Real-World Landscape of Genomic Medicine in Breast Cancer Revealed by the C-CAT Database: Reappraisal of Drug Accessibility and Clinical Utility in 6,307 Patients

Real-World Landscape of Genomic Medicine in Breast Cancer Revealed by the C-CAT Database: Reappraisal of Drug Accessibility and Clinical Utility in 6,307 Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-World Landscape of Genomic Medicine in Breast Cancer Revealed by the C-CAT Database: Reappraisal of Drug Accessibility and Clinical Utility in 6,307 Patients Midori Morita, Sae Kitano, Ryo Tsunashima, Tetsuhiro Yoshinami, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8861391/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Comprehensive genomic profiling (CGP) has advanced precision oncology in breast cancer, yet its real-world contribution to treatment selection in Japan remains unclear. Methods We analyzed 6,307 Japanese patients with breast cancer who underwent CGP in the nationwide Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database. Drug accessibility to Pharmaceuticals and Medical Devices Agency (PMDA)-approved therapies was assessed using two criteria, excluding biomarkers identifiable by prior companion diagnostics: Criterion I reflecting phase III breast cancer trials, and Criterion II reflecting phase I/II basket or tumor-agnostic trials. Evidence levels at testing were compared with those reassessed using evidence available as of October 2025. Results At least one genomic alteration was detected in 97.6% of cases; TP53 (53.9%), PIK3CA (37.7%) were most frequent. Re-evaluation increased Evidence Level A assignments from 3,757 to 5,874. Nevertheless, biomarkers linked to PMDA-approved therapies were limited to ten categories: PIK3CA/AKT1/PTEN alterations, ERBB2 amplification, BRCA1/2 mutations, NTRK fusions, BRAF V600 mutation, and TMB-H/MSI-H. Drug accessibility was 30.8% under Criterion I, driven exclusively by eligibility for capivasertib. Under Criterion II, the accessibility was 19.5%, comprising ERBB2 amplification (4.7%), somatic BRCA1/2 mutations (3.4%), NTRK fusions (0.1%), BRAF V600 mutation (0.2%), and non-overlapping TMB-H/MSI-H (11.1%). The prevalence of somatic BRCA1/2 mutations in germline BRCA1/2 noncarriers were identified in 3.4%, representing the first large-scale estimate in Japanese breast cancer. Conclusion Despite high detection rates, translation into PMDA-approved therapies remains limited, underscoring the need to optimize testing timing, expand trial access, harmonize molecular tumor boards practice, and consider breast cancer–specific mini-panels. Comprehensive genomic profiling Clinical utility drug accessibility rates the Center for Cancer Genomics and Advanced Therapeutics BRCA Figures Figure 1 Figure 2 Figure 3 Introduction Recent advances in molecular oncology have transformed the clinical management of cancer, positioning precision oncology—therapeutic decision-making based on tumor genomic alterations—at the center of contemporary practice[ 1 ]. Improvements in next-generation sequencing (NGS) technologies now enable comprehensive profiling of somatic mutations, gene fusions, and copy-number alterations, thereby facilitating the identification of actionable targets and prediction of therapeutic sensitivity[ 2 ]. Accordingly, cancer classification is shifting from morphology-based taxonomy to molecular subtyping, and the traditional “one-size-fits-all” approach is increasingly outdated. In Japan, comprehensive genomic profiling (CGP) testing was incorporated into the national health insurance system in 2019, leading to the nationwide establishment of a clinical genomic medicine infrastructure centered on designated core hospitals[ 3 ]. This system has expanded CGP from a technology largely restricted to research settings to a standardized component of routine oncology practice[ 4 ]. Simultaneously, liquid biopsy—analysis of circulating tumor DNA (ctDNA)—is increasingly recognized for its clinical utility in elucidating mechanisms of acquired resistance and enabling early detection of minimal residual disease (MRD)[ 5 , 6 ]. MRD-guided prediction of recurrence and monitoring of treatment response may further refine adjuvant therapy selection[ 7 ]. Artificial intelligence (AI) has also accelerated the interpretation of genomic and multi-omic data. AI-based models integrate large-scale omics datasets, support automated inference of the clinical significance of genomic alterations, and assist in predicting therapeutic sensitivity[ 8 ]. Large language models (LLMs), capable of surveying synthesizing rapidly expanding literature and real-world evidence, may further support evidence-based treatment recommendations. Collectively, these tools allow large-scale clinical CGP datasets to be analyzed more comprehensively and dynamically[ 9 ]. Despite these advances, several challenges continue to limit the clinical impact of precision oncology. First, only a minority of NGS-identified alterations represent truly actionable mutations with established therapeutic implications[ 10 ]. Many alterations remain variants of uncertain significance (VUS). Second, therapeutic response to identical genomic alterations may differ by tumor type, and intertumoral heterogeneity contributes to therapeutic resistance[ 11 ]. To overcome these gaps, integrated multi-omic approaches—incorporating transcriptomic or methylation data—are being explored; however, such methods may be irrelevant or impractical in real-world setting, and their clinical value remains uncertain[ 12 ]. To comprehensively evaluate the present state of precision oncology in breast cancer, we analyzed genomic and clinical data from 6,307 Japanese patients registered in the national Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database. We summarized the trajectory of cancer genomic medicine over the past six years and quantified drug accessibility rates. To evaluate clinical needs, we defined two criteria of drug accessibility[ 13 ]: Criterion I: The proportion of patients who, through CGP (but not companion diagnostics [CDx]), were first linked to PMDA-approved drugs supported by evidence from a phase III breast cancer trial. Criterion II: The proportion of patients who, through CGP (but not CDx), were first linked to PMDA-approved drugs supported by evidence from phase I/II basket or tumor-agnostic trials. Criterion I reflects the expectations of general clinicians who rely on conventional trial-based evidence; Criterion II represents the perspectives of genomic-medicine specialists who incorporate tumor-agnostic approvals. Both criteria were used to quantify drug accessibility and evaluate clinical utility. We additionally compared the Evidence Levels (ELs) assigned in the original C-CAT Expert Panel reports (EP-EL in C-CAT) with those updated using a simulated expert panel review based on evidence available as of October 2025 (EP-EL in SIM). This process enabled us to evaluate whether accumulated evidence increased drug accessibility and to identify unresolved challenges and future directions. Materials and Methods Study design and patients We conducted a retrospective analysis of clinical and genomic data from patients with breast cancer who underwent CGP testing between June 2019 and December 2024. Clinical and genomic information was collected from all patients who received any CGP assay. Data elements included CGP results (oncogenic alterations, tumor mutational burden [TMB]), patient characteristics (age, sex, Eastern Cooperative Oncology Group Performance Status [ECOG-PS], estrogen receptor [ER] status, HER2 status), assay type, specimen origin, and cancer-predisposing alterations identified via prior CDx testing (e.g., BRCA1/2 ). The counts of oncogenic alterations were defined as those categorized as “Oncogenic” or “Predisposing” in the C-CAT database. Target alterations were extracted from variants classified as “Oncogenic”, “Likely Oncogenic”, “Pathogenic”, or “Likely Pathogenic”. TMB-high (TMB-H) was defined as ≥ 10 muts/Mb for tissue-based assays and ≥ 14 muts/Mb for liquid biopsy assays. Microsatellite instability-high (MSI-H) was counted only for assays reporting MSI (GenMine TOP 🄬 does not report MSI). HER2 positivity was defined as immunohistochemistry (IHC) 3 + or in situ hybridization (ISH)-positive. For capivasertib eligibility, PIK3CA, AKT1, PTEN mutations, and PTEN deletions were identified in ER-positive, HER2-negative breast cancer. Somatic BRCA1/2 mutations ( sBRCA1/2 m) were assessed across all assays; germline BRCA1/2 mutations ( gBRCA1/2 m) were assessed only in assays capable of paired germline analysis (OncoGuide™ NCC Oncopanel [NOP], GenMine TOP 🄬 ). Here, we use the term ‘mutation’ to describe a deleterious sequence variant, and the term ‘carrier’ to describe an individual with a germline mutation. The Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database This study used the national C-CAT database maintained by the Ministry of Health, Labour and Welfare. C-CAT collects CGP results and clinical information from all insured CGP assays in Japan[ 4 ]. Since June 2019, FoundationOne 🄬 CDx (F1CDx) (Foundation Medicine Inc., Cambridge, MA, USA) and OncoGuideTM NCC Oncopanel (NOP) (Sysmex Co., Ltd., Kobe, Japan) have been started; FoundationOne 🄬 Liquid CDx (F1Liquid) (Foundation Medicine Inc., Cambridge, MA, USA) was added in 2021, and Guardant360 🄬 CDx (Guardant Health Inc., Palo Alto, CA, USA) and GenMine TOP 🄬 (Konica Minolta, Inc., Toyo, Japan) were approved in 2023. All assays were included in this analysis. Under Japanese insurance, CGP testing is permitted for patients with advanced solid tumors who have completed or are expected to complete standard therapy. By December 2024, approximately 7,000 patients with breast cancer had undergone CGP testing, of whom 99.7% consented to secondary use of their data[ 14 ]. In C-CAT, test results are interpreted and clinical annotations of genomic alterations are determined using the Cancer Knowledge Database (CKDB) and commercial tools (e.g., QIAGEN Clinical Insight, JAX-CKB). Evidence Levels (ELs) are defined from A to R (supplementary Table 1), and secondary germline findings reviewed by expert panels (molecular tumor boards [MTBs]). Some missing clinical and treatment data were noted in the dataset. Assessment of drug accessibility rates in clinical practice Drug accessibility was evaluated by extracting actionable alterations annotated as "Predictive" in C-CAT. For the top 100 most frequent alterations, we listed drugs matched to each alteration together with their EP-ELs as assigned in the original C-CAT reports. We then calculated the number of drugs classified as Evidence Level A (EL-A; EP-EL in C-CAT). Subsequently, we conducted an expert panel simulation in which C-CAT reports were re-evaluated by expert panel members from multiple designated core hospitals, using evidence available as of October 2025. We then recalculated the numbers of drugs classified as EL-A (EP-EL in SIM). Drug accessibility rates were calculated according to Criterion I and Criterion II defined above. The protocol was approved by the institutional review board of Kyoto Prefectural University of Medicine (ERB-C-2860) and the C-CAT Review Committee (AP20241127-01E). The requirement for informed consent was waived due to the retrospective nature of the study. Statistical analyses All statistical analyses were performed using R statistical software version 4.3.2. The oncoprint visualizations (Fig. 1 ) were created using the GenVisR package. Results Clinical and genomic characteristics In the C-CAT database, a total of 6,307 patients with breast cancer with analyzable data were included. The median age was 56 years (range, 22–91), and 99.4% were female (n = 6,269). ECOG-PS of 0–1 was observed in 92.3% (n = 5,823). ER positivity was 63.9% (Table 1 ). F1CDx was the most frequently used assay (71.3%), followed by F1Liquid (17.5%), NOP (8.3%), GenMineTOP 🄬 (1.6%), and Guardant360 🄬 (1.3%). Figure 1 presents an oncoprint of genomic alterations identified by CGP testing in 6,307 Japanese patients with breast cancer, integrating small variants, deletions, amplifications, and rearrangements, together with MSI, TMB, and ER and HER2 status extracted from clinical data. The most frequently altered genes were TP53 (53.9%), PIK3CA (37.7%), MYC (17.9%), CCND1 (15.8%), and GATA3 (14.7%), followed by ESR1 , which ranked tenth with a mutation frequency of 11.8%. The frequency of alterations outside the top ten genes was only a few percent. Overall, at least one genomic alteration was detected in 6,156 of 6,307 patients (97.6%) (Table 2 ). The median TMB was 4.0 muts/Mb, and TMB-H was observed in 11.1% of cases. MSI-H was identified in 0.4% of the cohort (Table 2 ). Table 1 Patient and Clinical Characteristics in C-CAT Database. Variable Classification N = 6307 (%) Sex Female 6269 (99.4) Male 38 (0.6) Median age, range 56 (22–91) Age group, years -29 42 (0.7) 30–39 363 (5.8) 40–49 1282 (20.3) 50–59 2071 (32.8) 60–69 1591 (25.2) 70–79 872 (13.8) 80- 86 (1.4) Alcohol Yes 270 (4.3) None 5257 (83.3) Unknown 780 (12.4) Smoking Yes 1190 (18.9) None 4621 (73.3) Unknown 496 (7.8) Estrogen receptor status Positive 4031 (63.9) Negative 1918 (30.4) Unknown/Uninspected 358 (5.7) Progesteron receptor status Positive 2969 (47.1) Negative 2947 (46.7) Unknown/Uninspected 391 (6.2) HER2 Positive 624 (9.9) Negative 5328 (84.5) Unknown/Uninspected 355 (5.6) gBRCA1 Positive 144 (2.3) Negative 3655 (57.9) Unknown/Uninspected 2508 (39.8) gBRCA2 Positive 264 (4.2) Negative 3515 (55.7) Unknown/Uninspected 2528 (40.1) ECOG-PS 0 3738 (59.3) 1 2085 (33.0) 2≦ 212 (3.4) Unknown 272 (4.3) Type of CGP test FoundationOne 🄬 CDx 4496 (71.3) FoundationOne 🄬 Liquid CDx 1104 (17.5) OncoGuide™ NCC Oncopanel System 522 (8.3) Guardant360 🄬 CDx 85 (1.3) GenMineTOP 🄬 CDx 100 (1.6) Specimens submitted for gene panel testing Primary lesion 2682 (42.5) Metastatic lesion 2430 (38.5) Liquid biopsy 1189 (18.9) Unknown 6 (0.1) C-CAT: Center for Cancer Genomics and Advanced Therapeutics ECOG-PS: Eastern Cooperative Oncology Group Performance Status CGP: Comprehensive genomic profiling Table 2 Genomic Characteristics in CGP Tests. Variable Classification N = 6307 (%) mutation Yes 6156 (97.6) No 151 (2.4) TMB (Muts/Mb) Low (tissue < 10, liquid < 14) 5523 (87.6) High (≥ 10 tissue, ≥ 14 liquid) 698 (11.1) Unknown 86 (1.3) Median TMB, range 4 (0-258.9) MSI-status ※ High 28 (0.4) Stable 4379 (69.4) Equivocal 24 (0.4) High not detected 1097 (17.4) Cannot be determined 360 (5.7) Unknown 419 (6.7) ※ GenMineTOP is included in Unknown. CGP: Comprehensive genomic profiling TMB: tumor mutation burden Muts/Mb: mutations-per-megabase MSI: microsatellite instability The TOP100 Genomic Alterations, Evidence Levels at the Time of CGP Testing (EP-EL in C-CAT) and the Reassessed Evidence Levels (EP-EL in SIM), and Drugs Recommended by Expert Panels and Drug Accessibility Rates Table 3 summarizes the 100 most frequent genomic alterations identified in Japanese patients with advanced or metastatic breast cancer, along with the evidence levels assigned by the expert panels at the time of CGP testing (EP-EL in C-CAT), as documented in the C-CAT reports. The proportion of alterations classified as evidence level A (EL-A) at the time of CGP testing is also shown. Furthermore, using the expert panel simulation incorporating updated evidence as of October 2025 (EP-EL in SIM), we reassessed evidence levels and corresponding therapeutic recommendations. Based on these data, Criteria I and II were calculated to assess drug accessibility and clinical applicability, and the results are presented in Table 3 . Table 3 Top 100 Genomic Alteration: Evidence Levels at the Time of CGP Tests (EP-EL in C-CAT) and the reassessed Evidence Levels (EP-EL in SIM), and Drugs Recommended by Expert Panels and Drug Accessibility Rates. No. Gene (%) EP in C-CAT EP in SIM The proportion first linked to PMDA-approved drugs with phase III trial evidence for breast cancer through CGP tests but not prior CDx (Criterion 1) (%) The proportion first linked to PMDA-approved drugs including based on phase I and II trial evidence through CGP tests but not prior CDx (Criterion 2) (%) EL ※ EL-A (N) EL ※ EL-A (N) Drugs recommended by the EP (including FDA-approval) Drugs recommended by the EP (PMDA-approval only) 1 TP53 53.9 C, E - - - - - 0 0 2 PIK3CA 37.7 A, B, C, D, E 2192 A_alpelisib A_capivasertib 2377 alpelisib, fulvestrant, alpelisib + fulvestrant If mutation, A_capivasertib + fulvestrant 23.2 (ER+HER2−) 0 3 MYC 17.9 - - - - - - 0 0 4 CCND1 15.8 C, E - - - - - 0 0 5 GATA3 14.7 - - - - - - 0 0 6 FGF19 14.3 - - If co-amplification FGF3/4/19, D_FGFRinhibitor - - - 0 0 7 PTEN 13.4 A, B, C, E 296 If loss and truncation, A_capivasertib C_everolimus 809 - C_everolimus If mutation and deletion, A_capivasertib + fulvestrant 6.3 (ER+HER2−) 0 8 ERBB2 13.1 A, B, C, D, E 620 If amplification, A_trastuzumab A_pertuzumab A_lapatinib A_T-DM1 A_T-DXd A_tucatinib A_neratinib If mutation, C_neratinib C_T-DXd 851 anit-HER2 drugs If amplification, A_trastuzumab A_pertuzumab A_lapatinib A_T-DM1 A_T-DXd 0 4.7 (changed from HER2 − based on IHC/ISH to HER2 + through CGP testing) 9 FGFR1 12.4 B, C, D, E - D_pemigatinib D_futibatinib D_erdafitinib D_infigratinib - - - 0 0 10 ESR1 11.8 A, B, C, E 405 A_elasestrant B_fulvestrant 686 elacestrant B_fulvestrant 0 0 11 RB1 9.0 - - - - - - 0 0 12 AKT1 8.4 A, B 191 A_capivasertib 459 - If mutation, A_capivasertib + fulvestrant 4.5 (ER+HER2−) 0 13 BRCA2 7.3 A, B, C 30 A_olaparib A_talazoparib A_niraparib C_rucaparib 459 olaparib, talazoparib A_olaparib A_talazoparib 0 2.2 (g BRCA2 − s BRCA2+ ) 14 CDKN2A 6.6 E - - - - - 0 0 15 MDM4 6.4 - - - - - - 0 0 16 NF1 6.2 C, E - C_MEKinhibitor - - - 0 0 17 CDH1 6.0 - - - - - - 0 0 18 RAD21 5.8 - - - - - - 0 0 19 ARID1A 5.5 E - - - - - 0 0 20 IKBKE 5.2 - - - - - - 0 0 21 ZNF703 5.0 - - - - - - 0 0 22 DDR2 5.0 - - - - - - 0 0 23 MCL1 5.0 - - - - - - 0 0 24 AURKA 4.9 - - - - - - 0 0 25 STK11 4.9 - - - - - 0 0 26 CDKN2B 4.4 - - - - - - 0 0 27 MDM2 4.1 C - - - - - 0 0 28 KRAS 4.1 C, D, E - If G12C, C_sotorasib - - - 0 0 29 MAP3K1 4.1 - - - - - - 0 0 30 AKT3 3.9 - - - - - - 0 0 31 MAP2K4 3.7 - - - - - - 0 0 32 DNMT3A 3.6 C - - - - - 0 0 33 BRCA1 3.4 A, B, C 14 A_olaparib A_talazoparib A_niraparib C_rucaparib 216 olaparib, talazoparib A_olaparib A_talazoparib 0 1.3 (g BRCA1 − s BRCA1+ ) 34 GNAS 3.2 - - - - - - 0 0 35 KDM5A 3.0 - - - - - - 0 0 36 ZNF217 3.0 - - - - - - 0 0 37 NTRK1 2.7 C - If fusion, A_entrectinib A_larotrectinib If amplification, D_TRKinhibitor 4 - If fusion, A_entrectinib A_larotrectinib 0 0.05 38 ATM 2.6 C, E - C_olaparib C_talazoparib C_niraparib C_rucaparib - - - 0 0 39 FGFR2 2.5 C, D, E - If fusion, C_pemigatinib C_futibatinib D_erdafitinib D_infigratinib If mutation, D_pemigatinib D_futibatinib C_erdafitinib D_infigratinib If not fusion and mutation, D_pemigatinib D_futibatinib D_erdafitinib D_infigratinib - - - 0 0 40 CHEK2 2 C - C_olaparib C_talazoparib C_niraparib C_rucaparib - - - 0 0 41 EGFR 2.2 C, D, E - If mutation (not exon20ins), C_gefitinib C_elrotinib C_afatinib C_osimertinib If mutation (exon20ins), C_osimertinib If amplification, C_afatinib D_EGFRinhibitor - - - 0 0 42 MUTYH 2.2 - - - - - - 0 0 43 CCNE1 2.2 E - - - - - 0 0 44 SF3B1 2.1 - - - - - - 0 0 45 TBX3 2.1 - - - - - - 0 0 46 CCND2 2.1 E - - - - - 0 0 47 SPEN 2.1 - - - - - - 0 0 48 MSH6 2.1 - - - - - - 0 0 49 EMSY 2.1 - - - - - - 0 0 50 CDK4 2.0 C - C_CDK4/6inhibitor - - - 0 0 51 TET2 2.0 - - - - - - 0 0 52 SMAD4 2.0 - - - - - - 0 0 53 JAK2 1.7 - - - - - - 0 0 54 VEGFA 1.7 - - - - - - 0 0 55 PALB2 1.6 C - C_olaparib C_talazoparib C_niraparib C_rucaparib - - - 0 0 56 KMT2D 1.6 - - - - - - 0 0 57 ERBB3 1.5 - - - - - - 0 0 58 CDKN1B 1.5 - - - - - - 0 0 59 CREBBP 1.5 - - - - - - 0 0 60 RICTOR 1.5 - - - - - - 0 0 61 BCL2L1 1.5 - - - - - - 0 0 62 ASXL1 1.5 - - - - - - 0 0 63 SRC 1.5 - - - - - - 0 0 64 CCND3 1.5 - - - - - - 0 0 65 BRAF 1.4 A, C, D, E 9 If V600, A_dabrafenib + trametinib A_dabrafenib A_trametinib If classII, C_MEKinhibitor If V600, 13 dabrafenib + trametinib dimethyl sulfoxide If V600, A_dabrafenib + trametinib A_dabrafenib A_trametinib 0 0.2 66 RAC1 1.4 - - - - - - 0 0 67 CDK12 1.6 C, D - C_olaparib C_talazoparib C_niraparib C_rucaparib If loss of function, C_immune checkpoint inhibitor - - - 0 0 68 NOTCH1 1.3 - - - - - - 0 0 69 SMO 1.3 E - - - - - 0 0 70 NKX2-1 1.3 - - - - - - 0 0 71 KIT 1.3 C, D - If mutation_Exson 17, R_imatinib R_sunitinib C_dasatinib C_avapritinib C_ponatinib If mutation_Exson 11, C_imatinib C_dasatinib C_ponatinib If amplification, C_imatinib C_dasatinib C_ponatinib - - - 0 0 72 CARD11 1.2 - - - - - - 0 0 73 RAF1 1.2 - - - - - - 0 0 74 FGFR4 1.2 D - - - - - 0 0 75 TERT 1.2 - - - - - - 0 0 76 BCL6 1.2 - - - - - - 0 0 77 EZH2 1.2 - - - - - - 0 0 78 HARS 1.1 - - - - - - 0 0 79 NOTCH3 1.1 - - - - - - 0 0 80 PIK3R1 1.1 - - - - - - 0 0 81 ROS1 1.1 - - If fusion, C_crizotinib C_entrectinib C_lorlatinib - - - 0 0 82 KDM6A 1.0 - - - - - - 0 0 83 CDK6 1.0 - - - - - - 0 0 84 JAK1 1.0 - - - - - - 0 0 85 PIK3CB 1.0 - - - - - - 0 0 86 MTAP 1.0 - - - - - - 0 0 87 NOTCH2 1.0 - - - - - - 0 0 88 APC 1.0 E - - - - - 0 0 89 SETD2 1.0 - - - - - - 0 0 90 TSC1 1.0 C, D - - - - - 0 0 91 AKT2 1.0 - - - - - - 0 0 92 BRD4 1.0 - - - - - - 0 0 93 MYCN 0.9 - - - - - - 0 0 94 CBFB 0.9 - - - - - - 0 0 95 MEN1 0.9 - - - - - - 0 0 96 TSC1 0.9 - - - - - - 0 0 97 AR 0.9 - - - - - - 0 0 98 RNF43 0.9 - - - - - - 0 0 99 SOX9 0.9 - - - - - - 0 0 100 FLT3 0.8 C - C_gilteritinib - - - 0 0 C-CAT: Center for Cancer Genomics and Advanced Therapeutics CGP: Comprehensive genomic profiling EP: Expert panel EL: Evidence level EL-A: Evidence level-A SIM: Simulation N: number of patients PMDA: Pharmaceuticals and Medical Devices Agency CDx: Companion diagnostics ER+: Estrogen receptor-positive HER2−: HER2-negative HER2+: HER2-positive gBRCA − sBRCA+ : germline breast cancer susceptibility gene-negative and somatic breast cancer susceptibility gene-positive ※Exclusion of EL-R Compared with EP-EL in C-CAT, the number of EL-A assignments increased substantially after re-evaluation using EP-EL in SIM, rising from 3,757 to 5,874. Among variants assigned as EL-A under EP-EL in SIM, the corresponding PMDA-approved agents included: anti-HER2 therapies for ERBB2 amplification, PARP inhibitors for BRCA1/2 mutations, capivasertib plus fulvestrant for PIK3CA/AKT1/PTEN alterations, entrectinib and larotrectinib for NTRK fusions, and dabrafenib plus trametinib for BRAF mutations. After excluding biomarkers already evaluable by pre-CGP CDx, clinically actionable alterations firstly identified by CGP were: ERBB2 amplification in HER2-negative breast cancer (4.3%), s BRCA1/2 m carriers in gBRCA1/2 m noncarriers (3.4%), PIK3CA/AKT1/PTEN alterations in ER-positive/HER2-negative breast cancer (non-overlapping; 30.8%), NTRK fusions (0.1%), and BRAF V600 mutations (0.2%). According to Criterion I, only PIK3CA/AKT1/PTEN alterations (30.8%) fulfilled the requirement of linking to PMDA-approved therapies supported by phase III trial evidence in breast cancer. According to Criterion II, which included PMDA-approved drugs supported by phase I/II basket trial or tumor-agnostic evidence, the drug accessibility rate comprised ERBB2 amplification (4.7%), sBRCA1/2 mutations (3.4%), NTRK fusions (0.1%), BRAF V600 mutations (0.2%), and non-overlapping TMB-H/MSI-H (11.1%). The overall Criterion II accessibility rate was 19.5% (Tables 3 and 4 ). When grouped by biomarkers, only ten actionable categories remained clinically relevant: Table 4 Summary of Evidence Level A in the Expert Panel Simulation. Biomarkers Drugs recommended by the EP (PMDA-approval only) Criterion 1 (%) phase III evidence Criterion 2 (%) phase I/II evidence PIK3CA/AKT1/PTEN alterations capivasertib 30.8 (ER+HER2−) TMB-high/MSI-high pembrolizumab 11.1 ERBB2 amplification anti-HER2 therapy 4.7 (HER2−) sBRCA1/2 mutation olaparib 3.4 ( gBRCA1/2 −) BRAF V600 mutation dabrafenib + trametinib 0.2 NTRK fusions entrectinib, larotrectinib 0.1 total 30.8 19.5 EP: Expert panel ER+: Estrogen receptor-positive HER2−: HER2-negative sBRCA : somatic breast cancer susceptibility gene gBRCA −: germline breast cancer susceptibility gene-negative PIK3CA, AKT1, PTEN, ERBB2, BRCA1, BRCA2, NTRK, BRAF , TMB, and MSI. Prevalence and Concordance of Germline and Somatic BRCA1/2 Mutations Germline BRCA1/2 mutations previously assessed by CDx (e.g., BRACAnalysis 🄬 ) were identified in 144 patients (2.3%) for gBRCA1 m carriers and 264 patients (4.2%) for gBRCA2 m carriers. Approximately 40% of patients had an unknown or untested germline BRCA1/2 m status at the time of CGP testing (Table 1 ). After excluding cases with unknown g BRCA1/2 test results, the prevalence was 3.8% for gBRCA1 m carriers (144/3799) and 7.0% for gBRCA2 m carriers (264/3779). Among patients with gBRCA1 m carriers (n = 144), sBRCA1 m were detected by CGP in 106 patients (73.6%). Similarly, among patients with gBRCA2 m carriers (n = 264), sBRCA2 m were detected in 246 (93.2%). In contrast, among CDx-confirmed germline mutation noncarriers, sBRCA1 m was detected by CGP in 49 of 3,655 patients (1.3%) who were gBRCA1 m noncarriers, and sBRCA2 m was detected in 77 of 3,515 patients (2.2%) who were gBRCA2 m noncarriers (Table 3 ). After excluding overlap, 116 patients (3.4%) who were gBRCA1/2 m noncarriers by CDx were found to harbor sBRCA1/2 m by CGP testing. Notably, among cases classified as g BRCA1/2 m noncarriers by CDx, none were reclassified as germline carriers based on CGP testing. determined to be g BRCA1/2 m carriers based on CGP testing. Discordance between HER2 positivity by IHC/ISH and ERBB2 amplification by CGP testing Discordance between HER2 status assessed by conventional IHC/ISH and ERBB2 amplification detected by CGP testing was observed (Fig. 3 ). When stratified by HER2 IHC score, the positivity rate of ERBB2 amplification by CGP testing was highest in IHC 3 + tumors (61.7%), followed by IHC 2+ (12.8%) and IHC 1+ (5.5%). Importantly, ERBB2 amplification was not identified in a substantial proportion of tumors even among those classified as IHC 3+. Similarly, concordance between HER2 positivity by ISH and ERBB2 amplification detected by CGP testing was limited, with ERBB2 amplification identified in only 39.2% of HER2 ISH–positive tumors. Notably, ERBB2 amplification was also detected in 4.7% of tumors classified as HER2-negative by IHC/ISH (Tables 3 and 4 ). Discussion The present study demonstrates that genomic medicine for breast cancer in Japan has steadily advanced through coordinated improvements in technology, regulatory frameworks, and clinical infrastructure. The approval of CGP testing, introduction of tumor–normal paired sequencing, and alignment with global standards for CDx and tumor-agnostic drug approvals have significantly expanded therapeutic opportunities. Among these developments, regulatory approval of a CDx for PIK3CA/AKT1/PTEN alterations represents a major milestone in precision oncology for breast cancer[ 15 ]. Accumulated data from the C-CAT have demonstrated a comparable prevalence of PIK3CA/AKT1/PTEN alterations in the domestic population[ 13 , 16 ]. In our cohort, CGP testing firstly enabled access to at least one PMDA-approved drug in 50.3% of patients when Criteria I and II were combined. This represents a substantial increase compared with our previous report of 37.9%[ 13 ], primarily attributable to an increased number of cases classified as EL-A and the regulatory approval of additional targeted therapies, including capivasertib for PIK3CA/AKT1/PTEN alterations and olaparib for sBRCA1/2 mutations. Notably, sBRCA1/2 mutations were identified in 3.4% of patients without gBRCA1/2 mutations. In a nationwide analysis of 7,051 Japanese patients with breast cancer, the prevalence of gBRCA1 and gBRCA2 mutations carriers has been reported to be 1.9% and 3.2%, respectively[ 17 ]; however, the prevalence of sBRCA1/2 m in this population had not previously been well characterized. To our knowledge, the present study is the first to demonstrate that 3.4% of Japanese patients with breast cancer who are gBRCA1/2 noncarriers harbor sBRCA1/2 mutations (1.3% in sBRCA1 m and 2.2% in sBRCA2 m). These findings are generally consistent with prior reports, including a Scandinavian cohort of 273 breast cancer cases showing an sBRCA m prevalence of 3.0%[ 18 ], a meta-analysis (3.4% for sBRCA1 m and 2.7% for sBRCA2 m)[ 19 ], and a study across Western and Asia-Pacific populations (1.6% sBRCA1 m and 4.7% sBRCA2 m)[ 20 ]. Collectively, these data suggest that ethnic differences in the prevalence of sBRCA1/2 m prevalence may be limited. Taken together, these real-world insights derived from CGP testing using the C-CAT database contribute substantially to the refinement of therapeutic targets and the optimization of patient selection in precision oncology for breast cancer. Despite these advances, significant limitations persist. According to Criterion I—the conventional standard requiring breast cancer-specific phase III evidence—only PIK3CA/AKT1/PTEN alterations (30.8%) were linked to actionable therapies. In practical terms, it is not an overstatement to suggest that, to date, CGP testing in breast cancer has largely been performed to identify candidates for capivasertib. Consequently, with the broader clinical implementation of the CDx (e.g., OncoGuide TM OncoScreen TM Plus CDx), it will be necessary to re-evaluate the incremental value of broad CGP beyond targeted CDx testing. Moreover, although Criterion II expanded the actionable population to 19.5%, most supporting evidence derives from tumor-agnostic phase I/II trials, and breast cancer was not always represented in these studies, warranting caution in clinical interpretation. The discordance between HER2 positivity assessed by conventional diagnostics and ERBB2 amplification detected by CGP testing should also be interpreted in this context. Although HER2 positivity by IHC or ISH has traditionally been considered concordant with ERBB2 amplification, our analysis demonstrated discordance. Specifically, ERBB2 amplification detected by CGP testing was observed in only 61.7% of HER2 IHC–positive tumors, and in merely 39.2% of HER2 ISH–positive tumors. This result was consistent with previous reports[ 21 ]. Furthermore, while ERBB2 amplification was detected in a small subset of HER2-negative tumors, the low frequency and uncertainty regarding its clinical significance further underscore the currently limited role of CGP in guiding HER2-targeted therapeutic decisions. Despite the fact that 97.6% of the 6,307 cases harbored at least one genomic alteration, only 10 genomic or molecular biomarkers ( PIK3CA, AKT1, PTEN, ERBB2, BRCA1, BRCA2, NTRK, BRAF , TMB, MSI) were clinically relevant for drug accessibility. From a practical and cost-effectiveness perspective, the development of a low-cost, high-efficiency, breast cancer–specific targeted gene panel focusing on this limited set of biomarkers may be warranted for real-world implementation. Thus, insights derived from large-scale CGP analyses using the C-CAT database provide an important foundation for optimizing precision oncology in the future. Nevertheless, several major gaps persist in real-world clinical implementation. The rate of conversion from alteration detection to actual treatment access remains low. A domestic report has suggested that only approximately 10% of patients undergo genotype-matched treatment after CGP testing[ 10 , 21 ]. CGP testing is often performed at an advanced stage of disease, frequently after the exhaustion of standard therapies; as a result, a subset of patients are unable to receive subsequent genotype-matched treatments because of disease progression or deterioration in performance status[ 22 ]. And, substantial variability exists in the operation of MTBs and in access to recommended treatments across institutions. For example, inter-institutional differences have been reported in MTB processes, including the quality and timeliness of genomic interpretation and reporting[ 23 ]. Persistent challenges remain in the management of genetic counseling, secondary findings, presumed germline pathogenic variants (PGPVs), and the establishment of robust follow-up systems. In our cohort, sBRCA1/2 m were detected in 3.4% of patients who were negative for gBRCA1/2 m (after excluding overlap). These findings underscore the need to further define optimal clinical handling and follow-up frameworks for such results, including appropriate counseling, confirmatory testing when indicated, and longitudinal management strategies. In addition, the C-CAT database currently lacks comprehensive clinical outcome data, limiting the ability to fully assess the real-world effectiveness of CGP-guided therapies. Taken together, genomic medicine in breast cancer appears to be transitioning from a phase focused primarily on “testing to detect alterations” to a phase in which the key objective is to “translate detected alterations into actionable treatment and supportive care.” However, at present, the field has not yet reached a stage of broad dissemination and routine, scalable implementation in clinical practice. Looking ahead over the next 5–10 years, the following directions for improvement will be very important. First, the timing of CGP testing warrants reconsideration. In breast cancer, CGP testing should ideally be performed not only at the time of recurrence or metastasis but also at an appropriate earlier stage, when expanding therapeutic options is most clinically meaningful[ 22 , 24 ]. Recently, a companion diagnostic assay targeting PIK3CA/AKT1/PTEN alterations for capivasertib has been implemented in clinical practice, demonstrating a high overall concordance rate of 95.7% with F1CDx[ 25 ]. As demonstrated in the present study, the proportion of patients eligible for approved therapies based on conventional criteria—namely, drugs supported by phase III trials in breast cancer (Criterion I)—was limited to alterations in these three genes, accounting for only 30.8% of cases. Given the availability of companion diagnostics for these three genes, it seems appropriate to re-evaluate both the optimal timing and clinical significance of comprehensive CGP testing. As discussed above, the development and implementation of a cost-effective, breast cancer–specific targeted gene mini-panel incorporating these three genes along with additional clinically relevant alterations may represent a practical and valuable alternative in real-world settings. Strategic integration of such targeted panels with CGP testing, performed at an appropriate time point, may further expand therapeutic sequencing options for patients. Second, improving access to matched therapies and securing sufficient clinical trial opportunities are critical. To streamline the transition from mutation detection to access to approved or investigational therapies, the establishment of a nationwide collaborative MTB network, reduction of inter-institutional and regional disparities, and strengthening of support systems for clinical trial enrollment are required[ 10 ]. In addition, the cost-effectiveness and accessibility of off-label treatments and non-reimbursed drugs should be carefully evaluated to ensure equitable access to precision oncology[ 26 ]. Third, the clinical implementation of next-generation technologies and assays requires both robust evidence generation and regulatory preparedness. Circulating tumor DNA–based monitoring (liquid biopsy), as well as RNA sequencing and methylation analyses, hold promise for early detection and the identification of emerging drug resistance. However, their adoption into routine clinical practice requires high-level evidence demonstrating analytical validity, reproducibility, clinical utility, and cost-effectiveness. Similarly, while AI-based interpretation support systems are promising, their clinical validity, fairness, and explainability must be rigorously evaluated alongside appropriate regulatory frameworks[ 8 , 12 ]. Fourth, workforce development and operational standardization within multidisciplinary and nationwide frameworks remain ongoing challenges. In March 2022, the Japanese Ministry of Health, Labour and Welfare announced that simplification of expert panel processes may be feasible by limiting reported results to high-evidence-level genomic alterations. However, such alterations account for less than 10% of all detected variants. Moreover, annotation of not only somatic variants but also germline variants and presumed germline pathogenic variants (PGPVs) relies on expertise informed by global databases, population-specific genomic data, and individual and family histories, making this process labor-intensive and technically demanding. Consequently, substantial human resources are still required at individual institutions, particularly at core hospitals[ 27 ]. Despite ongoing efforts to expand panel testing capacity, variability in expertise, experience, and institutional resources among MTB members continues to result in heterogeneity in discussion quality, posing a barrier to equitable genomic medicine delivery[ 23 ]. Additionally, balancing process simplification with workforce training remains challenging under constraints related to cost-effectiveness and healthcare labor reforms. Fifth, enhanced data utilization and clinical outcome assessment are essential. The C-CAT system was established to centrally collect, manage, and appropriately utilize genomic data for cancer genomic medicine in Japan[ 3 , 4 ]. However, the complexity of clinical data entry and the high proportion of missing data—particularly for subsequent treatments and survival outcomes—limit its current utility[ 13 ]. Standardized methodologies and metrics are needed to longitudinally evaluate the real-world impact of genomic medicine, including mutation detection rates, matched therapy rates, clinical trial enrollment, survival outcomes, and quality of life. Finally, patient and family education, ethical considerations, and follow-up systems must be strengthened. A substantial proportion of reported genomic alterations are variants of uncertain significance (VUS), posing interpretational challenges even for experts[ 23 ]. Communicating these findings without causing unrealistic expectations or undue anxiety is difficult. Comprehensive support frameworks encompassing pre-test informed consent, disclosure of secondary findings, genetic counseling, family testing and support, and post-test follow-up are therefore essential. Reduced patient satisfaction and increased psychological burden have been reported among patients who undergo CGP testing without receiving matched therapies, underscoring the importance of ethical communication and psychological support in routine implementation[ 27 , 28 ]. In summary, genomic medicine for breast cancer in Japan is progressing toward integration into routine clinical practice. In the short term, the above-mentioned improvement directions will be prioritized, and in the medium to long term, the key will be to build a system to quantify the effects of individualized treatment that combines C-CAT, a nationwide data infrastructure, and AI. However, the clinical impact of CGP remains limited by low rates of drug accessibility and the narrow range of biomarkers linked to approved therapies. Even with updated 2025 evidence, high-level evidence–based treatment opportunities remain concentrated in a small subset of genomic alterations. From the perspectives of cost-effectiveness, resource allocation, and real-world feasibility, CGP may currently be most appropriate for use within research-intensive cancer centers rather than universal deployment across all clinical settings. Continued national-level discussion and policy development will be essential to define the optimal role of CGP testing in Japanese breast cancer care. Abbreviations CGP comprehensive genomic profiling C-CAT Center for Cancer Genomics and Advanced Therapeutics CDx Companion diagnostics RCT randomized controlled trial pⅢ RCT Phase Ⅲ Randomized controlled trial HER2 human epidermal growth factor receptor type2 FDA Food and Drug Administration PMDA Pharmaceuticals and Medical Devices Agency EP Expert panel EL Evidence level SIM simulation ER estrogen receptor TMB-H tumor mutation burden-high MSI-H Microsatellite instability-high. Declarations Ethics statement Approval of the research protocol by the Ethics Review Committee of Kyoto Prefectural University of Medicine (ERB-C-2860) and by the review board of C-CAT (AP20241127-01E). Consent for publication Not applicable. Conflict of interest Midori Morita has received research funding from Murata outside the submitted work. Koichi Takayama received grants from Chugai Pharmaceutical and Ono Pharmaceutical, and personal fees from AstraZeneca, Chugai Pharmaceutical, MSD, Eli Lilly, Boehringer Ingelheim, and Daiichi-Sankyo. Yasuto Naoi has received research funding from Eisai, Shimazu, Murata, ONO, Daiichi-Sankyo and AstraZeneca, as well as honoraria from Eisai, AstraZeneca, Pfizer, Eli Lilly, Daiichi-Sankyo and Chugai outside the submitted work; he holds joint patents with Sysmex including Curebest™ 95GC Breast (JP.5725274.B2). The other authors declare no conflicts of interest. Funding No funding was received for this study. Author Contributions M Morita and Y Naoi contributed to the planning and design of the study. M Morita and R Tsunashima analyzed the data. T Yoshinami and M Nishida added annotation of the data. All authors approved the final version of the manuscript. References Garraway LA, Verweij J, Ballman KV. Precision oncology: an overview. 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Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yasuto","middleName":"","lastName":"Naoi","suffix":""}],"badges":[],"createdAt":"2026-02-12 11:18:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8861391/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8861391/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106070131,"identity":"50772d44-775a-4015-a2b0-be6b5cb7cee2","added_by":"auto","created_at":"2026-04-03 06:26:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the most frequent genomic alterations in C-CAT data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe top 37 mutation frequencies were extracted. TMB-high defined as ≥10 muts/Mb for tissue-based assays and ≥14 muts/Mb for liquid biopsy assays.\u003c/p\u003e","description":"","filename":"20260209MoritaCCATFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8861391/v1/35a755550937d37674733305.png"},{"id":106070135,"identity":"4bb00c2b-42b3-4355-b8a5-d14b4a22a2c3","added_by":"auto","created_at":"2026-04-03 06:26:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInclusion of capivasertib adaptation between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePIK3CA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAKT1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePTEN\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e alterations in ER-positive HER2-negative breast cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe venn diagram shows the overlap between \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003eAKT1\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e alterations in ER-positive HER2-negative breast cancer. It is 30.8% of all patients (1943/6307), accounting for 53.7% of ER-positive HER2-negative breast cancer (1943/3617).\u003c/p\u003e","description":"","filename":"20260209MoritaCCATFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8861391/v1/4b9f25b9ad6181db2bff18e6.png"},{"id":106070130,"identity":"7adf5725-4dff-4ad3-95c9-f4e2e999be1d","added_by":"auto","created_at":"2026-04-03 06:26:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eERBB2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e amplification positivity rates stratified by HER2 testing categories. (A) HER2 IHC categories. (B) HER2 ISH categories\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBars represent descriptive percentages derived from the study dataset. Numbers shown represent percentage of \u003cem\u003eERBB2\u003c/em\u003e amplification with corresponding numerator and denominator (n/N).\u003c/p\u003e","description":"","filename":"20260209MoritaCCATFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8861391/v1/fc9c701bd38ecdbf6d980717.png"},{"id":108806848,"identity":"d077a462-1dc6-480a-a863-7c322b8f3e36","added_by":"auto","created_at":"2026-05-08 15:29:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1553760,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8861391/v1/e9fccc84-7102-4df8-a97d-d927d5291eb4.pdf"},{"id":106070132,"identity":"b7adb995-13bc-4e40-9544-b5d3ee29cc12","added_by":"auto","created_at":"2026-04-03 06:26:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. Definition of Evidence Level in C-CAT.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"20260128MoritaCCATTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8861391/v1/7f38dbaddc7f252f111d648a.docx"}],"financialInterests":"","formattedTitle":"Real-World Landscape of Genomic Medicine in Breast Cancer Revealed by the C-CAT Database: Reappraisal of Drug Accessibility and Clinical Utility in 6,307 Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent advances in molecular oncology have transformed the clinical management of cancer, positioning precision oncology\u0026mdash;therapeutic decision-making based on tumor genomic alterations\u0026mdash;at the center of contemporary practice[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Improvements in next-generation sequencing (NGS) technologies now enable comprehensive profiling of somatic mutations, gene fusions, and copy-number alterations, thereby facilitating the identification of actionable targets and prediction of therapeutic sensitivity[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accordingly, cancer classification is shifting from morphology-based taxonomy to molecular subtyping, and the traditional \u0026ldquo;one-size-fits-all\u0026rdquo; approach is increasingly outdated.\u003c/p\u003e \u003cp\u003eIn Japan, comprehensive genomic profiling (CGP) testing was incorporated into the national health insurance system in 2019, leading to the nationwide establishment of a clinical genomic medicine infrastructure centered on designated core hospitals[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This system has expanded CGP from a technology largely restricted to research settings to a standardized component of routine oncology practice[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Simultaneously, liquid biopsy\u0026mdash;analysis of circulating tumor DNA (ctDNA)\u0026mdash;is increasingly recognized for its clinical utility in elucidating mechanisms of acquired resistance and enabling early detection of minimal residual disease (MRD)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. MRD-guided prediction of recurrence and monitoring of treatment response may further refine adjuvant therapy selection[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eArtificial intelligence (AI) has also accelerated the interpretation of genomic and multi-omic data. AI-based models integrate large-scale omics datasets, support automated inference of the clinical significance of genomic alterations, and assist in predicting therapeutic sensitivity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Large language models (LLMs), capable of surveying synthesizing rapidly expanding literature and real-world evidence, may further support evidence-based treatment recommendations. Collectively, these tools allow large-scale clinical CGP datasets to be analyzed more comprehensively and dynamically[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these advances, several challenges continue to limit the clinical impact of precision oncology. First, only a minority of NGS-identified alterations represent truly actionable mutations with established therapeutic implications[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Many alterations remain variants of uncertain significance (VUS). Second, therapeutic response to identical genomic alterations may differ by tumor type, and intertumoral heterogeneity contributes to therapeutic resistance[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To overcome these gaps, integrated multi-omic approaches\u0026mdash;incorporating transcriptomic or methylation data\u0026mdash;are being explored; however, such methods may be irrelevant or impractical in real-world setting, and their clinical value remains uncertain[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo comprehensively evaluate the present state of precision oncology in breast cancer, we analyzed genomic and clinical data from 6,307 Japanese patients registered in the national Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database. We summarized the trajectory of cancer genomic medicine over the past six years and quantified drug accessibility rates. To evaluate clinical needs, we defined two criteria of drug accessibility[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eCriterion I:\u003c/p\u003e \u003cp\u003eThe proportion of patients who, through CGP (but not companion diagnostics [CDx]), were first linked to PMDA-approved drugs supported by evidence from a phase III breast cancer trial.\u003c/p\u003e \u003cp\u003eCriterion II:\u003c/p\u003e \u003cp\u003eThe proportion of patients who, through CGP (but not CDx), were first linked to PMDA-approved drugs supported by evidence from phase I/II basket or tumor-agnostic trials.\u003c/p\u003e \u003cp\u003eCriterion I reflects the expectations of general clinicians who rely on conventional trial-based evidence; Criterion II represents the perspectives of genomic-medicine specialists who incorporate tumor-agnostic approvals. Both criteria were used to quantify drug accessibility and evaluate clinical utility.\u003c/p\u003e \u003cp\u003eWe additionally compared the Evidence Levels (ELs) assigned in the original C-CAT Expert Panel reports (EP-EL in C-CAT) with those updated using a simulated expert panel review based on evidence available as of October 2025 (EP-EL in SIM). This process enabled us to evaluate whether accumulated evidence increased drug accessibility and to identify unresolved challenges and future directions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective analysis of clinical and genomic data from patients with breast cancer who underwent CGP testing between June 2019 and December 2024. Clinical and genomic information was collected from all patients who received any CGP assay. Data elements included CGP results (oncogenic alterations, tumor mutational burden [TMB]), patient characteristics (age, sex, Eastern Cooperative Oncology Group Performance Status [ECOG-PS], estrogen receptor [ER] status, HER2 status), assay type, specimen origin, and cancer-predisposing alterations identified via prior CDx testing (e.g., \u003cem\u003eBRCA1/2\u003c/em\u003e). The counts of oncogenic alterations were defined as those categorized as \u0026ldquo;Oncogenic\u0026rdquo; or \u0026ldquo;Predisposing\u0026rdquo; in the C-CAT database.\u003c/p\u003e \u003cp\u003eTarget alterations were extracted from variants classified as \u0026ldquo;Oncogenic\u0026rdquo;, \u0026ldquo;Likely Oncogenic\u0026rdquo;, \u0026ldquo;Pathogenic\u0026rdquo;, or \u0026ldquo;Likely Pathogenic\u0026rdquo;. TMB-high (TMB-H) was defined as \u0026ge;\u0026thinsp;10 muts/Mb for tissue-based assays and \u0026ge;\u0026thinsp;14 muts/Mb for liquid biopsy assays. Microsatellite instability-high (MSI-H) was counted only for assays reporting MSI (GenMine TOP\u003csup\u003e\u0026#127276;\u003c/sup\u003e does not report MSI). HER2 positivity was defined as immunohistochemistry (IHC) 3\u0026thinsp;+\u0026thinsp;or in situ hybridization (ISH)-positive.\u003c/p\u003e \u003cp\u003eFor capivasertib eligibility, \u003cem\u003ePIK3CA, AKT1, PTEN\u003c/em\u003e mutations, and \u003cem\u003ePTEN\u003c/em\u003e deletions were identified in ER-positive, HER2-negative breast cancer. Somatic \u003cem\u003eBRCA1/2\u003c/em\u003e mutations (\u003cem\u003esBRCA1/2\u003c/em\u003em) were assessed across all assays; germline \u003cem\u003eBRCA1/2\u003c/em\u003e mutations (\u003cem\u003egBRCA1/2\u003c/em\u003em) were assessed only in assays capable of paired germline analysis (OncoGuide\u0026trade; NCC Oncopanel [NOP], GenMine TOP\u003csup\u003e\u0026#127276;\u003c/sup\u003e). Here, we use the term \u0026lsquo;mutation\u0026rsquo; to describe a deleterious sequence variant, and the term \u0026lsquo;carrier\u0026rsquo; to describe an individual with a germline mutation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database\u003c/h3\u003e\n\u003cp\u003eThis study used the national C-CAT database maintained by the Ministry of Health, Labour and Welfare. C-CAT collects CGP results and clinical information from all insured CGP assays in Japan[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since June 2019, FoundationOne\u003csup\u003e\u0026#127276;\u003c/sup\u003e CDx (F1CDx) (Foundation Medicine Inc., Cambridge, MA, USA) and OncoGuideTM NCC Oncopanel (NOP) (Sysmex Co., Ltd., Kobe, Japan) have been started; FoundationOne\u003csup\u003e\u0026#127276;\u003c/sup\u003e Liquid CDx (F1Liquid) (Foundation Medicine Inc., Cambridge, MA, USA) was added in 2021, and Guardant360\u003csup\u003e\u0026#127276;\u003c/sup\u003e CDx (Guardant Health Inc., Palo Alto, CA, USA) and GenMine TOP\u003csup\u003e\u0026#127276;\u003c/sup\u003e (Konica Minolta, Inc., Toyo, Japan) were approved in 2023. All assays were included in this analysis.\u003c/p\u003e \u003cp\u003eUnder Japanese insurance, CGP testing is permitted for patients with advanced solid tumors who have completed or are expected to complete standard therapy. By December 2024, approximately 7,000 patients with breast cancer had undergone CGP testing, of whom 99.7% consented to secondary use of their data[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn C-CAT, test results are interpreted and clinical annotations of genomic alterations are determined using the Cancer Knowledge Database (CKDB) and commercial tools (e.g., QIAGEN Clinical Insight, JAX-CKB). Evidence Levels (ELs) are defined from A to R (supplementary Table\u0026nbsp;1), and secondary germline findings reviewed by expert panels (molecular tumor boards [MTBs]). Some missing clinical and treatment data were noted in the dataset.\u003c/p\u003e\n\u003ch3\u003eAssessment of drug accessibility rates in clinical practice\u003c/h3\u003e\n\u003cp\u003eDrug accessibility was evaluated by extracting actionable alterations annotated as \"Predictive\" in C-CAT. For the top 100 most frequent alterations, we listed drugs matched to each alteration together with their EP-ELs as assigned in the original C-CAT reports. We then calculated the number of drugs classified as Evidence Level A (EL-A; EP-EL in C-CAT). Subsequently, we conducted an expert panel simulation in which C-CAT reports were re-evaluated by expert panel members from multiple designated core hospitals, using evidence available as of October 2025. We then recalculated the numbers of drugs classified as EL-A (EP-EL in SIM). Drug accessibility rates were calculated according to Criterion I and Criterion II defined above.\u003c/p\u003e \u003cp\u003e The protocol was approved by the institutional review board of Kyoto Prefectural University of Medicine (ERB-C-2860) and the C-CAT Review Committee (AP20241127-01E). The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R statistical software version 4.3.2. The oncoprint visualizations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were created using the GenVisR package.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical and genomic characteristics\u003c/h2\u003e \u003cp\u003eIn the C-CAT database, a total of 6,307 patients with breast cancer with analyzable data were included. The median age was 56 years (range, 22\u0026ndash;91), and 99.4% were female (n\u0026thinsp;=\u0026thinsp;6,269). ECOG-PS of 0\u0026ndash;1 was observed in 92.3% (n\u0026thinsp;=\u0026thinsp;5,823). ER positivity was 63.9% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). F1CDx was the most frequently used assay (71.3%), followed by F1Liquid (17.5%), NOP (8.3%), GenMineTOP\u003csup\u003e\u0026#127276;\u003c/sup\u003e (1.6%), and Guardant360\u003csup\u003e\u0026#127276;\u003c/sup\u003e (1.3%). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents an oncoprint of genomic alterations identified by CGP testing in 6,307 Japanese patients with breast cancer, integrating small variants, deletions, amplifications, and rearrangements, together with MSI, TMB, and ER and HER2 status extracted from clinical data. The most frequently altered genes were \u003cem\u003eTP53\u003c/em\u003e (53.9%), \u003cem\u003ePIK3CA\u003c/em\u003e (37.7%), \u003cem\u003eMYC\u003c/em\u003e (17.9%), \u003cem\u003eCCND1\u003c/em\u003e (15.8%), and \u003cem\u003eGATA3\u003c/em\u003e (14.7%), followed by \u003cem\u003eESR1\u003c/em\u003e, which ranked tenth with a mutation frequency of 11.8%. The frequency of alterations outside the top ten genes was only a few percent. Overall, at least one genomic alteration was detected in 6,156 of 6,307 patients (97.6%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The median TMB was 4.0 muts/Mb, and TMB-H was observed in 11.1% of cases. MSI-H was identified in 0.4% of the cohort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient and Clinical Characteristics in C-CAT Database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6307 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6269 (99.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian age, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (22\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eAge group, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1282 (20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2071 (32.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1591 (25.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e872 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5257 (83.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e780 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1190 (18.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4621 (73.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e496 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEstrogen receptor status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4031 (63.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1918 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown/Uninspected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProgesteron receptor status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2969 (47.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2947 (46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown/Uninspected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e391 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e624 (9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5328 (84.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown/Uninspected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003egBRCA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3655 (57.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown/Uninspected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2508 (39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003egBRCA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264 (4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3515 (55.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown/Uninspected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2528 (40.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eECOG-PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3738 (59.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2085 (33.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2≦\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eType of CGP test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFoundationOne\u003csup\u003e\u0026#127276;\u003c/sup\u003e CDx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4496 (71.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFoundationOne\u003csup\u003e\u0026#127276;\u003c/sup\u003e Liquid CDx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1104 (17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOncoGuide\u0026trade; NCC Oncopanel System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e522 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuardant360\u003csup\u003e\u0026#127276;\u003c/sup\u003e CDx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenMineTOP\u003csup\u003e\u0026#127276;\u003c/sup\u003e CDx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSpecimens submitted for gene\u003c/p\u003e \u003cp\u003epanel testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2682 (42.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetastatic lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2430 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiquid biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1189 (18.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eC-CAT: Center for Cancer Genomics and Advanced Therapeutics\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eECOG-PS: Eastern Cooperative Oncology Group Performance Status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCGP: Comprehensive genomic profiling\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenomic Characteristics in CGP Tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6307 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6156 (97.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTMB (Muts/Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (tissue\u0026thinsp;\u0026lt;\u0026thinsp;10, liquid\u0026thinsp;\u0026lt;\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5523 (87.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (\u0026ge;\u0026thinsp;10 tissue, \u0026ge;\u0026thinsp;14 liquid)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e698 (11.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian TMB, range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0-258.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMSI-status\u003csup\u003e※\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4379 (69.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquivocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh not detected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1097 (17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e419 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e※ GenMineTOP is included in Unknown.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCGP: Comprehensive genomic profiling\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eTMB: tumor mutation burden\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMuts/Mb: mutations-per-megabase\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMSI: microsatellite instability\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe TOP100 Genomic Alterations, Evidence Levels at the Time of CGP Testing (EP-EL in C-CAT) and the Reassessed Evidence Levels (EP-EL in SIM), and Drugs Recommended by Expert Panels and Drug Accessibility Rates\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the 100 most frequent genomic alterations identified in Japanese patients with advanced or metastatic breast cancer, along with the evidence levels assigned by the expert panels at the time of CGP testing (EP-EL in C-CAT), as documented in the C-CAT reports. The proportion of alterations classified as evidence level A (EL-A) at the time of CGP testing is also shown. Furthermore, using the expert panel simulation incorporating updated evidence as of October 2025 (EP-EL in SIM), we reassessed evidence levels and corresponding therapeutic recommendations. Based on these data, Criteria I and II were calculated to assess drug accessibility and clinical applicability, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 100 Genomic Alteration: Evidence Levels at the Time of CGP Tests (EP-EL in C-CAT) and the reassessed Evidence Levels (EP-EL in SIM), and Drugs Recommended by Expert Panels and Drug Accessibility Rates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEP in C-CAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eEP in SIM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe proportion first linked to PMDA-approved drugs with phase III trial evidence for breast cancer through CGP tests but not prior CDx\u003c/p\u003e \u003cp\u003e(Criterion 1) (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe proportion first linked to PMDA-approved drugs including based on phase I and II trial evidence through CGP tests but not prior CDx (Criterion 2) (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEL\u003csup\u003e※\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEL-A (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEL\u003csup\u003e※\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEL-A\u003c/p\u003e \u003cp\u003e(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDrugs recommended by the EP (including FDA-approval)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDrugs recommended by the EP (PMDA-approval only)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B, C, D, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA_alpelisib\u003c/p\u003e \u003cp\u003eA_capivasertib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ealpelisib, fulvestrant, alpelisib\u0026thinsp;+\u0026thinsp;fulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIf mutation,\u003c/p\u003e \u003cp\u003eA_capivasertib\u0026thinsp;+\u0026thinsp;fulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003cp\u003e(ER+HER2\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMYC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCCND1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGATA3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFGF19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf co-amplification FGF3/4/19,\u003c/p\u003e \u003cp\u003eD_FGFRinhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B, C, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf loss and truncation,\u003c/p\u003e \u003cp\u003eA_capivasertib\u003c/p\u003e \u003cp\u003eC_everolimus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC_everolimus\u003c/p\u003e \u003cp\u003eIf mutation and deletion,\u003c/p\u003e \u003cp\u003eA_capivasertib +\u003c/p\u003e \u003cp\u003efulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003cp\u003e(ER+HER2\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B, C, D, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf amplification,\u003c/p\u003e \u003cp\u003eA_trastuzumab\u003c/p\u003e \u003cp\u003eA_pertuzumab\u003c/p\u003e \u003cp\u003eA_lapatinib\u003c/p\u003e \u003cp\u003eA_T-DM1\u003c/p\u003e \u003cp\u003eA_T-DXd\u003c/p\u003e \u003cp\u003eA_tucatinib\u003c/p\u003e \u003cp\u003eA_neratinib\u003c/p\u003e \u003cp\u003eIf mutation,\u003c/p\u003e \u003cp\u003eC_neratinib\u003c/p\u003e \u003cp\u003eC_T-DXd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eanit-HER2 drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIf amplification,\u003c/p\u003e \u003cp\u003eA_trastuzumab\u003c/p\u003e \u003cp\u003eA_pertuzumab\u003c/p\u003e \u003cp\u003eA_lapatinib\u003c/p\u003e \u003cp\u003eA_T-DM1\u003c/p\u003e \u003cp\u003eA_T-DXd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003cp\u003e(changed from HER2\u0026thinsp;\u0026minus;\u0026thinsp;based on IHC/ISH\u003c/p\u003e \u003cp\u003eto HER2\u0026thinsp;+\u0026thinsp;through CGP testing)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFGFR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB, C, D, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eD_pemigatinib\u003c/p\u003e \u003cp\u003eD_futibatinib\u003c/p\u003e \u003cp\u003eD_erdafitinib\u003c/p\u003e \u003cp\u003eD_infigratinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eESR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B, C, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA_elasestrant\u003c/p\u003e \u003cp\u003eB_fulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eelacestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB_fulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAKT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA_capivasertib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIf mutation,\u003c/p\u003e \u003cp\u003eA_capivasertib\u0026thinsp;+\u0026thinsp;fulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003cp\u003e(ER+HER2\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBRCA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B, C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA_olaparib\u003c/p\u003e \u003cp\u003eA_talazoparib\u003c/p\u003e \u003cp\u003eA_niraparib\u003c/p\u003e \u003cp\u003eC_rucaparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eolaparib, talazoparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA_olaparib\u003c/p\u003e \u003cp\u003eA_talazoparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003cp\u003e(g\u003cem\u003eBRCA2\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;s\u003cem\u003eBRCA2+\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCDKN2A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMDM4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC_MEKinhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRAD21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eARID1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIKBKE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZNF703\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDDR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMCL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAURKA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSTK11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCDKN2B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMDM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, D, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf G12C,\u003c/p\u003e \u003cp\u003eC_sotorasib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMAP3K1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAKT3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMAP2K4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBRCA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, B, C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA_olaparib\u003c/p\u003e \u003cp\u003eA_talazoparib\u003c/p\u003e \u003cp\u003eA_niraparib\u003c/p\u003e \u003cp\u003eC_rucaparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eolaparib, talazoparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA_olaparib\u003c/p\u003e \u003cp\u003eA_talazoparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003cp\u003e(g\u003cem\u003eBRCA1\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;s\u003cem\u003eBRCA1+\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGNAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKDM5A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZNF217\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNTRK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf fusion,\u003c/p\u003e \u003cp\u003eA_entrectinib\u003c/p\u003e \u003cp\u003eA_larotrectinib\u003c/p\u003e \u003cp\u003eIf amplification,\u003c/p\u003e \u003cp\u003eD_TRKinhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIf fusion,\u003c/p\u003e \u003cp\u003eA_entrectinib\u003c/p\u003e \u003cp\u003eA_larotrectinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC_olaparib\u003c/p\u003e \u003cp\u003eC_talazoparib\u003c/p\u003e \u003cp\u003eC_niraparib\u003c/p\u003e \u003cp\u003eC_rucaparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFGFR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, D, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf fusion,\u003c/p\u003e \u003cp\u003eC_pemigatinib\u003c/p\u003e \u003cp\u003eC_futibatinib\u003c/p\u003e \u003cp\u003eD_erdafitinib\u003c/p\u003e \u003cp\u003eD_infigratinib\u003c/p\u003e \u003cp\u003eIf mutation,\u003c/p\u003e \u003cp\u003eD_pemigatinib\u003c/p\u003e \u003cp\u003eD_futibatinib\u003c/p\u003e \u003cp\u003eC_erdafitinib\u003c/p\u003e \u003cp\u003eD_infigratinib\u003c/p\u003e \u003cp\u003eIf not fusion and mutation,\u003c/p\u003e \u003cp\u003eD_pemigatinib\u003c/p\u003e \u003cp\u003eD_futibatinib\u003c/p\u003e \u003cp\u003eD_erdafitinib\u003c/p\u003e \u003cp\u003eD_infigratinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCHEK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC_olaparib\u003c/p\u003e \u003cp\u003eC_talazoparib\u003c/p\u003e \u003cp\u003eC_niraparib\u003c/p\u003e \u003cp\u003eC_rucaparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, D, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf mutation (not exon20ins),\u003c/p\u003e \u003cp\u003eC_gefitinib\u003c/p\u003e \u003cp\u003eC_elrotinib\u003c/p\u003e \u003cp\u003eC_afatinib\u003c/p\u003e \u003cp\u003eC_osimertinib\u003c/p\u003e \u003cp\u003eIf mutation (exon20ins),\u003c/p\u003e \u003cp\u003eC_osimertinib\u003c/p\u003e \u003cp\u003eIf amplification,\u003c/p\u003e \u003cp\u003eC_afatinib\u003c/p\u003e \u003cp\u003eD_EGFRinhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMUTYH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCCNE1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTBX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCCND2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSPEN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEMSY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCDK4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC_CDK4/6inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTET2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSMAD4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKMT2D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRICTOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBCL2L1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSRC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCCND3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBRAF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA, C, D, E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf V600,\u003c/p\u003e \u003cp\u003eA_dabrafenib\u0026thinsp;+\u0026thinsp;trametinib\u003c/p\u003e \u003cp\u003eA_dabrafenib\u003c/p\u003e \u003cp\u003eA_trametinib\u003c/p\u003e \u003cp\u003eIf classII,\u003c/p\u003e \u003cp\u003eC_MEKinhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIf V600, 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edabrafenib\u0026thinsp;+\u0026thinsp;trametinib dimethyl sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIf V600,\u003c/p\u003e \u003cp\u003eA_dabrafenib\u0026thinsp;+\u0026thinsp;trametinib\u003c/p\u003e \u003cp\u003eA_dabrafenib\u003c/p\u003e \u003cp\u003eA_trametinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRAC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCDK12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC_olaparib\u003c/p\u003e \u003cp\u003eC_talazoparib\u003c/p\u003e \u003cp\u003eC_niraparib\u003c/p\u003e \u003cp\u003eC_rucaparib\u003c/p\u003e \u003cp\u003eIf loss of function,\u003c/p\u003e \u003cp\u003eC_immune checkpoint inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNOTCH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSMO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNKX2-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKIT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC, D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf mutation_Exson 17,\u003c/p\u003e \u003cp\u003eR_imatinib\u003c/p\u003e \u003cp\u003eR_sunitinib\u003c/p\u003e \u003cp\u003eC_dasatinib\u003c/p\u003e \u003cp\u003eC_avapritinib\u003c/p\u003e \u003cp\u003eC_ponatinib\u003c/p\u003e \u003cp\u003eIf mutation_Exson 11,\u003c/p\u003e \u003cp\u003eC_imatinib\u003c/p\u003e \u003cp\u003eC_dasatinib\u003c/p\u003e \u003cp\u003eC_ponatinib\u003c/p\u003e \u003cp\u003eIf amplification,\u003c/p\u003e \u003cp\u003eC_imatinib\u003c/p\u003e \u003cp\u003eC_dasatinib\u003c/p\u003e \u003cp\u003eC_ponatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCARD11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRAF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFGFR4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTERT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBCL6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNOTCH3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eROS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf fusion,\u003c/p\u003e \u003cp\u003eC_crizotinib\u003c/p\u003e \u003cp\u003eC_entrectinib\u003c/p\u003e \u003cp\u003eC_lorlatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJAK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePIK3CB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBRD4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e 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align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMEN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTSC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRNF43\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSOX9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFLT3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC_gilteritinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eC-CAT: Center for Cancer Genomics and Advanced Therapeutics\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eCGP: Comprehensive genomic profiling\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eEP: Expert panel\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eEL: Evidence level\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eEL-A: Evidence level-A\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eSIM: Simulation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eN: number of patients\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003ePMDA: Pharmaceuticals and Medical Devices Agency\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eCDx: Companion diagnostics\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eER+: Estrogen receptor-positive\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eHER2\u0026minus;: HER2-negative\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eHER2+: HER2-positive\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003egBRCA\u003c/em\u003e\u0026minus;\u003cem\u003esBRCA+\u003c/em\u003e: germline breast cancer susceptibility gene-negative and somatic breast cancer susceptibility gene-positive\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e※Exclusion of EL-R\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCompared with EP-EL in C-CAT, the number of EL-A assignments increased substantially after re-evaluation using EP-EL in SIM, rising from 3,757 to 5,874. Among variants assigned as EL-A under EP-EL in SIM, the corresponding PMDA-approved agents included: anti-HER2 therapies for \u003cem\u003eERBB2\u003c/em\u003e amplification, PARP inhibitors for \u003cem\u003eBRCA1/2\u003c/em\u003e mutations, capivasertib plus fulvestrant for \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations, entrectinib and larotrectinib for \u003cem\u003eNTRK\u003c/em\u003e fusions, and dabrafenib plus trametinib for \u003cem\u003eBRAF\u003c/em\u003e mutations. After excluding biomarkers already evaluable by pre-CGP CDx, clinically actionable alterations firstly identified by CGP were: \u003cem\u003eERBB2\u003c/em\u003e amplification in HER2-negative breast cancer (4.3%), s\u003cem\u003eBRCA1/2\u003c/em\u003em carriers in \u003cem\u003egBRCA1/2\u003c/em\u003em noncarriers (3.4%), \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations in ER-positive/HER2-negative breast cancer (non-overlapping; 30.8%), \u003cem\u003eNTRK\u003c/em\u003e fusions (0.1%), and \u003cem\u003eBRAF\u003c/em\u003eV600 mutations (0.2%).\u003c/p\u003e \u003cp\u003eAccording to Criterion I, only \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations (30.8%) fulfilled the requirement of linking to PMDA-approved therapies supported by phase III trial evidence in breast cancer. According to Criterion II, which included PMDA-approved drugs supported by phase I/II basket trial or tumor-agnostic evidence, the drug accessibility rate comprised \u003cem\u003eERBB2\u003c/em\u003e amplification (4.7%), \u003cem\u003esBRCA1/2\u003c/em\u003e mutations (3.4%), \u003cem\u003eNTRK\u003c/em\u003e fusions (0.1%), \u003cem\u003eBRAF\u003c/em\u003eV600 mutations (0.2%), and non-overlapping TMB-H/MSI-H (11.1%). The overall Criterion II accessibility rate was 19.5% (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When grouped by biomarkers, only ten actionable categories remained clinically relevant:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Evidence Level A in the Expert Panel Simulation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrugs recommended by the EP\u003c/p\u003e \u003cp\u003e(PMDA-approval only)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriterion 1 (%)\u003c/p\u003e \u003cp\u003ephase III evidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCriterion 2 (%)\u003c/p\u003e \u003cp\u003ephase I/II evidence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecapivasertib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003cp\u003e(ER+HER2\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMB-high/MSI-high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e amplification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eanti-HER2 therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003cp\u003e(HER2\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esBRCA1/2\u003c/em\u003e mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eolaparib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003cp\u003e(\u003cem\u003egBRCA1/2\u003c/em\u003e\u0026minus;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBRAF\u003c/em\u003e V600 mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edabrafenib\u0026thinsp;+\u0026thinsp;trametinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNTRK\u003c/em\u003e fusions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eentrectinib, larotrectinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eEP: Expert panel\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eER+: Estrogen receptor-positive\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eHER2\u0026minus;: HER2-negative\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cem\u003esBRCA\u003c/em\u003e: somatic breast cancer susceptibility gene\u003c/p\u003e\n\u003cp\u003e\u003cem\u003egBRCA\u003c/em\u003e\u0026minus;: germline breast cancer susceptibility gene-negative\u003c/p\u003e \u003cp\u003e \u003cem\u003ePIK3CA, AKT1, PTEN, ERBB2, BRCA1, BRCA2, NTRK, BRAF\u003c/em\u003e, TMB, and MSI.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence and Concordance of Germline and Somatic BRCA1/2 Mutations\u003c/h3\u003e\n\u003cp\u003eGermline \u003cem\u003eBRCA1/2\u003c/em\u003e mutations previously assessed by CDx (e.g., BRACAnalysis\u003csup\u003e\u0026#127276;\u003c/sup\u003e) were identified in 144 patients (2.3%) for \u003cem\u003egBRCA1\u003c/em\u003em carriers and 264 patients (4.2%) for \u003cem\u003egBRCA2\u003c/em\u003em carriers. Approximately 40% of patients had an unknown or untested germline \u003cem\u003eBRCA1/2\u003c/em\u003em status at the time of CGP testing (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After excluding cases with unknown g\u003cem\u003eBRCA1/2\u003c/em\u003e test results, the prevalence was 3.8% for \u003cem\u003egBRCA1\u003c/em\u003em carriers (144/3799) and 7.0% for \u003cem\u003egBRCA2\u003c/em\u003em carriers (264/3779). Among patients with \u003cem\u003egBRCA1\u003c/em\u003em carriers (n\u0026thinsp;=\u0026thinsp;144), \u003cem\u003esBRCA1\u003c/em\u003em were detected by CGP in 106 patients (73.6%). Similarly, among patients with \u003cem\u003egBRCA2\u003c/em\u003em carriers (n\u0026thinsp;=\u0026thinsp;264), \u003cem\u003esBRCA2\u003c/em\u003em were detected in 246 (93.2%). In contrast, among CDx-confirmed germline mutation noncarriers, \u003cem\u003esBRCA1\u003c/em\u003em was detected by CGP in 49 of 3,655 patients (1.3%) who were \u003cem\u003egBRCA1\u003c/em\u003em noncarriers, and \u003cem\u003esBRCA2\u003c/em\u003em was detected in 77 of 3,515 patients (2.2%) who were \u003cem\u003egBRCA2\u003c/em\u003em noncarriers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After excluding overlap, 116 patients (3.4%) who were \u003cem\u003egBRCA1/2\u003c/em\u003em noncarriers by CDx were found to harbor \u003cem\u003esBRCA1/2\u003c/em\u003em by CGP testing. Notably, among cases classified as g\u003cem\u003eBRCA1/2\u003c/em\u003em noncarriers by CDx, none were reclassified as germline carriers based on CGP testing. determined to be g\u003cem\u003eBRCA1/2\u003c/em\u003em carriers based on CGP testing.\u003c/p\u003e\n\u003ch3\u003eDiscordance between HER2 positivity by IHC/ISH and ERBB2 amplification by CGP testing\u003c/h3\u003e\n\u003cp\u003eDiscordance between HER2 status assessed by conventional IHC/ISH and \u003cem\u003eERBB2\u003c/em\u003e amplification detected by CGP testing was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When stratified by HER2 IHC score, the positivity rate of \u003cem\u003eERBB2\u003c/em\u003e amplification by CGP testing was highest in IHC 3\u0026thinsp;+\u0026thinsp;tumors (61.7%), followed by IHC 2+ (12.8%) and IHC 1+ (5.5%). Importantly, \u003cem\u003eERBB2\u003c/em\u003e amplification was not identified in a substantial proportion of tumors even among those classified as IHC 3+. Similarly, concordance between HER2 positivity by ISH and \u003cem\u003eERBB2\u003c/em\u003e amplification detected by CGP testing was limited, with \u003cem\u003eERBB2\u003c/em\u003e amplification identified in only 39.2% of HER2 ISH\u0026ndash;positive tumors. Notably, \u003cem\u003eERBB2\u003c/em\u003e amplification was also detected in 4.7% of tumors classified as HER2-negative by IHC/ISH (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrates that genomic medicine for breast cancer in Japan has steadily advanced through coordinated improvements in technology, regulatory frameworks, and clinical infrastructure. The approval of CGP testing, introduction of tumor\u0026ndash;normal paired sequencing, and alignment with global standards for CDx and tumor-agnostic drug approvals have significantly expanded therapeutic opportunities. Among these developments, regulatory approval of a CDx for \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations represents a major milestone in precision oncology for breast cancer[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Accumulated data from the C-CAT have demonstrated a comparable prevalence of \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations in the domestic population[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our cohort, CGP testing firstly enabled access to at least one PMDA-approved drug in 50.3% of patients when Criteria I and II were combined. This represents a substantial increase compared with our previous report of 37.9%[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], primarily attributable to an increased number of cases classified as EL-A and the regulatory approval of additional targeted therapies, including capivasertib for \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations and olaparib for \u003cem\u003esBRCA1/2\u003c/em\u003e mutations. Notably, \u003cem\u003esBRCA1/2\u003c/em\u003e mutations were identified in 3.4% of patients without \u003cem\u003egBRCA1/2\u003c/em\u003e mutations. In a nationwide analysis of 7,051 Japanese patients with breast cancer, the prevalence of \u003cem\u003egBRCA1\u003c/em\u003e and \u003cem\u003egBRCA2\u003c/em\u003e mutations carriers has been reported to be 1.9% and 3.2%, respectively[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; however, the prevalence of \u003cem\u003esBRCA1/2\u003c/em\u003em in this population had not previously been well characterized. To our knowledge, the present study is the first to demonstrate that 3.4% of Japanese patients with breast cancer who are \u003cem\u003egBRCA1/2\u003c/em\u003e noncarriers harbor \u003cem\u003esBRCA1/2\u003c/em\u003e mutations (1.3% in \u003cem\u003esBRCA1\u003c/em\u003em and 2.2% in \u003cem\u003esBRCA2\u003c/em\u003em). These findings are generally consistent with prior reports, including a Scandinavian cohort of 273 breast cancer cases showing an \u003cem\u003esBRCA\u003c/em\u003em prevalence of 3.0%[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], a meta-analysis (3.4% for \u003cem\u003esBRCA1\u003c/em\u003em and 2.7% for \u003cem\u003esBRCA2\u003c/em\u003em)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and a study across Western and Asia-Pacific populations (1.6% \u003cem\u003esBRCA1\u003c/em\u003em and 4.7% \u003cem\u003esBRCA2\u003c/em\u003em)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Collectively, these data suggest that ethnic differences in the prevalence of \u003cem\u003esBRCA1/2\u003c/em\u003em prevalence may be limited. Taken together, these real-world insights derived from CGP testing using the C-CAT database contribute substantially to the refinement of therapeutic targets and the optimization of patient selection in precision oncology for breast cancer.\u003c/p\u003e \u003cp\u003eDespite these advances, significant limitations persist. According to Criterion I\u0026mdash;the conventional standard requiring breast cancer-specific phase III evidence\u0026mdash;only \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations (30.8%) were linked to actionable therapies. In practical terms, it is not an overstatement to suggest that, to date, CGP testing in breast cancer has largely been performed to identify candidates for capivasertib. Consequently, with the broader clinical implementation of the CDx (e.g., OncoGuide\u003csup\u003eTM\u003c/sup\u003eOncoScreen\u003csup\u003eTM\u003c/sup\u003ePlus CDx), it will be necessary to re-evaluate the incremental value of broad CGP beyond targeted CDx testing. Moreover, although Criterion II expanded the actionable population to 19.5%, most supporting evidence derives from tumor-agnostic phase I/II trials, and breast cancer was not always represented in these studies, warranting caution in clinical interpretation. The discordance between HER2 positivity assessed by conventional diagnostics and \u003cem\u003eERBB2\u003c/em\u003e amplification detected by CGP testing should also be interpreted in this context. Although HER2 positivity by IHC or ISH has traditionally been considered concordant with \u003cem\u003eERBB2\u003c/em\u003e amplification, our analysis demonstrated discordance. Specifically, \u003cem\u003eERBB2\u003c/em\u003e amplification detected by CGP testing was observed in only 61.7% of HER2 IHC\u0026ndash;positive tumors, and in merely 39.2% of HER2 ISH\u0026ndash;positive tumors. This result was consistent with previous reports[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, while \u003cem\u003eERBB2\u003c/em\u003e amplification was detected in a small subset of HER2-negative tumors, the low frequency and uncertainty regarding its clinical significance further underscore the currently limited role of CGP in guiding HER2-targeted therapeutic decisions. Despite the fact that 97.6% of the 6,307 cases harbored at least one genomic alteration, only 10 genomic or molecular biomarkers (\u003cem\u003ePIK3CA, AKT1, PTEN, ERBB2, BRCA1, BRCA2, NTRK, BRAF\u003c/em\u003e, TMB, MSI) were clinically relevant for drug accessibility. From a practical and cost-effectiveness perspective, the development of a low-cost, high-efficiency, breast cancer\u0026ndash;specific targeted gene panel focusing on this limited set of biomarkers may be warranted for real-world implementation. Thus, insights derived from large-scale CGP analyses using the C-CAT database provide an important foundation for optimizing precision oncology in the future.\u003c/p\u003e \u003cp\u003eNevertheless, several major gaps persist in real-world clinical implementation. The rate of conversion from alteration detection to actual treatment access remains low. A domestic report has suggested that only approximately 10% of patients undergo genotype-matched treatment after CGP testing[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. CGP testing is often performed at an advanced stage of disease, frequently after the exhaustion of standard therapies; as a result, a subset of patients are unable to receive subsequent genotype-matched treatments because of disease progression or deterioration in performance status[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. And, substantial variability exists in the operation of MTBs and in access to recommended treatments across institutions. For example, inter-institutional differences have been reported in MTB processes, including the quality and timeliness of genomic interpretation and reporting[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Persistent challenges remain in the management of genetic counseling, secondary findings, presumed germline pathogenic variants (PGPVs), and the establishment of robust follow-up systems. In our cohort, \u003cem\u003esBRCA1/2\u003c/em\u003em were detected in 3.4% of patients who were negative for \u003cem\u003egBRCA1/2\u003c/em\u003em (after excluding overlap). These findings underscore the need to further define optimal clinical handling and follow-up frameworks for such results, including appropriate counseling, confirmatory testing when indicated, and longitudinal management strategies. In addition, the C-CAT database currently lacks comprehensive clinical outcome data, limiting the ability to fully assess the real-world effectiveness of CGP-guided therapies. Taken together, genomic medicine in breast cancer appears to be transitioning from a phase focused primarily on \u0026ldquo;testing to detect alterations\u0026rdquo; to a phase in which the key objective is to \u0026ldquo;translate detected alterations into actionable treatment and supportive care.\u0026rdquo; However, at present, the field has not yet reached a stage of broad dissemination and routine, scalable implementation in clinical practice. Looking ahead over the next 5\u0026ndash;10 years, the following directions for improvement will be very important.\u003c/p\u003e \u003cp\u003eFirst, the timing of CGP testing warrants reconsideration. In breast cancer, CGP testing should ideally be performed not only at the time of recurrence or metastasis but also at an appropriate earlier stage, when expanding therapeutic options is most clinically meaningful[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Recently, a companion diagnostic assay targeting \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations for capivasertib has been implemented in clinical practice, demonstrating a high overall concordance rate of 95.7% with F1CDx[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As demonstrated in the present study, the proportion of patients eligible for approved therapies based on conventional criteria\u0026mdash;namely, drugs supported by phase III trials in breast cancer (Criterion I)\u0026mdash;was limited to alterations in these three genes, accounting for only 30.8% of cases. Given the availability of companion diagnostics for these three genes, it seems appropriate to re-evaluate both the optimal timing and clinical significance of comprehensive CGP testing. As discussed above, the development and implementation of a cost-effective, breast cancer\u0026ndash;specific targeted gene mini-panel incorporating these three genes along with additional clinically relevant alterations may represent a practical and valuable alternative in real-world settings. Strategic integration of such targeted panels with CGP testing, performed at an appropriate time point, may further expand therapeutic sequencing options for patients.\u003c/p\u003e \u003cp\u003eSecond, improving access to matched therapies and securing sufficient clinical trial opportunities are critical. To streamline the transition from mutation detection to access to approved or investigational therapies, the establishment of a nationwide collaborative MTB network, reduction of inter-institutional and regional disparities, and strengthening of support systems for clinical trial enrollment are required[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, the cost-effectiveness and accessibility of off-label treatments and non-reimbursed drugs should be carefully evaluated to ensure equitable access to precision oncology[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, the clinical implementation of next-generation technologies and assays requires both robust evidence generation and regulatory preparedness. Circulating tumor DNA\u0026ndash;based monitoring (liquid biopsy), as well as RNA sequencing and methylation analyses, hold promise for early detection and the identification of emerging drug resistance. However, their adoption into routine clinical practice requires high-level evidence demonstrating analytical validity, reproducibility, clinical utility, and cost-effectiveness. Similarly, while AI-based interpretation support systems are promising, their clinical validity, fairness, and explainability must be rigorously evaluated alongside appropriate regulatory frameworks[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFourth, workforce development and operational standardization within multidisciplinary and nationwide frameworks remain ongoing challenges. In March 2022, the Japanese Ministry of Health, Labour and Welfare announced that simplification of expert panel processes may be feasible by limiting reported results to high-evidence-level genomic alterations. However, such alterations account for less than 10% of all detected variants. Moreover, annotation of not only somatic variants but also germline variants and presumed germline pathogenic variants (PGPVs) relies on expertise informed by global databases, population-specific genomic data, and individual and family histories, making this process labor-intensive and technically demanding. Consequently, substantial human resources are still required at individual institutions, particularly at core hospitals[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite ongoing efforts to expand panel testing capacity, variability in expertise, experience, and institutional resources among MTB members continues to result in heterogeneity in discussion quality, posing a barrier to equitable genomic medicine delivery[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, balancing process simplification with workforce training remains challenging under constraints related to cost-effectiveness and healthcare labor reforms.\u003c/p\u003e \u003cp\u003eFifth, enhanced data utilization and clinical outcome assessment are essential. The C-CAT system was established to centrally collect, manage, and appropriately utilize genomic data for cancer genomic medicine in Japan[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the complexity of clinical data entry and the high proportion of missing data\u0026mdash;particularly for subsequent treatments and survival outcomes\u0026mdash;limit its current utility[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Standardized methodologies and metrics are needed to longitudinally evaluate the real-world impact of genomic medicine, including mutation detection rates, matched therapy rates, clinical trial enrollment, survival outcomes, and quality of life.\u003c/p\u003e \u003cp\u003eFinally, patient and family education, ethical considerations, and follow-up systems must be strengthened. A substantial proportion of reported genomic alterations are variants of uncertain significance (VUS), posing interpretational challenges even for experts[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Communicating these findings without causing unrealistic expectations or undue anxiety is difficult. Comprehensive support frameworks encompassing pre-test informed consent, disclosure of secondary findings, genetic counseling, family testing and support, and post-test follow-up are therefore essential. Reduced patient satisfaction and increased psychological burden have been reported among patients who undergo CGP testing without receiving matched therapies, underscoring the importance of ethical communication and psychological support in routine implementation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, genomic medicine for breast cancer in Japan is progressing toward integration into routine clinical practice. In the short term, the above-mentioned improvement directions will be prioritized, and in the medium to long term, the key will be to build a system to quantify the effects of individualized treatment that combines C-CAT, a nationwide data infrastructure, and AI. However, the clinical impact of CGP remains limited by low rates of drug accessibility and the narrow range of biomarkers linked to approved therapies. Even with updated 2025 evidence, high-level evidence\u0026ndash;based treatment opportunities remain concentrated in a small subset of genomic alterations.\u003c/p\u003e \u003cp\u003eFrom the perspectives of cost-effectiveness, resource allocation, and real-world feasibility, CGP may currently be most appropriate for use within research-intensive cancer centers rather than universal deployment across all clinical settings. Continued national-level discussion and policy development will be essential to define the optimal role of CGP testing in Japanese breast cancer care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCGP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomprehensive genomic profiling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-CAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenter for Cancer Genomics and Advanced Therapeutics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDx\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCompanion diagnostics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erandomized controlled trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epⅢ RCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhase Ⅲ Randomized controlled trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman epidermal growth factor receptor type2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood and Drug Administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePMDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePharmaceuticals and Medical Devices Agency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpert panel\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEvidence level\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esimulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestrogen receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMB-H\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor mutation burden-high\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSI-H\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrosatellite instability-high.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval of the research protocol by\u0026nbsp;the Ethics Review Committee of Kyoto Prefectural University of Medicine (ERB-C-2860) and by the review board of C-CAT (AP20241127-01E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMidori Morita\u0026nbsp;\u003c/strong\u003ehas received research funding from Murata outside the submitted work. \u003cstrong\u003e\u0026nbsp;Koichi Takayama\u003c/strong\u003e received grants from Chugai Pharmaceutical and Ono Pharmaceutical, and personal fees from AstraZeneca, Chugai Pharmaceutical, MSD, Eli Lilly, Boehringer Ingelheim, and Daiichi-Sankyo. \u003cstrong\u003eYasuto Naoi\u0026nbsp;\u003c/strong\u003ehas received research funding from Eisai, Shimazu, Murata, ONO, Daiichi-Sankyo and AstraZeneca, as well as honoraria from Eisai, AstraZeneca, Pfizer, Eli Lilly, Daiichi-Sankyo and Chugai outside the submitted work; he holds joint patents with Sysmex including Curebest\u0026trade; 95GC Breast (JP.5725274.B2). The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM Morita and Y Naoi contributed to the planning and design of the study. M Morita and R Tsunashima analyzed the data. T Yoshinami and M Nishida added annotation of the data. All authors approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGarraway LA, Verweij J, Ballman KV. Precision oncology: an overview. \u003cem\u003eJ Clin Oncol\u003c/em\u003e. 2013 May 20;31(15):1803-5. doi: 10.1200/JCO.2013.49.4799. Epub 2013 Apr 15. \u003c/li\u003e\n\u003cli\u003eMardis ER. 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Epub 2022 Jan 24. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Comprehensive genomic profiling, Clinical utility, drug accessibility rates, the Center for Cancer Genomics and Advanced Therapeutics, BRCA","lastPublishedDoi":"10.21203/rs.3.rs-8861391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8861391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eComprehensive genomic profiling (CGP) has advanced precision oncology in breast cancer, yet its real-world contribution to treatment selection in Japan remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed 6,307 Japanese patients with breast cancer who underwent CGP in the nationwide Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database. Drug accessibility to Pharmaceuticals and Medical Devices Agency (PMDA)-approved therapies was assessed using two criteria, excluding biomarkers identifiable by prior companion diagnostics: Criterion I reflecting phase III breast cancer trials, and Criterion II reflecting phase I/II basket or tumor-agnostic trials. Evidence levels at testing were compared with those reassessed using evidence available as of October 2025.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAt least one genomic alteration was detected in 97.6% of cases; \u003cem\u003eTP53\u003c/em\u003e (53.9%), \u003cem\u003ePIK3CA\u003c/em\u003e (37.7%) were most frequent. Re-evaluation increased Evidence Level A assignments from 3,757 to 5,874. Nevertheless, biomarkers linked to PMDA-approved therapies were limited to ten categories: \u003cem\u003ePIK3CA/AKT1/PTEN\u003c/em\u003e alterations, \u003cem\u003eERBB2\u003c/em\u003e amplification, \u003cem\u003eBRCA1/2\u003c/em\u003e mutations, \u003cem\u003eNTRK\u003c/em\u003e fusions, \u003cem\u003eBRAF\u003c/em\u003e V600 mutation, and TMB-H/MSI-H. Drug accessibility was 30.8% under Criterion I, driven exclusively by eligibility for capivasertib. Under Criterion II, the accessibility was 19.5%, comprising \u003cem\u003eERBB2\u003c/em\u003e amplification (4.7%), somatic \u003cem\u003eBRCA1/2\u003c/em\u003e mutations (3.4%), \u003cem\u003eNTRK\u003c/em\u003e fusions (0.1%), \u003cem\u003eBRAF\u003c/em\u003e V600 mutation (0.2%), and non-overlapping TMB-H/MSI-H (11.1%). The prevalence of somatic \u003cem\u003eBRCA1/2\u003c/em\u003e mutations in germline \u003cem\u003eBRCA1/2\u003c/em\u003e noncarriers were identified in 3.4%, representing the first large-scale estimate in Japanese breast cancer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDespite high detection rates, translation into PMDA-approved therapies remains limited, underscoring the need to optimize testing timing, expand trial access, harmonize molecular tumor boards practice, and consider breast cancer\u0026ndash;specific mini-panels.\u003c/p\u003e","manuscriptTitle":"Real-World Landscape of Genomic Medicine in Breast Cancer Revealed by the C-CAT Database: Reappraisal of Drug Accessibility and Clinical Utility in 6,307 Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 06:26:34","doi":"10.21203/rs.3.rs-8861391/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73dc6892-1bfe-456c-a82c-3d8b4350527d","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject","date":"2026-05-08T00:22:48+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T04:25:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 06:26:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8861391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8861391","identity":"rs-8861391","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}