Assessing In House Comprehensive Genomic Profiling by Liquid Biopsy for NSCLC Patients

Assessing In House Comprehensive Genomic Profiling by Liquid Biopsy for NSCLC 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 Article Assessing In House Comprehensive Genomic Profiling by Liquid Biopsy for NSCLC Patients Andrea Vingiani, Alberta Piccolo, Adele Busico, Iolanda Capone, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6264683/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 Liquid biopsy has emerged as a valuable tool for detecting therapeutic targets and resistance mechanisms. In this study, we aimed at evaluating the analytical performance of the TruSight Oncology 500 ctDNA (TSO500) compared to the FDA-approved Guardant360 CDx (G360) in detecting actionable alterations in NSCLC patients progressing on targeted therapies. We analysed 44 plasma samples from 36 consecutive metastatic NSCLC patients with known molecular drivers. The comparative analysis included 31 paired samples from 27 patients. TSO500 demonstrated high sensitivity in detecting G360-identified variants (81.02%). Concordance was particularly high for ESCAT I alterations (sensitivity: 95.2%), including gene fusions (100% sensitivity). Both LB assays identified resistance mutations in 12/26 patients, with TSO500 detecting all but 3 G360-identified resistance alterations. Our findings support TSO500's analytical validity and clinical utility, demonstrating high concordance with G360 for actionable alterations detection, highlighting its potential value in guiding treatment decisions. Biological sciences/Cancer/Cancer genomics Health sciences/Biomarkers/Diagnostic markers Health sciences/Biomarkers/Predictive markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cancer genomics showed an unprecedented improvement during the last decade. The widespread adoption of Next-Generation Sequencing (NGS) technologies at relatively low costs, combined with the availability of an increasing number of selective drugs, has led to a paradigm shift in oncology, thus leading to significant improvements in terms of patients’ outcome 1 – 6 . Given the increasing number of druggable alterations, NGS-based multi-gene testing represents the recommended approach for simultaneous detection of multiple gene alterations in a variety of solid malignancies, including non-small cell lung cancer (NSCLC), colorectal, prostatic and biliary cancer 7 , 8 . Liquid biopsy (LB) offers a non-invasive method for identifying tumor genomic aberrations. This approach addresses several limitations associated with traditional tumor tissue biopsies, such as feasibility, patient discomfort and morbidity 5 , 9 – 11 . In this context, NSCLC represents a model for LB application. In 2013, it was firstly demonstrated that LB could detect resistance mutations to first- and second-generation EGFR inhibitors (EGFR T790M) 12 , showing the clinical utility of LB in patients 13 . On June 2016, the U.S. Food and Drug Administration (FDA) approved the first plasma-based molecular assay, the Cobas® EGFR Mutation Test 2 (Roche Molecular Systems, Inc.), a real-time PCR test for the detection of EGFR exon 19 indels and the L858R mutations to identify NSCLC eligible for erlotinib [ https://www.fda.gov/drugs/resources-information-approved-drugs/cobas-egfr-mutation-test-v2 ]. Consistently, LB tests have recently been proven to represent a valuable tool in guiding treatment options for metastatic patients when obtaining diagnostic tissue material is challenging 14 . In particular, ESMO Clinical Practice Guidelines recommend the use of ctDNA testing in treatment-naive patients in which tissue biopsy is risky or contraindicated, and in pre-treated oncogene addicted patients for the detection of resistance mutations 15 – 17 . In recent years, several comprehensive NGS panels have been developed and commercialized, allowing an exhaustive characterization of tumor mutational landscape, including the detection gene mutations, fusions, copy number variation (CNV), tumor mutational burden (TMB), microsatellite instability (MSI), loss of heterozygosity (LOH) and homologous recombination 18 . These data provide a strong rationale to extend comprehensive genomic profiling (CGP) to LB in the clinical practice. To this aim, we designed a head-to-head comparison of two LB CGP assays, i.e. Guardant360 CDx® and TruSight Oncology 500 ctDNA, in a consecutive case cohort of NSCLS patients with known actionable targets. G360 is a FDA-approved, in service, DNA-based NGS panel, investigating single nucleotide variants (SNV), indels, gene fusions and CNV in up to 74 cancer-related genes, which has been extensively validated through numerous studies and clinical trials, showing its effectiveness in identifying actionable mutations and guiding targeted therapy in NSCLC and other cancer types 19 . Results Patients From May 2022 to March 2024, we enrolled 36 NSCLC patients progressing on tyrosine kinase inhibitors, previously characterized with molecular tests performed in our institution. Clinico-pathological and molecular characteristics of the study population are summarized in Table 1 . In 8 patients, a second blood drawn was taken at further disease progression, leading to an overall number of collected samples of 44. G360 provided valid reports in 42 out of 44 cases (95.45%). In the remaining 2 cases, G360 results did not meet the internal quality criteria. Regarding TSO500, in 12 out of 44 samples (27.2%), the ctDNA content was below 30 ng (median ctDNA content of unprocessed samples: 17.09 ng; range: 7-26.3 ng), and the test was not performed. The comparative analysis was therefore performed on 31 samples from 27 patients in which paired G360 and TSO500 data were available (Table 1 ). Study design is summarized in Fig. 1 . Table 1 Patients enrolled (n = 36) Comparative analysis patients (n = 27) Gender Male 7 (19.4%) 6 (22.2%) Female 29 (80.5%) 21 (77.8%) Age (years) Mean ± SD 64.6 ± 10.7 63.1 ± 10.02 Median (range) 65.7 (37.8–83.3) 60.7 (44.2–83.3) Smoking history Yes 18 (50%) 14 (51.8%) No 18 (50%) 12 (44.4%) Previous oncological surgery Yes 11 (30.5%) 7 (25.9%) No 25 (69.4%) 20 (74.07%) Histology Adenocarcinoma 33 (91.6%) 25 (92.5%) Large Cell Neuroendocrine Carcinoma 2 (5.5%) 2 (7.4%) Adenosquamous lung carcinoma 1 (2.7%) - Prior molecular characterization External 9 (22.2%) 5 (18.5%) Ion AmpliSeq Cancer Hotspot Panel (CHP) 6 (16.6%) 4 (14.8%) DNA Oncomine Research Assay Plus 10 (27.7%) 8 (29.6%) Targeted panel - LKB1 7 (19.4%) 7 (25.9%) Real Time PCR 5 (13.8%) 3 (11.1%) RNA Oncomine Research Assay Plus 16 (44.4%) 12 (44.4%) Archer FusionPlex Lung Panel 2 (5.5%) 2 (7.4%) Fluorescent In Situ Hybridization (FISH) 18 (50%) 13 (48.1%) Driver molecular alteration EGFR 20 (55.5%) 15 (55.5%) ALK 9 (25%) 5 (18.5%) ROS1 3 (8.33%) 3 (11.1%) RET 3 (8.33%) 3 (11.1%) BRAF 2 (5.55%) 2 (7.4%) Line of therapy I 26 (72.2%) 20 (74.04%) II 6 (16.6%) 3 (11.1%) >II 4 (11.1%) 4 (14.8%) Latest therapy TKI inhibitors 33 (91.6%) 24 (88.8%) Immunotherapy 1 (2.7%) 1 (3.7%) Chemotherapy 2 (5.5%) 2 (7.4%) Previous Radiotherapy Yes 25 (69.4%) 18 (66.6%) No 11 (30.5%) 9 (33.3%) G360 and TSO molecular results Among the 31 samples included in the comparative analysis, G360 identified 137 variants (mean per patient: 4.42, range 0–15). 95 variants were annotated as P/LP (mean per patient: 3.06, range: 0–9), and 42 as variants of unknown significance (VUS; mean per patient: 1.35, range: 0–9). In particular, G360 identified 117 SNV/indels, 5 gene fusions and 15 CNVs. TSO500 ctDNA assay identified 723 variants (mean per patient: 23.32, range: 7-123). 217 variants were P/LP (mean per patient: 7, range: 0–27), and 489 VUS (mean per patient: 15.77, range: 5–96), including 598 SNV/indels, 5 fusions and 103 CNV. No significant correlation was found between the DNA input and the number of variants obtained (Pearson’s r = -0.017, p = 0.92) (Supplementary Fig. 1A). The mean value of TMB obtained by TSO 500 ctDNA assay was 8.9 mut/Mb (range: 0–73.4). No significant correlation was found between the DNA input and TMB value (Pearson’s r = 0.132, p = 0.47) (Supplementary Fig. 1B). No significant association was found between ctDNA content and number of metastatic sites (Wilcoxon rank-sum test, p = 0.43) (Supplementary Fig. 2). G360 and TSO500 comparative analysis Overall, TSO500 correctly identified 111 out of the 137 variants (81.02%) detected by G360. TSO500 found 102 variants (range per sample: 1–19, median: 3) which were not identified by G360, leading to an overall specificity of 0.97 (range: 0.74-1) (Fig. 2 A and 2 B). TSO500 correctly identified 81 out of the 95 P/LP variants (85.26%) found by G360 (median sensitivity 0.89, range 0.33-1). Additionally, TSO500 detected 138 P/LP variants (range: 1–22, median: 3) not identified by G360 (specificity 0.99, range 0.88-1) (Fig. 2 C and 2 D). Comparative analysis through ESCAT tier classification For the evaluation of the potential clinical impact, we compared alterations identified by TSO500 and G360 across ESCAT classes. TSO500 and G360 were consistent in detecting ESCAT I (20 and 21 variants by TSO500 and G360, respectively) and ESCAT II variants (3 and 1 variants, respectively), while TSO500 identified a higher number of ESCAT III (17 and 12 variants) and ESCAT IV (30 and 9 variants) variants (Fig. 3 ). Compared with G360, TSO500 showed high sensitivity across ESCAT classes (95.2%, 100%, 58.3%, 88.8% for ESCAT I, II, III, IV class mutations, respectively). In particular, among standard of care actionable targets (ESCAT I), TSO500 did not detect an EGFR mutation (p.Leu858Arg) that was identified by G360; in this case, TSO500 run showed a low median coverage (927) (Fig. 4). Notably, all the 5 gene fusions identified by G360 (4 ALK and 1 RET fusions) were confirmed by TSO. TSO500 did not detect 5 ESCAT IIIA variants, including 2 PIK3CA, 1 BRCA1, 1 PTEN and 1 ATM variants. Except for the BRCA1 mutation (VAF: 8%), all these variants were characterized by a low variant allele frequency in G360 (range: 0.05%-0,38%). Of note, after a subsequent review of the BAM files using the IGV software, one of the PIK3CA variants and the PTEN variant were detected by TSO500, albeit at low frequency (PIK3CA, VAF: 0.07%) or affected by systematic noise (PTEN, VAF: 0.24). These variants had been excluded from the VCF files due to pre-specified quality filtering. On the other hand, no additional ESCAT I mutations missed by G360 were identified by TSO500 (Fig. 4). A high sensitivity (14/15, 93,3%) was also found with regard to CNV for EGFR (6 cases), BRAF (2), CCNE1 (2), MET (1), KRAS (1) and CCND1 (1). TSO500 identified 44 potential intrinsic or acquired resistance mutations in 12 out of 26 patients (46.2%), compared to 25 resistance mutations detected by G360 in 12 patients, including EGFR p.L718G, p.C797S and amplification, PIK3CA mutations, MET amplification, cell cycle genes alterations, FGF genes alterations and RAS genes alterations 19 – 26 . In particular, TSO500 detected all but 3 resistance mutations identified by G360, including the two aforementioned PIK3CA mutations in two patients treated with anti-EGFR therapy, and one ALK mutation (p.Gly1202Arg) in a patient with ALK fusion treated with lorlatinib. Finally, we observed a high correlation in variant allele frequencies among gene alterations detected by both TSO and G360 (Pearson’s r = 0.89, p-value = 0.000) (Supplementary Fig. 3). Comparison between LB and tissue NGS results at relapse In 9 out of 27 patients (33.3%), concurrent tissue-based CGP was performed at the same disease progression timepoint, allowing a direct comparison between tissue and LB NGS results. Overall, in 7/9 cases TSO500 identified 16 potentially actionable (ESCAT I-IV) and resistance mutations which were not identified by concurrent tissue NGS, including 2 ESCAT IA variants (two KRAS G12C), 5 ESCAT IIIA variants (two BRCA1 deletions, one PIK3CA mutation, one KRAS mutation and one KMT2A mutation), one ESCAT IIIB (MET mutation), 5 ESCAT IV variants (one CHEK2 mutation, and PIK3CA, FGFR3, BRAF, and CDK6 amplifications), and 3 EGFR resistance alterations (one amplification and 2 mutations). Of note, in a patient with an ALK-EML4 positive NSCLC progressing after two lines of ALK inhibitors (Alectinib and Lorlatinib), both G360 and TSO500 detected a KRAS p.G12C and a PIK3CA p.E542K mutation, which were not identified by concurrent tissue NGS. Upon further progression to a third line of treatment with carboplatin-pemetrexed, this patient was subjected to additional molecular profiling in both tissue and LB samples. The KRAS variant was detected in subsequent analysis both in tissue and LB samples, while the PIK3CA variant was identified in the plasma sample but not in tissue. Comparison between LB at relapse and tissue NGS at diagnosis Based on the availability of tissue NGS at diagnosis, we evaluated whether the resistance mechanisms identified by TSO500 LB were intrinsic or acquired. Comparable data for pre-treatment tumor tissue and LB at relapse were available for 5/12 patients (the remaining 7 patients were characterized in tissue at diagnosis by targeted small NGS panels). In 4 of these cases, TSO500 LB identified mutations which were not detected in tissue at diagnosis, thus representing putative acquired mutations. These included acquired EGFR L718 and BRAF p.V600E mutations in two patients with EGFR mutations treated with osimertinib; one MET amplification plus KRAS p.G12C mutation; and one BRAF p.V600E plus ALK missense mutation in two ALK-rearranged patients treated with lorlatinib. In the remaining patient, TSO500 LB did not detect any acquired variant, confirming the ERBB2, CCNE1, and EGFR amplifications previously identified in the pre-treatment tissue biopsy by CGP. At baseline, 11 patients had actionable gene fusions. At disease progression, LB detected gene fusions in 4 of these patients (5 out of 12 plasma samples, 41.6%). In the remaining 7 patients, G360 testing did not detect any gene fusion as well. Concurrent tissue biopsies at progression were available for 3 of these 7 patients; in 2 cases, tissue analysis confirmed the persistence of the original RET fusion, while in the third case, both IHC and ISH confirmed the loss of the ALK fusion. Among all tissue samples collected at progression, gene fusions were detected in 7 cases. TSO500 assay successfully identified gene fusions in 5 of these cases, yielding a detection rate of 71.4%. Comparative analysis between TMB values detected by tissue CGP (both prior target therapy and at disease progression) and LB TSO500 revealed poor correlation (Pearson’s r = 0.228, p = 0.498) (Supplementary Fig. 4) Discussion Our study provides the first real-world evaluation of the TSO500 ctDNA assay compared to the FDA-approved Guardant360 platform, demonstrating robust analytical performance and potential clinical utility in NSCLC patients progressing on targeted therapies. We observed a high concordance between TSO500 and G360 in detecting clinically actionable alterations, particularly for ESCAT I variants (sensitivity: 95.2%), in line with data previously reported by Woodhouse et al. who demonstrated > 95% concordance between FoundationOne Liquid CDx and different orthogonal methods in detecting actionable variants 27 . A key advantage of the comprehensive TSO500 panel lies in its ability to detect a broad spectrum of genomic alterations, including a significant number of ESCAT III and IV variants. While ESCAT I alterations guide standard-of-care therapies, lower-tier variants often hold relevance for patients who have exhausted approved treatment options. The identification of these additional alterations may facilitate patient enrollment in clinical trials or support off-label targeted therapy access, offering potential therapeutic strategies beyond conventional treatments. The sensitivity of TSO500 in detecting gene fusions is particularly noteworthy. While RNA-based panels are traditionally preferred, especially in tissue NGS, DNA-based LB tests also show a robust analytical validity 28 . González-Medina et al. demonstrated the effectiveness of the VHIO-iCCA custom NGS panel in monitoring FGFR2 fusion-positive patients during therapy, showing high sensitivity by detecting 16 fusions in plasma samples from 18 cholangiocarcinoma patients (sensitivity: 88.9%) with known FGFR fusions 29 . Similarly, Kasi and colleagues reported a high sensitivity of ctDNA testing for identifying actionable fusions across 53,842 patients profiled with FoundationOneLiquid CDx 30 . Conversely, in a comprehensive analysis of matched tissue and plasma samples from NSCLC patients, Lin et al. demonstrated that a 168 genes commercial LB NGS panel detected 67% (35/52) of fusions previously identified through RNA-based tissue NGS 31 . Although based on a small series, our study confirms these findings for the TSO500 LB assay, that was able to detect 5 out of 7 (71.4%, including 3 ALK-EML4 and 2 KIF5B-RET) gene fusions identified by concurrent tissue NGS. TMB measured by TSO500 did not show a strong correlation with tissue TMB, a finding consistent with other studies 32 . Given the critical role of TMB as a predictive biomarker for immunotherapy, further analytical and clinical validations of ctDNA-based TMB measurement remain a priority for ongoing research and care 33 , 34 . A potential flow of the TSO500 assay is represented by the relatively high output of ctDNA required for the analysis. In our study, 12 out of the 34 (27%) samples yielded less than DNA 30 ng of DNA and were therefore not profiled in accordance to manufacturers’ guidelines. The recent release of TSO500 v2, that requires a lower (20 ng) DNA input could significantly limit the failure rate. Actually, we found in our study that ctDNA quantity did not affect TMB values, and that SNV/InDels were detected at high sensitivity. Interestingly, using TSO500 v2 in an independent cohort of metastatic cancer patients of different histologies, allowed our group to obtain informative LB data in 89% of the cases (unpublished results). Our study underlines the complementary value of LB in detecting therapeutic targets and resistance mutations not identified in concurrent tissue samples 35 . In particular, TSO500 was more efficient than tissue biopsy in capturing tumor heterogeneity and emerging mutations associated with disease progression. Such findings highlights that applying CGP in LB allows to capture genomic alterations that might be missed by tissue NGS due to tumor heterogeneity in advanced disease. In conclusion, our findings support the analytical validity and clinical utility of TSO500 LB, demonstrating high concordance with the FDA-approved G360, high accuracy in the identification of clinically actionable variants, and robust sensitivity for gene fusions detection. These attributes position TSO500 as a robust tool in precision oncology, enabling tailored treatment decisions. Future research should focus on refining ctDNA-based TMB assessment and optimizing DNA input requirements to maximize the clinical utility of TSO500. MATERIAL AND METHODS Patients' cohort This is a prospective observational study conducted between May 2022 and March 2024, enrolling 36 consecutive locally advanced/metastatic NSCLC patients which were molecularly characterized on bioptic samples prior to target therapy initiation. At disease progression, plasma samples were collected from all patients included in the study for ctDNA analysis with both TruSight Oncology 500 ctDNA version 1 (TSO500, Illumina Inc, San Diego, CA, USA) and Guardant360 CDx® (G360, Guardant Health, Palo Alto, CA, USA). Whenever feasible, patients also underwent a tissue core biopsy of one of the metastatic deposits. Therapeutic decisions were undertaken on the basis of the results obtained with the G360 LB results. The study was approved by the Internal Audit Committee and Ethics Committee (INT 283 − 21). cfDNA extraction, next-generation sequencing and data analysis Plasma was obtained from whole blood collections (two streck tubes per assay). G360 is a comprehensive LB test detecting SNVs, indels, CNAs, and fusions across 74 genes in ctDNA, serving as a companion diagnostic (CDx) for multiple targeted therapies (EGFR, ERBB2, KRAS mutations) in NSCLC [ https://www.accessdata.fda.gov/cdrh_docs/pdf20/P200010S008C.pdf ]. The TSO500 is a LB NGS test for in-house use, designed to detect point mutations (SNV), indels and CNVs across 523 cancer-related genes, as well as fusions and splicing variants in 55 genes, and to characterize key immuno-oncology gene signatures, including TMB and MSI. After double centrifugation, cell-free DNA (cfDNA) was isolated using the QIAamp Circulating Nucleic Acid Kit (cat. 55114; Qiagen, Hilden, Germany). cfDNA was quantified using the Qubit Fluorometer 3.0 (Thermofisher, Waltham, MA, USA) and the dsDNA HS Assay (quantification range: 10 pg/µL–100 ng/µL; Thermofisher). The cfDNA fragment size was determined with the Cell-free DNA ScreenTape Analysis on TapeStation (Agilent Technologies, Inc., Santa Clara, CA). Briefly, at least 30 ng of cfDNA samples were used to generate libraries with TSO500 ctDNA KIT (Illumina). Libraries were quality-controlled with Qubit and Tapestation. Sequencing was carried out on the Illumina NovaSeq 6000, (800 million reads per sample). Raw data were analyzed using the Dragen TruSight Oncology 500 ctDNA v2.1.1 on an Illumina DRAGEN server v4. All variants that passed manufacturer’s quality check (“PASS” tag in VCF files) were used for further analyses. Polymorphisms were filtered by using publicly available (e.g. dbSNP, GnomAD, 1000Genomes). Variants were examined using OpenCravat 36 and clinically annotated using ClinVar 37 and cBio portal database 38 ; variants classified as benign or likely benign were excluded from further analyses. The Integrative Genomic Viewer IGV tool 39 was used for final check. Clinical actionability of pathogenic and likely pathogenic (P/LP) variants was defined according to the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT) 40 . The finally reported data were discussed by the institutional Molecular Tumor Board 18 . The FDA-approved G360 assay was used as a reference to evaluate the analytical performance of TSO500. For comparative analysis we considered the 74 genes shared between TSO500 and G360 tests. Standard packages for R software v 4.1.2 was used to explore the data and for descriptive statistics. Complexheatmap package in R was used to draw oncoplots of gene variants. Blood samples allocated for G360 were promptly shipped to Guardant Health facilities, and molecular reports delivered within 7–10 business days. Declarations Competing Interests AV has received payment or honoraria for lectures, presentations, speakers bureaus, or educational events from Roche and Illumina. CP declares having personal financial interest with AstraZeneca, Roche, MSD, Bristol Myers Squibb, Janssen, Sanofi; Pfizer, Lilly, Spectrum Pharmaceuticals, outside of the submitted work. declares receiving personal fees from Eli Lilly, Bristol-Myers Squibb, Italfarmaco, Novartis, AstraZeneca, Merck Sharp & Dohme, Takeda, Amgen, F. Hoffmann-La Roche, Sanofi, Pfizer and Glaxo SmithKline, outside of the submitted work. GP declared speaker honoraria from and/or being on the advisory board of ADS Biotec, Exact Sciences, Lilly, Novartis, and Roche; reported institutional research grant from Roche. All the remaining authors have no competing interests to declare. Author Contribution AV, LA, AB, AP and GP conceived and designed the study. IC, AB, EC and CDM, performed experiments and comprehensive genomic profiling data analysis. LA and AD performed bioinformatic analyses. LA, AD, AP, CMC and PV provided statistical analysis. CP, MB, and GLR enrolled patients and provided clinical data. FP, ET, DL, AB, AP, IC provided data interpretation. AV, AP, DL and GP were major contributors in writing the manuscript. GP coordinated the overall project. All authors reviewed and approved the final manuscript. Acknowledgement We thank all of the patients for their participation, as well as research nurses, molecular laboratory personnel and INT MTB members. The study was supported by CANDRIAM (no grant numbers). Data Availability The sequencing datasets used and analyzed during the current study are not publicly available due to privacy/ethical reasons. 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The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2 , 401-404 (2012). Robinson, J.T., Thorvaldsdottir, H., Wenger, A.M., Zehir, A. & Mesirov, J.P. Variant Review with the Integrative Genomics Viewer. Cancer Res 77 , e31-e34 (2017). Mateo, J. , et al. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). Ann Oncol 29 , 1895-1902 (2018). Additional Declarations Competing interest reported. AV has received payment or honoraria for lectures, presentations, speakers bureaus, or educational events from Roche and Illumina. CP declares having personal financial interest with AstraZeneca, Roche, MSD, Bristol Myers Squibb, Janssen, Sanofi; Pfizer, Lilly, Spectrum Pharmaceuticals, outside of the submitted work. declares receiving personal fees from Eli Lilly, Bristol-Myers Squibb, Italfarmaco, Novartis, AstraZeneca, Merck Sharp & Dohme, Takeda, Amgen, F. Hoffmann-La Roche, Sanofi, Pfizer and Glaxo SmithKline, outside of the submitted work. GP declared speaker honoraria from and/or being on the advisory board of ADS Biotec, Exact Sciences, Lilly, Novartis, and Roche; reported institutional research grant from Roche. All the remaining authors have no competing interests to declare. Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6264683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":445298658,"identity":"62667eba-19d3-432d-ae22-93ab77e3d0c7","order_by":0,"name":"Andrea Vingiani","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Vingiani","suffix":""},{"id":445298659,"identity":"3b5abacd-b7c3-43bf-8a82-42057e1b12cd","order_by":1,"name":"Alberta Piccolo","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Alberta","middleName":"","lastName":"Piccolo","suffix":""},{"id":445298660,"identity":"54aa119d-7e57-4348-8373-5843fbba8114","order_by":2,"name":"Adele Busico","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Adele","middleName":"","lastName":"Busico","suffix":""},{"id":445298661,"identity":"d4ff7854-b7c3-48d2-ac75-dbe6a765cfca","order_by":3,"name":"Iolanda Capone","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Iolanda","middleName":"","lastName":"Capone","suffix":""},{"id":445298662,"identity":"cb501b64-4017-4ca8-8073-ad5c5a76b523","order_by":4,"name":"Elena Tamborini","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei 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Pruneri","email":"data:image/png;base64,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","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":true,"prefix":"","firstName":"Giancarlo","middleName":"","lastName":"Pruneri","suffix":""}],"badges":[],"createdAt":"2025-03-19 22:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6264683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6264683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82073528,"identity":"6c8228c5-e283-4807-93f7-58e4c18df9f2","added_by":"auto","created_at":"2025-05-06 13:30:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98301,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6264683/v1/bd9004ec4cf025c96ca2ad19.png"},{"id":82074133,"identity":"3602c3da-71a6-43bc-bbf2-fc2a7e5c5ecf","added_by":"auto","created_at":"2025-05-06 13:38:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267818,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical performance of the TruSight Oncology 500 ctDNA v1 (TSO500) platform compared to the G360360 CDx test, in evaluating all variants (A,B) and pathogenic/likely pathogenic variants (C,D). The bar charts (A,C) display the proportion of correctly identified variants (sensitivity, blue) and correctly not identified (specificity, red) of TSO500 for each sample. Samples INT283/21_0025 and INT283/21_0035 resulted wild type by G360 testing, and therefore sensitivity calculation was not applicable. As well, sensitivity cannot be extracted in sample INT283/21_0046 where G360 did not identify pathogenic variants. Boxplots (B,D) show the sensitivity and specificity distributions across the whole dataset.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6264683/v1/05da7d5337477df505709e82.png"},{"id":82071635,"identity":"c9ad1904-8ca2-4729-80ff-316bd5445424","added_by":"auto","created_at":"2025-05-06 13:22:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23431,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the distribution of the absolute number of variants detected in each sample across ESCAT classes\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6264683/v1/d3ca413e78328b12d66e1653.png"},{"id":82071638,"identity":"2ceb7edb-6c7e-40b2-9c3f-c7ba1a1eb43d","added_by":"auto","created_at":"2025-05-06 13:22:05","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49252,"visible":true,"origin":"","legend":"\u003cp\u003eOncoplot showing therapeutically actionable variants (ESCAT I) in EGFR, ALK, KRAS (p.G12C) and RET genes, detected by G360 (left side of each column) and TSO500 (right side of each column). Single nucleotide and indels variants are represented by short red rectangles, while gene fusions by long yellow rectangles.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6264683/v1/3d13af28ebd72d9ae9c3c7f8.jpeg"},{"id":82207751,"identity":"6ff4a26d-8a22-4ad6-8ff6-45047b493bb8","added_by":"auto","created_at":"2025-05-07 18:01:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1344347,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6264683/v1/78be080e-7971-4907-8ef8-d5f9fcc41229.pdf"},{"id":82071633,"identity":"cc9db201-729d-4d2b-9ec3-4bcf1c7b7bc6","added_by":"auto","created_at":"2025-05-06 13:22:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":287876,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6264683/v1/4ab8d49bc22c6aeec9c87aff.docx"}],"financialInterests":"Competing interest reported. AV has received payment or honoraria for lectures, presentations, speakers bureaus, or educational events from Roche and Illumina. CP declares having personal financial interest with AstraZeneca, Roche, MSD, Bristol Myers Squibb, Janssen, Sanofi; Pfizer, Lilly, Spectrum Pharmaceuticals, outside of the submitted work. declares receiving personal fees from Eli Lilly, Bristol-Myers Squibb, Italfarmaco, Novartis, AstraZeneca, Merck Sharp \u0026 Dohme, Takeda, Amgen, F. Hoffmann-La Roche, Sanofi, Pfizer and Glaxo SmithKline, outside of the submitted work. GP declared speaker honoraria from and/or being on the advisory board of ADS Biotec, Exact Sciences, Lilly, Novartis, and Roche; reported institutional research grant from Roche. All the remaining authors have no competing interests to declare.","formattedTitle":"Assessing In House Comprehensive Genomic Profiling by Liquid Biopsy for NSCLC Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer genomics showed an unprecedented improvement during the last decade. The widespread adoption of Next-Generation Sequencing (NGS) technologies at relatively low costs, combined with the availability of an increasing number of selective drugs, has led to a paradigm shift in oncology, thus leading to significant improvements in terms of patients\u0026rsquo; outcome\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Given the increasing number of druggable alterations, NGS-based multi-gene testing represents the recommended approach for simultaneous detection of multiple gene alterations in a variety of solid malignancies, including non-small cell lung cancer (NSCLC), colorectal, prostatic and biliary cancer\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLiquid biopsy (LB) offers a non-invasive method for identifying tumor genomic aberrations. This approach addresses several limitations associated with traditional tumor tissue biopsies, such as feasibility, patient discomfort and morbidity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this context, NSCLC represents a model for LB application. In 2013, it was firstly demonstrated that LB could detect resistance mutations to first- and second-generation EGFR inhibitors (EGFR T790M)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, showing the clinical utility of LB in patients\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. On June 2016, the U.S. Food and Drug Administration (FDA) approved the first plasma-based molecular assay, the Cobas\u0026reg; EGFR Mutation Test 2 (Roche Molecular Systems, Inc.), a real-time PCR test for the detection of EGFR exon 19 indels and the L858R mutations to identify NSCLC eligible for erlotinib [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fda.gov/drugs/resources-information-approved-drugs/cobas-egfr-mutation-test-v2\u003c/span\u003e\u003cspan address=\"https://www.fda.gov/drugs/resources-information-approved-drugs/cobas-egfr-mutation-test-v2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. Consistently, LB tests have recently been proven to represent a valuable tool in guiding treatment options for metastatic patients when obtaining diagnostic tissue material is challenging\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In particular, ESMO Clinical Practice Guidelines recommend the use of ctDNA testing in treatment-naive patients in which tissue biopsy is risky or contraindicated, and in pre-treated oncogene addicted patients for the detection of resistance mutations\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, several comprehensive NGS panels have been developed and commercialized, allowing an exhaustive characterization of tumor mutational landscape, including the detection gene mutations, fusions, copy number variation (CNV), tumor mutational burden (TMB), microsatellite instability (MSI), loss of heterozygosity (LOH) and homologous recombination\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese data provide a strong rationale to extend comprehensive genomic profiling (CGP) to LB in the clinical practice. To this aim, we designed a head-to-head comparison of two LB CGP assays, i.e. Guardant360 CDx\u0026reg; and TruSight Oncology 500 ctDNA, in a consecutive case cohort of NSCLS patients with known actionable targets. G360 is a FDA-approved, in service, DNA-based NGS panel, investigating single nucleotide variants (SNV), indels, gene fusions and CNV in up to 74 cancer-related genes, which has been extensively validated through numerous studies and clinical trials, showing its effectiveness in identifying actionable mutations and guiding targeted therapy in NSCLC and other cancer types\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eFrom May 2022 to March 2024, we enrolled 36 NSCLC patients progressing on tyrosine kinase inhibitors, previously characterized with molecular tests performed in our institution. Clinico-pathological and molecular characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In 8 patients, a second blood drawn was taken at further disease progression, leading to an overall number of collected samples of 44. G360 provided valid reports in 42 out of 44 cases (95.45%). In the remaining 2 cases, G360 results did not meet the internal quality criteria. Regarding TSO500, in 12 out of 44 samples (27.2%), the ctDNA content was below 30 ng (median ctDNA content of unprocessed samples: 17.09 ng; range: 7-26.3 ng), and the test was not performed. The comparative analysis was therefore performed on 31 samples from 27 patients in which paired G360 and TSO500 data were available (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Study design is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\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\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients enrolled (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComparative analysis patients (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (80.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.7 (37.8\u0026ndash;83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7 (44.2\u0026ndash;83.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious oncological surgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (74.07%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (91.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (92.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge Cell Neuroendocrine Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosquamous lung carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior molecular characterization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIon AmpliSeq Cancer Hotspot Panel (CHP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA Oncomine Research Assay Plus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted panel - LKB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReal Time PCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNA Oncomine Research Assay Plus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArcher FusionPlex Lung Panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluorescent In Situ Hybridization (FISH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDriver molecular alteration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLine of therapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (74.04%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLatest therapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKI inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (91.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (88.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Radiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (66.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eG360 and TSO molecular results\u003c/h3\u003e\n\u003cp\u003eAmong the 31 samples included in the comparative analysis, G360 identified 137 variants (mean per patient: 4.42, range 0\u0026ndash;15). 95 variants were annotated as P/LP (mean per patient: 3.06, range: 0\u0026ndash;9), and 42 as variants of unknown significance (VUS; mean per patient: 1.35, range: 0\u0026ndash;9). In particular, G360 identified 117 SNV/indels, 5 gene fusions and 15 CNVs. TSO500 ctDNA assay identified 723 variants (mean per patient: 23.32, range: 7-123). 217 variants were P/LP (mean per patient: 7, range: 0\u0026ndash;27), and 489 VUS (mean per patient: 15.77, range: 5\u0026ndash;96), including 598 SNV/indels, 5 fusions and 103 CNV. No significant correlation was found between the DNA input and the number of variants obtained (Pearson\u0026rsquo;s r = -0.017, p\u0026thinsp;=\u0026thinsp;0.92) (Supplementary Fig.\u0026nbsp;1A). The mean value of TMB obtained by TSO 500 ctDNA assay was 8.9 mut/Mb (range: 0\u0026ndash;73.4). No significant correlation was found between the DNA input and TMB value (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.132, p\u0026thinsp;=\u0026thinsp;0.47) (Supplementary Fig.\u0026nbsp;1B). No significant association was found between ctDNA content and number of metastatic sites (Wilcoxon rank-sum test, p\u0026thinsp;=\u0026thinsp;0.43) (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003ch3\u003eG360 and TSO500 comparative analysis\u003c/h3\u003e\n\u003cp\u003eOverall, TSO500 correctly identified 111 out of the 137 variants (81.02%) detected by G360. TSO500 found 102 variants (range per sample: 1\u0026ndash;19, median: 3) which were not identified by G360, leading to an overall specificity of 0.97 (range: 0.74-1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). TSO500 correctly identified 81 out of the 95 P/LP variants (85.26%) found by G360 (median sensitivity 0.89, range 0.33-1). Additionally, TSO500 detected 138 P/LP variants (range: 1\u0026ndash;22, median: 3) not identified by G360 (specificity 0.99, range 0.88-1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComparative analysis through ESCAT tier classification\u003c/h3\u003e\n\u003cp\u003eFor the evaluation of the potential clinical impact, we compared alterations identified by TSO500 and G360 across ESCAT classes. TSO500 and G360 were consistent in detecting ESCAT I (20 and 21 variants by TSO500 and G360, respectively) and ESCAT II variants (3 and 1 variants, respectively), while TSO500 identified a higher number of ESCAT III (17 and 12 variants) and ESCAT IV (30 and 9 variants) variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared with G360, TSO500 showed high sensitivity across ESCAT classes (95.2%, 100%, 58.3%, 88.8% for ESCAT I, II, III, IV class mutations, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn particular, among standard of care actionable targets (ESCAT I), TSO500 did not detect an EGFR mutation (p.Leu858Arg) that was identified by G360; in this case, TSO500 run showed a low median coverage (927) (Fig.\u0026nbsp;4).\u003cdiv class=\"BlockQuote\"\u003e\u003c/p\u003e \u003cp\u003eNotably, all the 5 gene fusions identified by G360 (4 ALK and 1 RET fusions) were confirmed by TSO. TSO500 did not detect 5 ESCAT IIIA variants, including 2 PIK3CA, 1 BRCA1, 1 PTEN and 1 ATM variants. Except for the BRCA1 mutation (VAF: 8%), all these variants were characterized by a low variant allele frequency in G360 (range: 0.05%-0,38%). Of note, after a subsequent review of the BAM files using the IGV software, one of the PIK3CA variants and the PTEN variant were detected by TSO500, albeit at low frequency (PIK3CA, VAF: 0.07%) or affected by systematic noise (PTEN, VAF: 0.24). These variants had been excluded from the VCF files due to pre-specified quality filtering. On the other hand, no additional ESCAT I mutations missed by G360 were identified by TSO500 (Fig.\u0026nbsp;4). A high sensitivity (14/15, 93,3%) was also found with regard to CNV for EGFR (6 cases), BRAF (2), CCNE1 (2), MET (1), KRAS (1) and CCND1 (1). TSO500 identified 44 potential intrinsic or acquired resistance mutations in 12 out of 26 patients (46.2%), compared to 25 resistance mutations detected by G360 in 12 patients, including EGFR p.L718G, p.C797S and amplification, PIK3CA mutations, MET amplification, cell cycle genes alterations, FGF genes alterations and RAS genes alterations\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In particular, TSO500 detected all but 3 resistance mutations identified by G360, including the two aforementioned PIK3CA mutations in two patients treated with anti-EGFR therapy, and one ALK mutation (p.Gly1202Arg) in a patient with ALK fusion treated with lorlatinib.\u003c/p\u003e \u003cp\u003eFinally, we observed a high correlation in variant allele frequencies among gene alterations detected by both TSO and G360 (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.89, p-value\u0026thinsp;=\u0026thinsp;0.000) (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003ch3\u003eComparison between LB and tissue NGS results at relapse\u003c/h3\u003e\n\u003cp\u003eIn 9 out of 27 patients (33.3%), concurrent tissue-based CGP was performed at the same disease progression timepoint, allowing a direct comparison between tissue and LB NGS results.\u003c/p\u003e \u003cp\u003eOverall, in 7/9 cases TSO500 identified 16 potentially actionable (ESCAT I-IV) and resistance mutations which were not identified by concurrent tissue NGS, including 2 ESCAT IA variants (two KRAS G12C), 5 ESCAT IIIA variants (two BRCA1 deletions, one PIK3CA mutation, one KRAS mutation and one KMT2A mutation), one ESCAT IIIB (MET mutation), 5 ESCAT IV variants (one CHEK2 mutation, and PIK3CA, FGFR3, BRAF, and CDK6 amplifications), and 3 EGFR resistance alterations (one amplification and 2 mutations). Of note, in a patient with an ALK-EML4 positive NSCLC progressing after two lines of ALK inhibitors (Alectinib and Lorlatinib), both G360 and TSO500 detected a KRAS p.G12C and a PIK3CA p.E542K mutation, which were not identified by concurrent tissue NGS. Upon further progression to a third line of treatment with carboplatin-pemetrexed, this patient was subjected to additional molecular profiling in both tissue and LB samples. The KRAS variant was detected in subsequent analysis both in tissue and LB samples, while the PIK3CA variant was identified in the plasma sample but not in tissue.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison between LB at relapse and tissue NGS at diagnosis\u003c/h2\u003e \u003cp\u003eBased on the availability of tissue NGS at diagnosis, we evaluated whether the resistance mechanisms identified by TSO500 LB were intrinsic or acquired. Comparable data for pre-treatment tumor tissue and LB at relapse were available for 5/12 patients (the remaining 7 patients were characterized in tissue at diagnosis by targeted small NGS panels). In 4 of these cases, TSO500 LB identified mutations which were not detected in tissue at diagnosis, thus representing putative acquired mutations. These included acquired EGFR L718 and BRAF p.V600E mutations in two patients with EGFR mutations treated with osimertinib; one MET amplification plus KRAS p.G12C mutation; and one BRAF p.V600E plus ALK missense mutation in two ALK-rearranged patients treated with lorlatinib. In the remaining patient, TSO500 LB did not detect any acquired variant, confirming the ERBB2, CCNE1, and EGFR amplifications previously identified in the pre-treatment tissue biopsy by CGP.\u003c/p\u003e \u003cp\u003eAt baseline, 11 patients had actionable gene fusions. At disease progression, LB detected gene fusions in 4 of these patients (5 out of 12 plasma samples, 41.6%).\u003c/p\u003e \u003cp\u003eIn the remaining 7 patients, G360 testing did not detect any gene fusion as well. Concurrent tissue biopsies at progression were available for 3 of these 7 patients; in 2 cases, tissue analysis confirmed the persistence of the original RET fusion, while in the third case, both IHC and ISH confirmed the loss of the ALK fusion.\u003c/p\u003e \u003cp\u003eAmong all tissue samples collected at progression, gene fusions were detected in 7 cases. TSO500 assay successfully identified gene fusions in 5 of these cases, yielding a detection rate of 71.4%.\u003c/p\u003e \u003cp\u003eComparative analysis between TMB values detected by tissue CGP (both prior target therapy and at disease progression) and LB TSO500 revealed poor correlation (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.228, p\u0026thinsp;=\u0026thinsp;0.498) (Supplementary Fig.\u0026nbsp;4)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e Our study provides the first real-world evaluation of the TSO500 ctDNA assay compared to the FDA-approved Guardant360 platform, demonstrating robust analytical performance and potential clinical utility in NSCLC patients progressing on targeted therapies.\u003c/p\u003e \u003cp\u003eWe observed a high concordance between TSO500 and G360 in detecting clinically actionable alterations, particularly for ESCAT I variants (sensitivity: 95.2%), in line with data previously reported by Woodhouse et al. who demonstrated\u0026thinsp;\u0026gt;\u0026thinsp;95% concordance between FoundationOne Liquid CDx and different orthogonal methods in detecting actionable variants\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA key advantage of the comprehensive TSO500 panel lies in its ability to detect a broad spectrum of genomic alterations, including a significant number of ESCAT III and IV variants. While ESCAT I alterations guide standard-of-care therapies, lower-tier variants often hold relevance for patients who have exhausted approved treatment options. The identification of these additional alterations may facilitate patient enrollment in clinical trials or support off-label targeted therapy access, offering potential therapeutic strategies beyond conventional treatments.\u003c/p\u003e \u003cp\u003eThe sensitivity of TSO500 in detecting gene fusions is particularly noteworthy. While RNA-based panels are traditionally preferred, especially in tissue NGS, DNA-based LB tests also show a robust analytical validity\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Gonz\u0026aacute;lez-Medina et al. demonstrated the effectiveness of the VHIO-iCCA custom NGS panel in monitoring FGFR2 fusion-positive patients during therapy, showing high sensitivity by detecting 16 fusions in plasma samples from 18 cholangiocarcinoma patients (sensitivity: 88.9%) with known FGFR fusions\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Similarly, Kasi and colleagues reported a high sensitivity of ctDNA testing for identifying actionable fusions across 53,842 patients profiled with FoundationOneLiquid CDx\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Conversely, in a comprehensive analysis of matched tissue and plasma samples from NSCLC patients, Lin et al. demonstrated that a 168 genes commercial LB NGS panel detected 67% (35/52) of fusions previously identified through RNA-based tissue NGS\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Although based on a small series, our study confirms these findings for the TSO500 LB assay, that was able to detect 5 out of 7 (71.4%, including 3 ALK-EML4 and 2 KIF5B-RET) gene fusions identified by concurrent tissue NGS.\u003c/p\u003e \u003cp\u003eTMB measured by TSO500 did not show a strong correlation with tissue TMB, a finding consistent with other studies\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Given the critical role of TMB as a predictive biomarker for immunotherapy, further analytical and clinical validations of ctDNA-based TMB measurement remain a priority for ongoing research and care\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA potential flow of the TSO500 assay is represented by the relatively high output of ctDNA required for the analysis. In our study, 12 out of the 34 (27%) samples yielded less than DNA 30 ng of DNA and were therefore not profiled in accordance to manufacturers\u0026rsquo; guidelines. The recent release of TSO500 v2, that requires a lower (20 ng) DNA input could significantly limit the failure rate. Actually, we found in our study that ctDNA quantity did not affect TMB values, and that SNV/InDels were detected at high sensitivity. Interestingly, using TSO500 v2 in an independent cohort of metastatic cancer patients of different histologies, allowed our group to obtain informative LB data in 89% of the cases (unpublished results).\u003c/p\u003e \u003cp\u003eOur study underlines the complementary value of LB in detecting therapeutic targets and resistance mutations not identified in concurrent tissue samples\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In particular, TSO500 was more efficient than tissue biopsy in capturing tumor heterogeneity and emerging mutations associated with disease progression. Such findings highlights that applying CGP in LB allows to capture genomic alterations that might be missed by tissue NGS due to tumor heterogeneity in advanced disease.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings support the analytical validity and clinical utility of TSO500 LB, demonstrating high concordance with the FDA-approved G360, high accuracy in the identification of clinically actionable variants, and robust sensitivity for gene fusions detection. These attributes position TSO500 as a robust tool in precision oncology, enabling tailored treatment decisions. Future research should focus on refining ctDNA-based TMB assessment and optimizing DNA input requirements to maximize the clinical utility of TSO500.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatients' cohort\u003c/h2\u003e \u003cp\u003eThis is a prospective observational study conducted between May 2022 and March 2024, enrolling 36 consecutive locally advanced/metastatic NSCLC patients which were molecularly characterized on bioptic samples prior to target therapy initiation. At disease progression, plasma samples were collected from all patients included in the study for ctDNA analysis with both TruSight Oncology 500 ctDNA version 1 (TSO500, Illumina Inc, San Diego, CA, USA) and Guardant360 CDx\u0026reg; (G360, Guardant Health, Palo Alto, CA, USA). Whenever feasible, patients also underwent a tissue core biopsy of one of the metastatic deposits. Therapeutic decisions were undertaken on the basis of the results obtained with the G360 LB results. The study was approved by the Internal Audit Committee and Ethics Committee (INT 283\u0026thinsp;\u0026minus;\u0026thinsp;21).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ecfDNA extraction, next-generation sequencing and data analysis\u003c/h2\u003e \u003cp\u003ePlasma was obtained from whole blood collections (two streck tubes per assay). G360 is a comprehensive LB test detecting SNVs, indels, CNAs, and fusions across 74 genes in ctDNA, serving as a companion diagnostic (CDx) for multiple targeted therapies (EGFR, ERBB2, KRAS mutations) in NSCLC [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.accessdata.fda.gov/cdrh_docs/pdf20/P200010S008C.pdf\u003c/span\u003e\u003cspan address=\"https://www.accessdata.fda.gov/cdrh_docs/pdf20/P200010S008C.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. The TSO500 is a LB NGS test for in-house use, designed to detect point mutations (SNV), indels and CNVs across 523 cancer-related genes, as well as fusions and splicing variants in 55 genes, and to characterize key immuno-oncology gene signatures, including TMB and MSI. After double centrifugation, cell-free DNA (cfDNA) was isolated using the QIAamp Circulating Nucleic Acid Kit (cat. 55114; Qiagen, Hilden, Germany). cfDNA was quantified using the Qubit Fluorometer 3.0 (Thermofisher, Waltham, MA, USA) and the dsDNA HS Assay (quantification range: 10 pg/\u0026micro;L\u0026ndash;100 ng/\u0026micro;L; Thermofisher). The cfDNA fragment size was determined with the Cell-free DNA ScreenTape Analysis on TapeStation (Agilent Technologies, Inc., Santa Clara, CA). Briefly, at least 30 ng of cfDNA samples were used to generate libraries with TSO500 ctDNA KIT (Illumina). Libraries were quality-controlled with Qubit and Tapestation. Sequencing was carried out on the Illumina NovaSeq 6000, (800\u0026nbsp;million reads per sample). Raw data were analyzed using the Dragen TruSight Oncology 500 ctDNA v2.1.1 on an Illumina DRAGEN server v4. All variants that passed manufacturer\u0026rsquo;s quality check (\u0026ldquo;PASS\u0026rdquo; tag in VCF files) were used for further analyses. Polymorphisms were filtered by using publicly available (e.g. dbSNP, GnomAD, 1000Genomes). Variants were examined using OpenCravat\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and clinically annotated using ClinVar\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and cBio portal database\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e; variants classified as benign or likely benign were excluded from further analyses. The Integrative Genomic Viewer IGV tool\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e was used for final check. Clinical actionability of pathogenic and likely pathogenic (P/LP) variants was defined according to the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The finally reported data were discussed by the institutional Molecular Tumor Board\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The FDA-approved G360 assay was used as a reference to evaluate the analytical performance of TSO500. For comparative analysis we considered the 74 genes shared between TSO500 and G360 tests. Standard packages for R software v 4.1.2 was used to explore the data and for descriptive statistics. \u003cem\u003eComplexheatmap\u003c/em\u003e package in R was used to draw oncoplots of gene variants. Blood samples allocated for G360 were promptly shipped to Guardant Health facilities, and molecular reports delivered within 7\u0026ndash;10 business days.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eAV has received payment or honoraria for lectures, presentations, speakers bureaus, or educational events from Roche and Illumina. CP declares having personal financial interest with AstraZeneca, Roche, MSD, Bristol Myers Squibb, Janssen, Sanofi; Pfizer, Lilly, Spectrum Pharmaceuticals, outside of the submitted work. declares receiving personal fees from Eli Lilly, Bristol-Myers Squibb, Italfarmaco, Novartis, AstraZeneca, Merck Sharp \u0026amp; Dohme, Takeda, Amgen, F. Hoffmann-La Roche, Sanofi, Pfizer and Glaxo SmithKline, outside of the submitted work. GP declared speaker honoraria from and/or being on the advisory board of ADS Biotec, Exact Sciences, Lilly, Novartis, and Roche; reported institutional research grant from Roche. All the remaining authors have no competing interests to declare.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAV, LA, AB, AP and GP conceived and designed the study. IC, AB, EC and CDM, performed experiments and comprehensive genomic profiling data analysis. LA and AD performed bioinformatic analyses. LA, AD, AP, CMC and PV provided statistical analysis. CP, MB, and GLR enrolled patients and provided clinical data. FP, ET, DL, AB, AP, IC provided data interpretation. AV, AP, DL and GP were major contributors in writing the manuscript. GP coordinated the overall project. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all of the patients for their participation, as well as research nurses, molecular laboratory personnel and INT MTB members. The study was supported by CANDRIAM (no grant numbers).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe sequencing datasets used and analyzed during the current study are not publicly available due to privacy/ethical reasons. Datasets are available from the corresponding author (CC) on reasonable request and creation of a data usage agreement with our institution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndre, F.\u003cem\u003e, et al.\u003c/em\u003e Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: a multicentre, prospective trial (SAFIR01/UNICANCER). \u003cem\u003eLancet Oncol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 267-274 (2014).\u003c/li\u003e\n\u003cli\u003eLassen, U.N.\u003cem\u003e, et al.\u003c/em\u003e Precision oncology: a clinical and patient perspective. \u003cem\u003eFuture Oncol\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 3995-4009 (2021).\u003c/li\u003e\n\u003cli\u003eMassard, C.\u003cem\u003e, et al.\u003c/em\u003e High-Throughput Genomics and Clinical Outcome in Hard-to-Treat Advanced Cancers: Results of the MOSCATO 01 Trial. \u003cem\u003eCancer Discov\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 586-595 (2017).\u003c/li\u003e\n\u003cli\u003eThai, A.A., Solomon, B.J., Sequist, L.V., Gainor, J.F. \u0026amp; Heist, R.S. 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Variant Review with the Integrative Genomics Viewer. \u003cem\u003eCancer Res\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, e31-e34 (2017).\u003c/li\u003e\n\u003cli\u003eMateo, J.\u003cem\u003e, et al.\u003c/em\u003e A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). \u003cem\u003eAnn Oncol\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 1895-1902 (2018).\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":"","lastPublishedDoi":"10.21203/rs.3.rs-6264683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6264683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLiquid biopsy has emerged as a valuable tool for detecting therapeutic targets and resistance mechanisms. In this study, we aimed at evaluating the analytical performance of the TruSight Oncology 500 ctDNA (TSO500) compared to the FDA-approved Guardant360 CDx (G360) in detecting actionable alterations in NSCLC patients progressing on targeted therapies. We analysed 44 plasma samples from 36 consecutive metastatic NSCLC patients with known molecular drivers. The comparative analysis included 31 paired samples from 27 patients.\u003c/p\u003e \u003cp\u003eTSO500 demonstrated high sensitivity in detecting G360-identified variants (81.02%). Concordance was particularly high for ESCAT I alterations (sensitivity: 95.2%), including gene fusions (100% sensitivity). Both LB assays identified resistance mutations in 12/26 patients, with TSO500 detecting all but 3 G360-identified resistance alterations.\u003c/p\u003e \u003cp\u003eOur findings support TSO500's analytical validity and clinical utility, demonstrating high concordance with G360 for actionable alterations detection, highlighting its potential value in guiding treatment decisions.\u003c/p\u003e","manuscriptTitle":"Assessing In House Comprehensive Genomic Profiling by Liquid Biopsy for NSCLC Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 13:22:00","doi":"10.21203/rs.3.rs-6264683/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":"b72a7c9a-3131-4908-85c0-ac48c424786f","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47407586,"name":"Biological sciences/Cancer/Cancer genomics"},{"id":47407587,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":47407588,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-05-07T17:53:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 13:22:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6264683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6264683","identity":"rs-6264683","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}