Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma patients

Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma 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 Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma patients Mohammad Al Obeed Allah, Esraa Ali, Ivona Krus, Petr Holý, Vojtěch Haničinec, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5224537/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: Concerning the dismal prognosis of chemoresistant patients with epithelial ovarian carcinoma (EOC), we aimed to validate the findings of a previous whole exome sequencing study on 50 patients using an orthogonal Sanger sequencing method on the same patients and a separate set of 127 EOC patients (N=177). Methods: We focused on TP53 as a frequently mutated gene relevant for chemosensitivity, included KRAS as an additional therapeutically relevant target, complemented study with transcript levels of both genes, and compared results with clinical parameters. Results: All variants in TP53 and KRAS detected by exome sequencing were confirmed. KRAS mutated patients had significantly more frequently FIGO stages I or II (p=0.007) and other than high-grade serous tumor subtypes (nonHGSCs) (p<0.001), which was connected with lower KRAS transcript levels (p=0.004). Patients with nonHGSCs harboring TP53 missense variants disrupting the DNA binding loop had significantly poorer platinum-free interval than the rest (p=0.008). Tumors bearing nonsense, frameshift, or splice site TP53 variants had a significantly lower TP53 transcript level, while those with missense variants had significantly higher levels than wild-types (p<0.001). The normalized intratumoral TP53 and KRAS transcript levels were correlated, and three patients with both genes co-mutated had extremely poor survival. Conclusions: Our study points to KRAS as a target for future therapy of nonHGSCs and reveals the prognostic value of TP53 variants in the DNA binding loop. epithelial ovarian carcinoma platinum sensitivity TP53 KRAS variant transcript expression Figures Figure 1 Figure 2 Figure 3 Introduction Epithelial ovarian cancer (EOC), recognized as the eighth leading cause of cancer-related death among women, stands out as one of the most lethal gynecological malignancies 1 . Early detection of EOC is a challenge, because, in most cases, it is asymptomatic in the early stages (I or II based on The International Federation of Gynecology and Obstetrics, FIGO, guidelines). Typically, 75% of EOC cases occur at the advanced stage (III or IV), where the 5-year survival rate is approximately 20–45%, compared to 40–70% for stages I or II 2 , 3 . The standard treatment for advanced EOC has been primary debulking surgery followed by chemotherapy (platinum derivatives with paclitaxel) for the majority of cases 4 . However, most patients experience a relapse within the first five years after the initial diagnosis, with only 20–25% achieving cure 5 . Morphologically, EOCs are classified into four major subtypes: serous, endometrioid, clear cell, and mucinous 6 . Additionally, they can be divided into two primary types: type I, including endometrioid, mucinous, clear cell, and low-grade serous ovarian carcinomas (LGSCs), and type II, constituting 70% of the total and encompassing high-grade serous ovarian carcinomas (HGSCs), carcinosarcomas, and undifferentiated carcinomas. These classifications are integral to defining the aggressiveness of the cancer and its response to different chemotherapies 3 . A significant majority, exceeding 80% of identified EOC cases, fall under the histological classification of HGSC, characterized by an aggressive phenotype that correlates with elevated mortality rate 7 , which is attributed not only to diagnosis at the advanced stage but also to chemoresistance, where approximately 50% of the cases diagnosed at advanced stage relapse within the first five years 3 . The introduction of Poly(ADP-ribose) polymerase (PARP) inhibitors like olaparib and antiangiogenic agents, such as bevacizumab or pazopanib, has led to a significant improvement in the prognosis of the patients 8 , 9 . PARP inhibitors (PARPis) are primarily used for maintenance therapy for platinum-sensitive advanced EOCs 10 . Moreover, patients with BRCA1 / BRCA2 mutations demonstrate enhanced sensitivity to treatment with PARPi 11 . Next Generation Sequencing (NGS) enables the detection of genetic variability and its linkage to multidrug resistance. Based on genomic profiling, two major EOC types have been defined. EOC of type I is characterized by mutations in the MAPK pathway ( KRAS, BRAF, PTEN , and CTNNB1 , etc.) and type II mutations in TP53, BRCA1, BRCA2, KIT , and EGFR 12 . Moreover, DNA damage response and related alterations in DNA repair pathways play a crucial role in cancer development, including EOC. Germline mutations in DNA repair genes can predict hereditary forms of cancer, particularly BRCA1/2 mutations in breast and ovarian cancers 14 . Pathogenic somatic mutations in genes from the homologous recombination DNA repair pathway, such as BRCA1/2 , ATM , RAD51C , and RAD51D , were implicated in chemosensitivity and prognosis of EOC patients 15 , 16 . HGSC typically shows very high frequency of somatic TP53 mutations (~ 90%) and genomic heterogeneity 13 . Our study aimed to validate the results of a previous whole exome sequencing study of 50 patients 15 , which confirmed TP53 as the most frequently mutated gene in HGSC and EOC in general and suggested its relevance for chemosensitivity. Using direct Sanger sequencing of the same samples, we demonstrate the robustness of mutation detection and provide validation study results using an extended sample set analyzed by the same method. We also include KRAS as an additional target of interest and complement somatic mutation screening with an assessment of both genes’ transcript levels in tumor RNA. We compare the results with sensitivity to EOC therapy and patient survival for evaluation of the prognostic value of these biomarkers. Our study adds another dimension to exome or genome sequencing-based EOC projects published before 15 , 17 – 19 . Methods Patients For this study, we used samples of surgically resected, primary EOC tumors from 50 patients (confirmation set) with available whole exome data 15 and additional 127 EOC patients (validation set) without exome data. Patients were prospectively recruited at University Hospitals Motol, Královské Vinohrady (both in Prague, Czech Republic), and Pilsen (Czech Republic) between 2009 and 2020. Tumor samples were collected fresh and promptly frozen and stored at -80°C until isolation of nucleic acids. Peripheral blood samples were taken from all patients to enable tumor-normal matched analysis. Collaborating clinicians collected the following clinical data on each patient: age at diagnosis, FIGO stage (pTNM), the histological subtype and grade of the tumor, presence of distant metastasis or residuum after surgery, oncological treatments, chemosensitivity status, and overall survival (OS) from medical records. The chemosensitivity status was based on the platinum-free interval (PFI) measured as the time from the end of the platinum-based adjuvant chemotherapy to disease recurrence or progression 20 . Patients having PFI ≤ 6 months were considered platinum-resistant and patients with PFI ≥ 12 months platinum-sensitive. Several patients had PFI in the range of 7–12 months and were classified as partially platinum-sensitive 21 . These patients were tentatively included in the resistant group and all association analyses were performed both with and without them. Consensual results are provided. The OS was defined as the time elapsed between surgical resection and death of any cause or patient censoring. Detailed clinical characteristics of the patients are in Table 1 . Table 1 Clinical characteristics of EOC patients Parameters Number of patients Percentage Age at diagnosis 177 100 Median ± SD (years) 62.0 ± 11.5 FIGO stage I 12 7 II 10 6 III 139 82 IV 8 5 Data not available 8 ̶̶ Histologic grade (G) G1 11 6 G2 15 9 G3 146 85 Gx 5 ̶̶ Tumor subtype HGSC 143 84 Other* 28 16 Data not available 6 ̶̶ Distant metastasis Absent 161 95 Present 8 5 Data not available 8 ̶̶ Neoadjuvant chemotherapy Administered 57 32 Not administered 120 68 Residuum after surgery Present** 85 50 Absent (R0) 85 50 Data not available 7 ̶̶ Adjuvant chemotherapy Platinum-based # 165 96 Taxane monotherapy 2 1 Not administered 4 3 Data not available 6 ̶̶ Chemosensitivity status Resistant (PFI ≤ 6 months) 38 23 Intermediate (PFI 7–11 months) 24 15 Sensitive (PFI ≥ 12 months) 101 62 Data not available 14 ̶̶ Platinum-free interval 168 95 Median ± 95% confidence interval (months) 25 17.9–32.1 Overall survival 168 95 Median ± 95% confidence interval (months) 48 37.7–58.3 Footnotes: *Other subtypes include the following carcinomas: mucinous (n = 9), clear cell (n = 10), low grade serous (n = 5), endometrioid (n = 2), and borderline (n = 2). **Includes all ratings above R0 (R1, R2, unspecified). # Platinum-based chemotherapy regimens include n = 147 taxane (paclitaxel/docetaxel) with platinum (carboplatin/cisplatin), n = 9 platinum monotherapy, other (n = 1 FOLFOX, n = 1 platinum with anthracycline, n = 1 platinum with paclitaxel and anthracycline, and n = 6 platinum with paclitaxel and cyclophosphamide). FOLFOX = 5-fluorouracil, leucovorin, and oxaliplatin. Experimental protocol of the study was approved by the Institutional Review Boards of the National Institute of Public Health in Prague (approval reference no. IGA NS9803-4 of 2 February 2008), University Hospital Motol (approval reference no. EK-890/15 of 24 June 2015), University Hospital Královské Vinohrady (approval reference no. EK-VP/40/0/2017 of 28 June 2017), and University Hospital Pilsen (approval reference no. 16-29013A of 4 June 2015). All patients included in the study read and signed the Informed Consent of the Patient. Isolation of nucleic acids and cDNA synthesis DNA from peripheral blood lymphocytes was isolated using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany). Processing tumor tissue samples involved grinding them into a fine powder using a mortar and pestle under liquid nitrogen. Subsequently, we utilized the AllPrep DNA/RNA/Protein Mini Kit (Qiagen) according to the manufacturer's protocol for the isolation of total RNA and DNA. The quantity of the RNA and DNA samples was assessed using the Qubit 4 Nucleic Acid Fluorometric Quantification System (ThermoFisher Scientific, Waltham, MA, USA) and quality was checked by measuring the integrity number (RIN and DIN) using Agilent TapeStation 2200 (ThermoFisher Scientific). RNA was transcribed into cDNA with the help of the RevertAid™ First Strand cDNA Synthesis kit (ThermoFisher Scientific) according to the manufacturer's protocol and checked using the previously published method 21 . Quantitative Polymerase Chain Reaction Quantitative real-time PCR (qPCR) was performed using TaqMan® Gene Expression Assays (ThermoFisher), namely TP53 (Hs01034249_m1) and KRAS (Hs00364284_g1). PPIA (Hs99999904_m1), UBC (Hs00824723_m1), and YWHAZ (Hs03044281_g1), selected previously using NormFinder and geNorm software, served as reference genes for results normalization 22 . The reaction mixture with volume of 5 µL contained 1 µL of 5× Hot FirePol Probe qPCR Mix Plus (ROX) (Solis BioDyne OÜ, Tartu, Estonia), 0.25 µL of 20× TaqMan® Gene Expression Assay specified above, 1.75 µL of nuclease-free water, and 2 µL of 8-times diluted cDNA. qPCR reactions were performed in a 384-well block of the ViiA7 Real-Time PCR System and evaluated using the ViiA7 System Software (Life Technologies, Carlsbad, CA, USA). Cycling parameters were initially held at 50 ◦C for 2 min and 10 min denaturation at 95 ◦C, followed by 45 cycles consisting of 15 sec of denaturation at 95 ◦C and 60 sec of annealing/extension at 60 ◦C. The non-template control contained water instead of cDNA and negative cDNA synthesis controls (RNA transcribed without reverse transcriptase) were employed to control carry-over contamination. All samples were analyzed in duplicates and samples with a standard deviation > 0.5 Ct between replicates were re-analyzed. The qPCR process adhered to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines (MIQE) 23 . Differences between samples and groups of patients were calculated from raw Ct values with the comparative Ct method described previously 24 . The 2 −∆Ct method was used for relative quantification of gene expression, and the 2 −∆∆Ct method was used for fold change calculation in groups divided by differences in sensitivity to therapy or mutation classification. Direct sequencing Exons 2 and 3 of KRAS and 5–10 of TP53 were subjected to direct sequencing using the Sanger method. Briefly, DNA was amplified between oligonucleotide primer pairs specific for each amplicon ( Table S1 ) using regular PCR and after product length verification on agarose gel purified by ethanol precipitation. Each reaction was optimized to produce a strong single-band product. Sequencing reactions were then performed using the BigDye Terminator v3.1 Cycle Sequencing Kit (Invitrogen) with approximately 10 ng of PCR product and 2 pmol of sequencing primer in 10 µl final reaction volume according to the producer’s protocol. Separate sequencing reactions were run with both forward and reverse sequencing primers ( Supplementary Table S1 ). The acquired products were purified using ExoSAP-IT™ PCR Product Cleanup Reagent (Applied Biosystems, Foster City, CA). DNA sequencing was performed by a capillary electrophoresis-based system commercially (SEQme, s.r.o., Dobris, Czech Republic). Raw results were evaluated by BioEdit 7.2.5 program and Sequencing Analysis Software v5.2 (Applied Biosystems). External datasets For validation of somatic variants in TP53 and KRAS , the American Association for Cancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE) 15.0-public release dataset (released on 1 Feb, 2024), composed of tumor panel sequencing data from multiple major cancer centers, was utilized 25 . Only samples fulfilling the following criteria were used: EOC, primary tumor, any somatic mutation data found after matching by sample ID, and gene of interest ( TP53 and/or KRAS ) included in the respective sequencing panel (final dataset: n = 2210). For validation of expression levels and mutation data, we used the RNAseq gene expression (FPKM-UQ normalized) and DNAseq mutation data of the GDC TCGA-OV cohort (Mutect2 pipeline), downloaded from the University of California Santa Cruz Xenabrowser portal ( https://xenabrowser.net ), which were then filtered to only primary ovarian tumors (n = 374). The dataset does not contain minority subtypes, nor detailed histopathological annotation, with all samples being classified merely as serous, and therefore, subtype-sensitive analyses were not performed using this dataset. Statistical analysis Associations of categorical clinical data of patients (stage, grade of tumor, residuum, chemosensitivity status) with functional classification of mutations were analyzed using the Pearson chi-square or Fisher’s exact test. For the evaluation of associations of continuous variables such as age at diagnosis or transcript expression with categorical ones, the Kruskal-Wallis test was used. Correlations among continuous variables were tested with Spearman’s rho correlation. All tests were two-sided and p-values < 0.05 were considered statistically significant. Survival curves were plotted using the Kaplan-Meier method. Expression levels were distributed by quartiles and the “optimal cut-off” was defined as the highest statistical significance by the log-rank test. All statistical analyses were performed using the SPSS v16 program (SPSS, Chicago, IL, USA). Results Patients’ characteristics The main characteristics of all patients (N = 177) are in Table 1 . The median age of patients at the time of diagnosis was 62 years (range 24–89). Most patients presented with FIGO stage III (82%), grade G3 (85%), and HGSC subtype (84%). About one-third of patients (32%) underwent preoperative chemotherapy, and half of patients (50%) had disease residuum left after surgical tumor debulking. The vast majority of patients (96%) received platinum-based chemotherapy regimens in an adjuvant setting, two received taxane monotherapy, four did not receive any adjuvant treatment due to poor performance status, and for six patients the information about therapy was not available. The median PFI and OS were 25 and 48 months, respectively. Patients with FIGO stage III or IV, residuum after surgery (R1 or R2), or with PFI < 12 months had significantly poorer OS than the rest of the patients (p < 0.001 for all) ( Supplementary Fig. S1 A-C ). Somatic genetic variability All six KRAS variants found previously by exome sequencing (n = 50) were also detected by Sanger sequencing in the confirmation part of the study (n = 50). In the extended validation part (n = 125, two samples not assessed due to the lack of DNA), variants in a further six samples were observed (Table 2 ). Table 2 Molecular characteristics of EOC patients Gene Number of patients Percentage KRAS mutation status* KRAS wild-type 163 93 KRAS mutated 12 7 KRAS mutation spectrum p.Gly12Asp 5 42 p.Gly12Val 3 26 p.Gly12Cys 1 8 p.Gly12Ala 1 8 p.Gln61Arg 1 8 p.Gln61His 1 8 TP53 mutation status TP53 wild-type 92 52 TP53 mutated 85 48 TP53 mutation spectrum Hotspots p.Arg175His 8 p.Arg273His 5 p.Arg248Gln 5 p.His214Arg 3 p.Tyr220Cys 3 p.His179Gln 2 p.Arg248Trp 2 p.Asp259Tyr 2 p.Arg282Trp 2 p.Glu198Ter 3 p.Arg213Ter 3 Private missense mutations 27 Private frameshift or nonsense mutations 13 Private splice site mutations with pathogenic features 4 TP53 mutation functional consequences # Loss-of-function 37 56 Gain-of-function 29 44 Not classified 19 ̶̶ Dominant-negative effect (DNE) & loss-of-function (LOF) properties # DNE_LOF 54 83 notDNE_notLOF 6 9 notDNE_LOF 5 8 Not classified 20 ̶̶ Transactivation function # non-functional 51 34 functional or partially functional 97 66 Not classified 29 ̶̶ DNA binding loop affected # yes 43 24 no 134 76 Footnotes: *Result for two samples not available due to DNA of low quality/quantity. # Evaluated using The TP53 database of NCI ( https://tp53.isb-cgc.org/ ) and The Clinical Knowledgebase (CKB) powered by The Jackson Laboratory database ( https://ckb.jax.org/ ) and literature cited therein. All variants were missense single nucleotide substitutions in exon 2 (n = 10) or 3 (n = 2). Representative chromatograms are in Supplementary Fig. S2A, B . As for TP53 , the confirmation set showed exactly the same variants compared to exome sequencing, i.e., 39 mutated and 10 wild-type patients, except in one sample where originally the variant p.Pro75fs was detected in exon 4, but the exon was then not covered by the direct sequencing approach. In the validation set (n = 127), an additional 45 mutated samples were identified (Table 2 ). Representative chromatograms are presented in Supplementary Fig. S3A-J . Functional classifications enabled the distribution of TP53 variants to several categories: i/ missense (n = 62), out of which 27 were single private mutations and the rest mutational hotspots detected in two or more patients, ii/ two hotspot nonsense variants present in three patients each, and iii/ private frameshifts or nonsense variants (n = 13). The last category was splice site variants with pathogenic features, which were all private (n = 4). The TP53 database of NCI and The Clinical Knowledgebase (CKB) enabled more detailed stratification of variants into loss-of-function (n = 37) versus gain-of-function (n = 29) variants. Most of the somatic variants were classified as having the following properties: dominant-negative effect or loss-of-function (n = 54), non-functional transactivation (n = 51), and affecting DNA binding loop (n = 43) by these databases (Table 2 ). Three patients carried mutations in both TP53 and KRAS (co-mutations). One patient with the HGSC subtype had the combination of TP53 -Arg282Trp with KRAS -Gln61His, chemoresistant status, and OS of 16 months. The second patient had a clear cell subtype, TP53 -Arg248Gln with KRAS -Gln61Arg, chemoresistant status, and extremely short OS of 7 months. The third patient with the mucinous subtype had TP53 -Arg213Ter with KRAS -Gly12Asp mutation combination, chemoresistant status, and OS of 19 months. These three available cases suggest that carriage of TP53-KRAS co-mutations could be associated with chemoresistance and poor patient prognosis. All subsequent clinical genomic analyses were performed using the combined confirmation and validation cohorts (N = 177). Intratumoral KRAS and TP53 transcript levels To provide additional functional evidence, we analyzed by qPCR the TP53 and KRAS transcript levels in all available tumor samples together with genetic information. Five samples could not be determined due to low RNA quantity or quality and no tissue left. No extreme outliers were observed. The carriage or type of KRAS mutations did not significantly associate with the KRAS transcript level (p > 0.05). On the other hand, a significantly lower TP53 transcript level in tumors bearing nonsense, frameshift, or splice site types of variants compared to wild-type TP53 was observed (p < 0.001, Fig. 1A ). In contrast, tumors with missense TP53 variants had significantly higher transcript levels than wild-type ones (p < 0.001, Fig. 1A ). Higher TP53 transcript level was found in tumors with TP53 variants classified as gain-of-function compared to loss-of-function (p = 0.018), non-functional vs. functional transactivation (p = 0.001), or DNA binding loop affecting vs. other (p < 0.001) ( Supplementary Fig. S4A-C ). Carriage of co-mutated TP53-KRAS did not affect transcript expression (p = 0.224 for TP53 and p = 0.204 for KRAS). Interestingly, the normalized intratumoral TP53 and KRAS transcript levels were mutually significantly correlated (ρ = 0.384, p < 0.001, Fig. 1B ). Figure 1 Associations of TP53 and KRAS normalized transcript levels in tumors with characteristics of EOC patients (A) TP53 normalized transcript level with TP53 mutation type. (B) Mutual correlation between KRAS and TP53 transcript levels. (C) KRAS normalized transcript level with EOC subtype. HIGH means nonsense, frameshift, or splice site functional variant classification. Associations of somatic genetic variability and transcript levels with clinical data of patients Afterward, we performed statistical analysis of associations between transcript levels, mutational status, spectra, and functional classifications of both genes and clinical data of patients. Patients with FIGO stage I or II had significantly more frequently mutated KRAS compared to stage III or IV patients (p = 0.007, Table 3 ). On the other hand, patients with the HGSC tumor subtype had significantly less frequently mutated KRAS (p < 0.001, Table 3 ), and they had significantly higher KRAS transcript levels (p = 0.004, Fig. 1C ) compared to those with other EOC subtypes. KRAS mutation status, spectra, or transcript level were not significantly associated with the rest of the clinical parameters (age, grade of tumor, surgical radicality, chemosensitivity status, or OS, all p > 0.05), and this was true for the association between transcript level and stage as well. Patients with nonHGSC subtypes had significantly more often less advanced stages I/II than HGSCs (p < 0.001, Supplementary Table S2 ) and thus less aggressive disease. However, only the PFI of patients with clear cell subtype (n = 10) was any better than that of HGSC (n = 134), while for mucinous (n = 9) or LGSC (n = 5), it was not, and endometrioid patients had worst PFI (n = 2) ( Supplementary Fig. S5A ). However, for OS the difference between subtypes was not that apparent ( Supplementary Fig. S5B ).As for TP53 , its transcript level, mutation status, spectra, or functional classifications were not significantly associated with any of the clinical parameters (age, stage, grade of tumor, subtype, surgical radicality, chemosensitivity status, or OS, all p > 0.05). Table 3 Associations between KRAS mutational status and stage or tumor subtype of EOC patients Characteristics KRAS wild-type* KRAS mutated* p-value Stage I/II 17 5 0.007 Stage III/IV 139 6 HGSC 140 2 < 0.001 other subtypes 19 9 Footnotes: *Numbers of patients; for some patients clinical data or KRAS mutation status were not available. We further performed patient stratification into HGSC and nonHGSC subgroups, given the importance of the EOC subtype in previous analyses. No significant associations with clinical data were identified for KRAS or TP53 transcript levels, mutations, or their functional classifications in the HGSC subgroup (n = 143). However, patients with nonHGSC subtypes (n = 28) bearing any TP53 mutations had non-significantly poorer PFI than patients with the wild-type (p = 0.062, Supplementary Fig. S6 ), and patients with TP53 missense variants disrupting the DNA binding loop had significantly poorer PFI than patients without these alterations, including wild-type carriers (p = 0.011, Fig. 2A ). No association was found for OS or other clinical data, including chemosensitivity status. Patients with co-mutated TP53 - KRAS had significantly worse PFI and OS than wild-type patients or those with a single gene mutation (p < 0.001 for both, Fig. 2B, C ). Figure 2 Associations between patient survival and carriage of TP53 or KRAS mutations (A) Platinum-free interval stratified by carriage of TP53 DNA binding domain mutations in EOC patients with nonHGSC subtype. (B) Platinum-free interval and (C) overall survival in TP53 - KRAS co-mutated patients compared to wild-type or single gene mutated EOC patients of all subtypes. Validation using external datasets Finally, we attempted to validate our findings using the largest and most up-to-date publicly available EOC dataset within the GENIE project (n = 2210). The TP53 and KRAS mutation analysis confirmed the overrepresentation of KRAS mutations in nonHGSC compared to HGSC cases. In our dataset, 32% of nonHGSC patients harbored KRAS mutations, while only 1.4% of HGSC cases had such alterations (Table 3 ). Size of the GENIE dataset allowed the analysis of the distribution of KRAS mutations across all major nonHGSC subtypes. The frequency of KRAS mutations raised in the trend HGSC (1.2%) < < clear cell (13%) < endometrioid (27%) = LGSC (28%) < mucinous (67%). Even more interesting was the trend in the ratio of TP53 / KRAS mutability among subtypes, where mucinous and clear cell cases had a 1/1 ratio, while endometrioid and LGSC subtypes had more KRAS than TP53 mutations. Patients with the HGSC subtype had a ratio close to 100/1 in favor of TP53 . Most interestingly, analysis of the GENIE dataset revealed a considerable fraction of TP53-KRAS co-mutated patients, again with a high heterogeneity across subtypes. Almost half (46%) of patients with the mucinous subtype had both genes mutated. The other subtypes had a much lower proportion of such events, 4% for endometrioid and 1.5% for clear cell EOC. The occurrence of this phenomenon in LGSC and HGSC was comparable and less than 1% in both cases (Fig. 3 A). As GENIE does not contain expression data, we used the TCGA-OV dataset (n = 374) for the assessment of transcript levels. The comparison of TP53 transcript levels with main mutation classification groups confirmed the trend observed in our study, i.e., significantly higher level in tumors harboring missense mutations (p = 0.002) and lower in those with nonsense, frameshift, or splicing mutations of (p = 0.009) compared to wild-type (Fig. 3 B). For KRAS , no significant association of transcript expression with mutation spectra was found (p > 0.05, Fig. 3 C), perhaps due to the low number of observations (n = 3 mutated samples). A weak, non-significant correlation was observed between TP53 and KRAS transcripts (p = 0.052, Fig. 3 D). In terms of available clinical data, neither grade (G1 or G2 versus G3 or G4) nor stage (stage I or II versus III or IV) were significantly associated with KRAS or TP53 transcript level (p > 0.05, data not shown). Only two KRAS - TP53 co-mutations were found in the TCGA dataset. One patient (TCGA-29-1696-01A) had KRAS Gly12Arg with TP53 frameshift co-mutation, stage IIIC, G2, and died 34 months after diagnosis. The second patient (TCGA-61-2009-01A) had KRAS Glu61Leu with TP53 missense co-mutation, stage IIIC, G3, and was alive 40 months after diagnosis. Thus, external data do not seem to corroborate our observation of the considerably poorer prognosis of the three EOC patients with such co-mutations. Due to the absence of survival data in the public version of GENIE and the lack of histopathologically confirmed subtype stratification in the TCGA dataset (however, all tumors were serous), we could not attempt the validation of our prognostic associations ( Fig. 2 ). Discussion The present study confirmed by a gold standard direct sequencing method the presence of mutations in clinically actionable EOC oncodrivers TP53 and KRAS , reported by previous whole exome sequencing of 50 patients. We further screened both genes in the additional sample set of 127 EOC patients and complemented the somatic genotype with transcript expression data with the intent to provide deeper functional insight. In general, results show that the functional classification of mutations and disease subtype context matters much more than carrier status alone. These aspects need careful investigation before any sensible clinical exploitation. Specifically, HGSC differs from other EOC subtypes by extreme dominance of TP53 mutations, while KRAS mutations are more relevant to nonHGSC subtypes. This observation agrees with the generally accepted view 26 and may have clinical consequences 27 . The prevalence of KRAS mutations in patients with less advanced stages I/II found by us complies with the fact that some nonHGSC subtypes are more frequently diagnosed with less advanced disease than HGSC ones 28 . Nevertheless, this was reflected by non-significantly better PFI only for clear cell subtype and just mild effect on general prognosis in terms of OS. However, the clear cell subtype is considered less sensitive to platinum-based chemotherapy than other EOCs 29 . Thus, our observation may be due to the low number of samples evaluated (n = 10). The striking prevalence of KRAS mutations in nonHGSC subtypes calls for their integration into clinical trials with future KRAS inhibitors. Studies with KRAS -G12D (MRTX1133) 30 or pan-KRAS 31 inhibitors show promising results and the KRAS -G12C mutation inhibitors already have been approved for the personalization of therapy. Sotorasib has been approved for targeted therapy of non-small cell lung cancer (NSCLC) 32 and adagrasib for NSCLC and colorectal carcinoma 33 . Subtype-sensitive analyses brought interesting results also for TP53 mutations classified by their location in the sequence. Those disrupting the DNA binding loop predisposed patients with nonHGSC subtypes to poorer PFI compared to patients with mutations not disrupting this domain. Such an association, to our best knowledge, has not been published yet. Although it concerns a small fraction of patients, it may provide predictive value for therapy targeted to specific TP53 mutations and/or function 34 . The relevance of so-called loss-of-function or gain-of-function TP53 variants for disease progression and potential therapy targeting was discussed before 35 . Therefore, we used The TP53 database of and The Clinical Knowledgebase for the stratification of patients into subgroups with variants classified according to different functional effects (Table 2 ). Nevertheless, except for the above-mentioned variants disrupting the DNA binding domain, no stratification had clinical consequences. Perhaps the most important result for contemporary considerations on targeted therapy and immunotherapy appeared after the analysis of KRAS - TP53 co-mutated tumors. Previous studies in NSCLC 36 , 37 or pancreatic carcinoma 38 reported contradicting results. Despite both author groups demonstrating the dismal prognosis of patients harboring such alterations, the immunologically “hot” status has been claimed for NSCLC, while the “cold” status for pancreatic carcinoma with presumed consequences for the results of eventual immune checkpoint blockade therapy. Our study shows that EOC may be subject to further research in this area as all co-mutated patients in the present study (n = 3), no matter their subtype, proved to be chemoresistant and died very quickly, moving EOC closer in this to pancreatic cancer. External validation using the GENIE dataset (n = 2210) 25 helped to validate the observed correlation between TP53 mutation types and transcript expression. More importantly, it enabled a more precise evaluation of the distribution of TP53 and KRAS mutation status across EOC subtypes. This analysis revealed an increasing trend in the ratio TP53 / KRAS mutability: HGSC > > clear cell ≈ mucinous > endometrioid > > LGSC. An even more striking disproportion in TP53 - KRAS co-mutation frequency: mucinous (53/115) > > endometrioid (9/223) > clear cell (4/265) > HGSC (14/1467) > LGSC (1/140) was apparent. Despite the common occurrence of co-mutations in the mucinous subtype was already described 39 , both trends suggest enormous variability among EOC subtypes, which calls for exploitation in individualized therapy. Several limitations of the present study need to be mentioned. Firstly, the sample size precludes robust analysis of nonHGSC subtypes, which are very rare (< 10% of EOC each). Unfortunately, due to serious constraints in clinical data availability in both GENIE (missing survival) 25 and TCGA (missing EOC subtype), we could not externally validate our potentially clinically relevant results. Thus, more studies are necessary for this area and our study may contribute to meta-analyses. Second, patients analyzed in this study were untreated with PARPi or other targeted drugs. Platinum-based chemotherapy is considered the standard of care in EOC 40 , and chemosensitivity to platinum and PARPi overlaps 41 , 42 . Thus, our sample set is still relevant from this point of view. Lastly, transcript levels may not robustly correlate with protein levels as assessed by immunohistochemistry. However, while immunohistochemistry is routinely clinically used for TP53 assessment, it is not in the case of KRAS , where just the mutation status is considered for EGFR blockade therapy, and functional approval, especially for rarely occurring variants, is missing. Our study shows that this area needs further attention. Finally, the present study has clear benefits in ethnical homogeneity of the patient population, unified therapy regimen, and long-term complete clinical follow-up. In conclusion, our study confirms previous data on KRAS as a valid and hopefully soon druggable target for nonHGSC EOCs and identifies the prognostic value of TP53 mutations in the DNA binding loop for a fraction of patients. Furthermore, we describe an intriguing enrichment of TP53-KRAS co-mutations in the mucinous subtype of EOC based on analysis of an external dataset of 2210 samples. Our results further extend the area of precision oncology of EOC and suggest directions for future functional and preclinical studies. Abbreviations AACR The American Association for Cancer Research CKB The Clinical Knowledgebase EOC epithelial ovarian cancer FIGO The International Federation of Gynecology and Obstetrics FOLFOX 5–fluorouracil, leucovorin, and oxaliplatin GENIE Genomics Evidence Neoplasia Information Exchange HGSC high–grade serous ovarian carcinomas LGSC low–grade serous ovarian carcinomas MIQE the Minimum Information for Publication of Quantitative Real–Time PCR Experiments Guidelines NGS next generation sequencing nonHGSC other than high–grade serous carcinoma NSCLC non–small cell lung cancer OS overall survival PARPi poly(ADP–ribose) polymerase inhibitors PFI platinum–free interval qPCR quantitative real–time polymerase chain reaction Declarations Ethics approval and consent to participate Experimental protocol of the study was approved by the Institutional Review Boards of the National Institute of Public Health in Prague (approval reference no. IGA NS9803-4 of 2 February 2008), University Hospital Motol (approval reference no. EK-890/15 of 24 June 2015), University Hospital Kralovske Vinohrady (approval reference no. EK-VP/40/0/2017 of 28 June 2017), and University Hospital Pilsen (approval reference no. 16-29013A of 4 June 2015). All patients included in the study read and signed the Informed Consent of the Patient. Consent for publication Not applicable. Data access All data generated or analyzed during this study are included in this published article and its supplementary information files. Competing interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Funding This work was funded by the Czech Health Research Council grant no. NU22-08-00186 to P.S. Authors’ Contributions Conceptualization – R.V. & P.S. Methodology – M.A., E.A., I.K., V.H., & F.A. Formal Analysis – P.H., R.V., & P.S. Investigation – M.A., E.A., V.H., & I.K. Resources – L.R., M.H., M.M., K.K., A.B., & J.B. Writing – Original Draft – M.A., R.V., & P.S. Writing – Review & Editing – M.A., E.A., I.K., P.H., V.H., F.A., L.R., M.H., M.M., K.K., A.B., J.B., R.V. & P.S. Visualization – P.H & P.S. Supervision – R.V. & P.S. Project Administration – P.S. Funding Acquisition – P.S. Acknowledgements The authors would like to thank all participating patients for their kind consent to the study and the clinical personnel for outstanding support. References Cabasag CJ, Fagan PJ, Ferlay J, Vignat J, Laversanne M, Liu L, van der Aa MA, Bray F, Soerjomataram I. Ovarian cancer today and tomorrow: A global assessment by world region and Human Development Index using GLOBOCAN 2020. Int J Cancer . 2022;151(9):1535-1541. doi: 10.1002/ijc.34002. Matulonis UA, Sood AK, Fallowfield L, Howitt BE, Sehouli J, Karlan BY. Ovarian cancer. 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Supplementary Files AlObeedetalSupplementarymaterials.pdf Supplementary materials Figure S1: Associations between stage (A), residuum after surgery (B), and chemosensitivity status (C) and overall survival of EOC patients Figure S2: Representative chromatograms of KRAS mutations assessed in EOC patients by direct Sanger sequencing A – codon 12 in exon 2, B – codon 61 in exon 3 Figure S3: Representative chromatograms of TP53 mutations assessed in EOC patients by direct Sanger sequencing A – p.Arg175His, B – p.His179Gln, C – p.His214Arg, D – p.Tyr220Cys, E – p.Glu198Ter, F – p.Arg213Ter, G – p.Asp259Tyr, H – p.Arg273His, I – p.Arg282Trp, J – p.Arg248His/Trp. Figure S4: Differences in TP53 transcript levels between patients divided by functional classifications of TP53 mutations Gain-of-function vs. loss-of-function (A), non-functional vs. functional transactivation (B), and DNA binding loop affecting vs. other (C) Figure S5: Association between subtype and platinum-free (A) and overall (B) survival of EOC patients Figure S6: Association between TP53 mutation status and platinum-free interval of nonHGSC EOC patients Table S1: List of primers used for KRAS and TP53 mutation analysis in EOC patients by direct Sanger sequencing Table S2: Associations between EOC subtype and disease stage 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-5224537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364522986,"identity":"a9d82541-2681-49c4-b521-ffd545d4ea20","order_by":0,"name":"Mohammad Al Obeed Allah","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Al Obeed","lastName":"Allah","suffix":""},{"id":364522988,"identity":"bcf95526-64f8-4484-bc8e-01d42458eb0b","order_by":1,"name":"Esraa Ali","email":"","orcid":"","institution":"Charles 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10:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5224537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5224537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67199894,"identity":"7a0cefa0-966d-461d-8770-baa7523dc1ab","added_by":"auto","created_at":"2024-10-22 09:49:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82369,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of TP53 and KRAS normalized transcript levels in tumors with characteristics of EOC patients\u003c/p\u003e\n\u003cp\u003e(A) TP53 normalized transcript level with \u003cem\u003eTP53\u003c/em\u003e mutation type. (B) Mutual correlation between KRAS and TP53 transcript levels. (C) KRAS normalized transcript level with EOC subtype.\u003c/p\u003e\n\u003cp\u003eHIGH means nonsense, frameshift, or splice site functional variant classification.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-5224537/v1/21e9242fc21b2c42c7b2383b.png"},{"id":67199895,"identity":"d30842a3-06d6-4612-af3b-d987e96791aa","added_by":"auto","created_at":"2024-10-22 09:49:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152361,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between patient survival and carriage of \u003cem\u003eTP53\u003c/em\u003e or \u003cem\u003eKRAS\u003c/em\u003e mutations\u003c/p\u003e\n\u003cp\u003e(A) Platinum-free interval stratified by carriage of \u003cem\u003eTP53\u003c/em\u003e DNA binding domain mutations in EOC patients with nonHGSC subtype. (B) Platinum-free interval and (C) overall survival in \u003cem\u003eTP53\u003c/em\u003e-\u003cem\u003eKRAS\u003c/em\u003eco-mutated patients compared to wild-type or single gene mutated EOC patients of all subtypes.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-5224537/v1/f49fa5c39f19a03fddc6efc6.png"},{"id":67201366,"identity":"65e03ffb-5021-4507-a72d-7aa5cf15f00e","added_by":"auto","created_at":"2024-10-22 09:58:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":679592,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e mutational spectra in EOC subtypes (A) and intratumoral transcript levels in non-stratified EOC cases (B – \u003cem\u003eTP53\u003c/em\u003e, C - \u003cem\u003eKRAS\u003c/em\u003e, D – lack of mutual correlation) using external datasets\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-5224537/v1/e37c3ddc583e4098125c5643.png"},{"id":67201673,"identity":"b5e73028-2091-4772-a2c7-d7436600425d","added_by":"auto","created_at":"2024-10-22 10:06:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1889130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5224537/v1/3c199930-4b7d-4933-8007-1ff19c971f80.pdf"},{"id":67199897,"identity":"e493d91a-ea7f-4f0d-aba8-0b1a2a0b9390","added_by":"auto","created_at":"2024-10-22 09:49:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2157985,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1: \u003c/strong\u003eAssociations between stage (A), residuum after surgery (B), and chemosensitivity status (C) and overall survival of EOC patients\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2: \u003c/strong\u003eRepresentative chromatograms of \u003cem\u003eKRAS\u003c/em\u003e mutations assessed in EOC patients by direct Sanger sequencing\u003c/p\u003e\n\u003cp\u003eA – codon 12 in exon 2, B – codon 61 in exon 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3: \u003c/strong\u003eRepresentative chromatograms of \u003cem\u003eTP53\u003c/em\u003emutations assessed in EOC patients by direct Sanger sequencing\u003c/p\u003e\n\u003cp\u003eA – p.Arg175His, B – p.His179Gln, C – p.His214Arg, D – p.Tyr220Cys, E – p.Glu198Ter, F – p.Arg213Ter, G – p.Asp259Tyr, H – p.Arg273His, I – p.Arg282Trp, J – p.Arg248His/Trp.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4: \u003c/strong\u003eDifferences in TP53 transcript levels between patients divided by functional classifications of \u003cem\u003eTP53\u003c/em\u003emutations\u003c/p\u003e\n\u003cp\u003eGain-of-function vs. loss-of-function (A), non-functional vs. functional transactivation (B), and DNA binding loop affecting vs. other (C)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S5\u003c/strong\u003e: Association between subtype and platinum-free (A) and overall (B) survival of EOC patients\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S6: \u003c/strong\u003eAssociation between \u003cem\u003eTP53\u003c/em\u003emutation status and platinum-free interval of nonHGSC EOC patients\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1: \u003c/strong\u003eList of primers used for \u003cem\u003eKRAS\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e mutation analysis in EOC patients by direct Sanger sequencing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2: \u003c/strong\u003eAssociations between EOC subtype and disease stage\u003c/p\u003e","description":"","filename":"AlObeedetalSupplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5224537/v1/a8a048bcfb9491bea0e1f9c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEpithelial ovarian cancer (EOC), recognized as the eighth leading cause of cancer-related death among women, stands out as one of the most lethal gynecological malignancies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Early detection of EOC is a challenge, because, in most cases, it is asymptomatic in the early stages (I or II based on The International Federation of Gynecology and Obstetrics, FIGO, guidelines). Typically, 75% of EOC cases occur at the advanced stage (III or IV), where the 5-year survival rate is approximately 20\u0026ndash;45%, compared to 40\u0026ndash;70% for stages I or II\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The standard treatment for advanced EOC has been primary debulking surgery followed by chemotherapy (platinum derivatives with paclitaxel) for the majority of cases\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, most patients experience a relapse within the first five years after the initial diagnosis, with only 20\u0026ndash;25% achieving cure\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMorphologically, EOCs are classified into four major subtypes: serous, endometrioid, clear cell, and mucinous\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additionally, they can be divided into two primary types: type I, including endometrioid, mucinous, clear cell, and low-grade serous ovarian carcinomas (LGSCs), and type II, constituting 70% of the total and encompassing high-grade serous ovarian carcinomas (HGSCs), carcinosarcomas, and undifferentiated carcinomas. These classifications are integral to defining the aggressiveness of the cancer and its response to different chemotherapies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A significant majority, exceeding 80% of identified EOC cases, fall under the histological classification of HGSC, characterized by an aggressive phenotype that correlates with elevated mortality rate\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, which is attributed not only to diagnosis at the advanced stage but also to chemoresistance, where approximately 50% of the cases diagnosed at advanced stage relapse within the first five years\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe introduction of Poly(ADP-ribose) polymerase (PARP) inhibitors like olaparib and antiangiogenic agents, such as bevacizumab or pazopanib, has led to a significant improvement in the prognosis of the patients\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. PARP inhibitors (PARPis) are primarily used for maintenance therapy for platinum-sensitive advanced EOCs\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Moreover, patients with \u003cem\u003eBRCA1\u003c/em\u003e/\u003cem\u003eBRCA2\u003c/em\u003e mutations demonstrate enhanced sensitivity to treatment with PARPi\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext Generation Sequencing (NGS) enables the detection of genetic variability and its linkage to multidrug resistance. Based on genomic profiling, two major EOC types have been defined. EOC of type I is characterized by mutations in the MAPK pathway (\u003cem\u003eKRAS, BRAF, PTEN\u003c/em\u003e, and \u003cem\u003eCTNNB1\u003c/em\u003e, etc.) and type II mutations in \u003cem\u003eTP53, BRCA1, BRCA2, KIT\u003c/em\u003e, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, DNA damage response and related alterations in DNA repair pathways play a crucial role in cancer development, including EOC. Germline mutations in DNA repair genes can predict hereditary forms of cancer, particularly \u003cem\u003eBRCA1/2\u003c/em\u003e mutations in breast and ovarian cancers\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Pathogenic somatic mutations in genes from the homologous recombination DNA repair pathway, such as \u003cem\u003eBRCA1/2\u003c/em\u003e, \u003cem\u003eATM\u003c/em\u003e, \u003cem\u003eRAD51C\u003c/em\u003e, and \u003cem\u003eRAD51D\u003c/em\u003e, were implicated in chemosensitivity and prognosis of EOC patients\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. HGSC typically shows very high frequency of somatic \u003cem\u003eTP53\u003c/em\u003e mutations (~\u0026thinsp;90%) and genomic heterogeneity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study aimed to validate the results of a previous whole exome sequencing study of 50 patients\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which confirmed \u003cem\u003eTP53\u003c/em\u003e as the most frequently mutated gene in HGSC and EOC in general and suggested its relevance for chemosensitivity. Using direct Sanger sequencing of the same samples, we demonstrate the robustness of mutation detection and provide validation study results using an extended sample set analyzed by the same method. We also include \u003cem\u003eKRAS\u003c/em\u003e as an additional target of interest and complement somatic mutation screening with an assessment of both genes\u0026rsquo; transcript levels in tumor RNA. We compare the results with sensitivity to EOC therapy and patient survival for evaluation of the prognostic value of these biomarkers. Our study adds another dimension to exome or genome sequencing-based EOC projects published before\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eFor this study, we used samples of surgically resected, primary EOC tumors from 50 patients (confirmation set) with available whole exome data\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and additional 127 EOC patients (validation set) without exome data. Patients were prospectively recruited at University Hospitals Motol, Kr\u0026aacute;lovsk\u0026eacute; Vinohrady (both in Prague, Czech Republic), and Pilsen (Czech Republic) between 2009 and 2020. Tumor samples were collected fresh and promptly frozen and stored at -80\u0026deg;C until isolation of nucleic acids. Peripheral blood samples were taken from all patients to enable tumor-normal matched analysis.\u003c/p\u003e \u003cp\u003eCollaborating clinicians collected the following clinical data on each patient: age at diagnosis, FIGO stage (pTNM), the histological subtype and grade of the tumor, presence of distant metastasis or residuum after surgery, oncological treatments, chemosensitivity status, and overall survival (OS) from medical records. The chemosensitivity status was based on the platinum-free interval (PFI) measured as the time from the end of the platinum-based adjuvant chemotherapy to disease recurrence or progression\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Patients having PFI\u0026thinsp;\u0026le;\u0026thinsp;6 months were considered platinum-resistant and patients with PFI\u0026thinsp;\u0026ge;\u0026thinsp;12 months platinum-sensitive. Several patients had PFI in the range of 7\u0026ndash;12 months and were classified as partially platinum-sensitive\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These patients were tentatively included in the resistant group and all association analyses were performed both with and without them. Consensual results are provided. The OS was defined as the time elapsed between surgical resection and death of any cause or patient censoring. Detailed clinical characteristics of the patients are in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eClinical characteristics of EOC patients\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\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\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\u003eAge at diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \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\u003e\u003cb\u003eFIGO stage\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\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\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\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic grade (G)\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\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor subtype\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\u003eHGSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistant metastasis\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\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeoadjuvant chemotherapy\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\u003eAdministered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot administered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResiduum after 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\u003ePresent**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent (R0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdjuvant chemotherapy\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\u003ePlatinum-based\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxane monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot administered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemosensitivity status\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\u003eResistant (PFI\u0026thinsp;\u0026le;\u0026thinsp;6 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate (PFI 7\u0026ndash;11 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitive (PFI\u0026thinsp;\u0026ge;\u0026thinsp;12 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatinum-free interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u0026thinsp;\u0026plusmn;\u0026thinsp;95% confidence interval (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u0026ndash;32.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall survival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u0026thinsp;\u0026plusmn;\u0026thinsp;95% confidence interval (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.7\u0026ndash;58.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFootnotes:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Other subtypes include the following carcinomas: mucinous (n\u0026thinsp;=\u0026thinsp;9), clear cell (n\u0026thinsp;=\u0026thinsp;10), low grade serous (n\u0026thinsp;=\u0026thinsp;5), endometrioid (n\u0026thinsp;=\u0026thinsp;2), and borderline (n\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e \u003cp\u003e**Includes all ratings above R0 (R1, R2, unspecified).\u003c/p\u003e \u003cp\u003e \u003csup\u003e#\u003c/sup\u003ePlatinum-based chemotherapy regimens include n\u0026thinsp;=\u0026thinsp;147 taxane (paclitaxel/docetaxel) with platinum (carboplatin/cisplatin), n\u0026thinsp;=\u0026thinsp;9 platinum monotherapy, other (n\u0026thinsp;=\u0026thinsp;1 FOLFOX, n\u0026thinsp;=\u0026thinsp;1 platinum with anthracycline, n\u0026thinsp;=\u0026thinsp;1 platinum with paclitaxel and anthracycline, and n\u0026thinsp;=\u0026thinsp;6 platinum with paclitaxel and cyclophosphamide). FOLFOX\u0026thinsp;=\u0026thinsp;5-fluorouracil, leucovorin, and oxaliplatin.\u003c/p\u003e \u003cp\u003e Experimental protocol of the study was approved by the Institutional Review Boards of the National Institute of Public Health in Prague (approval reference no. IGA NS9803-4 of 2 February 2008), University Hospital Motol (approval reference no. EK-890/15 of 24 June 2015), University Hospital Kr\u0026aacute;lovsk\u0026eacute; Vinohrady (approval reference no. EK-VP/40/0/2017 of 28 June 2017), and University Hospital Pilsen (approval reference no. 16-29013A of 4 June 2015). All patients included in the study read and signed the Informed Consent of the Patient.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIsolation of nucleic acids and cDNA synthesis\u003c/h3\u003e\n\u003cp\u003eDNA from peripheral blood lymphocytes was isolated using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany). Processing tumor tissue samples involved grinding them into a fine powder using a mortar and pestle under liquid nitrogen. Subsequently, we utilized the AllPrep DNA/RNA/Protein Mini Kit (Qiagen) according to the manufacturer's protocol for the isolation of total RNA and DNA. The quantity of the RNA and DNA samples was assessed using the Qubit 4 Nucleic Acid Fluorometric Quantification System (ThermoFisher Scientific, Waltham, MA, USA) and quality was checked by measuring the integrity number (RIN and DIN) using Agilent TapeStation 2200 (ThermoFisher Scientific). RNA was transcribed into cDNA with the help of the RevertAid\u0026trade; First Strand cDNA Synthesis kit (ThermoFisher Scientific) according to the manufacturer's protocol and checked using the previously published method\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eQuantitative Polymerase Chain Reaction\u003c/h3\u003e\n\u003cp\u003eQuantitative real-time PCR (qPCR) was performed using TaqMan\u0026reg; Gene Expression Assays (ThermoFisher), namely TP53 (Hs01034249_m1) and KRAS (Hs00364284_g1). PPIA (Hs99999904_m1), UBC (Hs00824723_m1), and YWHAZ (Hs03044281_g1), selected previously using NormFinder and geNorm software, served as reference genes for results normalization\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The reaction mixture with volume of 5 \u0026micro;L contained 1 \u0026micro;L of 5\u0026times; Hot FirePol Probe qPCR Mix Plus (ROX) (Solis BioDyne O\u0026Uuml;, Tartu, Estonia), 0.25 \u0026micro;L of 20\u0026times; TaqMan\u0026reg; Gene Expression Assay specified above, 1.75 \u0026micro;L of nuclease-free water, and 2 \u0026micro;L of 8-times diluted cDNA. qPCR reactions were performed in a 384-well block of the ViiA7 Real-Time PCR System and evaluated using the ViiA7 System Software (Life Technologies, Carlsbad, CA, USA). Cycling parameters were initially held at 50 ◦C for 2 min and 10 min denaturation at 95 ◦C, followed by 45 cycles consisting of 15 sec of denaturation at 95 ◦C and 60 sec of annealing/extension at 60 ◦C. The non-template control contained water instead of cDNA and negative cDNA synthesis controls (RNA transcribed without reverse transcriptase) were employed to control carry-over contamination. All samples were analyzed in duplicates and samples with a standard deviation\u0026thinsp;\u0026gt;\u0026thinsp;0.5 Ct between replicates were re-analyzed. The qPCR process adhered to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines (MIQE)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDifferences between samples and groups of patients were calculated from raw Ct values with the comparative Ct method described previously\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The 2\u003csup\u003e\u0026minus;∆Ct\u003c/sup\u003e method was used for relative quantification of gene expression, and the 2\u003csup\u003e\u0026minus;∆∆Ct\u003c/sup\u003e method was used for fold change calculation in groups divided by differences in sensitivity to therapy or mutation classification.\u003c/p\u003e\n\u003ch3\u003eDirect sequencing\u003c/h3\u003e\n\u003cp\u003eExons 2 and 3 of \u003cem\u003eKRAS\u003c/em\u003e and 5\u0026ndash;10 of \u003cem\u003eTP53\u003c/em\u003e were subjected to direct sequencing using the Sanger method. Briefly, DNA was amplified between oligonucleotide primer pairs specific for each amplicon (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) using regular PCR and after product length verification on agarose gel purified by ethanol precipitation. Each reaction was optimized to produce a strong single-band product. Sequencing reactions were then performed using the BigDye Terminator v3.1 Cycle Sequencing Kit (Invitrogen) with approximately 10 ng of PCR product and 2 pmol of sequencing primer in 10 \u0026micro;l final reaction volume according to the producer\u0026rsquo;s protocol. Separate sequencing reactions were run with both forward and reverse sequencing primers (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The acquired products were purified using ExoSAP-IT\u0026trade; PCR Product Cleanup Reagent (Applied Biosystems, Foster City, CA). DNA sequencing was performed by a capillary electrophoresis-based system commercially (SEQme, s.r.o., Dobris, Czech Republic). Raw results were evaluated by BioEdit 7.2.5 program and Sequencing Analysis Software v5.2 (Applied Biosystems).\u003c/p\u003e\n\u003ch3\u003eExternal datasets\u003c/h3\u003e\n\u003cp\u003eFor validation of somatic variants in \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e, the American Association for Cancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE) 15.0-public release dataset (released on 1 Feb, 2024), composed of tumor panel sequencing data from multiple major cancer centers, was utilized\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Only samples fulfilling the following criteria were used: EOC, primary tumor, any somatic mutation data found after matching by sample ID, and gene of interest (\u003cem\u003eTP53\u003c/em\u003e and/or \u003cem\u003eKRAS\u003c/em\u003e) included in the respective sequencing panel (final dataset: n\u0026thinsp;=\u0026thinsp;2210). For validation of expression levels and mutation data, we used the RNAseq gene expression (FPKM-UQ normalized) and DNAseq mutation data of the GDC TCGA-OV cohort (Mutect2 pipeline), downloaded from the University of California Santa Cruz Xenabrowser portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which were then filtered to only primary ovarian tumors (n\u0026thinsp;=\u0026thinsp;374). The dataset does not contain minority subtypes, nor detailed histopathological annotation, with all samples being classified merely as serous, and therefore, subtype-sensitive analyses were not performed using this dataset.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAssociations of categorical clinical data of patients (stage, grade of tumor, residuum, chemosensitivity status) with functional classification of mutations were analyzed using the Pearson chi-square or Fisher\u0026rsquo;s exact test. For the evaluation of associations of continuous variables such as age at diagnosis or transcript expression with categorical ones, the Kruskal-Wallis test was used. Correlations among continuous variables were tested with Spearman\u0026rsquo;s rho correlation. All tests were two-sided and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Survival curves were plotted using the Kaplan-Meier method. Expression levels were distributed by quartiles and the \u0026ldquo;optimal cut-off\u0026rdquo; was defined as the highest statistical significance by the log-rank test. All statistical analyses were performed using the SPSS v16 program (SPSS, Chicago, IL, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eThe main characteristics of all patients (N\u0026thinsp;=\u0026thinsp;177) are in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of patients at the time of diagnosis was 62 years (range 24\u0026ndash;89). Most patients presented with FIGO stage III (82%), grade G3 (85%), and HGSC subtype (84%). About one-third of patients (32%) underwent preoperative chemotherapy, and half of patients (50%) had disease residuum left after surgical tumor debulking. The vast majority of patients (96%) received platinum-based chemotherapy regimens in an adjuvant setting, two received taxane monotherapy, four did not receive any adjuvant treatment due to poor performance status, and for six patients the information about therapy was not available. The median PFI and OS were 25 and 48 months, respectively. Patients with FIGO stage III or IV, residuum after surgery (R1 or R2), or with PFI\u0026thinsp;\u0026lt;\u0026thinsp;12 months had significantly poorer OS than the rest of the patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all) (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-C\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSomatic genetic variability\u003c/h2\u003e \u003cp\u003eAll six \u003cem\u003eKRAS\u003c/em\u003e variants found previously by exome sequencing (n\u0026thinsp;=\u0026thinsp;50) were also detected by Sanger sequencing in the confirmation part of the study (n\u0026thinsp;=\u0026thinsp;50). In the extended validation part (n\u0026thinsp;=\u0026thinsp;125, two samples not assessed due to the lack of DNA), variants in a further six samples were observed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eMolecular characteristics of EOC patients\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=\"char\" char=\".\" 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\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e mutation status*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e wild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e mutated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKRAS\u003c/b\u003e \u003cb\u003emutation spectrum\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\u003ep.Gly12Asp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Gly12Val\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Gly12Cys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Gly12Ala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Gln61Arg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Gln61His\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP53\u003c/b\u003e \u003cb\u003emutation status\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\u003e\u003cem\u003eTP53\u003c/em\u003e wild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mutated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP53\u003c/b\u003e \u003cb\u003emutation spectrum\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\u003e\u003cem\u003eHotspots\u003c/em\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\u003ep.Arg175His\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \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\u003ep.Arg273His\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \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\u003ep.Arg248Gln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \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\u003ep.His214Arg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \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\u003ep.Tyr220Cys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \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\u003ep.His179Gln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \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\u003ep.Arg248Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \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\u003ep.Asp259Tyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \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\u003ep.Arg282Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \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\u003ep.Glu198Ter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \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\u003ep.Arg213Ter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \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\u003e\u003cem\u003ePrivate missense mutations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \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\u003e\u003cem\u003ePrivate frameshift or nonsense mutations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \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\u003e\u003cem\u003ePrivate splice site mutations with pathogenic features\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \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\u003e\u003cb\u003eTP53\u003c/b\u003e \u003cb\u003emutation functional consequences\u003c/b\u003e\u003csup\u003e#\u003c/sup\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\u003eLoss-of-function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGain-of-function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot classified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDominant-negative effect (DNE) \u0026amp; loss-of-function (LOF) properties\u003c/em\u003e\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \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\u003eDNE_LOF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enotDNE_notLOF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enotDNE_LOF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot classified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTransactivation function\u003c/em\u003e\u003csup\u003e#\u003c/sup\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\u003enon-functional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efunctional or partially functional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot classified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e̶̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDNA binding loop affected\u003c/em\u003e\u003csup\u003e#\u003c/sup\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFootnotes:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Result for two samples not available due to DNA of low quality/quantity.\u003c/p\u003e \u003cp\u003e \u003csup\u003e#\u003c/sup\u003eEvaluated using The TP53 database of NCI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tp53.isb-cgc.org/\u003c/span\u003e\u003cspan address=\"https://tp53.isb-cgc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and The Clinical Knowledgebase (CKB) powered by The Jackson Laboratory database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ckb.jax.org/\u003c/span\u003e\u003cspan address=\"https://ckb.jax.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and literature cited therein.\u003c/p\u003e \u003cp\u003eAll variants were missense single nucleotide substitutions in exon 2 (n\u0026thinsp;=\u0026thinsp;10) or 3 (n\u0026thinsp;=\u0026thinsp;2). Representative chromatograms are in \u003cb\u003eSupplementary Fig. S2A, B\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAs for \u003cem\u003eTP53\u003c/em\u003e, the confirmation set showed exactly the same variants compared to exome sequencing, i.e., 39 mutated and 10 wild-type patients, except in one sample where originally the variant p.Pro75fs was detected in exon 4, but the exon was then not covered by the direct sequencing approach. In the validation set (n\u0026thinsp;=\u0026thinsp;127), an additional 45 mutated samples were identified (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Representative chromatograms are presented in \u003cb\u003eSupplementary Fig. S3A-J\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFunctional classifications enabled the distribution of \u003cem\u003eTP53\u003c/em\u003e variants to several categories: i/ missense (n\u0026thinsp;=\u0026thinsp;62), out of which 27 were single private mutations and the rest mutational hotspots detected in two or more patients, ii/ two hotspot nonsense variants present in three patients each, and iii/ private frameshifts or nonsense variants (n\u0026thinsp;=\u0026thinsp;13). The last category was splice site variants with pathogenic features, which were all private (n\u0026thinsp;=\u0026thinsp;4). The TP53 database of NCI and The Clinical Knowledgebase (CKB) enabled more detailed stratification of variants into loss-of-function (n\u0026thinsp;=\u0026thinsp;37) versus gain-of-function (n\u0026thinsp;=\u0026thinsp;29) variants. Most of the somatic variants were classified as having the following properties: dominant-negative effect or loss-of-function (n\u0026thinsp;=\u0026thinsp;54), non-functional transactivation (n\u0026thinsp;=\u0026thinsp;51), and affecting DNA binding loop (n\u0026thinsp;=\u0026thinsp;43) by these databases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThree patients carried mutations in both \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e (co-mutations). One patient with the HGSC subtype had the combination of \u003cem\u003eTP53\u003c/em\u003e-Arg282Trp with \u003cem\u003eKRAS\u003c/em\u003e-Gln61His, chemoresistant status, and OS of 16 months. The second patient had a clear cell subtype, \u003cem\u003eTP53\u003c/em\u003e-Arg248Gln with \u003cem\u003eKRAS\u003c/em\u003e-Gln61Arg, chemoresistant status, and extremely short OS of 7 months. The third patient with the mucinous subtype had \u003cem\u003eTP53\u003c/em\u003e-Arg213Ter with \u003cem\u003eKRAS\u003c/em\u003e-Gly12Asp mutation combination, chemoresistant status, and OS of 19 months. These three available cases suggest that carriage of \u003cem\u003eTP53-KRAS\u003c/em\u003e co-mutations could be associated with chemoresistance and poor patient prognosis.\u003c/p\u003e \u003cp\u003eAll subsequent clinical genomic analyses were performed using the combined confirmation and validation cohorts (N\u0026thinsp;=\u0026thinsp;177).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIntratumoral KRAS and TP53 transcript levels\u003c/h2\u003e \u003cp\u003eTo provide additional functional evidence, we analyzed by qPCR the TP53 and KRAS transcript levels in all available tumor samples together with genetic information. Five samples could not be determined due to low RNA quantity or quality and no tissue left. No extreme outliers were observed.\u003c/p\u003e \u003cp\u003eThe carriage or type of \u003cem\u003eKRAS\u003c/em\u003e mutations did not significantly associate with the KRAS transcript level (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). On the other hand, a significantly lower TP53 transcript level in tumors bearing nonsense, frameshift, or splice site types of variants compared to wild-type \u003cem\u003eTP53\u003c/em\u003e was observed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e). In contrast, tumors with missense \u003cem\u003eTP53\u003c/em\u003e variants had significantly higher transcript levels than wild-type ones (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e). Higher TP53 transcript level was found in tumors with \u003cem\u003eTP53\u003c/em\u003e variants classified as gain-of-function compared to loss-of-function (p\u0026thinsp;=\u0026thinsp;0.018), non-functional vs. functional transactivation (p\u0026thinsp;=\u0026thinsp;0.001), or DNA binding loop affecting vs. other (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eSupplementary Fig. S4A-C\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eCarriage of co-mutated \u003cem\u003eTP53-KRAS\u003c/em\u003e did not affect transcript expression (p\u0026thinsp;=\u0026thinsp;0.224 for TP53 and p\u0026thinsp;=\u0026thinsp;0.204 for KRAS).\u003c/p\u003e \u003cp\u003eInterestingly, the normalized intratumoral TP53 and KRAS transcript levels were mutually significantly correlated (ρ\u0026thinsp;=\u0026thinsp;0.384, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cb\u003eFig.\u0026nbsp;1B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 1\u003c/strong\u003e \u003cp\u003eAssociations of TP53 and KRAS normalized transcript levels in tumors with characteristics of EOC patients\u003c/p\u003e \u003c/p\u003e \u003cp\u003e(A) TP53 normalized transcript level with \u003cem\u003eTP53\u003c/em\u003e mutation type. (B) Mutual correlation between KRAS and TP53 transcript levels. (C) KRAS normalized transcript level with EOC subtype.\u003c/p\u003e \u003cp\u003eHIGH means nonsense, frameshift, or splice site functional variant classification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of somatic genetic variability and transcript levels with clinical data of patients\u003c/h2\u003e \u003cp\u003eAfterward, we performed statistical analysis of associations between transcript levels, mutational status, spectra, and functional classifications of both genes and clinical data of patients.\u003c/p\u003e \u003cp\u003ePatients with FIGO stage I or II had significantly more frequently mutated \u003cem\u003eKRAS\u003c/em\u003e compared to stage III or IV patients (p\u0026thinsp;=\u0026thinsp;0.007, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the other hand, patients with the HGSC tumor subtype had significantly less frequently mutated \u003cem\u003eKRAS\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and they had significantly higher KRAS transcript levels (p\u0026thinsp;=\u0026thinsp;0.004, \u003cb\u003eFig.\u0026nbsp;1C\u003c/b\u003e) compared to those with other EOC subtypes. \u003cem\u003eKRAS\u003c/em\u003e mutation status, spectra, or transcript level were not significantly associated with the rest of the clinical parameters (age, grade of tumor, surgical radicality, chemosensitivity status, or OS, all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and this was true for the association between transcript level and stage as well. Patients with nonHGSC subtypes had significantly more often less advanced stages I/II than HGSCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cb\u003eSupplementary Table S2\u003c/b\u003e) and thus less aggressive disease. However, only the PFI of patients with clear cell subtype (n\u0026thinsp;=\u0026thinsp;10) was any better than that of HGSC (n\u0026thinsp;=\u0026thinsp;134), while for mucinous (n\u0026thinsp;=\u0026thinsp;9) or LGSC (n\u0026thinsp;=\u0026thinsp;5), it was not, and endometrioid patients had worst PFI (n\u0026thinsp;=\u0026thinsp;2) (\u003cb\u003eSupplementary Fig. S5A\u003c/b\u003e). However, for OS the difference between subtypes was not that apparent (\u003cb\u003eSupplementary Fig. S5B\u003c/b\u003e).As for \u003cem\u003eTP53\u003c/em\u003e, its transcript level, mutation status, spectra, or functional classifications were not significantly associated with any of the clinical parameters (age, stage, grade of tumor, subtype, surgical radicality, chemosensitivity status, or OS, all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eAssociations between \u003cem\u003eKRAS\u003c/em\u003e mutational status and stage or tumor subtype of EOC patients\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e wild-type*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e mutated*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\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\u003eHGSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother subtypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFootnotes:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Numbers of patients; for some patients clinical data or \u003cem\u003eKRAS\u003c/em\u003e mutation status were not available.\u003c/p\u003e \u003cp\u003eWe further performed patient stratification into HGSC and nonHGSC subgroups, given the importance of the EOC subtype in previous analyses. No significant associations with clinical data were identified for KRAS or TP53 transcript levels, mutations, or their functional classifications in the HGSC subgroup (n\u0026thinsp;=\u0026thinsp;143). However, patients with nonHGSC subtypes (n\u0026thinsp;=\u0026thinsp;28) bearing any \u003cem\u003eTP53\u003c/em\u003e mutations had non-significantly poorer PFI than patients with the wild-type (p\u0026thinsp;=\u0026thinsp;0.062, \u003cb\u003eSupplementary Fig. S6\u003c/b\u003e), and patients with \u003cem\u003eTP53\u003c/em\u003e missense variants disrupting the DNA binding loop had significantly poorer PFI than patients without these alterations, including wild-type carriers (p\u0026thinsp;=\u0026thinsp;0.011, \u003cb\u003eFig.\u0026nbsp;2A\u003c/b\u003e). No association was found for OS or other clinical data, including chemosensitivity status.\u003c/p\u003e \u003cp\u003ePatients with co-mutated \u003cem\u003eTP53\u003c/em\u003e-\u003cem\u003eKRAS\u003c/em\u003e had significantly worse PFI and OS than wild-type patients or those with a single gene mutation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both, \u003cb\u003eFig.\u0026nbsp;2B, C\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 2\u003c/strong\u003e \u003cp\u003eAssociations between patient survival and carriage of \u003cem\u003eTP53\u003c/em\u003e or \u003cem\u003eKRAS\u003c/em\u003e mutations\u003c/p\u003e \u003c/p\u003e \u003cp\u003e(A) Platinum-free interval stratified by carriage of \u003cem\u003eTP53\u003c/em\u003e DNA binding domain mutations in EOC patients with nonHGSC subtype. (B) Platinum-free interval and (C) overall survival in \u003cem\u003eTP53\u003c/em\u003e-\u003cem\u003eKRAS\u003c/em\u003e co-mutated patients compared to wild-type or single gene mutated EOC patients of all subtypes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation using external datasets\u003c/h2\u003e \u003cp\u003eFinally, we attempted to validate our findings using the largest and most up-to-date publicly available EOC dataset within the GENIE project (n\u0026thinsp;=\u0026thinsp;2210).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e mutation analysis confirmed the overrepresentation of \u003cem\u003eKRAS\u003c/em\u003e mutations in nonHGSC compared to HGSC cases. In our dataset, 32% of nonHGSC patients harbored \u003cem\u003eKRAS\u003c/em\u003e mutations, while only 1.4% of HGSC cases had such alterations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Size of the GENIE dataset allowed the analysis of the distribution of \u003cem\u003eKRAS\u003c/em\u003e mutations across all major nonHGSC subtypes. The frequency of \u003cem\u003eKRAS\u003c/em\u003e mutations raised in the trend HGSC (1.2%)\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;clear cell (13%)\u0026thinsp;\u0026lt;\u0026thinsp;endometrioid (27%)\u0026thinsp;=\u0026thinsp;LGSC (28%)\u0026thinsp;\u0026lt;\u0026thinsp;mucinous (67%). Even more interesting was the trend in the ratio of \u003cem\u003eTP53\u003c/em\u003e/\u003cem\u003eKRAS\u003c/em\u003e mutability among subtypes, where mucinous and clear cell cases had a 1/1 ratio, while endometrioid and LGSC subtypes had more \u003cem\u003eKRAS\u003c/em\u003e than \u003cem\u003eTP53\u003c/em\u003e mutations. Patients with the HGSC subtype had a ratio close to 100/1 in favor of \u003cem\u003eTP53\u003c/em\u003e. Most interestingly, analysis of the GENIE dataset revealed a considerable fraction of \u003cem\u003eTP53-KRAS\u003c/em\u003e co-mutated patients, again with a high heterogeneity across subtypes. Almost half (46%) of patients with the mucinous subtype had both genes mutated. The other subtypes had a much lower proportion of such events, 4% for endometrioid and 1.5% for clear cell EOC. The occurrence of this phenomenon in LGSC and HGSC was comparable and less than 1% in both cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eAs GENIE does not contain expression data, we used the TCGA-OV dataset (n\u0026thinsp;=\u0026thinsp;374) for the assessment of transcript levels. The comparison of TP53 transcript levels with main mutation classification groups confirmed the trend observed in our study, i.e., significantly higher level in tumors harboring missense mutations (p\u0026thinsp;=\u0026thinsp;0.002) and lower in those with nonsense, frameshift, or splicing mutations of (p\u0026thinsp;=\u0026thinsp;0.009) compared to wild-type (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For \u003cem\u003eKRAS\u003c/em\u003e, no significant association of transcript expression with mutation spectra was found (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), perhaps due to the low number of observations (n\u0026thinsp;=\u0026thinsp;3 mutated samples). A weak, non-significant correlation was observed between TP53 and KRAS transcripts (p\u0026thinsp;=\u0026thinsp;0.052, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In terms of available clinical data, neither grade (G1 or G2 versus G3 or G4) nor stage (stage I or II versus III or IV) were significantly associated with KRAS or TP53 transcript level (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, data not shown).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOnly two \u003cem\u003eKRAS\u003c/em\u003e-\u003cem\u003eTP53\u003c/em\u003e co-mutations were found in the TCGA dataset. One patient (TCGA-29-1696-01A) had \u003cem\u003eKRAS\u003c/em\u003e Gly12Arg with \u003cem\u003eTP53\u003c/em\u003e frameshift co-mutation, stage IIIC, G2, and died 34 months after diagnosis. The second patient (TCGA-61-2009-01A) had \u003cem\u003eKRAS\u003c/em\u003e Glu61Leu with \u003cem\u003eTP53\u003c/em\u003e missense co-mutation, stage IIIC, G3, and was alive 40 months after diagnosis. Thus, external data do not seem to corroborate our observation of the considerably poorer prognosis of the three EOC patients with such co-mutations.\u003c/p\u003e \u003cp\u003eDue to the absence of survival data in the public version of GENIE and the lack of histopathologically confirmed subtype stratification in the TCGA dataset (however, all tumors were serous), we could not attempt the validation of our prognostic associations (\u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study confirmed by a gold standard direct sequencing method the presence of mutations in clinically actionable EOC oncodrivers \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e, reported by previous whole exome sequencing of 50 patients. We further screened both genes in the additional sample set of 127 EOC patients and complemented the somatic genotype with transcript expression data with the intent to provide deeper functional insight.\u003c/p\u003e \u003cp\u003eIn general, results show that the functional classification of mutations and disease subtype context matters much more than carrier status alone. These aspects need careful investigation before any sensible clinical exploitation. Specifically, HGSC differs from other EOC subtypes by extreme dominance of \u003cem\u003eTP53\u003c/em\u003e mutations, while \u003cem\u003eKRAS\u003c/em\u003e mutations are more relevant to nonHGSC subtypes. This observation agrees with the generally accepted view\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and may have clinical consequences\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The prevalence of \u003cem\u003eKRAS\u003c/em\u003e mutations in patients with less advanced stages I/II found by us complies with the fact that some nonHGSC subtypes are more frequently diagnosed with less advanced disease than HGSC ones\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Nevertheless, this was reflected by non-significantly better PFI only for clear cell subtype and just mild effect on general prognosis in terms of OS. However, the clear cell subtype is considered less sensitive to platinum-based chemotherapy than other EOCs\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Thus, our observation may be due to the low number of samples evaluated (n\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e \u003cp\u003eThe striking prevalence of \u003cem\u003eKRAS\u003c/em\u003e mutations in nonHGSC subtypes calls for their integration into clinical trials with future \u003cem\u003eKRAS\u003c/em\u003e inhibitors. Studies with \u003cem\u003eKRAS\u003c/em\u003e-G12D (MRTX1133)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e or pan-KRAS\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e inhibitors show promising results and the \u003cem\u003eKRAS\u003c/em\u003e-G12C mutation inhibitors already have been approved for the personalization of therapy. Sotorasib has been approved for targeted therapy of non-small cell lung cancer (NSCLC)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and adagrasib for NSCLC and colorectal carcinoma\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubtype-sensitive analyses brought interesting results also for \u003cem\u003eTP53\u003c/em\u003e mutations classified by their location in the sequence. Those disrupting the DNA binding loop predisposed patients with nonHGSC subtypes to poorer PFI compared to patients with mutations not disrupting this domain. Such an association, to our best knowledge, has not been published yet. Although it concerns a small fraction of patients, it may provide predictive value for therapy targeted to specific \u003cem\u003eTP53\u003c/em\u003e mutations and/or function\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The relevance of so-called loss-of-function or gain-of-function \u003cem\u003eTP53\u003c/em\u003e variants for disease progression and potential therapy targeting was discussed before\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Therefore, we used The TP53 database of and The Clinical Knowledgebase for the stratification of patients into subgroups with variants classified according to different functional effects (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nevertheless, except for the above-mentioned variants disrupting the DNA binding domain, no stratification had clinical consequences.\u003c/p\u003e \u003cp\u003ePerhaps the most important result for contemporary considerations on targeted therapy and immunotherapy appeared after the analysis of \u003cem\u003eKRAS\u003c/em\u003e-\u003cem\u003eTP53\u003c/em\u003e co-mutated tumors. Previous studies in NSCLC\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e or pancreatic carcinoma\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e reported contradicting results. Despite both author groups demonstrating the dismal prognosis of patients harboring such alterations, the immunologically \u0026ldquo;hot\u0026rdquo; status has been claimed for NSCLC, while the \u0026ldquo;cold\u0026rdquo; status for pancreatic carcinoma with presumed consequences for the results of eventual immune checkpoint blockade therapy. Our study shows that EOC may be subject to further research in this area as all co-mutated patients in the present study (n\u0026thinsp;=\u0026thinsp;3), no matter their subtype, proved to be chemoresistant and died very quickly, moving EOC closer in this to pancreatic cancer.\u003c/p\u003e \u003cp\u003eExternal validation using the GENIE dataset (n\u0026thinsp;=\u0026thinsp;2210)\u003csup\u003e25\u003c/sup\u003e helped to validate the observed correlation between \u003cem\u003eTP53\u003c/em\u003e mutation types and transcript expression. More importantly, it enabled a more precise evaluation of the distribution of \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e mutation status across EOC subtypes. This analysis revealed an increasing trend in the ratio \u003cem\u003eTP53\u003c/em\u003e/\u003cem\u003eKRAS\u003c/em\u003e mutability: HGSC\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;clear cell\u0026thinsp;\u0026asymp;\u0026thinsp;mucinous\u0026thinsp;\u0026gt;\u0026thinsp;endometrioid\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;LGSC. An even more striking disproportion in \u003cem\u003eTP53\u003c/em\u003e-\u003cem\u003eKRAS\u003c/em\u003e co-mutation frequency: mucinous (53/115)\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;endometrioid (9/223)\u0026thinsp;\u0026gt;\u0026thinsp;clear cell (4/265)\u0026thinsp;\u0026gt;\u0026thinsp;HGSC (14/1467)\u0026thinsp;\u0026gt;\u0026thinsp;LGSC (1/140) was apparent. Despite the common occurrence of co-mutations in the mucinous subtype was already described\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, both trends suggest enormous variability among EOC subtypes, which calls for exploitation in individualized therapy.\u003c/p\u003e \u003cp\u003eSeveral limitations of the present study need to be mentioned. Firstly, the sample size precludes robust analysis of nonHGSC subtypes, which are very rare (\u0026lt;\u0026thinsp;10% of EOC each). Unfortunately, due to serious constraints in clinical data availability in both GENIE (missing survival)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and TCGA (missing EOC subtype), we could not externally validate our potentially clinically relevant results. Thus, more studies are necessary for this area and our study may contribute to meta-analyses. Second, patients analyzed in this study were untreated with PARPi or other targeted drugs. Platinum-based chemotherapy is considered the standard of care in EOC\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and chemosensitivity to platinum and PARPi overlaps\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Thus, our sample set is still relevant from this point of view. Lastly, transcript levels may not robustly correlate with protein levels as assessed by immunohistochemistry. However, while immunohistochemistry is routinely clinically used for \u003cem\u003eTP53\u003c/em\u003e assessment, it is not in the case of \u003cem\u003eKRAS\u003c/em\u003e, where just the mutation status is considered for EGFR blockade therapy, and functional approval, especially for rarely occurring variants, is missing. Our study shows that this area needs further attention. Finally, the present study has clear benefits in ethnical homogeneity of the patient population, unified therapy regimen, and long-term complete clinical follow-up.\u003c/p\u003e \u003cp\u003eIn conclusion, our study confirms previous data on \u003cem\u003eKRAS\u003c/em\u003e as a valid and hopefully soon druggable target for nonHGSC EOCs and identifies the prognostic value of \u003cem\u003eTP53\u003c/em\u003e mutations in the DNA binding loop for a fraction of patients. Furthermore, we describe an intriguing enrichment of \u003cem\u003eTP53-KRAS\u003c/em\u003e co-mutations in the mucinous subtype of EOC based on analysis of an external dataset of 2210 samples. Our results further extend the area of precision oncology of EOC and suggest directions for future functional and preclinical studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAACR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe American Association for Cancer Research\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Clinical Knowledgebase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eepithelial ovarian cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe International Federation of Gynecology and Obstetrics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOLFOX\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e5\u0026ndash;fluorouracil, leucovorin, and oxaliplatin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGENIE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomics Evidence Neoplasia Information Exchange\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh\u0026ndash;grade serous ovarian carcinomas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow\u0026ndash;grade serous ovarian carcinomas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIQE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Minimum Information for Publication of Quantitative Real\u0026ndash;Time PCR Experiments Guidelines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enext generation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003enonHGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eother than high\u0026ndash;grade serous carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon\u0026ndash;small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePARPi\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoly(ADP\u0026ndash;ribose) polymerase inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplatinum\u0026ndash;free interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqPCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equantitative real\u0026ndash;time polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental protocol of the study was approved by the Institutional Review Boards of the National Institute of Public Health in Prague (approval reference no. IGA NS9803-4 of 2 February 2008), University Hospital Motol (approval reference no. EK-890/15 of 24 June 2015), University Hospital Kralovske Vinohrady (approval reference no. EK-VP/40/0/2017 of 28 June 2017), and University Hospital Pilsen (approval reference no. 16-29013A of 4 June 2015). All patients included in the study read and signed the Informed Consent of the Patient.\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\u003eData access\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Czech Health Research Council grant no. NU22-08-00186 to P.S.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization – R.V. \u0026amp; P.S.\u003c/p\u003e\n\u003cp\u003eMethodology\u0026nbsp;–\u0026nbsp;M.A., E.A., I.K., V.H., \u0026amp; F.A.\u003c/p\u003e\n\u003cp\u003eFormal Analysis – P.H., R.V., \u0026amp; P.S.\u003c/p\u003e\n\u003cp\u003eInvestigation\u0026nbsp;–\u0026nbsp;M.A., E.A., V.H., \u0026amp; I.K.\u003c/p\u003e\n\u003cp\u003eResources – L.R., M.H., M.M., K.K., A.B., \u0026amp; J.B.\u003c/p\u003e\n\u003cp\u003eWriting – Original Draft – M.A., R.V., \u0026amp; P.S.\u003c/p\u003e\n\u003cp\u003eWriting – Review \u0026amp; Editing –\u0026nbsp;M.A., E.A., I.K., P.H., V.H., F.A., L.R., M.H., M.M., K.K., A.B., J.B., R.V. \u0026amp; P.S.\u003c/p\u003e\n\u003cp\u003eVisualization – P.H \u0026amp; P.S.\u003c/p\u003e\n\u003cp\u003eSupervision –\u0026nbsp;R.V. \u0026amp; P.S.\u003c/p\u003e\n\u003cp\u003eProject Administration – P.S.\u003c/p\u003e\n\u003cp\u003eFunding Acquisition – P.S.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participating patients for their kind consent to the study and the clinical personnel for outstanding support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCabasag CJ, Fagan PJ, Ferlay J, Vignat J, Laversanne M, Liu L, van der Aa MA, Bray F, Soerjomataram I. 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Distinct mechanisms of innate and adaptive immune regulation underlie poor oncologic outcomes associated with KRAS-TP53 co-alteration in pancreatic cancer. \u003cem\u003eOncogene\u003c/em\u003e. 2022;41(28):3640-3654. doi: 10.1038/s41388-022-02368-w.\u003c/li\u003e\n\u003cli\u003eRechsteiner M, Zimmermann AK, Wild PJ, Caduff R, von Teichman A, Fink D, Moch H, Noske A. TP53 mutations are common in all subtypes of epithelial ovarian cancer and occur concomitantly with KRAS mutations in the mucinous type. \u003cem\u003eExp Mol Pathol\u003c/em\u003e. 2013;95(2):235-41. doi: 10.1016/j.yexmp.\u003c/li\u003e\n\u003cli\u003eSoberanis Pina P, Lheureux S. Overcoming PARP inhibitor resistance in ovarian cancer. \u003cem\u003eInt J Gynecol Cancer.\u003c/em\u003e 2023;33(3):364-376. doi: 10.1136/ijgc-2022-003698.\u003c/li\u003e\n\u003cli\u003eCoelho R, Tozzi A, Disler M, Lombardo F, Fedier A, L\u0026oacute;pez MN, Freuler F, Jacob F, Heinzelmann-Schwarz V. Overlapping gene dependencies for PARP inhibitors and carboplatin response identified by functional CRISPR-Cas9 screening in ovarian cancer. \u003cem\u003eCell Death Dis\u003c/em\u003e. 2022;13(10):909. doi: 10.1038/s41419-022-05347-x.\u003c/li\u003e\n\u003cli\u003eMcMullen M, Karakasis K, Madariaga A, Oza AM. Overcoming Platinum and PARP-Inhibitor Resistance in Ovarian Cancer. \u003cem\u003eCancers\u003c/em\u003e 2020;12:1607. doi:10.3390/cancers12061607.\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":"epithelial ovarian carcinoma, platinum sensitivity, TP53, KRAS, variant, transcript expression","lastPublishedDoi":"10.21203/rs.3.rs-5224537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5224537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground: \u003c/em\u003eConcerning the dismal prognosis of chemoresistant patients with epithelial ovarian carcinoma (EOC), we aimed to validate the findings of a previous whole exome sequencing study on 50 patients using an orthogonal Sanger sequencing method on the same patients and a separate set of 127 EOC patients (N=177).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods:\u003c/em\u003e We focused on \u003cem\u003eTP53\u003c/em\u003e as a frequently mutated gene relevant for chemosensitivity, included \u003cem\u003eKRAS\u003c/em\u003e as an additional therapeutically relevant target, complemented study with transcript levels of both genes, and compared results with clinical parameters.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults: \u003c/em\u003eAll variants in \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e detected by exome sequencing were confirmed. \u003cem\u003eKRAS\u003c/em\u003e mutated patients had significantly more frequently FIGO stages I or II (p=0.007) and other than high-grade serous tumor subtypes (nonHGSCs) (p\u0026lt;0.001), which was connected with lower KRAS transcript levels (p=0.004). Patients with nonHGSCs harboring \u003cem\u003eTP53\u003c/em\u003e missense variants disrupting the DNA binding loop had significantly poorer platinum-free interval than the rest (p=0.008). Tumors bearing nonsense, frameshift, or splice site \u003cem\u003eTP53\u003c/em\u003e variants had a significantly lower TP53 transcript level, while those with missense variants had significantly higher levels than wild-types (p\u0026lt;0.001). The normalized intratumoral TP53 and KRAS transcript levels were correlated, and three patients with both genes co-mutated had extremely poor survival.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions:\u003c/em\u003e Our study points to \u003cem\u003eKRAS\u003c/em\u003e as a target for future therapy of nonHGSCs and reveals the prognostic value of \u003cem\u003eTP53\u003c/em\u003evariants in the DNA binding loop.\u003c/p\u003e","manuscriptTitle":"Functional validation of somatic variability in TP53 and KRAS for prediction of platinum sensitivity and prognosis in epithelial ovarian carcinoma patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 09:49:50","doi":"10.21203/rs.3.rs-5224537/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":"23f5166b-e1b8-46f9-99b8-1694bd6a7fc0","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-22T09:49:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-22 09:49:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5224537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5224537","identity":"rs-5224537","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}