Multi-Modal Data Integration Reveals Functionally Credible Predictive Biomarkers in Ovarian Cancer

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Muranen, Andreas Hainari, Daria Afenteva, Wojciech Senkowski, and 26 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8860995/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Precision oncology aims to tailor treatment according to tumor-specific molecular alterations, but the success of aberration-guided therapies has been limited in clinical trials. Here, we develop an integrated whole-genome and transcriptome workflow to systematically distinguish functionally credible, predictive driver aberrations from non-functional alterations across all classes of genomic events. Methods We applied the integrated omics workflow to 335 patients with ovarian high-grade serous cancer (HGSC) enrolled in the observational DECIDER trial. Tumor samples were collected from multiple cancer sites as a part of the standard cancer care. DNA and RNA were extracted together from snap-frozen tumor samples and sent to whole-genome and transcriptome sequencing. Sequencing data were processed with the Anduril 2 pipeline for detection and validation of short somatic changes and with the HMW toolkit and the nf-core/rnafusion pipeline for assessment of structural changes. Aberration-specific drug sensitivity was tested in patient-derived organoids with a drug screen combining targeted agents and chemotherapy. Results Using an agnostic integrated omics analysis, we identified clinically relevant ESCAT Tier II–III alterations in more than 40% of the patients, even though 60% of all nominally pathogenic variants proved to be false positives. Credible aberrations were predominantly clonal, detected across anatomical sites, and preserved from diagnosis to relapse, indicating early establishment during tumor evolution. The most recurrent actionable event was NF1 deficiency, which was associated with a robust transcriptional footprint and marked sensitivity to KRAS- and MEK-inhibition in patient-derived organoids. Notably, integrated DNA-RNA analysis enabled discrimination of treatment-guiding aberrations from false-positive findings that would otherwise misinform treatment selection and confound clinical trial outcomes. Conclusions Our findings provide a strategy for more reliable biomarker detection in precision oncology, inform biomarker-guided clinical trial design, and reveal unexploited therapeutic vulnerabilities in HGSC. Trial registration ClinicalTrials.gov: NCT04846933. Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC (DECIDER) Precision oncology targeted therapy mutation biomarker high-grade serous cancer multi-omics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Precision oncology promises improved cancer management by leveraging individual, cancer-specific driver mutations to guide targeted therapies (Subbiah, 2024). Recently, the precision oncology paradigm has been evaluated in large pan-cancer trials, including NCI-MATCH (O’Dwyer, 2023), DRUP (Haj Mohammad, 2024), and SCRUM-MONSTAR (Hashimoto, 2024), where thousands of cancer patients have received targeted therapies guided by genomic biomarkers. These studies show that while actionable aberrations are detected in about a third of patients, clinical benefits vary largely between cancer types, with overall response rates reported between 10% and 33% and survival benefit of only a few months. Most targeted therapies act on specific genomic alterations, which are assumed to either be essential to continuous tumor cell proliferation via oncogene addiction or cause detrimental cellular deficiencies (Passaro, 2024; Petroni, 2022). However, the identification of such central events is complicated by non-affecting passenger mutations or co-driver aberrations with synergistic oncogenic effects (O’Dwyer, 2023). Consequently, the precision oncology trials are challenged by false negative cases, where an existing driver event remains undetected, and by false positives, where nominally actionable aberrations lack functional effect in tumor cells. Indeed, several studies have suggested that combining whole-genome and transcriptome sequencing would enhance the detection of actionable biomarker aberrations (Cuppen, 2022; Pleasance, 2022; Rodon, 2019; Horton, 2024; Owen, 2024), although their focus remains on expanding the number of potential actionable alterations rather than improving precision by systematically assessing the functional impact of genomic alterations. Here, we report a precision oncology workflow that combines whole-genome and transcriptome sequencing data to comprehensively identify targetable molecular alterations and to distinguish credible driver events from redundant passenger aberrations. We applied the workflow to characterize the actionable landscape of prospectively collected patients with ovarian high-grade serous carcinoma (HGSC), which is a copy number-driven cancer with nearly universal TP53 dysfunction (Jamalzadeh, 2024; TCGA, 2011), and five-year survival rate below 40% (Torre, 2018). Current actionable molecular biomarkers in HGSC are limited to BRCA1/2 gene mutation and homologous recombination deficiency (HRD) (González-Martín, 2023). However, upon development of resistance to the standard platinum-based chemotherapy, few efficient treatment options remain. Our results show that tumors of about 40% of patients with HGSC harbor unexploited targetable aberrations and that combining RNA-level data considerably improves the detection of credible aberrations. Furthermore, our pre-clinical research in patient-derived organoids supports the use of KRAS or MEK inhibitors for patients with NF1-deficient tumors. Methods Patients and samples The analysis was based on real-world data on patients with high-grade serous carcinoma enrolled in the prospective, observational, longitudinal DECIDER study (ClinicalTrials.gov: NCT04846933) between October 2009 and September 2024. Patients were treated at Turku University Hospital and stratified to primary debulking surgery (PDS) followed by adjuvant carboplatin and paclitaxel chemotherapy or to neoadjuvant chemotherapy (NACT) followed by interval debulking surgery and adjuvant chemotherapy. Follow-up was centralized to Turku University Hospital for five years after diagnosis as part of routine cancer care. Solid tumor, ascites, or pleural fluid samples were collected during the standard care. Nucleic acids were extracted from snap-frozen tumor samples and sequenced as previously described (Lahtinen, 2023; Micoli, 2025; Afenteva, 2025) and summarized in the Supplementary methods (Additional file 1). Quality-controlled tumor sequencing data with at least 10% tumor fraction were available for 335 patients and 946 samples (Fig. 1 a, Additional file 1: Table S1 ). Genomic sequencing data analysis Sequencing data were processed using the Anduril 2 pipeline (Cervera, 2019), following GATK best practices (Van der Auwera & O'Connor, 2020 , details in Additional file 1: Supplementary methods). Somatic variants were called using GATK 4.1.9.0 Mutect2, patient-specific germline reference samples and a Panel of Normals. Variants were annotated with ANNOVAR 20250302 (Wang, 2010), prediction algorithms CGI (Tamborero, 2018), AlphaMissense (Cheng, 2023), PolyPhen2 (Adzhubei, 2013), SIFT (Vaser, 2016), and dbscSNV 1.1 (Jian, 2014), and variant databases ClinVar (Landrum, 2025) or OncoKB (Chakravarty, 2017). Copy-number aberrations, tumor-cell fraction, and tumor ploidy were analyzed using GATK version 4.1.4.1 (DePristo, 2011) and ASCAT algorithm (Van Loo, 2010). GATK-ASCAT output was used for identification of samples with representative tumor-cell fraction (> 10%) and for detection of gene amplifications and large deep deletions. Structural variants were detected using GRIDSS (Cameron, 2017 II) and the HMF toolkit (Priestley, 2019). Additionally, structural aberrations affecting BRCA1 , BRCA2 , RAD51C , or RAD51D , were curated with BasePlayer (Katainen, 2018) and SegmentModels Spy (Lavikka, 2024). RNA sequencing data analysis RNA sequencing data were processed with Anduril 2 using the SePIA pipeline (Icay, 2016, details in Additional file 1: Supplementary methods). Amplification-driven gene hyperexpression was detected from a normalized gene expression matrix. Expression of mutant alleles was assessed by force calling the somatic mutations in genome-aligned RNA-sequencing data. Gene fusion events were detected using the nf-core/rnafusion pipeline v. 2.0.0 (Ewels, 2020). Transcriptional footprint analyses of recurrent driver aberrations were performed using the PROGENy (Schubert, 2018) and CollecTRI (Müller-Dott, 2023) algorithms. Workflow for identification of credible driver events The precision oncology workflow employs a systematic, stepwise strategy with stringent criteria to identify credible driver aberrations for oncogenes and tumor suppressor genes (Fig. 1 b). To verify the functional impact of genomics aberrations in oncogenes, we confirmed that the relevant alleles were active. Aberrations in tumor suppressor genes were assessed by probability of complete loss of all functional gene copies. Briefly, short mutations, copy-number changes, and structural aberrations were called using specific data processing pipelines, as described above, resulting in annotations on variant identity, protein-level consequence, and predicted or known pathogenicity (Additional file 1: Supplementary methods). Multi-site sampling with phylogenetic analysis revealed the behavior of the aberrations along cancer evolution, as described previously (Lahtinen, 2023). For pathogenic short mutations in tumor suppressor genes, we calculated the expected allele frequency given that the aberration affects all gene copies in cancer cells, using the following formula: $$\:\frac{\left(CNmut\times\:TF\right)}{\left(CNtot\times\:TF+2\times\:\left(1-TF\right)\right)}$$ where CNmut (number of aberrant gene copies in cancer) and CNtot (number of all gene copies in cancer) are given the same value, the locus copy number, and TF is the tumor fraction, from the GATK-ASCAT copy-number analysis pipeline. For aberrations co-occurring with loss-of-heterozygocity, we assessed the probability of all gene copies being affected by the aberration. With a null hypothesis that all gene copies are affected, we calculated the binomial probability of the observed read count given the sequencing depth. Probability values below 0.01 rejected the null hypothesis and prompted us to assume that the aberration is only sub-clonal. Copy-number losses were interpreted to affect all gene copies, if the estimated copy number of the lowest copy-number segment in the gene was less than 0.5. The number of affected and intact alleles in tandem duplications, translocations, and inversions was calculated as the difference in copy number between the adjacent segments. If the estimated number of intact copies was less than 0.5, all gene copies were assumed to be affected. For the oncogenes, whose impact arises from aberrant protein function, transcriptomic-level evidence was considered a prerequisite for credibility. Therefore, their credibility was assessed with RNA sequencing data in an aberration type-specific manner, as follows. An amplified gene was required both to be associated with elevated expression overall, as previously reported (Jamalzadeh, 2024, CNI > 0.46, CNA transition point > 2), and to have especially high expression in the cancer sample with the amplification (highest 20% in the cohort). A structural change, where the target gene coding region was joined to another genomic region, was considered credible only if an expressed fusion gene was detected and a somatic missense mutation only upon detection of allele-specific expression. Ambiguous cases and splice-aberrations were manually curated by an expert geneticist from BAM files with the help of BasePlayer visualization software (Katainen, 2018). Assembly of drug target and biomarker data We first made a comprehensive search for cancer drugs using the Open Targets Platform (Buniello, 2025), which resulted in 388 approved and investigational cancer drugs. Then we identified the drug targets followed by delineation of 388 predictive and likely predictive biomarkers (Fig. 1 c, Additional file 1: Supplementary methods, Additional file 2). Together, these form a comprehensive collection of drug-gene pairs that characterize the actionable landscape of HGSC. To ensure clinical relevance, genomic aberrations were classified using the ESCAT (Mateo, 2018), where Tiers IA-C represent clinical actionability in routine care, Tiers II–III denote actionability in clinical trials, and Tiers IV, V, and X represent progressively lower levels of evidence for biomarker–treatment associations. Organoid analysis Long-term organoids were established from dissociated, fresh tumor tissue samples, verified to match the original tissues, and tested for drug sensitivity as described previously (Senkowski, 2023). Briefly, organoids were each maintained in their optimal growth media and screened at two concentrations for each drug, selected based on literature to match physiological doses, alone and in combinations (Additional file 1: Table S2 .). Medium was replaced every 72-96h and the organoids were exposed to drugs for 7 days, with drug replenishment after 4 days of treatment, together with the culture medium change. Cell viability was measured after the total of 7 days of drug exposure using automated fluorescent confocal microscopy (Image Xpress Confocal, Molecular Devices) and fluorescent live/dead cell staining with CellTox Green #G8743, Promega, final concentration 1X) and Hoechst 33342 (#B2261, Sigma, final concentration 5 µg/mL) and normalized according to reference conditions of DMSO or 10 µM Bortezomib. Cytotoxicities were estimated through image analysis using MetaXpress (Molecular Devices) software (RRID: SCR_016654). Statistical analyses Associations were tested with two-sided Fisher’s exact test. Aberration homogeneity in cancer cells was assessed at loci of full loss-of-heterozygocity using binomial probability and null hypothesis that all gene copies are affected, as described above. Results We performed systematic evaluation of somatic aberrations in 388 drug target and biomarker genes with an integrated omics analysis using tumor sequencing data of 946 samples from 355 patients with HGSC enrolled in the DECIDER study (Fig. 1 a). The workflow consisted of three major phases as follows: aberration discovery in the DNA sequencing data, assessment of the aberration’s pathogenicity and impact on protein structure, and functional evaluation with distinct criteria for tumor suppressors and oncogenes (Fig. 1 c). Aberrations in tumor suppressor genes fulfilled the functional criterion when all gene copies in cancer cells were affected. Aberrations in oncogenes were required to produce an aberrant transcript for functional validation. Thus, the analysis enabled stepwise prioritization of variants of interest as shown in Fig. 2 . Actionable landscape of HGSC In tumor suppressor genes, we identified 542 short mutations and 316 structural aberrations (Fig. 2 a). The majority of short mutations mapped to TP53 , consistent with the near-universal prevalence of TP53 mutations in HGSC (Zarei, 2020; TCGA, 2011). Beyond TP53 mutations, 75% of the pathogenic short mutations and 34% of the structural aberrations resulted in complete loss of gene function. For oncogenes, structural aberrations represented the most prevalent aberration class ( n = 1584) followed by amplifications of wild-type genes ( n = 1195) and short mutations ( n = 650) as shown in Fig. 2 b. Most structural aberrations showed no evidence of functional impact at mRNA level, reducing their relevance as predictive biomarkers. In contrast, about half of the short mutations were expressed. Notably, gene amplifications frequently resulted in marked overexpression of the affected genes, consistent with prior evidence highlighting the central role of copy-number alterations in HGSC (Jamalzadeh, 2024; TCGA, 2011). To assess clinical relevance of the genomic aberrations, we matched them to ESCAT tiers as shown in Fig. 2 c. Currently, HRD and PARP inhibitors are the only Tier IA combination of a biomarker and therapy for patients with HGSC. In our cohort, we detected HRD in 145 patients (43%) and 42% of them had causal mutations in BRCA1/2 ( n = 49) or RAD51C/D ( n = 12). High mutational burden, a Tier IC biomarker for immune checkpoint inhibitor therapy, was detected in a one patient, who had mismatch-repair deficiency caused by an MSH6 mutation. Tier II–IV drug-gene pairs represent the unexploited potential of precision oncology in HGSC. In our cohort, approximately half of the patients ( n = 137, 41%) harbored at least one functionally credible Tier II or III aberration with potential for off-label drug use (Fig. 2 c, Additional file 3). Notably, 10% harbored more than one actionable alteration, including four patients with three aberrations and 30 patients with two. Tier IIIA loss of NF1 was the most prevalent genomic event ( n = 70, 21%), suggesting benefit from the MEK inhibitors. Other prominent targetable categories were aberrations associated with AKT ( n = 27, 8%), MTOR ( n = 23, 7%) or ERBB2 ( n = 16, 5%) inhibitors. In general, credible Tier II–III aberrations were detected in both HRD and HR proficient (HRP) tumors with equal frequency. However, both NF1 aberrations (OR: 2.17; 95% CI: 1.24–3.84; p = 0.005) and PTEN loss-of-function events (OR: 5.34; 95% CI: 1.65–22.6; p = 0.002) were enriched in HRD tumors. Notably, the actionable variants were not recurrent hotspot mutations but unique genomic changes. False-positive actionable variants are minimized by allele-specific DNA-RNA analysis We then characterized to what extent the presence of a Tier I-III aberration implies clinical actionability. We identified 684 nominally pathogenic aberrations in 58 Tier I-III genes and 283 patients. Strikingly, less than half ( n = 271, 40%) of these aberrations met the criteria for credible alterations, i.e. , alle-specific expression (oncogenes) or complete loss of all functional gene copies (tumor suppressor genes), as shown in Fig. 2 d. The proportion of false-positive findings varied between biomarker genes. For instance, 78% of deleterious NF1 aberrations caused full loss of the gene function, suggesting clinical relevance. Similarly, most aberration in PTEN (76%), and MTOR (67%) were found credible. In contrast to NF1 , PTEN and MTOR , pathogenic missense mutations or translocations in receptor tyrosine kinase genes, such as ALK , FLT3 , KIT , MET , RET , ROS1 , NTRK1-3 , were predominantly non-expressed passenger events (Fig. 2 d). This limits their clinical relevance, which is also supported by their borderline expression in the tissue of origin, the fallopian tube (GTEx v10, Additional file 1: Figure S1 ). Notably, false-positive biomarkers were also discovered for PARP inhibitors, including primarily subclonal or heterozygous mutations in secondary HR-pathway genes ( TR , BARD1 , BRIP1 , CDK12 , CHEK1 , CHEK2 , FANCA , FANCL , MLH1 , MRE11 , NBN , RAD51B , and RAD54L ) and occasional heterozygous alterations in BRCA1/2 . Accordingly, these aberrations showed no significant association with HRD (OR:1.21; 95% CI: 0.77–1.93; p = 0.44). Amplifications of wild-type oncogenes are credible driver events Next, we explored the impact of Tier IVB wild-type proto-oncogene amplifications, which are characteristics to HGSC. The most recurrent amplifications supported by transcriptional overexpression were CCNE1 ( n = 56, 17%), AKT2 ( n = 31, 9%), PTK2 ( n = 23, 7%), and KRAS ( n = 22, 7%) as shown in Fig. 2 c. These amplifications were enriched in HRP tumors (OR: 2.78; CI: 1.64–4.80; p = 0.00008), with AKT2 amplifications occurring almost exclusively in HRP cases and PTK2 amplifications showing comparable frequencies in HRP and HRD tumors. Allele-specific expression analysis showed that CCNE1 , AKT2 , PTK2 , and KRAS amplifications cause hyper-expression of the wild-type alleles. Moreover, the amplifications represented credible driver events modulating the activity of cancer-associated pathways, each characterized by a distinct transcriptional signature (Fig. 3 , Additional file 1: Figure S2 ). Amplifications of AKT2 , PTK2 , or KRAS were linked to aberrant activation of MAPK and PI3K signaling cascades, mirroring loss-of-function events in NF1 and PTEN . Furthermore, amplifications of AKT2 or KRAS triggered elevated activity of TGF-β-dependent signaling and epithelial-to-mesenchymal transition via SMAD-family or homeobox transcription factors, whereas PTK2 amplification phenocopied NF1 deficiency, showing high activity of TNF-α and NF-κB signaling and their associated transcriptional regulators. CCNE1 amplification was not associated with perturbed signaling cascades, aligning with its role as a regulator of cell cycle progression. Pre-treatment samples are informative for clinical decisions at relapse To guide optimal sampling strategies for identifying predictive biomarkers at relapse, we analyzed Tier II–III aberrations across multiple anatomical locations before and during therapy. Credible aberrations were detected roughly four times more frequently across all anatomical sites compared with false-positive events (OR: 3.66; CI: 2.53–5.37; p = 2.4·10 13 ). Among credible aberrations, 73% were present in all sampled anatomical locations, with no significant differences between sites ( p = 0.62). This implies that credible aberrations with clinical utility are predominantly clonal events. To further explore the longitudinal stability of credible aberrations, we used paired primary and relapse samples from 22 patients with 34 Tier II–III aberrations. Most of the aberrations (70%) were identical in primary and relapse samples, indicating early emergence and subsequent clonal stabilization, like in an example patient illustrated in Fig. 4 a. Further exploration of the discordant aberrations between primary and relapse samples revealed that subclonality is often detectable at diagnosis as shown in Fig. 4 b-d. Briefly, two patients had tubo-ovarian-specific mutations that were not present in relapses, whereas treatment-naïve metastasis samples represented the actionable biomarkers in relapse (Fig. 4 b-c). Interestingly, one of these and another patient harbored converging subclonal aberrations with the same gene-level consequence, suggesting strong selective pressure to maintain activation of key growth signaling pathways (Fig. 4 c). Furthermore, four patients had actionable aberrations present in both tubo-ovarian and metastatic samples, but low variant allele frequencies in the metastatic samples revealed the subclonality (Fig. 4 d). Patient-derived organoids suggest MEK and KRAS targeting therapies effective in HGSC To investigate the therapeutic potential of NF1 deficiency, which was the most recurrent Tier III biomarker, we performed a focused drug screen using patient-derived organoids (PDOs). Organoid cultures were established from three patient cases harboring deleterious and homogeneous NF1 aberrations, three cases with KRAS wild-type amplification, and three reference cases lacking driver events in the RAS-MAPK pathway. To inhibit both up- and downstream components of the pathway, we used five inhibitors as monotherapies and in combination with other compounds (Fig. 5 a, Methods). Monotherapy with the investigational KRAS inhibitor RMC-7977 demonstrated the strongest activity against NF1-deficient PDOs, exceeding the effect of the MEK inhibitor cobimetinib, which is a Tier III drug for NF1-deficient tumors (Fig. 5 b,c; Additional file 4). Sensitivity to both RMC-7977 and cobimetinib was specific to NF1 deficiency, as no reduction in viability was observed in the reference PDOs. In contrast, compounds targeting upstream (SHP2 or SOS1) or downstream (ERK) components of the pathway had little impact on PDO growth regardless of mutation status (Additional file 4). RMC-7977 or cobimetinib re-sensitized the platinum-resistant PDO to carboplatin. In contrast, the two platinum-sensitive NF1-deficient PDOs showed no additional benefit from the combination. Instead, the response to the combination mimicked the response to carboplatin alone, a pattern known as single-agent dominance, which is frequently seen in vitro for clinically effective treatment combinations (Richards, 2020). Thus, these findings suggest that platinum re-challenge combined with targeted KRAS or MEK inhibition could represent an optimal therapeutic strategy for patients with NF1-deficient HGSC. Unlike the NF1-deficient models, organoids with KRAS amplification were resistant to RMC-7977, cobimetinib, and all other compounds tested (Fig. 5 c; Additional file 4). This suggests KRAS amplification confers resistance rather than sensitivity to RAS-MAPK-focused therapies. Discussion Recognizing that false-positive calls remain a major obstacle to effective precision oncology trials (Waarts, 2022; O’Dwyer, 2023), we developed an integrated whole-genome and transcriptome workflow designed to reliably identify clinically actionable aberrations. In the prospective DECIDER trial, we detected credible ESCAT Tier II–III alterations with potential therapeutic relevance in approximately 40% of 335 patients. Notably, 60% of all variants initially classified as pathogenic were found to be false positives. For example, mutations in secondary HR-pathway genes rarely resulted in complete loss of gene function or a corresponding HRD phenotype, and Tier IC NTRK1–3 alterations were credible in only a minority of cases. These findings indicate that many previously proposed predictive biomarkers lack true functional relevance. Thus, trials that enroll patients based on presumed pathogenic alterations in target genes are likely to be substantially affected by false-positive biomarkers, diminishing the observable benefit of the therapies under investigation. Longitudinal analysis of paired primary and relapse tumors revealed that most actionable aberrations were clonally stable and present across multiple sites. This suggests that true driver aberrations underlying oncogene addiction are established early in tumor evolution, consistent with the current evolutionary models (Petroni, 2022; Sottoriva, 2015). Therefore, archival diagnostic samples could reliably capture most clinically relevant driver events in HGSC. However, the biomarker detection should be based on metastatic samples, instead of the tubo-ovarian sites that are enriched with private mutations (Lahtinen, 2023). We characterized the actionable landscape HGSC and identified several recurring credible targetable aberrations. For example, NF1 and PTEN loss were often found credible and supported by strong impact on cell transcriptional programs. Furthermore, wild-type amplifications of oncogenes including AKT2 , PTK2 , and KRAS were identified as credible driver events in a substantial proportion of patients with HGSC, highlighting the importance of preclinical studies to assess their potential therapeutic vulnerabilities. We explored the translational potential of NF1 deficiency, which was the most recurrent actionable aberration, with a focused drug screen targeting RAS–MAPK pathway components in PDOs stratified by mutation status. PDOs harboring NF1 loss-of-function aberrations displayed marked sensitivity to KRAS- and MEK-inhibition. Although MEK inhibitors have shown limited efficacy as monotherapy in HGSC (Manoharan, 2024; Vergote, 2020), unlike in low-grade serous carcinoma (Gershenson, 2022), our data suggest that RAS-MAPK pathway inhibition may still provide therapeutic value in combination regimens. Indeed, in a platinum-resistant NF1-deficient PDO, inhibition of MEK or KRAS effectively re-sensitized the cells to carboplatin. The observational design of the real-world DECIDER trial presents both strengths and limitations. It supports unbiased and comprehensive identification of actionable aberrations but precludes direct evaluation of biomarker-specific treatment responses. Accordingly, dedicated clinical trials are needed to evaluate the therapeutic benefit of the proposed combinations. We note that several of the findings presented here were reviewed by the Molecular Tumor Board at Turku University Hospital and informed the referral of three patients to the biomarker-guided FINPROVE clinical trial (NCT05159245). Conclusions Our multi-omics workflow provides a generalizable strategy to overcome a major barrier to precision oncology by minimizing the number of false-positive predictive biomarkers that otherwise could reduce the treatment efficacy of targeted therapies in clinical trials. We comprehensively characterized the actionable landscape in HGSC and identified clinically relevant vulnerabilities in over 40% of patients that could expand precision-therapy options. Furthermore, using PDOs, we provide preclinical evidence supporting combination therapy of carboplatin and targeted drugs for HGSC patients with chemoresistant disease. Declarations Ethics approval and consent to participate The study was approved by Ethics Committee of the Hospital District of Southwest Finland (VARHA/28314/13.02.02/2023) and is being conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (WMA). All patients provided written informed consent. We adhered to the REMARK guidelines in reporting, where applicable, and used the STROBE reporting guideline to draft this manuscript (Elm, 2007; Elm, 2025; Additional file 5). Consent for publication Not applicable Availability of data and materials Source files for sequencing data are made public in the European Genome-phenome Archive (DNA: EGAS00001006775, RNA: EGAS00001004714). Three of the nine tested organoids are deposited in the Auria Biobank (https://www.auria.fi/biopankki/en/). Data used for panel-of-normals in mutation calling was partly obtained from The Cancer Genome Atlas managed by the NCI and NHGRI (dbGaP accession number phs000178). GTEx v10 data were obtained from: the GTEx Portal https://www.gtexportal.org on 2025-06-02. All analyses and data processing were performed with open-source, published software, as described above. Custom analysis scripts are provided in https://github.com/HautaniemiLab/ Actionable. The workflow is currently being integrated into an open-source platform of clinical decision making https://github.com/oncodash/oncodash. Competing interests JSR reports in the last 3 years funding from GSK and Pfizer & fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer, Grunenthal, Tempus and Moderna. The other authors declare no conflicts of interest. Funding This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965193 for DECIDER, the Sigrid Jusélius Foundation, Cancer Foundation Finland, and OvaCure. Authors’ contributions Study conception: TAM, AV, JSR, EF, BS, EA, SHa, JH; Study design: TAM, WS, JO, AL, ACO, JS, SHi, AV, EA, KW, SHa, JH; Data acquisition: TAM, AH, DA, WS, JO, KD, SHo, VMI, KL, YL, ML, IM, GMa, FM, GMi, ACO, JS, SHi, AV, EF, KW, SHa, JH; Data analysis: TAM, AH, DA, WS, JO, JD, KL, YL, ML, IM, GMa, FM, GMi, MN, PRM, JSR, EF, BS; Interpretation of data: TAM, AH, DA, WS, JS, SHi, AV, EA, KW, SHa, JH; Creation of new software: TAM, AH, GMi; Drafting of the manuscript: TAM; All authors participated in the revision of the manuscript text and approved the submitted manuscript version. Acknowledgments The authors wish to acknowledge the CSC-IT Center for Science (Finland) for computational resources, all contributing research nurses, lab technicians, and administrative personnel, especially Elina Valkonen, Peppi Alho, Nina Halme, and Jenni Lahtinen for their valuable contributions. 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Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Ann-Christin","middleName":"","lastName":"Ostwaldt","suffix":""},{"id":601497188,"identity":"52935635-e70f-4e50-acd2-19cb07ffb2f4","order_by":19,"name":"Pablo Rodriguez-Mier","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Rodriguez-Mier","suffix":""},{"id":601497189,"identity":"355f40e5-8796-4948-b0fe-57670e3222c7","order_by":20,"name":"Jenni Söderlund","email":"","orcid":"","institution":"Turku University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jenni","middleName":"","lastName":"Söderlund","suffix":""},{"id":601497192,"identity":"5d20b50e-8f9b-4cd1-9465-bfe3f4f14902","order_by":21,"name":"Sakari Hietanen","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Sakari","middleName":"","lastName":"Hietanen","suffix":""},{"id":601497194,"identity":"df3f2567-674e-4af3-909a-0399535d0199","order_by":22,"name":"Anni Virtanen","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Anni","middleName":"","lastName":"Virtanen","suffix":""},{"id":601497196,"identity":"1fc2b3ae-58e2-4f43-aabc-0842736310cd","order_by":23,"name":"Julio Saez-Rodriguez","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Julio","middleName":"","lastName":"Saez-Rodriguez","suffix":""},{"id":601497197,"identity":"3008ea39-3bab-438b-aa26-fa733ce885ce","order_by":24,"name":"Elisa Ficarra","email":"","orcid":"","institution":"University of Modena and Reggio Emilia","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"","lastName":"Ficarra","suffix":""},{"id":601497198,"identity":"81e7bf7f-f26f-48fd-be5a-f5fa571a72e9","order_by":25,"name":"Benno Schwikowski","email":"","orcid":"","institution":"Institut Pasteur","correspondingAuthor":false,"prefix":"","firstName":"Benno","middleName":"","lastName":"Schwikowski","suffix":""},{"id":601497202,"identity":"84839992-0517-48b0-ab42-ade87381835e","order_by":26,"name":"Erika Alanne","email":"","orcid":"","institution":"Turku University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"Alanne","suffix":""},{"id":601497205,"identity":"63f40e2f-4208-4589-9203-f31a197679f1","order_by":27,"name":"Krister Wennerberg","email":"","orcid":"","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Krister","middleName":"","lastName":"Wennerberg","suffix":""},{"id":601497207,"identity":"16663190-21c2-431f-a040-c4dd659926fa","order_by":28,"name":"Sampsa Hautaniemi","email":"data:image/png;base64,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","orcid":"","institution":"University of Helsinki","correspondingAuthor":true,"prefix":"","firstName":"Sampsa","middleName":"","lastName":"Hautaniemi","suffix":""},{"id":601497208,"identity":"37f6cfb3-5a79-41d9-989e-86d8c80f452a","order_by":29,"name":"Johanna Hynninen","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Johanna","middleName":"","lastName":"Hynninen","suffix":""}],"badges":[],"createdAt":"2026-02-12 10:39:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8860995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8860995/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780464,"identity":"ce3f1794-1d39-488e-afd7-9b3a85a70c88","added_by":"auto","created_at":"2026-03-17 07:53:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":686306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the multi-omics workflow and its application to HGSC samples.\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003ea)\u003c/strong\u003e Tumor samples from primary and metastatic sites and ascites were collected from patients with HGSC for whole-genome and transcriptome sequencing. Pie charts describe the number of samples from each tumor site category and the proportion of samples from primary debulking surgery (PDS), interval debulking surgery (IDS) and palliative relapse operations. Edges between the pie charts describe the frequency of the two sites having been analyzed from the same patient. \u003cstrong\u003eb) \u003c/strong\u003eSchematic representation of the multi-omics workflow for systematic evaluation of somatic genomic aberrations. \u003cstrong\u003ec)\u003c/strong\u003e Logical and functional relationships between drugs, their target proteins, associated biomarker genes, and the different types of aberrations (solid lines) enabled connecting all somatic aberrations to targeted cancer drugs (dashed line).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/cdb99a5003902b29da5f7f17.png"},{"id":104780465,"identity":"a92d0e0d-36f6-484d-adbd-b4e4a1ff0b71","added_by":"auto","created_at":"2026-03-17 07:53:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1014443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCredible actionable aberrations.\u003c/strong\u003e \u003cbr\u003e\nCounts of different aberration types, including point mutations, structural variants, and copy-number alterations affecting \u003cstrong\u003ea)\u003c/strong\u003e tumor suppressor genes or \u003cstrong\u003eb)\u003c/strong\u003e oncogenes (including all genes encoding drug target proteins). All aberrations affecting the coding sequences of the selected genes, detected in any of the analyzed cancer samples were counted once per a patient. DNA–RNA integration was applied to genes with presumed gain-of-function mechanisms to confirm functional activity of oncogenic events, whereas quantitative DNA analysis was used for tumor suppressor genes to identify alterations causing complete loss of gene function. \u003cstrong\u003e*\u003c/strong\u003eStructural aberrations in tumor suppressor genes include one TP53 driver aberration. \u003cstrong\u003eb)\u003c/strong\u003e Oncoplot illustrating the recurrent, credible, actionable aberrations classified as ESCAT Tier II–IV. Tier II–III aberrations were detected in 40% of patients (top rows; aberrations grouped by associated drug and color-coded by affected gene). Recurrent amplifications of wild-type oncogenes (Tier IVB) are summarized in the middle rows, and HRD-associated genes, mutational signatures, and \u003cem\u003eTP53 \u003c/em\u003emutations in the bottom rows. \u003cstrong\u003e*\u003c/strong\u003eAberrations affecting biomarker genes were not analyzed for hypermutated cancer with mismatch-repair deficiency. \u003cstrong\u003ed)\u003c/strong\u003e Evaluation of nominally pathogenic genomic alterations for allele-specific expression or complete loss of all gene copies reclassified more than half of the aberrations as false positives (grey area). False-positive aberrations were identified across all analyzed genes and drug categories. Representative drugs corresponding to the gene-specific categories are listed in the accompanying table, including at least one approved drug per a target gene.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/f58b6692af378f5324ee374b.png"},{"id":104780410,"identity":"075e8b4f-a646-4dd6-b00f-43dfc67efa86","added_by":"auto","created_at":"2026-03-17 07:52:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":395505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional footprints of recurring driver aberrations.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003eDifferential pathway activity, measured from aberration-specific differential gene-expression with a PROGENy analysis. Enrichment scores and significance levels presented for all curated PROGENy pathways (Schubert, 2018). \u003cstrong\u003eb)\u003c/strong\u003eAberration-induced differential activity of transcription factors, analyzed with the CollecTRI algorithm (Müller-Dott, 2023). Only significant associations (after correction for multiple testing) are shown.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/2bab23e609c7bb906399a137.png"},{"id":104780367,"identity":"c7e87141-1acb-472e-a033-6c6b32f96036","added_by":"auto","created_at":"2026-03-17 07:52:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample cases of longitudinal patterns\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003ea)\u003c/strong\u003e Patient EOC299 had clonal aberrations in two treatment-guiding biomarker genes. Consequently, the same aberrations were detected in all primary and relapse samples. \u003cstrong\u003eb)\u003c/strong\u003e Patient EOC998 had a clonal translocation causing full loss of \u003cem\u003eNF1\u003c/em\u003e detected in all cancer samples, and a site-specific aberration in \u003cem\u003eKMT2A\u003c/em\u003e, detected merely in the dextral fallopian tube. \u003cstrong\u003ec)\u003c/strong\u003e Patient EOC69 exemplified convergent evolution with non-oncogene addiction to loss of \u003cem\u003eNF1\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e. The PIK3CA missense mutation was seen only in the ovarian sample. \u003cstrong\u003ed)\u003c/strong\u003e Patient EOC105 had sub-clonal alterations in \u003cem\u003eNF1\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e. At diagnosis, the allele frequencies varied by the sample sub-clonal composition, so that the sub-clonality was detectable only in the omental and peritoneal samples. Neither of the aberrations was detected in relapse samples, representing an evolutionary branch that was not captured in the diagnostic samples.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/d8afe8eea2d0c125a612a651.png"},{"id":104470369,"identity":"21659667-dab6-4214-b939-19a70dcca314","added_by":"auto","created_at":"2026-03-12 07:19:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":547978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug sensitivity of patient-derived organoids.\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003ea)\u003c/strong\u003e Schematic presentation of the RAS-MAPK signaling cascade. The pathway components targeted in the drug screen in organoids are marked with red asterisks. (GF: growth factor, RTK: receptor tyrosine kinase) \u003cstrong\u003eb)\u003c/strong\u003e Drug response measured as percent viability after treatment with KRAS inhibitor (RMC-7977, upper panel) or MEK-inhibitor (cobimetinib, lower panel) at two concentrations as monotherapy and in combination with two carboplatin concentrations. The plots include median and range over organoids from different patients, each measured in two replicates, except organoid EOC989, the only platinum (Plt)-resistant organoid with a distinct response pattern, where average over two technical replicates is shown.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/415f86fa8e35957eab0fef53.png"},{"id":104784453,"identity":"bccc9aa8-ad93-4a61-a4b6-375b4c5d5ffe","added_by":"auto","created_at":"2026-03-17 08:07:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3688306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/755b9e40-3ac0-44c8-8e54-fc8d3c121c7f.pdf"},{"id":104780338,"identity":"22bea1d9-a6d0-4149-84dc-13623fce014f","added_by":"auto","created_at":"2026-03-17 07:52:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":420080,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/016dd20cb316ecbda5e8fffa.docx"},{"id":104470372,"identity":"8b6f1f89-27b0-4898-ac54-c2fc904694ad","added_by":"auto","created_at":"2026-03-12 07:19:28","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":99096,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatadrugs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/39490012d2d4a2ec416d6f95.xlsx"},{"id":104470376,"identity":"b340b2dc-7a92-4cac-a471-c906a4df32b4","added_by":"auto","created_at":"2026-03-12 07:19:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":435278,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydataaberrations.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/741fe8b148bb74d56e6176d5.xlsx"},{"id":104470377,"identity":"01002589-3e68-48bb-8124-15f43d7335e6","added_by":"auto","created_at":"2026-03-12 07:19:28","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30601,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydataorganoids.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/b618d4eadca42e35eb4e8f04.xlsx"},{"id":104780617,"identity":"88c4f0c1-bafc-4404-ad6e-e72e1220b76d","added_by":"auto","created_at":"2026-03-17 07:53:24","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":200481,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydatastrobe.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8860995/v1/864b943572423cec5ac8230d.pdf"}],"financialInterests":"Competing interest reported. JSR reports in the last 3 years funding from GSK and Pfizer \u0026 fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer, Grunenthal, Tempus and Moderna. The other authors declare no conflicts of interest.","formattedTitle":"Multi-Modal Data Integration Reveals Functionally Credible Predictive Biomarkers in Ovarian Cancer","fulltext":[{"header":"Background","content":"\u003cp\u003ePrecision oncology promises improved cancer management by leveraging individual, cancer-specific driver mutations to guide targeted therapies (Subbiah, 2024). Recently, the precision oncology paradigm has been evaluated in large pan-cancer trials, including NCI-MATCH (O\u0026rsquo;Dwyer, 2023), DRUP (Haj Mohammad, 2024), and SCRUM-MONSTAR (Hashimoto, 2024), where thousands of cancer patients have received targeted therapies guided by genomic biomarkers. These studies show that while actionable aberrations are detected in about a third of patients, clinical benefits vary largely between cancer types, with overall response rates reported between 10% and 33% and survival benefit of only a few months.\u003c/p\u003e \u003cp\u003eMost targeted therapies act on specific genomic alterations, which are assumed to either be essential to continuous tumor cell proliferation via oncogene addiction or cause detrimental cellular deficiencies (Passaro, 2024; Petroni, 2022). However, the identification of such central events is complicated by non-affecting passenger mutations or co-driver aberrations with synergistic oncogenic effects (O\u0026rsquo;Dwyer, 2023). Consequently, the precision oncology trials are challenged by false negative cases, where an existing driver event remains undetected, and by false positives, where nominally actionable aberrations lack functional effect in tumor cells. Indeed, several studies have suggested that combining whole-genome and transcriptome sequencing would enhance the detection of actionable biomarker aberrations (Cuppen, 2022; Pleasance, 2022; Rodon, 2019; Horton, 2024; Owen, 2024), although their focus remains on expanding the number of potential actionable alterations rather than improving precision by systematically assessing the functional impact of genomic alterations.\u003c/p\u003e \u003cp\u003eHere, we report a precision oncology workflow that combines whole-genome and transcriptome sequencing data to comprehensively identify targetable molecular alterations and to distinguish credible driver events from redundant passenger aberrations. We applied the workflow to characterize the actionable landscape of prospectively collected patients with ovarian high-grade serous carcinoma (HGSC), which is a copy number-driven cancer with nearly universal TP53 dysfunction (Jamalzadeh, 2024; TCGA, 2011), and five-year survival rate below 40% (Torre, 2018). Current actionable molecular biomarkers in HGSC are limited to \u003cem\u003eBRCA1/2\u003c/em\u003e gene mutation and homologous recombination deficiency (HRD) (Gonz\u0026aacute;lez-Mart\u0026iacute;n, 2023). However, upon development of resistance to the standard platinum-based chemotherapy, few efficient treatment options remain.\u003c/p\u003e \u003cp\u003eOur results show that tumors of about 40% of patients with HGSC harbor unexploited targetable aberrations and that combining RNA-level data considerably improves the detection of credible aberrations. Furthermore, our pre-clinical research in patient-derived organoids supports the use of KRAS or MEK inhibitors for patients with NF1-deficient tumors.\u003c/p\u003e"},{"header":"Methods","content":" \u003cp\u003ePatients and samples\u003c/p\u003e \u003cp\u003eThe analysis was based on real-world data on patients with high-grade serous carcinoma enrolled in the prospective, observational, longitudinal DECIDER study (ClinicalTrials.gov: NCT04846933) between October 2009 and September 2024. Patients were treated at Turku University Hospital and stratified to primary debulking surgery (PDS) followed by adjuvant carboplatin and paclitaxel chemotherapy or to neoadjuvant chemotherapy (NACT) followed by interval debulking surgery and adjuvant chemotherapy. Follow-up was centralized to Turku University Hospital for five years after diagnosis as part of routine cancer care.\u003c/p\u003e \u003cp\u003eSolid tumor, ascites, or pleural fluid samples were collected during the standard care. Nucleic acids were extracted from snap-frozen tumor samples and sequenced as previously described (Lahtinen, 2023; Micoli, 2025; Afenteva, 2025) and summarized in the Supplementary methods (Additional file 1). Quality-controlled tumor sequencing data with at least 10% tumor fraction were available for 335 patients and 946 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenomic sequencing data analysis\u003c/p\u003e \u003cp\u003eSequencing data were processed using the Anduril 2 pipeline (Cervera, 2019), following GATK best practices (Van der Auwera \u0026amp; O'Connor, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, details in Additional file 1: Supplementary methods). Somatic variants were called using GATK 4.1.9.0 Mutect2, patient-specific germline reference samples and a Panel of Normals. Variants were annotated with ANNOVAR 20250302 (Wang, 2010), prediction algorithms CGI (Tamborero, 2018), AlphaMissense (Cheng, 2023), PolyPhen2 (Adzhubei, 2013), SIFT (Vaser, 2016), and dbscSNV 1.1 (Jian, 2014), and variant databases ClinVar (Landrum, 2025) or OncoKB (Chakravarty, 2017). Copy-number aberrations, tumor-cell fraction, and tumor ploidy were analyzed using GATK version 4.1.4.1 (DePristo, 2011) and ASCAT algorithm (Van Loo, 2010). GATK-ASCAT output was used for identification of samples with representative tumor-cell fraction (\u0026gt;\u0026thinsp;10%) and for detection of gene amplifications and large deep deletions. Structural variants were detected using GRIDSS (Cameron, 2017 II) and the HMF toolkit (Priestley, 2019). Additionally, structural aberrations affecting \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eBRCA2\u003c/em\u003e, \u003cem\u003eRAD51C\u003c/em\u003e, or \u003cem\u003eRAD51D\u003c/em\u003e, were curated with BasePlayer (Katainen, 2018) and SegmentModels Spy (Lavikka, 2024).\u003c/p\u003e \u003cp\u003eRNA sequencing data analysis\u003c/p\u003e \u003cp\u003eRNA sequencing data were processed with Anduril 2 using the SePIA pipeline (Icay, 2016, details in Additional file 1: Supplementary methods). Amplification-driven gene hyperexpression was detected from a normalized gene expression matrix. Expression of mutant alleles was assessed by force calling the somatic mutations in genome-aligned RNA-sequencing data. Gene fusion events were detected using the nf-core/rnafusion pipeline v. 2.0.0 (Ewels, 2020). Transcriptional footprint analyses of recurrent driver aberrations were performed using the PROGENy (Schubert, 2018) and CollecTRI (M\u0026uuml;ller-Dott, 2023) algorithms.\u003c/p\u003e \u003cp\u003eWorkflow for identification of credible driver events\u003c/p\u003e \u003cp\u003eThe precision oncology workflow employs a systematic, stepwise strategy with stringent criteria to identify credible driver aberrations for oncogenes and tumor suppressor genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). To verify the functional impact of genomics aberrations in oncogenes, we confirmed that the relevant alleles were active. Aberrations in tumor suppressor genes were assessed by probability of complete loss of all functional gene copies.\u003c/p\u003e \u003cp\u003eBriefly, short mutations, copy-number changes, and structural aberrations were called using specific data processing pipelines, as described above, resulting in annotations on variant identity, protein-level consequence, and predicted or known pathogenicity (Additional file 1: Supplementary methods). Multi-site sampling with phylogenetic analysis revealed the behavior of the aberrations along cancer evolution, as described previously (Lahtinen, 2023).\u003c/p\u003e \u003cp\u003eFor pathogenic short mutations in tumor suppressor genes, we calculated the expected allele frequency given that the aberration affects all gene copies in cancer cells, using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\left(CNmut\\times\\:TF\\right)}{\\left(CNtot\\times\\:TF+2\\times\\:\\left(1-TF\\right)\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eCNmut\u003c/em\u003e (number of aberrant gene copies in cancer) and \u003cem\u003eCNtot\u003c/em\u003e (number of all gene copies in cancer) are given the same value, the locus copy number, and \u003cem\u003eTF\u003c/em\u003e is the tumor fraction, from the GATK-ASCAT copy-number analysis pipeline. For aberrations co-occurring with loss-of-heterozygocity, we assessed the probability of all gene copies being affected by the aberration. With a null hypothesis that all gene copies are affected, we calculated the binomial probability of the observed read count given the sequencing depth. Probability values below 0.01 rejected the null hypothesis and prompted us to assume that the aberration is only sub-clonal.\u003c/p\u003e \u003cp\u003eCopy-number losses were interpreted to affect all gene copies, if the estimated copy number of the lowest copy-number segment in the gene was less than 0.5. The number of affected and intact alleles in tandem duplications, translocations, and inversions was calculated as the difference in copy number between the adjacent segments. If the estimated number of intact copies was less than 0.5, all gene copies were assumed to be affected.\u003c/p\u003e \u003cp\u003eFor the oncogenes, whose impact arises from aberrant protein function, transcriptomic-level evidence was considered a prerequisite for credibility. Therefore, their credibility was assessed with RNA sequencing data in an aberration type-specific manner, as follows. An amplified gene was required both to be associated with elevated expression overall, as previously reported (Jamalzadeh, 2024, CNI\u0026thinsp;\u0026gt;\u0026thinsp;0.46, CNA transition point\u0026thinsp;\u0026gt;\u0026thinsp;2), and to have especially high expression in the cancer sample with the amplification (highest 20% in the cohort). A structural change, where the target gene coding region was joined to another genomic region, was considered credible only if an expressed fusion gene was detected and a somatic missense mutation only upon detection of allele-specific expression. Ambiguous cases and splice-aberrations were manually curated by an expert geneticist from BAM files with the help of BasePlayer visualization software (Katainen, 2018).\u003c/p\u003e \u003cp\u003eAssembly of drug target and biomarker data\u003c/p\u003e \u003cp\u003eWe first made a comprehensive search for cancer drugs using the Open Targets Platform (Buniello, 2025), which resulted in 388 approved and investigational cancer drugs. Then we identified the drug targets followed by delineation of 388 predictive and likely predictive biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Additional file 1: Supplementary methods, Additional file 2). Together, these form a comprehensive collection of drug-gene pairs that characterize the actionable landscape of HGSC. To ensure clinical relevance, genomic aberrations were classified using the ESCAT (Mateo, 2018), where Tiers IA-C represent clinical actionability in routine care, Tiers II\u0026ndash;III denote actionability in clinical trials, and Tiers IV, V, and X represent progressively lower levels of evidence for biomarker\u0026ndash;treatment associations.\u003c/p\u003e \u003cp\u003eOrganoid analysis\u003c/p\u003e \u003cp\u003eLong-term organoids were established from dissociated, fresh tumor tissue samples, verified to match the original tissues, and tested for drug sensitivity as described previously (Senkowski, 2023). Briefly, organoids were each maintained in their optimal growth media and screened at two concentrations for each drug, selected based on literature to match physiological doses, alone and in combinations (Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.). Medium was replaced every 72-96h and the organoids were exposed to drugs for 7 days, with drug replenishment after 4 days of treatment, together with the culture medium change. Cell viability was measured after the total of 7 days of drug exposure using automated fluorescent confocal microscopy (Image Xpress Confocal, Molecular Devices) and fluorescent live/dead cell staining with CellTox Green #G8743, Promega, final concentration 1X) and Hoechst 33342 (#B2261, Sigma, final concentration 5 \u0026micro;g/mL) and normalized according to reference conditions of DMSO or 10 \u0026micro;M Bortezomib. Cytotoxicities were estimated through image analysis using MetaXpress (Molecular Devices) software (RRID: SCR_016654).\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eAssociations were tested with two-sided Fisher\u0026rsquo;s exact test. Aberration homogeneity in cancer cells was assessed at loci of full loss-of-heterozygocity using binomial probability and null hypothesis that all gene copies are affected, as described above.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe performed systematic evaluation of somatic aberrations in 388 drug target and biomarker genes with an integrated omics analysis using tumor sequencing data of 946 samples from 355 patients with HGSC enrolled in the DECIDER study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The workflow consisted of three major phases as follows: aberration discovery in the DNA sequencing data, assessment of the aberration\u0026rsquo;s pathogenicity and impact on protein structure, and functional evaluation with distinct criteria for tumor suppressors and oncogenes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Aberrations in tumor suppressor genes fulfilled the functional criterion when all gene copies in cancer cells were affected. Aberrations in oncogenes were required to produce an aberrant transcript for functional validation. Thus, the analysis enabled stepwise prioritization of variants of interest as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eActionable landscape of HGSC\u003c/p\u003e \u003cp\u003eIn tumor suppressor genes, we identified 542 short mutations and 316 structural aberrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The majority of short mutations mapped to \u003cem\u003eTP53\u003c/em\u003e, consistent with the near-universal prevalence of \u003cem\u003eTP53\u003c/em\u003e mutations in HGSC (Zarei, 2020; TCGA, 2011). Beyond \u003cem\u003eTP53\u003c/em\u003e mutations, 75% of the pathogenic short mutations and 34% of the structural aberrations resulted in complete loss of gene function.\u003c/p\u003e \u003cp\u003eFor oncogenes, structural aberrations represented the most prevalent aberration class (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1584) followed by amplifications of wild-type genes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1195) and short mutations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;650) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. Most structural aberrations showed no evidence of functional impact at mRNA level, reducing their relevance as predictive biomarkers. In contrast, about half of the short mutations were expressed. Notably, gene amplifications frequently resulted in marked overexpression of the affected genes, consistent with prior evidence highlighting the central role of copy-number alterations in HGSC (Jamalzadeh, 2024; TCGA, 2011).\u003c/p\u003e \u003cp\u003eTo assess clinical relevance of the genomic aberrations, we matched them to ESCAT tiers as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec. Currently, HRD and PARP inhibitors are the only Tier IA combination of a biomarker and therapy for patients with HGSC. In our cohort, we detected HRD in 145 patients (43%) and 42% of them had causal mutations in \u003cem\u003eBRCA1/2\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;49) or \u003cem\u003eRAD51C/D\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12). High mutational burden, a Tier IC biomarker for immune checkpoint inhibitor therapy, was detected in a one patient, who had mismatch-repair deficiency caused by an \u003cem\u003eMSH6\u003c/em\u003e mutation.\u003c/p\u003e \u003cp\u003eTier II\u0026ndash;IV drug-gene pairs represent the unexploited potential of precision oncology in HGSC. In our cohort, approximately half of the patients (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;137, 41%) harbored at least one functionally credible Tier II or III aberration with potential for off-label drug use (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Additional file 3). Notably, 10% harbored more than one actionable alteration, including four patients with three aberrations and 30 patients with two. Tier IIIA loss of \u003cem\u003eNF1\u003c/em\u003e was the most prevalent genomic event (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70, 21%), suggesting benefit from the MEK inhibitors. Other prominent targetable categories were aberrations associated with AKT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27, 8%), MTOR (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23, 7%) or ERBB2 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16, 5%) inhibitors. In general, credible Tier II\u0026ndash;III aberrations were detected in both HRD and HR proficient (HRP) tumors with equal frequency. However, both \u003cem\u003eNF1\u003c/em\u003e aberrations (OR: 2.17; 95% CI: 1.24\u0026ndash;3.84; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and \u003cem\u003ePTEN\u003c/em\u003e loss-of-function events (OR: 5.34; 95% CI: 1.65\u0026ndash;22.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) were enriched in HRD tumors. Notably, the actionable variants were not recurrent hotspot mutations but unique genomic changes.\u003c/p\u003e \u003cp\u003eFalse-positive actionable variants are minimized by allele-specific DNA-RNA analysis\u003c/p\u003e \u003cp\u003eWe then characterized to what extent the presence of a Tier I-III aberration implies clinical actionability. We identified 684 nominally pathogenic aberrations in 58 Tier I-III genes and 283 patients. Strikingly, less than half (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;271, 40%) of these aberrations met the criteria for credible alterations, \u003cem\u003ei.e.\u003c/em\u003e, alle-specific expression (oncogenes) or complete loss of all functional gene copies (tumor suppressor genes), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed. The proportion of false-positive findings varied between biomarker genes. For instance, 78% of deleterious \u003cem\u003eNF1\u003c/em\u003e aberrations caused full loss of the gene function, suggesting clinical relevance. Similarly, most aberration in \u003cem\u003ePTEN\u003c/em\u003e (76%), and \u003cem\u003eMTOR\u003c/em\u003e (67%) were found credible.\u003c/p\u003e \u003cp\u003eIn contrast to \u003cem\u003eNF1\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e and \u003cem\u003eMTOR\u003c/em\u003e, pathogenic missense mutations or translocations in receptor tyrosine kinase genes, such as \u003cem\u003eALK\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eKIT\u003c/em\u003e, \u003cem\u003eMET\u003c/em\u003e, \u003cem\u003eRET\u003c/em\u003e, \u003cem\u003eROS1\u003c/em\u003e, \u003cem\u003eNTRK1-3\u003c/em\u003e, were predominantly non-expressed passenger events (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This limits their clinical relevance, which is also supported by their borderline expression in the tissue of origin, the fallopian tube (GTEx v10, Additional file 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Notably, false-positive biomarkers were also discovered for PARP inhibitors, including primarily subclonal or heterozygous mutations in secondary HR-pathway genes (\u003cem\u003eTR\u003c/em\u003e, \u003cem\u003eBARD1\u003c/em\u003e, \u003cem\u003eBRIP1\u003c/em\u003e, \u003cem\u003eCDK12\u003c/em\u003e, \u003cem\u003eCHEK1\u003c/em\u003e, \u003cem\u003eCHEK2\u003c/em\u003e, \u003cem\u003eFANCA\u003c/em\u003e, \u003cem\u003eFANCL\u003c/em\u003e, \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMRE11\u003c/em\u003e, \u003cem\u003eNBN\u003c/em\u003e, \u003cem\u003eRAD51B\u003c/em\u003e, and \u003cem\u003eRAD54L\u003c/em\u003e) and occasional heterozygous alterations in \u003cem\u003eBRCA1/2\u003c/em\u003e. Accordingly, these aberrations showed no significant association with HRD (OR:1.21; 95% CI: 0.77\u0026ndash;1.93; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.44).\u003c/p\u003e \u003cp\u003eAmplifications of wild-type oncogenes are credible driver events\u003c/p\u003e \u003cp\u003eNext, we explored the impact of Tier IVB wild-type proto-oncogene amplifications, which are characteristics to HGSC. The most recurrent amplifications supported by transcriptional overexpression were \u003cem\u003eCCNE1\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;56, 17%), \u003cem\u003eAKT2\u003c/em\u003e (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;31, 9%), \u003cem\u003ePTK2\u003c/em\u003e (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;23, 7%), and \u003cem\u003eKRAS\u003c/em\u003e (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;22, 7%) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec. These amplifications were enriched in HRP tumors (OR: 2.78; CI: 1.64\u0026ndash;4.80; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00008), with \u003cem\u003eAKT2\u003c/em\u003e amplifications occurring almost exclusively in HRP cases and \u003cem\u003ePTK2\u003c/em\u003e amplifications showing comparable frequencies in HRP and HRD tumors.\u003c/p\u003e \u003cp\u003eAllele-specific expression analysis showed that \u003cem\u003eCCNE1\u003c/em\u003e, \u003cem\u003eAKT2\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, and \u003cem\u003eKRAS\u003c/em\u003e amplifications cause hyper-expression of the wild-type alleles. Moreover, the amplifications represented credible driver events modulating the activity of cancer-associated pathways, each characterized by a distinct transcriptional signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Additional file 1: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Amplifications of \u003cem\u003eAKT2\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, or \u003cem\u003eKRAS\u003c/em\u003e were linked to aberrant activation of MAPK and PI3K signaling cascades, mirroring loss-of-function events in \u003cem\u003eNF1\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e. Furthermore, amplifications of \u003cem\u003eAKT2\u003c/em\u003e or \u003cem\u003eKRAS\u003c/em\u003e triggered elevated activity of TGF-β-dependent signaling and epithelial-to-mesenchymal transition via SMAD-family or homeobox transcription factors, whereas \u003cem\u003ePTK2\u003c/em\u003e amplification phenocopied \u003cem\u003eNF1\u003c/em\u003e deficiency, showing high activity of TNF-α and NF-κB signaling and their associated transcriptional regulators. \u003cem\u003eCCNE1\u003c/em\u003e amplification was not associated with perturbed signaling cascades, aligning with its role as a regulator of cell cycle progression.\u003c/p\u003e \u003cp\u003ePre-treatment samples are informative for clinical decisions at relapse\u003c/p\u003e \u003cp\u003eTo guide optimal sampling strategies for identifying predictive biomarkers at relapse, we analyzed Tier II\u0026ndash;III aberrations across multiple anatomical locations before and during therapy. Credible aberrations were detected roughly four times more frequently across all anatomical sites compared with false-positive events (OR: 3.66; CI: 2.53\u0026ndash;5.37; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4\u0026middot;10\u003csup\u003e13\u003c/sup\u003e). Among credible aberrations, 73% were present in all sampled anatomical locations, with no significant differences between sites (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62). This implies that credible aberrations with clinical utility are predominantly clonal events.\u003c/p\u003e \u003cp\u003eTo further explore the longitudinal stability of credible aberrations, we used paired primary and relapse samples from 22 patients with 34 Tier II\u0026ndash;III aberrations. Most of the aberrations (70%) were identical in primary and relapse samples, indicating early emergence and subsequent clonal stabilization, like in an example patient illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003eFurther exploration of the discordant aberrations between primary and relapse samples revealed that subclonality is often detectable at diagnosis as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-d. Briefly, two patients had tubo-ovarian-specific mutations that were not present in relapses, whereas treatment-na\u0026iuml;ve metastasis samples represented the actionable biomarkers in relapse (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c). Interestingly, one of these and another patient harbored converging subclonal aberrations with the same gene-level consequence, suggesting strong selective pressure to maintain activation of key growth signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Furthermore, four patients had actionable aberrations present in both tubo-ovarian and metastatic samples, but low variant allele frequencies in the metastatic samples revealed the subclonality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003ePatient-derived organoids suggest MEK and KRAS targeting therapies effective in HGSC\u003c/p\u003e \u003cp\u003eTo investigate the therapeutic potential of NF1 deficiency, which was the most recurrent Tier III biomarker, we performed a focused drug screen using patient-derived organoids (PDOs). Organoid cultures were established from three patient cases harboring deleterious and homogeneous \u003cem\u003eNF1\u003c/em\u003e aberrations, three cases with \u003cem\u003eKRAS\u003c/em\u003e wild-type amplification, and three reference cases lacking driver events in the RAS-MAPK pathway. To inhibit both up- and downstream components of the pathway, we used five inhibitors as monotherapies and in combination with other compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Methods).\u003c/p\u003e \u003cp\u003eMonotherapy with the investigational KRAS inhibitor RMC-7977 demonstrated the strongest activity against NF1-deficient PDOs, exceeding the effect of the MEK inhibitor cobimetinib, which is a Tier III drug for NF1-deficient tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb,c; Additional file 4). Sensitivity to both RMC-7977 and cobimetinib was specific to NF1 deficiency, as no reduction in viability was observed in the reference PDOs. In contrast, compounds targeting upstream (SHP2 or SOS1) or downstream (ERK) components of the pathway had little impact on PDO growth regardless of mutation status (Additional file 4).\u003c/p\u003e \u003cp\u003eRMC-7977 or cobimetinib re-sensitized the platinum-resistant PDO to carboplatin. In contrast, the two platinum-sensitive NF1-deficient PDOs showed no additional benefit from the combination. Instead, the response to the combination mimicked the response to carboplatin alone, a pattern known as single-agent dominance, which is frequently seen \u003cem\u003ein vitro\u003c/em\u003e for clinically effective treatment combinations (Richards, 2020). Thus, these findings suggest that platinum re-challenge combined with targeted KRAS or MEK inhibition could represent an optimal therapeutic strategy for patients with NF1-deficient HGSC.\u003c/p\u003e \u003cp\u003eUnlike the NF1-deficient models, organoids with \u003cem\u003eKRAS\u003c/em\u003e amplification were resistant to RMC-7977, cobimetinib, and all other compounds tested (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec; Additional file 4). This suggests KRAS amplification confers resistance rather than sensitivity to RAS-MAPK-focused therapies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecognizing that false-positive calls remain a major obstacle to effective precision oncology trials (Waarts, 2022; O\u0026rsquo;Dwyer, 2023), we developed an integrated whole-genome and transcriptome workflow designed to reliably identify clinically actionable aberrations. In the prospective DECIDER trial, we detected credible ESCAT Tier II\u0026ndash;III alterations with potential therapeutic relevance in approximately 40% of 335 patients. Notably, 60% of all variants initially classified as pathogenic were found to be false positives. For example, mutations in secondary HR-pathway genes rarely resulted in complete loss of gene function or a corresponding HRD phenotype, and Tier IC NTRK1\u0026ndash;3 alterations were credible in only a minority of cases. These findings indicate that many previously proposed predictive biomarkers lack true functional relevance. Thus, trials that enroll patients based on presumed pathogenic alterations in target genes are likely to be substantially affected by false-positive biomarkers, diminishing the observable benefit of the therapies under investigation.\u003c/p\u003e \u003cp\u003eLongitudinal analysis of paired primary and relapse tumors revealed that most actionable aberrations were clonally stable and present across multiple sites. This suggests that true driver aberrations underlying oncogene addiction are established early in tumor evolution, consistent with the current evolutionary models (Petroni, 2022; Sottoriva, 2015). Therefore, archival diagnostic samples could reliably capture most clinically relevant driver events in HGSC. However, the biomarker detection should be based on metastatic samples, instead of the tubo-ovarian sites that are enriched with private mutations (Lahtinen, 2023).\u003c/p\u003e \u003cp\u003eWe characterized the actionable landscape HGSC and identified several recurring credible targetable aberrations. For example, \u003cem\u003eNF1\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e loss were often found credible and supported by strong impact on cell transcriptional programs. Furthermore, wild-type amplifications of oncogenes including \u003cem\u003eAKT2\u003c/em\u003e, \u003cem\u003ePTK2\u003c/em\u003e, and \u003cem\u003eKRAS\u003c/em\u003e were identified as credible driver events in a substantial proportion of patients with HGSC, highlighting the importance of preclinical studies to assess their potential therapeutic vulnerabilities.\u003c/p\u003e \u003cp\u003eWe explored the translational potential of NF1 deficiency, which was the most recurrent actionable aberration, with a focused drug screen targeting RAS\u0026ndash;MAPK pathway components in PDOs stratified by mutation status. PDOs harboring \u003cem\u003eNF1\u003c/em\u003e loss-of-function aberrations displayed marked sensitivity to KRAS- and MEK-inhibition. Although MEK inhibitors have shown limited efficacy as monotherapy in HGSC (Manoharan, 2024; Vergote, 2020), unlike in low-grade serous carcinoma (Gershenson, 2022), our data suggest that RAS-MAPK pathway inhibition may still provide therapeutic value in combination regimens. Indeed, in a platinum-resistant NF1-deficient PDO, inhibition of MEK or KRAS effectively re-sensitized the cells to carboplatin.\u003c/p\u003e \u003cp\u003eThe observational design of the real-world DECIDER trial presents both strengths and limitations. It supports unbiased and comprehensive identification of actionable aberrations but precludes direct evaluation of biomarker-specific treatment responses. Accordingly, dedicated clinical trials are needed to evaluate the therapeutic benefit of the proposed combinations. We note that several of the findings presented here were reviewed by the Molecular Tumor Board at Turku University Hospital and informed the referral of three patients to the biomarker-guided FINPROVE clinical trial (NCT05159245).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur multi-omics workflow provides a generalizable strategy to overcome a major barrier to precision oncology by minimizing the number of false-positive predictive biomarkers that otherwise could reduce the treatment efficacy of targeted therapies in clinical trials. We comprehensively characterized the actionable landscape in HGSC and identified clinically relevant vulnerabilities in over 40% of patients that could expand precision-therapy options. Furthermore, using PDOs, we provide preclinical evidence supporting combination therapy of carboplatin and targeted drugs for HGSC patients with chemoresistant disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was approved by Ethics Committee of the Hospital District of Southwest Finland (VARHA/28314/13.02.02/2023) and is being conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (WMA). All patients provided written informed consent. We adhered to the REMARK guidelines in reporting, where applicable, and used the STROBE reporting guideline to draft this manuscript (Elm, 2007; Elm, 2025; Additional file 5).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eSource files for sequencing data are made public in the European Genome-phenome Archive (DNA: EGAS00001006775, RNA: EGAS00001004714). Three of the nine tested organoids are deposited in the Auria Biobank (https://www.auria.fi/biopankki/en/). Data used for panel-of-normals in mutation calling was partly obtained from The Cancer Genome Atlas managed by the NCI and NHGRI (dbGaP accession number phs000178). GTEx v10 data were obtained from: the GTEx Portal https://www.gtexportal.org on 2025-06-02.\u003c/p\u003e\n\u003cp\u003eAll analyses and data processing were performed with open-source, published software, as described above. Custom analysis scripts are provided in \u0026nbsp;https://github.com/HautaniemiLab/\u003cbr\u003e\u0026nbsp;Actionable. The workflow is currently being integrated into an open-source platform of clinical decision making https://github.com/oncodash/oncodash.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eJSR reports in the last 3 years funding from GSK and Pfizer \u0026amp; fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer, Grunenthal, Tempus and Moderna. The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis project has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under grant agreement No 965193 for DECIDER, the Sigrid Jus\u0026eacute;lius Foundation, Cancer Foundation Finland, and OvaCure.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eStudy conception: TAM, AV, JSR, EF, BS, EA, SHa, JH; Study design: TAM, WS, JO, AL, ACO, JS, SHi, AV, EA, KW, SHa, JH; Data acquisition: TAM, AH, DA, WS, JO, KD, SHo, VMI, KL, YL, ML, IM, GMa, FM, GMi, ACO, JS, SHi, AV, EF, KW, SHa, JH; Data analysis: TAM, AH, DA, WS, JO, JD, KL, YL, ML, IM, GMa, FM, GMi, MN, PRM, JSR, EF, BS; Interpretation of data: TAM, AH, DA, WS, JS, SHi, AV, EA, KW, SHa, JH; Creation of new software: TAM, AH, GMi; Drafting of the manuscript: TAM; All authors participated in the revision of the manuscript text and approved the submitted manuscript version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors wish to acknowledge the CSC-IT Center for Science (Finland) for computational resources,\u0026nbsp;all contributing research nurses, lab technicians, and administrative personnel, especially Elina Valkonen, Peppi Alho, Nina Halme, and Jenni Lahtinen for their valuable contributions. The authors are grateful to all study participants for donating their samples and allowing the use of their personal data for scientific advancement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. 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Am J Surg Pathol. 2020;44(3):316\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/PAS.0000000000001419\u003c/span\u003e\u003cspan address=\"10.1097/PAS.0000000000001419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"genome-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)","snPcode":"13073","submissionUrl":"https://submission.springernature.com/new-submission/13073/3","title":"Genome Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Precision oncology, targeted therapy, mutation, biomarker, high-grade serous cancer, multi-omics","lastPublishedDoi":"10.21203/rs.3.rs-8860995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8860995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrecision oncology aims to tailor treatment according to tumor-specific molecular alterations, but the success of aberration-guided therapies has been limited in clinical trials. Here, we develop an integrated whole-genome and transcriptome workflow to systematically distinguish functionally credible, predictive driver aberrations from non-functional alterations across all classes of genomic events.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe applied the integrated omics workflow to 335 patients with ovarian high-grade serous cancer (HGSC) enrolled in the observational DECIDER trial. Tumor samples were collected from multiple cancer sites as a part of the standard cancer care. DNA and RNA were extracted together from snap-frozen tumor samples and sent to whole-genome and transcriptome sequencing. Sequencing data were processed with the Anduril 2 pipeline for detection and validation of short somatic changes and with the HMW toolkit and the nf-core/rnafusion pipeline for assessment of structural changes. Aberration-specific drug sensitivity was tested in patient-derived organoids with a drug screen combining targeted agents and chemotherapy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing an agnostic integrated omics analysis, we identified clinically relevant ESCAT Tier II\u0026ndash;III alterations in more than 40% of the patients, even though 60% of all nominally pathogenic variants proved to be false positives. Credible aberrations were predominantly clonal, detected across anatomical sites, and preserved from diagnosis to relapse, indicating early establishment during tumor evolution. The most recurrent actionable event was NF1 deficiency, which was associated with a robust transcriptional footprint and marked sensitivity to KRAS- and MEK-inhibition in patient-derived organoids. Notably, integrated DNA-RNA analysis enabled discrimination of treatment-guiding aberrations from false-positive findings that would otherwise misinform treatment selection and confound clinical trial outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings provide a strategy for more reliable biomarker detection in precision oncology, inform biomarker-guided clinical trial design, and reveal unexploited therapeutic vulnerabilities in HGSC.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eClinicalTrials.gov: NCT04846933. 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