Integrated immunogenomic analyses of high-grade serous ovarian cancer reveal vulnerability to combination immunotherapy

Background High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecologic malignancy despite new therapeutic concepts, including poly-ADP-ribose polymerase inhibitors (PARPis) and antiangiogenic therapy. The efficacy of immunotherapies is modest, but clinical trials investigating the potential of combination immunotherapy with PARPis are underway. Homologous recombination repair deficiency (HRD) or BRCAness and the composition of the tumor microenvironment appear to play a critical role in determining the therapeutic response. Methods We conducted comprehensive immunogenomic analyses of HGSOC using data from several patient cohorts, including a new cohort from the Medical University of Innsbruck (MUI). Machine learning methods were used to develop a classification model for BRCAness from gene expression data. Integrated analysis of bulk and single-cell RNA sequencing data was used to delineate the tumor immune microenvironment and was validated by immunohistochemistry. The impact of PARPi and BRCA1 mutations on the activation of immune-related pathways was studied in vitro using ovarian cancer cell lines, RNA sequencing, and immunofluorescence analysis. Results We identified a predictive 24-gene signature to determine BRCAness. Comprehensive analysis of the tumor microenvironment allowed us to identify patient samples with BRCAness and high immune infiltration. Further characterization of these samples revealed increased infiltration of immunosuppressive cells, including tumor-associated macrophages (TAMs) expressing TREM2 , C1QA, and LILRB4, as identified by further analysis of single-cell RNA sequencing data and gene expression analysis of samples from patients receiving combination therapy with PARPi and anti-PD-1. PARPi activated the cGAS-STING signaling pathway and the downstream innate immune response in a similar manner to HGSOC patients with BRCAness status. We have developed a web application () and an associated R package OvRSeq, which allow for comprehensive characterization of ovarian cancer patient samples and assessment of a vulnerability score that enables stratification of patients to predict response to the mentioned combination immunotherapy. Conclusions Genomic instability in HGSOC affects the tumor immune environment, and TAMs play a crucial role in modulating the immune response. Based on various datasets, we have developed a diagnostic application that uses RNA sequencing data not only to comprehensively characterize HGSOC but also to predict vulnerability and response to combination immunotherapy. ### Competing Interest Statement AGZ reports consulting fees from Amgen, Astra Zeneca, GSK, MSD, Novartis, PharmaMar, Roche-Diagnostic, Seagen; honoraria from Amgen, Astra Zeneca, GSK, MSD, Novartis, PharmaMar, Roche, Seagen; travel expenses from Astra Zeneca, Gilead, Roche; participation on advisory boards from Amgen, Astra Zeneca, GSK, MSD, Novartis, Pfizer, PharmaMar, Roche, Seagen. CM reports consulting fees and honoraria from Roche, Novartis, Amgen, MSD, PharmaMar, Astra Zeneca, GSK, Seagen; travel expenses from Roche, Astra Zeneca; participation on advisory boards from Roche, Novartis, Amgen, MSD, Astra Zeneca, Pfizer, PharmaMar, GSK, Seagen. HH has received research funding via Catalym and Secarna. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ### Funding Statement This research was funded in whole, or in part, by the Anniversary Fund of the National Bank of Austria (OeNB) (grant number 18279 to HH). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: For the pilot study (validation cohort from the Medical University of Innsbruck), written informed consent was needed for all patients before enrollment. The study was reviewed and approved by the Ethics Committee of the Medical University of Innsbruck (reference number: 1189/2019) and conducted in accordance with the Declaration of Helsinki. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as [ClinicalTrials.gov][1]. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes RNA sequencing data from in vitro experiments are available via the Gene Expression Omnibus (GEO) (GSE237361). RNA sequencing data and patient information of the validation cohort (MUI) are available at Zenodo . The R package OvRSeq is available from GitHub () under MIT license and the corresponding web application (). The analysis scripts used in this manuscript are available at GitHub (). * ### Abbreviations AUC : Area under curve BRCA : Breast cancer DNA repair-associated genes BRIT : BRCAness immune type CTL : Cytotoxic T lymphocytes CYT : Cytolytic activity C1QA : Complement C1q A chain C2C : Transformed CYT to C1QA ratio DIF : Differentiated molecular subtype EOC : Epithelial ovarian cancer FDR : False discovery rate GSEA : Gene set enrichment analysis GSVA : Gene set variation analysis HGSOC : High-grade serous ovarian cancer HR : Hazard ratio HRD : Homologous recombination repair deficiency HRR : Homologous recombination repair IMR : Immune reactive molecular subtype IPS : Immunophenoscore LOH : Loss of Heterogeneity LST : Large-scale transitions MDSC : Myeolid-derived suppressor cell MES : Mesenchymal molecular subtype MutSig3 : Mutational signature 3 NES : Normalized enrichment score PARP : Poly (ADP-Ribose) Polymerase PARPi : PARP inhibitor (olaparib, niraparib PCA : Principal component analysis PD-1 : Programmed cell death 1 ( PDCD1 ) PRO : Proliferative molecular subtype PROGENy : Pathway RespOnsive GENes for activity inference ROC : Receiver operating characteristics TAI : Telomeric allelic imbalance TAM : Tumor-associated macrophages TCGA : The Cancer Genome Atlas TPM : Transcript per millions Tregs : Regulatory CD4+ T cells UMAP : Uniform manifold approximation and projection [1]: http://ClinicalTrials.gov