Deciphering cancer genomes with GenomeSpy: a grammar-based visualization toolkit.

BACKGROUND: Visualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remains a distinct need for tailored solutions. However, their implementation typically requires extensive programming expertise from bioinformaticians and software developers, especially when building interactive applications. Toolkits based on visualization grammars offer a more accessible, declarative way to author new visualizations. Yet, current grammar-based solutions fall short in adequately supporting the interactive analysis of large datasets with extensive sample collections, a pivotal task often encountered in cancer research. FINDINGS: We present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. By using combinatorial building blocks and a declarative language, users can implement new visualization designs easily and embed them in web pages or end-user-oriented applications. A distinctive element of GenomeSpy's architecture is its effective use of the graphics processing unit in all rendering, enabling a high frame rate and smoothly animated interactions, such as navigation within a genome. We demonstrate the utility of GenomeSpy by characterizing the genomic landscape of 753 ovarian cancer samples from patients in the DECIDER clinical trial. Our results expand the understanding of the genomic architecture in ovarian cancer, particularly the diversity of chromosomal instability. CONCLUSIONS: GenomeSpy is a visualization toolkit applicable to a wide range of tasks pertinent to genome analysis. It offers high flexibility and exceptional performance in interactive analysis. The toolkit is open source with an MIT license, implemented in JavaScript, and available at https://genomespy.app/.

PIK3R1 fusion drives chemoresistance in ovarian cancer by activating ERK1/2 and inducing rod and ring-like structures.

Gene fusions are common in high-grade serous ovarian cancer (HGSC). Such genetic lesions may promote tumorigenesis, but the pathogenic mechanisms are currently poorly understood. Here, we investigated the role of a PIK3R1-CCDC178 fusion identified from a patient with advanced HGSC. We show that the fusion induces HGSC cell migration by regulating ERK1/2 and increases resistance to platinum treatment. Platinum resistance was associated with rod and ring-like cellular structure formation. These structures contained, in addition to the fusion protein, CIN85, a key regulator of PI3K-AKT-mTOR signaling. Our data suggest that the fusion-driven structure formation induces a previously unrecognized cell survival and resistance mechanism, which depends on ERK1/2-activation.

Genome-wide quantification of copy-number aberration impact on gene expression in ovarian high-grade serous carcinoma.

Copy-number alterations (CNAs) are a hallmark of cancer and can regulate cancer cell states via altered gene expression values. Herein, we have developed a copy-number impact (CNI) analysis method that quantifies the degree to which a gene expression value is impacted by CNAs and leveraged this analysis at the pathway level. Our results show that a high CNA is not necessarily reflected at the gene expression level, and our method is capable of detecting genes and pathways whose activity is strongly influenced by CNAs. Furthermore, the CNI analysis enables unbiased categorization of CNA categories, such as deletions and amplifications. We identified six CNI-driven pathways associated with poor treatment response in ovarian high-grade serous carcinoma (HGSC), which we found to be the most CNA-driven cancer across 14 cancer types. The key driver in most of these pathways was amplified wild-type KRAS, which we validated functionally using CRISPR modulation. Our results suggest that wild-type KRAS amplification is a driver of chemotherapy resistance in HGSC and may serve as a potential treatment target.

Locus-specific LINE-1 expression in clinical ovarian cancer specimens at the single-cell level.

Long interspersed nuclear elements (LINE-1s/L1s) are a group of retrotransposons that can copy themselves within a genome. In humans, it is the most successful transposon in nucleotide content. L1 expression is generally mild in normal human tissues, but the activity has been shown to increase significantly in many cancers. Few studies have examined L1 expression at single-cell resolution, thus it is undetermined whether L1 reactivation occurs solely in malignant cells within tumors. One of the cancer types with frequent L1 activity is high-grade serous ovarian carcinoma (HGSOC). Here, we identified locus-specific L1 expression with 3' single-cell RNA sequencing in pre- and post-chemotherapy HGSOC sample pairs from 11 patients, and in fallopian tube samples from five healthy women. Although L1 expression quantification with the chosen technique was challenging due to the repetitive nature of the element, we found evidence of L1 expression primarily in cancer cells, but also in other cell types, e.g. cancer-associated fibroblasts. The expression levels were similar in samples taken before and after neoadjuvant chemotherapy, indicating that L1 transcriptional activity was unaffected by clinical platinum-taxane treatment. Furthermore, L1 activity was negatively associated with the expression of MYC target genes, a finding that supports earlier literature of MYC being an L1 suppressor.

HRD related signature 3 predicts clinical outcome in advanced tubo-ovarian high-grade serous carcinoma.

OBJECTIVES: We evaluated usability of single base substitution signature 3 (Sig3) as a biomarker for homologous recombination deficiency (HRD) in tubo-ovarian high-grade serous carcinoma (HGSC). MATERIALS AND METHODS: This prospective observational trial includes 165 patients with advanced HGSC. Fresh tissue samples (n = 456) from multiple intra-abdominal areas at diagnosis and after neoadjuvant chemotherapy (NACT) were collected for whole-genome sequencing. Sig3 was assessed by fitting samples independently with COSMIC v3.2 reference signatures. An HR scar assay was applied for comparison. Progression-free survival (PFS) and overall survival (OS) were studied using Kaplan-Meier and Cox regression analysis. RESULTS: Sig3 has a bimodal distribution, eliminating the need for an arbitrary cutoff typical in HR scar tests. Sig3 could be assessed from samples with low (10%) cancer cell proportion and was consistent between multiple samples and stable during NACT. At diagnosis, 74 (45%) patients were HRD (Sig3+), while 91 (55%) were HR proficient (HRP, Sig3-). Sig3+ patients had longer PFS and OS than Sig3- patients (22 vs. 13 months and 51 vs. 34 months respectively, both p < 0.001). Sig3 successfully distinguished the poor prognostic HRP group among BRCAwt patients (PFS 19 months for Sig3+ and 13 months for Sig3- patients, p < 0.001). However, Sig3 at diagnosis did not predict chemoresponse anymore in the first relapse. The patient-level concordance between Sig3 and HR scar assay was 87%, and patients with HRD according to both tests had the longest median PFS. CONCLUSIONS: Sig3 is a prognostic marker in advanced HGSC and useful tool in patient stratification for HRD.

Quantification of cell death and proliferation of patient-derived ovarian cancer organoids through 3D imaging and image analysis.

Patient-derived organoids (PDOs) are ideal ex vivo model systems to study cancer progression and drug resistance mechanisms. Here, we present a protocol for measuring drug efficacy in three-dimensional (3D) high-grade serous ovarian cancer PDO cultures through quantification of cytotoxicity using propidium iodide incorporation in dead cells. We also provide detailed steps to analyze proliferation of PDOs using the Ki67 biomarker. We describe steps for sample processing, immunofluorescent staining, high-throughput confocal imaging, and image-based quantification for 3D cultures. For complete details on the use and execution of this protocol, please refer to Lahtinen et al. (2023).1.

Circulating tumor DNA-based copy-number profiles enable monitoring treatment effects during therapy in high-grade serous carcinoma.

Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for detecting and profiling longitudinal genomics changes in cancer. While copy-number alterations (CNAs) play a major role in cancers, treatment effect monitoring using copy-number profiles has received limited attention as compared to mutations. A major reason for this is the insensitivity of CNA analysis for the real-life tumor-fraction ctDNA samples. We performed copy-number analysis on 152 plasma samples obtained from 29 patients with high-grade serous ovarian cancer (HGSC) using a sequencing panel targeting over 500 genes. Twenty-one patients had temporally matched tissue and plasma sample pairs, which enabled assessing concordance with tissues sequenced with the same panel or whole-genome sequencing and to evaluate sensitivity. Our approach could detect concordant CNA profiles in most plasma samples with as low as 5% tumor content and highly amplified regions in samples with ∼1% of tumor content. Longitudinal profiles showed changes in the CNA profiles in seven out of 11 patients with high tumor-content plasma samples at relapse. These changes included focal acquired or lost copy-numbers, even though most of the genome remained stable. Two patients displayed major copy-number profile changes during therapy. Our analysis revealed ctDNA-detectable subclonal selection resulting from both surgical operations and chemotherapy. Overall, longitudinal ctDNA data showed acquired and diminished CNAs at relapse when compared to pre-treatment samples. These results highlight the importance of genomic profiling during treatment as well as underline the usability of ctDNA.

Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma.

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we integrate clonal composition and topology using whole-genome sequencing data from 510 samples of 148 patients with HGSC in the prospective, longitudinal, multiregion DECIDER study. Our results reveal three evolutionary states, which have distinct features in genomics, pathways, and morphological phenotypes, and significant association with treatment response. Nested pathway analysis suggests two evolutionary trajectories between the states. Experiments with five tumor organoids and three PI3K inhibitors support targeting tumors with enriched PI3K/AKT pathway with alpelisib. Heterogeneity analysis of samples from multiple anatomical sites shows that site-of-origin samples have 70% more unique clones than metastatic tumors or ascites. In conclusion, these analysis and visualization methods enable integrative tumor evolution analysis to identify patient subtypes using data from longitudinal, multiregion cohorts.

A platform for efficient establishment and drug-response profiling of high-grade serous ovarian cancer organoids.

The broad research use of organoids from high-grade serous ovarian cancer (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here, we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (53% vs. 23%-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic, and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in a culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data are explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.

Aberrant glycosylation of α3 integrins as diagnostic markers in epithelial ovarian cancer.

BACKGROUND: Glycans are strongly involved in stability and function of integrins (ITG) and tetraspanin protein CD63 and their respective interaction partners as they are dysregulated in the tumorigenic processes. Glycosylation changes is a universal phenomenon of cancer cells. In this study, glycosylation changes in epithelial ovarian cancer (EOC) are explored using tetraspanin and integrin molecules. METHODS: ITG and CD63 were immobilized from 10 EOC and 5 benign ovarian cyst fluid on microtiter wells and traced with 3 glycan binding proteins (STn, WGA, UEA) conjugated on europium nanoparticles. Total protein measurements (ITG & CD63 immunoassays) were also performed. The most promising glycovariant candidates identified were then clinically evaluated on the whole cohort of 77 ovarian cyst fluids. Additional testing was performed in ascites fluid samples of liver cirrhosis (n = 2) and EOC (n = 4). RESULTS: Sialylated Tn antibody based glycovariants of ITGα3 (ITGα3STn) and CD63 (CD63STn) performed better than corresponding protein epitope-based immunoassays, ITGα3IA and CD63IA respectively. Combined ITGα3 based assays (ITGα3IA + ITGα3STn) detected 49 out of 55 malignant & borderline cases without detecting any of the 22 benign and healthy cysts. CONCLUSION: Our findings indicate the potential diagnostic application of ITGα3STn along with total ITGα3IA, which could help reduce the unnecessary surgeries. The results encourage studying further the potential use of these novel assays to detect EOC at earlier clinical stages.

Toxicity and therapy outcome associations in LIG3, SLCO1B3, ABCB1, OPRM1 and GSTP1 in high-grade serous ovarian cancer.

Adverse effects are the major limiting factors in combinatorial chemotherapies. To identify genetic associations in ovarian cancer chemotherapy-induced toxicities and therapy outcomes, we examined a cohort of 101 patients receiving carboplatin-paclitaxel treatment with advanced high-grade serous ovarian cancers. Based on literature and database searches, we selected 19 candidate polymorphisms, designed a multiplex single nucleotide polymorphism-genotyping assay and applied Cox regression analysis, case-control association statistics and the log-rank Mantel-Cox test. In the Cox regression analysis, the SLCO1B3 rs1052536 AA-genotype was associated with a reduced risk of any severe toxicity (hazard ratio = 0.35, p = 0.023). In chi-square allelic test, the LIG3 rs1052536 T-allele was associated with an increased risk of neuropathy (odds ratio [OR] = 2.79, p = 0.031) and GSTP1 rs1695 G allele with a poorer response in the first-line chemotherapy (OR = 2.65, p = 0.026). In Kaplan-Meier survival analysis, ABCB1 rs2032582 TT-genotype was associated with shorter overall survival (uncorrected p = 0.025) and OPRM1 rs544093 GG and GT genotypes with shorter platinum-free interval (uncorrected p = 0.027) and progression-free survival (uncorrected p = 0.012). Results suggest that SLCO1B3 and LIG3 variants are associated with the risk of adverse effects in patients receiving carboplatin-paclitaxel treatment, the GSTP1 variant may affect the treatment response and ABCB1 and OPRM1 variants may influence the prognosis.

Functional Homologous Recombination Assay on FFPE Specimens of Advanced High-Grade Serous Ovarian Cancer Predicts Clinical Outcomes.

PURPOSE: Deficiency in homologous recombination (HR) repair of DNA damage is characteristic of many high-grade serous ovarian cancers (HGSC). It is imperative to identify patients with homologous recombination-deficient (HRD) tumors as they are most likely to benefit from platinum-based chemotherapy and PARP inhibitors (PARPi). Existing methods measure historical, not necessarily current HRD and/or require high tumor cell content, which is not achievable for many patients. We set out to develop a clinically feasible assay for identifying functionally HRD tumors that can predict clinical outcomes. EXPERIMENTAL DESIGN: We quantified RAD51, a key HR protein, in immunostained formalin-fixed, paraffin-embedded (FFPE) tumor samples obtained from chemotherapy-naïve and neoadjuvant chemotherapy (NACT)-treated HGSC patients. We defined cutoffs for functional HRD separately for these sample types, classified the patients accordingly as HRD or HR-proficient, and analyzed correlations with clinical outcomes. From the same specimens, genomics-based HRD estimates (HR gene mutations, genomic signatures, and genomic scars) were also determined, and compared with functional HR (fHR) status. RESULTS: fHR status significantly predicted several clinical outcomes, including progression-free survival (PFS) and overall survival (OS), when determined from chemo-naïve (PFS, P < 0.0001; OS, P < 0.0001) as well as NACT-treated (PFS, P < 0.0001; OS, P = 0.0033) tumor specimens. The fHR test also identified as HRD those PARPi-at-recurrence-treated patients with longer OS (P = 0.0188). CONCLUSIONS: We developed an fHR assay performed on routine FFPE specimens, obtained from either chemo-naïve or NACT-treated HGSC patients, that can significantly predict real-world platinum-based chemotherapy and PARPi response. See related commentary by Garg and Oza, p. 2957.

Effects of Wee1 inhibitor adavosertib on patient-derived high-grade serous ovarian cancer cells are multiple and independent of homologous recombination status.

Objective: A major challenge in the treatment of platinum-resistant high-grade serous ovarian cancer (HGSOC) is lack of effective therapies. Much of ongoing research on drug candidates relies on HGSOC cell lines that are poorly documented. The goal of this study was to screen for effective, state-of-the-art drug candidates using primary HGSOC cells. In addition, our aim was to dissect the inhibitory activities of Wee1 inhibitor adavosertib on primary and conventional HGSOC cell lines. Methods: A comprehensive drug sensitivity and resistance testing (DSRT) on 306 drug compounds was performed on three patient-derived genetically unique HGSOC cell lines and two commonly used ovarian cancer cell lines. The effect of adavosertib on the cell lines was tested in several assays, including cell-cycle analysis, apoptosis induction, proliferation, wound healing, DNA damage, and effect on nuclear integrity. Results: Several compounds exerted cytotoxic activity toward all cell lines, when tested in both adherent and spheroid conditions. In further cytotoxicity tests, adavosertib exerted the most consistent cytotoxic activity. Adavosertib affected cell-cycle control in patient-derived and conventional HGSOC cells, inducing G2/M accumulation and reducing cyclin B1 levels. It induced apoptosis and inhibited proliferation and migration in all cell lines. Furthermore, the DNA damage marker γH2AX and the number of abnormal cell nuclei were clearly increased following adavosertib treatment. Based on the homologous recombination (HR) signature and functional HR assays of the cell lines, the effects of adavosertib were independent of the cells' HR status. Conclusion: Our study indicates that Wee1 inhibitor adavosertib affects several critical functions related to proliferation, cell cycle and division, apoptosis, and invasion. Importantly, the effects are consistent in all tested cell lines, including primary HGSOC cells, and independent of the HR status of the cells. Wee1 inhibition may thus provide treatment opportunities especially for patients, whose cancer has acquired resistance to platinum-based chemotherapy or PARP inhibitors.

Diagnostic potential of nanoparticle aided assays for MUC16 and MUC1 glycovariants in ovarian cancer.

Our study reports the discovery and evaluation of nanoparticle aided sensitive assays for glycovariants of MUC16 and MUC1 in a unique collection of paired ovarian cyst fluids and serum samples obtained at or prior to surgery for ovarian carcinoma suspicion. Selected glycovariants and the immunoassays for CA125, CA15-3 and HE4 were compared and validated in 347 cyst fluid and serum samples. Whereas CA125 and CA15-3 performed poorly in cyst fluid to separate carcinoma and controls, four glycovariants including MUC16MGL , MUC16STn , MUC1STn and MUC1Tn provided highly improved separations. In serum, the two STn glycovariants outperformed conventional CA125, CA15-3 and HE4 assays in all subcategories analyzed with main benefits obtained at high specificities and at postmenopausal and early-stage disease. Serum MUC16STn performed best at high specificity (90%-99%), but sensitivity was also improved by the other glycovariants and CA15-3. The highly improved specificity, excellent analytical sensitivity and robustness of the nanoparticle assisted glycovariant assays carry great promise for improved identification and early detection of ovarian carcinoma in routine differential diagnostics.

Ovarian Cancers with Low CIP2A Tumor Expression Constitute an APR-246-Sensitive Disease Subtype.

Identification of ovarian cancer patient subpopulations with increased sensitivity to targeted therapies could offer significant clinical benefit. We report that 22% of the high-grade ovarian cancer tumors at diagnosis express CIP2A oncoprotein at low levels. Furthermore, regardless of their significantly lower likelihood of disease relapse after standard chemotherapy, a portion of relapsed tumors retain their CIP2A-deficient phenotype. Through a screen for therapeutics that would preferentially kill CIP2A-deficient ovarian cancer cells, we identified reactive oxygen species inducer APR-246, tested previously in ovarian cancer clinical trials. Consistent with CIP2A-deficient ovarian cancer subtype in humans, CIP2A is dispensable for development of MISIIR-Tag-driven mouse ovarian cancer tumors. Nevertheless, CIP2A-null ovarian cancer tumor cells from MISIIR-Tag mice displayed APR-246 hypersensitivity both in vitro and in vivo. Mechanistically, the lack of CIP2A expression hypersensitizes the ovarian cancer cells to APR-246 by inhibition of NF-κB activity. Accordingly, combination of APR-246 and NF-κB inhibitor compounds strongly synergized in killing of CIP2A-positive ovarian cancer cells. Collectively, the results warrant consideration of clinical testing of APR-246 for CIP2A-deficient ovarian cancer tumor subtype patients. Results also reveal CIP2A as a candidate APR-246 combination therapy target for ovarian cancer.

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability.

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.

Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer.

Chemotherapy resistance is a critical contributor to cancer mortality and thus an urgent unmet challenge in oncology. To characterize chemotherapy resistance processes in high-grade serous ovarian cancer, we prospectively collected tissue samples before and after chemotherapy and analyzed their transcriptomic profiles at a single-cell resolution. After removing patient-specific signals by a novel analysis approach, PRIMUS, we found a consistent increase in stress-associated cell state during chemotherapy, which was validated by RNA in situ hybridization and bulk RNA sequencing. The stress-associated state exists before chemotherapy, is subclonally enriched during the treatment, and associates with poor progression-free survival. Co-occurrence with an inflammatory cancer-associated fibroblast subtype in tumors implies that chemotherapy is associated with stress response in both cancer cells and stroma, driving a paracrine feed-forward loop. In summary, we have found a resistant state that integrates stromal signaling and subclonal evolution and offers targets to overcome chemotherapy resistance.

Dubious effects of methadone as an "anticancer" drug on ovarian cancer cell-lines and patient-derived tumor-spheroids.

BACKGROUND: The opioid agonist D,L-methadone exerts analgesic effects via the mu opioid receptor, encoded by OPRM1 and therefore plays a role in chronic pain management. In preclinical tumor-models D,L-methadone shows apoptotic and chemo-sensitizing effects and was therefore hyped as an off-label "anticancer" drug without substantiation from clinical trials. Its effects in ovarian cancer (OC) are completely unexplored. METHODS: We analyzed OPRM1-mRNA expression in six cisplatin-sensitive, two cisplatin-resistant OC cell-lines, 170 OC tissue samples and 12 non-neoplastic control tissues. Pro-angiogenetic, cytotoxic and apoptotic effects of D,L-methadone were evaluated in OC cell-lines and four patient-derived tumor-spheroid models. RESULTS: OPRM1 was transcriptionally expressed in 69% of OC-tissues and in three of eight OC cell-lines. D,L-methadone exposure significantly reduced cell-viability in five OC cell-lines irrespective of OPRM1 expression. D,L-methadone, applied alone or combined with cisplatin, showed no significant effects on apoptosis or VEGF secretion in cell-lines. Notably, in two of the four spheroid models, treatment with D,L-methadone significantly enhanced cell growth (by up to 121%), especially after long-term exposure. This is consistent with the observed attenuation of the inhibitory effects of cisplatin in three spheroid models when adding D,L-methadone. The effect of methadone treatment on VEGF secretion in tumor-spheroids was inconclusive. CONCLUSIONS: Our study demonstrates that certain OC samples express OPRM1, which, however, is not a prerequisite for D,L-methadone function. As such, D,L-methadone may exert also detrimental effects by stimulating the growth of certain OC-cells and abrogating cisplatin's therapeutic effect.

Targeting DNA Homologous Repair Proficiency With Concomitant Topoisomerase II and c-Abl Inhibition.

Critical DNA repair pathways become deranged during cancer development. This vulnerability may be exploited with DNA-targeting chemotherapy. Topoisomerase II inhibitors induce double-strand breaks which, if not repaired, are detrimental to the cell. This repair process requires high-fidelity functional homologous recombination (HR) or error-prone non-homologous end joining (NHEJ). If either of these pathways is defective, a compensatory pathway may rescue the cells and induce treatment resistance. Consistently, HR proficiency, either inherent or acquired during the course of the disease, enables tumor cells competent to repair the DNA damage, which is a major problem for chemotherapy in general. In this context, c-Abl is a protein tyrosine kinase that is involved in DNA damage-induced stress. We used a low-dose topoisomerase II inhibitor mitoxantrone to induce DNA damage which caused a transient cell cycle delay but allowed eventual passage through this checkpoint in most cells. We show that the percentage of HR and NHEJ efficient HeLa cells decreased more than 50% by combining c-Abl inhibitor imatinib with mitoxantrone. This inhibition of DNA repair caused more than 87% of cells in G2/M arrest and a significant increase in apoptosis. To validate the effect of the combination treatment, we tested it on commercial and patient-derived cell lines in high-grade serous ovarian cancer (HGSOC), where chemotherapy resistance correlates with HR proficiency and is a major clinical problem. Results obtained with HR-proficient and deficient HGSOC cell lines show a 50-85% increase of sensitivity by the combination treatment. Our data raise the possibility of successful targeting of treatment-resistant HR-proficient cancers.

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.