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.

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.

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.

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.

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.

FUNGI: FUsioN Gene Integration toolset.

MOTIVATION: Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. RESULTS: We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. AVAILABILITYAND IMPLEMENTATION: FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

MOTIVATION: A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS: To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION: https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

ALDH1A1-related stemness in high-grade serous ovarian cancer is a negative prognostic indicator but potentially targetable by EGFR/mTOR-PI3K/aurora kinase inhibitors.

Poor chemotherapy response remains a major treatment challenge for high-grade serous ovarian cancer (HGSC). Cancer stem cells are the major contributors to relapse and treatment failure as they can survive conventional therapy. Our objectives were to characterise stemness features in primary patient-derived cell lines, correlate stemness markers with clinical outcome and test the response of our cells to both conventional and exploratory drugs. Tissue and ascites samples, treatment-naive and/or after neoadjuvant chemotherapy, were prospectively collected. Primary cancer cells, cultured under conditions favouring either adherent or spheroid growth, were tested for stemness markers; the same markers were analysed in tissue and correlated with chemotherapy response and survival. Drug sensitivity and resistance testing was performed with 306 oncology compounds. Spheroid growth condition HGSC cells showed increased stemness marker expression (including aldehyde dehydrogenase isoform I; ALDH1A1) as compared with adherent growth condition cells, and increased resistance to platinum and taxane. A set of eight stemness markers separated treatment-naive tumours into two clusters and identified a distinct subgroup of HGSC with enriched stemness features. Expression of ALDH1A1, but not most other stemness markers, was increased after neoadjuvant chemotherapy and its expression in treatment-naive tumours correlated with chemoresistance and reduced survival. In drug sensitivity and resistance testing, five compounds, including two PI3K-mTOR inhibitors, demonstrated significant activity in both cell culture conditions. Thirteen compounds, including EGFR, PI3K-mTOR and aurora kinase inhibitors, were more toxic to spheroid cells than adherent cells. Our results identify stemness markers in HGSC that are associated with a decreased response to conventional chemotherapy and reduced survival if expressed by treatment-naive tumours. EGFR, mTOR-PI3K and aurora kinase inhibitors are candidates for targeting this cell population. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A longitudinal analysis of CA125 glycoforms in the monitoring and follow up of high grade serous ovarian cancer.

OBJECTIVE: Cancer antigen 125 (CA125) is generally considered the gold standard of biomarkers in the diagnosis and monitoring of high grade serous ovarian carcinoma (HGSC). We recently reported, that two CA125 glycoforms (CA125-STn and CA125-MGL) have a high specificity to HGSC and further hypothesized, that these cancer specific glycoforms are feasible candidates as biomarkers in HGSC treatment and follow up. METHODS: Our cohort consisted of 122 patients diagnosed with HGSC. Serum samples were collected longitudinally at the time of diagnosis, during treatment and follow up. Serum levels of CA125, CA125-STn and CA125-MGL were determined and compared or correlated with different end points (tumor load assessed intraoperatively, residual disease, treatment response, progression free survival). RESULTS: Serum CA125-STn levels at diagnosis differentiated patients with low tumor load and high tumor load (p = 0,030), indicating a favorable detection of tumor volume. Similarly, the CA125-STn levels at diagnosis were significantly lower in patients with subsequent complete cytoreduction than in patients with suboptimal cytoreduction (p = 0,025). Conventional CA125 did not differentiate these patients (p = 0,363 and p = 0,154). The CA125-STn nadir value predicted the progression free survival of patients. The detection of disease relapse was improved with CA125-STn, which presented higher fold increase in 80,0% of patients and earlier increase in 37,0% of patients. CONCLUSIONS: CA125-STn showed promise as a useful biomarker in the monitoring and follow up of patients with HGSC utilizing a robust and affordable technique. Our findings are topical as a suitable indicator of tumor load facilitates patient selection in an era of new targeted therapies.

HE4 in the evaluation of tumor load and prognostic stratification of high grade serous ovarian carcinoma.

OBJECTIVE: Human epididymis protein 4 (HE4) is a validated, complementary biomarker to cancer antigen 125 (CA125) for high grade serous ovarian carcinoma (HGSC). Currently, there are insufficient data on the utility of longitudinal HE4 measurement during HGSC treatment and follow up. We set to provide a comprehensive analysis on the kinetics and prognostic performance of HE4 with serial measurements during HGSC treatment and follow up. METHODS: This prospective study included 143 patients with advanced HGSC (ClinicalTrials.gov identifier: NCT01276574). Serum CA125 and HE4 were measured at baseline, before each cycle of chemotherapy and during follow up until first progression. Baseline biomarker values were compared to the tumor load assessed during surgery and to residual disease. Biomarker nadir values and concentrations at progression were correlated to survival. RESULTS: The baseline HE4 concentration distinguished patients with a high tumor load from patients with a low tumor load assessed during surgery (p<.0001). The baseline CA125 level was not associated with tumor load to a similar extent (p=.067). At progression, the HE4 level was an independent predictor of worse survival in the multivariate analysis (p=.002). All patients that were alive 3 years post-progression had a serum HE4 concentration below 199.20 pmol/l at the 1st recurrence. CONCLUSION: HE4 is a feasible biomarker in the treatment monitoring and prognostic stratification of patients with HGSC. Specifically, the serum level of HE4 at first relapse was associated with the survival of patients and it may be a useful complementary tool in the selection of second line treatments. This is to the best of our knowledge the first time this finding has been reported.

Hyper-phosphorylation of Sequestosome-1 Distinguishes Resistance to Cisplatin in Patient Derived High Grade Serous Ovarian Cancer Cells.

Platinum-resistance is a major limitation to effective chemotherapy regimens in high-grade serous ovarian cancer (HGSOC). To better understand the mechanisms involved we characterized the proteome and phosphoproteome in cisplatin sensitive and resistant HGSOC primary cells using a mass spectrometry-based proteomic strategy. PCA analysis identified a distinctive phosphoproteomic signature between cisplatin sensitive and resistant cell lines. The most phosphorylated protein in cisplatin resistant cells was sequestosome-1 (p62/SQSTM1). Changes in expression of apoptosis and autophagy related proteins Caspase-3 and SQSTM1, respectively, were validated by Western blot analysis. A significant increase in apoptosis in the presence of cisplatin was observed in only the sensitive cell line while SQSTM1 revealed increased expression in the resistant cell line relative to sensitive cell line. Furthermore, site-specific phosphorylation on 20 amino acid residues of SQSTM1 was detected indicating a hyper-phosphorylation phenotype. This elevated hyper-phosphorylation of SQSTM1 in resistant HGSOC cell lines was validated with Western blot analysis. Immunofluoresence staining of s28-pSQSTM1 showed inducible localization to autophagosomes upon cisplatin treatment in the sensitive cell line while being constitutively expressed to autophagosomes in the resistant cell. Furthermore, SQSTM1 expression was localized in cancer cells of clinical high-grade serous tumors. Here, we propose hyper-phosphorylation of SQSTM1 as a marker and a key proteomic change in cisplatin resistance development in ovarian cancers by activating the autophagy pathway and influencing down-regulation of apoptosis.

The Expression of HSD17B12 Is Associated with COX-2 Expression and Is Increased in High-Grade Epithelial Ovarian Cancer.

OBJECTIVE: The aim of this study was to characterize the expression of hydroxysteroid (17ß) dehydrogenase type 12 (HSD17B12), an enzyme involved in the synthesis of arachidonic acid (AA), in ovarian cancer, and to study its coexpression with its upstream and downstream enzymes in the AA pathway, namely elongation of very long chain fatty acids protein 5 (ELOVL5) and cyclooxygenase-2 (COX-2), respectively. MATERIALS AND METHODS: Samples from benign and malignant ovarian neoplastic lesions were immunohistochemically stained with HSD17B12, ELOVL5, and COX-2. The staining intensities were quantified with the QuantCenter program, and the results were confirmed with visual inspection. Statistical significances were calculated with the Student t test, the Mann-Whitney test, linear regression, or ANOVA. RESULTS: The expression of the HSD17B12, ELOVL5, and COX-2 enzymes increased according to the grade of the endometrioid ovarian adenocarcinomas. In contrast, in serous adenocarcinomas, staining with ELOVL5 was constantly weak, whereas the expression of HSD17B12 and COX-2 increased with the grade or FIGO stage of the cancer, respectively. CONCLUSIONS: The expression of HSD17B12 increased along with the severity of ovarian cancer, and the expression mimicked COX-2 expression and intensity. This further suggests the involvement of HSD17B12 in AA production, and its coexpression with COX-2 indicates a role for the enzyme in the increased prostaglandin production during ovarian cancer progression.

A Nanoparticle-Lectin Immunoassay Improves Discrimination of Serum CA125 from Malignant and Benign Sources.

BACKGROUND: Measurement of serum cancer antigen 125 (CA125) is the standard approach for epithelial ovarian cancer (EOC) diagnostics and follow-up. However, the clinical specificity is not optimal because increased values are also detected in healthy controls and in benign diseases. CA125 is known to be differentially glycosylated in EOC, potentially offering a way to construct CA125 assays with improved cancer specificity. Our goal was to identify carbohydrate-reactive lectins for discriminating between CA125 originating from EOC and noncancerous sources. METHODS: CA125 from the OVCAR-3 cancer cell line, placental homogenate, and ascites fluid from patients with cirrhosis were captured on anti-CA125 antibody immobilized on microtitration wells. A panel of lectins, each coated onto fluorescent europium-chelate-doped 97-nm nanoparticles (Eu(+3)-NPs), was tested for detection of the immobilized CA125. Serum samples from high-grade serous EOC or patients with endometriosis and healthy controls were analyzed. RESULTS: By using macrophage galactose-type lectin (MGL)-coated Eu(+3)-NPs, an analytically sensitive CA125 assay (CA125(MGL)) was achieved that specifically recognized the CA125 isoform produced by EOC, whereas the recognition of CA125 from nonmalignant conditions was reduced. Serum CA125(MGL) measurement better discriminated patients with EOC from endometriosis compared to conventional immunoassay. The discrimination was particularly improved for marginally increased CA125 values and for earlier detection of EOC progression. CONCLUSIONS: The new CA125(MGL) assay concept could help reduce the false-positive rates of conventional CA125 immunoassays. The improved analytical specificity of this test approach is dependent on a discriminating lectin immobilized in large numbers on Eu(+3)-NPs, providing both an avidity effect and signal amplification.

Ovarian endometriosis signatures established through discovery and directed mass spectrometry analysis.

New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor ß-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silico studies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays.

Serum concentrations of HE4 change little during in vitro fertilization.

OBJECTIVE: Human epididymal secretory protein 4 (HE4) is a biomarker for ovarian cancer. The effect of follicle stimulating hormone (FSH) stimulation during in vitro fertilization (IVF) on HE4 was evaluated to determine whether substantial hormonal stimulation of the ovaries affects the serum levels of this marker. Findings were compared with serum concentrations of cancer antigen 125 (CA125), currently the most commonly used marker for ovarian cancer. DESIGN: Prospective cohort study. SETTING: University hospital infertility clinic. POPULATION: Infertility patients undergoing IVF treatment. METHODS: We determined the serum concentration of HE4 and CA125 in serial blood samples in 20 women treated with IVF. Samples were taken following gonadotrophin-releasing hormone agonist suppression, two to three times during FSH stimulation, at ovum pick-up and 2 weeks following embryo transfer at the time of the pregnancy test. Ovarian follicles were counted and serum estradiol concentrations measured throughout treatment. MAIN OUTCOME MEASURES: Serum HE4, CA125 and estradiol concentrations. RESULTS: Serum HE4 levels did not increase significantly during the FSH stimulation. The majority of values remained below the normal reference range (<70 pm) throughout the treatment. Serum CA125 concentration also remained low during stimulation. Serum estradiol concentration reflected the severity of ovarian stimulation during IVF, increasing 3.9-fold with stimulation. CONCLUSIONS: Serum HE4 concentrations respond little if at all to supraphysiological ovarian stimulation, suggesting that the amount of circulating HE4 in women with normal ovaries is independent of gonadotropin stimulation. Hormonal stimulation of the ovaries is unlikely to affect the differential diagnosis of ovarian tumors with HE4.

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.

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/.

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.

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.