Anni Virtanen

Decoding the Genomic and Functional Landscape of Emerging Subtypes in Ovarian Cancer

Abstract Ovarian high-grade serous carcinoma (HGSC) is characterized by pervasive genomic instability and high inter- and intra-tumor heterogeneity. Approximately half of HGSC tumors harbor homologous recombination deficiency (HRD), rendering them vulnerable to PARP inhibitors and platinum-based chemotherapy. In contrast, patients lacking HRD (HR-proficient, HRP) generally respond poorly to current therapies. To overcome heterogeneity and identify relevant HGSC subtypes, we characterized the genomic landscape of 640 tumors from 243 patients using whole-genome sequencing. Our chromosomal instability signature–based analysis characterized the structural variation landscape and revealed five HGSC subtypes, validated in an independent dataset. Two HRD subtypes, associated with BRCA1- or BRCA2-driven alterations, demonstrated favorable treatment responses. Strikingly, three HRP subtypes emerged, marked by unique structural alterations and gene expression patterns, tumor microenvironment interactions, and different chemotherapy responses. Notably, organoid experiments showed subtype-specific sensitivity to CHK1 inhibition, suggesting prexasertib as a potential targeted treatment for most currently untreatable HRP patients. Significance: These findings demonstrate that HGSC tumors can be divided into functionally and clinically distinct subtypes, offering new insights into the underlying biology of HGSC and providing a foundation to develop tailored therapeutic strategies for HRP tumors, which currently lack effective options.

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

Abstract 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/.

Predictors for regression and progression of actively surveilled cervical intraepithelial neoplasia grade 2: A prospective cohort study

AbstractIntroductionTo evaluate predicting clinical factors for regression and progression of cervical intraepithelial neoplasia (CIN) grade 2 (CIN2) in young women during two years of active surveillance.Material and MethodsThis was a single‐center prospective observational cohort study. Women under 31 years of age giving written informed consent with histologically confirmed CIN2 were followed with colposcopy, cytology, and biopsies every 6 months up to 24 months. At baseline, HPV genotyping was performed on cervical samples. The rates of regression and progression were recorded for every timepoint and at the end of study overall and stratified according to clinical factors and HPV genotypes at baseline. Risk ratio (RR) was used to estimate the relative risks for regression and progression. The study was registered in the ISRCTN registry (ISRCTN91953024).ResultsIn total, 205/243 (84.4%) women completed the study. Complete regression (normal histology and/or normal or atypical squamous cells of undetermined significance (ASC‐US) cytology) was detected in 64.4.% (n = 132) while 16.1% (n = 33) of the lesions progressed to CIN grade 3 (CIN3) or worse including 31 CIN3 cases, one adenocarcinoma in situ and one cervical cancer case. Factors associated with progression were initial large (>50% of the transformation zone) lesion size, risk ratio (RR) 3.06 (95% confidence interval (CI) 1.40–6.69), and high‐grade referral cytology RR 4.73 (95% CI 1.18–19.03). Compared with baseline HPV negativity or having only low‐risk HPV genotypes present, high‐risk HPV (hrHPV) positivity was associated with lower likelihood of regression RR 0.74 (95% CI 0.60–0.91). Age, cigarette smoking, use of combined oral contraceptives or baseline high‐risk HPV genotype, including HPV16, were not associated with the outcomes.ConclusionsThe majority of CIN2 lesions regress in young women. Women with large lesions and/or high‐grade referral cytology should perhaps more often be treated instead of active surveillance. Initial hrHPV genotype does not appear to predict outcomes while not harboring hrHPV favors regression.

Ex Vivo Immuno-Oncology Platform Reveals Spatial T-cell Infiltration Patterns Linked to ATR Inhibition Responses in High-Grade Serous Ovarian Cancer

Abstract Identifying new therapeutic approaches in high-grade serous ovarian cancer (HGSC) requires the development of more accurate preclinical models that replicate the patient-specific tumor and its microenvironment. To address this, we established immunocompetent patient-derived cultures (iPDC) for HGSC, cultured on a physiologically relevant human omentum gel matrix. We developed a high-throughput platform that combines drug testing, histologic analysis, genomic profiling, single-cell studies, and spatial biomarker discovery. Our results from 47 tumors showed that iPDCs recapitulated the tumor genomic and histologic characteristics while also retaining the intratumoral immune cells. The iPDC treatment responses correlated significantly with the patients’ clinical treatment responses. Using iPDCs and single-cell RNA sequencing, we identified potentially effective therapeutic options for patients with recurrent HGSC linked to distinct tumor cell states and mechanisms of resistance. High-throughput drug response profiling with single-cell imaging identified ataxia telangiectasia and Rad3-related inhibitor (ATRi) combined with an immunotherapy targeting autotaxin as a promising new combination treatment for HGSC. Using hyperplexed imaging and spatial analysis, we discovered that ATRi responses were associated with significant increases in both intra- and peritumoral T-cell infiltration, particularly in PD-1+ CD8+ T cells. Additionally, the ATRi-induced reactivation of CD8+ T cells was linked to spatial interactions with replication stress–positive tumor cells. Thus, our iPDC platform presents a representative high-throughput ex vivo model to advance precision oncology in HGSC, uncovering the ATRi-immunotherapy combination as a potentially effective therapeutic option for clinical translation.