Sampsa Hautaniemi

Dynamic and Ongoing De Novo L1 Retrotransposition Contributes to Genome Plasticity and Intrapatient Heterogeneity in Ovarian Cancer

Abstract Long interspersed element-1 (L1) retrotransposons are the only protein-coding active transposable elements in the human genome. Although typically silenced in normal cells, they are highly expressed in many human epithelial cancers, including high-grade serous ovarian cancer (HGSC), and can integrate into the genome through retrotransposition. De novo L1 insertions are known to contribute to genomic instability and cancer evolution in epithelial malignancies, including HGSC, suggesting that they might also play a role in intrapatient tumor heterogeneity. In this study, we quantified de novo L1 insertions in clinical HGSC specimens and uncovered high heterogeneity in total L1 insertion events (L1 burden) between patients. HGSC tumors with high L1 burden were highly proliferative, whereas tumors with low or no L1 insertions showed enrichment of immune response and cell death pathways. Although the overall L1 burden was similar across different tumor sites within the same patient, the specific L1 insertions (L1 profiles) diverged significantly more than their single-nucleotide variants profiles. Taken together, these findings demonstrate that L1 activity and retrotransposition are highly dynamic in vivo and can contribute substantially to tumor genome plasticity, especially at late stages of cancer progression. The patient-specific propensity of acquiring L1 insertions (L1 burden) could be driven by molecular properties of the progenitor tumor. Retrotransposition-associated DNA damage and/or replication stress could be a potential molecular vulnerability for precision cancer medicine approaches. Significance: L1 retrotransposition is a dynamic process that continues at late stages of high-grade serous ovarian cancer and can substantially contribute to intrapatient tumor heterogeneity.

Multi-omics analysis reveals the attenuation of the interferon pathway as a driver of chemo-refractory ovarian cancer

Ovarian high-grade serous carcinoma (HGSC) is one of the deadliest gynecological malignancies, with 10%-15% of patients exhibiting primary resistance to first-line chemotherapy. To characterize the molecular drivers of chemo-refractoriness, we perform multi-omics profiling of treatment-naive biopsies from patients with refractory HGSC enrolled in the DECIDER observational trial. We demonstrate that chemo-refractory HGSC is characterized by diminished interferon type I (IFN-I) and enhanced hypoxia pathway activity, and baseline IFN-I activity in chemo-naive cancer is an independent prognostic factor. Single-cell RNA sequencing and spatial protein profiling analyses corroborate the importance of elevated IFN-I activity in response to chemotherapy. Importantly, in vitro experiments demonstrate that high levels of IFN-I signaling increase cell chemosensitivity to platinum in a cell-autonomous manner. Together, these findings indicate that the IFN-I pathway activity in HGSC cancer cells predicts response to first-line chemotherapy in HGSC, proposing the stimulation of the IFN-I response as a therapeutic strategy. The study is registered at ClinicalTrials.gov (NCT04846933).

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.

Jellyfish: integrative visualization of spatio-temporal tumor evolution and clonal dynamics

Abstract Summary Spatial and temporal intra-tumor heterogeneity drives tumor evolution and therapy resistance. Existing visualization tools often fail to capture both dimensions simultaneously. To address this, we developed Jellyfish, a tool that integrates phylogenetic and sample trees into a single plot, providing a holistic view of tumor evolution and capturing both spatial and temporal evolution. Available as a JavaScript library and R package, Jellyfish generates interactive visualizations from tumor phylogeny and clonal composition data. We demonstrate its ability to visualize complex subclonal dynamics using data from ovarian high-grade serous carcinoma. Availability and implementation Jellyfish is freely available with MIT license at https://github.com/HautaniemiLab/jellyfish (JavaScript library) and https://github.com/HautaniemiLab/jellyfisher (R package).

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/.

A synthetic lethal dependency on casein kinase 2 in response to replication-perturbing therapeutics in RB1-deficient cancer cells

Resistance to therapy commonly develops in patients with high-grade serous ovarian carcinoma (HGSC) and triple-negative breast cancer (TNBC), urging the search for improved therapeutic combinations and their predictive biomarkers. Starting from a CRISPR knockout screen, we identified that loss of RB1 in TNBC or HGSC cells generates a synthetic lethal dependency on casein kinase 2 (CK2) for surviving the treatment with replication-perturbing therapeutics such as carboplatin, gemcitabine, or PARP inhibitors. CK2 inhibition in RB1-deficient cells resulted in the degradation of another RB family cell cycle regulator, p130, which led to S phase accumulation, micronuclei formation, and accelerated PARP inhibition–induced aneuploidy and mitotic cell death. CK2 inhibition was also effective in primary patient-derived cells. It selectively prevented the regrowth of RB1-deficient patient HGSC organoids after treatment with carboplatin or niraparib. As about 25% of HGSCs and 40% of TNBCs have lost RB1 expression, CK2 inhibition is a promising approach to overcome resistance to standard therapeutics in large strata of patients.

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.

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

AbstractPoor 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.

Drug target proteome profiling identifies HES1-driven mitotic catastrophe in ovarian serous carcinoma.

Ovarian high-grade serous cancer (HGSC) is the most aggressive ovarian cancer subtype with limited treatment options. We identify the PDPK1 inhibitor BX-912 as a promising candidate, showing strong single-agent activity and synergy with the PARP inhibitor olaparib, independent of BRCA status. Unexpectedly, BX-912 induces multinucleation, a phenotype not seen with other PDPK1 inhibitors. Proteome Integral Solubility Alteration (PISA) assay reveals HES1 as a functional off-target, while structural modeling suggested BX-912 acts as a protein-protein interaction modulator, driving nuclear accumulation of HES1 complexes and hence inducing mitotic catastrophe. Cell-cycle analyses confirm enhanced DNA damage response and G2/M arrest when combined with olaparib. These findings uncover a novel mechanism for BX-912, establish HES1 inhibition as a therapeutic strategy in HGSC, demonstrate proteomics' power to reveal hidden drug activities, and propose sequential cell-cycle targeting to improve treatment efficacy.

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.

Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC

Chemotherapy resistance is the greatest contributor to mortality in advanced cancers and severe challenges remain in finding effective treatment modalities to cancer patients with metastasized and relapsed disease. High-grade serous ovarian cancer (HGSOC) is typically diagnosed at a stage where the disease is already widely spread to the abdomen and current standard of practice treatment consists of surgery followed by platinum-taxane based chemotherapy and maintenance therapy. While 90% of HGSOC patients show no clinically detectable signs of cancer after surgery and chemotherapy, only 43% of the patients are alive five years after diagnosis because of chemoresistant cancer. This prospective, observational trial focuses on revealing major mechanisms causing chemoresistance in HGSOG patients and derive personalized treatment regimens for chemotherapy resistant HGSOC patients. The investigators recruit newly diagnosed advanced stage HGSOC patients who are then thoroughly followed during their cancer treatment. Longitudinal sampling includes digitalized H\&E stained histology slides mainly collected during routine diagnostics, fresh tumor \& ascites samples for next-generation sequencing/proteomics (WGS, RNA-seq, DNA-methylation, ATAC-seq, ChIP-seq, mass cytometry, etc.) and ex vivo experiments, plasma samples for circulating tumor DNA (ctDNA) analyses. Broad range of clinical parameters such as laboratory and radiologic parameters (e.g., FDG PET/CT), given cancer treatments and their outcomes are collected. Radiomic analyses are performed to PET/CT and CT scans. Long-term patient derived organoid lines are established from fresh tumor tissues. Actionable genomic alterations are searched. The general objective is to establish a clinically useful precision oncology approach based on multi-level data collected in longitudinal setting, and translate the most potent and validated discoveries into clinical use. DECIDER project will produce AI-powered diagnostic tools, cutting-edge software platforms for clinical decision-making, novel data analysis \& integration methods, and high-throughput ex vivo drug screening approaches.