Anna Vähärautio

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.

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.

Iron Chelation Therapy Elicits Innate Immune Control of Metastatic Ovarian Cancer

Abstract Iron accumulation in tumors contributes to disease progression and chemoresistance. Although targeting this process can influence various hallmarks of cancer, the immunomodulatory effects of iron chelation in the tumor microenvironment are unknown. Here, we report that treatment with deferiprone, an FDA-approved iron chelator, unleashes innate immune responses that restrain ovarian cancer. Deferiprone reprogrammed ovarian cancer cells toward an immunostimulatory state characterized by the production of type-I IFN and overexpression of molecules that activate NK cells. Mechanistically, these effects were driven by innate sensing of mitochondrial DNA in the cytosol and concomitant activation of nuclear DNA damage responses triggered upon iron chelation. Deferiprone synergized with chemotherapy and prolonged the survival of mice with ovarian cancer by bolstering type-I IFN responses that drove NK cell-dependent control of metastatic disease. Hence, iron chelation may represent an alternative immunotherapeutic strategy for malignancies that are refractory to current T-cell–centric modalities. Significance: This study uncovers that targeting dysregulated iron accumulation in ovarian tumors represents a major therapeutic opportunity. Iron chelation therapy using an FDA-approved agent causes immunogenic stress responses in ovarian cancer cells that delay metastatic disease progression and enhance the effects of first-line chemotherapy. See related commentary by Bell and Zou, p. 1771

Transgelin 2 guards T cell lipid metabolism and antitumour function

Mounting effective immunity against pathogens and tumours relies on the successful metabolic programming of T cells by extracellular fatty acids

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Abstract Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

Tracing back primed resistance in cancer via sister cells

Abstract Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

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.