We are pursuing these mechanisms by single-cell transcriptomics, an approach that offers an unbiased approach to study these mechanisms at multiple levels: in individual cells, sub-populations and tissues. Single-cell transcriptomics also enables us to perform analyses of aberrations, whether readily found in cancer specimens or attained through genome editing, and their effects on transcriptional networks that define cellular states, in a scale that is sufficient to gain both biologically and clinically relevant information.
ERC funded project by Anna Vähärautio studies how past stress encounters induce treatment resistance in ovarian cancer cells. The project will reveal biographies of individual cancer cells to understand how they become resilient. How do previous adaptations to various stressors shape the adaptation during anti-cancer treatment? Is it possible to block treatment induced adaptation by correctly timed co-treatments?
The ERA PerMed PARIS project combines preclinical research with advanced bioinformatics and medical ethics research to evolve the state of the art in the personalised treatment of chemoresistant high-grade serous ovarian cancer.
We have several sub-projects that apply ReSisTrace, our lineage tracing method that takes advantage of the similarity of sister cells to couple cell state to its future fate. These projects are funded by Cancer Foundation and Academy of Finland (collaboration with Jing Tang, Markus Vähä-Koskela and Hanna Seppälä also involving pancreatic cancer organoids).
Using the scRNA-seq data from clinical HGSOC tumor specimens we have accumulated so far; we study how various features of the tumors affect their drug response. We are especially interested in the immune and non-immune tumor microenvironment, cancer cell states in relationship to those of normal post-menopausal fimbria (the tissue of origin for HGSOC), as well as how distinct genetic drivers modulate HGSOC cell states and their treatment responses.
High-grade serous ovarian cancer (HGSOC), which is the most aggressive type of ovarian cancer, is characterized by the mutation of gene TP53 and extensive copy number variations (CNVs). HGSOC tumors typically show an initial favorable response to standard treatments, however they often acquire resistance and relapse. Currently, there is a need for new therapeutic approaches to combat emerging drug-resistant subpopulations. Although, the scarcity of common targetable oncogenic mutations has complicated the development of directed therapies, the study of CNVs offers a promising opportunity to find new mechanisms of resistance and develop alternative treatments, as it has been described how CNVs can model clonal fitness and therapeutic resistance in other types of cancer. Here, we seek to explore the impact of the CNVs on the treatment response of HGSOC patients and their distal effects on the transcriptome using convolutional neural networks. This complex machine learning model will be trained with the largest available longitudinal cohort of HGSOC samples at the single-cell resolution and reveal which CNVs, and their specific combinations, have a relevant role in shaping the HGSOC tumors upon treatment. Employing a systems biology approach, we plan to identify convergent phenotypes within these relevant CNV profiles and then validate their effects on treatment using data from both external cohorts and drug-treated patient derived organoid models. This novel approach enables revealing resistance mechanisms driven by complex genotypes, and thus allows finding specific vulnerabilities to combat emerging resistance in HGSOC.