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.
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.
Despite scientific advances, many solid cancers, such as pancreatic adenocarcinoma (PAC) or recurrent high grade serous ovarian cancer (HGSOC), have remained practically incurable. We will identify each tumor’s vulnerabilities separately for distinct subpopulations in order to combine the most potent drugs for maximal efficacy. This enables the design of new rational combinations to target multiple pre-existing and adaptive drug resistance mechanisms. By developing PAC and HGSOC organoid models, we aim to show the feasibility of these systems medicine methods to understand drug resistance for individual patients, that will ultimately inform an evidence-based treatment decision, or to provide insights into the development of new combinatorial therapies. This project is funded by Academy of Finland.
High-grade serous ovarian cancer (HGSOC) responds initially well to chemotherapy but relapses later in high frequency. The standard treatment, platinum-based chemotherapy, was introduced more than 30 years ago and has since changed little, as has the 5-year survival rate, still less than 50% owing to the large heterogeneity of these late-diagnosed malignancies.
We are characterizing single-cell transcriptomes from fresh tumor samples. So far, we have already analyzed tens of thousands of cells from more than 100 specimens from primary, metastatic and relapsed tumors.
Currently, our special focus in HGSOC is on how the genotype of each cell, especially its copy number alterations, affects its transcriptional phenotype. How does the genotype tumor cells modulate the composition of tumor microenvironment, or the response to chemotherapy? What is the contribution of genomic aberrations, and what is that of environmental factors to the transcriptional aberrations present in individual tumor cells? Answering these questions will aid us to understand HGSOC tumors to find specific vulnerabilities that can be exploited in tailored treatments. This is essential to improve the current poor survival odds of women with HGSOC diagnosis.
Cancers in each tissue typically have a specific set of mutations that differ between tissues. This is because cells of different types have specific regulators that transmit the developmental and environmental signals relevant in each tissue, even though the main regulators driving cell proliferation are shared. To fully understand how individual cells control their proliferation, we need to characterize the role of each regulator in each individual cell. We will aim to accomplish this by individually depleting cells of each regulator (transcription factor) and observing how expression of all genes in single colon cancer cells is affected. By combining gene expression data from a large number of single cells, we aim to explain gene regulation of colon cancer cells in higher precision than previously possible. To better understand tissue-specific and shared (with other epithelia-derived malignancies) regulation mechanisms, we will also perform the experiment in ovarian tumor cells with different genetic aberrations, tissue-specific TFs and applied chemotherapy regimens. Overall, high-resolution gene regulatory network of single cells we expect to obtain will help us to identify the tissue-specific factors that transmit signals relevant for colon cancer cells.
This project is funded by Academy of Finland.