Viscido, Gaetano (2021) A single-cell atlas of breast cancer cell lines to study tumour heterogeneity and drug response. [Tesi di dottorato]

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Breast Cancer (BC) patient stratification is driven by receptor status and histological grading and subtyping, with about 20% of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention. Clinical decision for breast cancer patients still relies primarily on the expression status of three biomarkers of therapeutic agents: the estrogen and progesterone receptors (ESR1 and PgR, respectively), and the aberrant expression/amplification of the epidermal growth factor receptor 2 (HER2/ERBB2). However, current clinical approaches for the diagnosis of such biomarkers do not account for the whole transcriptional landscape of the cell and the intrapopulation gene expression heterogeneity of tumors, that may be responsible for drug resistance in cancer patients. It is therefore necessary to discover and establish new predictive and prognostic biomarkers for patient stratification and personalized medicine that take into account tumor heterogeneity. Here, I evaluated the potentiality of single-cell RNA-sequencing (scRNA-seq) for automated diagnosis and drug treatment of BC. To this end, I implemented Drop-seq in the lab, a droplet-based microfluidic platform that enables to measure the gene expression profile in single-cell for thousands of cells. By means of Drop-seq, I transcriptionally profiled 35,276 individual cells from 32 cell lines covering all BC subtypes, showing that with scRNA-seq we successfully measured the expression of clinically relevant receptors. This breast cancer single-cell atlas can be used to computationally map single cell transcriptional profiles of patients' tumor biopsies to the atlas to determine their composition in terms of cell lines. By this approach, I found that each tumor is heterogeneous and composed of multiple cell lines mostly, but not exclusively, of the same subtype. I observed that in most cell lines there is a high degree of heterogeneity in the expression of BC receptors. I focused on whether such heterogeneity impacts a cell line's overall drug sensitivity. By correlating the percentage of cells expressing a given drug target (e.g. HER2, etc.) to the known toxicity of the relevant drug across the 33 cell lines, I observed a significant negative correlation (the higher the % of cells, the higher the toxicity). I then focused on the MDA-MB-361 cell-line of the luminal B subtype with a gain in genomic copy number of the locus containing the ERRB2 gene coding for HER2. Despite HER2 amplification, scRNA-seq showed that only about 70% of cells express its mRNA. To investigate the origin of this heterogeneity, I performed fluorescence-activating cell sorting (FACS) to isolate HER2 expressing cells (HER2+) from non-expressing cells (HER2-) in the MDA-MB-361 cell population. After approximately three weeks, both subpopulations re-established the original heterogeneity, thus showing that heterogeneity in HER2 expression in these cells is dynamic and not regulated by genetic mechanisms. This observation led us to the development of a bioinformatic approach named DREEP (DRug Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from scRNA-seq and confirmed the validity of the approach using published large-scale studies on drug sensitivity. Application of DREEP to the MDA-MB-361 cell line identified drugs able to selectively inhibit the growth of the HER2- subpopulation. Etoposide was predicted to selectively inhibit the growth of the HER2- cells but not HER2+ cells. I experimentally validated the DREEP prediction of the effect of etoposide on the HER2- subpopulation. However, DREEP predicted afatinib, a specific and selective HER2 inhibitor, to be equally effective on both subpopulations, even though HER2- cells do not express the target of afatinib. Surprisingly, the experimental validation that I performed confirmed this counter-intuitive prediction. We thus developed a mathematical model to explain this counterintuitive result, in which we show that the afatinib treatment has the same effect on both subpopulations if the interconversion time between the two HER2 states is comparable to the cell cycle duration. Finally, I experimentally validated the model prediction by testing the interconversion dynamics of the HER2 state upon afatinib perturbation in MDA-MB-361 cell line.

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