A single-cell atlas of breast cancer cell lines to study tumour heterogeneity and drug response

Abstract

Brest Cancer (BC) patient stratification is mainly driven by receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention to apply. Here, we evaluated the potentiality of single-cell transcriptomics for automated diagnosis and drug treatment of breast cancer. We transcriptionally profiled 35,276 individual cells from 33 BC cell-lines covering all main BC subtypes to yield a Breast Cancer Single Cell Atlas. We show that single cell transcriptomics can successfully detect clinically relevant BC biomarkers and that atlas can be used to automatically predict cancer subtype and composition from a patient's tumour biopsy. We found that BC cell lines arbour a high degree of heterogeneity in the expression of clinically relevant BC biomarkers and that such heterogeneity enables cells with differential drug sensitivity to co-exist even within a genomically stable isogenic cell line. Finally, we developed a novel bioinformatics approach named DREEP (DRug Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We validated DREEP both in-silico and in-vitro by selectively inhibiting the growth of the HER2-deficient subpopulation in the MDAMB361 cell line. Our work shows transcriptional heterogeneity is common, dynamic and plays a relevant role in determining drug sensitivity. Moreover, our Breast Cancer Single Cell Atlas and DREEP approach are a unique resource for the BC research community and to advance the use of single-cell sequencing in the clinics.

A mechanistic model captures the emergence and implications of non-genetic heterogeneity and reversible drug resistance in ER+ breast cancer cells

“…Classification of luminal subtypes as ER-positive and that of HER2+ and basal-like as ER-negative is endorsed by recent transcriptomic profiling of 35 276 cells from 32 breast cancer cell lines capturing the percentage of cells positive for expression of ESR1 and various EMT players in cell lines representing different breast cancer subtypes. While luminal cell lines such as MCF7, BT474 and T47D have higher percentage of cells that are positive for ESR1 and the ones negative for SLUG and ZEB1, HER2+ and triple negative cell lines such as MDA-MB-453, BT549 and MDA-MB-468 showed a reverse trend ( 52 ). Put together, this analysis supports our prediction about an association between activation of EMT program and compromised ERα signaling activity in ER-positive breast cancer cases, supported both by bulk and single-cell data analysis.…”

MetaSCDrug: Meta-Transfer Learning for Single-Cell-Level Drug Response Prediction from Transcriptome and Molecular Representations

“…Intratumor heterogeneity has become a key factor in the failure of cancer treatments, arising from a variety of factors, including the genetic diversity of cells and numerous distinct cell subpopulations within a tumor [2,3].This diversity poses substantial challenges in predicting responses to personalized anti-cancer treatments [4]. In this context, single-cell RNA sequencing (scRNA-seq) analysis has emerged as a powerful tool, providing novel insights into the cellular composition of tumors [5] and enabling the identification of distinct cellular subpopulations that contribute to resistance [6,7]. The analysis of scRNA-seq holds great promise for enhancing drug response prediction, tailoring therapies to individual patients, and facilitating personalized oncology [8].…”