Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

Abstract

Single-cell RNA sequencing is a powerful tool to explore cancer heterogeneity. However, the expression of lncRNAs in single cells is still to be studied extensively and methods to deal with the sparsity of this type of data are lacking. Here, we propose a topic modeling approach to investigate the transcriptional heterogeneity of luminal and triple negative breast cancer cells using patient-derived xenograft models of acquired resistance to chemotherapy and targeted therapy. We show that using an integrative clustering that combines the information coming from mRNAs and lncRNAs treated as disjoint omic layers greatly improves the accuracy of cell classification. Topics associated with specific breast cancer subpopulations show a clear enrichment for pathways involved in subtyping and progression of breast cancer and to sets of lncRNA encoded in the open chromatin regions of breast cancer cell lines. We identified lncRNAs strongly associated with cell clusters already well known in the literature, such as MALAT1 and NEAT1, and highlighted some others that may be clinically relevant.