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Abstract Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

While transcriptomics have enhanced our understanding for cancer, spatial transcriptomics enable the characterisation of cellular interactions. Here, the authors integrate single cell data with spatial information for HER2 + tumours and develop tools for the prediction of interactions between tumour-infiltrating cells.

Subtypes of cancer associated fibroblasts can both promote and suppress tumorigenesis. Here, the authors investigate how p53 status in pancreatic cancer cells affects their interaction with cancer associated fibroblasts, and report perlecan as a mediator of the pro-metastatic environment.

Single cell RNA sequencing generates short reads from one end of a template, providing incomplete transcript coverage and limiting identification of diverse sequences such as antigen receptors. Here the authors combine long read nanopore sequencing with short read profiling of barcoded libraries to generate full-length antigen receptor sequences.

Summary: Inhibitor of differentiation (ID) proteins dimerize with basic HLH (bHLH) transcription factors, repressing transcription of lineage-specification genes across diverse cellular lineages. ID4 is a key regulator of mammary stem cells; however, the mechanism by which it achieves this is unclear. Here, we show that ID4 has a cell autonomous role in preventing myoepithelial differentiation of basal cells in mammary organoids and in vivo. ID4 positively regulates proliferative genes and negatively regulates genes involved in myoepithelial function. Mass spectrometry reveals that ID4 interacts with the bHLH protein HEB, which binds to E-box motifs in regulatory elements of basal developmental genes involved in extracellular matrix and the contractile cytoskeleton. We conclude that high ID4 expression in mammary basal stem cells antagonizes HEB transcriptional activity, preventing myoepithelial differentiation and allowing for appropriate tissue morphogenesis. Downregulation of ID4 during pregnancy modulates gene regulated by HEB, promoting specialization of basal cells into myoepithelial cells.

Stromal cell recruitment, activation and crosstalk with cancer cells is poorly understood. Here, the authors demonstrate that cancer cell-derived Hedgehog ligand triggers stromal remodeling that in turn induces a cancer-stem-cell like, drug-resistant phenotype of nearby cancer cells while treatment with smoothened inhibitors reverses these phenotypes.