Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature

Single-cell analysis has revolutionised genomic science in recent years. However, due to cost and other practical considerations, single-cell analyses are impossible for studies based on medium or large patient cohorts. For example, a single-cell analysis usually costs thousands of euros for one tissue sample from one volunteer, meaning that typical studies using single-cell analyses are based on very few individuals. While single-cell genomic data can be used to examine the phenotype of individual cells, cell-type deconvolution methods are required to track the quantities of these cells in bulk-tissue genomic data. Hormone receptor negative breast cancers are highly aggressive, and are thought to originate from a subtype of epithelial cells called the luminal progenitor. In this paper, we show how to quantify the number of luminal progenitor cells as well as other epithelial subtypes in breast tissue samples using DNA and RNA based measurements. We find elevated levels of cells which resemble these hormone receptor negative luminal progenitor cells in breast tumour biopsies of hormone receptor negative cancers, as well as in healthy breast tissue samples from BRCA1 (FANCS) mutation carriers. We also find that breast tumours from carriers of heterozygous mutations in non-BRCA Fanconi Anaemia pathway genes are much more likely to be hormone receptor negative. These findings have implications for understanding hormone receptor negative breast cancers, and for breast cancer screening in carriers of heterozygous mutations of Fanconi Anaemia pathway genes.

Anoikis resistance in mammary epithelial cells is mediated by semaphorin 7a

Semaphorin-7a (SEMA7A), best known as a neuroimmune molecule, plays a diverse role in many cellular processes and pathologies. Here, we show that SEMA7A promotes anoikis resistance in cultured mammary epithelial cells through integrins and activation of pro-survival kinase AKT, which led us to investigate a role for SEMA7A during postpartum mammary gland involution—a normal developmental process where cells die by anoikis. Our results reveal that SEMA7A is expressed on live mammary epithelial cells during involution, that SEMA7A expression is primarily observed in α6-integrin expressing cells, and that luminal progenitor cells, specifically, are decreased in mammary glands of SEMA7A−/− mice during involution. We further identify a SEMA7A-α6/β1-integrin dependent mechanism of mammosphere formation and chemoresistance in mammary epithelial cells and suggest that this mechanism is relevant for recurrence in breast cancer patients.

Anoikis resistance in mammary epithelial cells is mediated by semaphorin-7a.

Semaphorin-7a (SEMA7A), best known as a neuroimmune molecule, plays a diverse role in many cellular processes and pathologies. Here, we show that SEMA7A promotes anoikis resistance in cultured mammary epithelial cells through integrin mediated activation of pro-survival kinase AKT, which led us to investigate a role for SEMA7A during postpartum mammary gland involution- a normal process in development where cells die by anoikis. Our results reveal that SEMA7A is expressed on live mammary epithelial cells during involution, that SEMA7A expression is primarily observed in a6-integrin expressing cells, and that luminal progenitor cells, specifically, are decreased in mammary glands of SEMA7A-/- mice during involution. We further identify a SEMA7A/b1-integrin dependent mechanism of cell survival that promotes anoikis resistance, mammosphere formation, and chemoresistance in mammary epithelial cells and suggest that this mechanism is relevant for recurrence in breast cancer patients.

In silico Computed Clusters of Prostate Luminal Progenitors Match FACS-Enriched LSCmed cells

Several groups recently published single-cell (sc) expression atlases of the adult mouse prostate cells based on RNA sequencing (scRNA-seq) data. All studies identified one computerized cluster of non-secretory luminal progenitor cells enriched in luminal and stemness-related gene transcripts. The actual correspondence between these luminal progenitor cell clusters has not been investigated. In addition, the presence of Krt4 (encoding cytokeratin 4) in these in silico-identified luminal progenitors suggested the overlap with FACS-enriched LSCmed luminal progenitor cells earlier identified as a stem-like, castration-tolerant and tumor-initiating cell population. Here, we used a unified bioinformatics pipeline to re-analyze published prostate scRNA-seq datasets and perform various pan-transcriptomic comparisons including the LSCmed cell signature. Our study demonstrates that i) the mouse prostate luminal progenitor cell clusters identified in the different scRNA-seq studies largely overlap and can be defined by a common 15-gene signature including Krt4, ii) mouse LSCmed cells match both mouse and human luminal progenitors identified by scRNA-seq analysis. Bridging these in silico-identified and ex vivo-characterized prostate luminal progenitor subsets should benefit our understanding of their actual involvement in prostate diseases.

Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature

Hormone receptor negative breast cancers are highly aggressive, and are thought to originate from a subtype of epithelial cells called the luminal progenitor. In this paper, we show how to quantify the number of luminal progenitor cells as well as other epithelial subtypes in breast tissue samples using DNA and RNA based measurements. We find elevated levels of these hormone receptor negative luminal progenitor cells in breast tumour biopsies of hormone receptor negative cancers, as well as in healthy breast tissue samples from BRCA1 (FANCS) mutation carriers. We also find that breast tumours from carriers of heterozygous mutations in non-BRCA Fanconi Anaemia pathway genes are much more likely to be hormone receptor negative.

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA-sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation without prior knowledge has remained challenging. Here we describe a simple yet robust determinant of developmental potential—the number of detectably expressed genes per cell— and leverage this measure of transcriptional diversity to develop a new framework for predicting ordered differentiation states from scRNA-seq data. When evaluated on ~150,000 single-cell transcriptomes spanning 53 lineages and five species, our approach, called CytoTRACE, outperformed previous methods and ~19,000 molecular signatures for resolving experimentally-confirmed developmental trajectories. In addition, it enabled unbiased identification of tissue-resident stem cells, including cells with long-term regenerative potential. When used to analyze human breast tumors, we discovered candidate genes associated with less-differentiated luminal progenitor cells and validated GULP1 as a novel gene involved in tumorigenesis. Our study establishes a key RNA-based correlate of developmental potential and provides a new platform for robust delineation of cellular hierarchies (https://cytotrace.stanford.edu).