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A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is an evolving technology used to elucidate the cellular architecture of adult organs. Previous scRNA-seq on breast tissue utilized reduction mammoplasty samples, which are often histologically abnormal. We report a rapid tissue collection/processing protocol to perform scRNA-seq of breast biopsies of healthy women and identify 23 breast epithelial cell clusters. Putative cell-of-origin signatures derived from these clusters are applied to analyze transcriptomes of ~3,000 breast cancers. Gene signatures derived from mature luminal cell clusters are enriched in ~68% of breast cancers, whereas a signature from a luminal progenitor cluster is enriched in ~20% of breast cancers. Overexpression of luminal progenitor cluster-derived signatures in HER2+, but not in other subtypes, is associated with unfavorable outcome. We identify TBX3 and PDK4 as genes co-expressed with estrogen receptor (ER) in the normal breasts, and their expression analyses in >550 breast cancers enable prognostically relevant subclassification of ER+ breast cancers.

SUBMITTER: Bhat-Nakshatri P

PROVIDER: S-EPMC7974552 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells.

Bhat-Nakshatri Poornima P Gao Hongyu H Sheng Liu L McGuire Patrick C PC Xuei Xiaoling X Wan Jun J Liu Yunlong Y Althouse Sandra K SK Colter Austyn A Sandusky George G Storniolo Anna Maria AM Nakshatri Harikrishna H

Cell reports. Medicine 20210316 3

Single-cell RNA sequencing (scRNA-seq) is an evolving technology used to elucidate the cellular architecture of adult organs. Previous scRNA-seq on breast tissue utilized reduction mammoplasty samples, which are often histologically abnormal. We report a rapid tissue collection/processing protocol to perform scRNA-seq of breast biopsies of healthy women and identify 23 breast epithelial cell clusters. Putative cell-of-origin signatures derived from these clusters are applied to analyze transcrip ...[more]

PMID: 33763657

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Project description:BackgroundWhole-genome sequencing (WGS) is a powerful method for revealing the diversity and complexity of the somatic mutation burden of tumours. Here, we investigated the utility of tumour and matched germline WGS for understanding aetiology and treatment opportunities for high-risk individuals with familial breast cancer.Patients and methodsWe carried out WGS on 78 paired germline and tumour DNA samples from individuals carrying pathogenic variants in BRCA1 (n = 26) or BRCA2 (n = 22) or from non-carriers (non-BRCA1/2; n = 30).ResultsMatched germline/tumour WGS and somatic mutational signature analysis revealed patients with unreported, dual pathogenic germline variants in cancer risk genes (BRCA1/BRCA2; BRCA1/MUTYH). The strategy identified that 100% of tumours from BRCA1 carriers and 91% of tumours from BRCA2 carriers exhibited biallelic inactivation of the respective gene, together with somatic mutational signatures suggestive of a functional deficiency in homologous recombination. A set of non-BRCA1/2 tumours also had somatic signatures indicative of BRCA-deficiency, including tumours with BRCA1 promoter methylation, and tumours from carriers of a PALB2 pathogenic germline variant and a BRCA2 variant of uncertain significance. A subset of 13 non-BRCA1/2 tumours from early onset cases were BRCA-proficient, yet displayed complex clustered structural rearrangements associated with the amplification of oncogenes and pathogenic germline variants in TP53, ATM and CHEK2.ConclusionsOur study highlights the role that WGS of matched germline/tumour DNA and the somatic mutational signatures can play in the discovery of pathogenic germline variants and for providing supporting evidence for variant pathogenicity. WGS-derived signatures were more robust than germline status and other genomic predictors of homologous recombination deficiency, thus impacting the selection of platinum-based or PARP inhibitor therapy. In this first examination of non-BRCA1/2 tumours by WGS, we illustrate the considerable heterogeneity of these tumour genomes and highlight that complex genomic rearrangements may drive tumourigenesis in a subset of cases.

Project description:BackgroundProstate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.MethodsA metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).ResultsValidated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.ConclusionsThe metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

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A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells.

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A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells.

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