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Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns.

ABSTRACT: Cell lines are commonly used as cancer models. The tissue of origin provides context for understanding biological mechanisms and predicting therapy response. We therefore systematically examined whether cancer cell lines exhibit features matching the presumed cancer type of origin. Gene expression and DNA methylation classifiers trained on ~9000 tumors identified 35 (of 614 examined) cell lines that better matched a different tissue or cell type than the one originally assigned. Mutational patterns further supported most reassignments. For instance, cell lines identified as originating from the skin often exhibited a UV mutational signature. We cataloged 366 "golden set" cell lines in which transcriptomic and epigenomic profiles strongly resemble the cancer type of origin, further proposing their assignments to subtypes. Accounting for the uncertain tissue of origin in cell line panels can change the interpretation of drug screening and genetic screening data, revealing previously unknown genomic determinants of sensitivity or resistance.

SUBMITTER: Salvadores M

PROVIDER: S-EPMC7458440 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns.

Salvadores Marina M Fuster-Tormo Francisco F Supek Fran F

Science advances 20200701 27

Cell lines are commonly used as cancer models. The tissue of origin provides context for understanding biological mechanisms and predicting therapy response. We therefore systematically examined whether cancer cell lines exhibit features matching the presumed cancer type of origin. Gene expression and DNA methylation classifiers trained on ~9000 tumors identified 35 (of 614 examined) cell lines that better matched a different tissue or cell type than the one originally assigned. Mutational patte ...[more]

PMID: 32937430

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Project description:While recent integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with multiple subtypes and many oncogenic/tumour suppressor driver events, much less is known about the role of DNA methylation in shaping breast cancer taxonomy and defining driver events.Here, we applied a powerful integrative network algorithm to matched DNA methylation and RNA-Seq data for 724 estrogen receptor (ER)-positive (ER+) breast cancers and 111 normal adjacent tissue specimens from The Cancer Genome Atlas (TCGA) project, in order to identify putative epigenetic driver events and to explore the resulting molecular taxonomy. This revealed the existence of nine functionally deregulated epigenetic hotspots encompassing a total of 146 genes, which we were able to validate in independent data sets encompassing over 1000 ER+ breast cancers. Integrative clustering of the matched messenger RNA (mRNA) and DNA methylation data over these genes resulted in only two clusters, which correlated very strongly with the luminal-A and luminal B subtypes. Overall, luminal-A and luminal-B breast cancers shared the same epigenetically deregulated hotspots but with luminal-B cancers exhibiting increased aberrant DNA methylation patterns relative to normal tissue. We show that increased levels of DNA methylation and mRNA expression deviation from the normal state define a marker of poor prognosis. Our data further implicates epigenetic silencing of WNT signalling antagonists and bone morphogenetic proteins (BMP) as key events underlying both luminal subtypes but specially of luminal-B breast cancer. Finally, we show that DNA methylation changes within the identified epigenetic interactome hotspots do not exhibit mutually exclusive patterns within the same cancer sample, instead exhibiting coordinated changes within the sample.Our results indicate that the integrative DNA methylation and transcriptomic landscape of ER+ breast cancer is surprisingly homogeneous, defining two main subtypes which strongly correlate with luminal-A/B subtype status. In particular, we identify WNT and BMP signalling as key epigenetically deregulated tumour suppressor pathways in luminal ER+ breast cancer, with increased deregulation seen in luminal-B breast cancer.

Dataset Information

Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns.

Publications

Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns.

Similar Datasets

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