Dataset Information

Mutation Screening of 1,237 Cancer Genes across Six Model Cell Lines of Basal-Like Breast Cancer.

ABSTRACT: Basal-like breast cancer is an aggressive subtype generally characterized as poor prognosis and lacking the expression of the three most important clinical biomarkers, estrogen receptor, progesterone receptor, and HER2. Cell lines serve as useful model systems to study cancer biology in vitro and in vivo. We performed mutational profiling of six basal-like breast cancer cell lines (HCC38, HCC1143, HCC1187, HCC1395, HCC1954, and HCC1937) and their matched normal lymphocyte DNA using targeted capture and next-generation sequencing of 1,237 cancer-associated genes, including all exons, UTRs and upstream flanking regions. In total, 658 somatic variants were identified, of which 378 were non-silent (average 63 per cell line, range 37-146) and 315 were novel (not present in the Catalogue of Somatic Mutations in Cancer database; COSMIC). 125 novel mutations were confirmed by Sanger sequencing (59 exonic, 48 3'UTR and 10 5'UTR, 1 splicing), with a validation rate of 94% of high confidence variants. Of 36 mutations previously reported for these cell lines but not detected in our exome data, 36% could not be detected by Sanger sequencing. The base replacements C/G>A/T, C/G>G/C, C/G>T/A and A/T>G/C were significantly more frequent in the coding regions compared to the non-coding regions (OR 3.2, 95% CI 2.0-5.3, P<0.0001; OR 4.3, 95% CI 2.9-6.6, P<0.0001; OR 2.4, 95% CI 1.8-3.1, P<0.0001; OR 1.8, 95% CI 1.2-2.7, P = 0.024, respectively). The single nucleotide variants within the context of T[C]T/A[G]A and T[C]A/T[G]A were more frequent in the coding than in the non-coding regions (OR 3.7, 95% CI 2.2-6.1, P<0.0001; OR 3.8, 95% CI 2.0-7.2, P = 0.001, respectively). Copy number estimations were derived from the targeted regions and correlated well to Affymetrix SNP array copy number data (Pearson correlation 0.82 to 0.96 for all compared cell lines; P<0.0001). These mutation calls across 1,237 cancer-associated genes and identification of novel variants will aid in the design and interpretation of biological experiments using these six basal-like breast cancer cell lines.

SUBMITTER: Olsson E

PROVIDER: S-EPMC4684399 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Mutation Screening of 1,237 Cancer Genes across Six Model Cell Lines of Basal-Like Breast Cancer.

Olsson Eleonor E Winter Christof C George Anthony A Chen Yilun Y Törngren Therese T Bendahl Pär-Ola PO Borg Åke Å Gruvberger-Saal Sofia K SK Saal Lao H LH

PloS one 20151215 12

Basal-like breast cancer is an aggressive subtype generally characterized as poor prognosis and lacking the expression of the three most important clinical biomarkers, estrogen receptor, progesterone receptor, and HER2. Cell lines serve as useful model systems to study cancer biology in vitro and in vivo. We performed mutational profiling of six basal-like breast cancer cell lines (HCC38, HCC1143, HCC1187, HCC1395, HCC1954, and HCC1937) and their matched normal lymphocyte DNA using targeted capt ...[more]

PMID: 26670335

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Project description:BackgroundMethylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis.ResultsOur two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically significant negative correlation between methylation profiles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identified 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes.ConclusionsOur results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

Dataset Information

Mutation Screening of 1,237 Cancer Genes across Six Model Cell Lines of Basal-Like Breast Cancer.

Publications

Mutation Screening of 1,237 Cancer Genes across Six Model Cell Lines of Basal-Like Breast Cancer.

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