We are analyzing https://link.springer.com/article/10.1007/s10549-014-2991-x.

Title:
Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data | Breast Cancer Research and Treatment
Description:
Breast cancer is one of the most commonly diagnosed cancers in women. While there are several effective therapies for breast cancer and important single gene prognostic/predictive markers, more than 40,000 women die from this disease every year. The increasing availability of large-scale genomic datasets provides opportunities for identifying factors that influence breast cancer survival in smaller, well-defined subsets. The purpose of this study was to investigate the genomic landscape of various breast cancer subtypes and its potential associations with clinical outcomes. We used statistical analysis of sequence data generated by the Cancer Genome Atlas initiative including somatic mutation load (SML) analysis, Kaplan–Meier survival curves, gene mutational frequency, and mutational enrichment evaluation to study the genomic landscape of breast cancer. We show that ER+, but not ER−, tumors with high SML associate with poor overall survival (HR = 2.02). Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes. While it is known that somatic mutations in specific genes affect breast cancer survival, this study is the first to identify that SML may constitute an important global signature for a subset of ER+ tumors prone to high mortality. Moreover, although somatic mutations in individual DNA damage genes affect clinical outcome, our results indicate that coincident mutations in DNA damage response and signature ER genes may prove more informative for ER+ breast cancer survival. Next generation sequencing may prove an essential tool for identifying pathways underlying poor outcomes and for tailoring therapeutic strategies.
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Keywords {🔍}

tumors, breast, cancer, genes, mutations, survival, hml, mutation, ddr, sml, pubmed, gene, fig, load, article, lml, clinical, pathway, google, scholar, pathways, subset, data, dna, analysis, poor, repair, signature, mutational, outcome, table, cas, damage, status, frequency, significant, mmr, estrogen, genomic, high, receptor, prognostic, tumor, checkpoint, significantly, size, test, somatic, identified, tcga,

Topics {✒️}

diseasetype=brca&diseasename=breast%20invasive%20carcinoma braf/nras wild-type melanomas large-scale genomic datasets article download pdf table 1 kegg-generated list stat3-induced apoptosis requires consultant/advisor board member dna damage repair cancer genome atlas anti-estrogen-based therapies triple-negative breast cancer genome-wide phenotypic signatures dna damage response kaplan–meier survival curves article haricharan dna damage checkpoint double-strand break lymph node involvement ddr-related genes included somatic mutation load estrogen receptor-positive anderson cancer center prediction/prognosis enables easy t-cell marker pathways base excision repair nucleotide excision repair estrogen receptor status high mutation load increasing font size low mutation load target genes involved bar graphs representing kaplan–meier curves discussion mutation load privacy choices/manage cookies cancer-related death full access er+ cancers associates increased mutation load breast cancer subtypes breast cancer confirmed familial breast cancer primary breast cancer breast cancer occurence exome somatic variants mutating individual pathways independent prognostic factor genome wide sequencing table 3 descriptive characteristics er− breast cancer

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         headline:Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data
         description:Breast cancer is one of the most commonly diagnosed cancers in women. While there are several effective therapies for breast cancer and important single gene prognostic/predictive markers, more than 40,000 women die from this disease every year. The increasing availability of large-scale genomic datasets provides opportunities for identifying factors that influence breast cancer survival in smaller, well-defined subsets. The purpose of this study was to investigate the genomic landscape of various breast cancer subtypes and its potential associations with clinical outcomes. We used statistical analysis of sequence data generated by the Cancer Genome Atlas initiative including somatic mutation load (SML) analysis, Kaplan–Meier survival curves, gene mutational frequency, and mutational enrichment evaluation to study the genomic landscape of breast cancer. We show that ER+, but not ER−, tumors with high SML associate with poor overall survival (HR = 2.02). Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes. While it is known that somatic mutations in specific genes affect breast cancer survival, this study is the first to identify that SML may constitute an important global signature for a subset of ER+ tumors prone to high mortality. Moreover, although somatic mutations in individual DNA damage genes affect clinical outcome, our results indicate that coincident mutations in DNA damage response and signature ER genes may prove more informative for ER+ breast cancer survival. Next generation sequencing may prove an essential tool for identifying pathways underlying poor outcomes and for tailoring therapeutic strategies.
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      headline:Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data
      description:Breast cancer is one of the most commonly diagnosed cancers in women. While there are several effective therapies for breast cancer and important single gene prognostic/predictive markers, more than 40,000 women die from this disease every year. The increasing availability of large-scale genomic datasets provides opportunities for identifying factors that influence breast cancer survival in smaller, well-defined subsets. The purpose of this study was to investigate the genomic landscape of various breast cancer subtypes and its potential associations with clinical outcomes. We used statistical analysis of sequence data generated by the Cancer Genome Atlas initiative including somatic mutation load (SML) analysis, Kaplan–Meier survival curves, gene mutational frequency, and mutational enrichment evaluation to study the genomic landscape of breast cancer. We show that ER+, but not ER−, tumors with high SML associate with poor overall survival (HR = 2.02). Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes. While it is known that somatic mutations in specific genes affect breast cancer survival, this study is the first to identify that SML may constitute an important global signature for a subset of ER+ tumors prone to high mortality. Moreover, although somatic mutations in individual DNA damage genes affect clinical outcome, our results indicate that coincident mutations in DNA damage response and signature ER genes may prove more informative for ER+ breast cancer survival. Next generation sequencing may prove an essential tool for identifying pathways underlying poor outcomes and for tailoring therapeutic strategies.
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               type:PostalAddress
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      affiliation:
            name:The University of Texas M.D. Anderson Cancer Center
            address:
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               type:PostalAddress
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      name:Codified Genomics, LLC, Houston, USA
      name:Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston, USA
      name:Department of Clinical Cancer Prevention, Unit 1360, The University of Texas M.D. Anderson Cancer Center, Houston, USA

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