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Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data.

Haricharan S, Bainbridge MN, Scheet P, Brown PH - Breast Cancer Res. Treat. (2014)

Bottom Line: Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Cancer Prevention, Unit 1360, The University of Texas M.D. Anderson Cancer Center, P.O. Box 301439, Houston, TX, 77030-1439, USA, sharicharan@mdanderson.org.

ABSTRACT
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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Coincident mutations in DDR and ER signature genes associate with poor survival irrespective of mutation load. a Percentage of tumors with mutations in genes associated with either good or poor prognosis in specified subsets. Fisher’s exact test was used to determine the p-value. b Kaplan–Meier survival curves of indicated groups. Log-rank test was used to generate p-values. c Bar graph depicting the percentage of tumors with mutations in the specified pathways. Fisher’s exact test was used to identify p-values. The list of ER signature genes is presented in “Materials and methods” section. d–f Kaplan–Meier survival curves of indicated groups. Log-rank test was used to determine p-values. ER, ER signature genes; DDR, genes from the five major DNA damage response pathways; Chkpt, genes from the DNA damage checkpoint; mut, tumors with non-silent mutations in genes from the specified pathway; NL, tumors with no identified mutations in genes from the specified pathway; ns, not significant
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Fig4: Coincident mutations in DDR and ER signature genes associate with poor survival irrespective of mutation load. a Percentage of tumors with mutations in genes associated with either good or poor prognosis in specified subsets. Fisher’s exact test was used to determine the p-value. b Kaplan–Meier survival curves of indicated groups. Log-rank test was used to generate p-values. c Bar graph depicting the percentage of tumors with mutations in the specified pathways. Fisher’s exact test was used to identify p-values. The list of ER signature genes is presented in “Materials and methods” section. d–f Kaplan–Meier survival curves of indicated groups. Log-rank test was used to determine p-values. ER, ER signature genes; DDR, genes from the five major DNA damage response pathways; Chkpt, genes from the DNA damage checkpoint; mut, tumors with non-silent mutations in genes from the specified pathway; NL, tumors with no identified mutations in genes from the specified pathway; ns, not significant

Mentions: Next, we investigated potential pathways underlying the poor survival phenotype associated with HML tumors using a candidate approach. To determine whether the HML subset of ER+ tumors is enriched for mutations associated with poor prognosis, we generated a list of known prognostic genes mutated at >10 % frequency in human breast cancer based on the existing literature [4, 16–18] (see “Materials and methods” section and Table 5). We assessed the proportion of tumors with mutations in these genes in both the HML and LML ER+ subsets. Our results demonstrate that the LML subset has a significantly higher proportion of good prognostic mutations than poor prognostic mutations (p = 0.002), (Fig. 4a). However, there were no significant associations found between these known prognostic mutations and overall survival in either HML or LML subsets (Fig. 4b). These data indicate that mechanisms other than those associated with known prognostic genetic mutations mediate the association between SML and breast cancer survival.Table 5


Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data.

Haricharan S, Bainbridge MN, Scheet P, Brown PH - Breast Cancer Res. Treat. (2014)

Coincident mutations in DDR and ER signature genes associate with poor survival irrespective of mutation load. a Percentage of tumors with mutations in genes associated with either good or poor prognosis in specified subsets. Fisher’s exact test was used to determine the p-value. b Kaplan–Meier survival curves of indicated groups. Log-rank test was used to generate p-values. c Bar graph depicting the percentage of tumors with mutations in the specified pathways. Fisher’s exact test was used to identify p-values. The list of ER signature genes is presented in “Materials and methods” section. d–f Kaplan–Meier survival curves of indicated groups. Log-rank test was used to determine p-values. ER, ER signature genes; DDR, genes from the five major DNA damage response pathways; Chkpt, genes from the DNA damage checkpoint; mut, tumors with non-silent mutations in genes from the specified pathway; NL, tumors with no identified mutations in genes from the specified pathway; ns, not significant
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig4: Coincident mutations in DDR and ER signature genes associate with poor survival irrespective of mutation load. a Percentage of tumors with mutations in genes associated with either good or poor prognosis in specified subsets. Fisher’s exact test was used to determine the p-value. b Kaplan–Meier survival curves of indicated groups. Log-rank test was used to generate p-values. c Bar graph depicting the percentage of tumors with mutations in the specified pathways. Fisher’s exact test was used to identify p-values. The list of ER signature genes is presented in “Materials and methods” section. d–f Kaplan–Meier survival curves of indicated groups. Log-rank test was used to determine p-values. ER, ER signature genes; DDR, genes from the five major DNA damage response pathways; Chkpt, genes from the DNA damage checkpoint; mut, tumors with non-silent mutations in genes from the specified pathway; NL, tumors with no identified mutations in genes from the specified pathway; ns, not significant
Mentions: Next, we investigated potential pathways underlying the poor survival phenotype associated with HML tumors using a candidate approach. To determine whether the HML subset of ER+ tumors is enriched for mutations associated with poor prognosis, we generated a list of known prognostic genes mutated at >10 % frequency in human breast cancer based on the existing literature [4, 16–18] (see “Materials and methods” section and Table 5). We assessed the proportion of tumors with mutations in these genes in both the HML and LML ER+ subsets. Our results demonstrate that the LML subset has a significantly higher proportion of good prognostic mutations than poor prognostic mutations (p = 0.002), (Fig. 4a). However, there were no significant associations found between these known prognostic mutations and overall survival in either HML or LML subsets (Fig. 4b). These data indicate that mechanisms other than those associated with known prognostic genetic mutations mediate the association between SML and breast cancer survival.Table 5

Bottom Line: Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Cancer Prevention, Unit 1360, The University of Texas M.D. Anderson Cancer Center, P.O. Box 301439, Houston, TX, 77030-1439, USA, sharicharan@mdanderson.org.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus