Sign up for email alerts to receive notifications of new articles published in Cancer Informatics
Background: Breast tumors have been described by molecular subtypes characterized by pervasively different gene expression profiles. The subtypes are associated with different clinical parameters and origin of precursor cells. However, the biological pathways and chromosomal aberrations that differ between the subgroups are less well characterized. The molecular subtypes are associated with different risk of metastatic recurrence of the disease. Nevertheless, the performance of these overall patterns to predict outcome is far from optimal, suggesting that biological mechanisms that extend beyond the subgroups impact metastasis.
Results: We have scrutinized publicly available gene expression datasets and identified molecular subtypes in 1,394 breast tumors with outcome data. By analysis of chromosomal regions and pathways using “Gene set enrichment analysis” followed by a meta-analysis, we identified comprehensive mechanistic differences between the subgroups. Furthermore, the same approach was used to investigate mechanisms related to metastasis within the subgroups. A striking finding is that the molecular subtypes account for the majority of biological mechanisms associated with metastasis. However, some mechanisms, aside from the subtypes, were identified in a training set of 1,239 tumors and confirmed by survival analysis in two independent validation datasets from the same type of platform and consisting of very comparable node-negative patients that did not receive adjuvant medical therapy. The results show that high expression of 5q14 genes and low levels of TNFR2 pathway genes were associated with poor survival in basal-like cancers. Furthermore, low expression of 5q33 genes and interleukin-12 pathway genes were associated with poor outcome exclusively in ERBB2-like tumors.
Conclusion: The identified regions, genes, and pathways may be potential drug targets in future individualized treatment strategies.
PDF (1.78 MB PDF FORMAT)
RIS citation (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)
BibTex citation (BIBDESK, LATEX)
Compared with other journals we considered for publishing, Cancer Informatics provided extremely rapid but quality turnaround from draft submission to a flawlessly typeset final publication. Moreover, sharing the article is now as easy as sharing a link with no subscriptions required, and additional code and data files are equally accessible, supporting reproducible research. Because it has published many of our references we feel confident that our target readership must follow the journal. This is further ...