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Prediction of Molecular Mechanisms of Breast Cancer Metastasis

Metastasis -the spread of cancer to other parts of the body- causes 90% of cancer deaths, underlies major health complications in cancer patients and renders most cancers incurable. Unfortunately, the molecular mechanisms underlying the process are poorly understood and therapeutics to block it remain elusive. Here, we present a computational technique for scanning genome-scale regulatory networks for potential genes associated with metastasis. First, we demonstrate that in the breast cancer cell line MCF7, the commonly dysregulated cancer biomarkers TP53, ERBB2, ESR1 and PGR are closely connected to known metastasis genes with a significant proportion being 2nd degree neighbors of a given biomarker. Next, we identify genes whose 2nd degree neighbors are connected in a similar manner to these biomarkers. Consequently, these are referred to as metastasis associated genes or MAGs. We identify 190 genes that are TP53-MAGs, 22 ERBB2-MAGs, 240 ESR1-MAGs and 84 PGR-MAGs (FDR adjusted P <0.001). Analysis of the MAGs reveals statistically significant enrichment with biological functions previously associated with metastasis including the extracellular matrix (ECM) receptor interaction, focal adhesion, cytokine-cytokine receptor interaction and chemokine signaling. The biological significance of MAGs is further supported by their enrichment with experimentally validated binding sites for transcription factors that regulate metastasis, for example BACH1- a master regulator of breast cancer metastasis to bone. The predicted MAGs are also clinically relevant as therapeutic targets for metastasis blocking agents. Specifically, genes that are perturbed by drugs and miRNAs that influence metastasis are enriched with MAGs. Furthermore, some MAGs are associated with patient survival and provide insights into the proclivity for breast cancer subtypes to preferentially spread to specific organs. The results of this study imply that aberrations in primary tumors may constrict metastasis trajectories. This could enable the prediction of organ specific metastases based on aberrations in the primary tumor and lay a foundation for future studies on individualized or personalized models of metastasis. The approach is potentially scalable across other cancers and has clinical implications.