We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancersignaling architectural and functional organization. From the network, we extracted an oncogenesignaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few 'oncogene-signaling-dependent blocks' in which frequently used oncogenesignaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancerassociated genes and biomarkers.

Expression of profilin-1 (Pfn1) is downregulated in breast cancer cells, the functional significance of which is yet to be understood. To address this question, in this study we evaluated how perturbing Pfn1 affects motility and invasion of breast cancer cells. We show that loss of Pfn1 expression leads to enhanced motility and matrigel invasiveness of MDA-MB-231 breast cancer cells. Interestingly, silencing Pfn1 expression is associated with downregulation of both cell -cell and cell -matrix adhesions with concomitant increase in motility and dramatic scattering of normal human mammary epithelial cells. Thus, these data for the first time suggest that loss of Pfn1 expression may have significance in breast cancer progression. Consistent with these findings, even a moderate overexpression of Pfn1 induces actin stress-fibres, upregulates focal adhesion, and dramatically inhibits motility and matrigel invasiveness of MDA-MB-231 cells. Using mutants of Pfn1 that are defective in binding to either actin or proline-rich ligands, we further show that overexpressed Pfn1 must have a functional actin-binding site to suppress cell motility. Finally, animal experiments reveal that overexpression of Pfn1 suppresses orthotopic tumorigenicity and micro-metastasis of MDA-MB-231 cells in nude mice. These data imply that perturbing Pfn1 could be a good molecular strategy to limit the aggressiveness of breast cancer cells.

Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations), making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival) onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.

Tumor size is strongly correlated with breast cancer metastasis and patient survival. Increased tumor size contributes to hypoxic and metabolic gradients in the solid tumor and to an aggressive tumor phenotype. Thus, it is important to develop three-dimensional (3D) breast tumor models that recapitulate size-induced microenvironmental changes and consequently, natural tumor progression in real time without the use of artificial culture conditions or gene manipulations. Here, we developed size-controlled multicellular aggregates (“microtumors”) of subtype-specific breast cancer cells by using non-adhesive polyethylene glycol dimethacrylate hydrogel microwells of defined sizes (150–600 μm). These 3D microtumor models faithfully represent size-induced microenvironmental changes such as hypoxic gradients, cellular heterogeneity and spatial distribution of necrotic/proliferating cells. These microtumors acquire hallmarks of tumor progression in the same cell lines within 6 days. Of note, large microtumors of hormone receptor positive cells exhibited an aggressive phenotype characterized by collective cell migration and upregulation of mesenchymal markers at mRNA and protein level, which was not observed in small microtumors. Interestingly, triple negative breast cancer (TNBC) cell lines did not show size-dependent upregulation of mesenchymal markers. In conclusion, size-controlled microtumor models successfully recapitulated clinically observed positive association between tumor size and aggressive phenotype in hormone receptor positive breast cancer while maintaining clinically proven poor correlation of tumor size with aggressive phenotype in TNBC. Such clinically relevant 3D models generated under controlled experimental conditions can serve as precise preclinical models to study mechanisms involved in breast tumor progression as well as antitumor drug effects as a function of tumor progression.