Literature context: # 05-525 (RRID:AB_309782)
Many lines of evidence have indicated that both genetic and non-genetic determinants can contribute to intra-tumor heterogeneity and influence cancer outcomes. Among the best described sub-population of cancer cells generated by non-genetic mechanisms are cells characterized by a CD44+/CD24- cell surface marker profile. Here, we report that human CD44+/CD24- cancer cells are genetically highly unstable because of intrinsic defects in their DNA-repair capabilities. In fact, in CD44+/CD24- cells, constitutive activation of the TGF-beta axis was both necessary and sufficient to reduce the expression of genes that are crucial in coordinating DNA damage repair mechanisms. Consequently, we observed that cancer cells that reside in a CD44+/CD24- state are characterized by increased accumulation of DNA copy number alterations, greater genetic diversity and improved adaptability to drug treatment. Together, these data suggest that the transition into a CD44+/CD24- cell state can promote intra-tumor genetic heterogeneity, spur tumor evolution and increase tumor fitness.
Literature context: 13B3/2C6) Millipore Cat#05-525; RRID:AB_309782 Mouse polyclonal anti-Rad51 Che
Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.