The hallmarks of cancer deem biological pathways and molecules to be conserved. This approach may be useful for deriving a prognostic gene signature. Weighted Gene Co-expression Network Analysis of gene expression datasets in eleven cancer types identified modules of highly correlated genes and interactive networks conserved across glioblastoma, breast, ovary, colon, rectal and lung cancers, from which a universal classifier for tumor stratification was extracted. Specific conserved gene modules were validated across different microarray platforms and datasets. Strikingly, preserved genes within these modules defined regulatory networks associated with immune regulation, cell differentiation, metastases, cell migration, metastases, oncogenic transformation, and resistance to apoptosis and senescence, with AIF1 and PRRX1 being suggested to be master regulators governing these biological processes. A universal classifier from these conserved networks enabled execution of common set of principles across different cancers that revealed distinct, differential correlation of biological functions with patient survival in a cancer-specific manner. Correlation analysis further identified a panel of 15 risk genes with potential prognostic value, termed as the GBOCRL-IIPr panel [(GBM-Breast-Ovary-Colon-Rectal-Lung)–Immune–Invasion–Prognosis], that surprisingly, were not amongst the master regulators or important network hubs. This panel may now be integrated in predicting patient outcomes in the six cancers.

Tumor heterogeneity and the presence of drug-sensitive and refractory populations within the same tumor are almost never assessed in the drug discovery pipeline. Such incomplete assessment of drugs arising from spatial and temporal tumor cell heterogeneity reflects on their failure in the clinic and considerable wasted costs in the drug discovery pipeline. Here we report the derivation of a flow cytometry-based tumor deconstruction platform for resolution of at least 18 discrete tumor cell fractions. This is achieved through concurrent identification, quantification and analysis of components of cancer stem cell hierarchies, genetically instable clones and differentially cycling populations within a tumor. We also demonstrate such resolution of the tumor cytotype to be a potential value addition in drug screening through definitive cell target identification. Additionally, this real-time definition of intra-tumor heterogeneity provides a convenient, incisive and analytical tool for predicting drug efficacies through profiling perturbations within discrete tumor cell subsets in response to different drugs and candidates. Consequently, possible applications in informed therapeutic monitoring and drug repositioning in personalized cancer therapy would complement rational design of new candidates besides achieving a re-evaluation of existing drugs to derive non-obvious combinations that hold better chances of achieving remission.