A vast majority of the published cancer studies in the past few decades was conducted on cancer cells rather than cancer tissues. Knowing that the microenvironment plays key roles in cancer initiation, development and metastasis, we must reassess the true relevance of many of these published results to cancer. We have recently developed a new framework for cancer studies by treating cancer as a survival process under increasingly more challenging stresses, which evolve as a cancer evolves. Our main hypothesis is that cell proliferation is a sustained and common pathway to survival under all major cancer-associated stresses. The availability of large-scale cancer tissue omic data enables us to systematically identify various stress types present in each tissue and how each cancer tumor responds to the encountered stresses, ultimately validating, refining or rejecting this fundamentally novel hypothesis. In this presentation, I will discuss (1) how data mining can be used to identify such stresses and their responses, leading to substantially improved understanding about cancer evolution from its onset; and (2) how data mining-based discoveries can be integrated with cell-based experimental findings, leading to more comprehensive understanding about the key drivers and facilitators of cancer evolution, hence potentially leading to much improved treatment paradigms for challenging cancer cases.