Tumour heterogeneity and the evolutionary trade-offs of cancer

Resistance of human tumors to anticancer drugs is most often ascribed to gene mutations, gene amplification, or epigenetic changes that influence the uptake, metabolism, or export of drugs from single cells. Another important yet little-appreciated cause of anticancer drug resistance is the limited ability of drugs to penetrate tumor tissue and to reach all of the tumor cells in a potentially lethal concentration. To reach all viable cells in the tumor, anticancer drugs must be delivered efficiently through the tumor vasculature, cross the vessel wall, and traverse the tumor tissue. In addition, heterogeneity within the tumor microenvironment leads to marked gradients in the rate of cell proliferation and to regions of hypoxia and acidity, all of which can influence the sensitivity of the tumor cells to drug treatment. In this review, we describe how the tumor microenvironment may be involved in the resistance of solid tumors to chemotherapy and discuss potential strategies to improve the effectiveness of drug treatment by modifying factors relating to the tumor microenvironment. Show more

Significance Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes. Show more