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This Week in Science: Apr 8, 2016

As part of a special issue of Science this week that focuses on cancer, Weill Cornell Medicine researcher and New York Genome Center member Harold Varmus discusses the uncertainty around cancer research funding despite growing interest among policymakers in tackling the disease. He highlights the need for resources to take advantage of the momentum already achieved in combating cancer, while offering ways that national leaders can do so "even without the certainty of additional dollars."

Also in Science, a pair of UK researchers discusses the various aspects of the genetics underlying tumor metastasis, touching on the challenges intratumoral heterogeneity presents to interpreting cancer genome data and reviewing findings from recent phylogenetic studies across different cancer types.

Meanwhile, researchers from Stanford University review the latest research into the role that hypoxia plays in promoting multiple steps of the metastatic cascade. They also discuss a recently identified receptor tyrosine kinase, AXL, as a critical mediator of HIF-dependent invasion and metastasis, and a potential therapeutic target for metastatic disease.

A pair of cancer investigators from the Fred Hutchinson Cancer Research Center and Johns Hopkins University examine recent data suggesting that tumor cells act collectively rather than as single cells at different stages of the metastatic cascade, invading local tissue and then entering circulation to promote tumor growth at distant sites.

In a perspective piece, two European scientists examine the role of neutrophils in metastasis and discuss investigational strategies to block neutrophil activity, and a group of US and Israeli investigators reports the results of study using single-cell sequencing technology to look at the full spectrum of cell types present in metastatic melanoma and the identification of a subset that develop treatment resistance.

Finally, in Science Translational Medicine, a University of California, Los Angeles-led team reports clinical data on a new computational tool for predicting the optimal drug dose for patients. Called parabolic personalized dosing (PPD), the method uses an individual’s clinical data, including the concentration of a drug in the bloodstream, to predict their next optimal drug dose. In a pilot study of liver transplant patients, PPD was applied to the use of immunosuppressants and led to shorter hospital stays compared with standard physician-guided dosing.