Computational Approach Relates Gene Signatures With Cancer Outcomes

Researchers at Stanford University conducted an exhaustive meta-anaysis of gene expression signatures from ~18,000 human tumors across 39 malignancies and evaluated overall survival outcomes. The study, published online in the journal Nature Medicine, identified a forkhead box MI regulatory network as a major predictor of adverse outcomes, and found that expression of favorably prognostic genes, including KLRB1, largely reflect tumor-associated leukocytes.

Their computational approach included using the analaytical tool CIBERSORT, which helped associate 22 distinct leukocyte subsets and cancer survival. Based on their analysis, the authors predict that "This resource and can help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets."

Dr Ash Alizadeh, the study's senior author thinks that the information can be translated into ways to predict a cancer patient's outlook and help guide treatment choices. Eventually, it might also prove useful in zeroing in on patients who are likely to respond to new cancer treatments that target the immune system.

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