Strength in numbers: predicting response to checkpoint inhibitors from large clinical datasets
Albrecht Stenzinger1, Daniel Kazdal2, Solange Peters3
1Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Heidelberg partner site, Heidelberg, Germany; German Center for Lung Research (DZL), Heidelberg partner site, Heidelberg, Germany.
Abstract:
The advent of immune checkpoint blockers for cancer therapy has spawned great interest in identifying molecular features reflecting the complexity of tumor immunity, which can subsequently be leveraged as predictive biomarkers. In a thorough big-data approach analyzing the largest series of homogenized molecular and clinical datasets, Litchfield et al. identified a set of genomic biomarkers that identifies immunotherapy responders across cancer types.
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