Strength in numbers: predicting response to checkpoint inhibitors from large clinical datasets

被引：5

作者：

Stenzinger, Albrecht ^{[1
,2
,3
]}

Kazdal, Daniel ^{[1
,3
]}

Peters, Solange ^{[4
]}

机构：

[1] Univ Hosp Heidelberg, Inst Pathol, Heidelberg, Germany

[2] German Canc Consortium DKTK, Heidelberg Partner Site, Heidelberg, Germany

[3] German Ctr Lung Res DZL, Heidelberg Partner Site, Heidelberg, Germany

[4] Lausanne Univ Hosp, Dept Oncol, Lausanne, Switzerland

来源：

CELL | 2021年 / 184卷 / 03期

关键词：

TUMOR;

D O I：

10.1016/j.cell.2021.01.008

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：571 / 573

页数：3