Predicting treatment response based on RNA expression in large datasets

Aaron S. Mansfield; Jin Jen

doi:10.1158/1078-0432.CCR-18-2823

Predicting treatment response based on RNA expression in large datasets

Research output: Contribution to journal › Article › peer-review

Abstract

PD-L1 expression levels derived from >16,000 samples guided the selection of tumor types likely to benefit from pembrolizuamb monotherapy in clinical trials. Although not fail-proof, FDA approvals for most of the prioritized indications speak to the power of RNA expression profiling and the value of large genomic datasets.

Original language	English (US)
Pages (from-to)	1443-1445
Number of pages	3
Journal	Clinical Cancer Research
Volume	25
Issue number	5
DOIs	https://doi.org/10.1158/1078-0432.CCR-18-2823
State	Published - Mar 1 2019

ASJC Scopus subject areas

Oncology
Cancer Research

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10.1158/1078-0432.CCR-18-2823

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title = "Predicting treatment response based on RNA expression in large datasets",

abstract = "PD-L1 expression levels derived from >16,000 samples guided the selection of tumor types likely to benefit from pembrolizuamb monotherapy in clinical trials. Although not fail-proof, FDA approvals for most of the prioritized indications speak to the power of RNA expression profiling and the value of large genomic datasets.",

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Predicting treatment response based on RNA expression in large datasets

Abstract

ASJC Scopus subject areas

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