Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma
Name
s41591-019-0654-5.pdf
Description
Published version
Size
3.5 MB
Format
Adobe PDF
Checksum (MD5)
695503077a027a0ed51000803a526617
Author(s)
Liu, David V.
Liu, Derek
Jerby-Arnon, Livnat
Vokes, Natalie I.
Margolis, Claire A.
Conway, Jake
He, Meng Xiao
Elmarakeby, Haitham
Dietlein, Felix
Miao, Diana
Date Issued
December 2019
Journal
Nature Medicine
Publisher
Springer Science and Business Media LLC
Citation
Liu, David, et al., "Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma." Nature Medicine 25 (Dec. 2019): p. 1916-27 doi 10.1038/s41591-019-0654-5 ©2019 Author(s)
Version
Final published version
Abstract
Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.
MIT Department
Broad Institute of MIT and Harvard
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41591-019-0654-5
