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Improved prediction of immune checkpoint blockade efficacy across multiple cancer types

Abstract

Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose1. Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions.

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Fig. 1: Overview of development of the model for integrated clinical-genetic prediction of ICB response.
Fig. 2: Model performance across multiple cancer types in the test set.
Fig. 3: Model predicts OS and PFS across multiple cancer types in the test set.

Data availability

All de-identified data needed to replicate all analyses are in Supplementary Table 3 and are available online at https://www.ioexplorer.org.

Code availability

The code used in this study is deposited at https://github.com/CCF-ChanLab/MSK-IMPACT-IO.

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Acknowledgements

We thank the Chan lab and members of the Immunogenomics and Precision Oncology Platform for advice and input. This work was supported, in part, by NIH R35 CA232097 (T.A.C.), NIH RO1 CA205426 (T.A.C.), the PaineWebber Chair (T.A.C.), NIH/NCI Cancer Center Support Grant (P30 CA008748), Fundación Alfonso Martín Escudero (C.V.), NIH K08 DE024774, NIH R01 DE027738, the Sebastian Nativo Fund and the Jayme and Peter Flowers Fund (to L.G.T.M.).

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Authors

Contributions

D.C., S.-K.Y., C.V., A.P., L.G.T.M., N.W. and T.A.C conceived and designed the study. D.C. and S.-K.Y. developed the machine learning model. D.C., S.-K.Y., C.V., A.P., C.K., M.L., D.H., H.S., D.W.K., N.P., V.M., K.W., T.L., R.M.S., N.R., P.S.A., V.P.B., G.P., A.A.H., A.N.S., M.A.P., R.J.M, M.L., A.Z., M.F.B., L.G.T.M. and N.W. acquired, analyzed or interpreted the data. M.G. provided statistical advice. All authors critically revised the manuscript for important intellectual content. L.G.T.M., N.W. and T.A.C. supervised the study.

Corresponding authors

Correspondence to Luc G. T. Morris, Nils Weinhold or Timothy A. Chan.

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Competing interests

T.A.C. is a co-founder of Gritstone Oncology and holds equity. T.A.C. holds equity in An2H. T.A.C. acknowledges grant funding from Bristol Myers Squibb, AstraZeneca, Illumina, Pfizer, An2H and Eisai. T.A.C. has served as an advisor for Bristol Myers, MedImmune, Squibb, Illumina, Eisai, AstraZeneca and An2H. T.A.C., L.G.T.M. and D.C. hold ownership of intellectual property on using tumor mutational burden to predict immunotherapy response, with a pending patent, which has been licensed to PGDx. M.A.P. reports consulting fees from Bristol Myers Squibb, Merck, Array BioPharma, Novartis, Incyte, NewLink Genetics, Aduro and Eisai; honoraria from Bristol Myers Squibb and Merck; and institutional support from RGenix, Infinity, Bristol Myers Squibb, Merck, Array BioPharma, Novartis and AstraZeneca. M.L. has received advisory board compensation from Merck and Bristol Myers Squibb. The remaining authors declare no competing interests.

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Chowell, D., Yoo, SK., Valero, C. et al. Improved prediction of immune checkpoint blockade efficacy across multiple cancer types. Nat Biotechnol 40, 499–506 (2022). https://doi.org/10.1038/s41587-021-01070-8

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