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Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Open accessAbstract
Immune checkpoint inhibitors, which unleash a patient’s own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti–PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy.
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“Indeed, as mentioned in the previous section, both PD-1 and CTLA-4 blockades are more effective in tumors that are infiltrated by T cells or that have high mutation rates and are therefore more immunogenic prior to treatment (86–88).”
From Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations (2018) · cites this paper as [86]
In the citing paper
“This was particularly noted in anti-CTLA-4-treated melanoma patients whose tumors displayed neo-antigens (87) and similarly in anti-PD-1-treated patients with colorectal cancers or non-small cell lung cancers that were mismatch-repair deficient or had high mutation rates, respectively (78, 86).”
From Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations (2018) · cites this paper as [86]
In the citing paper
“other features such as TMB appear to also be associated with clinical benefit to PD-1 / PD-L1 antibodies”
From Diverse EGFR Exon 20 Insertions and Co-Occurring Molecular Alterations Identified by Comprehensive Genomic Profiling of NSCLC (2018) · cites this paper as [27]
In the citing paper
“The predictive value of TMB, MSI, and dMMR as biomarkers of response to ICBs has been supported by several studies”
From Hallmarks of response, resistance, and toxicity to immune checkpoint blockade (2021) · cites this paper as Rizvi et al., 2015
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“also shown to be true for PD- 1 blockade in NSCLC”
From Hallmarks of response, resistance, and toxicity to immune checkpoint blockade (2021) · cites this paper as Rizvi et al., 2015
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“Previous reports have shown that the total number of somatic coding mutations in a cancer genome correlates with response to ICB (7, 8, 10–12, 17).”
From Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy (2018) · cites this paper as [10]
In the citing paper
“Another well-recognized biomarker that predicts response or nonresponse to ICIs is the mutational burden of the tumor, with higher tumor mutational loads strongly associated with response to both anti-CTLA-4 and anti-PD-1 across multiple cancer indications (83)(84)(85)(86).”
From Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance (2021) · cites this paper as [86]
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Metadata
- Authors
- Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., Miller, M. L., Rekhtman, N., Moreira, A. L., Ibrahim, F., Bruggeman, C., Gasmi, B., Zappasodi, R., Maeda, Y., Sander, C., Garon, E. B., Merghoub, T., Wolchok, J. D., Schumacher, T. N., & Chan, T. A.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- The Netherlands Cancer Institute (NL)
- Columbia University (US)
- University of California, Los Angeles (US)
- Corresponding author
- Rizvi, Naiyer A.
- Hellmann, Matthew D.
- Snyder, Alexandra
- Chan, Timothy A.
- ORCID
- Rizvi, Naiyer A.
- Hellmann, Matthew D.
- Snyder, Alexandra
- Kvistborg, Pia
- Makarov, Vladimir
- Havel, Jonathan J.
- Lee, William
- Yuan, Jianda
- Wong, Phillip
- Miller, Martin L.
- Rekhtman, Natasha
- Moreira, Andre L.
- Gasmi, Billel
- Zappasodi, Roberta
- Sander, Chris
- Garon, Edward B.
- Merghoub, Taha
- Wolchok, Jedd D.
- Schumacher, Ton N.
- Chan, Timothy A.
- Venue
- Science
- Published
- 2015-04-03
- Volume
- 348
- Issue
- 6230
- Pages
- 124-128
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4993154
- OpenAlex
- W2049553585
- Semantic Scholar
- d0a5bc5ff2c33c640e97009377304357fe8d2838
- Cited by
- 7,802
- References
- 68
- Citations by year
- 2026: 258 · 2025: 389 · 2024: 473 · 2023: 598 · 2022: 824 · 2021: 930 · 2020: 956 · 2019: 881 · 2018: 883 · 2017: 762 · 2016: 614 · 2015: 216
- FWCI
- 360.34
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Lung Cancer Research Foundation
- The STARR Foundation
- Geoffrey Beene Cancer Research Center
- Society for Memorial Sloan Kettering Cancer Center
- Frederick Adler Chair Fund
- The One Ball Matt Memorial Golf Tournament
- Queen Wilhelmina Cancer Research Award
- Ludwig Trust
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- non-small cell lung cancer, anti-PD-1 therapy, pembrolizumab, immune checkpoint inhibitors, mutational burden, non-synonymous mutations, neoantigens, CD8+ T cell responses, treatment response prediction, tumor genomic landscape
- MeSH terms
- Antineoplastic Agents, Carcinoma, Non-Small-Cell Lung, DNA Repair, Humans, Lung Neoplasms, Mutation, Smoking, Cohort Studies, CD8-Positive T-Lymphocytes, Disease-Free Survival, Drug Resistance, Neoplasm, Programmed Cell Death 1 Receptor, Antibodies, Monoclonal, Humanized
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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