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. 2015 Apr 3;348(6230):124-8.
doi: 10.1126/science.aaa1348. Epub 2015 Mar 12.

Cancer Immunology. Mutational Landscape Determines Sensitivity to PD-1 Blockade in Non-Small Cell Lung Cancer

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Free PMC article

Cancer Immunology. Mutational Landscape Determines Sensitivity to PD-1 Blockade in Non-Small Cell Lung Cancer

Naiyer A Rizvi et al. Science. .
Free PMC article

Abstract

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.

Figures

Fig. 1
Fig. 1. Nonsynonymous mutation burden associated with clinical benefit of anti–PD-1 therapy
(A) Nonsynonymous mutation burden in tumors from patients with DCB (n = 7) or with NDB (n = 9) (median 302 versus 148, Mann-Whitney P = 0.02). (B) PFS in tumors with higher nonsynonymous mutation burden (n = 8) compared to tumors with lower nonsynonymous mutation burden (n = 8) in patients in the discovery cohort (HR 0.19, 95% CI 0.05 to 0.70, log-rank P = 0.01). (C) Nonsynonymous mutation burden in tumors with DCB (n = 7) compared to those with NDB (n = 8) in patients in the validation cohort (median 244 versus 125, Mann-Whitney P = 0.04). (D) PFS in tumors with higher nonsynonymous mutation burden (n = 9) compared to those with lower nonsynonymous mutation burden (n = 9) in patients in the validation cohort (HR 0.15, 95% CI 0.04 to 0.59, log-rank P = 0.006). (E) ROC curve for the correlation of nonsynonymous mutation burden with DCB in discovery cohort. AUC is 0.86 (95% CI 0.66 to 1.05, null hypothesis test P = 0.02). Cut-off of ≥178 nonsynonymous mutations is designated by triangle. (F) Nonsynonymous mutation burden in patients with DCB (n = 14) compared to those with NDB (n = 17) for the entire set of sequenced tumors (median 299 versus 127, Mann-Whitney P = 0.0008). (G) PFS in those with higher nonsynonymous mutation burden (n = 17) compared to those with lower nonsynonymous mutation burden (n = 17) in the entire set of sequenced tumors (HR 0.19, 95% CI 0.08–0.47, log-rank P = 0.0004). In (A), (C), and (F), median and interquartile ranges of total nonsynonymous mutations are shown, with individual values for each tumor shown with dots.
Fig. 2
Fig. 2. Molecular smoking signature is significantly associated with improved PFS in NSCLC patients treated with pembrolizumab
PFS in tumors characterized as TH by molecular smoking signature classifier (n = 16) compared to TL tumors (n = 18) (HR 0.15, 95% 0.06 to 0.39, log-rank P = 0.0001).
Fig. 3
Fig. 3. Mutation burden, clinical response, and factors contributing to mutation burden
Total exonic mutation burden for each sequenced tumor with nonsynonymous (dark shading), synonymous (medium shading), and indels/frameshift mutations (light shading) displayed in the histogram. Columns are shaded to indicate clinical benefit status: DCB, green; NDB, red; not reached 6 months follow-up (NR), blue. The cohort identification (D, discovery; V, validation), best objective response (PR, partial response; SD, stable disease; PD, progression of disease), and PFS (censored at the time of data lock) are reported in the table. Those with ongoing progression-free survival are labeled with ++. The presence of the molecular smoking signature is displayed in the table with TH cases (purple) and TL cases (orange). The presence of deleterious mutations in specific DNA repair/replication genes is indicated by the arrows.
Fig. 4
Fig. 4. Candidate neoantigens, neoantigen-specific T cell response, and response to pembrolizumab
(A) Neoantigen burden in patients with DCB (n = 14) compared to NDB (n = 17) across the overall set of sequenced tumors (median 203 versus 83, Mann-Whitney P = 0.001). (B) PFS in tumors with higher candidate neoantigen burden (n = 17) compared to tumors with lower candidate neoantigen burden (n = 17) (HR 0.23, 95%CI 0.09 to 0.58, log-rank P = 0.002). (C) (Top) Representative computed tomography (CT) images of a liver metastasis before and after initiation of treatment. (Middle) Change in radiographic response. (Bottom) Magnitude of the HERC1 P3278S reactive CD8+ T cell response measured in peripheral blood. (D) The proportion of CD8+ T cell population in serially collected autologous PBLs recognizing the HERC1 P3278S neoantigen (ASNASSAAK) before and during pembrolizumab treatment. Each neoantigen is encoded by a unique combination of two fluorescently labeled peptide-MHC complexes (represented individually on each axis); neoantigen-specific T cells are represented by the events in the double positive position indicated with black dots. Percentages indicate the number of CD8+ MHC multimer+ cells out of total CD8 cells. (E) Autologous T cell response to wild-type HERC1 peptide (black), mutant HERC1 P3278S neoantigen (red), or no stimulation (blue), as detected by intracellular cytokine staining. T cell costains for IFNγ and CD8, TNFα, CD107a, and CCL4, respectively, are displayed for the Day 63 and Day 297 time points.

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