Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer

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.

Copyright © 2015, American Association for the Advancement of Science.

Figures

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. 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. 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. 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.