Molecular profiling of long-term responders to immune checkpoint inhibitors in advanced non-small cell lung cancer

Abstract

Immunotherapy has transformed advanced non-small cell lung cancer (NSCLC) treatment strategies and has led to unprecedented long-lasting responses in some patients. However, the molecular determinants driving these long-term responses remain elusive. To address this issue, we performed an integrative analysis of genomic and transcriptomic features of long-term immune checkpoint inhibitors (ICIs)-associated responders. We assembled a cohort of 47 patients with NSCLC receiving ICIs that was enriched in long-term responders [>18 months of progression-free survival (PFS)]. We performed whole-exome sequencing from tumor samples, estimated the tumor mutational burden (TMB), and inferred the somatic copy number alterations (SCNAs). We also obtained gene transcription data for a subset of patients using Nanostring, which we used to assess the tumor immune infiltration status and PD-L1 expression. Our results indicate that there is an association between TMB and benefit to ICIs, which is driven by those patients with long-term response. Additionally, high SCNAs burden is associated with poor response and negatively correlates with the presence of several immune cell types (B cells, natural killers, regulatory T cells or effector CD8 T cells). Also, CD274 (PD-L1) expression is increased in patients with benefit, mainly in those with long-term response. In our cohort, combined assessment of TMB and SCNAs burden enabled identification of long-term responders (considering PFS and overall survival). Notably, the association between TMB, SCNAs burden, and PD-L1 expression with the outcomes of ICIs treatment was validated in two public datasets of ICI-treated patients with NSCLC. Thus, our data indicate that TMB is associated with long-term benefit following ICIs treatment in NSCLC and that TMB, SCNAs burden, and PD-L1 are complementary determinants of response to ICIs.

Keywords: NSCLC; PD-L1; chromosomal alterations burden; copy number alterations; immune checkpoint inhibitors; long-term benefit; tumor mutational burden.

Fig. 1

Tumor genomic characterization and response to ICIs. (A, B) Tumor mutational burden (TMB) distribution across groups of ICIs benefit. Color indicates best response to ICIs (CR: complete response, PR: partial response, SD: stable disease, and PD: progressive disease). (C) Kaplan–Meier plot dividing the cohort into TMB tertiles. (D, E) Somatic copy number alterations (SCNAs) burden distribution across groups of ICIs benefit. Color indicates best response. (F) Kaplan–Meier plot survival curves dividing the cohort into SCNAs burden tertiles. (G) Genomic alterations in selected genes. Columns and rows represent patients and genes, respectively. Mann–Whitney–Wilcoxon tests have been used to determine differences between ICIs benefit groups (A, B, D, E). Log‐rank tests have been used to determine differences between TMB groups and between SCNAs groups (C, F). ns: 0.05 < P ≤ 1.0; *: 0.01 < P <0.05; **: 0.001 < P ≤ 0.01.

Fig. 2

Immune expression profiling of tumor’s microenvironment. (A, B) TEFF and cytotoxic score distribution across groups of ICIs benefit. For each patient, the value of each signature has been computed as the geometric mean of the expression of all genes in the signature. Next, for each of the scores, all values have been standardized. Color indicates best response to ICIs (CR: complete response, PR: partial response, SD: stable disease, and PD: progressive disease). (C) Distribution of expression signatures belonging to four different immune cell populations across groups of benefit. Values computed as in panel A. Color indicates best response to ICIs. (D) Representation of different immune populations across 22 patients. Columns and rows correspond to patients and specific immune cell populations, respectively. Patients have been clustered using a hierarchical clustering algorithm based on their degree of similarity considering amount and type of tumor immune infiltration. (E) CD274 expression distribution according to indicated group of benefit. Color indicates best response. Mann–Whitney–Wilcoxon tests have been used to determine differences between ICIs benefit groups (A, B, C, E, F); ns: 0.05 < P ≤ 1.0; *: 0.01 < P <0.05; **: 0.001 < P ≤ 0.01.

Fig. 3

Interplay between biomarkers of response to ICIs. (A) Spearman correlation between indicated features. Color represents the correlation coefficient. The P value of each correlation is shown within each cell. (B) Scatter plot representation of TMB and SCNAs burden. Horizontal and vertical gray lines represent the SCNAs burden and TMB mean, respectively. Dot color represents the group of benefit. (C) Kaplan–Meier plot survival curves dividing the cohort into four groups based on the quadrants defined in panel 3B. Long‐rank tests have been used to determine differences between TMB‐SCNAs groups. ns: 0.05 < P ≤ 1.0; *: 0.01 < P <0.05; **: 0.001 < P ≤ 0.01. (D) Univariate and multivariate Cox proportional hazards model for SCNAs burden, ICIs target, sex, and TMB. Multivariate analysis has been stratified by histology and smoking history. Features represented in blue are statistically significant (P < 0.05).

Fig. 4

NSCLC‐MSK cohort analysis. (A) TMB, (B) FGA (SCNAs burden), and (C) PD‐L1 distribution across groups of ICIs benefit. Mann–Whitney–Wilcoxon tests have been used to determine differences between clinical benefit groups (A, B, C). ns: 0.05 < P ≤ 1.0; *: 0.01 < P < 0.05. (D) Multivariate Cox proportional hazards model for FGA (SCNAs burden), age, TMB, smoking status, sex, and PD‐L1, stratified by histology. Features represented in blue are statistically significant (P < 0.05).