Purpose: Immune checkpoint inhibitors (ICIs) have revolutionized cancer management. However, molecular determinants of response to ICIs remain incompletely understood. Experimental Design:We performed genomic profiling of 78 non-small-cell lung cancer (NSCLC) patients who underwent anti-PD-(L)1 therapies by both whole exome and targeted next-generation sequencing (a 422-cancer-gene panel) to explore the predictive biomarkers of ICI response. Tumor mutation burden (TMB), and specific somatic mutations and copy number alterations (CNAs) were evaluated for their associations with immunotherapy response. Results:We confirmed that high TMB was associated with improved clinical outcomes, and TMB quantified by gene panel strongly correlated with WES results (Spearman's ρ=0.81). Compared with wild-type, patients with FAT1 mutations had higher durable clinical benefit (DCB, 71.4% vs 22.7%, p=0.01) and objective response rates (ORR, 57.1% vs 15.2%, p=0.02). On the other hand, patients with activating mutations in EGFR/ERBB2 had reduced median progression-free survival (mPFS) compared with others (51.0 vs 70.5 days, p=0.0037, HR=2.47 [95% CI, 1.32-4.62]). Additionally, copy number loss in specific chromosome 3p segments containing the tumor suppressor ITGA9 and several chemokine receptor pathway genes, were highly predictive of poor clinical outcome (survival rates at 6 months, 0% vs 31%, p=0.012, HR= 2.08 [95% CI, 1.09-4.00]). Our findings were further validated in two independently published datasets comprising multiple cancer types. Conclusions:We identified novel genomic biomarkers that were predictive of response to anti-PD-(L)1 therapies. Our findings suggest that comprehensive profiling of TMB and the aforementioned molecular markers could result in greater predictive power of response to ICI therapies in NSCLC.

Background: We aimed to investigate whether more years spent in education are causally associated with a lower risk of lung cancer, through a two-sample Mendelian randomization study. Methods: The main analysis used publicly available genetic summary data from two large consortia [International Lung Cancer Consortium (ILCCO) and Social Science Genetic Association Consortium (SSGAC)]. Genetic variants used as instrumental variables for years of education were derived from SSGAC. Finally, genetic data from three additional consortia (TAG, GLGC, GIANT) were analysed to investigate whether education could causally alter common lung cancer risk factors. The exposure was the genetic predisposition to higher levels of education, measured by 73 single nucleotide polymorphisms from SSGAC. The primary outcome was the risk of lung cancer (11 348 events in ILCCO). Secondary outcomes based on different histological subtypes were also examined. Analyses were performed using the package TwoSampleMR in R. Results: Genetic predisposition towards 3.6 years of additional education was associated with a 52% lower risk of lung cancer (odds ratio 0.48, 95% confidence interval 0.34 to 0.66; P = 1.02 × 10 − 5). Sensitivity analyses were consistent with a causal interpretation in which major bias from genetic pleiotropy was unlikely. The Mendelian randomization assumptions did not seem to be violated. Genetic predisposition towards longer education was additionally associated with less smoking, lower body mass index and a favourable blood lipid profile. Conclusions: Our study indicated that low education is a causal risk factor in the development of lung cancer. Further work is needed to elucidate the potential mechanisms.

Pulmonary lymphoepithelioma-like carcinoma (LELC) is a rare and distinct subtype of primary lung cancer characterized by Epstein-Barr virus (EBV) infection. Herein, we reported the mutational landscape of pulmonary LELC using whole-exome sequencing, targeted deep sequencing and single-nucleotide polymorphism arrays. We identify a low degree of somatic mutation but widespread existence of copy number variations. We reveal predominant signature 2 mutations and frequent loss of type I interferon genes that are involved in the host-virus counteraction. Integrated analysis shows enrichment of genetic lesions affecting several critical pathways, including NF-κB, JAK/STAT, and cell cycle. Notably, multi-dimensional comparison unveils that pulmonary LELC resemble NPC but are clearly different from other lung cancers, natural killer/T-cell lymphoma or EBV-related gastric cancer in terms of genetic features. In all, our study illustrates a distinct genomic landscape of pulmonary LELC and provides a road map to facilitate genome-guided personalized treatment.

High-level tissue tumor mutational burden (tTMB) or blood TMB (bTMB) are associated with better response of immunotherapy in non-small cell lung cancer (NSCLC) patients. However, the correlations of single-region tTMB, multi-region tTMB and bTMB remain to be determined. Moreover, whether intratumor heterogeneity (ITH) has impact on TMB should be clarified. We collected multi-region tumor tissues with matched blood from 32 operative NSCLC and evaluated single-region tTMB, multi-region tTMB and bTMB through a 1021-gene panel sequencing. TMB of > 9 mutations/Mb was classified as high. Besides, we used tTMB fold-change to evaluate the influence of the enrolled region number on tTMB. We found both of single-region tTMB and bTMB showed strong correlations with multi-region tTMB, while the former correlated better (Pearson r = 0.94, P = 2E-84; Pearson r = 0.47, P = 0.0067). It showed extremely high specificity (100%) but low sensitivity (43%) when using bTMB to define TMB-high patients, while most false-negative predictions were in early-stage patients. Compared to single region, we found significantly enhanced tTMB fold-change if taking multi-regions for consideration. However, it showed insignificant tTMB fold-change increase if the included regions’ number more than three. Moreover, ITH-high patients had significantly higher tTMB fold-change compared with ITH-low patients (2.32 vs. 1.02, P = 8.879e-05). The conversion rate of tTMB level (tTMB-low to tTMB-high) was numerically higher in ITH-high group than that in ITH-low group (16.67% vs. 3.84%). In summary, single-region tTMB has stronger correlation with multi-region tTMB compared with bTMB. ITH has an impact on tTMB, especially in high-level ITH patients.