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.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

BP2 consolidates the analytical evidence for interchangeability of the 22C3, 28-8, and SP263 assays and lower sensitivity of the SP142 assay for determining tumor proportion score on TCs and demonstrates greater sensitivity of the 73-10 assay compared with that of the other assays.

SummaryWe have examined the mechanism of thalidomide inhibition of lipopolysaccharide (LPS)-induced tumor necrosis factor ot (TNF<~) production and found that the drug enhances the degradation of TNF-oe mRNA. Thus, the half-life of the molecule was reduced from "o30 to ~o17 rain in the presence of 50/zg/ml of thalidomide. Inhibition of TNF-ot production was selective, as other LPS-induced monocyte cytokines were unaffected. PentoxifyUine and dexamethasone, two other inhibitors of TNF-c~ production, are known to exert their effects by means of different mechanisms, suggesting that the three agents inhibit TNF-c~ synthesis at distinct points of the cytokine biosynthetic pathway. These observations provide an explanation for the synergistic effects of these drugs. The selective inhibition of TNF-cr production makes thalidomide an ideal candidate for the treatment of inflammatory conditions where TNF-c~-induced toxicities are observed and where immunity must remain intact.

Purpose: Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a highly aggressive neoplasm, whose biologic relationship to small cell lung carcinoma (SCLC) versus non-SCLC (NSCLC) remains unclear, contributing to uncertainty regarding optimal clinical management. To clarify these relationships, we analyzed genomic alterations in LCNEC compared with other major lung carcinoma types. Experimental Design: LCNEC (n = 45) tumor/normal pairs underwent targeted next-generation sequencing of 241 cancer genes by Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) platform and comprehensive histologic, immunohistochemical, and clinical analysis. Genomic data were compared with MSK-IMPACT analysis of other lung carcinoma histologies (n = 242). Results: Commonly altered genes in LCNEC included TP53 (78%), RB1 (38%), STK11 (33%), KEAP1 (31%), and KRAS (22%). Genomic profiles segregated LCNEC into 2 major and 1 minor subsets: SCLC-like (n = 18), characterized by TP53+RB1 co-mutation/loss and other SCLC-type alterations, including MYCL amplification; NSCLC-like (n = 25), characterized by the lack of coaltered TP53+RB1 and nearly universal occurrence of NSCLC-type mutations (STK11, KRAS, and KEAP1); and carcinoid-like (n = 2), characterized by MEN1 mutations and low mutation burden. SCLC-like and NSCLC-like subsets revealed several clinicopathologic differences, including higher proliferative activity in SCLC-like tumors (P &lt; 0.0001) and exclusive adenocarcinoma-type differentiation marker expression in NSCLC-like tumors (P = 0.005). While exhibiting predominant similarity with lung adenocarcinoma, NSCLC-like LCNEC harbored several distinctive genomic alterations, including more frequent mutations in NOTCH family genes (28%), implicated as key regulators of neuroendocrine differentiation. Conclusions: LCNEC is a biologically heterogeneous group of tumors, comprising distinct subsets with genomic signatures of SCLC, NSCLC (predominantly adenocarcinoma), and rarely, highly proliferative carcinoids. Recognition of these subsets may inform the classification and management of LCNEC patients. Clin Cancer Res; 22(14); 3618–29. ©2016 AACR.