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Improved identification of anti-tumor T cells is needed to advance cancer immunotherapies. CD39 expression is a promising surrogate of tumor-reactive CD8+ T cells. Here, we comprehensively profiled CD39 expression in human lung cancer. CD39 expression enriched for CD8+ T cells with features of exhaustion, tumor reactivity, and clonal expansion. Flow cytometry of 440 lung cancer biospecimens revealed weak association between CD39+ CD8+ T cells and tumoral features, such as programmed death-ligand 1 (PD-L1), tumor mutation burden, and driver mutations. Immune checkpoint blockade (ICB), but not cytotoxic chemotherapy, increased intratumoral CD39+ CD8+ T cells. Higher baseline frequency of CD39+ CD8+ T cells conferred improved clinical outcomes from ICB therapy. Furthermore, a gene signature of CD39+ CD8+ T cells predicted benefit from ICB, but not chemotherapy, in a phase III clinical trial of non-small cell lung cancer. These findings highlight CD39 as a proxy of tumor-reactive CD8+ T cells in human lung cancer. • CD39+ CD8+ T cells express features of exhaustion and tumor reactivity • CD39 expression enriches for CD8+ TCRs with tumor reactivity • CD39 expression on CD8+ T cells is non-redundant to tumor-based biomarkers • CD39+ CD8+ T cells are predictive of benefit from ICB Factors predicting benefit of immune checkpoint blockade (ICB) are needed. Here, Chow et al. demonstrate that CD39 expression marks tumor-reactive CD8+ T cells. High baseline levels of CD39+ CD8+ T cells are associated with ICB efficacy in lung cancer. Thus, CD39 is a potential tumor-extrinsic biomarker for guiding cancer management. [ABSTRACT FROM AUTHOR]

Summary Heritability and genome stability are shaped by meiotic recombination, which is initiated via hundreds of DNA double-strand breaks (DSBs). The distribution of DSBs throughout the genome is not random, but mechanisms molding this landscape remain poorly understood. Here, we exploit genome-wide maps of mouse DSBs at unprecedented nucleotide resolution to uncover previously invisible spatial features of recombination. At fine scale, we reveal a stereotyped hotspot structure—DSBs occur within narrow zones between methylated nucleosomes—and identify relationships between SPO11, chromatin, and the histone methyltransferase PRDM9. At large scale, DSB formation is suppressed on non-homologous portions of the sex chromosomes via the DSB-responsive kinase ATM, which also shapes the autosomal DSB landscape at multiple size scales. We also provide a genome-wide analysis of exonucleolytic DSB resection lengths and elucidate spatial relationships between DSBs and recombination products. Our results paint a comprehensive picture of features governing successive steps in mammalian meiotic recombination. [ABSTRACT FROM AUTHOR]

Summary: The nonrandom distribution of meiotic recombination influences patterns of inheritance and genome evolution, but chromosomal features governing this distribution are poorly understood. Formation of the DNA double-strand breaks (DSBs) that initiate recombination results in the accumulation of Spo11 protein covalently bound to small DNA fragments. By sequencing these fragments, we uncover a genome-wide DSB map of unprecedented resolution and sensitivity. We use this map to explore how DSB distribution is influenced by large-scale chromosome structures, chromatin, transcription factors, and local sequence composition. Our analysis offers mechanistic insight into DSB formation and early processing steps, supporting the view that the recombination terrain is molded by combinatorial and hierarchical interaction of factors that work on widely different size scales. This map illuminates the occurrence of DSBs in repetitive DNA elements, repair of which can lead to chromosomal rearrangements. We also discuss implications for evolutionary dynamics of recombination hot spots. [ABSTRACT FROM AUTHOR]

Activation of mitogenic signaling pathways is a common oncogenic driver of many solid tumors including lung cancer. Although activating mutations in the mitogen-activated protein kinase (MAPK) pathway are prevalent in non-small cell lung cancers, MAPK pathway activity, counterintuitively, is relatively suppressed in the more aggressively proliferative small cell lung cancer (SCLC). Here, we elucidate the role of the MAPK pathway and how it interacts with other signaling pathways in SCLC. We find that the most common SCLC subtype, SCLC-A associated with high expression of ASCL1 , is selectively sensitive to MAPK activation in vitro and in vivo through induction of cell-cycle arrest and senescence. We show strong upregulation of ERK negative feedback regulators and STAT signaling upon MAPK activation in SCLC-A lines. These findings provide insight into the complexity of signaling networks in SCLC and suggest subtype-specific mitogenic vulnerabilities., Competing Interests: C.M.R. has consulted regarding oncology drug development with Amgen, Daiichi Sankyo, Genentech/Roche, Ipsen, Jazz, Merck, Pfizer, Syros, and Vavotek. C.M.R. serves on the scientific advisory boards of Bridge Medicines, Earli, and Harpoon Therapeutics., (© 2021 The Authors.)

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids., (Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)