Exhausted T cells in chronic infections and cancer have sustained expression of the inhibitory receptor programmed cell death 1 (PD-1). Therapies that block the PD-1 pathway have shown promising clinical results in a significant number of advanced-stage cancer patients. Nonetheless, a better understanding of the immunological responses induced by PD-1 blockade in cancer patients is lacking. Identification of predictive biomarkers is a priority in the field, but whether peripheral blood analysis can provide biomarkers to monitor or predict patients’ responses to treatment remains to be resolved. In this study, we analyzed longitudinal blood samples from advanced stage non–small cell lung cancer (NSCLC) patients (n = 29) receiving PD-1–targeted therapies. We detected an increase in Ki-67+ PD-1+ CD8 T cells following therapy in ∼70% of patients, and most responses were induced after the first or second treatment cycle. This T-cell activation was not indiscriminate because we observed only minimal effects on EBV-specific CD8 T cells, suggesting that responding cells may be tumor specific. These proliferating CD8 T cells had an effector-like phenotype (HLA-DR+, CD38+, Bcl-2lo), expressed costimulatory molecules (CD28, CD27, ICOS), and had high levels of PD-1 and coexpression of CTLA-4. We found that 70% of patients with disease progression had either a delayed or absent PD-1+ CD8 T-cell response, whereas 80% of patients with clinical benefit exhibited PD-1+ CD8 T-cell responses within 4 wk of treatment initiation. Our results suggest that peripheral blood analysis may provide valuable insights into NSCLC patients’ responses to PD-1–targeted therapies.

SummaryMultiple signatures of somatic mutations have been identified in cancer genomes. Exome sequences of 1,001 human cancer cell lines and 577 xenografts revealed most common mutational signatures, indicating past activity of the underlying processes, usually in appropriate cancer types. To investigate ongoing patterns of mutational-signature generation, cell lines were cultured for extended periods and subsequently DNA sequenced. Signatures of discontinued exposures, including tobacco smoke and ultraviolet light, were not generated in vitro. Signatures of normal and defective DNA repair and replication continued to be generated at roughly stable mutation rates. Signatures of APOBEC cytidine deaminase DNA-editing exhibited substantial fluctuations in mutation rate over time with episodic bursts of mutations. The initiating factors for the bursts are unclear, although retrotransposon mobilization may contribute. The examined cell lines constitute a resource of live experimental models of mutational processes, which potentially retain patterns of activity and regulation operative in primary human cancers.

Somatic cells acquire mutations throughout the course of an individual’s life. Mutations occurring early in embryogenesis will often be present in a substantial proportion of, but not all, cells in the post-natal human and thus have particular characteristics and impact1. Depending upon their location in the genome and the proportion of cells they are present in, these mosaic mutations can cause a wide range of genetic disease syndromes2 and predispose to cancer3,4. They have a high chance of being transmitted to offspring as de novo germline mutations and, in principle, can provide insights into early human embryonic cell lineages and their contributions to adult tissues5. Although it is known that gross chromosomal abnormalities are remarkably common in early human embryos6 our understanding of early embryonic somatic mutations is very limited. Here, we use whole genome sequences of adult normal blood from 241 individuals to identify 163 early embryonic mutations. We estimate that approximately three base substitution mutations occur per cell per cell-doubling in early human embryogenesis and these are mainly attributable to two known mutational signatures7. We used the mutations to reconstruct developmental lineages of adult cells and demonstrate that the two daughter cells of many early embryonic cell doublings contribute asymmetrically to adult blood at an approximately 2:1 ratio. This study therefore provides insights into the mutation rates, the mutational processes and the developmental outcomes of cell dynamics operative during early human embryogenesis.

Background Genetic research regarding blood lipids has largely focused on DNA sequence variation; few studies have explored epigenetic effects. Genome-wide surveys of DNA methylation may uncover epigenetic factors influencing lipid metabolism. Methods and Results To identify whether differential methylation of cytosine-guanine dinucleotides (CpGs) correlated with lipid phenotypes, we isolated DNA from CD4+ T-cells and quantified proportion of sample methylation at over 450,000 CpGs using the Illumina Infinium HumanMethylation450 Beadchip in 991 participants of the Genetics of Lipid Lowering Drugs and Diet Network. We modeled percent methylation at individual CpGs as a function of fasting very low density lipoprotein cholesterol (VLDL-C) and triglycerides (TG) using mixed linear regression adjusted for age, gender, study site, cell purity, and family structure. Four CpGs (cg00574958, cg17058475, cg01082498, cg09737197) in intron 1 of carnitine palmitoyltransferase 1A (CPT1A) were strongly associated with VLDL-C (P=1.8*10-21 to 1.6*10-8) and TG (P=1.6*10-26 to 1.5*10-9). Array findings were validated by bisulfite sequencing. We performed qPCR experiments demonstrating that methylation of the top CpG (cg00574958) was correlated with CPT1A expression. The association of cg00574958 with TG and CPT1A expression were replicated in the Framingham Heart Study (P=4.1*10-14 and 3.1*10-13, respectively). DNA methylation at CPT1A cg00574958 explained 11.6% and 5.5% of the variation in TG in the discovery and replication cohorts, respectively. Conclusions This genome-wide epigenomic study identified CPT1A methylation as strongly and robustly associated with fasting VLDL-C and TG. Identifying novel epigenetic contributions to lipid traits may inform future efforts to identify new treatment targets and/or biomarkers of disease risk.