DGKα/ζ inhibitors combine with PD-1 checkpoint therapy to promote T cell-mediated antitumor immunity.

Programmed cell death protein 1 (PD-1) immune checkpoint blockade therapy has revolutionized cancer treatment. Although PD-1 blockade is effective in a subset of patients with cancer, many fail to respond because of either primary or acquired resistance. Thus, next-generation strategies are needed to expand the depth and breadth of clinical responses. Toward this end, we designed a human primary T cell phenotypic high-throughput screening strategy to identify small molecules with distinct and complementary mechanisms of action to PD-1 checkpoint blockade. Through these efforts, we selected and optimized a chemical series that showed robust potentiation of T cell activation and combinatorial activity with αPD-1 blockade. Target identification was facilitated by chemical proteomic profiling with a lipid-based photoaffinity probe, which displayed enhanced binding to diacylglycerol kinase α (DGKα) in the presence of the active compound, a phenomenon that correlated with the translocation of DGKα to the plasma membrane. We further found that optimized leads within this chemical series were potent and selective inhibitors of both DGKα and DGKζ, lipid kinases that constitute an intracellular T cell checkpoint that blunts T cell signaling through diacylglycerol metabolism. We show that dual DGKα/ζ inhibition amplified suboptimal T cell receptor signaling mediated by low-affinity antigen presentation and low major histocompatibility complex class I expression on tumor cells, both hallmarks of resistance to PD-1 blockade. In addition, DGKα/ζ inhibitors combined with αPD-1 therapy to elicit robust tumor regression in syngeneic mouse tumor models. Together, these findings support targeting DGKα/ζ as a next-generation T cell immune checkpoint strategy.

Analysis of Pre- and Posttreatment Tissues from the SWOG S0800 Trial Reveals an Effect of Neoadjuvant Chemotherapy on the Breast Cancer Genome.

PURPOSE: We performed whole-exome sequencing (WES) of pre- and posttreatment cancer tissues to assess the somatic mutation landscape of tumors before and after neoadjuvant taxane and anthracycline chemotherapy with or without bevacizumab. EXPERIMENTAL DESIGN: Twenty-nine pretreatment biopsies from the SWOG S0800 trial were subjected to WES to identify mutational patterns associated with response to neoadjuvant chemotherapy. Nine matching samples with residual cancer after therapy were also analyzed to assess changes in mutational patterns in response to therapy. RESULTS: In pretreatment samples, a higher proportion of mutation signature 3, a BRCA-mediated DNA repair deficiency mutational signature, was associated with higher rate of pathologic complete response (pCR; median signature weight 24%, range 0%-38% in pCR vs. median weight 0%, range 0%-19% in residual disease, Wilcoxon rank sum, Bonferroni P = 0.007). We found no biological pathway level mutations associated with pCR or enriched in posttreatment samples. We observed statistically significant enrichment of high functional impact mutations in the "E2F targets" and "G2-M checkpoint" pathways in residual cancer samples implicating these pathways in resistance to therapy and a significant depletion of mutations in the "myogenesis pathway" suggesting the cells harboring these variants were effectively eradicated by therapy. CONCLUSIONS: These results suggest that genomic disturbances in BRCA-related DNA repair mechanisms, reflected by a dominant mutational signature 3, confer increased chemotherapy sensitivity. Cancers that survive neoadjuvant chemotherapy frequently have alterations in cell-cycle-regulating genes but different genes of the same pathways are affected in different patients.

CD36-Mediated Metabolic Rewiring of Breast Cancer Cells Promotes Resistance to HER2-Targeted Therapies.

Although it is established that fatty acid (FA) synthesis supports anabolic growth in cancer, the role of exogenous FA uptake remains elusive. Here we show that, during acquisition of resistance to HER2 inhibition, metabolic rewiring of breast cancer cells favors reliance on exogenous FA uptake over de novo FA synthesis. Through cDNA microarray analysis, we identify the FA transporter CD36 as a critical gene upregulated in cells with acquired resistance to the HER2 inhibitor lapatinib. Accordingly, resistant cells exhibit increased exogenous FA uptake and metabolic plasticity. Genetic or pharmacological inhibition of CD36 suppresses the growth of lapatinib-resistant but not lapatinib-sensitive cells in vitro and in vivo. Deletion of Cd36 in mammary tissues of MMTV-neu mice significantly attenuates tumorigenesis. In breast cancer patients, CD36 expression increases following anti-HER2 therapy, which correlates with a poor prognosis. Our results define CD36-mediated metabolic rewiring as an essential survival mechanism in HER2-positive breast cancer.

Association of T-Cell Receptor Repertoire Use With Response to Combined Trastuzumab-Lapatinib Treatment of HER2-Positive Breast Cancer: Secondary Analysis of the NeoALTTO Randomized Clinical Trial.

Importance: Dual anti-HER2 blockade increased the rate of pathologic complete response (pCR) in the Neoadjuvant Lapatinib and/or Trastuzumab Treatment Optimisation (NeoALTTO) trial, and high immune gene expression was associated with pCR in all treatment arms. So far, no marker has been identified that is specifically associated with the benefit from dual HER2 blockade. Objective: To examine if use of the T-cell ß chain variable genes adds to the potential association of immune gene signatures with response to dual HER2 blockade. Design, Setting, and Participants: In the NeoALTTO trial, HER2-positive patients recruited between January 5, 2008, and May 27, 2010, were treated with paclitaxel plus either lapatinib or trastuzumab or both as neoadjuvant therapy. In this study, RNA sequencing data from baseline tumor specimens of 245 patients in the NeoALTTO trial were analyzed and reads were aligned to TRBV gene reference sequences using a previously published Basic Local Alignment Search Tool T-cell receptor mapping pipeline. Total TRBV gene use, Shannon entropy, and gene richness were calculated for each tumor, and nonnegative matrix factorization was used to define TRBV co-use metagenes (TMGs). The association between TRBV metrics, tumor genomic metrics, and response was assessed with multivariable logistic regression. Statistical analysis was performed from January 23 to December 2, 2017. Main Outcomes and Measures: The association between TRBV use metrics and pCR. Results: Among the 245 women with available data (mean [SD] age, 49 [11] years), total TRBV use correlated positively with a gene expression signature for immune activity (Spearman ρ = 0.93; P < .001). High use of TRBV11-3 and TMG2, characterized by high use of TRBV4.3, TRBV6.3, and TRBV7.2, was associated with a higher rate of pCR to dual HER2-targeted therapy (TRBV11-3 interaction: odds ratio, 2.63 [95% CI, 1.22-6.47]; P = .02; TMG2 interaction: odds ratio, 3.39 [95% CI, 1.57-8.27]; P = .004). Immune-rich cancers with high TMG2 levels (n = 92) had significantly better response to dual HER2-targeted treatment compared with the single therapy arms (rate of pCR, 68% [95% CI, 52%-83%] vs 21% [95% CI, 10%-31%]; P < .001), whereas those with low TMG2 levels did not benefit from dual therapy. High TMG2 levels were also associated with a higher rate of pCR to the combined therapy in immune-poor tumors (n = 30; pCR, 50% [95% CI, 22%-78%] vs 6% [95% CI, 0%-16%]; P = .009). Conclusions and Relevance: Use patterns of TRBV genes potentially provide information about the association with response to dual HER2 blockade beyond immune gene signatures. High use of TRBV11.3 or TRBV4.3, TRBV6.3, and TRBV7.2 identifies patients who have a better response to dual HER2 targeted therapy. Trial Registration: ClinicalTrials.gov Identifier: NCT00553358.

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS.

Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.

Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer's disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson's disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at http://genocanyon.med.yale.edu/GenoSkyline.

Leveraging functional annotations in genetic risk prediction for human complex diseases.

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.

Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies.

Extensive efforts have been made to understand genomic function through both experimental and computational approaches, yet proper annotation still remains challenging, especially in non-coding regions. In this manuscript, we introduce GenoSkyline, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations. GenoSkyline successfully identified a variety of non-coding regulatory machinery including enhancers, regulatory miRNA, and hypomethylated transposable elements in extensive case studies. Integrative analysis of GenoSkyline annotations and results from genome-wide association studies (GWAS) led to novel biological insights on the etiologies of a number of human complex traits. We also explored using tissue-specific functional annotations to prioritize GWAS signals and predict relevant tissue types for each risk locus. Brain and blood-specific annotations led to better prioritization performance for schizophrenia than standard GWAS p-values and non-tissue-specific annotations. As for coronary artery disease, heart-specific functional regions was highly enriched of GWAS signals, but previously identified risk loci were found to be most functional in other tissues, suggesting a substantial proportion of still undetected heart-related loci. In summary, GenoSkyline annotations can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases. GenoSkyline is available at http://genocanyon.med.yale.edu/GenoSkyline.

Scalable and cost-effective NGS genotyping in the cloud.

BACKGROUND: While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10's of dollars. RESULTS: We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. CONCLUSIONS: Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.