Keston Aquino-Michaels

Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy.

Gut microbes affect immunotherapy The unleashing of antitumor T cell responses has ushered in a new era of cancer treatment. Although these therapies can cause dramatic tumor regressions in some patients, many patients inexplicably see no benefit. Mice have been used in two studies to investigate what might be happening. Specific members of the gut microbiota influence the efficacy of this type of immunotherapy (see the Perspective by Snyder et al.). Vétizou et al. found that optimal responses to anticytotoxic T lymphocyte antigen blockade required specific Bacteroides spp. Similarly, Sivan et al. discovered that Bifidobacterium spp. enhanced the efficacy of antiprogrammed cell death ligand 1 therapy. Science, this issue, p. 1079 and p. 1084; see also p. 1031 Gut microbes modulate the effectiveness of cancer immunotherapies in mice. [Also see Perspective by Snyder et al.] T cell infiltration of solid tumors is associated with favorable patient outcomes, yet the mechanisms underlying variable immune responses between individuals are not well understood. One possible modulator could be the intestinal microbiota. We compared melanoma growth in mice harboring distinct commensal microbiota and observed differences in spontaneous antitumor immunity, which were eliminated upon cohousing or after fecal transfer. Sequencing of the 16S ribosomal RNA identified Bifidobacterium as associated with the antitumor effects. Oral administration of Bifidobacterium alone improved tumor control to the same degree as programmed cell death protein 1 ligand 1 (PD-L1)–specific antibody therapy (checkpoint blockade), and combination treatment nearly abolished tumor outgrowth. Augmented dendritic cell function leading to enhanced CD8+ T cell priming and accumulation in the tumor microenvironment mediated the effect. Our data suggest that manipulating the microbiota may modulate cancer immunotherapy.

Ethnicity-specific pharmacogenetics: The case of warfarin in African Americans

Using a derivation cohort (N=349), we developed the first warfarin dosing algorithm that includes recently discovered polymorphisms in VKORC1 and CYP2C9 associated with warfarin dose requirement in African Americans (AAs). We tested our novel algorithm in an independent cohort of 129 AAs and compared the dose prediction to the International Warfarin Pharmacogenetics Consortium (IWPC) dosing algorithms. Our algorithm explains more of the phenotypic variation (R2=0.27) than the IWPC pharmacogenomics (R2=0.15) or clinical (R2=0.16) algorithms. Among high-dose patients, our algorithm predicted a higher proportion of patients within 20% of stable warfarin dose (45% vs 29% and 2% in the IWPC pharmacogenomics and clinical algorithms, respectively). In contrast to our novel algorithm, a significant inverse correlation between predicted dose and percent West African ancestry was observed for the IWPC pharmacogenomics algorithm among patients requiring ⩾60 mg per week (β=−2.04, P=0.02).

Poly-Omic Prediction of Complex Traits: OmicKriging

High‐confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome‐wide association studies have discovered thousands of well‐replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics‐level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems‐level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics‐level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

Association of the GGCX (CAA)16/17 repeat polymorphism with higher warfarin dose requirements in African Americans

Objective Little is known about genetic contributors to higher than usual warfarin dose requirements, particularly for African Americans. This study tested the hypothesis that the &ggr;-glutamyl carboxylase (GGCX) genotype contributes to warfarin dose requirements greater than 7.5 mg/day in an African American population. Methods A total of 338 African Americans on a stable dose of warfarin were enrolled. The GGCX rs10654848 (CAA)n, rs12714145 (G>A), and rs699664 (p.R325Q); VKORC1 c.-1639G>A and rs61162043; and CYP2C9*2, *3, *5, *8, *11, and rs7089580 genotypes were tested for their association with dose requirements greater than 7.5 mg/day alone and in the context of other variables known to influence dose variability. Results The GGCX rs10654848 (CAA)16 or 17 repeat occurred at a frequency of 2.6% in African Americans and was overrepresented among patients requiring greater than 7.5 mg/day versus those who required lower doses (12 vs. 3%, P=0.003; odds ratio 4.0, 95% confidence interval, 1.5–10.5). The GGCX rs10654848 genotype remained associated with high dose requirements on regression analysis including age, body size, and VKORC1 genotype. On linear regression, the GGCX rs10654848 genotype explained 2% of the overall variability in warfarin dose in African Americans. An examination of the GGCX rs10654848 genotype in warfarin-treated Caucasians revealed a (CAA)16 repeat frequency of only 0.27% (P=0.008 compared with African Americans). Conclusion These data support the GGCX rs10654848 genotype as a predictor of higher than usual warfarin doses in African Americans, who have a 10-fold higher frequency of the (CAA)16/17 repeat compared with Caucasians.

Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues.

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).

The composition of the microbiota modulates allograft rejection

Transplantation is the only cure for end-stage organ failure, but without immunosuppression, T cells rapidly reject allografts. While genetic disparities between donor and recipient are major determinants of the kinetics of transplant rejection, little is known about the contribution of environmental factors. Because colonized organs have worse transplant outcome than sterile organs, we tested the influence of host and donor microbiota on skin transplant rejection. Compared with untreated conventional mice, pretreatment of donors and recipients with broad-spectrum antibiotics (Abx) or use of germ-free (GF) donors and recipients resulted in prolonged survival of minor antigen-mismatched skin grafts. Increased graft survival correlated with reduced type I IFN signaling in antigen-presenting cells (APCs) and decreased priming of alloreactive T cells. Colonization of GF mice with fecal material from untreated conventional mice, but not from Abx-pretreated mice, enhanced the ability of APCs to prime alloreactive T cells and accelerated graft rejection, suggesting that alloimmunity is modulated by the composition of microbiota rather than the quantity of bacteria. Abx pretreatment of conventional mice also delayed rejection of major antigen-mismatched skin and MHC class II-mismatched cardiac allografts. This study demonstrates that Abx pretreatment prolongs graft survival, suggesting that targeting microbial constituents is a potential therapeutic strategy for enhancing graft acceptance.

Integrated analysis of genetic variation and gene expression reveals novel variant for increased warfarin dose requirement in African Americans.

Essentials Genetic variants controlling gene regulation have not been explored in pharmacogenomics. We tested liver expression quantitative trait loci for association with warfarin dose response. A novel predictor for increased warfarin dose response in African Americans was identified. Precision medicine must take into account population‐specific variation in gene regulation.

PrediXcan: Trait Mapping Using Human Transcriptome Regulation

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Abstract 3276: Identifying cross-phenotype inflammatory effects in cancer

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Through genome-wide association studies we now better understand many of the genetic factors contributing to cancer. However, the overlap between cancer associated loci and inflammation associated loci remains poorly understood. Inflammation has long been considered the underlying basis to several complex traits and plays well-established roles in elevating risk to various cancer types. In fact, genome-wide association studies have identified loci associated with inflammatory biomarkers and risk of cancer. Therefore we sought to test cross-phenotype genetic effects between cancer and inflammation biomarkers. While genome-wide significant loci on their own typically account for very little of the measured narrow-sense heritability, polygenic approaches show greater promise to account for the missing heritability of complex traits. Therefore we built polygenic signatures from inflammatory biomarker genome-wide association studies. Preliminary analysis of a case control pancreatic cancer dataset (304 cases/1425 controls) where we applied seven inflammatory signatures indicates that polygenic scores built from IL-10 and C-reactive protein (CRP) are significant predictors of case control status. Importantly the directions of effect for IL-10 and CRP are consistent with the canonical roles of these proteins. We extended this approach to a breast cancer dataset (1082 cases/1086 controls) where we found that polygenic scores built from CRP are significant predictors of case control status. In both the pancreatic and breast cancer datasets CRP polygenic scores decrease risk of case status. This approach will not only inform us of the cancers with overlapping inflammatory effects, but also allow us to identify the components of the immune system that mediate cancer risk. Ultimately this understanding may lead to the use of anti-inflammatory medication for individuals at risk for a particular cancer. Therefore this research addresses cancer prevention by studying the inflammatory genetic component to risk. Citation Format: Keston Aquino-Michaels, Vasya Trubetskoy, Hae Kyung Im, Nancy Cox. Identifying cross-phenotype inflammatory effects in cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3276. doi:10.1158/1538-7445.AM2014-3276