Urinary Proteomics and Outcomes in Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Although several studies have addressed plasma proteomics in heart failure with preserved ejection fraction, limited data are available on the prognostic value of urinary proteomics. The objective of our study was to identify urinary proteins/peptides associated with death and heart failure admission in patients with heart failure with preserved ejection fraction. METHODS AND RESULTS: The study population included participants enrolled in TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist Trial). The relationship between urine protein levels and the risk of death or heart failure admission was assessed using Cox regression, in both nonadjusted analyses and adjusting for urine creatinine levels, and the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) score. A total of 426 (12.4%) TOPCAT participants had urinary protein data and were included. There were 40 urinary proteins/peptides significantly associated with death or heart failure admission in nonadjusted analyses, 21 of which were also significant adjusted analyses. Top proteins in the adjusted analysis included ANGPTL2 (angiopoietin-like protein 2) (hazard ratio [HR], 0.5731 [95% CI, 0.47-0.7]; P=3.13E-05), AMY2A (α amylase 2A) (HR, 0.5496 [95% CI, 0.44-0.69]; P=0.0001), and DNASE1 (deoxyribonuclease-1) (HR, 0.5704 [95% CI, 0.46-0.71]; P=0.0002). Higher urinary levels of proteins involved in fibrosis (collagen VI α-1, collagen XV α-1), metabolism (pancreatic α-amylase 2A/B, mannosidase α class 1A member 1), and inflammation (heat shock protein family D member 1, inducible T cell costimulatory ligand) were associated with a lower risk of death or heart failure admission. CONCLUSIONS: Our study identifies several novel associations between urinary proteins/peptides and outcomes in heart failure with preserved ejection fraction. Many of these associations are independent of clinical risk scores and may aid in risk stratification in this patient population.

A Molecular Epidemiological Analysis Of Programmed Cell Death Ligand-1 (PD-L1) Protein Expression, Mutations And Survival In Non-Small Cell Lung Cancer.

PURPOSE: To characterize programmed cell death ligand-1 (PD-L1) expression in relation to survival and gene mutation status in patients with advanced NSCLC. The study also explored the influence of tumor mutational burden (TMB) on PD-L1 expression and patient characteristics. PATIENTS AND METHODS: Adult patients with histologically or cytologically documented Stage IIIB/Stage IV/recurrent/progressive NSCLC, Eastern Cooperative Oncology Group performance status 0 to 3, and >2 lines of prior systemic treatment regimens were included in this retrospective analysis. Patients were treated from 1997 to 2015 at H. Lee Moffitt Cancer Center and Research Institute, Tampa, or at 7 community centers across the United States. PD-L1 expression level was determined using the VENTANA PD-L1 (SP263) Assay. EGFR and KRAS mutation status and ALK rearrangements were determined by targeted DNA sequencing; these were obtained from clinical records where targeted DNA sequencing was not performed. TMB was calculated as the total number of somatic mutations per sample. RESULTS: From a total of 136 patients included in the study, 23.5% had tumors with high PD-L1 expression (≥25%). There were no significant differences in patient characteristics, overall survival (OS), and progression-free survival (PFS) between patients with high PD-L1 expression (median OS: 39.5 months; median PFS: 15.8 months) vs low PD-L1 expression (<25%; median OS: 38.1 months; median PFS: 18.6 months). PD-L1 expression level correlated (P=0.05) with TMB and was consistent with The Cancer Genome Atlas data. CONCLUSION: In this retrospective analysis, survival outcomes of patients with advanced NSCLC were comparable by PD-L1 expression level. EGFR and KRAS mutation status were not found to be significantly associated with PD-L1 expression level, while TMB was weakly associated with PD-L1 expression level. Overall, PD-L1 expression level was not observed to be an independent prognostic biomarker in this cohort of patients with advanced NSCLC treated with chemotherapy.

Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer.

INTRODUCTION: Tumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. OBJECTIVE: Here we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase III clinical trial. METHODS: In the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small-cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne® CDx assay. RESULTS: TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman's r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearman's r = 0.90). CONCLUSIONS: Parameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.

Circulating T Cell Subpopulations Correlate With Immune Responses at the Tumor Site and Clinical Response to PD1 Inhibition in Non-Small Cell Lung Cancer.

Agents targeting the PD1-PDL1 axis have transformed cancer therapy. Factors that influence clinical response to PD1-PDL1 inhibitors include tumor mutational burden, immune infiltration of the tumor, and local PDL1 expression. To identify peripheral correlates of the anti-tumor immune response in the absence of checkpoint blockade, we performed a retrospective study of circulating T cell subpopulations and matched tumor gene expression in melanoma and non-small cell lung cancer (NSCLC) patients. Notably, both melanoma and NSCLC patients whose tumors exhibited increased inflammatory gene transcripts presented high CD4+ and CD8+ central memory T cell (CM) to effector T cell (Eff) ratios in blood. Consequently, we evaluated CM/Eff T cell ratios in a second cohort of NSCLC. The data showed that high CM/Eff T cell ratios correlated with increased tumor PDL1 expression. Furthermore, of the 22 patients within this NSCLC cohort who received nivolumab, those with high CM/Eff T cell ratios, had longer progression-free survival (PFS) (median survival: 91 vs. 215 days). These findings show that by providing a window into the state of the immune system, peripheral T cell subpopulations inform about the state of the anti-tumor immune response and identify potential blood biomarkers of clinical response to checkpoint inhibitors in melanoma and NSCLC.

STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma.

KRAS is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that STK11/LKB1 (KL) or TP53 (KP) comutations define distinct subgroups of KRAS-mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups (P < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with KRAS-mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; P = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free (P < 0.001) and overall (P = 0.0015) survival compared with KRASMUT;STK11/LKB1WT LUAC. Among 924 LUACs, STK11/LKB1 alterations were the only marker significantly associated with PD-L1 negativity in TMBIntermediate/High LUAC. The impact of STK11/LKB1 alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In Kras-mutant murine LUAC models, Stk11/Lkb1 loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify STK11/LKB1 alterations as a major driver of primary resistance to PD-1 blockade in KRAS-mutant LUAC.Significance: This work identifies STK11/LKB1 alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in KRAS-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. Cancer Discov; 8(7); 822-35. ©2018 AACR.See related commentary by Etxeberria et al., p. 794This article is highlighted in the In This Issue feature, p. 781.

Comparative analysis of primary versus relapse/refractory DLBCL identifies shifts in mutation spectrum.

Current understanding of the mutation spectrum of relapsed/refractory (RR) tumors is limited. We performed whole exome sequencing (WES) on 47 diffuse large B cell lymphoma (DLBCL) tumors that persisted after R-CHOP treatment, 8 matched to primary biopsies. We compared genomic alterations from the RR cohort against two treatment-naïve DLBCL cohorts (n=112). While the overall number and types of mutations did not differ significantly, we identified frequency changes in DLBCL driver genes. The overall frequency of MYD88 mutant samples increased (12% to 19%), but we noted a decrease in p.L265P (8% to 4%) and increase in p.S219C mutations (2% to 6%). CARD11 p.D230N, PIM1 p.K115N and CD79B p.Y196C mutations were not observed in the RR cohort, although these mutations were prominent in the primary DLBCL samples. We observed an increase in BCL2 mutations (21% to 38% of samples), BCL2 amplifications (3% to 6% of samples) and CREBBP mutations (31% to 42% of samples) in the RR cohort, supported by acquisition of mutations in these genes in relapsed compared to diagnostic biopsies from the same patient. These increases may reflect the genetic characteristics of R-CHOP RR tumors expected to be enriched for during clinical trial enrollment. These findings hold significance for a number of emerging targeted therapies aligned to genetic targets and biomarkers in DLBCL, reinforcing the importance of time-of-treatment biomarker screening during DLBCL therapy selection.

PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR.

Rational Selection of Syngeneic Preclinical Tumor Models for Immunotherapeutic Drug Discovery.

Murine syngeneic tumor models are critical to novel immuno-based therapy development, but the molecular and immunologic features of these models are still not clearly defined. The translational relevance of differences between the models is not fully understood, impeding appropriate preclinical model selection for target validation, and ultimately hindering drug development. Across a panel of commonly used murine syngeneic tumor models, we showed variable responsiveness to immunotherapies. We used array comparative genomic hybridization, whole-exome sequencing, exon microarray analysis, and flow cytometry to extensively characterize these models, which revealed striking differences that may underlie these contrasting response profiles. We identified strong differential gene expression in immune-related pathways and changes in immune cell-specific genes that suggested differences in tumor immune infiltrates between models. Further investigation using flow cytometry showed differences in both the composition and magnitude of the tumor immune infiltrates, identifying models that harbor "inflamed" and "non-inflamed" tumor immune infiltrate phenotypes. We also found that immunosuppressive cell types predominated in syngeneic mouse tumor models that did not respond to immune-checkpoint blockade, whereas cytotoxic effector immune cells were enriched in responsive models. A cytotoxic cell-rich tumor immune infiltrate has been correlated with increased efficacy of immunotherapies in the clinic, and these differences could underlie the varying response profiles to immunotherapy between the syngeneic models. This characterization highlighted the importance of extensive profiling and will enable investigators to select appropriate models to interrogate the activity of immunotherapies as well as combinations with targeted therapies in vivo Cancer Immunol Res; 5(1); 29-41. ©2016 AACR.

A multigene mutation classification of 468 colorectal cancers reveals a prognostic role for APC.

Colorectal cancer (CRC) is a highly heterogeneous disease, for which prognosis has been relegated to clinicopathologic staging for decades. There is a need to stratify subpopulations of CRC on a molecular basis to better predict outcome and assign therapies. Here we report targeted exome-sequencing of 1,321 cancer-related genes on 468 tumour specimens, which identified a subset of 17 genes that best classify CRC, with APC playing a central role in predicting overall survival. APC may assume 0, 1 or 2 truncating mutations, each with a striking differential impact on survival. Tumours lacking any APC mutation carry a worse prognosis than single APC mutation tumours; however, two APC mutation tumours with mutant KRAS and TP53 confer the poorest survival among all the subgroups examined. Our study demonstrates a prognostic role for APC and suggests that sequencing of APC may have clinical utility in the routine staging and potential therapeutic assignment for CRC.

Co-evolution of somatic variation in primary and metastatic colorectal cancer may expand biopsy indications in the molecular era.

INTRODUCTION: Metastasis is thought to be a clonal event whereby a single cell initiates the development of a new tumor at a distant site. However the degree to which primary and metastatic tumors differ on a molecular level remains unclear. To further evaluate these concepts, we used next generation sequencing (NGS) to assess the molecular composition of paired primary and metastatic colorectal cancer tissue specimens. METHODS: 468 colorectal tumor samples from a large personalized medicine initiative were assessed by targeted gene sequencing of 1,321 individual genes. Eighteen patients produced genomic profiles for 17 paired primary:metastatic (and 2 metastatic:metastatic) specimens. RESULTS: An average of 33.3 mutations/tumor were concordant (shared) between matched samples, including common well-known genes (APC, KRAS, TP53). An average of 2.3 mutations/tumor were discordant (unshared) among paired sites. KRAS mutational status was always concordant. The overall concordance rate for mutations was 93.5%; however, nearly all (18/19 (94.7%)) paired tumors showed at least one mutational discordance. Mutations were seen in: TTN, the largest gene (5 discordant pairs), ADAMTS20, APC, MACF1, RASA1, TP53, and WNT2 (2 discordant pairs), SMAD2, SMAD3, SMAD4, FBXW7, and 66 others (1 discordant pair). CONCLUSIONS: Whereas primary and metastatic tumors displayed little variance overall, co-evolution produced incremental mutations in both. These results suggest that while biopsy of the primary tumor alone is likely sufficient in the chemotherapy-naïve patient, additional biopsies of primary or metastatic disease may be necessary to precisely tailor therapy following chemotherapy resistance or insensitivity in order to adequately account for tumor evolution.

Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene.

Decreased insulin sensitivity, also referred to as insulin resistance (IR), is a fundamental abnormality in patients with type 2 diabetes and a risk factor for cardiovascular disease. While IR predisposition is heritable, the genetic basis remains largely unknown. The GENEticS of Insulin Sensitivity consortium conducted a genome-wide association study (GWAS) for direct measures of insulin sensitivity, such as euglycemic clamp or insulin suppression test, in 2,764 European individuals, with replication in an additional 2,860 individuals. The presence of a nonsynonymous variant of N-acetyltransferase 2 (NAT2) [rs1208 (803A>G, K268R)] was strongly associated with decreased insulin sensitivity that was independent of BMI. The rs1208 "A" allele was nominally associated with IR-related traits, including increased fasting glucose, hemoglobin A1C, total and LDL cholesterol, triglycerides, and coronary artery disease. NAT2 acetylates arylamine and hydrazine drugs and carcinogens, but predicted acetylator NAT2 phenotypes were not associated with insulin sensitivity. In a murine adipocyte cell line, silencing of NAT2 ortholog Nat1 decreased insulin-mediated glucose uptake, increased basal and isoproterenol-stimulated lipolysis, and decreased adipocyte differentiation, while Nat1 overexpression produced opposite effects. Nat1-deficient mice had elevations in fasting blood glucose, insulin, and triglycerides and decreased insulin sensitivity, as measured by glucose and insulin tolerance tests, with intermediate effects in Nat1 heterozygote mice. Our results support a role for NAT2 in insulin sensitivity.

Weight loss after gastric bypass is associated with a variant at 15q26.1.

The amount of weight loss attained after Roux-en-Y gastric bypass (RYGB) surgery follows a wide and normal distribution, and recent evidence indicates that this weight loss is due to physiological, rather than mechanical, mechanisms. To identify potential genetic factors associated with weight loss after RYGB, we performed a genome-wide association study (GWAS) of 693 individuals undergoing RYGB and then replicated this analysis in an independent population of 327 individuals undergoing RYGB. We found that a 15q26.1 locus near ST8SIA2 and SLCO3A1 was significantly associated with weight loss after RYGB. Expression of ST8SIA2 in omental fat of these individuals at baseline was significantly associated with weight loss after RYGB. Gene expression analysis in RYGB and weight-matched, sham-operated (WMS) mice revealed that expression of St8sia2 and Slco3a1 was significantly altered in metabolically active tissues in RYGB-treated compared to WMS mice. These findings provide strong evidence for specific genetic influences on weight loss after RYGB and underscore the biological nature of the response to RYGB.

Inverse regulation of inflammation and mitochondrial function in adipose tissue defines extreme insulin sensitivity in morbidly obese patients.

Obesity is associated with insulin resistance, a major risk factor for type 2 diabetes and cardiovascular disease. However, not all obese individuals are insulin resistant, which confounds our understanding of the mechanistic link between these conditions. We conducted transcriptome analyses on 835 obese subjects with mean BMI of 48.8, on which we have previously reported genetic associations of gene expression. Here, we selected ~320 nondiabetic (HbA(1c) <7.0) subjects and further stratified the cohort into insulin-resistant versus insulin-sensitive subgroups based on homeostasis model assessment-insulin resistance. An unsupervised informatics analysis revealed that immune response and inflammation-related genes were significantly downregulated in the omental adipose tissue of obese individuals with extreme insulin sensitivity and, to a much lesser extent, in subcutaneous adipose tissue. In contrast, genes related to ß-oxidation and the citric acid cycle were relatively overexpressed in adipose of insulin-sensitive patients. These observations were verified by querying an independent cohort of our published dataset of 37 subjects whose subcutaneous adipose tissue was sampled before and after treatment with thiazolidinediones. Whereas the immune response and inflammation pathway genes were downregulated by thiazolidinedione treatment, ß-oxidation and citric acid cycle genes were upregulated. This work highlights the critical role that omental adipose inflammatory pathways might play in the pathophysiology of insulin resistance, independent of body weight.

Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets.

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin(ob/ob) mutation (Lep(ob)). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein-protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Integrating genetic association, genetics of gene expression, and single nucleotide polymorphism set analysis to identify susceptibility Loci for type 2 diabetes mellitus.

Large-scale genome-wide association studies (GWAS) have identified over 40 genomic regions significantly associated with type 2 diabetes mellitus. However, GWAS results are not always straightforward to interpret, and linking these loci to meaningful disease etiology is often difficult without extensive follow-up studies. The authors expanded on previously reported type 2 diabetes mellitus GWAS from the nested case-control studies of 2 prospective US cohorts by incorporating expression single nucleotide polymorphism (SNP) information and applying SNP set enrichment analysis to identify sets of SNPs associated with genes that could provide further biologic insight to traditional genome-wide analysis. Using data collected between 1989 and 1994 in these previous studies to form a nested case-control study, the authors found that 3 of the most significantly associated SNPs to type 2 diabetes mellitus in their study are expression SNPs to the lymphocyte antigen 75 gene (LY75), the ubiquitin-specific peptidase 36 gene (USP36), and the phosphatidylinositol transfer protein, cytoplasmic 1 gene (PITPNC1). SNP set enrichment analysis of the GWAS results identified enrichment for expression SNPs to the macrophage-enriched module and the Gene Ontology (GO) biologic process fat cell differentiation human, which includes the transcription factor 7-like 2 gene (TCF7L2), as well as other type 2 diabetes mellitus-associated genes. Integrating genome-wide association, gene expression, and gene set analysis may provide valuable biologic support for potential type 2 diabetes mellitus susceptibility loci and may be useful in identifying new targets or pathways of interest for the treatment and prevention of type 2 diabetes mellitus.

Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women.

Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. Subcutaneous and visceral fat were quantified in 5,560 women and 4,997 men from 4 population-based studies. Genome-wide genotyping was performed using standard arrays and imputed to ~2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), VAT adjusted for body mass index, and VAT/SAT ratio (a metric of the propensity to store fat viscerally as compared to subcutaneously) in the overall sample and in women and men separately. A weighted z-score meta-analysis was conducted. For the VAT/SAT ratio, our most significant p-value was rs11118316 at LYPLAL1 gene (p = 3.1 × 10E-09), previously identified in association with waist-hip ratio. For SAT, the most significant SNP was in the FTO gene (p = 5.9 × 10E-08). Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, rs1659258 near THNSL2 (p = 1.6 × 10-08), but not men (p = 0.75). Validation of this SNP in the GIANT consortium data demonstrated a similar sex-specific pattern, with observed significance in women (p = 0.006) but not men (p = 0.24) for BMI and waist circumference (p = 0.04 [women], p = 0.49 [men]). Finally, we interrogated our data for the 14 recently published loci for body fat distribution (measured by waist-hip ratio adjusted for BMI); associations were observed at 7 of these loci. In contrast, we observed associations at only 7/32 loci previously identified in association with BMI; the majority of overlap was observed with SAT. Genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in women. More refined phenotypes for body composition and fat distribution can detect new loci not previously uncovered in large-scale GWAS of anthropometric traits.

Clinicopathologic and gene expression parameters predict liver cancer prognosis.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. METHODS: Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. RESULTS: HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. CONCLUSION: When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.

Dissecting cis regulation of gene expression in human metabolic tissues.

Complex diseases such as obesity and type II diabetes can result from a failure in multiple organ systems including the central nervous system and tissues involved in partitioning and disposal of nutrients. Studying the genetics of gene expression in tissues that are involved in the development of these diseases can provide insights into how these tissues interact within the context of disease. Expression quantitative trait locus (eQTL) studies identify mRNA expression changes linked to proximal genetic signals (cis eQTLs) that have been shown to affect disease. Given the high impact of recent eQTL studies, it is important to understand what role sample size and environment plays in identification of cis eQTLs. Here we show in a genotyped obese human population that the number of cis eQTLs obey precise scaling laws as a function of sample size in three profiled tissues, i.e. omental adipose, subcutaneous adipose and liver. Also, we show that genes (or transcripts) with cis eQTL associations detected in a small population are detected at approximately 90% rate in the largest population available for our study, indicating that genes with strong cis acting regulatory elements can be identified with relatively high confidence in smaller populations. However, by increasing the sample size we allow for better detection of weaker and more distantly located cis-regulatory elements. Yet, we determined that the number of tissue specific cis eQTLs saturates in a modestly sized cohort while the number of cis eQTLs common to all tissues fails to reach a maximum value. Understanding the power laws that govern the number and specificity of eQTLs detected in different tissues, will allow a better utilization of genetics of gene expression to inform the molecular mechanism underlying complex disease traits.

Heritability of the weight loss response to gastric bypass surgery.

CONTEXT: The use of Roux-en-Y gastric bypass (RYGB) surgery to treat severe obesity has grown dramatically. RYGB is highly effective, but the response in individual patients varies widely, and clinical predictors have been able to explain only a fraction of this variation. OBJECTIVE: Our objective was to determine whether there is a significant genetic contribution to weight loss after RYGB. METHODS: We genotyped 848 patients undergoing RYGB. Using identity-by-descent methods, we identified 13 pairs of first-degree relatives. We identified an additional 10 pairs of individuals who were living together but are not genetically related and randomly paired the remaining 794 individuals. We then compared weight loss within and across pairs. RESULTS: First-degree relative pairs had a similar response to surgery, with a 9% mean difference in excess weight loss between members of each pair. This similarity was not seen with cohabitating individuals (26% mean difference; P = 0.005 vs. first-degree pairs) or unrelated individuals (25% mean difference; P = 0.001). Cohabitating individuals had within-pair differences in weight loss no more similar than randomly paired individuals (P = 0.60). The pair relationship explained a significant portion of the variation in weight loss in first-degree relatives [intraclass correlation coefficient (ICC) = 70.4%; P = 0.02] but not in random subjects (ICC = 0.9%; P = 0.48) or genetically unrelated cohabitating individuals (ICC = 14.3%; P = 0.67). CONCLUSIONS: Genetic factors strongly influence the effect of RYGB on body weight. Identification of the specific genes that mediate this effect will advance our understanding of the biological mechanisms of weight loss after RYGB and should help identify patients who will benefit the most from this intervention.