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.

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.

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.

A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort.

To map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1000 patients undergoing Roux-en-Y gastric bypass (RYGB) and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than 100,000 gene expression traits representing four metabolically relevant tissues: liver, omental adipose, subcutaneous adipose, and stomach. We successfully identified 24,531 eSNPs corresponding to about 10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high-quality disease map for each tissue in morbidly obese patients to not only inform genetic associations identified in this cohort, but in previously published genome-wide association studies as well. These data can aid in elucidating the key networks associated with morbid obesity, response to RYGB, and disease as a whole.

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.

Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes.

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

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.

Common body mass index-associated variants confer risk of extreme obesity.

To investigate the genetic architecture of severe obesity, we performed a genome-wide association study of 775 cases and 3197 unascertained controls at approximately 550,000 markers across the autosomal genome. We found convincing association to the previously described locus including the FTO gene. We also found evidence of association at a further six of 12 other loci previously reported to influence body mass index (BMI) in the general population and one of three associations to severe childhood and adult obesity and that cases have a higher proportion of risk-conferring alleles than controls. We found no evidence of homozygosity at any locus due to identity-by-descent associating with phenotype which would be indicative of rare, penetrant alleles, nor was there excess genome-wide homozygosity in cases relative to controls. Our results suggest that variants influencing BMI also contribute to severe obesity, a condition at the extreme of the phenotypic spectrum rather than a distinct condition.

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.

Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.

Type 2 diabetes results from severe insulin resistance coupled with a failure of b cells to compensate by secreting sufficient insulin. Multiple genetic loci are involved in the development of diabetes, although the effect of each gene on diabetes susceptibility is thought to be small. MicroRNAs (miRNAs) are noncoding 19-22-nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand the relationship between miRNA regulation and obesity-induced diabetes, we quantitatively profiled approximately 220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs profiled were expressed in all three tissues, with many miRNAs in each tissue showing significant changes in response to genetic obesity. Furthermore, several miRNAs in each tissue were differentially responsive to obesity in B6 versus BTBR mice, suggesting that they may be involved in the pathogenesis of diabetes. In liver there were approximately 40 miRNAs that were downregulated in response to obesity in B6 but not BTBR mice, indicating that genetic differences between the mouse strains play a critical role in miRNA regulation. In order to elucidate the genetic architecture of hepatic miRNA expression, we measured the expression of miRNAs in genetically obese F2 mice. Approximately 10% of the miRNAs measured showed significant linkage (miR-eQTLs), identifying loci that control miRNA abundance. Understanding the influence that obesity and genetics exert on the regulation of miRNA expression will reveal the role miRNAs play in the context of obesity-induced type 2 diabetes.

Identification of possible genetic alterations in the breast cancer cell line MCF-7 using high-density SNP genotyping microarray.

CONTEXT: Cancer cell lines are used extensively in various research. Knowledge of genetic alterations in these lines is important for understanding mechanisms underlying their biology. However, since paired normal tissues are usually unavailable for comparison, precisely determining genetic alterations in cancer cell lines is difficult. To address this issue, a highly efficient and reliable method is developed. AIMS: Establishing a highly efficient and reliable experimental system for genetic profiling of cell lines. MATERIALS AND METHODS: A widely used breast cancer cell line, MCF-7, was genetically profiled with 4,396 single nucleotide polymorphisms (SNPs) spanning 11 whole chromosomes and two other small regions using a newly developed high-throughput multiplex genotyping approach. RESULTS: The fractions of homozygous SNPs in MCF-7 (13.3%) were significantly lower than those in the control cell line and in 24 normal human individuals (25.1% and 27.4%, respectively). Homozygous SNPs in MCF-7 were found in clusters. The sizes of these clusters were significantly larger than the expected based on random allelic combination. Fourteen such regions were found on chromosomes 1p, 1q, 2q, 6q, 13, 15q, 16q, 17q and 18p in MCF-7 and two in the small regions. CONCLUSIONS: These results are generally concordant with those obtained using different approaches but are better in defining their chromosomal positions. The used approach provides a reliable way to detecting possible genetic alterations in cancer cell lines without paired normal tissues.

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.

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.

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays.

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of approximately 160,000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300,000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from http://www2.umdnj.edu/lilabweb/publications/AccuTyping.html.

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.

Pretreatment gene expression profiles can be used to predict response to neoadjuvant chemoradiotherapy in esophageal cancer.

BACKGROUND: The use of neoadjuvant therapy, in particular chemoradiotherapy (CRT), in the treatment of esophageal cancer (EC) remains controversial. The ability to predict treatment response in an individual EC patient would greatly aid therapeutic planning. Gene expression profiles of EC were measured and relationship to therapeutic response assessed. METHODS: Tumor biopsy samples taken from 46 EC patients before neoadjuvant CRT were analyzed on 10.5K cDNA microarrays. Response to treatment was assessed and correlated to gene expression patterns by using a support vector machine learning algorithm. RESULTS: Complete clinical response at conclusion of CRT was achieved in 6 of 21 squamous cell carcinoma (SCC) and 11 of 25 adenocarcinoma (AC) patients. CRT response was an independent prognostic factor for survival (P < .001). A range of support vector machine models incorporating 10 to 1000 genes produced a predictive performance of tumor response to CRT peaking at 87% in SCC, but a distinct positive prediction profile was unobtainable for AC. A 32-gene classifier was produced, and by means of this classifier, 10 of 21 SCC patients could be accurately identified as having disease with an incomplete response to therapy, and thus unlikely to benefit from neoadjuvant CRT. CONCLUSIONS: Our study identifies a 32-gene classifier that can be used to predict response to neoadjuvant CRT in SCC. However, because of the molecular diversity between the two histological subtypes of EC, when considering the AC and SCC samples as a single cohort, a predictive profile could not be resolved, and a negative predictive profile was observed for AC.

High-throughput genotyping of single nucleotide polymorphisms with high sensitivity.

The ability to analyze a large number of genetic markers consisting of single nucleotide polymorphisms (SNPs) may bring about significant advance in understanding human biology. Recent development of several high-throughput genotyping approaches has significantly facilitated large-scale SNP analysis. However, because of their relatively low sensitivity, application of these approaches, especially in studies involving a small amount of material, has been limited. In this chapter, detailed experimental procedures for a high-throughput and highly sensitive genotyping system are described. The system involves using computer program selected primers that are expected not to generate a significant amount of nonspecific products during PCR amplification. After PCR, a small aliquot of the PCR product is used as templates to generate single-stranded DNA (ssDNA). ssDNA sequences from different SNP loci are then resolved by hybridizing these sequences to the probes arrayed onto glass surface. The probes are designed in such a way that hybridizing to the ssDNA templates places their 3'-ends next to the polymorphic sites. Therefore, the probes can be labeled in an allele-specific way using fluorescently labeled dye terminators. The allelic states of the SNPs can then be determined by analyzing the amounts of different fluorescent colors incorporated to the corresponding probes. The genotyping system is highly accurate and capable of analyzing >1000 SNPs in individual haploid cells.

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.

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.