Altered regulation of DPF3, a member of the SWI/SNF complexes, underlies the 14q24 renal cancer susceptibility locus.

Our study investigated the underlying mechanism for the 14q24 renal cell carcinoma (RCC) susceptibility risk locus identified by a genome-wide association study (GWAS). The sentinel single-nucleotide polymorphism (SNP), rs4903064, at 14q24 confers an allele-specific effect on expression of the double PHD fingers 3 (DPF3) of the BAF SWI/SNF complex as assessed by massively parallel reporter assay, confirmatory luciferase assays, and eQTL analyses. Overexpression of DPF3 in renal cell lines increases growth rates and alters chromatin accessibility and gene expression, leading to inhibition of apoptosis and activation of oncogenic pathways. siRNA interference of multiple DPF3-deregulated genes reduces growth. Our results indicate that germline variation in DPF3, a component of the BAF complex, part of the SWI/SNF complexes, can lead to reduced apoptosis and activation of the STAT3 pathway, both critical in RCC carcinogenesis. In addition, we show that altered DPF3 expression in the 14q24 RCC locus could influence the effectiveness of immunotherapy treatment for RCC by regulating tumor cytokine secretion and immune cell activation.

PCAmatchR: a flexible R package for optimal case-control matching using weighted principal components.

SUMMARY: A concern when conducting genome-wide association studies (GWAS) is the potential for population stratification, i.e. ancestry-based genetic differences between cases and controls, that if not properly accounted for, could lead to biased association results. We developed PCAmatchR as an open source R package for performing optimal case-control matching using principal component analysis (PCA) to aid in selecting controls that are well matched by ancestry to cases. PCAmatchR takes user supplied PCA outputs and selects matching controls for cases by utilizing a weighted Mahalanobis distance metric which weights each principal component by the percentage of genetic variation explained. Results from the 1000 Genomes Project data demonstrate both the functionality and performance of PCAmatchR for selecting matching controls for case populations as well as reducing inflation of association test statistics. PCAmatchR improves genomic similarity between matched cases and controls, which minimizes the effects of population stratification in GWAS analyses. AVAILABILITY AND IMPLEMENTATION: PCAmatchR is freely available for download on GitHub (https://github.com/machiela-lab/PCAmatchR) or through CRAN (https://CRAN.R-project.org/package=PCAmatchR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application.

BACKGROUND: Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. RESULTS: We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. CONCLUSIONS: sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

Detectible mosaic truncating PPM1D mutations, age and breast cancer risk.

Mosaic protein truncating variants (PTVs) in the phosphatase, Mg2+/Mn2+dependent 1D (PPM1D) gene in blood-derived DNA have been associated with increased risk of breast cancer. We analyzed PPM1D PTVs in blood from 3817 breast cancer cases and 3058 controls by deep sequencing of a previously defined region in exon 6 of PPM1D. We identified 50 of 6875 (0.73%) participants having a mosaic PPM1D PTV. We observed a higher frequency of mosaic PPM1D PTVs with increasing age (Ptrend = 2.9 × 10-6). We did not observe an overall association between PPM1D PTVs and increased breast cancer risk (OR = 1.51, 95% CI = 0.84-2.71). Evidence for an association was observed in a subset of cases with DNA collected 1-year or more before breast cancer diagnosis (OR = 3.44, 95% CI = 1.62-7.30, P-value = 0.001); however, no significant association was observed for the larger series of cases with DNA collected post diagnosis (OR = 1.01, 95% CI = 0.51-2.01, P-value = 0.98). Our study indicates that the PPM1D PTVs are present at higher rates than previously reported and the frequency of PPM1D PTVs increases with age. We observed limited evidence for an association between mosaic PPM1D PTVs and breast cancer risk, suggesting mosaic PPM1D PTVs in the blood likely do not influence risk of breast cancer.

LDlinkR: An R Package for Rapidly Calculating Linkage Disequilibrium Statistics in Diverse Populations.

Genomic research involving human genetics and evolutionary biology relies heavily on linkage disequilibrium (LD) to investigate population-specific genetic structure, functionally map regions of disease susceptibility and uncover evolutionary history. Interactive and powerful tools are needed to calculate population-specific LD estimates for integrative genomics research. LDlink is an interactive suite of web-based tools developed to query germline variants in 1000 Genomes Project population groups of interest and generate interactive tables and plots of LD estimates. As an expansion to this resource, we have developed an R package, LDlinkR, designed to rapidly calculate statistics for large lists of variants and LD attributes that eliminates the time needed to perform repetitive requests from the web-based LDlink tool. LDlinkR accelerates genomic research by providing efficient and user-friendly functions to programmatically interrogate and download pairwise LD estimates from expansive lists of genetic variants. LDlinkR is a free and publicly available R package that can be installed from the Comprehensive R Archive Network (CRAN) or downloaded from https://github.com/CBIIT/LDlinkR.

Landscape of Combination Immunotherapy and Targeted Therapy to Improve Cancer Management.

Cancer treatments composed of immune checkpoint inhibitors and oncogene-targeted drugs might improve cancer management, but there has been little investigation of their combined potential as yet. To estimate the fraction of cancer cases that might benefit from such combination therapy, we conducted an exploratory study of cancer genomic datasets to determine the proportion with somatic mutation profiles amenable to either immunotherapy or targeted therapy. We surveyed 13,349 genomic profiles from public databases for cases with specific mutations targeted by current agents or a burden of exome-wide nonsynonymous mutations (NsM) that exceed a proposed threshold for response to checkpoint inhibitors. Overall, 8.9% of cases displayed profiles that could benefit from combination therapy, which corresponded to approximately 11.2% of U.S. annual incident cancer cases. Frequently targetable mutations were in PIK3CA, BRAF, NF1, NRAS, and PTEN We also noted a high burden of NsM in cases with targetable mutations in SMO, DDR2, FGFR1, PTCH1, FGFR2, and MET Our results indicate that a significant proportion of solid tumor patients are eligible for immuno-targeted combination therapy, and they suggest prioritizing specific cancers for trials of certain targeted and checkpoint inhibitor drugs. Cancer Res; 77(13); 3666-71. ©2017 AACR.

Burden of Nonsynonymous Mutations among TCGA Cancers and Candidate Immune Checkpoint Inhibitor Responses.

Immune checkpoint inhibitor treatment represents a promising approach toward treating cancer and has been shown to be effective in a subset of melanoma, non-small cell lung cancer (NSCLC), and kidney cancers. Recent studies have suggested that the number of nonsynonymous mutations (NsM) can be used to select melanoma and NSCLC patients most likely to benefit from checkpoint inhibitor treatment. It is hypothesized that a higher burden of NsM generates novel epitopes and gene products, detected by the immune system as foreign. We conducted an assessment of NsM across 7,757 tumor samples drawn from 26 cancers sequenced in the Cancer Genome Atlas (TCGA) Project to estimate the subset of cancers (both types and fractions thereof) that fit the profile suggested for melanoma and NSCLC. An additional independent set of 613 tumors drawn from 5 cancers were analyzed for replication. An analysis of the receiver operating characteristic curves of published data on checkpoint inhibitor response in melanoma and NSCLC data estimates a cutoff of 192 NsM with 74% sensitivity and 59.3% specificity to discriminate potential clinical benefit. Across the 7,757 samples of TCGA, 16.2% displayed an NsM count that exceeded the threshold of 192. It is notable that more than 30% of bladder, colon, gastric, and endometrial cancers have NsM counts above 192, which was also confirmed in melanoma and NSCLC. Our data could inform the prioritization of tumor types (and subtypes) for possible clinical trials to investigate further indications for effective use of immune checkpoint inhibitors, particularly in adult cancers. Cancer Res; 76(13); 3767-72. ©2016 AACR.

Functional characterization of the 12p12.1 renal cancer-susceptibility locus implicates BHLHE41.

Genome-wide association studies have identified multiple renal cell carcinoma (RCC) susceptibility loci. Here, we use regional imputation and bioinformatics analysis of the 12p12.1 locus to identify the single-nucleotide polymorphism (SNP) rs7132434 as a potential functional variant. Luciferase assays demonstrate allele-specific regulatory activity and, together with data from electromobility shift assays, suggest allele-specific differences at rs7132434 for AP-1 transcription factor binding. In an analysis of The Cancer Genome Atlas data, SNPs highly correlated with rs7132434 show allele-specific differences in BHLHE41 expression (trend P value=6.3 × 10(-7)). Cells overexpressing BHLHE41 produce larger mouse xenograft tumours, while RNA-seq analysis reveals that constitutively increased BHLHE41 induces expression of IL-11. We conclude that the RCC risk allele at 12p12.1 maps to rs7132434, a functional variant in an enhancer that upregulates BHLHE41 expression which, in turn, induces IL-11, a member of the IL-6 cytokine family.