Kocher, Jean‐Pierre

Abstract Purpose: Homozygous deletions play important roles in carcinogenesis. The genome-wide screening for homozygously deleted genes in many different cancer types with a large number of patient specimens representing the tumor heterogeneity has not been done. Experimental Design: We performed integrative analyses of the copy-number profiles of 10,759 patients across 31 cancer types from The Cancer Genome Atlas project. Results: We found that the type-I interferon, α-, and β-defensin genes were homozygously deleted in 19 cancer types with high frequencies (7%–31%, median = 12%; interquartile range = 10%–16.5%). Patients with homozygous deletion of interferons exhibited significantly shortened overall or disease-free survival time in a number of cancer types, whereas patients with homozygous deletion of defensins did not significantly associate with worse overall or disease-free survival. Gene expression analyses suggested that homozygous deletion of interferon and defensin genes could activate genes involved in oncogenic and cell-cycle pathways but repress other genes involved in immune response pathways, suggesting their roles in promoting tumorigenesis and helping cancer cells evade immune surveillance. Further analysis of the whole exomes of 109 patients with melanoma demonstrated that the homozygous deletion of interferon (P = 0.0029, OR = 11.8) and defensin (P = 0.06, OR = 2.79) genes are significantly associated with resistance to anti-CTLA4 immunotherapy. Conclusions: Our analysis reveals that the homozygous deletion of interferon and defensin genes is prevalent in human cancers, and importantly this feature can be used as a novel prognostic biomarker for immunotherapy resistance. Clin Cancer Res; 24(14); 3299–308. ©2018 AACR.

Abstract Background Illumina paired-end sequencing has been increasingly popular for 16S rRNA gene-based microbiota profiling. It provides higher phylogenetic resolution than single-end reads due to a longer read length. However, the reverse read (R2) often has significant low base quality, and a large proportion of R2s will be discarded after quality control, resulting in a mixture of paired-end and single-end reads. A typical 16S analysis pipeline usually processes either paired-end or single-end reads but not a mixture. Thus, the quantification accuracy and statistical power will be reduced due to the loss of a large amount of reads. As a result, rare taxa may not be detectable with the paired-end approach, or low taxonomic resolution will result in a single-end approach. Results To have both the higher phylogenetic resolution provided by paired-end reads and the higher sequence coverage by single-end reads, we propose a novel OTU-picking pipeline, hybrid-denovo, that can process a hybrid of single-end and paired-end reads. Using high-quality paired-end reads as a gold standard, we show that hybrid-denovo achieved the highest correlation with the gold standard and performed better than the approaches based on paired-end or single-end reads in terms of quantifying the microbial diversity and taxonomic abundances. By applying our method to a rheumatoid arthritis (RA) data set, we demonstrated that hybrid-denovo captured more microbial diversity and identified more RA-associated taxa than a paired-end or single-end approach. Conclusions Hybrid-denovo utilizes both paired-end and single-end 16S sequencing reads and is recommended for 16S rRNA gene targeted paired-end sequencing data.

Chromosome instability (CIN) is widely observed in both sporadic and hereditary colorectal cancer (CRC). Defects in APC and WNT signaling are primarily associated with CIN in hereditary CRC, but the genetic causes for CIN in sporadic CRC remain elusive. Using high‐density SNP array and exome data from The Cancer Genome Atlas (TCGA), we characterized loss of heterozygosity (LOH) and copy number variation (CNV) in the peripheral blood, normal colon, and corresponding tumor tissue in 15 CRC patients with proficient mismatch repair (MMR) and 24 CRC patients with deficient MMR. We found a high frequency of 18q LOH in tumors and arm‐specific enrichment of genetic aberrations on 18q in the normal colon (primarily copy neutral LOH) and blood (primarily copy gain). These aberrations were specific to the sporadic, pMMR CRC. Though in tumor samples genetic aberrations were observed for genes commonly mutated in hereditary CRC (eg, APC, CTNNB1, SMAD4, BRAF), none of them showed LOH or CNV in the normal colon or blood. DCC located on 18q21.1 topped the list of genes with genetic aberrations in the tumor. In an independent cohort of 13 patients subjected to Whole Genome Sequencing (WGS), we found LOH and CNV on 18q in adenomatous polyp and tumor tissues. Our data suggests that patients with sporadic CRC may have genetic aberrations preferentially enriched on 18q in their blood, normal colon epithelium, and non‐malignant polyp lesions that may prove useful as a clinical marker for sporadic CRC detection and risk assessment.

Abstract Summary: The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method. Availability and implementation: CpGFilter is implemented in R and publicly available under CRAN via the R package ‘CpGFilter’. Contact: chen.jun2@mayo.edu or xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Summary: One central theme of modern high-throughput genomic data analysis is to identify relevant genomic features as well as build up a predictive model based on selected features for various tasks such as personalized medicine. Correlating the large number of ‘omics’ features with a certain phenotype is particularly challenging due to small sample size (n) and high dimensionality (p). To address this small n, large p problem, various forms of sparse regression models have been proposed by exploiting the sparsity assumption. Among these, network-constrained sparse regression model is of particular interest due to its ability to utilize the prior graph/network structure in the omics data. Despite its potential usefulness for omics data analysis, no efficient R implementation is publicly available. Here we present an R software package ‘glmgraph’ that implements the graph-constrained regularization for both sparse linear regression and sparse logistic regression. We implement both the L1 penalty and minimax concave penalty for variable selection and Laplacian penalty for coefficient smoothing. Efficient coordinate descent algorithm is used to solve the optimization problem. We demonstrate the use of the package by applying it to a human microbiome dataset, where phylogeny structure among bacterial taxa is available. Availability and implementation: ‘glmgraph’ is implemented in R and C++ Armadillo and publicly available under CRAN. Contact: chen.jun2@mayo.edu or hongzhe@upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.