ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery

The next steps in next-gen sequencing of cancer genomes

The necessary infrastructure to carry out genomics-driven oncology is now widely available and has resulted in the exponential increase in characterized cancer genomes. While a subset of genomic alterations is clinically actionable, the majority of somatic events remain classified as variants of unknown significance and will require functional characterization. A careful cataloging of the genomic alterations and their response to therapeutic intervention should allow the compilation of an “actionability atlas” and the creation of a genomic taxonomy stratified by tumor type and oncogenic pathway activation. The next phase of genomic medicine will therefore require talented bioinformaticians, genomic navigators, and multidisciplinary approaches to decode complex cancer genomes and guide potential therapy. Equally important will be the ethical and interpretable return of results to practicing oncologists. Finally, the integration of genomics into clinical trials is likely to speed the development of predictive biomarkers of response to targeted therapy as well as define pathways to acquired resistance

Cafeteria diet-induced obesity causes oxidative damage in white adipose

Obesity continues to be one of the most prominent public health dilemmas in the world. The complex interaction among the varied causes of obesity makes it a particularly challenging problem to address. While typical high-fat purified diets successfully induce weight gain in rodents, we have described a more robust model of diet-induced obesity based on feeding rats a diet consisting of highly palatable, energy-dense human junk foods – the “cafeteria” diet (CAF, 45-53% kcal from fat). We previously reported that CAF-fed rats became hyperphagic, gained more weight, and developed more severe hyperinsulinemia, hyperglycemia, and glucose intolerance compared to the lard-based 45% kcal from fat high fat diet–fed group. In addition, the CAF diet-fed group displayed a higher degree of inflammation in adipose and liver, mitochondrial dysfunction, and an increased concentration of lipid-derived, pro-inflammatory mediators. Building upon our previous findings, we aimed to determine mechanisms that underlie physiologic findings in the CAF diet. We investigated the effect of CAF diet-induced obesity on adipose tissue specifically using expression arrays and immunohistochemistry. Genomic evidence indicated the CAF diet induced alterations in the white adipose gene transcriptome, with notable suppression of glutathione-related genes and pathways involved in mitigating oxidative stress. Immunohistochemical analysis indicated a doubling in adipose lipid peroxidation marker 4-HNE levels compared to rats that remained lean on control standard chow diet. Our data indicates that the CAF diet drives an increase in oxidative damage in white adipose tissue that may affect tissue homeostasis. Oxidative stress drives activation of inflammatory kinases that can perturb insulin signaling leading to glucose intolerance and diabetes

Association between differential gene expression and body mass index among endometrial cancers from The Cancer Genome Atlas Project

The Cancer Genome Atlas (TCGA) identified four integrated clusters for endometrial cancer (EC): POLE, MSI, CNL and CNH. We evaluated differences in gene expression profiles of obese and non-obese women with EC and examined the association of body mass index (BMI) within the clusters identified in TCGA

Hedgehog-GLI Signaling Inhibition Suppresses Tumor Growth in Squamous Lung Cancer

Lung squamous cell carcinoma (LSCC) currently lacks effective targeted therapies. Previous studies reported overexpression of HEDGEHOG (HH)-GLI signaling components in LSCC. However, they addressed neither the tumor heterogeneity nor the requirement for HH-GLI signaling. Here, we investigated the role of HH-GLI signaling in LSCC, and studied the therapeutic potential of HH-GLI suppression

Genetic Landscape of Human Papillomavirus–Associated Head and Neck Cancer and Comparison to Tobacco-Related Tumors

Head and neck cancer is the fifth most common cancer worldwide. It is often amenable to curative intent therapy when localized to the head and neck region, but it carries a poor prognosis when it is recurrent or metastatic. Therefore, initial treatment decisions are critical to improve patient survival. However, multimodality therapy used with curative intent is toxic. The balance between offering intensive versus tolerable and function-preserving therapy has been thrown into sharp relief with the recently described epidemic of human papillomavirus–associated head and neck squamous cell carcinomas characterized by improved clinical outcomes compared with smoking-associated head and neck tumors. Model systems and clinical trials have been slow to address the clinical questions that face the field to date. With this as a background, a host of translational studies have recently reported the somatic alterations in head and neck cancer and have highlighted the distinct genetic and biologic differences between viral and tobacco-associated tumors. This review seeks to summarize the main findings of studies, including The Cancer Genome Atlas, for the clinician scientist, with a goal of leveraging this new knowledge toward the betterment of patients with head and neck cancer

BlackOPs: Increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequen-cing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical arti-fact results from incorrectly aligning experimen-tally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We de-veloped BlackOPs, an open-source tool that simu-lates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklist

Molecular Evolution Patterns in Metastatic Lymph Nodes Reflect the Differential Treatment Response of Advanced Primary Lung Cancer

Tumor heterogeneity influences the clinical outcome of patients with cancer, and the diagnostic method to measure the tumor heterogeneity needs to be developed. We analyzed genomic features on pairs of primary and multiple metastatic lymph nodes from six patients with lung cancer using whole-exome sequencing and RNA sequencing. Although somatic single-nucleotide variants were shared in primary lung cancer and metastases, tumor evolution predicted by the pattern of genomic alterations was matched to anatomic location of the tumors. Four of six cases exhibited a branched clonal evolution pattern. Lymph nodes with acquired somatic variants demonstrated resistance to the cancer treatment. In this study, we demonstrated that multiple biopsies and sequencing strategies for different tumor regions are required for a comprehensive understanding of the landscape of genetic alteration and for guiding targeted therapy in advanced primary lung cancer. Cancer Res; 76(22); 6568-76. ©2016 AACR