Accumulating evidence demonstrated that while a small minority of the human genome encodes proteins a large portion of the genome encodes non-protein coding transcripts. Thus it has been suggested that such RNAs, including the recently described family of long non-coding RNAs (lncRNA) are key regulators of cellular homeostasis and transformation leading to tumorigenesis. Here we tested this hypothesis focusing on the role of lncRNAs in T cell leukemia (T-ALL), a pediatric blood cancer, characterized by Notch pathway activation. Using RNA-Sequencing we identified and catalogued a large number of T-ALL-specific lncRNAs targeted by the NOTCH pathway. We further focused on a single such transcript, LUNAR1 (LeUkemia-induced Non-coding Activator RNA-1), and demonstrated that through chromosomal looping it is able to control the expression of the IGF1R gene, IGF1 signaling and growth of T-ALL, providing evidence that suggests that lncRNAs could be used as biomarkers as well as therapy targets in human cancer.

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.

The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.

Breast cancer progression and metastasis are driven by complex and reciprocal interactions, between epithelial cancer cells and their surrounding stromal microenvironment. We have previously shown that a loss of stromal Cav-1 expression is associated with an increased risk of early tumor recurrence, metastasis and decreased overall survival. To identify and characterize the signaling pathways that are activated in Cav-1 negative tumor stroma, we performed gene expression profiling using laser microdissected breast cancer-associated stroma. Tumor stroma was laser capture microdissected from 4 cases showing high stromal Cav-1 expression and 7 cases with loss of stromal Cav-1. Briefly, we identified 238 gene transcripts that were upregulated and 232 gene transcripts that were downregulated in the stroma of tumors showing a loss of Cav-1 expression (p ≤ 0.01 and fold-change ≥ 1.5). Gene set enrichment analysis (GSEA) revealed "stemness," inflammation, DNA damage, aging, oxidative stress, hypoxia, autophagy and mitochondrial dysfunction in the tumor stroma of patients lacking stromal Cav-1. Our findings are consistent with the recently proposed "Reverse Warburg Effect" and the "Autophagic Tumor Stroma Model of Cancer Metabolism." In these two complementary models, cancer cells induce oxidative stress in adjacent stromal cells, which then forces these stromal fibroblasts to undergo autophagy/mitophagy and aerobic glycolysis. This, in turn, produces recycled nutrients (lactate, ketones and glutamine) to feed anabolic cancer cells, which are undergoing oxidative mitochondrial metabolism. Our results are also consistent with previous biomarker studies showing that the increased expression of known autophagy markers (such as ATG16L and the cathepsins) in the tumor stroma is specifically associated with metastatic tumor progression and/or poor clinical outcome.

We report results from the analysis of complete mitochondrial DNA (mtDNA) sequences from 112 Japanese semi-supercentenarians (aged above 105 years) combined with previously published data from 96 patients in each of three non-disease phenotypes: centenarians (99–105 years of age), healthy non-obese males, obese young males and four disease phenotypes, diabetics with and without angiopathy, and Alzheimer's and Parkinson's disease patients. We analyze the correlation between mitochondrial polymorphisms and the longevity phenotype using two different methods. We first use an exhaustive algorithm to identify all maximal patterns of polymorphisms shared by at least five individuals and define a significance score for enrichment of the patterns in each phenotype relative to healthy normals. Our study confirms the correlations observed in a previous study showing enrichment of a hierarchy of haplogroups in the D clade for longevity. For the extreme longevity phenotype we see a single statistically significant signal: a progressive enrichment of certain “beneficial” patterns in centenarians and semi-supercentenarians in the D4a haplogroup. We then use Principal Component Spectral Analysis of the SNP-SNP Covariance Matrix to compare the measured eigenvalues to a Null distribution of eigenvalues on Gaussian datasets to determine whether the correlations in the data (due to longevity) arises from some property of the mutations themselves or whether they are due to population structure. The conclusion is that the correlations are entirely due to population structure (phylogenetic tree). We find no signal for a functional mtDNA SNP correlated with longevity. The fact that the correlations are from the population structure suggests that hitch-hiking on autosomal events is a possible explanation for the observed correlations.

Next‐generation sequencing (NGS) has emerged as an affordable and reproducible means to query tumors for somatic genetic anomalies. To help interpret somatic NGS data, many institutions have created a molecular tumor board to analyze the results of NGS and make recommendations. This article evaluates the utility of cognitive computing systems to analyze data for clinical decision‐making.

We report new results from the re-analysis of 672 complete mitochondrial (mtDNA) genomes of unrelated Japanese individuals stratified into seven equal sized groups by the phenotypes: diabetic patients, diabetic patients with severe angiopathy, healthy non-obese young males, obese young males, patients with Alzheimer's disease, patients with Parkinson's disease and centenarians. Each phenotype had 96 samples over 27 known haplogroups: A, B4a, B4b, B4c, B*, B5, D*, F1, F2, M*, M7a, M7b, M8, M9, D4a, D4b1, D4b2, D4d, D4e, D4g, D4h, D5, G, Z, M*, N9a, and N9b. A t-test comparing the fraction of samples in a haplogroup to healthy young males showed a significant enrichment of haplogroups D4a, D5, and D4b2 in centenarians. The D4b2 enrichment was limited to a subgroup of 40 of 61 samples which had the synonymous mutation 9296C > T. We identified this cluster as a distinct haplogroup and labeled it as D4b2b. Using an exhaustive procedure, we constructed the complete list of "mutation patterns" for centenarians and showed that the most significant patterns were in D4a, D5, and D4b2b. We argue that if a selection for longevity appeared only once, it was probably an autosomal event which could be dated to after the appearance of the D mega-group but before the coalescent time of D4a, D5, and D4b2b. Using a simple procedure, we estimated that this event occurred 24.4 +/- 0.9 kYBP.

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.