NOTCH1 mutations have been reported to occur in 10 to 15% of head and neck squamous cell carcinomas (HNSCC). To determine the significance of these mutations, we embarked upon a comprehensive study of NOTCH signaling in a cohort of 44 HNSCC tumors and 25 normal mucosal samples through a set of expression, copy number, methylation and mutation analyses. Copy number increases were identified in NOTCH pathway genes including the NOTCH ligand JAG1. Gene set analysis defined a differential expression of the NOTCH signaling pathway in HNSCC relative to normal tissues. Analysis of individual pathway-related genes revealed overexpression of ligands JAG1 and JAG2 and receptor NOTCH3. In 32% of the HNSCC examined, activation of the downstream NOTCH effectors HES1/HEY1 was documented. Notably, exomic sequencing identified 5 novel inactivating NOTCH1 mutations in 4/37 of the tumors analyzed, with none of these tumors exhibiting HES1/HEY1 overexpression. Our results revealed a bimodal pattern of NOTCH pathway alterations in HNSCC, with a smaller subset exhibiting inactivating NOTCH1 receptors mutations but a larger subset exhibiting other NOTCH1 pathway alterations, including increases in expression or gene copy number of the receptor or ligands as well as downstream pathway activation. Our results imply that therapies that target the NOTCH pathway may be more widely suitable for HNSCC treatment than appreciated currently.

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

BackgroundEpigenetic alterations have been implicated in the pathogenesis of solid tumors, however, proto-oncogenes activated by promoter demethylation have been sporadically reported. We used an integrative method to analyze expression in primary head and neck squamous cell carcinoma (HNSCC) and pharmacologically demethylated cell lines to identify aberrantly demethylated and expressed candidate proto-oncogenes and cancer testes antigens in HNSCC.Methodology/Principal FindingsWe noted coordinated promoter demethylation and simultaneous transcriptional upregulation of proto-oncogene candidates with promoter homology, and phylogenetic footprinting of these promoters demonstrated potential recognition sites for the transcription factor BORIS. Aberrant BORIS expression correlated with upregulation of candidate proto-oncogenes in multiple human malignancies including primary non-small cell lung cancers and HNSCC, induced coordinated proto-oncogene specific promoter demethylation and expression in non-tumorigenic cells, and transformed NIH3T3 cells.Conclusions/SignificanceCoordinated, epigenetic unmasking of multiple genes with growth promoting activity occurs in aerodigestive cancers, and BORIS is implicated in the coordinated promoter demethylation and reactivation of epigenetically silenced genes in human cancers.

Coordinated Gene Activity in Pattern Sets (CoGAPS) provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. CoGAPS improves on other enrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a thresholdindependent statistic inferring activity on gene sets. The software is provided as open source C++ code built on top of JAGS software with an R interface. Availability: The R package CoGAPS and the C++ package GAPS-JAGS are provided open source under the GNU Lesser Public License (GLPL) with a users manual containing installation and operating instructions. CoGAPS is available through Bioconductor and depends on the rjags package available through CRAN to interface CoGAPS with GAPS-JAGS.