Genomes for Kids: The Scope of Pathogenic Mutations in Pediatric Cancer Revealed by Comprehensive DNA and RNA Sequencing.

Genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. Here, we used a three-platform sequencing approach, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-seq), to examine tumor and germline genomes from 309 prospectively identified children with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type. Eighty-six percent of patients harbored diagnostic (53%), prognostic (57%), therapeutically relevant (25%), and/or cancer-predisposing (18%) variants. Inclusion of WGS enabled detection of activating gene fusions and enhancer hijacks (36% and 8% of tumors, respectively), small intragenic deletions (15% of tumors), and mutational signatures revealing of pathogenic variant effects. Evaluation of paired tumor-normal data revealed relevance to tumor development for 55% of pathogenic germline variants. This study demonstrates the power of a three-platform approach that incorporates WGS to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers. SIGNIFICANCE: Pediatric cancers are driven by diverse genomic lesions, and sequencing has proven useful in evaluating high-risk and relapsed/refractory cases. We show that combined WGS, WES, and RNA-seq of tumor and paired normal tissues enables identification and characterization of genetic drivers across the full spectrum of pediatric cancers. This article is highlighted in the In This Issue feature, p. 2945.

A bioinformatics approach reveals novel interactions of the OVOL transcription factors in the regulation of epithelial - mesenchymal cell reprogramming and cancer progression.

BACKGROUND: Mesenchymal to Epithelial Transition (MET) plasticity is critical to cancer progression, and we recently showed that the OVOL transcription factors (TFs) are critical regulators of MET. Results of that work also posed the hypothesis that the OVOLs impact MET in a range of cancers. We now test this hypothesis by developing a model, OVOL Induced MET (OI-MET), and sub-model (OI-MET-TF), to characterize differential gene expression in MET common to prostate cancer (PC) and breast cancer (BC). RESULTS: In the OI-MET model, we identified 739 genes differentially expressed in both the PC and BC models. For this gene set, we found significant enrichment of annotation for BC, PC, cancer, and MET, as well as regulation of gene expression by AP1, STAT1, STAT3, and NFKB1. Focusing on the target genes for these four TFs plus the OVOLs, we produced the OI-MET-TF sub-model, which shows even greater enrichment for these annotations, plus significant evidence of cooperation among these five TFs. Based on known gene/drug interactions, we prioritized targets in the OI-MET-TF network for follow-on analysis, emphasizing the clinical relevance of this work. Reflecting these results back to the OI-MET model, we found that binding motifs for the TF pair AP1/MYC are more frequent than expected and that the AP1/MYC pair is significantly enriched in binding in cancer models, relative to non-cancer models, in these promoters. This effect is seen in both MET models (solid tumors) and in non-MET models (leukemia). These results are consistent with our hypothesis that the OVOLs impact cancer susceptibility by regulating MET, and extend the hypothesis to include mechanisms not specific to MET. CONCLUSIONS: We find significant evidence of the OVOL, AP1, STAT1, STAT3, and NFKB1 TFs having important roles in MET, and more broadly in cancer. We prioritize known gene/drug targets for follow-up in the clinic, and we show that the AP1/MYC TF pair is a strong candidate for intervention.

LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types.

BACKGROUND: The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. We hypothesize that during carcinogenesis, certain pathways or biological gene sets are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method to implicate important biological pathways in high-throughput data is well established. RESULTS: We developed a web-based gene set enrichment application called LRpath with clustering functionality that allows for identification and comparison of pathway signatures across multiple studies. Here, we employed LRpath analysis to unravel the commonly altered pathways and other gene sets across ten cancer studies employing DNA methylation data profiled with the Illumina HumanMethylation27 BeadChip. We observed a surprising level of concordance in differential methylation across multiple cancer types. For example, among commonly hypomethylated groups, we identified immune-related functions, peptidase activity, and epidermis/keratinocyte development and differentiation. Commonly hypermethylated groups included homeobox and other DNA-binding genes, nervous system and embryonic development, and voltage-gated potassium channels. For many gene sets, we observed significant overlap in the specific subset of differentially methylated genes. Interestingly, fewer DNA repair genes were differentially methylated than expected by chance. CONCLUSIONS: Clustering analysis performed with LRpath revealed tightly clustered concepts enriched for differential methylation. Several well-known cancer-related pathways were significantly affected, while others were depleted in differential methylation. We conclude that DNA methylation changes in cancer tend to target a subset of the known cancer pathways affected by genetic aberrations.