High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios.

The 1000 Genomes Project (1kGP) is the largest fully open resource of whole-genome sequencing (WGS) data consented for public distribution without access or use restrictions. The final, phase 3 release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low-coverage WGS. Here, we present a high-coverage 3,202-sample WGS 1kGP resource, which now includes 602 complete trios, sequenced to a depth of 30X using Illumina. We performed single-nucleotide variant (SNV) and short insertion and deletion (INDEL) discovery and generated a comprehensive set of structural variants (SVs) by integrating multiple analytic methods through a machine learning model. We show gains in sensitivity and precision of variant calls compared to phase 3, especially among rare SNVs as well as INDELs and SVs spanning frequency spectrum. We also generated an improved reference imputation panel, making variants discovered here accessible for association studies.

Deep whole-genome sequencing of 3 cancer cell lines on 2 sequencing platforms.

To test the performance of a new sequencing platform, develop an updated somatic calling pipeline and establish a reference for future benchmarking experiments, we performed whole-genome sequencing of 3 common cancer cell lines (COLO-829, HCC-1143 and HCC-1187) along with their matched normal cell lines to great sequencing depths (up to 278x coverage) on both Illumina HiSeqX and NovaSeq sequencing instruments. Somatic calling was generally consistent between the two platforms despite minor differences at the read level. We designed and implemented a novel pipeline for the analysis of tumor-normal samples, using multiple variant callers. We show that coupled with a high-confidence filtering strategy, the use of combination of tools improves the accuracy of somatic variant calling. We also demonstrate the utility of the dataset by creating an artificial purity ladder to evaluate the somatic pipeline and benchmark methods for estimating purity and ploidy from tumor-normal pairs. The data and results of the pipeline are made accessible to the cancer genomics community.

HIF activation causes synthetic lethality between the VHL tumor suppressor and the EZH1 histone methyltransferase.

Inactivation of the von Hippel-Lindau tumor suppressor protein (pVHL) is the signature lesion in the most common form of kidney cancer, clear cell renal cell carcinoma (ccRCC). pVHL loss causes the transcriptional activation of hypoxia-inducible factor (HIF) target genes, including many genes that encode histone lysine demethylases. Moreover, chromatin regulators are frequently mutated in this disease. We found that ccRCC displays increased H3K27 acetylation and a shift toward mono- or unmethylated H3K27 caused by an HIF-dependent increase in H3K27 demethylase activity. Using a focused short hairpin RNA library, as well as CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein 9) and a pharmacological inhibitor, we discovered that pVHL-defective ccRCC cells are hyperdependent on the H3K27 methyltransferase EZH1 for survival. Therefore, targeting EZH1 could be therapeutically useful in ccRCC.

Radiation Resistance in KRAS-Mutated Lung Cancer Is Enabled by Stem-like Properties Mediated by an Osteopontin-EGFR Pathway.

Lung cancers with activating KRAS mutations are characterized by treatment resistance and poor prognosis. In particular, the basis for their resistance to radiation therapy is poorly understood. Here, we describe a radiation resistance phenotype conferred by a stem-like subpopulation characterized by mitosis-like condensed chromatin (MLCC), high CD133 expression, invasive potential, and tumor-initiating properties. Mechanistic investigations defined a pathway involving osteopontin and the EGFR in promoting this phenotype. Osteopontin/EGFR-dependent MLCC protected cells against radiation-induced DNA double-strand breaks and repressed putative negative regulators of stem-like properties, such as CRMP1 and BIM. The MLCC-positive phenotype defined a subset of KRAS-mutated lung cancers that were enriched for co-occurring genomic alterations in TP53 and CDKN2A. Our results illuminate the basis for the radiation resistance of KRAS-mutated lung cancers, with possible implications for prognostic and therapeutic strategies. Cancer Res; 77(8); 2018-28. ©2017 AACR.

A system biology approach for understanding the miRNA regulatory network in colon rectal cancer.

In this paper we present a systems biology approach to the understanding of the miRNA-regulatory network in colon rectal cancer. An initial set of significant genes in Colon Rectal Cancer (CRC) were obtained by mining relevant literature. An initial set of cancer-related miRNAs were obtained from three databases: miRBase, miRWalk, Targetscan and GEO microarray experiment. First principle methods were then used to generate the global miRNA-gene network. Significant miRNAs and associated transcription factors in the global miRNA-gene network were identified using topological and sub-graph analyses. Eleven novel miRNAs were identified and three of the novel miRNAs, hsa-miR-630, hsa-miR-100 and hsa-miR-99a, were further analysed to elucidate their role in CRC. The proposed methodology effectively made use of literature data and was able to show novel, significant miRNA-transcription associations in CRC.

Cliques for the identification of gene signatures for colorectal cancer across population.

BACKGROUND: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Studies have correlated risk of CRC development with dietary habits and environmental conditions. Gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. Such processes can be used to evaluate potential drug targets. Though recognition of CRC gene-signatures across populations is crucial to better understanding potential novel treatment options for CRC, it remains a challenging task. RESULTS: We developed a topological and biological feature-based network approach for identifying the gene signatures across populations. In this work, we propose a novel approach of using cliques to understand the variability within population. Cliques are more conserved and co-expressed, therefore allowing identification and comparison of cliques across a population which can help researchers study gene variations. Our study was based on four publicly available expression datasets belonging to four different populations across the world. We identified cliques of various sizes (0 to 7) across the four population networks. Cliques of size seven were further analyzed across populations for their commonality and uniqueness. Forty-nine common cliques of size seven were identified. These cliques were further analyzed based on their connectivity profiles. We found associations between the cliques and their connectivity profiles across networks. With these clique connectivity profiles (CCPs), we were able to identify the divergence among the populations, important biological processes (cell cycle, signal transduction, and cell differentiation), and related gene pathways. Therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. In this work we demonstrate the power and effectiveness of cliques to study CRC across populations. CONCLUSIONS: We developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in CRC gene profiles across four populations with unique dietary habits.