Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers.

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types.

Enhancing Recovery After Major Bladder Cancer Surgery: Comprehensive Review and Assessment of Application of the Enhanced Recovery After Surgery Guidelines.

Radical cystectomy with pelvic lymphadenectomy and urinary diversion is the standard treatment for patients diagnosed with localized muscle-invasive bladder cancer. Enhanced recovery after surgery (ERAS) is a multimodal perioperative care pathway comprising recommendations on different items with variable evidence that are aimed at improving outcomes. This review provides an overview of the application of specific elements of the ERAS guidelines.

Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets.

In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR) dependence leads to clinically aggressive tumors with few therapeutic options. We used ATAC-seq (assay for transposase-accessible chromatin sequencing), RNA-seq, and DNA sequencing to investigate 22 organoids, six patient-derived xenografts, and 12 cell lines. We identified the well-characterized AR-dependent and neuroendocrine subtypes, as well as two AR-negative/low groups: a Wnt-dependent subtype, and a stem cell-like (SCL) subtype driven by activator protein-1 (AP-1) transcription factors.

Systems biology analysis of human genomes points to key pathways conferring spina bifida risk.

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance, and environmental influences that hamper genome-wide association studies approaches to untargeted discovery.

CNCDatabase: a database of non-coding cancer drivers.

Most mutations in cancer genomes occur in the non-coding regions with unknown impact on tumor development. Although the increase in the number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies making it difficult to understand their roles in tumorigenesis of different cancer types. We have developed CNCDatabase, Cornell Non-coding Cancer driver Database (https://cncdatabase.

DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.

Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on variants from whole-genome sequences of 1834 patients of twelve cancer types revealed 672 insulator loops disrupted in at least 10% of patients.

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.

Identification of Cancer Drivers at CTCF Insulators in 1,962 Whole Genomes.

Recent studies have shown that mutations at non-coding elements, such as promoters and enhancers, can act as cancer drivers. However, an important class of non-coding elements, namely CTCF insulators, has been overlooked in the previous driver analyses. We used insulator annotations from CTCF and cohesin ChIA-PET and analyzed somatic mutations in 1,962 whole genomes from 21 cancer types.

Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network.

We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes.

Detailed analysis of inversions predicted between two human genomes: errors, real polymorphisms, and their origin and population distribution.

The growing catalogue of structural variants in humans often overlooks inversions as one of the most difficult types of variation to study, even though they affect phenotypic traits in diverse organisms. Here, we have analysed in detail 90 inversions predicted from the comparison of two independently assembled human genomes: the reference genome (NCBI36/HG18) and HuRef. Surprisingly, we found that two thirds of these predictions (62) represent errors either in assembly comparison or in one of the assemblies, including 27 misassembled regions in HG18.

Validation and genotyping of multiple human polymorphic inversions mediated by inverted repeats reveals a high degree of recurrence.

In recent years different types of structural variants (SVs) have been discovered in the human genome and their functional impact has become increasingly clear. Inversions, however, are poorly characterized and more difficult to study, especially those mediated by inverted repeats or segmental duplications. Here, we describe the results of a simple and fast inverse PCR (iPCR) protocol for high-throughput genotyping of a wide variety of inversions using a small amount of DNA.

InvFEST, a database integrating information of polymorphic inversions in the human genome.

The newest genomic advances have uncovered an unprecedented degree of structural variation throughout genomes, with great amounts of data accumulating rapidly. Here we introduce InvFEST (http://invfestdb.uab.

PeSV-Fisher: identification of somatic and non-somatic structural variants using next generation sequencing data.

Unlabelled: Next-generation sequencing technologies expedited research to develop efficient computational tools for the identification of structural variants (SVs) and their use to study human diseases. As deeper data is obtained, the existence of higher complexity SVs in some genomes becomes more evident, but the detection and definition of most of these complex rearrangements is still in its infancy. The full characterization of SVs is a key aspect for discovering their biological implications.