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1. Incorporating mutational heterogeneity to identify genes that are enriched for synonymous mutations in cancer

   https://doi.org/10.1186/s12859-023-05521-8  

   Rao, Yiyun ; Ahmed, Nabeel ; Pritchard, Justin ; O’Brien, Edward P. ( December 2023 , BMC Bioinformatics)

   Synonymous mutations, which change the DNA sequence but not the encoded protein sequence, can affect protein structure and function, mRNA maturation, and mRNA half-lives. The possibility that synonymous mutations might be enriched in cancer has been explored in several recent studies. However, none of these studies control for all three types of mutational heterogeneity (patient, histology, and gene) that are known to affect the accurate identification of non-synonymous cancer-associated genes. Our goal is to adopt the current standard for non-synonymous mutations in an investigation of synonymous mutations.

   Here, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify cancer-associated genes that are enriched for synonymous mutations based on a non-coding background model that takes into account the mutational heterogeneity across these levels. Using MutSigCVsyn, we first analyzed 2572 cancer whole-genome samples from the Pan-cancer Analysis of Whole Genomes (PCAWG) to identify non-synonymous cancer drivers as a quality control. Indicative of the algorithm accuracy we find that 58.6% of these candidate genes were also found in Cancer Census Gene (CGC) list, and 66.2% were found within the PCAWG cancer driver list. We then applied it to identify 30 putative cancer-associated genes that are enriched for synonymous mutations within the same samples. One of the promising gene candidates is the B cell lymphoma 2 (BCL-2) gene. BCL-2 regulates apoptosis by antagonizing the action of proapoptotic BCL-2 family member proteins. The synonymous mutations in BCL2 are enriched in its anti-apoptotic domain and likely play a role in cancer cell proliferation.

   Our study introduces MutSigCVsyn, an algorithm that accounts for mutational heterogeneity at patient, histology, and gene levels, to identify cancer-associated genes that are enriched for synonymous mutations using whole genome sequencing data. We identified 30 putative candidate genes that will benefit from future experimental studies on the role of synonymous mutations in cancer biology.

    

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2. Network propagation-based prioritization of long tail genes in 17 cancer types

   https://doi.org/10.1186/s13059-021-02504-x  

   Mohsen, Hussein ; Gunasekharan, Vignesh ; Qing, Tao ; Seay, Montrell ; Surovtseva, Yulia ; Negahban, Sahand ; Szallasi, Zoltan ; Pusztai, Lajos ; Gerstein, Mark B. ( October 2021 , Genome Biology)

   The diversity of genomic alterations in cancer poses challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implications in cancer development. The study of cancer-relevant genes often requires integrative approaches pooling together multiple types of biological data. Network propagation methods demonstrate high efficacy in achieving this integration. Yet, the majority of these methods focus their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on prioritizing long tail genes with potential functional impact on cancer development.

   We identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets “upward mobility genes” and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on upward mobility genes that are not previously identified using driver genes alone, validate their role in cancer cell survival in vitro using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting in the validation of 18 previously unreported genes.

   Our analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets.

    

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3. iDINGO—integrative differential network analysis in genomics with Shiny application

   https://doi.org/10.1093/bioinformatics/btx750  

   Class, Caleb A. ; Ha, Min Jin ; Baladandayuthapani, Veerabhadran ; Do, Kim-Anh ; Berger, ed., Bonnie ( November 2017 , Bioinformatics)

   Differential network analysis is an important way to understand network rewiring involved in disease progression and development. Building differential networks from multiple ‘omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple ‘omics data independently does not account for the hierarchical structure of the data.

   We developed the iDINGO R package to estimate group-specific dependencies and make inferences on the integrative differential networks, considering the biological hierarchy among the platforms. A Shiny application has also been developed to facilitate easier analysis and visualization of results, including integrative differential networks and hub gene identification across platforms.

   R package is available on CRAN (https://cran.r-project.org/web/packages/iDINGO) and Shiny application at https://github.com/MinJinHa/iDINGO.

   Supplementary data are available at Bioinformatics online.

    

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4. A weighted exact test for mutually exclusive mutations in cancer

   https://doi.org/10.1093/bioinformatics/btw462  

   Leiserson, Mark DM ; Reyna, Matthew A. ; Raphael, Benjamin J. ( August 2016 , Bioinformatics)

   The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision.

   We formulate a weighted exact test for assessing the significance of mutual exclusivity in an arbitrary number of mutational events. Our test conditions on the number of samples with a mutation as well as per-event, per-sample mutation probabilities. We provide a recursive formula to compute P-values for the weighted test exactly as well as a highly accurate and efficient saddlepoint approximation of the test. We use our test to approximate a commonly used permutation test for exclusivity that conditions on per-event, per-sample mutation frequencies. However, our test is more efficient and it recovers more significant results than the permutation test. We use our Weighted Exclusivity Test (WExT) software to analyze hundreds of colorectal and endometrial samples from The Cancer Genome Atlas, which are two cancer types that often have extremely high mutation rates. On both cancer types, the weighted test identifies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier approaches.

   See http://compbio.cs.brown.edu/projects/wext for software.

   braphael@cs.brown.edu

   Supplementary data are available at Bioinformatics online.

    

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5. Current State of Circulating MicroRNAs as Cancer Biomarkers

   https://doi.org/10.1373/clinchem.2015.241190  

   He, Yuqing ; Lin, Juanjuan ; Kong, Danli ; Huang, Mingyuan ; Xu, Chengkai ; Kim, Taek-Kyun ; Etheridge, Alton ; Luo, Yanhong ; Ding, Yuanlin ; Wang, Kai ( January 2020 , Clinical Chemistry)

   Numerous studies have demonstrated the existence of stable regulatory RNAs, microRNAs (miRNAs), in the circulation and have shown that the spectrum of these extracellular miRNAs is affected by various pathologic conditions including cancers.

   Circulating miRNAs have been the focus of numerous cancer biomarker discovery efforts over the past few years; however, a considerable number of these studies have yielded inconsistent and irreproducible findings. Here, we have summarized and compared the results of studies covering 8 different cancer types to address key questions, including the possibility of using circulating miRNA to detect cancers and what factors may affect miRNA signatures. Although identifying circulating miRNA signatures to detect specific types of early stage cancers can be challenging, study results suggest that it may be possible to use miRNAs to detect cancers in general.

   Circulating miRNA is a rich source for potential disease biomarkers; however, factors, both intrinsic and extrinsic, that may affect measurement of circulating miRNA have not been fully characterized. Better understanding of intra- and intercellular miRNA trafficking and the fundamental biology of cancer cell–derived lipid vesicles may facilitate the development of circulating miRNA-based biomarkers for cancer detection and classification.

    

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