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1. 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)

   Abstract BackgroundThe 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. ResultsWe 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. ConclusionOur analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets. 

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2. Platform-integrated mRNA isoform quantification

   https://doi.org/10.1093/bioinformatics/btz932  

   Sun, Jiao; Chang, Jae-Woong; Zhang, Teng; Yong, Jeongsik; Kuang, Rui; Zhang, Wei; Gorodkin, ed., Jan (December 2019, Bioinformatics)

   Abstract MotivationAccurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. ResultsIn the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementationSource code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. Identifying simultaneous rearrangements in cancer genomes

   https://doi.org/10.1093/bioinformatics/btx745  

   Oesper, Layla; Dantas, Simone; Raphael, Benjamin J.; Curtis, ed., Christina (November 2017, Bioinformatics)

   Abstract MotivationThe traditional view of cancer evolution states that a cancer genome accumulates a sequential ordering of mutations over a long period of time. However, in recent years it has been suggested that a cancer genome may instead undergo a one-time catastrophic event, such as chromothripsis, where a large number of mutations instead occur simultaneously. A number of potential signatures of chromothripsis have been proposed. In this work, we provide a rigorous formulation and analysis of the ‘ability to walk the derivative chromosome’ signature originally proposed by Korbel and Campbell. In particular, we show that this signature, as originally envisioned, may not always be present in a chromothripsis genome and we provide a precise quantification of under what circumstances it would be present. We also propose a variation on this signature, the H/T alternating fraction, which allows us to overcome some of the limitations of the original signature. ResultsWe apply our measure to both simulated data and a previously analyzed real cancer dataset and find that the H/T alternating fraction may provide useful signal for distinguishing genomes having acquired mutations simultaneously from those acquired in a sequential fashion. Availability and implementationAn implementation of the H/T alternating fraction is available at https://bitbucket.org/oesperlab/ht-altfrac. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Modeling clinical and molecular covariates of mutational process activity in cancer

   https://doi.org/10.1093/bioinformatics/btz340  

   Robinson, Welles; Sharan, Roded; Leiserson, Mark D (July 2019, Bioinformatics)

   Abstract Motivation Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes). Results To begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor’s observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort—using cancer type as a covariate—and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas. Availability and implementation TCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. PlantSimLab - a modeling and simulation web tool for plant biologists

   https://doi.org/10.1186/s12859-019-3094-9  

   Ha, S; Dimitrova, E; Hoops, S; Altarawy, D; Ansariola, M; Deb, D; Glazebrook, J; Hillmer, R; Shahin, H; Katagiri, F; et al (December 2019, BMC Bioinformatics)

   Abstract BackgroundAt the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. ResultsThis paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. ConclusionsMathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise. 

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