e-MutPath: Computational modelling reveals the functional landscape of genetic mutations rewiring interactome networks

ABSTRACTUnderstanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the ‘edgotype’ of a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath, a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect. The code for e-MutPath is available as a user-friendly R package at the GitHub website (https://github.com/lyshaerbin/eMutPath).

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e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Nucleic Acids Research ◽

10.1093/nar/gkaa1015 ◽

2020 ◽

Author(s):

Yongsheng Li ◽

Brandon Burgman ◽

Ishaani S Khatri ◽

Sairahul R Pentaparthi ◽

Zhe Su ◽

...

Keyword(s):

Drug Targets ◽

Disease Risk ◽

Computational Method ◽

Genetic Mutations ◽

Precision Oncology ◽

Functional Impact ◽

Critical Knowledge ◽

Specific Effects ◽

Pathway Perturbation ◽

Biological Signaling Networks

Abstract Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

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Understanding Schizophrenia: Genetic Causes and Treatment

Current Neuropsychiatry and Clinical Neuroscience Reports ◽

10.33702/cncnr.2019.1.1.2 ◽

2019 ◽

Vol 1 (1) ◽

pp. 6-12

Author(s):

Fatima Javeria ◽

Shazma Altaf ◽

Alishah Zair ◽

Rana Khalid Iqbal

Keyword(s):

Copy Number ◽

Drug Targets ◽

Disease Risk ◽

Mental Disease ◽

Copy Number Variants ◽

Aminobutyric Acid ◽

Epigenetic Mechanisms ◽

Gamma Aminobutyric Acid ◽

Wide Range ◽

Severe Mental Disease

Schizophrenia is a severe mental disease. The word schizophrenia literally means split mind. There are three major categories of symptoms which include positive, negative and cognitive symptoms. The disease is characterized by symptoms of hallucination, delusions, disorganized thinking and speech. Schizophrenia is related to many other mental and psychological problems like suicide, depression, hallucinations. Including these, it is also a problem for the patient’s family and the caregiver. There is no clear reason for the disease, but with the advances in molecular genetics; certain epigenetic mechanisms are involved in the pathophysiology of the disease. Epigenetic mechanisms that are mainly involved are the DNA methylation, copy number variants. With the advent of GWAS, a wide range of SNPs is found linked with the etiology of schizophrenia. These SNPs serve as ‘hubs’; because these all are integrating with each other in causing of schizophrenia risk. Until recently, there is no treatment available to cure the disease; but anti-psychotics can reduce the disease risk by minimizing its symptoms. Dopamine, serotonin, gamma-aminobutyric acid, are the neurotransmitters which serve as drug targets in the treatment of schizophrenia. Due to the involvement of genetic and epigenetic mechanisms, drugs available are already targeting certain genes involved in the etiology of the disease.

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A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

npj Precision Oncology ◽

10.1038/s41698-021-00191-2 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Istvan Petak ◽

Maud Kamal ◽

Anna Dirner ◽

Ivan Bieche ◽

Robert Doczi ◽

...

Keyword(s):

Clinical Benefit ◽

Progressive Disease ◽

Genetic Alterations ◽

Outcome Data ◽

Computational Method ◽

Molecular Profile ◽

Precision Oncology ◽

Driver Genes ◽

Oncology Clinical Trial ◽

Molecularly Targeted Agents

AbstractPrecision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

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On the scope and limitations of baker's yeast as a model organism for studying human tissue-specific pathways

10.1101/011858 ◽

2014 ◽

Author(s):

Shahin Mohammadi ◽

Baharak Saberidokht ◽

Shankar Subramaniam ◽

Ananth Grama

Keyword(s):

Drug Targets ◽

Model Organism ◽

Functional Space ◽

Computational Method ◽

Therapeutic Interventions ◽

Human Tissues ◽

Human Interactome ◽

Tissue Specific ◽

Complex Disorders ◽

Human Genes

Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue. We propose a novel computational and statistical framework to systematically quantify the suitability of yeast as a model organism for different human tissues. We develop a computational method for dissecting the human interactome into tissue-specific cellular networks. Using these networks, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. We study these sub-spaces in detail, and relate them to the overall similarity of each tissue with yeast. Many complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. We show that human-specific subsets of tissue-selective genes are significantly associated with the onset and development of a number of pathologies. Consequently, they provide excellent candidates as drug targets for therapeutic interventions. We also present a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans.

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Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology

Nature Cancer ◽

10.1038/s43018-021-00243-3 ◽

2021 ◽

Author(s):

Brendan Reardon ◽

Nathanael D. Moore ◽

Nicholas S. Moore ◽

Eric Kofman ◽

Saud H. AlDubayan ◽

...

Keyword(s):

Point Of Care ◽

Single Gene ◽

Knowledge Bases ◽

Computational Method ◽

Precision Oncology ◽

Clinical Interpretation ◽

First Order ◽

Molecular Features ◽

Molecular Oncology ◽

Molecular Profiles

AbstractTumor molecular profiling of single gene-variant (‘first-order’) genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these ‘second-order’ alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles.

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Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

Wellcome Open Research ◽

10.12688/wellcomeopenres.15334.2 ◽

2019 ◽

Vol 4 ◽

pp. 113 ◽

Cited By ~ 12

Author(s):

Venexia M Walker ◽

Neil M Davies ◽

Gibran Hemani ◽

Jie Zheng ◽

Philip C Haycock ◽

...

Keyword(s):

Risk Factors ◽

Web Application ◽

Drug Targets ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Causal Effect ◽

R Package ◽

Sensitivity Analyses ◽

Link Type ◽

Study Results

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

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STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btaa755 ◽

2020 ◽

Cited By ~ 1

Author(s):

Massimo Andreatta ◽

Santiago J Carmona

Keyword(s):

Single Cell ◽

Distance Measure ◽

Source Code ◽

Cell Types ◽

R Package ◽

Computational Method ◽

Biological Variability ◽

Rna Seq ◽

Batch Effects ◽

Guide Trees

Abstract Summary STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. Availability and implementation Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

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A Bayesian linear mixed model for prediction of complex traits

Bioinformatics ◽

10.1093/bioinformatics/btaa1023 ◽

2020 ◽

Author(s):

Yang Hai ◽

Yalu Wen

Keyword(s):

Complex Traits ◽

Mixed Model ◽

Linear Mixed Model ◽

Rare Variants ◽

Disease Risk ◽

R Package ◽

Underlying Disease ◽

Supplementary Information ◽

True Effect Size ◽

Bayes Algorithm

Abstract Motivation Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. Results We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. Availability The R-package is available at https://github.com/yhai943/BLMM Supplementary information Supplementary data are available at Bioinformatics online.

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TADBD: a sensitive and fast method for detection of typologically associated domain boundaries

BioTechniques ◽

10.2144/btn-2019-0165 ◽

2020 ◽

Vol 69 (1) ◽

pp. 18-25

Author(s):

Hongqiang Lyu ◽

Lin Li ◽

Zhifang Wu ◽

Tian Wang ◽

Jiguang Zheng ◽

...

Keyword(s):

R Package ◽

Computational Method ◽

Genome Architecture ◽

Fast Method ◽

3D Genome ◽

Multi Scale ◽

Comparison Results ◽

Contact Matrix ◽

Domain Boundaries ◽

Template Size

A topologically associated domain (TAD) is a self-interacting genomic block. Detection of TAD boundaries on Hi-C contact matrix is one of the most important issues in the analysis of 3D genome architecture at TAD level. Here, we present TAD boundary detection (TADBD), a sensitive and fast computational method for detection of TAD boundaries on Hi-C contact matrix. This method implements a Haar-based algorithm by considering Haar diagonal template, acceleration via a compact integrogram, multi-scale aggregation at template size and statistical filtering. In most cases, comparison results from simulated and experimental data show that TADBD outperforms the other five methods. In addition, a new R package for TADBD is freely available online.

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Identifying functional loss of ATM gene in patients with advanced cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.3629 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 3629-3629

Author(s):

Patrick Glen Pilie ◽

Jinesh S. Gheeya ◽

Keith Kyewalabye ◽

Rohit Vivek Goswamy ◽

Khalida M Wani ◽

...

Keyword(s):

Advanced Cancer ◽

Predictive Biomarker ◽

Cancer Center ◽

Precision Oncology ◽

Cell Nuclei ◽

Functional Impact ◽

Unknown Significance ◽

Atm Protein ◽

Md Anderson ◽

Coding Variants

3629 Background: ATM is frequently mutated in cancer, and defects may serve as a putative predictive biomarker. However, the functional impact of most ATM variants is not well known. In this study, we examined the relationship between ATM variants and ATM protein expression to better discern ATM functional defects in patients (pts) with advanced cancer. Methods: We retrospectively identified pts seen at MD Anderson Cancer Center who had ATM variants detected on CLIA-certified next generation sequencing (NGS) assays. ATM immunohistochemistry (IHC) was performed on available tumors. We then prospectively assessed ATM IHC on tumors from pts who were referred for DNA damage repair inhibitor (DDRi) trials. Functional classification of the variants was performed via published in silico tools and/or precision oncology decision support (PODS). An IHC cut-off of 100% loss in tumor cell nuclei defined ATM loss of protein (LOP). Results: Of 1394 ATM-mutant tumors identified retrospectively, ATM alterations were classified as 16% (N = 216) inactivating, 12% (N = 163) potentially inactivating, 71% (N = 993) variant of unknown significance (VUS), and 2% (N = 22) benign. Coding variants were seen across the ATM exonic structure/splice sites, and 20 individual variants were shared in > 10 pts. 263/297 available retrospective tumor samples had interpretable IHC results; 27% (N = 72) had ATM LOP. LOP was most prevalent in tumors with inactivating ATM variants (39/100, 39%); but, importantly, LOP was seen in 20% (N = 33/162) of potentially inactivating/VUS, thus better clarifying their functional impact. In the prospective cohort of 217 pt tumors, 17% (N = 37) had ATM LOP. 29% (N = 62/217) of this cohort also had ATM variants. ATM LOP was seen in 48% of tumors with inactivating variants (N = 14/29), 25% of tumors with potentially/VUS(N = 9/36), and 9% (N = 14/156) of tumors without ATM variants identified. ATM LOP was detected most commonly in colorectal (24%; N = 8/34), cholangiocarcinoma (20%; N = 6/30), prostate (16%; N = 16/104) and pancreatic (9%; N = 1/11) cancers among this cohort of pts referred for DDRi trials. Conclusions: ATM coding variants occurred across the gene, with certain variants shared across tumor types. The functional impact of most ATM variants was VUS, and ATM LOP can help clarify function in up to 25% of these VUS. Also, ATM LOP can be seen even in tumors without ATM variants identified, suggesting epigenetic or post-translational loss. Future prospective studies assessing predictive capability of paired DNA and protein-level profiling of ATM are warranted.

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