Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Download Full-text

Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

Briefings in Bioinformatics ◽

10.1093/bib/bbaa032 ◽

2020 ◽

Cited By ~ 2

Author(s):

Minsik Oh ◽

Sungjoon Park ◽

Sun Kim ◽

Heejoon Chae

Keyword(s):

Machine Learning ◽

Gene Regulation ◽

State Of The Art ◽

Patient Specific ◽

Specific Gene ◽

Omics Data ◽

Gene Expressions ◽

Learning Methods ◽

Machine Learning Methods ◽

Disease Subtype

Abstract Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

Download Full-text

Identification of ovarian cancer driver genes by using module network integration of multi-omics data

Interface Focus ◽

10.1098/rsfs.2013.0013 ◽

2013 ◽

Vol 3 (4) ◽

pp. 20130013 ◽

Cited By ~ 37

Author(s):

Olivier Gevaert ◽

Victor Villalobos ◽

Branimir I. Sikic ◽

Sylvia K. Plevritis

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Specific Gene ◽

Omics Data ◽

Driver Genes ◽

Module Network ◽

Cancer Driver ◽

Specific Gene Expression ◽

Cancer Drivers ◽

Disease Specific

The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles. We developed AMARETTO, an algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue. AMARETTO first models the effects of genomic/epigenomic data on disease-specific gene expression. AMARETTO's second step involves constructing a module network to connect the cancer drivers with their downstream targets. We observed that more gene expression variation can be explained when using disease-specific gene expression data. We applied AMARETTO to the ovarian cancer TCGA data and identified several cancer driver genes of interest, including novel genes in addition to known drivers of cancer. Finally, we showed that certain modules are predictive of good versus poor outcome, and the associated drivers were related to DNA repair pathways.

Download Full-text

Novel cancer subtyping method based on patient-specific gene regulatory network

Scientific Reports ◽

10.1038/s41598-021-02394-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mai Adachi Nakazawa ◽

Yoshinori Tamada ◽

Yoshihisa Tanaka ◽

Marie Ikeguchi ◽

Kako Higashihara ◽

...

Keyword(s):

Gene Networks ◽

Regulatory Networks ◽

The Cancer Genome Atlas ◽

Patient Specific ◽

Specific Gene ◽

Omics Data ◽

Cancer Subtypes ◽

Molecular Systems ◽

Molecular Features ◽

Identification Processes

AbstractThe identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Download Full-text

Constructing cancer patient-specific and group-specific gene networks with multi-omics data

BMC Medical Genomics ◽

10.1186/s12920-020-00736-7 ◽

2020 ◽

Vol 13 (S6) ◽

Author(s):

Wook Lee ◽

De-Shuang Huang ◽

Kyungsook Han

Keyword(s):

Cancer Patient ◽

Gene Networks ◽

Patient Specific ◽

Specific Gene ◽

Omics Data

Download Full-text

Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

The International Journal of Biostatistics ◽

10.1515/ijb-2021-0009 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Shinjo Yada

Keyword(s):

Neural Network ◽

Clinical Trials ◽

Precision Medicine ◽

Early Phase ◽

Patient Specific ◽

Specific Gene ◽

Cancer Type ◽

Background Characteristics ◽

Number Of Patients ◽

Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

Download Full-text

Depression patient-derived cortical neurons reveal potential biomarkers for antidepressant response

Translational Psychiatry ◽

10.1038/s41398-021-01319-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yishai Avior ◽

Shiri Ron ◽

Dana Kroitorou ◽

Claudia Albeldas ◽

Vitaly Lerner ◽

...

Keyword(s):

Cortical Neurons ◽

Antidepressant Treatment ◽

Patient Specific ◽

Specific Gene ◽

Specific Information ◽

Patient Response ◽

Patient Will ◽

Gene Expression Alterations ◽

Treatment Alternatives

AbstractMajor depressive disorder is highly prevalent worldwide and has been affecting an increasing number of people each year. Current first line antidepressants show merely 37% remission, and physicians are forced to use a trial-and-error approach when choosing a single antidepressant out of dozens of available medications. We sought to identify a method of testing that would provide patient-specific information on whether a patient will respond to a medication using in vitro modeling. Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression study were used to rapidly generate cortical neurons and screen them for bupropion effects, for which the donor patients showed remission or non-remission. We provide evidence for biomarkers specific for bupropion response, including synaptic connectivity and morphology changes as well as specific gene expression alterations. These biomarkers support the concept of personalized antidepressant treatment based on in vitro platforms and could be utilized as predictors to patient response in the clinic.

Download Full-text

Abstract 289: Transcriptomic Analysis of Inter- and Intra-patient Variation in Human iPSCs: Platform for Precision Medicine to Predict Drug Toxicity

Circulation Research ◽

10.1161/res.119.suppl_1.289 ◽

2016 ◽

Vol 119 (suppl_1) ◽

Author(s):

Elena Matsa ◽

Paul W Burridge ◽

Kun-Hsing Yu ◽

Haodi Wu ◽

Vittavat Termglinchan ◽

...

Keyword(s):

Gene Expression ◽

Precision Medicine ◽

Disease Modeling ◽

Sendai Virus ◽

Expression Profiles ◽

Cardiac Differentiation ◽

Patient Specific ◽

Specific Gene ◽

Drug Induced ◽

Human Ipscs

Rapid improvements in human induced pluripotent stem cell (hiPSC) differentiation methodologies have allowed previously unattainable access to high-purity, patient-specific cardiomyocytes (CMs) for use in disease modeling, cardiac regeneration, and drug testing. In the present study, we investigate the ability of hiPSC-derived cardiomyocytes (hiPSC-CMs) to reflect the donor’s genetic identity and serve as preclinical functional readout platforms for precision medicine. We used footprint-free Sendai virus to create two separate hiPSC clones from the fibroblasts of five different individuals lacking known mutations associated with cardiovascular disease. Whole genome expression profiling of hiPSC-CMs showed that inter-patient variation was greater than intra-patient variation, thereby verifying that reprogramming and cardiac differentiation technologies can preserve patient-specific gene expression signatures. Gene ontologies (GOs) accounting for inter-patient variation were mostly metabolic or epigenetic. Toxicology analysis based on gene expression profiles predicted patient-specific susceptibility of hiPSC-CMs to cardiotoxicity, and functional assays using drugs targeting key regulators in pathways predicted to produce cardiotoxicity showed inter-patient differential responses in hiPSC-CMs. Our data suggest that hiPSC-CMs can be used in vitro to predict and help prevent patient-specific drug-induced cardiotoxicity, potentially enabling personalized patient consultation in the future.

Download Full-text

COMP-04. MODELING PRECISION ONCOLOGY FOR GLIOBLASTOMA THROUGH INTEGRATION OF DESCRIPTIVE, FUNCTIONAL, AND NETWORK-BASED GENOMICS

Neuro-Oncology ◽

10.1093/neuonc/noz175.247 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi61-vi62

Author(s):

Pia Hoellerbauer ◽

Megan Kufeld ◽

Sonali Arora ◽

Emily Girard ◽

James Olson ◽

...

Keyword(s):

Membrane Trafficking ◽

Mitochondrial Protein ◽

Survival Rates ◽

Mitotic Catastrophe ◽

Patient Specific ◽

Specific Gene ◽

Precision Oncology ◽

Genome Wide ◽

F Box Protein ◽

Phosphatidylinositol 4

Abstract Precision oncology is largely based on the notion that identification and targeting of oncogenic drivers will lead to improved clinical outcomes. However, the promise of precision oncology awaits to be fulfilled for many cancers, including Glioblastoma (GBM), where identification of oncogenic drivers has yet to improve survival rates. Here, we have attempted to systematically identify GBM vulnerabilities by performing genome-wide CRISRP-Cas9 lethality screens in patient-derived GBM stem-like cells (GSCs). In validation studies, we comprehensively retested GSC-specific hits in multiple GSC isolates, which were also genomically profiled (e.g. RNA-seq, exome-seq, CNV), and further integrated these data with CRISPR-Cas9 lethality screens from over 500 human cell lines from the Broad Institute’s CRISPR Avana dataset. As a result, we have begun making GBM dependency predictions and functional associations for top scoring hits, including: tumor developmental subtype; loss of functional redundancy with other genes/proteins; cancer-specific subnetworks of genes involved in mitochondrial protein turnover and membrane trafficking; and genes of unknown function essential for subset of GBMs. A few examples of these categories include the following scenarios. We find ADAR (Adenosine Deaminase RNA Specific) gene dependency is associated with the mesenchymal GBM subtype. The EFR3Agene, which has roles in maintaining active pools of phosphatidylinositol 4-kinase, appears required when the expression of its paralog EFR3Bis low or absent in tumor cells. The F-box protein-encoding gene FBXO42appears non-essential to most human cells lines and neural stem cells, but when knocked out in sensitive GSCs causes mitotic arrest, mitotic catastrophe, and cell death. While still a work in progress, we hope to use these results as a foundation for exploring and illuminating patient-specific molecular vulnerabilities for brain tumors. The results also underscore the need for integration of functional genetic approaches, where gene activities are inhibited, into precision oncology paradigms.

Download Full-text

In vivo CRISPR/Cas9 targeting of fusion oncogenes for selective elimination of cancer cells

Nature Communications ◽

10.1038/s41467-020-18875-x ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

M. Martinez-Lage ◽

R. Torres-Ruiz ◽

P. Puig-Serra ◽

P. Moreno-Gaona ◽

M. C. Martin ◽

...

Keyword(s):

Cancer Cells ◽

Tumor Development ◽

Tumor Burden ◽

Patient Specific ◽

Specific Gene ◽

In Vivo Models ◽

New Horizons ◽

Selective Elimination ◽

Cancer Types

Abstract Fusion oncogenes (FOs) are common in many cancer types and are powerful drivers of tumor development. Because their expression is exclusive to cancer cells and their elimination induces cell apoptosis in FO-driven cancers, FOs are attractive therapeutic targets. However, specifically targeting the resulting chimeric products is challenging. Based on CRISPR/Cas9 technology, here we devise a simple, efficient and non-patient-specific gene-editing strategy through targeting of two introns of the genes involved in the rearrangement, allowing for robust disruption of the FO specifically in cancer cells. As a proof-of-concept of its potential, we demonstrate the efficacy of intron-based targeting of transcription factors or tyrosine kinase FOs in reducing tumor burden/mortality in in vivo models. The FO targeting approach presented here might open new horizons for the selective elimination of cancer cells.

Download Full-text