ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules

AbstractThe identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.

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Interpreting pathways to discover cancer driver genes with Moonlight

Nature Communications ◽

10.1038/s41467-019-13803-0 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 9

Author(s):

Antonio Colaprico ◽

Catharina Olsen ◽

Matthew H. Bailey ◽

Gabriel J. Odom ◽

Thilde Terkelsen ◽

...

Keyword(s):

Tumor Suppressors ◽

Molecular Mechanisms ◽

Dual Role ◽

Tissue Type ◽

Driver Gene ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Therapeutic Decisions ◽

Cancer Driver Genes

AbstractCancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.

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P03.25 Neutralizing extracellular CHP-1 impairs tumor growth and metastasis formation

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-itoc7.63 ◽

2020 ◽

Vol 8 (Suppl 2) ◽

pp. A32.2-A33

Author(s):

L Seclì ◽

F Fusella ◽

M Brancaccio

Keyword(s):

Cancer Progression ◽

Immune Modulation ◽

Molecular Mechanisms ◽

Immune Cell ◽

Tumor Grade ◽

Metastasis Formation ◽

Cancer Subtypes ◽

Cancer Hallmarks ◽

Clinical Models

BackgroundFound in the extracellular compartment, Heat Shock Proteins (HSPs) are actively secreted proteins that modulate the tumor behavior. Extracellular HSPs play a unique role as extracellular chaperones and receptors-binding molecules, favoring the establishment and maintenance of different cancer hallmarks, including immune modulation and evasion. CHP-1, is a ubiquitously expressed protein with chaperone activity and its high expression correlates with high tumor grade and lymph node positivity in different breast and lung cancer subtypes. In addition, CHP-1 is actively and uncanonically secreted by cancer cells in the tumor microenvironment (TME).Materials and MethodsSera cancer patients were analyzed for the presence of CHP-1. To assess the role of extracellular CHP-1 (eCHP-1) in the TME, in vitro experiments on different cell populations have been performed. To dissect the molecular mechanisms, through which eCHP-1 induces cancer progression, have been analyzed specific signaling pathways in cancer and immune cells. Immune cell composition in presence of eCHP-1 in tumors has been identified using flow-cytometry. The characterization of eCHP-1 inhibition as therapeutic approach has been conducted in breast and colon cancer pre-clinical models.ResultseCHP-1 activates an autocrine signaling through TLR2, TLR4 and LRP1, promoting tumor progression and metastasis formation in different pre-clinical models. Moreover, eCHP-1 can modulate the immune composition of the TME, making interesting the analysis of its inhibition in cancer immunotherapy.ConclusionseCHP-1 represents a easy accessible protein for diagnosis and targeting in very aggressive canncers.Disclosure InformationL. Seclì: None. F. Fusella: None. M. Brancaccio: None.

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Identifying cancer type specific oncogenes and tumor suppressors using limited size data

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500311 ◽

2016 ◽

Vol 14 (06) ◽

pp. 1650031 ◽

Cited By ~ 4

Author(s):

Ana B. Pavel ◽

Cristian I. Vasile

Keyword(s):

Tumor Suppressors ◽

Molecular Mechanisms ◽

Lung Squamous Cell Carcinoma ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Type ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Subtypes ◽

Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

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Genomic Instability Profiles at the Single Cell Level in Mouse Colorectal Cancers of Defined Genotypes

Cancers ◽

10.3390/cancers13061267 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1267

Author(s):

Vasilis S. Dionellis ◽

Maxim Norkin ◽

Angeliki Karamichali ◽

Giacomo G. Rossetti ◽

Joerg Huelsken ◽

...

Keyword(s):

Genomic Instability ◽

Large Scale ◽

Molecular Mechanisms ◽

Single Cells ◽

Genetic Alterations ◽

Nucleotide Substitutions ◽

Driver Genes ◽

Normal Cells ◽

Cancer Driver ◽

Intratumoural Heterogeneity

The genomes of many human CRCs have been sequenced, revealing a large number of genetic alterations. However, the molecular mechanisms underlying the accumulation of these alterations are still being debated. In this study, we examined colorectal tumours that developed in mice with Apclox/lox, LSL-KrasG12D, and Tp53lox/lox targetable alleles. Organoids were derived from single cells and the spectrum of mutations was determined by exome sequencing. The number of single nucleotide substitutions (SNSs) correlated with the age of the tumour, but was unaffected by the number of targeted cancer-driver genes. Thus, tumours that expressed mutant Apc, Kras, and Tp53 alleles had as many SNSs as tumours that expressed only mutant Apc. In contrast, the presence of large-scale (>10 Mb) copy number alterations (CNAs) correlated strongly with Tp53 inactivation. Comparison of the SNSs and CNAs present in organoids derived from the same tumour revealed intratumoural heterogeneity consistent with genomic lesions accumulating at significantly higher rates in tumour cells compared to normal cells. The rate of acquisition of SNSs increased from the early stages of cancer development, whereas large-scale CNAs accumulated later, after Tp53 inactivation. Thus, a significant fraction of the genomic instability present in cancer cells cannot be explained by aging processes occurring in normal cells before oncogenic transformation.

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Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

Cancers ◽

10.3390/cancers13174376 ◽

2021 ◽

Vol 13 (17) ◽

pp. 4376

Author(s):

Amin Ghareyazi ◽

Amir Mohseni ◽

Hamed Dashti ◽

Amin Beheshti ◽

Abdollah Dehzangi ◽

...

Keyword(s):

Pancreatic Cancer ◽

Signaling Pathways ◽

Molecular Mechanisms ◽

De Novo ◽

Treatment Options ◽

Point Mutations ◽

Growth Factor Receptor ◽

Causative Mutation ◽

Whole Genome Analysis ◽

Cancer Subtypes

It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

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Mutations in Cancer Driver Genes: An Insight into Prostate Cancer Progression

annals of urologic oncology ◽

10.32948/auo.2019.09.12 ◽

2019 ◽

pp. 1-7

Author(s):

Sahdeo Prasad ◽

Sanjay K Srivastava

Keyword(s):

Prostate Cancer ◽

Cancer Progression ◽

Drug Targets ◽

Cellular Functions ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Initiation ◽

The Status ◽

Using Data ◽

Insight Into

Prostate cancer is one of the most common uro-oncological disease in men and is globally leading cause of cancer related deaths in males. The somatic mutation has a strong link in the occurrence of cancer. Mutation in the oncogenes and tumor suppressor genes that alter key cellular functions can lead to prostate cancer initiation and progression. Whole genome sequencing has identified numerous genetic alternations and further provided a detail view of the mutations in genes that drive progression of prostate cancer. TP53, SPOP, PTEN, ATM, AR, CTNNB1, FOXA1, KMT2D, BRACA2 and APC were found as frequently mutated genes in prostate cancer. Using data from cBioPortal and PubMed, this review summarizes the status and possible impact of mutations in these driver genes on survival, progression, and metastasis of prostate cancer. This study will contribute a better understanding of biological basis for clinical variability in prostate cancer patients and may provide new genetic diagnostic markers and drug targets.

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DriverGroup: A novel method for identifying driver gene groups

10.1101/2020.04.23.058719 ◽

2020 ◽

Author(s):

Vu VH Pham ◽

Lin Liu ◽

Cameron P Bracken ◽

Gregory J Goodall ◽

Jiuyong Li ◽

...

Keyword(s):

Cancer Progression ◽

Single Gene ◽

Gene Interaction ◽

Computational Method ◽

Supplementary Information ◽

Driver Gene ◽

Driver Genes ◽

Cancer Driver ◽

Critical Nodes ◽

Novel Method

AbstractMotivationIdentifying cancer driver genes is a key task in cancer informatics. Most exisiting methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesise that there are driver gene groups that work in concert to regulate cancer and we develop a novel computational method to detect those driver gene groups.ResultsWe develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (1) Constructing the gene network, (2) Discovering critical nodes of the constructed network, and (3) Identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify coding and non-coding driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup.Availability and implementationDriverGroup is available at https://github.com/pvvhoang/[email protected] informationSupplementary data are available at Bioinformatics online.

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DriverGroup: a novel method for identifying driver gene groups

Bioinformatics ◽

10.1093/bioinformatics/btaa797 ◽

2020 ◽

Vol 36 (Supplement_2) ◽

pp. i583-i591

Author(s):

Vu V H Pham ◽

Lin Liu ◽

Cameron P Bracken ◽

Gregory J Goodall ◽

Jiuyong Li ◽

...

Keyword(s):

Cancer Progression ◽

Single Gene ◽

Gene Interaction ◽

Computational Method ◽

Supplementary Information ◽

Driver Gene ◽

Driver Genes ◽

Cancer Driver ◽

Critical Nodes ◽

Novel Method

Abstract Motivation Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. Results We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. Availability and implementation DriverGroup is available at https://github.com/pvvhoang/DriverGroup Supplementary information Supplementary data are available at Bioinformatics online.

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Regulation of Neuroendocrine-like Differentiation in Prostate Cancer by Non-Coding RNAs

Non-Coding RNA ◽

10.3390/ncrna7040075 ◽

2021 ◽

Vol 7 (4) ◽

pp. 75

Author(s):

Eva Slabáková ◽

Zuzana Kahounová ◽

Jiřina Procházková ◽

Karel Souček

Keyword(s):

Prostate Cancer ◽

Cancer Cells ◽

Cancer Progression ◽

Molecular Mechanisms ◽

De Novo ◽

Treatment Resistance ◽

Response To Treatment ◽

Neuroendocrine Prostate Cancer ◽

Non Coding Rnas ◽

New Treatment

Neuroendocrine prostate cancer (NEPC) represents a variant of prostate cancer that occurs in response to treatment resistance or, to a much lesser extent, de novo. Unravelling the molecular mechanisms behind transdifferentiation of cancer cells to neuroendocrine-like cancer cells is essential for development of new treatment opportunities. This review focuses on summarizing the role of small molecules, predominantly microRNAs, in this phenomenon. A published literature search was performed to identify microRNAs, which are reported and experimentally validated to modulate neuroendocrine markers and/or regulators and to affect the complex neuroendocrine phenotype. Next, available patients’ expression datasets were surveyed to identify deregulated microRNAs, and their effect on NEPC and prostate cancer progression is summarized. Finally, possibilities of miRNA detection and quantification in body fluids of prostate cancer patients and their possible use as liquid biopsy in prostate cancer monitoring are discussed. All the addressed clinical and experimental contexts point to an association of NEPC with upregulation of miR-375 and downregulation of miR-34a and miR-19b-3p. Together, this review provides an overview of different roles of non-coding RNAs in the emergence of neuroendocrine prostate cancer.

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Mutational likeliness and entropy help to identify driver mutations and their functional role in cancer

10.1101/354324 ◽

2018 ◽

Author(s):

Giorgio Mattiuz ◽

Salvatore Di Giorgio ◽

Lorenzo Tofani ◽

Antonio Frandi ◽

Francesco Donati ◽

...

Keyword(s):

Cancer Progression ◽

Somatic Mutations ◽

Driver Mutations ◽

Cancer Evolution ◽

Loss Of Function ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Genomes ◽

Passenger Mutations ◽

Mutational Processes

AbstractAlterations in cancer genomes originate from mutational processes taking place throughout oncogenesis and cancer progression. We show that likeliness and entropy are two properties of somatic mutations crucial in cancer evolution, as cancer-driver mutations stand out, with respect to both of these properties, as being distinct from the bulk of passenger mutations. Our analysis can identify novel cancer driver genes and differentiate between gain and loss of function mutations.

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