scholarly journals Integrated analysis of gene expression and copy number identified potential cancer driver genes with amplification-dependent overexpression in 1,454 solid tumors

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Keiichi Ohshima ◽  
Keiichi Hatakeyama ◽  
Takeshi Nagashima ◽  
Yuko Watanabe ◽  
Kaori Kanto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Solid Tumors ◽  
Copy Number ◽  
Integrated Analysis ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Potential Cancer

Discovering potential cancer driver genes by an integrated network-based approach

2016 ◽  
Vol 12 (9) ◽  
pp. 2921-2931 ◽  
Author(s):  
Kai Shi ◽  
Lin Gao ◽  
Bingbo Wang
Keyword(s):  
Gene Expression ◽  
Gene Mutations ◽  
Driver Genes ◽  
Integrated Network ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
The Relationship ◽  
Potential Cancer ◽  
Patient Mutation ◽  
Network Diffusion

An integrated network-based approach is proposed to nominate driver genes. It is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation.

Dots Witer: Prediction of Potential Cancer Driver Genes Using Hybrid Approach

2019 ◽  
Author(s):  
P. A. Amalka Iloshini ◽  
M.W. A. C. R. Wijesinghe ◽  
T. Kartheeswaran ◽  
W. M. P. S. Weerasooriya
Keyword(s):  
Hybrid Approach ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Potential Cancer

Comparative Oncogenomics Identifies PSMB4 and SHMT2 as Potential Cancer Driver Genes

2014 ◽  
Vol 74 (11) ◽  
pp. 3114-3126 ◽  
Author(s):  
Genee Y. Lee ◽  
Peter M. Haverty ◽  
Li Li ◽  
Noelyn M. Kljavin ◽  
Richard Bourgon ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Potential Cancer

scholarly journals DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Di Zhang ◽  
Yannan Bin
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Prognostic Biomarkers ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Critical Challenge ◽  
Novel Algorithm

Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.

Integrated Analysis Of Genomic and Gene Expression Profiles In Follicular Lymphoma Reveals Subsets and Driver Genes Of Potential Microenvironmental Importance

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2487-2487
Author(s):  
Ondrej Havranek ◽  
Jason R. Westin ◽  
Min Zhang ◽  
Seema Rawal ◽  
Larry W. Kwak ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Follicular Lymphoma ◽  
B Cell ◽  
Immune Cells ◽  
Copy Number ◽  
Integrated Analysis ◽  
Driver Genes ◽  
Gene Modules ◽  
Cell Fractions

Abstract Background The immune microenvironment in follicular lymphoma (FL) impacts its clinical course, but the interaction between FL cells and host immune cells is poorly understood, and may be influenced by large genetic abnormalities in FL cells. Regions of copy number variation (CNV) and copy-neutral loss of heterozygosity (cnLOH) are detectable by single nucleotide polymorphism (SNP) arrays and frequently found in FL, but the critical “driver genes” within them are largely unidentified. Methods Cell suspensions from tumor biopsies of 66 untreated FL patients were sorted into “B cell” and non-B fractions by immunomagnetic depletion targeting CD3 or CD19 and CD20 respectively. High-resolution Illumina Omni5 SNP arrays were used to profile genomic DNA from B cell fractions and germline DNA (from non-B fractions or peripheral blood cells). Nexus Copy Number software (BioDiscovery) compared paired profiles to determine tumor-specific CNV and cnLOH abnormalities of each patient. Genes within overlapping recurrently-altered regions were identified by the JISTIC algorithm (PMID: 20398270). For 43 of these patients, whole-genome gene expression profiling (GEP) of both fractions was done on Illumina HT12v4 arrays. CONEXIC module network analysis (PMID: 21129771) identified candidate driver genes, based on correlation of their expression in B-cell fractions with that of modules of genes in B-cell or non-B fractions. Results Comparing tumor vs. germline profiles in SNP array analysis clarified the detection of tumor-specific CNV, and enabled the detection of cnLOH. The aggregate genomic profile of regions affected by CNV in our 66 FL samples was highly similar to results of previous FL studies. Most frequent (each in 25-35% of samples) were deletions of 1p36 or a large part of 6q, amplifications of 1q, 7p/q, 12q, 17q, or 18p/q, and cnLOH at 16p. The distribution of these abnormalities suggested that FL can be divided into subgroups based on several large mutually-exclusive genomic aberrations: -10q, -16p, +12q, and, less clearly, -1p/1q+. Novel analysis combining copy number values with corresponding SNP frequencies also identified abnormalities of lower frequency within samples, suggestive of tumor subclones with potential growth advantages, notably including deletions at 13q14 and 19p12 and amplification of 16p13. JISTIC identified 715 expressed genes within amplified regions and 413 expressed genes within deleted regions (329 genes) or regions of cnLOH (84 genes). CONEXIC identified 62 and 68 of these genes as candidate drivers regulating expression of gene modules in tumor B cells and infiltrating immune cells, respectively. Several regulators of B-cell modules were already described in FL or other hematological malignancies: MDM2 (12q15, amplified in 26%), an E3 ubiquitin ligase whose targets include TP53; NME1 (17q21.33, amplified in 21%), part of the nucleoside diphosphate kinase complex, overexpressed and correlated with poor prognosis in AML; or B-cell receptor-associated CD79B (17q23.3, amplified in 21%), mutated and functionally significant in diffuse large B-cell lymphoma. Validating MDM2 as a driver gene, Gene Set Enrichment Analysis showed strong positive association between expression of MDM2 and that of proliferation signatures in B cells, including signatures of genes downregulated by TP53. Genes affecting the interaction between tumor B cells and the FL microenvironment plausibly regulate module expression in both B cells and non-B cells. Such dual candidate driver genes included PHIP (6q14.1, deleted in 27%), a binding partner of insulin receptor substrate-1, overexpressed in melanoma and linked to its metastasis and progression; SMARCC2 (12q13.2, amplified in 25%), part of the ATP-dependent chromatin remodeling complex SNF/SWI, mutated in some carcinomas; SFR1 (10q25.1, deleted in 18%), involved in DNA homologous recombination; and BUD31(7q22.1, amplified in 21%), a homolog of a yeast protein involved in pre-mRNA splicing. Conclusions CNV and cnLOH abnormalities are frequent in FL, and may identify subgroups within FL. Integrated analysis finds known candidate driver genes within recurrently-altered regions, appearing to regulate expression of gene modules in B cells. Novel candidate driver genes that appear to regulate modules in both B and non-B cells may shape the FL microenvironment in important ways, and are being investigated experimentally. Disclosures: No relevant conflicts of interest to declare.

Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level

2017 ◽  
Vol 24 (2) ◽  
pp. 138-152 ◽  
Author(s):  
Heewon Park ◽  
Atsushi Niida ◽  
Seiya Imoto ◽  
Satoru Miyano
Keyword(s):  
Feature Selection ◽  
Copy Number ◽  
Expression Level ◽  
Driver Genes ◽  
Cancer Driver ◽  
Selection For ◽  
Cancer Driver Genes

scholarly journals Identifying potential cancer driver genes by genomic data integration

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Yong Chen ◽  
Jingjing Hao ◽  
Wei Jiang ◽  
Tong He ◽  
Xuegong Zhang ◽  
...  
Keyword(s):  
Data Integration ◽  
Genomic Data ◽  
Driver Genes ◽  
Cancer Driver ◽  
Genomic Data Integration ◽  
Cancer Driver Genes ◽  
Potential Cancer

Integrated Analysis of High-Resolution Gene Expression and Copy Number Profiling Identified Biallelic Deletion of CDKN2A/2B Tumor Suppressor Locus As the Most Frequent and Unique Genomic Abnormality in Diffuse Large B-Cell Lymphoma (DLBCL) with Strong Prognostic Value in Both GCB and ABC Subtypes and Not Overcome by a Dose-Intensive Immunochemotherapy Regimen Plus Rituximab. Results of a Prospective GELA Clinical Trial Program

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 415-415 ◽  
Author(s):  
Fabrice Jardin ◽  
Sylvain Mareschal ◽  
Martin Figeac ◽  
Jean-Philippe Jais ◽  
Karen Leroy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Trial ◽  
High Resolution ◽  
Tumor Suppressor ◽  
B Cell ◽  
Copy Number ◽  
Gene Copy ◽  
Integrated Analysis ◽  
Driver Genes ◽  
Clinical Trial Program

Abstract Abstract 415 Background and aim of the study Genomic gains and losses play a crucial role in the development and progression of DLBCL and are closely related to gene expression profiles (GEP), including the germinal center B-cell like (GCB) and activated B-cell like (ABC) cell of origin (COO) molecular signatures. To identify new oncogenes or tumor suppressor genes (TSG) involved in DLBCL pathogenesis and to determine their prognostic values, an integrated analysis of high-resolution gene expression and copy number profiling was performed. Patients and methods Two hundred and eight adult patients with de novo CD20+ DLBCL enrolled in the prospective multicentric randomized LNH-03 GELA trials (LNH03-1B, -2B, -3B, 39B, -5B, -6B, -7B) with available frozen tumour samples, centralized reviewing and adequate DNA/RNA quality were selected. 116 patients were treated by Rituximab(R)-CHOP/R-miniCHOP and 92 patients were treated by the high dose (R)-ACVBP regimen dedicated to patients younger than 60 years (y) in frontline. Tumour samples were simultaneously analysed by high resolution comparative genomic hybridization (CGH, Agilent, 144K) and gene expression arrays (Affymetrix, U133+2). Minimal common regions (MCR), as defined by segments that affect the same chromosomal region in different cases, were delineated. Gene expression and MCR data sets were merged using Gene expression and dosage integrator algorithm (GEDI, Lenz et al. PNAS 2008) to identify new potential driver genes. Results A total of 1363 recurrent (defined by a penetrance > 5%) MCRs within the DLBCL data set, ranging in size from 386 bp, affecting a single gene, to more than 24 Mb were identified by CGH. Of these MCRs, 756 (55%) showed a significant association with gene expression: 396 (59%) gains, 354 (52%) single-copy deletions, and 6 (67%) homozygous deletions. By this integrated approach, in addition to previously reported genes (CDKN2A/2B, PTEN, DLEU2, TNFAIP3, B2M, CD58, TNFRSF14, FOXP1, REL…), several genes targeted by gene copy abnormalities with a dosage effect and potential physiopathological impact were identified, including genes with TSG activity involved in cell cycle (HACE1, CDKN2C) immune response (CD68, CD177, CD70, TNFSF9, IRAK2), DNA integrity (XRCC2, BRCA1, NCOR1, NF1, FHIT) or oncogenic functions (CD79b, PTPRT, MALT1, AUTS2, MCL1, PTTG1…) with distinct distribution according to COO signature. The CDKN2A/2B tumor suppressor locus (9p21) was deleted homozygously in 27% of cases and hemizygously in 9% of cases. Biallelic loss was observed in 49% of ABC DLBCL and in 10% of GCB DLBCL. This deletion was strongly correlated to age and associated to a limited number of additional genetic abnormalities including trisomy 3, 18 and short gains/losses of Chr. 1, 2, 19 regions (FDR < 0.01), allowing to identify genes that may have synergistic effects with CDKN2A/2B inactivation. With a median follow-up of 42.9 months, only CDKN2A/2B biallelic deletion strongly correlates (FDR p.value < 0.01) to a poor outcome in the entire cohort (4y PFS = 44% [32–61] respectively vs. 74% [66–82] for patients in germline configuration; 4y OS = 53% [39–72] vs 83% [76–90]). In a Cox proportional hazard prediction of the PFS, CDKN2A/2B deletion remains predictive (HR = 1.9 [1.1–3.2], p = 0.02) when combined with IPI (HR = 2.4 [1.4–4.1], p = 0.001) and GCB status (HR = 1.3 [0.8–2.3], p = 0.31). This difference remains predictive in the subgroup of patients treated by R-CHOP (4y PFS = 43% [29–63] vs. 66% [55–78], p=0.02), in patients treated by R-ACVBP (4y PFS = 49% [28–84] vs. 83% [74–92], p=0.003), and in GCB (4y PFS = 50% [27–93] vs. 81% [73–90], p=0.02), or ABC/unclassified (5y PFS = 42% [28–61] vs. 67% [55–82] p = 0.009) molecular subtypes (Figure 1). Conclusion We report for the first time an integrated genetic analysis of a large cohort of DLBCL patients included in a prospective multicentric clinical trial program allowing identifying new potential driver genes with pathogenic impact. However CDKN2A/2B deletion constitutes the strongest and unique prognostic factor of chemoresistance to R-CHOP, regardless the COO signature, which is not overcome by a more intensified immunochemotherapy. Patients displaying this frequent genomic abnormality warrant new and dedicated therapeutic approaches. Disclosures: Salles: roche: Consultancy.

Specific clonal expansion at disease progression (PD) in solid cancers pinpointed by cell free DNA analysis.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13144-e13144
Author(s):  
Elisa Frullanti ◽  
Maria Palmieri ◽  
Margherita Baldassarri ◽  
Francesca Fava ◽  
Alessandra Fabbiani ◽  
...  
Keyword(s):  
Solid Tumors ◽  
Primary Tumor ◽  
Dna Analysis ◽  
Copy Number Variations ◽  
Driver Mutations ◽  
Molecular Profile ◽  
Sequencing Analysis ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes

e13144 Background: More than 50% of solid cancers sooner or later escape control of standard treatments. Detection and analysis of cell free circulating DNA (cfDNA) now offer the possibility to detect key mutations of cancer driver genes which may play a major role in the therapy escaping mechanism. We sought to identify clones of solid tumors escaping standard treatments in order to assess personalized treatment at PD. Methods: A cohort of patients with 10 different solid tumors progressing after standard therapy were selected. CfDNA analysis was performed using PAXgene blood ccfDNA tubes (QIAGEN), MagMAX cell-free total nucleic acid isolation kit, and ION PROTON platform (ThermoFisher Scientific). Results: Next generation sequencing analysis of 52 cancer-driver genes of cfDNA samples of 39 patients allowed for picking up clones plausibly involved in the PD mechanism in 60% of cases. A mean of 1.3 mutated genes (range 1-3) for each tumor was found. Point mutations in TP53, PIK3CA, and CNV in FGFR3 were the most commonly observed, with a rate of 41%, 16%, and 13%, respectively. Increased copy number variations of FGF receptors were identified in patients with non-small cell lung, pancreatic, and gastric cancer, and cholangiocarcinoma. Other clones had mutations in ESR1 (breast), CTNNB1 (uterus), KRAS and CCND2 (pancreas), EGFR and BRAF (lung). Interestingly, retinoblastomas resistant to Melphalan showed expanding mutated clones in PTEN or SMAD4. Increased levels of cfDNA were observed in the plasma of all patients. Conclusions: The results presented here show that irrespective of the primary tumor mutational burden and subsequent complex clonal evolution, a simplified mutational load is present at PD. One or few “sniper” clones drive progression and the molecular profile has a weak correlation with the primary tumor. Single driver mutations in TP53 remain the main target of a not yet developed specific therapy in most tumors such as breast, ovarian, uterine, lung, gastric cancers and glioblastoma. Among the actionable mutations, PIK3CA were found, not only in breast cancers, but also in uterine carcinoma, Sezary syndrome and glioblastoma, pinpointing the needs of specific trials in these tumors.

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