Gene amplifications in cancer TCGA datasets – Hosein Kouros-Mehr

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways that drive cancer types. We recently published* our efforts to identify genes that are commonly amplified in cancers and display a cancer driver signature.  Put in another way, we devised a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets

 

We carried out a GISTIC bioinformatics analysis of TCGA datasets spanning 16 cancer subtypes and identified 486 genes that were amplified in two or more datasets.  These cancer types include BLCA – Bladder Urothelial Carcinoma, BRCA – Breast invasive carcinoma, CESC – Cervical squamous cell carcinoma and endocervical adenocarcinoma, CRC – Colorectal Cancer (COAD and READ studies combined together), GBM – Glioblastoma multiforme, HNSC – Head and Neck squamous cell carcinoma, KIRC – Kidney renal clear cell carcinoma, LGG – Brain Lower Grade Glioma, LUAD- Lung adenocarcinoma, LUSC -
Lung squamous cell carcinoma, OV – Ovarian serous cystadenocarcinoma, PAAD – Pancreatic adenocarcinoma, PRAD – Prostate adenocarcinoma, SKCM – Skin Cutaneous Melanoma, STAD – Stomach adenocarcinoma, UCEC – Uterine Corpus Endometrioid Carcinoma.

 

 

From the 486 genes, we identified 75 cancer-associated genes with potential “druggable” properties. The majority of the genes were localized to 14 amplicons spread across the genome.  Genes within an amplicon tended to be amplified in the same cancer subtypes.  To further identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 42 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets.

 

The amplified cancer driver genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 42 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapters GRB2 and GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes.

 

The data presented can be used for patient tailoring efforts — in other words, to tailor novel therapeutics to the patients whose cancers contain the genetics drivers in question and would benefit most from the targeted therapy.  The data can also be used to identify potential novel opportunities for drug discovery efforts.

 

Hosein Kouros-Mehr

* Chen Y, McGee J, Chen X, Doman TN, Gong X, Zhang Y, Hamm N, Ma X, Higgs RE, Bhagwat SV, Buchanan S, Peng SB, Staschke KA,Yadav V, Yue Y, Kouros-Mehr H. (2014)  Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets.  PLoS One. 2014 May 29;9(5):e98293. doi: 10.1371/journal.pone.0098293. eCollection 2014.