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

Abstract

Identification of driver genes contributes to the understanding of cancer etiology and is imperative for the development of individualized therapies. Gene amplification is a major event in oncogenesis. Driver genes with tumor-specific amplification-dependent overexpression can be therapeutic targets. In this study, we aimed to identify amplification-dependent driver genes in 1,454 solid tumors, across more than 15 cancer types, by integrative analysis of gene expression and copy number. Amplification-dependent overexpression of 64 known driver oncogenes were found in 587 tumors (40%); genes frequently observed were MYC (25%) and MET (18%) in colorectal cancer; SKP2 (21%) in lung squamous cell carcinoma; HIST1H3B (19%) and MYCN (13%) in liver cancer; KIT (57%) in gastrointestinal stromal tumors; and FOXL2 (12%) in squamous cell carcinoma across tissues. Genomic aberrations in 138 known cancer driver genes and 491 established fusion genes were found in 1,127 tumors (78%). Further analyses of 820 cancer-related genes revealed 16 as potential driver genes, with amplification-dependent overexpression restricted to the remaining 22% of samples (327 tumors) initially undetermined genetic drivers. Among them, AXL, which encodes a receptor tyrosine kinase, was recurrently overexpressed and amplified in sarcomas. Our studies of amplification-dependent overexpression identified potential drug targets in individual tumors.

Evolutionary Origins of Recurrent Pancreatic Cancer

“…2, Table S6) were notable for somatic alterations in additional genes, some of which are predicted to or likely to activate Mapk/Erk signaling (G12V mutation in KRAS, I679Dfs*21 mutation in NF1, R111X mutation in PPP6C) (23,24), PI3K/Akt/MTOR signaling (D323H hotspot mutation in AKT1, E542K hotspot mutation in PIK3CA, homozygous deletion of STK11) (25), or MYC/MAX regulated gene expression (Y1312X mutation in CHD8, W1004X mutation in MGA, X863_ splicing mutation in NOTCH1, up to 16-fold amplification of MYC) (26,27). Somatic alterations in genes predicted to affect chromatinmediated gene expression (up to 38-fold focal amplification of HIST1H3B, frameshift mutations in KMT2B, KMT2C, KMT2D, c.4471-1N>A splicing mutations in TRIP12) (28)(29)(30), nuclear export (E571K hotspot mutation in XPO1) (31) and DNA damage repair (c.497-1N>A splicing mutation in ATM, F134V mutation in TP53) were also found (32,33). Recurrences from two patients contained copy number alterations of genes implicated in innate immunity signaling (PRKCI 38fold focal amplification, TRAF3 homozygous deletion) (34,35).…”

CYP3A4 Gene Is a Novel Biomarker for Predicting a Poor Prognosis in Hepatocellular Carcinoma

“…Total RNA was extracted from approximately 10 mg of minced tissue samples using the miRNeasy Mini Kit (Qiagen, Hilden, Germany) as described previously (14). RNA samples with RNA integrity number ≥6.0 was used for microarray analysis.…”

“…RNA samples with RNA integrity number ≥6.0 were used for the microarray analysis. Microarray analysis was performed as described previously (14). Briefly, total RNA (100 ng) was fluorescence-labeled and hybridized to the SurePrint G3 Human Gene Expression 8×60 K v2 Microarray (Agilent Technologies, Santa Clara, CA, USA).…”

“…To extract genes, we narrowed down to 820 cancer-related genes (SCC-820) (14) in order to focus on genes with oncogenic characteristics. Genes with aberrant expression were selected by the difference of expression levels with ≥10-fold change between tumor and non-tumor tissues.…”

Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines

“…We observed that the estimated copy number gains and losses of known oncogenes (n=23) and tumor suppressor genes (n=40) [33], relative to the average ploidy per PDX sample, generally results in expression fold change (relative to the average expression at copy number normal state) in the same direction ( Supplementary Table S8) [11,34,35]. Most of these genes show significant over-expression with copy number gain and significant under-expression with copy number loss across the PDX samples (p<0.05) ( Figure 5C and Supplementary Figure S10).…”