QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency

BMC Bioinformatics

Table 3 Putative driver gene sets discovered by QuaDMutNetEx

Gene	c	D/R	QuanDMutEx	COSMIC	DDBv2	Gene	c	D/R	QuanDMutEx	COSMIC	DDBv2
TN: Triple-negative breast cancer
TP53	35	R	✓	✓	✓	PARK2	6	∙	✓	∙	✓
ATR	4	D	∙	✓	✓	SAGE1	3	∙	✓	∙	✓
NR3C1	3	∙	✓	∙	✓	CREBBP	2	D/R	∙	✓	✓
DAPK1	2	∙	∙	∙	✓	NCOA1	2	D	∙	✓	✓
SLC39A7	2	∙	∙	∙	✓	IDH3B	2	∙	✓	∙	✓
HIST1H4A	2	∙	∙	∙	✓	HIF1A	2	D	∙	✓	✓
MLL	2	D	∙	✓	✓
GBM: Glioblastoma multiforme
CDKN2A	55	R	✓	✓	✓	TP53	38	R	✓	✓	✓
MDM2	13	D	∙	✓	✓	MDM4	5	D	∙	✓	✓
MAPK9	2	∙	∙	∙	✓	RPL11	2	∙	∙	∙	✓
HGS: high-grade serous ovarian cancer
TP53	249	R	✓	✓	✓	SOS1	3	∙	∙	∙	✓
CTNNB1	2	D	∙	✓	✓	DAG1	2	∙	∙	∙	✓
ERBB2	2	D	∙	✓	✓	FANCA	2	R	∙	✓	✓
GRB2	2	∙	∙	∙	✓	PIK3R1	2	R	∙	✓	✓
TSHR	2	D	∙	✓	✓	DNAJA3	2	∙	∙	∙	✓
HSP90AA1	2	D	∙	✓	✓	HSPA5	2	∙	∙	∙	✓
MST1R	2	∙	∙	∙	✓	PTK2	2	∙	✓	∙	✓
STAT3	2	D	∙	✓	✓	UBC	2	∙	∙	∙	✓
VAV3	2	∙	∙	∙	✓	WRN	2	R	✓	✓	✓
ZAP70	2	∙	∙	∙	✓	ERBB3	2	D	∙	✓	✓
NTRK2	2	∙	∙	∙	✓	SPRY2	2	∙	∙	∙	✓
DHHC11	2	∙	∙	∙	✓	JAK2	2	D	∙	✓	✓
CDKN2A	2	R	∙	✓	✓
METABRIC: breast cancer
ERBB2	84	D	∙	✓	✓	FGFR1	50	D	∙	✓	✓
GAB2	35	∙	∙	∙	✓	PSG11	28	∙	∙	∙	✓
MACROD2	19	∙	✓	∙	✓	PTEN	16	D	✓	✓	✓
FRS2	10	∙	∙	∙	✓	IGF1R	10	∙	∙	∙	✓
CRK	10	∙	∙	∙	✓	JAK2	7	D	∙	✓	✓
AC116165.7-2	6	∙	✓	∙	∙	IRS4	6	∙	∙	✓	✓
PTK2	5	∙	∙	∙	✓	IL6ST	4	D	∙	✓	✓
EGFR	4	D	∙	✓	✓	GRB2	4	∙	∙	∙	✓
PTPN1	4	∙	∙	∙	✓	CREBBP	3	D/R	∙	✓	✓
DOK6	3	∙	∙	∙	✓	JAK1	2	D	∙	✓	✓
EGF	2	∙	∙	∙	✓	PIK3R1	2	R	∙	✓	✓
SYK	2	D	∙	✓	✓	PTPN6	2	∙	∙	✓	✓
VAV1	2	∙	∙	✓	✓

Number of patients in the dataset that had a mutation in the gene is in c column. D/R stand for dominant or recessive otherwise unknown. Genes discovered by the quadratic mutual-exclusivity approach that does not include the network connectivity term are in QuanDMutEx column. COSMIC [26, 27] column represent if the gene present in COSMIC Cancer Gene Census. Genes present in DriverDBv2 [28] are in DDBv2 column

ISSN: 1471-2105