Prediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression

Table 2 Kinases with the highest difference in the regression coefficients for the log transformed data of the secondary screen

Kinase	Cancer beta coefficient	Normal beta coefficient	Difference
TGFBR2	0.061	0.000	0.061
EGFR	0.060	0.000	0.060
PHKG1	0.051	0.014	0.037
RIPK2	0.032	-0.002	0.034
PRKG2	0.012	0.045	0.033
CDK4	0.021	-0.008	0.029
MAP3K10	0.038	0.014	0.024
MARK4	0.000	0.022	0.022
PAK1	0.025	0.004	0.021
MAP4K5	0.021	0.000	0.021
MARK2	0.006	0.026	0.021
MARK3	0.000	0.020	0.020
TBK1	0.012	0.031	0.020
ERBB2	0.021	0.001	0.019
NUAK1	-0.029	-0.010	0.019
ULK2	0.018	0.000	0.018
MYLK2	-0.024	-0.006	0.018
MAP4K4	0.004	-0.014	0.018
CDK5	0.002	-0.016	0.018
GSK3B	0.021	0.004	0.017
PAK2	0.019	0.002	0.017
CDC42BPB	0.023	0.006	0.017
DSTYK	0.006	-0.010	0.016
RPS6KA2	0.000	-0.016	0.016
FGFR1	-0.004	0.012	0.016
PAK7	0.015	0.000	0.015
PIM1	-0.015	0.000	0.015
CDK3	0.015	0.000	0.015
IRAK1	-0.002	-0.017	0.015

A larger difference is associated with a selective response of A549 upon inhibition. Note that in addition to TGFB2R and CDK4, which were identified with the correlation approach of Table 1, additional kinases known to have an important role in lung cancer such as EGFR [24, 25] and PHKG1 [26] are found using the elastic net approach.

ISSN: 1752-0509