A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

Table 3 Single-node interventions which in combination with PI3K inhibitors yield an increase in Apoptosis_norm from 0.7 and/or a decrease in Proliferation_norm from 0.25

Perturbation	Apoptosis_norm	Proliferation_norm	Mechanism
BCL2 = 0	1.00	0.25	Apoptosis
ER_transcription = 0	0.84	0.00	ER
ER_transcription < 2	0.84	0.13	ER
Fulvestrant = 1	0.83	0.00	ER
KMT2D = 0	0.83	0.13	ER
FOXA1 = 0	0.83	0.13	ER
BCL2_T = 0	0.81	0.25	Apoptosis
MCL1 = 0	0.75	0.25	Apoptosis
mTORC1 = 0	0.74	0.25	mTORC1
Everolimus = 1	0.73	0.25	mTORC1
cycD_CDK46 = 0	0.70	0.00	Proliferation
Palbociclib = 1	0.70	0.00	Proliferation
pRb = 0	0.70	0.00	Proliferation
E2F = 0	0.70	0.00	Proliferation
MYC = 0	0.70	0.00	ER
pRb < 2	0.70	0.13	Proliferation
cycD_CDK46 < 2	0.70	0.13	Proliferation
MYC < 2	0.70	0.13	ER
FOXO3 = 0	0.33	0.13	AKT

The entries are ordered by their effect on Apoptosis. Certain node perturbations that are equivalent in the network sense and lead to the same effect are grouped, specifically: ESR1 = OFF, ER = OFF, and ER_transcription = 0; ESR1 < 2 and ER_transcription < 2; PBX1 = OFF and FOXA1 = OFF; translation = OFF, S6K=OFF, EIF4F=OFF, and mTORC1 = OFF; cyclinD = 0, CDK4/6 = OFF, and cycD_CDK4/6 = 0; cyclinD < 2, and cycD_CDK4/6 < 2; FOXO3_Ub = ON and FOXO3 = OFF

ISSN: 2366-6196