Targeting Adaptation to Cancer Treatment by Drug Combinations

Abstract

Adaptation of tumors to therapeutic interventions contributes to dismal long-term patient outcomes. Adaptation to therapy involves co-action of functionally related proteins that together activate cell survival programs and compensate for the therapeutic impact. Oncogenic dependencies to such adaptive events, however, can generate new therapeutic vulnerabilities that can be targeted with drug combinations. The precision medicine approaches in which targeted drugs are matched to pre-existing genomic aberrations fail to address the adaptive responses and resulting vulnerabilities. Here, we provide the mathematical formulation, implementation and validation of the TargetScore method. The TargetScore identifies collective adaptive responses to targeted interventions as concurrent changes of phospho-proteins that are connected within a signaling network. Based on the adaptive responses, the method predicts drug-induced vulnerabilities. Using TargetScore, we inferred the adaptive responses with short-term (i.e., days) stress and long-term (i.e., months) acquired resistance to inhibitors of anti-apoptotic mediators, MCL1 and BCL2. With experiments guided by the predictions, we identified synergistic interactions between inhibitors of PARP, SHP2, and MCL1 in breast cancer cells. TargetScore is readily applicable to existing precision oncology efforts by matching targeted drug combinations to emerging molecular signatures under therapeutic stress.

BET inhibition induces vulnerability to MCL1 targeting through upregulation of fatty acid synthesis pathway in breast cancer

“…We analyzed the proteomic responses to BETis using our TargetScore network modeling method (Figure 2) (Wang et al, 2021). Our goal was to identify collective adaptive response mechanisms that can drive drug resistance and drug-induced vulnerabilities to combination therapies (Figures 2A and S2).…”

“…The algorithm is presented in detail in (Wang et al, 2021). The aim of the network-level description of adaptive responses is to better explain collective mechanisms, in contrast with conventional analysis which focuses on individual genes/proteins.…”