Overcoming resistance to BRAFV600E inhibition in melanoma by deciphering and targeting personalized protein network alterations

Abstract

BRAFV600E melanoma patients, despite initially responding to the clinically prescribed anti-BRAFV600E therapy, often relapse, and their tumors develop drug resistance. While it is widely accepted that these tumors are originally driven by the BRAFV600E mutation, they often eventually diverge and become supported by various signaling networks. Therefore, patient-specific altered signaling signatures should be deciphered and treated individually. In this study, we design individualized melanoma combination treatments based on personalized network alterations. Using an information-theoretic approach, we compute high-resolution patient-specific altered signaling signatures. These altered signaling signatures each consist of several co-expressed subnetworks, which should all be targeted to optimally inhibit the entire altered signaling flux. Based on these data, we design smart, personalized drug combinations, often consisting of FDA-approved drugs. We validate our approach in vitro and in vivo showing that individualized drug combinations that are rationally based on patient-specific altered signaling signatures are more efficient than the clinically used anti-BRAFV600E or BRAFV600E/MEK targeted therapy. Furthermore, these drug combinations are highly selective, as a drug combination efficient for one BRAFV600E tumor is significantly less efficient for another, and vice versa. The approach presented herein can be broadly applicable to aid clinicians to rationally design patient-specific anti-melanoma drug combinations.

Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures

“…The TCGA dataset comprising of 747 breast tumors was a part of a large dataset consisting of 3467 human tumors, which were profiled on a reverse-phase protein array (RPPA) for 181 cancer associated proteins, and analyzed using surprisal analysis as described in [ 18 , 19 , 22 ]. The input data was obtained from TCPA (The Cancer Proteome Atlas) portal.…”

“…It is important to note that each protein can participate in many unbalanced processes. In this study, we analyze changes in the proteomic processes as described previously [ 18 , 19 , 22 ].…”

“…Not all tumors are influenced by all unbalanced processes. So for each tumor, a set of unbalanced processes is generated, which is converted into a patient-specific barcode as described in [ 18 , 19 , 22 ]. Briefly, for each patient, λ α (k) ( α = 1, 2, 3, …, n ) values are normalized as follows: If λ α (k) > 2 (and is therefore significant according to calculation of threshold values [ 22 ]) then it was normalized to 1; if λ α (k) < −2 (significant according to threshold values as well) then it was normalized to −1; and if −2 < λ α (k) < 2 then it was normalized to 0.…”

“…Each patient is assigned then a barcode using −1, 1 and 0 values—which represents a patient-specific combination of the active processes ( Table S1 ). This combination is a patient-specific signaling signature that can be used further to devise patient-specific drug therapies [ 18 , 19 ] as shown in Figure 2. For more details see Figure S1 describing the flow of the analysis and references [ 18 , 19 ].…”

“…We suggest that a key for promising therapies lies in an accurate resolution of the PaSSS in each TNBC. All patient-specific processes should be inhibited, through targeting one or two central protein hubs in each process, in order to reduce the tumor-specific biochemical flux [ 19 ]. PaSSS analysis suggested that only a small part of TNBC tumors (~30%) would benefit from anti-EGFR therapies and only when they are combined with anti-ER therapies.…”