Method Development: Efficient Computer Vision Based Algorithms

Division Of Basic Sciences - Nci

Investigators

Linked publications, trials & patents

Abstract

Cancer requires drug treatment. The oncologist is faced with the problem: which drugs to prescribe and which regimen? Much is still unknown, and the number of possible drugs, and their combinations is vast. At the outset, the oncologist reckons with at least two established facts: (i) patients receiving successive single molecules treatments are likely to experience drug resistance, and (ii), to select optimal drug combinations requires to pick the 'best' protein drug target combinations. Intuitively, target selection should precede drug selection, implying that well-informed strategies would opt to first consider drug targets - not drugs - combinations. Nowadays, drug combinations that oncologists consider are empirical and limited. They are restricted primarily by observations and praxis, that is, scant clinical experience with their application. We are developing a signaling-based method to discover optimal proteins for the oncologist to co-target with drug combinations. Our innovative signaling-based approach harnesses nature, mimicking the cancer's prolife tactics. A fundamental cancer tenet for evading anti-drugs is circumventing them. Since the drugs commonly block pathways, cancer often maneuvers around the blockade through emerging mutations, and (or) altering expression levels, that allow bypassing it. Our signaling- and mutations- informed method aims to capture cancer's ploy. To accomplish this aim we identified potential targets for combination drugs using a network-based approach. Candidates were selected from pivotal subnetworks nodes, characterized by high betweenness centrality, serving as essential communication hubs. As such, they often serve as homeostatic guardians, making them ideal targets for combination drugs aimed at disrupting oncogenic signaling. By analyzing our compiled coexisting mutations and their associated subnetworks, we elucidated the principal communication pathways, employing genes on the shortest paths as seeds. This methodology enabled us to determine key oncogenic signaling nodes and essential connector proteins that reveal potential rewiring routes. The mounting number of new targeted therapies has exponentially expanded the therapeutic combinations search space calling for an effective solution to this challenge. Selecting optimal small molecule combinations among this vast array renders comprehensive clinical testing impractical. Combinatorial kinase inhibitors aim to disrupt specific resistance signaling, inhibiting multiple targets simultaneously. Combination strategies entail blocking pathways upstream and downstream, exploiting synthetic lethality, and concerted targeting of multiple pathways. Network-based, signaling-primed combination drugs represent a transformative paradigm in precision oncology and personalized medicine. It addresses the commonly recognized limitations of monotherapies. It offsets compensatory mechanisms and by temporally rotating the combinations, it lessens drug resistance. High-throughput omics data harnessed by sophisticated computational algorithms, is a recipe to therapeutic regimens to the heterogeneous nature of cancers. Within this framework, signaling-learned network-informed combination drugs establish a first step in designing a combinatorial strategy that precisely targets tumor-specific molecular alterations. This approach also expedites drug discovery through the repurposing of existing compounds and the identification of novel synergies. We acknowledge that incorporating extensively studied cancer subtypes, such as HER2-positive breast cancer or BRAF'PIK3CA-mutant colorectal cancer, may introduce bias toward these well-characterized contexts. This could potentially lead to overfitting, where predicted therapeutic combinations are disproportionately influenced by cancers with abundant molecular and clinical data. Such bias might limit the generalizability of our findings to less-studied cancer types or molecular subtypes. Future work should focus on systematically validating the identified combinations across a broader spectrum of tumor types with varying levels of molecular annotation to ensure the robustness and clinical applicability of our approach across diverse oncological contexts. While our study prioritizes therapeutic combinations based on molecular and cellular signaling interactions, effective cancer therapy must also account for the tissue context and the influence of the tumor microenvironment, including interactions with immune cells and stromal components. These factors can impact drug efficacy and resistance and incorporating such spatial and immunological dimensions remains an important avenue for future research. Additionally, although our computational approach identifies biologically plausible combinations, the safety profiles of these combinations-including potential on- and off-target toxicities-must be rigorously evaluated in preclinical models. The lack of absolute specificity and selectivity of many targeted agents poses challenges in clinical implementation, underlining the need for careful toxicological screening alongside efficacy studies. Here we launch the first concept-based signaling mechanism-informed strategy, which takes up the question of "which signaling pathway and protein to select to mitigate the patient's expected drug resistance"3 deescalating the massive number of possibilities facing the physician and fitting the solution to the patient status. Applications of our strategy are validated by existing patient-based xenograft models. However, translating these into clinical practice necessitates further rigorous validation in preclinical models and meticulously conducted clinical trials.