Engineering Multidimensional Evolutionary Forces to Combat Cancer

Abstract

With advances in technology and bioinformatics, we are now positioned to view and manage cancer through an evolutionary lens. This perspective is critical as our appreciation for the role of tumor heterogeneity, tumor immune compartment, and tumor microenvironment on cancer pathogenesis and evolution grows. Here, we explore recent knowledge on the evolutionary basis of cancer pathogenesis and progression, viewing tumors as multilineage, multicomponent organisms whose growth is regulated by subcomponent fi tness relationships. We propose reconsidering some current tenets of the cancer management paradigm in order to take better advantage of crucial fi tness relationships to improve outcomes of patients with cancer. Signifi cance: Tumor and tumor immune compartment and microenvironment heterogeneity, and their evolution, are critical disease features that affect treatment response. The impact and interplay of these components during treatment are viable targets to improve clinical response. In this article, we consider how tumor cells, the tumor immune compartment and microenvironment, and epigenetic factors interact and also evolve during treatment. We evaluate the convergence of these factors and suggest innovative treatment concepts that leverage evolutionary relationships to limit tumor growth and drug resistance. Research.

Treating Cancer as an Invasive Species

“…Intersystem comparisons may help clarify parameter ranges for which heterogeneity engineering approaches will be effective (55). It is promising that there have been recent calls to expand clinical trial testing of cancer adaptive therapy and treatment switching (56). The results of such studies would be highly informative for the development of two-population heterogeneity engineering and shed light on when higher order population engineering might be most necessary.…”

Acquired Resistance in Lung Cancer

“…Improved strategies for early detection may ultimately yield less disseminated disease and lower initial tumor burdens, but to meaningfully address acquired resistance, combination therapies designed to forestall predictable mechanisms of acquired resistance will be essential. The question of how best to combine or sequence targeted therapies will benefit from mathematical models and empirical data on the resistance mechanisms actually arising in a given patient (McCoach & Bivona 2019). On-target resistance is now largely anticipated based on structural modeling of mutations within the kinase that abrogate or alter drug binding.…”

A mathematical study on the drug resistant virus emergence with HIV/AIDS treatment cases

“…An optimal control problem for the HIV schedules has been addressed in [ 16 ] particularly based on the cost function including the constant probability of drug resistance risk, and a more extensive study can be found in [ 17 ]. Besides HIV/AIDS disease, it is notable that drug resistance in cancer treatment has been investigated in [ 18 ] while antimicrobial resistance in bacterial treatment with antibiotic usage has been researched in [ 19 ].…”