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 fitness relationships. We propose reconsidering some current tenets of the cancer management paradigm in order to take better advantage of crucial fitness relationships to improve outcomes of patients with cancer. SIGNIFICANCE: 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.

Figure 1.. Tumor cell influences and evolution.

Depiction of the evolutionary paths of tumor cells. The center portion of this image depicts a group of heterogeneous tumor cells. There are four predominant, and often overlapping, categories of evolutionary influence that can impact tumor cells during cancer progression and treatment that impact response to therapies. Each category depicts several individual mechanisms of tumor promotion and evolution. These mechanisms are not mutually exclusive and can occur concurrently. Abbreviations: APC- antigen present cell, CAF- Cancer associated Fibroblast, EGF – Endothelial growth factor, EMT – epithelial to mesenchymal transition, FGF – Fibroblast growth factor, miRNA- micro-RNA, MMP- Matrix metalloproteinases, PDGF – platelet derived growth factor, TAM – Tumor associated macrophage, TC- tumor cell, TGF- β – transforming growth factor beta, TNF- α – tumor necrosis factor alpha, VEGF/ VEGF-A - vascular endothelial growth factor (A),

Figure 2.. Depiction of tumor evolution.

Shown is a theoretical illustration of tumor growth and evolution demonstrating how some of the different modes of tumor promotion and evolution in response to therapy may occur and evolve concurrently. Abbreviations: CAF- Cancer associated Fibroblast, EMT – epithelial to mesenchymal transition, miRNA- micro-RNA, TAM – Tumor associated macrophage, TGF- β – transforming growth factor beta, TNF- α – tumor necrosis factor alpha

Figure 3.. Biological and learning based molecular treatment of cancer.

Depiction of a clinical trial design in which tumors and cfDNA are evaluated longitudinally during treatment to tailored therapy combinations based on the evolution of the persistent, or emergent clonal populations identified during active treatment and before treatment resistance is detected using more conventional clinical or radiographic assessments. Abbreviations: cfDNA – cell-free DNA, IO – immunotherapy, TMB – tumor mutation burden, epi – epigenetically targeted agent.

Figure 4.. Clonal predominance in alternating therapy

Depiction of clonal predominance switching during alternating treatment with 2 different EGFR TKI therapies to maintain a steady state population until outgrowth of a clonal population resistant to both therapies occurs, at which point a new therapy would be employed.