Tumor-immune metaphenotypes orchestrate an evolutionary bottleneck that promotes metabolic transformation

Abstract

Metabolism plays a complex role in the evolution of cancerous tumors, including inducing a multifaceted effect on the immune system to aid immune escape. Immune escape is, by definition, a collective phenomenon by requiring the presence of two cell types interacting in close proximity: tumor and immune. The microenvironmental context of these interactions is influenced by the dynamic process of blood vessel growth and remodelling, creating heterogeneous patches of well-vascularized tumor or acidic niches. Here, we present a multiscale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shape acid-mediated invasion and immune escape over a biologically-realistic time scale. The model explores several immune escape mechanisms such as i) acid inactivation of immune cells, ii) competition for glucose, and iii) inhibitory immune checkpoint receptor expression (PD-L1). We also explore the efficacy of anti-PD-L1 and sodium bicarbonate buffer agents for treatment. To aid in understanding immune escape as a collective cellular phenomenon, we define immune escape in the context of six collective phenotypes (termed “meta-phenotypes”): Self-Acidify, Mooch Acid, PD-L1 Attack, Mooch PD-L1, Proliferate Fast, and Starve Glucose. Fomenting a stronger immune response leads to initial benefits (additional cytotoxicity), but this advantage is offset by increased cell turnover that leads to accelerated evolution and the emergence of aggressive phenotypes. This creates a bimodal therapy landscape: either the immune system should be maximized for complete cure, or kept in check to avoid rapid evolution of invasive cells. These constraints are dependent on heterogeneity in vascular context, microenvironmental acidification, and the strength of immune response. This model helps to untangle the key constraints on evolutionary costs and benefits of three key phenotypic axes on tumor invasion and treatment: acid-resistance, glycolysis, and PD-L1 expression. The benefits of concomitant anti-PD-L1 and buffer treatments is a promising treatment strategy to limit the adverse effects of immune escape.

Phenotype switching in a global method for agent-based models of biological tissue

“…The global method still has room for improvements and future innovations to be adaptable to a wider range of phenomena. As one example, the parameters governing cells can vary from cell-to-cell within a given cell type as is the case in studies that include evolution [13][14][15]. If these parameters affect the cellular exchange or the intracellular signaling modules of the molecular dynamics, then the mean dynamics of these processes depend on the distribution of state variables as well as parameters.…”

Modeling tumors as species-rich ecological communities

“…In low dimensional form it has been thoroughly applied to the study of tumor growth and competition (see Box 2,[17,62]). We propose here that each cancerous population, defined by a set of signatures yielding a functional phenotypic behavior µ [12,27,29,43], needs to be understood as an individual species with abundance c µ growing while interacting with the other cellular populations c ν . Depending on the dynamics at play, here c µ could be restricted to populations with equivalent mutational or antigenic features (so-called genetic clones, [12]), or even include additional layers of the non-cancerous tissue such as the stroma [63] or the immune system [43].…”

“…However, the improvement in our understanding of the richness of tumor diversity has led to the realization that cancer population dynamics makes sense under a community ecology picture [24]. In this context, heterogeneity at the mutational, phenotypic and cell type levels, dictates that tumors are in fact complex adaptive ecosystems built of many interacting populations (See BOX 1, [25][26][27]). Furthermore, despite the traditional dominance of competition as a driver of cancer dynamics, evidence accumulates indicating that ecological interactions between cancer cells are not only competitive: cooperation or commensalism could also be at play [25,[28][29][30].…”

Phenotype switching in a global method for agent-based models of biological tissue

“…The global method still has room for improvements and future innovations to be adaptable to a wider range of phenomena. As one example, the parameters governing cells can vary from cell-to-cell within a given cell type as is the case in studies that include evolution [15][16][17]. If these parameters affect the cellular exchange or the intracellular signaling modules of the molecular dynamics, then the mean dynamics of these processes depend on the distribution of state variables as well as parameters.…”