Associate Professor HARVARD MEDICAL SCHOOL, United States
Introduction: Immunotherapy approaches have shown efficacy and even cures in some cancers. Although extremely encouraging, not all patients benefit, and a deep understanding of the mechanisms of cancer and immune interactions is needed to improve outcomes. Immune responses are generally initiated in lymph nodes, where antigen drains and accumulates. The dissemination of antigen and induction of immune response in the tumor draining lymph nodes (TDLNs) and other, more distant lymph nodes (LNs) is a key factor. However, an immune response in the TDLNs or distant LNs doesn’t necessarily enhance ICB performance, because biochemical cues and structural barriers can prohibit immune cell arrival and penetration into the tumor. In this context, cancers can be classified into categories including "cold" (immune desert; excluding immune cells), and "hot" (hosting immune cells). The hotness of the tumor is a crucial factor for immunotherapy outcome and depends on the spatiotemporal distribution of effector T cells within the tumor. The distribution of T cells in a tumor is influenced by their activation in local or distant lymph nodes, which in turn, is affected by tumor antigen production. Once activated, the T cells enter the blood stream and their arrival and penetration in the tumor are affected by the tumor structure, tumor vessel adhesion molecules, cancer metabolism, and chemotactic signaling between cancer and T cells. Once in the tumor, the immune cells must reach and kill as many cancer cells as possible.
Materials and
Methods: We developed a multiscale, systems biology model of key aspects of the local immune responses within the TME to identify new paradigms for improving response rates to immunotherapies. The multi-scale model domain represents a 10×10×8 mm region of the tissue and considers T cell and cancer cell dynamics on a discrete matrix. We also calculate continuous gradients of oxygen, nutrients (glucose), and carbon dioxide, VEGF, extracellular matrix (ECM) and matrix metalloproteinases (MMPs), Angiopoietins- 1 and 2 (ang-1 and -2), and cancer-induced immune attractive and suppressive agents. Angiogenic blood vessels initiate from an idealized circular “mother vessel” that surrounds the tumor at the mid-plane. Angiogenic sprouts migrate from the mother vessel in a biased random walk toward sources of VEGF and haptotactic factors. Sprout extension depends on endothelial cell proliferation. At each time step, cancer cells are assigned a vitality, which is determined by local nutrient and metabolite concentrations; cell viability and proliferation depend on this vitality score.
Results, Conclusions, and Discussions: Results
Spatiotemporal mechanistic models effectively recapitulate spatial heterogeneities in tumor growth and treatment. We developed a 3D, multi-scale model encompassing molecular, cellular, and tissue scales to simulate the tumor microenvironment (TME) (Fig.1A). This model predicts cancer response to environmental factors and chemotherapy, aligning with clinical observations (Fig.1B). The spatiotemporal growth of heterogeneous cancer with angiogenic vessels is illustrated in Fig.2B. This model elucidates and quantifies T cell dynamics and distribution following systemic activation, with varying levels of cancer attraction intensity. Effector T cells enter the angiogenic vessels, assessing parameters that produce cold vs. hot cancers (Fig.1C). We hypothesize a spectrum for cold-to-hot cancers, including immune-dessert cold avascular tumors, immune-dessert cold vascular tumors, immune-excluded cold tumors, and immune-inflamed hot tumors (Fig.1C). Using this multi-scale approach, we map T cell distribution within tumors, reflecting the efficacy of subsequent immune checkpoint blockade (ICB) treatment. Tumor responses to ICB for different hotness classifications are shown in Fig. 1D.
Conclusions
1.Our multiscale model simulates the heterogeneous TME at molecular, cellular, and tissue scales. 2.The model serves as a computational tool to determine T-cell infiltration into tumors based on interactions with TME agents. 3.Co-option mechanisms can increase cancer resistance to treatment and immune response. 4.T-cells traversing angiogenic vessels are arrested at adhesion sites and extravasate into the tumor interstitium. 5.Increasing haptotactic attraction of T cells to tumors enhances extravasation and migration in the tumor interstitium, determining cancer hotness. Discussion
As illustrated in Fig.1, hot/cold cancer scenarios depend on the spatiotemporal distribution of cancer and T cells in the TME. This distribution is influenced by local tumor heterogeneity and dynamic TME processes, such as angiogenesis, cancer cell migration, and cytokine release. Timing of T cell activation, proliferation, and arrival in the tumor is crucial, relying on endothelial adhesion, transmigration, and subsequent migration into the tumor. These steps must coincide with the tumor's susceptibility to immune attack, ensuring tumor killing before T-cell exhaustion. We hypothesize that orchestrating local and systemic interactions can promote Tcell accumulation and tumor killing. The overarching goal is to develop a cancer patient digital twin to timely predict responses to immunotherapy and optimize treatment.
