Sequential adjustment of cytotoxic T lymphocyte densities improves efficacy in controlling tumor growth

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Mechanistic model of radiotherapy-induced lung fibrosis using coupled 3D agent-based and Monte Carlo simulations

BACKGROUND

Mechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological normal tissue complication probability models. The latter, currently used in the clinics, rely exclusively on limited patient data and neglect spatial dose distribution information. Among the various approaches, agent-based models are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision.

METHODS

In this work, we develop and characterize a coupled 3D agent-based - Monte Carlo model that mechanistically simulates the onset of the radiation-induced lung fibrosis in an alveolar segment. To the best of our knowledge, this is the first such model.

RESULTS

Our model replicates extracellular matrix patterns, radiation-induced lung fibrosis severity indexes and functional subunits survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher functional subunits survival and lower radiation-induced lung fibrosis severity indexes are achieved when a 5-fractions treatment is simulated. Finally, the model shows increased sensitivity to more uniform protons dose distributions with respect to more heterogeneous ones from photon irradiation.

CONCLUSIONS

This study lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of radiation-induced lung fibrosis via mechanistic modelling.

"Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy"

Despite recent progress in adoptive T cell therapy for cancer, understanding and predicting the kinetics of infused T cells remains a challenge. Multiple factors can impact the distribution, expansion, and decay or persistence of infused T cells in patients. We have developed a novel quantitative systems pharmacology (QSP) model of TCR-transgenic T cell therapy in patients with solid tumors to describe the kinetics of endogenous T cells and multiple memory subsets of engineered T cells after infusion. These T cells undergo lymphodepletion, proliferation, trafficking, differentiation, and apoptosis in blood, lymph nodes, tumor site, and other peripheral tissues. Using the model, we generated patient-matched digital twins that recapitulate the circulating T cell kinetics reported from a clinical trial of TCR-engineered T cells targeting E7 in patients with metastatic HPV-associated epithelial cancers. Analyses of key parameters influencing cell kinetics and differences among digital twins identify stem cell-like memory T cells (Tscm) cells as an important determinant of both expansion and persistence and suggest that Tscm-related differences contribute significantly to the observed variability in cellular kinetics among patients. We simulated in silico clinical trials using digital twins and predict that Tscm enrichment in the infused product improves persistence of the engineered T cells and could enable administration of a lower dose. Finally, we verified the broader relevance of the QSP model, the digital twins, and findings on the importance of Tscm enrichment by predicting kinetics for two patients with pancreatic cancer treated with KRAS G12D targeting T cell therapy. This work offers insight into the key role of Tscm biology on T cell kinetics and provides a quantitative framework to evaluate cellular kinetics for future efforts in the development and clinical application of TCR-engineered T cell therapies.

Cytotoxic T Lymphocytes Control Growth of B16 Tumor Cells in Collagen-Fibrin Gels by Cytolytic and Non-Lytic Mechanisms

Cytotoxic T lymphocytes (CTLs) are important in controlling some viral infections, and therapies involving the transfer of large numbers of cancer-specific CTLs have been successfully used to treat several types of cancers in humans. While the molecular mechanisms of how CTLs kill their targets are relatively well understood, we still lack a solid quantitative understanding of the kinetics and efficiency by which CTLs kill their targets in vivo. Collagen-fibrin-gel-based assays provide a tissue-like environment for the migration of CTLs, making them an attractive system to study T cell cytotoxicity in in vivo-like conditions. Budhu.et al. systematically varied the number of peptide (SIINFEKL)-pulsed B16 melanoma cells and SIINFEKL-specific CTLs (OT-1) and measured the remaining targets at different times after target and CTL co-inoculation into collagen-fibrin gels. The authors proposed that their data were consistent with a simple model in which tumors grow exponentially and are killed by CTLs at a per capita rate proportional to the CTL density in the gel. By fitting several alternative mathematical models to these data, we found that this simple "exponential-growth-mass-action-killing" model did not precisely describe the data. However, determining the best-fit model proved difficult because the best-performing model was dependent on the specific dataset chosen for the analysis. When considering all data that include biologically realistic CTL concentrations (E≤107cell/mL), the model in which tumors grow exponentially and CTLs suppress tumor's growth non-lytically and kill tumors according to the mass-action law (SiGMA model) fit the data with the best quality. A novel power analysis suggested that longer experiments (∼3-4 days) with four measurements of B16 tumor cell concentrations for a range of CTL concentrations would best allow discriminating between alternative models. Taken together, our results suggested that the interactions between tumors and CTLs in collagen-fibrin gels are more complex than a simple exponential-growth-mass-action killing model and provide support for the hypothesis that CTLs' impact on tumors may go beyond direct cytotoxicity.

Mathematical modeling suggests cytotoxic T lymphocytes control growth of B16 tumor cells in collagin-fibrin gels by cytolytic and non-lytic mechanisms

Cytotoxic T lymphocytes (CTLs) are important in controlling some viral infections, and therapies involving transfer of large numbers of cancer-specific CTLs have been successfully used to treat several types of cancers in humans. While molecular mechanisms of how CTLs kill their targets are relatively well understood we still lack solid quantitative understanding of the kinetics and efficiency at which CTLs kill their targets in different conditions. Collagen-fibrin gel-based assays provide a tissue-like environment for the migration of CTLs, making them an attractive system to study the cytotoxicity in vitro. Budhu et al. [1] systematically varied the number of peptide (SIINFEKL)- pulsed B16 melanoma cells and SIINFEKL-specific CTLs (OT-1) and measured remaining targets at different times after target and CTL co-inoculation into collagen-fibrin gels. The authors proposed that their data were consistent with a simple model in which tumors grow exponentially and are killed by CTLs at a per capita rate proportional to the CTL density in the gel. By fitting several alternative mathematical models to these data we found that this simple "exponential-growth-mass-action-killing" model does not precisely fit the data. However, determining the best fit model proved difficult because the best performing model was dependent on the specific dataset chosen for the analysis. When considering all data that include biologically realistic CTL concentrations ( E ≤ 10 ⁷ cell/ml) the model in which tumors grow exponentially and CTLs suppress tumor's growth non-lytically and kill tumors according to the mass-action law (SiGMA model) fitted the data with best quality. Results of power analysis suggested that longer experiments (∼ 3 - 4 days) with 4 measurements of B16 tumor cell concentrations for a range of CTL concentrations would best allow to discriminate between alternative models. Taken together, our results suggest that interactions between tumors and CTLs in collagen-fibrin gels are more complex than a simple exponential-growth- mass-action killing model and provide support for the hypothesis that CTLs impact on tumors may go beyond direct cytotoxicity.

Interdependence of sequential cytotoxic T lymphocyte and natural killer cell cytotoxicity against melanoma cells

Cytotoxic T lymphocytes (CTL) and natural killer (NK) cells recognize and eliminate cancer cells. However, immune evasion, downregulation of immune function by the tumour microenvironment and resistance of cancer cells are major problems. Although CTL and NK cells are both important to eliminate cancer, most studies address them individually. We quantified sequential primary human CTL and NK cell cytotoxicity against the melanoma cell line SK-Mel-5. At high effector-to-target ratios, NK cells or melan-A (MART-1)-specific CTL eliminated all SK-Mel-5 cells within 24 h, indicating that SK-Mel-5 cells are not resistant initially. However, at lower effector-to-target ratios, which resemble numbers of the immune contexture in human cancer, a substantial number of SK-Mel-5 cells survived. Pre-exposure to CTL induced resistance in surviving SK-Mel-5 cells to subsequent CTL or NK cell cytotoxicity, and pre-exposure to NK cells induced resistance in surviving SK-Mel-5 cells to NK cells. Higher human leucocyte antigen class I expression or interleukin-6 levels were correlated with resistance to NK cells, whereas reduction in MART-1 antigen expression was correlated with reduced CTL cytotoxicity. The CTL cytotoxicity was rescued beyond control levels by exogenous MART-1 antigen. In contrast to the other three combinations, CTL cytotoxicity against SK-Mel-5 cells was enhanced following NK cell pre-exposure. Our assay allows quantification of sequential CTL and NK cell cytotoxicity and might guide strategies for efficient CTL-NK cell anti-melanoma therapies. KEY POINTS: Cytotoxic T lymphocytes (CTL) and natural killer (NK) cells eliminate cancer cells. Both CTL and NK cells attack the same targets, but most studies address them individually. In a sequential cytotoxicity model, the interdependence of antigen-specific CTL and NK cell cytotoxicity against melanoma is quantified. High numbers of antigen-specific CTL and NK cells eliminate all melanoma cells. However, lower numbers induce resistance if secondary CTL or NK cell exposure follows initial CTL exposure or if secondary NK cell exposure follows initial NK cell exposure. On the contrary, if secondary CTL exposure follows initial NK cell exposure, cytotoxicity is enhanced. Alterations in human leucocyte antigen class I expression and interleukin-6 levels are correlated with resistance to NK cells, whereas a reduction in antigen expression is correlated with reduced CTL cytotoxicity; CTL cytotoxicity is rescued beyond control levels by exogenous antigen. This assay and the results on interdependencies will help us to understand and optimize immune therapies against cancer.

Greek Fire, Poison Arrows, and Scorpion Bombs: How Tumor Cells Defend Against the Siege Weapons of Cytotoxic T Lymphocytes

CD8⁺ cytotoxic T lymphocytes (CTLs) are the main cellular effectors of the adaptive immune response against cancer cells, which in turn have evolved sophisticated cellular defense mechanisms to withstand CTL attack. Herein we provide a critical review of the pertinent literature on early and late attack/defense events taking place at the CTL/target cell lytic synapse. We examine the earliest steps of CTL-mediated cytotoxicity (“the poison arrows”) elicited within seconds of CTL/target cell encounter, which face commensurately rapid synaptic repair mechanisms on the tumor cell side, providing the first formidable barrier to CTL attack. We examine how breach of this first defensive barrier unleashes the inextinguishable “Greek fire” in the form of granzymes whose broad cytotoxic potential is linked to activation of cell death executioners, injury of vital organelles, and destruction of intracellular homeostasis. Herein tumor cells deploy slower but no less sophisticated defensive mechanisms in the form of enhanced autophagy, increased reparative capacity, and dysregulation of cell death pathways. We discuss how the newly discovered supra-molecular attack particles (SMAPs, the “scorpion bombs”), seek to overcome the robust defensive mechanisms that confer tumor cell resistance. Finally, we discuss the implications of the aforementioned attack/defense mechanisms on the induction of regulated cell death (RCD), and how different contemporary RCD modalities (including apoptosis, pyroptosis, and ferroptosis) may have profound implications for immunotherapy. Thus, we propose that understanding and targeting multiple steps of the attack/defense process will be instrumental to enhance the efficacy of CTL anti-tumor activity and meet the outstanding challenges in clinical immunotherapy.

Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer

CD4 T cells have been implicated in cancer immunity for their helper functions. Moreover, their direct cytotoxic potential has been shown in some patients with cancer. Here, by mining single-cell RNA-seq datasets, we identified CD4 T cell clusters displaying cytotoxic phenotypes in different human cancers, resembling CD8 T cell profiles. Using the peptide-MHCII-multimer technology, we confirmed ex vivo the presence of cytolytic tumor-specific CD4 T cells. We performed an integrated phenotypic and functional characterization of these cells, down to the single-cell level, through a high-throughput nanobiochip consisting of massive arrays of picowells and machine learning. We demonstrated a direct, contact-, and granzyme-dependent cytotoxic activity against tumors, with delayed kinetics compared to classical cytotoxic lymphocytes. Last, we found that this cytotoxic activity was in part dependent on SLAMF7. Agonistic engagement of SLAMF7 enhanced cytotoxicity of tumor-specific CD4 T cells, suggesting that targeting these cells might prove synergistic with other cancer immunotherapies.

Evaluation of CD8 T cell killing models with computer simulations of 2-photon imaging experiments

In vivo imaging of cytotoxic T lymphocyte (CTL) killing activity revealed that infected cells have a higher observed probability of dying after multiple contacts with CTLs. We developed a three-dimensional agent-based model to discriminate different hypotheses about how infected cells get killed based on quantitative 2-photon in vivo observations. We compared a constant CTL killing probability with mechanisms of signal integration in CTL or infected cells. The most likely scenario implied increased susceptibility of infected cells with increasing number of CTL contacts where the total number of contacts was a critical factor. However, when allowing in silico T cells to initiate new interactions with apoptotic target cells (zombie contacts), a contact history independent killing mechanism was also in agreement with experimental datasets. The comparison of observed datasets to simulation results, revealed limitations in interpreting 2-photon data, and provided readouts to distinguish CTL killing models.

To Remember or to Forget: The Role of Good and Bad Memories in Adoptive T Cell Therapy for Tumors

The generation of immunological memory is a hallmark of adaptive immunity by which the immune system "remembers" a previous encounter with an antigen expressed by pathogens, tumors, or normal tissues; and, upon secondary encounters, mounts faster and more effective recall responses. The establishment of T cell memory is influenced by both cell-intrinsic and cell-extrinsic factors, including genetic, epigenetic and environmental triggers. Our current knowledge of the mechanisms involved in memory T cell differentiation has instructed new opportunities to engineer T cells with enhanced anti-tumor activity. The development of adoptive T cell therapy has emerged as a powerful approach to cure a subset of patients with advanced cancers. Efficacy of this approach often requires long-term persistence of transferred T cell products, which can vary according to their origin and manufacturing conditions. Host preconditioning and post-transfer supporting strategies have shown to promote their engraftment and survival by limiting the competition with a hostile tumor microenvironment and between pre-existing immune cell subsets. Although in the general view pre-existing memory can confer a selective advantage to adoptive T cell therapy, here we propose that also "bad memories"-in the form of antigen-experienced T cell subsets-co-evolve with consequences on newly transferred lymphocytes. In this review, we will first provide an overview of selected features of memory T cell subsets and, then, discuss their putative implications for adoptive T cell therapy.