Rethinking the immunotherapy numbers game

Spatial interactions modulate tumor growth and immune infiltration

Lenia, a cellular automata framework used in artificial life, provides a natural setting to implement mathematical models of cancer incorporating features such as morphogenesis, homeostasis, motility, reproduction, growth, stimuli response, evolvability, and adaptation. Historically, agent-based models of cancer progression have been constructed with rules that govern birth, death and migration, with attempts to map local rules to emergent global growth dynamics. In contrast, Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining an interaction kernel governing density-dependent growth dynamics. Lenia can recapitulate a range of cancer model classifications including local or global, deterministic or stochastic, non-spatial or spatial, single or multi-population, and off or on-lattice. Lenia is subsequently used to develop data-informed models of 1) single-population growth dynamics, 2) multi-population cell-cell competition models, and 3) cell migration or chemotaxis. Mathematical modeling provides important mechanistic insights. First, short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects. Next, we find that asymmetric interaction tumor-immune kernels lead to poor immune response. Finally, modeling recapitulates immune-ECM interactions where patterns of collagen formation provide immune protection, indicated by an emergent inverse relationship between disease stage and immune coverage.

The synergistic immunotherapeutic impact of engineered CAR-T cells with PD-1 blockade in lymphomas and solid tumors: a systematic review

Currently, therapies such as chimeric antigen receptor-T Cell (CAR-T) and immune checkpoint inhibitors like programmed cell death protein-1 (PD-1) blockers are showing promising results for numerous cancer patients. However, significant advancements are required before CAR-T therapies become readily available as off-the-shelf treatments, particularly for solid tumors and lymphomas. In this review, we have systematically analyzed the combination therapy involving engineered CAR-T cells and anti PD-1 agents. This approach aims at overcoming the limitations of current treatments and offers potential advantages such as enhanced tumor inhibition, alleviated T-cell exhaustion, heightened T-cell activation, and minimized toxicity. The integration of CAR-T therapy, which targets tumor-associated antigens, with PD-1 blockade augments T-cell function and mitigates immune suppression within the tumor microenvironment. To assess the impact of combination therapy on various tumors and lymphomas, we categorized them based on six major tumor-associated antigens: mesothelin, disialoganglioside GD-2, CD-19, CD-22, CD-133, and CD-30, which are present in different tumor types. We evaluated the efficacy, complete and partial responses, and progression-free survival in both pre-clinical and clinical models. Additionally, we discussed potential implications, including the feasibility of combination immunotherapies, emphasizing the importance of ongoing research to optimize treatment strategies and improve outcomes for cancer patients. Overall, we believe combining CAR-T therapy with PD-1 blockade holds promise for the next generation of cancer immunotherapy.

Spatial interactions modulate tumor growth and immune infiltration

Direct observation of immune cell trafficking patterns and tumor-immune interactions is unlikely in human tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen formation evolve as a mechanism of immune escape, but the exact nature of the interaction between immune cells and collagen is poorly understood. Spatial data quantifying the degree of collagen fiber alignment in squamous cell carcinomas indicates that late stage disease is associated with highly aligned fibers. Here, we introduce a computational modeling framework (called Lenia) to discriminate between two hypotheses: immune cell migration that moves 1) parallel or 2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-ECM interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. We also illustrate the capabilities of Lenia to model the evolution of tumor progression and immune predation. Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining a kernel cell-cell interaction function that governs tumor growth dynamics under immune predation with immune cell migration. Mathematical modeling provides important mechanistic insights into cell interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interactions drives tumor growth and infiltration.

Optimal dosage protocols for mathematical models of synergy of chemo- and immunotherapy

The release of tumor antigens during traditional cancer treatments such as radio- or chemotherapy leads to a stimulation of the immune response which provides synergistic effects these treatments have when combined with immunotherapies. A low-dimensional mathematical model is formulated which, depending on the values of its parameters, encompasses the 3 E's (elimination, equilibrium, escape) of tumor immune system interactions. For the escape situation, optimal control problems are formulated which aim to revert the process to the equilibrium scenario. Some numerical results are included.

Application of adoptive cell therapy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a global health challenge. Novel treatment modalities are urgently needed to extend the overall survival of patients. The liver plays an immunomodulatory function due to its unique physiological structural characteristics. Therefore, following surgical resection and radiotherapy, immunotherapy regimens have shown great potential in the treatment of hepatocellular carcinoma. Adoptive cell immunotherapy is rapidly developing in the treatment of hepatocellular carcinoma. In this review, we summarize the latest research on adoptive immunotherapy for hepatocellular carcinoma. The focus is on chimeric antigen receptor (CAR)-T cells and T cell receptor (TCR) engineered T cells. Then tumour-infiltrating lymphocytes (TILs), natural killer (NK) cells, cytokine-induced killer (CIK) cells, and macrophages are briefly discussed. The main overview of the application and challenges of adoptive immunotherapy in hepatocellular carcinoma. It aims to provide the reader with a comprehensive understanding of the current status of HCC adoptive immunotherapy and offers some strategies. We hope to provide new ideas for the clinical treatment of hepatocellular carcinoma.

Circulating tumor DNA adds specificity to PET after axicabtagene ciloleucel in large B-cell lymphoma

We examined the meaning of metabolically active lesions on 1-month restaging nuclear imaging of patients with relapsed/refractory large B-cell lymphoma receiving axicabtagene ciloleucel (axi-cel) by assessing the relationship between total metabolic tumor volume (MTV) on positron emission tomography (PET) scans and circulating tumor DNA (ctDNA) in the plasma. In this prospective multicenter sample collection study, MTV was retrospectively calculated via commercial software at baseline, 1, and 3 months after chimeric antigen receptor (CAR) T-cell therapy; ctDNA was available before and after axi-cel administration. Spearman correlation coefficient (rs) was used to study the relationship between the variables, and a mathematical model was constructed to describe tumor dynamics 1 month after CAR T-cell therapy. The median time between baseline scan and axi-cel infusion was 33 days (range, 1-137 days) for all 57 patients. For 41 of the patients with imaging within 33 days of axi-cel or imaging before that time but no bridging therapy, the correlation at baseline became stronger (rs, 0.61; P < .0001) compared with all patients (rs, 0.38; P = .004). Excluding patients in complete remission with no measurable residual disease, ctDNA and MTV at 1 month did not correlate (rs, 0.28; P = .11) but correlated at 3 months (rs, 0.79; P = .0007). Modeling of tumor dynamics, which incorporated ctDNA and inflammation as part of MTV, recapitulated the outcomes of patients with positive radiologic 1-month scans. Our results suggested that nonprogressing hypermetabolic lesions on 1-month PET represent ongoing treatment responses, and their composition may be elucidated by concurrently examining the ctDNA.

In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.

Mathematical modeling of cancer immunotherapy for personalized clinical translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.