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Mathematical modeling of cancer immunotherapy for personalized clinical translation.

ABSTRACT: 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.

SUBMITTER: Butner JD

PROVIDER: S-EPMC10732566 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Mathematical modeling of cancer immunotherapy for personalized clinical translation.

Butner Joseph D JD Dogra Prashant P Chung Caroline C Pasqualini Renata R Arap Wadih W Lowengrub John J Cristini Vittorio V Wang Zhihui Z

Nature computational science 20221219 12

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 transition ...[more]

PMID: 38126024

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Project description:BackgroundTherapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients. We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simple mathematical models.Methodology/principal findingsWe developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient's training set and his validation set. The training set, used for model personalization, contained the patient's initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set. The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients (the coefficient of determination between the predicted and observed PSA values was R(2) = 0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Conclusions/significanceUsing a few initial measurements, we constructed robust patient-specific models of PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols.

Project description:Increasing evidence supports targeting the adenosine pathway in immuno-oncology with several clinical programs directed at adenosine A2 receptor (A2AR, A2BR), CD73 and CD39 in development. Through a cyclic-AMP-mediated intracellular cascade, adenosine shifts the cytokine and cellular profile of the tumor microenvironment away from cytotoxic T cell inflammation toward one of immune tolerance. A perpetuating cycle of tumor cell proliferation, tissue injury, dysregulated angiogenesis, and hypoxia promote adenosine accumulation via ATP catabolism. Adenosine receptor (eg, A2AR, A2BR) stimulation of both the innate and adaptive cellular precursors lead to immunosuppressive phenotypic differentiation. Preclinical work in various tumor models with adenosine receptor inhibition has demonstrated restoration of immune cell function and tumor regression. Given the broad activity but known limitations of anti-programmed cell death protein (PD1) therapy and other checkpoint inhibitors, ongoing studies have sought to augment the successful outcomes of anti-PD1 therapy with combinatorial approaches, particularly adenosine signaling blockade. Preliminary data have demonstrated an optimal safety profile and enhanced overall response rates in several early phase clinical trials with A2AR and more recently CD73 inhibitors. However, beneficial outcomes for both monotherapy and combinations have been mostly lower than expected based on preclinical studies, indicating a need for more nuanced patient selection or biomarker integration that might predict and optimize patient outcomes. In the context of known immuno-oncology biomarkers such as tumor mutational burden and interferon-associated gene expression, a comparison of adenosine-related gene signatures associated with clinical response indicates an underlying biology related to immunosuppression, angiogenesis, and T cell inflammation. Importantly, though, adenosine associated gene expression may point to a unique intratumoral phenotype independent from IFN-γ related pathways. Here, we discuss the cellular and molecular mechanisms of adenosine-mediated immunosuppression, preclinical investigation of adenosine signaling blockade, recent response data from clinical trials with A2AR, CD73, CD39 and PD1/L1 inhibitors, and ongoing development of predictive gene signatures to enhance combinatorial immune-based therapies.

Dataset Information

Mathematical modeling of cancer immunotherapy for personalized clinical translation.

Publications

Mathematical modeling of cancer immunotherapy for personalized clinical translation.

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