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Imaging cellular immunotherapies and immune cell biomarkers: from preclinical studies to patients.

ABSTRACT: Cellular immunotherapies have emerged as a successful therapeutic approach to fight a wide range of human diseases, including cancer. However, responses are limited to few patients and tumor types. An in-depth understanding of the complexity and dynamics of cellular immunotherapeutics, including what is behind their success and failure in a patient, the role of other immune cell types and molecular biomarkers in determining a response, is now paramount. As the cellular immunotherapy arsenal expands, whole-body non-invasive molecular imaging can shed a light on their in vivo fate and contribute to the reliable assessment of treatment outcome and prediction of therapeutic response. In this review, we outline the non-invasive strategies that can be tailored toward the molecular imaging of cellular immunotherapies and immune-related components, with a focus on those that have been extensively tested preclinically and are currently under clinical development or have already entered the clinical trial phase. We also provide a critical appraisal on the current role and consolidation of molecular imaging into clinical practice.

SUBMITTER: Volpe A

PROVIDER: S-EPMC9511655 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Imaging cellular immunotherapies and immune cell biomarkers: from preclinical studies to patients.

Volpe Alessia A Adusumilli Prasad S PS Schöder Heiko H Ponomarev Vladimir V

Journal for immunotherapy of cancer 20220901 9

Cellular immunotherapies have emerged as a successful therapeutic approach to fight a wide range of human diseases, including cancer. However, responses are limited to few patients and tumor types. An in-depth understanding of the complexity and dynamics of cellular immunotherapeutics, including what is behind their success and failure in a patient, the role of other immune cell types and molecular biomarkers in determining a response, is now paramount. As the cellular immunotherapy arsenal expa ...[more]

PMID: 36137649

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Project description:Early prediction of COVID-19 in-hospital mortality relies usually on patients' preexisting comorbidities and is rarely reproducible in independent cohorts. We wanted to compare the role of routinely measured biomarkers of immunity, inflammation, and cellular damage with preexisting comorbidities in eight different machine-learning models to predict mortality, and evaluate their performance in an independent population. We recruited and followed-up consecutive adult patients with SARS-Cov-2 infection in two different Italian hospitals. We predicted 60-day mortality in one cohort (development dataset, n = 299 patients, of which 80% was allocated to the development dataset and 20% to the training set) and retested the models in the second cohort (external validation dataset, n = 402). Demographic, clinical, and laboratory features at admission, treatments and disease outcomes were significantly different between the two cohorts. Notably, significant differences were observed for %lymphocytes (p < 0.05), international-normalized-ratio (p < 0.01), platelets, alanine-aminotransferase, creatinine (all p < 0.001). The primary outcome (60-day mortality) was 29.10% (n = 87) in the development dataset, and 39.55% (n = 159) in the external validation dataset. The performance of the 8 tested models on the external validation dataset were similar to that of the holdout test dataset, indicating that the models capture the key predictors of mortality. The shap analysis in both datasets showed that age, immune features (%lymphocytes, platelets) and LDH substantially impacted on all models' predictions, while creatinine and CRP varied among the different models. The model with the better performance was model 8 (60-day mortality AUROC 0.83 ± 0.06 in holdout test set, 0.79 ± 0.02 in external validation dataset). The features that had the greatest impact on this model's prediction were age, LDH, platelets, and %lymphocytes, more than comorbidities or inflammation markers, and these findings were highly consistent in both datasets, likely reflecting the virus effect at the very beginning of the disease.

Dataset Information

Imaging cellular immunotherapies and immune cell biomarkers: from preclinical studies to patients.

Publications

Imaging cellular immunotherapies and immune cell biomarkers: from preclinical studies to patients.

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