Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Personalized Cell Therapy for Patients With Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-based Responder and Non-responder Prediction

Version 1 : Received: 12 November 2021 / Approved: 15 November 2021 / Online: 15 November 2021 (11:18:43 CET)

How to cite: Salybekov, A.; Wolfien, M.; Kobayashi, S.; Asahara, T. Personalized Cell Therapy for Patients With Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-based Responder and Non-responder Prediction. Preprints 2021, 2021110253 (doi: 10.20944/preprints202111.0253.v1). Salybekov, A.; Wolfien, M.; Kobayashi, S.; Asahara, T. Personalized Cell Therapy for Patients With Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-based Responder and Non-responder Prediction. Preprints 2021, 2021110253 (doi: 10.20944/preprints202111.0253.v1).

Abstract

Stem/progenitor cell transplantation is a potential novel therapeutic strategy to induce angiogenesis in ischemic tissue, which can prevent major amputation in patients with advanced peripheral artery disease (PAD). Thus, clinicians can use cell therapies worldwide to treat PAD. However, some cell therapy studies did not report beneficial outcomes. Clinical researchers suggested that classical risk factors and comorbidities may adversely affect the efficacy of cell therapy. Some studies have indicated that the response to stem cell therapy varies among patients even in those harboring limited risk factors. This suggested the role of undetermined risk factors, including genetic alterations, somatic mutations, and clonal hematopoiesis. Personalized stem cell-based therapy can be developed by analyzing individual risk factors. These approaches must consider several clinical biomarkers and perform studies (such as genome-wide association studies (GWAS)) on disease-related genetic traits and integrate the findings with those of transcriptome-wide association studies (TWAS) and whole-genome sequencing in PAD. Additional unbiased analyses with state-of-the-art computational methods, such as machine learning-based patient stratification, are suited for predictions in clinical investigations. The integration of these complex approaches into a unified analysis procedure for the identification of responders and non-responders before stem cell therapy, which can decrease treatment expenditure, is a major challenge to increase the efficacy of therapies.

Keywords

Cell therapy; chronic limb-threating ischemia; peripheral artery disease; diabetes; atherosclerosis obliterans; thromboangiitis obliterans; personalized medicine; artificial intelligence; machine learning; genome-wide association studies; transcriptome-wide association studies; clonal hematopoiesis of indeterminate potential.

Subject

MEDICINE & PHARMACOLOGY, Cardiology

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