Buerger's disease or Thromboangiitis Obliterans (TAO) is a nonatherosclerotic segmental vascular disease which affects small and medium arteries and veins in the upper and lower extremities. Based on pathological findings, TAO can be considered as a distinct form of vasculitis that is most prevalent in young male smokers. There is no definitive cure for this disease as therapeutic modalities are limited in number and efficacy. Surgical bypass has limited utility and 24% of patients will ultimately require amputation. Recently, studies have shown that therapeutic angiogenesis and immunomodulatory approaches through the delivery of cells to target tissues are potential options for ischemic lesion treatment. In this review, we summarize the current knowledge of TAO treatment and provide an overview of stem cell-based treatment modalities.

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.