CELL INTELLIGENCE · 肿瘤细胞治疗研究

Acute lymphoblastic leukemia (ALL) represents the predominant cancer in pediatric populations, though its occurrence in adults is relatively rare. Pre-treatment risk stratification is crucial for predicting prognosis. Important factors for assessment include patient age, white blood cell (WBC) count at diagnosis, extramedullary involvement, immunophenotype, and cytogenetic aberrations. Minimal residual disease (MRD), primarily assessed by flow cytometry following remission, plays a substantial role in guiding management plans. Over the past decade, significant advancements in ALL outcomes have been witnessed. Conventional chemotherapy has remarkably reduced mortality rates; however, its intensive nature raises safety concerns and has led to the emergence of treatment-resistant cases with recurrence of relapses. Consequently, The U.S. Food and Drug Administration (FDA) has approved several novel treatments for relapsed/refractory ALL due to their demonstrated efficacy, as indicated by improved complete remission and survival rates. These treatments include tyrosine kinase inhibitors (TKIs), the anti-CD19 monoclonal antibody blinatumomab, anti-CD22 inotuzumab ozogamicin, anti-CD20 rituximab, and chimeric antigen receptor (CAR) T-cell therapy. Identifying the variables that influence treatment decisions is a pressing necessity for tailoring therapy based on heterogeneous patient characteristics. Key predictive factors identified in various observational studies and clinical trials include prelymphodepletion disease burden, complex genetic abnormalities, and MRD. Furthermore, the development of serious adverse events following treatment could be anticipated through predictive models, allowing for appropriate prophylactic measures to be considered. The ultimate aim is to incorporate the concept of precision medicine in the field of ALL through valid prediction platform to facilitate the selection of the most suitable treatment approach.