Deep learning based transcriptome model robustly predicts survival in T-cell acute lymphoblastic leukemia

Identifying subgroups of T-cell acute lymphoblastic leukemia (T-ALL) with poorer survival will significantly influence patient treatment options and improve patient survival expectations. Current efforts to unambiguously predict T-ALL survival expectations in multiple patient cohorts are lacking. A deep learning (DL)-based model was developed in order to reliably determine the prognostic staging of T-ALL patients. We used transcriptome sequencing data from TARGET to build a DL-based survival model on 265 T-ALL patients, we found that patients could be divided into two subgroups (K0 and K1) with significant differences (P < 0.0001) in survival rates. The more malignant subgroup was significantly associated with some tumor-related pathways, such as PI3K-Akt, cGMP-PKG and TGF-beta signaling pathway. DL-based model also showed good performance in a cohort of patients from our clinical center (P = 0.0248). T-ALL patients survival was successfully predicted using a DL-based model, and we hope to apply it to our clinical practice in the future. Overall design: Analysis of 20 samples from patients with T-ALL.