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

Lenghe Zhang; Lijuan Zhou; Yulian Wang; Chao Li; Pengjun Liao; Liye Zhong; Suxia Geng; Peilong Lai; Xin Du; Jianyu Weng

doi:10.3389/fonc.2022.1057153

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

Front Oncol. 2022 Nov 2:12:1057153. doi: 10.3389/fonc.2022.1057153. eCollection 2022.

Authors

Lenghe Zhang^{1

2}, Lijuan Zhou¹, Yulian Wang², Chao Li², Pengjun Liao², Liye Zhong², Suxia Geng², Peilong Lai^{1

2}, Xin Du^{1

2}, Jianyu Weng^{1

2}

Affiliations

¹ The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
² Department of Hematology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

Abstract

Identifying subgroups of T-cell acute lymphoblastic leukemia (T-ALL) with poor survival will significantly influence patient treatment options and improve patient survival expectations. Current efforts to predict T-ALL survival expectations in multiple patient cohorts are lacking. A deep learning (DL)-based model was developed to determine the prognostic staging of T-ALL patients. We used transcriptome sequencing data from TARGET to build a DL-based survival model using 265 T-ALL patients. We found that patients could be divided into two subgroups (K0 and K1) with significant difference (P< 0.0001) in survival rate. The more malignant subgroup was significantly associated with some tumor-related signaling pathways, such as PI3K-Akt, cGMP-PKG and TGF-beta signaling pathway. DL-based model 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 clinical practice in the future.

Keywords: T-cell acute lymphoblastic leukemia (T-ALL); deep learning; k-means; survival; transcriptome sequencing.