Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer

Ziwei Wei; Dunsheng Han; Cong Zhang; Shiyu Wang; Jinke Liu; Fan Chao; Zhenyu Song; Gang Chen

doi:10.3389/fonc.2022.893424

Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer

Front Oncol. 2022 Jun 23:12:893424. doi: 10.3389/fonc.2022.893424. eCollection 2022.

Authors

Ziwei Wei¹, Dunsheng Han¹, Cong Zhang¹, Shiyu Wang¹, Jinke Liu¹, Fan Chao², Zhenyu Song³, Gang Chen¹

Affiliations

¹ Department of Urology, Jinshan Hospital, Fudan University, Shanghai, China.
² Department of Urology, Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, China.
³ Ovarian Cancer Program, Department of Gynecologic Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.

Abstract

Objective: Post-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.

Methods: Five-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.

Results: The DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.

Conclusion: We developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

Keywords: H2O package; autoencoder; deep learning; multi-omics; prostate cancer; relapse prediction.