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Zaccaria GM, Altini N, Mezzolla G, Vegliante MC, Stranieri M, Pappagallo SA, Ciavarella S, Guarini A, Bevilacqua V. SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107966. [PMID: 38091844 DOI: 10.1016/j.cmpb.2023.107966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intelligence (XAI) to stratify prognosis and derive a gene-based signature. METHODS AE was exploited to learn an unsupervised representation of the gene expression (GE) from three publicly available datasets, each with its own technology. Multi-layer perceptron (MLP) was used to classify prognosis from latent representation. GE data were preprocessed as normalized, scaled, and standardized. Four different AE architectures (Large, Medium, Small and Extra Small) were compared to find the most suitable for GE data. The joint AE-MLP classified patients on six different outcomes: overall survival at 12, 36, 60 months and progression-free survival (PFS) at 12, 36, 60 months. XAI techniques were used to derive a gene-based signature aimed at refining the Revised International Prognostic Index (R-IPI) risk, which was validated in a fourth independent publicly available dataset. We named our tool SurvIAE: Survival prediction with Interpretable AE. RESULTS From the latent space of AEs, we observed that scaled and standardized data reduced the batch effect. SurvIAE models outperformed R-IPI with Matthews Correlation Coefficient up to 0.42 vs. 0.18 for the validation-set (PFS36) and to 0.30 vs. 0.19 for the test-set (PFS60). We selected the SurvIAE-Small-PFS36 as the best model and, from its gene signature, we stratified patients in three risk groups: R-IPI Poor patients with High levels of GAB1, R-IPI Poor patients with Low levels of GAB1 or R-IPI Good/Very Good patients with Low levels of GPR132, and R-IPI Good/Very Good patients with High levels of GPR132. CONCLUSIONS SurvIAE showed the potential to derive a gene signature with translational purpose in DLBCL. The pipeline was made publicly available and can be reused for other pathologies.
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Affiliation(s)
- Gian Maria Zaccaria
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy
| | - Nicola Altini
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy.
| | - Giuseppe Mezzolla
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy
| | - Maria Carmela Vegliante
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Marianna Stranieri
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy
| | - Susanna Anita Pappagallo
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Sabino Ciavarella
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Attilio Guarini
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco, 65, Bari 70124, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy; Apulian Bioengineering srl, Via delle Violette, 14, Modugno 70026, Italy
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