Abstract 4800: Reducing health disparities for prostate adenocarcinoma by integrating multi-omics data via a multi-modal transfer learning approach

Abstract

Abstract Prostate adenocarcinoma (PRAD) is the most common subtype of prostate cancer, which is the second leading cause of cancer death in American men. Although significant progress has been made to improve PRAD prognosis, existing studies indicated that Black American men have disproportionately high incidence and mortality rate in prostate cancer compared with non-Hispanic White American men. With artificial intelligence (AI) and machine learning (ML) being increasingly applied to PRAD research and clinical decision-making, PRAD data disparities would introduce bias to AI/ML models and further enhance negative impacts on healthcare towards underrepresented groups. Transfer learning has shown potential to reduce racial disparities in PRAD. However, its performance may be deteriorated due to: (1) its model requires large-scale training samples which are difficult to obtain in clinical settings, and (2) it only uses single-omics data without integrating multi-omics information. To address these concerns, we propose to develop a multi-modal transfer learning model to integrate multi-omics data for reducing health disparities. Specifically, we first investigated two multi-modal ensemble methods, Pearson Correlation Coefficient (PCC) based patient-pairwise similarity, and variational autoencoder (VAE) to integrate different types of omics data. Then, we leveraged a transfer learning model based on domain adaptation to pre-train the model on the majority group (White Americans) and fine-tune the model using the minority group (Black Americans). To further address the imbalanced data among ethnic groups, we explored implementing a data augmentation method, Synthetic Minority Oversampling Technique (SMOTE), to increase the size of minority group data. We evaluated our model on multi-omics data (mRNA, miRNA and methylation) of PRAD from The Cancer Genome Atlas (TCGA) database. Results suggested that our proposed approach achieved better performance of reducing health disparities for Black Americans compared with the mixture model and the independent model as well as the conventional transfer learning model in predicting progression-free interval (PFI) prognosis for PRAD patients. Furthermore, we also demonstrated that SMOTE alleviated the data imbalanced problem and improved the performance of the prognosis classification for ethnic minority groups. In summary, our results demonstrated that our proposed multi-modal transfer learning approach could effectively reduce the performance gap between majority and minority groups in PRAD prognosis, thereby mitigating health disparities. We expect that our proposed multi-model transfer learning framework can be customized and extensible to reduce health disparities in other types of cancer. Citation Format: Lusheng Li, Jieqiong Wang, Shibiao Wan. Reducing health disparities for prostate adenocarcinoma by integrating multi-omics data via a multi-modal transfer learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4800.