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Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with
Charles Blatti1, Jesús de la Fuente2, Huanyao Gao3
1NCSA, University of Illinois at Urbana-Champaign, Champaign, Illinois.
Abstract:
Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions.
Significance:
The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.
Insights
A new computational method, TraRe, reveals that disrupted immune response pathways and specific transcription factors like ELK3, MXD1, and MYB are key to abiraterone resistance in metastatic castration-resistant prostate cancer (mCRPC). This offers new therapeutic targets for improving patient survival rates.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Metastatic castration-resistant prostate cancer (mCRPC) patients have poor survival rates due to resistance to therapies like abiraterone (Abi).
- Understanding the molecular mechanisms of abiraterone resistance is crucial for developing effective treatments.
- Bulk transcriptomics has limitations in detailing complex cellular transcriptional dynamics, hindering insights into resistance mechanisms.
Purpose of the Study:
- To develop a computational method (TraRe) for analyzing transcriptional network structures to understand abiraterone resistance in mCRPC.
- To identify specific molecular targets and pathways involved in abiraterone resistance.
- To provide a global and functional perspective on abiraterone resistance mechanisms.
Main Methods:
- Developed TraRe, a computational method utilizing sparse Bayesian models to analyze transcriptional networks, regulons, and transcription factors (TFs).
- Applied TraRe to transcriptomic data from 46 mCRPC patients with clinical abiraterone response data.
- Experimentally validated key predictions in prostate cancer cell lines.
Main Results:
- Identified abrogated immune response transcriptional modules significantly differentially regulated in abiraterone-responsive versus abiraterone-resistant patients.
- Replicated these findings in an independent mCRPC study cohort.
- Experimentally validated the differential roles of TFs ELK3, MXD1, and MYB in cell survival and confirmed ELK3's regulation of cell migration in abiraterone-resistant prostate cancer cells.
Conclusions:
- Disruption of specific signaling cascades involving ELK3, MXD1, and MYB impacts abiraterone resistance in prostate cancer.
- The TraRe method is a valuable tool for generating hypotheses on transcriptional network disruptions driving drug resistance.
- Findings shed light on mechanisms of abiraterone response and resistance, potentially leading to novel therapeutic strategies.
