Integrative Multi-Omics Analysis of Melanoma: Uncovering Pathways Associated with Immunotherapy Outcomes
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Purpose
The treatment of advanced malignant melanoma with immune checkpoint blockade (ICB) therapies such as anti-CTLA4 and anti-PD1 has been transformative, yet a significant proportion of patients demonstrate intrinsic resistance or develop severe immune-related adverse events (irAEs), complicating treatment strategies. This study aimed to integrate clinical and molecular data using multi-omics factor analysis (MOFA) To better understand the multifaceted interactions governing ICB resistance and irAE development.
Methods
Melanoma patient-derived xenograft tumors with transcriptomic and microbiome data were analyzed using the MOFA2 R package. Simulations assessed MOFA2’s performance with small sample sizes. Transcriptomic and microbiome data were normalized and analyzed with MOFA2, and gene set enrichment analysis (GSEA) was performed.
Results
MOFA2 demonstrated robust performance with small sample sizes in simulations and accurately recapitulated findings from published data. Analysis identified five latent factors associating the tumor transcriptome, tumor microbiome, or both with differences in tumor subtypes, ICB response, and specific irAEs. GSEA highlighted pathways related to oxidative phosphorylation, DNA replication, and immune responses.
Conclusion
Integrative analysis of multi-omics data using MOFA2 provides insights into melanoma biology, uncovering distinct molecular pathways underlying clinical phenotypes. These insights contribute to our understanding of the complex biological mechanisms contributing to differences in melanoma clinical and tumor characteristics and treatment response, offering potential insight towards future development of more personalized and effective diagnostic, prognostic, and therapeutic strategies for patients.
Context Summary
This study aims to integrate multi-omics data, specifically transcriptomic and microbiome datasets, using the MOFA2 computational framework, to understand the complex interplay driving melanoma heterogeneity and response to immune checkpoint blockade (ICB) therapy. The study demonstrates that MOFA2 performs effectively even with small sample sizes, successfully capturing factors that distinguish tumor subtypes, ICB response, and immune-related adverse events (irAEs). It identifies associations between molecular features and clinical outcomes, shedding light on potential mechanisms underlying melanoma pathogenesis and treatment response. By integrating clinical and molecular data, the findings offer insights into the biological underpinnings of melanoma treatment response. Understanding these mechanisms could inform the development of more effective diagnostic, prognostic, and therapeutic strategies for melanoma patients, moving towards personalized oncology approaches.
