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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Targeted Transcriptomic Profiling Identifies Biomarkers and Molecular Features Associated With Immunotherapy Response
Leticia Hamana1, Mario L Marques-Piubelli1, Lu Wei1
1Department of Translational Molecular Pathology, University of Texas, MD Anderson Cancer Center, Houston, Texas.
Summary
Classic Hodgkin lymphoma (CHL) transcriptomics reveal distinct phenotypes in immunotherapy non-responders. Non-responders show an "oncogene-driven" profile, while responders exhibit an "inflammatory immune-scape," guiding future treatment strategies.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Classic Hodgkin lymphoma (CHL) is highly curable, but relapsed/refractory disease necessitates better treatment response predictors.
- Immune-checkpoint inhibitors improve outcomes, yet many patients have suboptimal responses, highlighting the need for deeper understanding of CHL biology.
- Transcriptomic analysis can improve patient stratification and guide treatment approaches for CHL.
Purpose of the Study:
- To characterize the transcriptomic landscape of Classic Hodgkin lymphoma (CHL) in treatment-naive and relapsed patients.
- To identify tumor-intrinsic and microenvironmental features associated with CHL biology and response to nivolumab-based immunotherapy.
- To elucidate transcriptional differences between immunotherapy responders and non-responders in CHL.
Main Methods:
- Targeted mRNA-Next-Generation Sequencing (NGS) using the HTG EdgeSeq Precision Immune-Oncology Panel on formalin-fixed paraffin-embedded CHL tissue.
- Differential gene expression and pathway enrichment analyses were performed on 25 CHL samples.
- Analysis included treatment-naive and post-chemotherapy relapsed patients treated with nivolumab-based protocols.
Main Results:
- CHL samples showed overexpression of immune-checkpoint genes (CD274, CTLA-4), immunoregulatory genes (IL6, IL13), and macrophage-related markers (CD163, MMP2, TIMP1).
- Immunotherapy non-responders (20% overall) displayed a proliferative, cytokine-activated, immunologically cold phenotype with activated E2F, G2M, MYC, and PI3K/AKT/mTOR pathways.
- Responders exhibited an "inflammatory immune-scape" phenotype, contrasting with the "oncogene-driven" profile of non-responders.
Conclusions:
- The CHL transcriptomic landscape is complex and diverse, influencing treatment response.
- Immunotherapy non-responders share an "oncogene-driven" transcriptional profile, irrespective of prior treatment.
- These findings provide a foundation for precision-focused studies to improve CHL outcomes.
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