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Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
NLRC5/CITA expression correlates with efficient response to checkpoint blockade immunotherapy.
Sayuri Yoshihama1,2, Steven X Cho3, Jason Yeung1
1Department of Microbial Pathogenesis and Immunology, Texas A&M Health Science Center, 415A Reynolds Medical Building, College Station, TX, 77843, USA.
NLRC5 expression predicts response to cancer immunotherapy. Higher NLRC5 levels in melanoma tumors correlate with better outcomes from checkpoint blockade therapies like anti-CTLA-4 and anti-PD1.
Area of Science:
- Immunology
- Oncology
- Genetics
Background:
- Checkpoint blockade immunotherapy shows promise for various cancers.
- Cancer cells can evade immune detection, limiting immunotherapy effectiveness.
- Identifying suitable patients is vital due to treatment costs and side effects.
Purpose of the Study:
- To investigate NLRC5 as a predictive biomarker for checkpoint blockade immunotherapy response.
- To evaluate NLRC5 expression in relation to anti-CTLA-4 and anti-PD1 therapies in melanoma.
Main Methods:
- Analysis of NLRC5 and MHC class I gene expression in melanoma tumors from immunotherapy responders and non-responders.
- Multivariate analysis incorporating tumor mutation number, neo-antigen load, and PD-L2 expression.
- Correlation of NLRC5 expression/methylation and mutation load with patient survival.
Main Results:
- Melanoma patients responding to immunotherapy had higher NLRC5 and MHC class I gene expression.
- Multivariate analysis improved stratification of responders versus non-responders to anti-CTLA-4 therapy.
- NLRC5 expression and tumor mutation load significantly correlated with increased patient survival.
Conclusions:
- NLRC5 tumor expression is a potential predictive biomarker for anti-CTLA-4 and anti-PD1 immunotherapy response in melanoma.
- Combined assessment of NLRC5 and tumor mutation load may offer valuable prognostic insights.
- These findings could guide patient selection for immunotherapy, optimizing treatment efficacy and resource allocation.
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