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Pan-cancer analysis reveals CCL5/CSF2 as potential predictive biomarkers for immune checkpoint inhibitors
Yi-Chao Chen1, Wei-Zhong Zheng2, Chun-Peng Liu3
1Clinical Research Center, Shantou Central Hospital, Shantou, 515041, China.
Background:
Currently, there are no optimal biomarkers available for distinguishing patients who will respond to immune checkpoint inhibitors (ICIs) therapies. Consequently, the exploration of novel biomarkers that can predict responsiveness to ICIs is crucial in the field of immunotherapy.
Methods:
We estimated the proportions of 22 immune cell components in 10 cancer types (6,128 tumors) using the CIBERSORT algorithm, and further classified patients based on their tumor immune cell proportions in a pan-cancer setting using k-means clustering. Differentially expressed immune genes between the patient subgroups were identified, and potential predictive biomarkers for ICIs were explored. Finally, the predictive value of the identified biomarkers was verified in patients with urothelial carcinoma (UC) and esophageal squamous cell carcinoma (ESCC) who received ICIs.
Results:
Our study identified two subgroups of patients with distinct immune infiltrating phenotypes and differing clinical outcomes. The patient subgroup with improved outcomes displayed tumors enriched with genes related to immune response regulation and pathway activation. Furthermore, CCL5 and CSF2 were identified as immune-related hub-genes and were found to be prognostic in a pan-cancer setting. Importantly, UC and ESCC patients with high expression of CCL5 and low expression of CSF2 responded better to ICIs.
Conclusion:
We demonstrated CCL5 and CSF2 as potential novel biomarkers for predicting the response to ICIs in patients with UC and ESCC. The predictive value of these biomarkers in other cancer types warrants further evaluation in future studies.
Insights
Novel biomarkers CCL5 and CSF2 can predict response to immune checkpoint inhibitors (ICIs) in urothelial carcinoma and esophageal squamous cell carcinoma. This finding is crucial for advancing immunotherapy and personalizing cancer treatment strategies.
Area of Science:
- Oncology
- Immunotherapy
- Biomarker Discovery
Background:
- Optimal biomarkers for predicting immune checkpoint inhibitor (ICI) therapy response are lacking.
- Identifying predictive biomarkers is crucial for advancing immunotherapy.
- Current treatment selection relies on limited predictive markers.
Purpose of the Study:
- To identify novel biomarkers for predicting ICI response across multiple cancer types.
- To explore immune cell infiltration patterns and their correlation with clinical outcomes.
- To validate potential biomarkers in urothelial carcinoma (UC) and esophageal squamous cell carcinoma (ESCC).
Main Methods:
- Utilized CIBERSORT algorithm to estimate immune cell proportions in 6,128 tumors across 10 cancer types.
- Employed k-means clustering for pan-cancer classification based on immune cell phenotypes.
- Identified differentially expressed immune genes and validated biomarker predictive value in UC and ESCC patients receiving ICIs.
Main Results:
- Identified two patient subgroups with distinct immune infiltration phenotypes and clinical outcomes.
- Discovered CCL5 and CSF2 as immune-related hub genes with prognostic value.
- Found that high CCL5 and low CSF2 expression predicted better ICI response in UC and ESCC patients.
Conclusions:
- CCL5 and CSF2 are demonstrated as potential novel biomarkers for predicting ICI response in UC and ESCC.
- These biomarkers offer a promising avenue for personalizing immunotherapy selection.
- Further evaluation of CCL5 and CSF2 in other cancer types is warranted.
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