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A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to
Fyza Y Shaikh1,2, James R White3, Joell J Gills1,2
1The Bloomberg-Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Purpose:
While immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer by producing durable antitumor responses, only 10%-30% of treated patients respond and the ability to predict clinical benefit remains elusive. Several studies, small in size and using variable analytic methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations.
Experimental Design:
We have reanalyzed the available raw 16S rRNA amplicon and metagenomic sequencing data across five recently published ICI studies (n = 303 unique patients) using a uniform computational approach.
Results:
Herein, we identify novel bacterial signals associated with clinical responders (R) or nonresponders (NR) and develop an integrated microbiome prediction index. Unexpectedly, the NR-associated integrated index shows the strongest and most consistent signal using a random effects model and in a sensitivity and specificity analysis (P < 0.01). We subsequently tested the integrated index using validation cohorts across three distinct and diverse cancers (n = 105).
Conclusions:
Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.
Insights
Predicting cancer treatment response to immune checkpoint inhibitors (ICI) is challenging. This study found that gut microbiome biomarkers predicting nonresponse were more consistent and reliable than those predicting response, offering new intervention strategies.
Area of Science:
- Oncology
- Immunotherapy
- Microbiome Research
Background:
- Immune checkpoint inhibitors (ICI) have transformed cancer treatment, yet response rates remain low (10-30%).
- Predicting clinical benefit from ICI therapy is difficult.
- The gut microbiome is a potential biomarker for ICI response, but previous findings are inconsistent.
Purpose of the Study:
- To reanalyze existing ICI treatment studies using a standardized computational method.
- To identify bacterial signals associated with treatment response or nonresponse.
- To develop and validate a microbiome-based prediction index for ICI outcomes.
Main Methods:
- Reanalysis of 16S rRNA amplicon and metagenomic sequencing data from five ICI studies (n=303 patients).
- Uniform computational approach applied to identify bacterial signals.
- Development and validation of an integrated microbiome prediction index across three cancer types (n=105 patients).
Main Results:
- Novel bacterial signals associated with responders (R) and nonresponders (NR) were identified.
- An integrated microbiome prediction index was developed.
- The NR-associated index demonstrated the strongest and most consistent predictive signal (P < 0.01).
Conclusions:
- Biomarkers for predicting nonresponse to ICI are more robust than those for response.
- This study suggests a new approach to identify patients who may not benefit from current ICI therapy.
- Findings support the development of microbiome-based interventions to improve ICI treatment outcomes.
