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Assessing the Performance of a Novel Stool-Based Microbiome Test That Predicts Response to First Line Immune
Irina Robinson1, Maximilian Johannes Hochmair1, Manuela Schmidinger2
1Department of Respiratory and Critical Care Medicine, Karl Landsteiner Institute for Lung Research and Pulmonary Oncology, Klinik Floridsdorf, Vienna Healthcare Group, 1210 Vienna, Austria.
Cancers
|July 14, 2023
Summary
The BiomeOne test predicts response to immune checkpoint inhibitor (ICI) therapies using gut microbiome profiles. This validation study shows its potential for guiding treatment decisions in cancer patients.
Area of Science:
- Oncology
- Microbiome Research
- Immunotherapy
Background:
- The gut microbiome significantly influences immunotherapy response but lacks validated predictive tools.
- Immune checkpoint inhibitors (ICIs) are crucial cancer treatments, yet predicting patient response remains a challenge.
Purpose of the Study:
- To validate BiomeOne®, a microbiome-based algorithm, for predicting clinical benefit from ICI therapy.
- To assess BiomeOne®'s performance in patients with melanoma, NSCLC, and RCC undergoing ICI treatment.
Main Methods:
- A multi-centric study analyzed stool microbiome profiles of 63 patients receiving ICIs.
- The BiomeOne® algorithm classified patients as responders (Rs) or non-responders (NRs).
- Microbiome composition associated with response and immune-related adverse events (irAEs) was identified.
Main Results:
- BiomeOne® achieved 81% sensitivity and 50% specificity in predicting ICI response.
- Specific bacterial taxa like *Oscillospira* and *Prevotella copri* were associated with favorable responses.
- Patients experiencing irAEs showed increased microbial diversity and depletion in *Agathobacter*.
- In NSCLC patients, BiomeOne® demonstrated higher sensitivity (78.6%) than PD-L1 testing (67.9%) for predicting response.
Conclusions:
- BiomeOne® shows promise as the first microbiome-based diagnostic for predicting ICI response.
- Further validation across indications is needed for clinical integration of microbiome diagnostics.
- This approach could enhance personalized cancer treatment strategies.
