Abstract CT022: Evaluation of oral microbiome profiling as a response biomarker in squamous cell carcinoma of the head and neck: Analyses from CheckMate 141
Bibliographic record
Abstract
Abstract Introduction: Recent studies indicated that specific intestinal microbiota may modulate efficacy of anti-PD-1 and anti-CTLA-4 immunotherapy in preclinical tumor models (Sivan et al, Science. 2015;350:1084-9; Vétizou et al, Science. 2015;350:1079-84). However, little is known regarding the association of the oral microbiome with checkpoint blockade immunotherapy. In CheckMate 141 (NCT02105636), a randomized global phase 3 study comparing nivolumab with investigator’s choice (IC) therapy in patients with platinum-refractory squamous cell carcinoma of the head and neck (SCCHN), nivolumab improved median overall survival compared with IC (7.5 vs 5.1 months; P=0.01) (Ferris et al, NEJM. 2016;375:1856-67). This analysis assessed if oral microbiome profiling would yield prognostic biomarkers of response to anti-PD-1 immunotherapy in patients with SCCHN treated in CheckMate 141. Methods: Saliva samples were obtained at screening (nivolumab n=85, IC n=31) and week 7 of treatment (nivolumab n=77, IC n=28), and profiled using high-throughput 16S ribosomal RNA sequencing. Bacterial abundance was estimated using a combined differential abundance modeling approach, normalized using cumulative sum scaling to correct for sequencing depth, and analyzed using a linear model for association with response (complete response/partial response vs stable disease vs progressive disease), tumor PD-L1 expression, HPV16 status, treatment history, and patient demographics. Results: Among 221 saliva samples analyzed, bacteria from 13 phyla and 542 species were detected. At baseline, no significant associations were detected in richness of bacterial diversity with best overall response, tumor PD-L1 expression, or HPV16 status. No associations in microbial alpha and beta diversity were detected with treatment modality (nivolumab vs IC) or treatment duration (baseline vs week 7). Patients with prior radiation therapy (n=97) had lower abundance of bacteria from the families Prevotellaceae and Flavobacteriaceae than patients without prior radiation therapy (n=17). The abundance of bacteria from the families Desulfobulbaceae and Flavobacteriaceae was higher in European (n=56) than North American (n=46) patients. Conclusion: Differences in certain bacterial species were observed in patients with prior radiation therapy and between different geographical locations. However, no significant associations were detected between oral bacterial diversity and clinical response, tumor PD-L1 expression, HPV16 status, or treatment modality. This exploratory analysis is the first to evaluate the oral microbiome as a biomarker in a randomized, phase 3 clinical trial for immunotherapy in SCCHN and may serve as a basis for future assessments and experimental design. Correlation of salivary and intestinal microbiota with that from tumor specimens is warranted. Citation Format: Robert L. Ferris, George Blumenschein, Kevin Harrington, Jérôme Fayette, Joël Guigay, A. Dimitrios Colevas, Lisa Licitra, Everett Vokes, Maura Gillison, Caroline Even, Cheryl Ho, Makoto Tahara, Robert Haddad, Mark Lynch, Manish Monga, Somnath Bandyopadhyay, Omar Jabado, Henry Kao. Evaluation of oral microbiome profiling as a response biomarker in squamous cell carcinoma of the head and neck: Analyses from CheckMate 141 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr CT022. doi:10.1158/1538-7445.AM2017-CT022
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".