Fluctuations in Gut Microbiome Composition During Immune Checkpoint Inhibitor Therapy
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) such as programmed cell death protein-1 (PD-1) inhibitors or PD-1 ligand-1 (PD-L1) inhibitors have led to remarkable improvement in outcomes of non-small cell lung cancer (NSCLC). Unfortunately, the significant benefits of ICI therapy are frequently limited by resistance to treatment and adverse effects, and the predictive value of pre-treatment tumor tissue PD-L1 expression is limited. Development of less invasive biomarkers that could identify responders and non-responders in early on-treatment could markedly improve the treatment regimen. Accumulating evidence suggests that baseline gut microbiota profile is associated with response to PD-1/PD-L1 blockade therapy. However, change in the gut microbiome composition during PD-1/PD-L1 blockade therapy and its relation to response remain unclear. Methods: Here, we analyzed pre- and on-treatment fecal samples from five NSCLC patients receiving anti-PD-1 immunotherapy, alone or in tandem with chemotherapy, and performed 16S rRNA sequencing. Results: The overall alpha diversity of the baseline gut microbiome was similar between three responders and two non-responders. While the gut microbiome composition remained stable overall during treatment (R2 = 0.145), responders showed significant changes in microbiome diversity between pre- and on-treatment samples during anti-PD-1 therapy compared to non-responders (P = 0.0274). Within the diverse microbiota, responders showed decreases in the abundance of genera Odoribacter , Gordonibacter , Candidatus Stoquefichus , Escherichia-Shigella , and Collinsella , and increase in abundance of Clostridium sensu stricto 1 . In contrast, non-responders demonstrated on-treatment increases in genera Prevotella , Porphyromonas , Streptococcus , and Escherichia-Shigella , and decrease in abundance of Akkermansia . Conclusions: This pilot study identified a substantial change in gut microbiome diversity between pre- and on-treatment samples in NSCLC patients responding to anti-PD-1 therapy compared to non-responders. Our findings highlight the potential utility of gut microbiota dynamics as a noninvasive biomarker to predict response to PD-1/PD-L1 blockade therapy for a wide variety of malignancies, which sets a path for future investigation in larger prospective studies. World J Oncol. 2023;14(3):178-187 doi: https://doi.org/10.14740/wjon1587
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".