Coupling culturomics and metagenomics sequencing to characterize the gut microbiome of patients with cancer treated with immune checkpoint inhibitors
Bibliographic record
Abstract
BACKGROUND: The gut microbiome represents a novel biomarker for melanoma and non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICI). Gut microbiome metagenomics profiling studies of patients treated with immunotherapy identified bacteria associated with ICI efficacy, while others have been linked to resistance. However, limitations of metagenomics sequencing, such as complex bioinformatic processing requirements, necessity of a threshold for positive detection, and the inability to detect live organisms, have hindered our ability to fully characterize the gut microbiome. Therefore, combining metagenomics with high-throughput culture-based techniques (culturomics) represents an ideal strategy to fully characterize microbiome composition to more robustly position the microbiome as a biomarker of response to ICI. METHODS: We performed culturomics using fecal samples from 22 patients from two academic centres in Canada and the United Kingdom with NSCLC and cutaneous melanoma treated with ICI (cancer group), comparing their microbiome composition to that of 7 healthy volunteers (HV), along with matching shotgun metagenomics sequencing. RESULTS: For culturomics results, 221 distinct species were isolated. Among these 221 distinct species, 182 were identified in the cancer group and 110 in the HV group. In the HV group, the mean species richness was higher compared to the cancer group (34 vs. 18, respectively, p = 0.002). Beta diversity revealed separate clusters between groups (p = 0.004). Bifidobacterium spp. and Bacteroides spp. were enriched in HV, while cancer patients showed an overrepresentation of Enterocloster species, as well as Veillonella parvula. Next, comparing cancer patients' clinical outcomes to ICI, we observed that among the 20 most abundant bacteria present in non-responder patients, 2 belonged to the genus Enterocloster, along with an enrichment of Hungatella hathewayi and Cutibacterium acnes. In contrast, responders to ICI exhibited a predominance of Bacteroides spp. In NSCLC patients, metagenomics analysis revealed that of the 154 bacteria species isolated through culturomics, 61/154 (39%) were also identified by metagenomics sequencing. Importantly, 94 individual species were uniquely detected by culturomics. CONCLUSION: These findings highlight that culturomics and metagenomics can serve as complementary tools to characterize the microbiome in patients with cancer. This integrated approach uncovers specific microbiome signatures that differentiate HV from cancer patients, and identifies specific species associated with therapy response and resistance.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".