Cross-laboratory replication of pseudomyxoma peritonei tumor microbiome reveals reproducible microbial signatures
Bibliographic record
Abstract
Recent work has demonstrated that cancer-specific microbial communities often colonize tumor tissues. However, untangling low-biomass signals from environmental contamination makes this research technically challenging. We utilize pseudomyxoma peritonei (PMP), a cancer characterized by the spread of mucus-secreting cells throughout the peritoneal cavity, to develop a robust workflow for identifying reproducible tumor microbiomes. Typically originating from the rupture of an appendiceal tumor into the peritoneal cavity, metastasized tumors have been previously shown to harbor a core set of microbes. However, that work did not control for the potential contamination of these low microbial biomass samples. We expand upon these prior findings by characterizing the microbiome of 70 additional PMP tumors and six normal peritoneal control tissues along with appropriate laboratory controls. Additionally, DNA from a subset of 25 tissues was extracted and sequenced at an independent laboratory. We found evidence of reproducible microbial signatures between the replicates of six different PMP tumors that include a set of core taxa that may be introduced from surgical contamination, as well as patient-specific taxa that are also commonly implicated in colorectal cancer. In addition, preoperative chemotherapy treatment was found to reduce tumor microbiome diversity. Our findings demonstrate how independent sample replication can be a powerful approach to investigate low-biomass microbial communities associated with tumor tissues that will improve low microbial biomass research.IMPORTANCERecent work has demonstrated that microbial communities colonize over 30 different types of tumor tissues. The origin of these communities and their possible involvement in carcinogenesis or cancer treatment outcomes remains an unclear, yet important area of research. A current major challenge in characterizing low-biomass, tumor-associated microbiomes is the introduction of environmental contamination during collection, handling, DNA extraction, PCR, and sequencing. Here, we provide a framework for replicating low-biomass tumor microbiome samples to help identify tumors with robust microbial signals and low background contamination. Using this replication approach, we show that pseudomyxoma peritonei (PMP) tumors host reproducible microbial communities, including organisms that have previously been associated with colorectal cancer. Incorporating sample replication into future tumor microbiome studies is a promising approach that will help identify robust signals and increase reproducibility in the field.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".