Identification of intratumoral bacteria that enhance breast tumor metastasis
Bibliographic record
Abstract
ABSTRACT The central, mortality-associated hallmark of cancer is the process of metastasis. It is increasingly recognized that bacteria influence multiple facets of cancer progression, but the extent to which tumor microenvironment-associated bacteria control metastasis in cancer is poorly understood. To identify tumor-associated bacteria and their role in metastasis, we utilized established murine models of non-metastatic and metastatic breast tumors to identify bacteria capable of driving metastatic disease. We found several species of the Bacillus genus that were unique to metastatic tumors, and found that breast tumor cells cultured with a Bacillus bacterium isolated from metastatic tumors, Bacillus thermoamylovorans , produced nearly 3× the metastatic burden as control cells or cells cultured with bacteria from non-metastatic breast tumors. We then performed targeted metabolomics on tumor cells cultured with different bacterial species and found that B. thermoamylovorans differentially regulated tumor cell metabolite profiles compared to bacteria isolated from non-metastatic tumors. Using these bacteria, we performed de novo sequencing and tested for the presence of genes that were unique to the bacterium isolated from metastatic tumors in a patient population to provide a proof-of-concept for identifying how specific bacterial functions are associated with the metastatic process in cancer independent of bacterial species. Together, our data directly demonstrate the ability of specific bacteria to promote metastasis through interaction with cancer cells. IMPORTANCE Metastasis is a major barrier to long-term survival for cancer patients, and therapeutic options for patients with aggressive, metastatic forms of breast cancer remain limited. It is therefore critical to understand the differences between non-metastatic and metastatic disease to identify potential methods for slowing or even stopping metastasis. In this work, we identify a bacterial species present with metastatic breast tumors capable of increasing the metastatic capabilities of tumor cells. We isolated and sequenced this bacteria, as well as a control species which failed to promote metastasis, and identified specific bacterial genes that were unique to the metastasis-promoting species. We tested for the presence of these bacterial genes in patient tumor samples and found they were more likely to be associated with mortality. We also identified enrichment of specific bacterial functions, providing insight into possible sources of bacteria-driven increases in the metastatic potential of multiple cancer types.
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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".