Abstract A047: Tumor-derived bacteria drive breast cancer metastasis
Bibliographic record
Abstract
Abstract Metastasis is a major barrier to long-term survival and therapeutic options for aggressive, metastatic forms of breast cancer remain limited. Studies using patient samples have identified tumor-resident bacteria that preferentially associate with specific breast cancer types including highly aggressive TNBC. However, it is not yet understood how intratumoral bacteria directly contributes to disease progression and metastatic propensity independent of other prognostic factors. It is therefore the goal of the Dedhar and Finlay labs to identify how specific bacteria within metastatic breast cancer control immune and tumor cell functions to regulate metastatic potential and determine the outcome of disease progression. Using the syngenic, immunocompetent 4T1 and 67NR breast cancer models of metastatic and non-metastatic disease, we found microbiome depletion significantly reduces primary tumor growth highly metastatic 4T1 tumors specifically. We also found bacterial depletion reduces metastatic burden and extends survival time compared to microbiome-replete controls. Along with alterations in disease progression, microbiome depletion induces changes in immune cell function that occur specifically in the metastatic 4T1 tumors, revealing differential microbial-based regulation of metastatic versus non-metastatic disease. To identify bacteria that control metastasis in microbiome-replete controls, we plated surgically resected tumor suspensions on bacterial growth media and compared bacteria from the 4T1 and 67NR primary tumors. We identified several species of the Bacillus genus that were unique to 4T1 tumors and were present both within the primary tumor as well as metastatic nodules. To determine how these bacteria effect disease progression, we designed several in vivo model systems to directly test the ability of the isolated bacteria to promote metastasis. Using an orthotopic inoculation model with 4T1 or EMT6 cells, we found that following intratumoral injection, the 4T1- derived Bacillus species was actually able to augment metastasis when introduced directly back into primary tumors. To determine the specificity of this phenomenon, we then compared the effects of the 4T1 and 67NR-isolated bacteria on metastasis by injecting 4T1 cells that had been co-cultured with either bacteria prior to injection. Interestingly, we found that while the 67NR-derived bacteria had little effect on metastasis, the 4T1-derived Bacillus species significantly enhanced metastatic tumor burden compared to all other groups including those cultured with the 67NR-derived bacteria. These data demonstrate the ability of certain bacteria to promote metastatic disease. Based on these findings, we hypothesize specific bacteria play a causative role in augmenting metastatic propensity, and seek to determine functional differences between intratumoral bacteria to identify mechanistic targets for prevention of metastasis. We also seek to expand this work into clinical models to identify potential prognostic factors as well as mechanistic targets for disease treatment. Citation Format: Zachary J. Gerbec, Antonio Serapio-Palacios, Sarah E. Woodword, Jorge Pena Diaz, Brett Finlay, Shoukat Dedhar. Tumor-derived bacteria drive breast cancer metastasis [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A047.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".