Molecular imaging reveals a high degree of cross-seeding of spontaneous metastases in a novel mouse model of synchronous bilateral breast cancer
Bibliographic record
Abstract
ABSTRACT Synchronous bilateral breast cancer (SBBC) patients present with cancer in both breasts at the time of diagnosis or within a short time interval. They show higher rates of metastasis and lower overall survival compared to women with unilateral breast cancer. However, the lack of a preclinical model has led to a dearth in knowledge regarding the patterns of SBBC metastasis. Here we established an SBBC model and used molecular imaging to visualize the development of spontaneous lung metastases arising from each primary tumor. We engineered human breast cancer cells to express either Akaluc or Antares2 for bioluminescence imaging (BLI), and tdTomato or zsGreen for ex vivo fluorescence microscopy. Both cell populations were implanted into contralateral mammary fat pads of mice (n=10) and BLI was performed weekly for up to day 29 (n=3), 38 (n=4), or 42 (n=3). Signal from both Antares2 and Akaluc was first detected in the lungs on day 28 and was present in 9 of 10 mice at endpoint. Ex vivo fluorescence microscopy of the lungs revealed that for mice sacrificed on day 38, a significant percentage of micrometastases were composed of cancer cells from both primary tumors (mean 37%; range 27% to 45%), while two mice sacrificed on day 42 showed percentages of 51% and 70%. These results reveal a high degree of metastatic cross-seeding which may contribute to faster metastatic growth and intratumoral heterogeneity. We posit our work will help understand treatment resistance and optimal planning of treatment for SBBC patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".