Spatiotemporal assessment of spontaneous metastasis formation using multimodal in vivo imaging in HER2+ and triple negative metastatic breast cancer xenograft models in mice
Bibliographic record
Abstract
BACKGROUND: Preclinical breast cancer models recapitulating the clinical course of metastatic disease are crucial for drug development. Highly metastatic cell lines forming spontaneous metastasis following orthotopic implantation were previously developed and characterized regarding their biological and histological characteristics. This study aimed to non-invasively and longitudinally characterize the spatiotemporal pattern of metastasis formation and progression in the MDA-MB-231-derived triple negative LM2-4 and HER2+ LM2-4H2N cell lines, using bioluminescence imaging (BLI), contrast enhanced computed tomography (CT), fluorescence imaging, and 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography ([18F]FDG-PET). MATERIAL AND METHODS: LM2-4, LM2-4H2N, and MDA-MB-231 tumors were established in the right inguinal mammary fat pad (MFP) of female SCID mice and resected 14-16 days later. Metastasis formation was monitored using BLI. Metabolic activity of primary and metastatic lesions in mice bearing LM2-4 or LM2-4H2N was assessed by [18F]FDG-PET. Metastatic burden at study endpoint was assessed by CT and fluorescence imaging following intravenous dual-modality liposome agent administration. RESULTS: Comparable temporal metastasis patterns were observed using BLI for the highly metastatic cell lines LM2-4 and LM2-4H2N, while metastasis formed about 10 days later for MDA-MB-231. 21 days post primary tumor resection, metastases were detected in 86% of LM2-4, 69% of LM2-4H2N, and 60% of MDA-MB-231 inoculated mice, predominantly in the axillary region, contralateral MFP, and liver/lung. LM2-4 and LM2-4H2N tumors displayed high metabolism based on [18F]FDG-PET uptake. Lung metastases were detected as the [18F]FDG-PET uptake increased significantly between pre- and post-metastasis scan. Using a liposomal dual-modality agent, CT and fluorescence confirmed BLI detected lesions and identified additional metastatic nodules in the intraperitoneal cavity and lung. CONCLUSIONS: The combination of complementary anatomical and functional imaging techniques can provide high sensitivity characterization of metastatic disease spread, progression and overall disease burden. The described models and imaging toolset can be implemented as an effective means for quantitative treatment response evaluation in metastatic breast cancer.
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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 | 0.000 | 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 | 0.001 |
| 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".