Imaging Biomarkers Predict Response to Anti-HER2 (ErbB2) Therapy in Preclinical Models of Breast Cancer
Bibliographic record
Abstract
PURPOSE: To evaluate noninvasive imaging methods as predictive biomarkers of response to trastuzumab in mouse models of HER2-overexpressing breast cancer. The correlation between tumor regression and molecular imaging of apoptosis, glucose metabolism, and cellular proliferation was evaluated longitudinally in responding and nonresponding tumor-bearing cohorts. EXPERIMENTAL DESIGN: Mammary tumors from MMTV/HER2 transgenic female mice were transplanted into syngeneic female mice. BT474 human breast carcinoma cell line xenografts were grown in athymic nude mice. Tumor cell apoptosis (NIR700-Annexin V accumulation), glucose metabolism [2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography ([18F]FDG-PET)], and proliferation [3'-[18F]fluoro-3'-deoxythymidine-PET ([18F]FLT-PET)] were evaluated throughout a biweekly trastuzumab regimen. Imaging metrics were validated by direct measurement of tumor size and immunohistochemical analysis of cleaved caspase-3, phosphorylated AKT, and Ki67. RESULTS: NIR700-Annexin V accumulated significantly in trastuzumab-treated MMTV/HER2 and BT474 tumors that ultimately regressed but not in nonresponding or vehicle-treated tumors. Uptake of [18F]FDG was not affected by trastuzumab treatment in MMTV/HER2 or BT474 tumors. [18F]FLT-PET imaging predicted trastuzumab response in BT474 tumors but not in MMTV/HER2 tumors, which exhibited modest uptake of [18F]FLT. Close agreement was observed between imaging metrics and immunohistochemical analysis. CONCLUSIONS: Molecular imaging of apoptosis accurately predicts trastuzumab-induced regression of HER2+ tumors and may warrant clinical exploration to predict early response to neoadjuvant trastuzumab. Trastuzumab does not seem to alter glucose metabolism substantially enough to afford [18F]FDG-PET significant predictive value in this setting. Although promising in one preclinical model, further studies are required to determine the overall value of [18F]FLT-PET as a biomarker of response to trastuzumab in HER2+ 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".