Abstract P4-02-05: A novel 64Cu-liposomal PET agent (MM-DX-929) predicts response to liposomal chemotherapeutics in preclinical breast cancer models
Bibliographic record
Abstract
Abstract Background: Liposomal anthracyclines (such as pegylated liposomal doxorubicin (PLD) or HER2-targeted liposomal doxorubicin (MM-302)) are being used and/or evaluated for the clinical management of breast cancer, but responses vary from patient to patient. It is hypothesized that variability in the deposition of liposomal therapeutics within tumors leads to differences in drug exposure, thereby directly influencing tumor response. We have developed MM-DX-929, a novel 64Cu-liposomal PET imaging agent, as a clinically-implementable tool to investigate whether image-based quantification of liposome deposition in tumors can predict treatment response to liposomal chemotherapeutics, including PLD, MM-302 and/or liposomal irinotecan (MM-398). Objectives: Our primary objective is to demonstrate that the extent of tumor uptake of MM-DX-929 is predictive of tumor response to MM-302 in preclinical breast cancer xenograft models. A secondary objective is to enable clinical translation of MM-DX-929 to enable incorporation into existing therapeutic trials. Methods: Mice bearing BT474-M3 mammary and subcutaneous tumors were injected intravenously with MM-DX-929 prior to dosing with MM-302. PET/CT imaging was performed at 16h post MM-DX-929 injection, and tumor uptake was determined. Response to treatment was quantified as tumor volume changes measured over a 2-month period by MRI. Results: Tumor deposition of MM-DX-929 administration correlated well with treatment response to MM-302 (Spearman correlation coefficient of −0.891 and a p-value of 0.0004). MM-DX-929 accumulation in tumors prior to the start of the MM-302 treatment successfully predicted improved tumor growth inhibition following MM-302 treatment. Conclusion: These findings support trials of MM-DX-929 as a predictive imaging agent to select patients who are most likely to respond to liposomal therapies. The clinical development of MM-DX-929 for identification of breast cancer patients likely to respond to liposomal therapeutics is currently being pursued. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-02-05.
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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) | 0.003 | 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".