Liposomal Irinotecan Achieves Significant Survival and Tumor Burden Control in a Triple Negative Breast Cancer Model of Spontaneous Metastasis
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) represents a significant therapeutic challenge due to its highly aggressive nature and lack of effective treatment options. Liposomal irinotecan (nal-IRI, ONIVYDE) was approved in 2015 (by the Food and Drug Administration, European Medicines Agency, and Therapeutic Goods Administration) and is a topoisomerase inhibitor indicated, in combination with fluorouracil and leucovorin, for the treatment of patients with metastatic adenocarcinoma of the pancreas after disease progression following gemcitabine-based therapy. This study investigates the potential therapeutic benefit of nal-IRI for the treatment of advanced TNBC in a clinically relevant mouse model of spontaneous metastasis (LM2-4). Female SCID mice were orthotopically inoculated with TNBC LM2-4-luc cells in the lower mammary fat pad. Following primary tumor resection, bioluminescence imaging (BLI) was used to monitor both metastasis formation and spread as well as response to treatment with nal-IRI. Weekly treatment with 10 mg/kg of nal-IRI provided a 4.9-times longer median survival compared to both 50 mg/kg irinotecan treated and untreated animals. The survival benefit was supported by a significant delay in the regrowth of the primary tumor, effective control, and eventual regression of metastases assessed using longitudinal BLI, which was confirmed at the study end point with magnetic resonance (MR) imaging and post-mortem observation. This preclinical investigation demonstrates that, at a five-times lower dose compared to the free drug, liposomal irinotecan provides significant survival benefit and effective management of metastatic disease burden in a clinically relevant model of spontaneous TNBC metastases. These findings support the evaluation of nal-IRI in patients with advanced and metastatic TNBC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".