Systemic toxicities of trastuzumab‐emtansine predict tumor response in <scp>HER2</scp>+ metastatic breast cancer
Bibliographic record
Abstract
The mechanism by which trastuzumab-emtansine (T-DM1) causes systemic toxicities apart from trastuzumab alone is currently unknown. We hypothesized that the systemic toxicities from T-DM1 may have been caused by the free and active maytansine released from the lysed HER2+ tumor cells, and if so, they may correlate with the response to treatment and eventually disease-free survival or patient outcome. In a retrospective, observational study, we evaluated 73 patients from three centers in the United States and Canada with advanced HER2+ breast cancer that received at least one dose of T-DM1. Toxicity grades were summed to create a corresponding toxicity sum score (TSS), and its association with clinical outcomes was analyzed. A higher TSS was significantly associated with longer progression-free survival with an HR = 0.66 [95% confidence interval [CI]: 0.47-0.92], P = .014, for each 1-point increase in the TSS score. Adjusted for baseline platelet count, aspartate transaminase and alanine transaminase, higher TSS remains significantly associated with longer progression-free survival with adjusted HR = 0.67 [95% CI: 0.47-0.93], P = .020. The analysis suggests that the systemic toxicities of T-DM1 were significantly correlated with its clinical efficacy. This is the first report to correlate the systemic toxicities of T-DM1 with clinical outcome. Further, this suggests that systemic toxicities of antibody-drug conjugates (ADCs) may serve as a predictive biomarker, particularly if noncleavable linkers are used. If confirmed in larger prospective studies, the present finding is significant because most ADCs do not have a biomarker predictive of clinical outcome other than the presence or absence of the antibody target.
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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) | 0.000 | 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) | 0.001 | 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".