Sequencing Antibody Drug Conjugates in Breast Cancer: Exploring Future Roles
Bibliographic record
Abstract
Antibody drug conjugates (ADCs) have emerged as a highly effective treatment strategy across breast cancer (BC) subtypes, including human epidermal growth factor receptor 2-positive (HER2+), hormone-receptor positive (ER/PR+), and triple-negative breast cancer (TNBC). Over the past twenty years, ADCs have undergone relevant evolutions, from target diversity to payload ratio, to linker design, allowing for a progressive increase in their efficacy. From the first-generation ADC, trastuzumab emtansine (T-DM1), approved in 2013 for HER2+ breast cancer, to next generation ADCs such as sacituzumab govitecan and trastuzumab deruxtecan, to emerging ADCs on the horizon, we continue to see unparalleled efficacy compared to traditional chemotherapy. However, each ADC has brought a new cadre of adverse events for clinicians and patients to manage. Importantly, with the development and approval of several ADCs to treat metastatic breast cancer, there are unanswered clinical questions surrounding how to optimally sequence treatment for patients who may be candidates for more than one ADC and, in general, how to treat patients beyond progression on ADCs. From bench to bedside, in this review, we will discuss the pharmacology and current indications for the novel ADCs trastuzumab deruxtecan and sacituzumab govitecan. Highlighting emerging ADCs and ongoing clinical trials, we will anticipate the changes in the breast cancer treatment paradigm. Lastly, we will outline the available data and current approaches for adverse event management and sequencing strategies for ADCs in clinical practice, including proposed mechanisms of resistance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".