Antibody-Drug Conjugates in Breast Cancer: Current Landscape and Future Targets
Bibliographic record
Abstract
Antibody-drug conjugates (ADCs) have transformed therapeutic options for patients with breast cancer, delivering targeted cytotoxic agents with enhanced efficacy, albeit with systemic toxicity. Since the approval of trastuzumab emtansine in 2012, the ADC landscape has rapidly expanded to include agents targeting HER2, TROP-2, and other novel targets. Currently, four ADCs are approved in breast cancer, showing clinical benefit across HER2-positive, HER2-low, hormone receptor (HR)-positive and triple-negative subtypes. Trastuzumab deruxtecan has demonstrated superior outcomes compared to earlier HER2-targeted ADCs and is the preferred treatment in multiple settings. Anti-TROP-2 ADCs, such as sacituzumab govitecan and datopotamab deruxtecan, have provided improvements in progression-free survival in both triple-negative and HR-positive/HER2-negative disease. Ongoing research is exploring additional targets, such as HER3, Nectin-4, B7-H4, and CD166, with several promising candidates showing efficacy in early phase trials. As ADCs move into earlier lines of therapy and combination regimens, understanding optimal sequencing, toxicity management, and cost considerations will be essential. This review summarizes the current ADC landscape in breast cancer and highlights future directions for this rapidly evolving therapeutic class.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".