Antibody–Drug Conjugates in Breast Cancer: Ascent to Destiny and Beyond—A 2023 Review
Bibliographic record
Abstract
Antibody-drug conjugates (ADCs) are revolutionizing cancer treatment, adding another important new class of systemic therapy. ADCs are a specially designed class of therapeutics that target cells expressing specific cancer antigens using directed antibody-drug delivery and release a cytotoxic chemotherapeutic payload. Over the past two decades, improvements in ADC design, development, and research, particularly in breast cancer, have led to several recent landmark publications. These advances have significantly changed various treatment paradigms and revamped traditional classifications of breast cancer with the introduction of a potential new subtype: "HER2-low". This review will focus on several ADCs developed for breast cancer treatment, including trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), sacituzumab govitecan (SG) and other newer emerging agents. It will provide an overview of the role of ADCs in breast cancer and discuss the opportunities and challenges they present. Additionally, our review will discuss future research directions to improve the selection of targets, combination therapies, and aim to improve drug safety. Important first-line metastatic and adjuvant clinical trials are underway, which may expand the role of ADC therapy in breast cancer. We foresee ADCs driving a new era of breast cancer treatment, adding to the steady incremental survival advantage observed in recent years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".