Antibody-Drug Conjugates to Promote Immune Surveillance: Lessons Learned from Breast Cancer
Bibliographic record
Abstract
Antibody-drug conjugates (ADCs) represent an effective class of agents for the treatment of several tumor types, including breast cancer (BC), featuring approved molecules such as trastuzumab-emtansine, trastuzumab-deruxtecan, and sacituzumab-govitecan. Immune-checkpoint inhibitors (ICIs) also showed activity in selected BC subtypes, and two agents, pembrolizumab and atezolizumab, are currently approved for the treatment of triple-negative BC patients. The potential synergy between ADCs and immunotherapy in BC remains an area of active investigation. Preclinical studies suggest that ADCs promote immune surveillance, modulating tumor microenvironment, inducing immunogenic cell death, and enhancing antitumor immunity. Translational evidence has shown potential predictive biomarkers for ADCs alone or in combination with immunotherapy, including expression of target antigen, oncogenic pathways, tumor-infiltrating lymphocytes, and neutrophil-to-lymphocyte ratio. Given this background, several clinical trials evaluated ADC-ICI combinations in BC patients, demonstrating promising outcomes with an overall manageable toxicity profile, and many studies are currently ongoing to confirm the efficacy and feasibility of this therapeutic approach. In the present review, we summarized the available evidence about the integration of ADCs and immunotherapy for the management of BC, emphasizing the need for further translational and clinical investigations to optimize this treatment strategy and elucidate predictive biomarkers, eventually improving patient outcomes.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".