Antibody-drug conjugates targeting TROP-2: Clinical development in metastatic breast cancer
Bibliographic record
Abstract
Antibody drug conjugates (ADCs) combine the potent cytotoxicity of chemotherapy with the antigen -specific targeted approach of antibodies into one single molecule. Trophoblast cell surface antigen 2 (TROP-2) is a transmembrane glycoprotein involved in calcium signal transduction and is expressed in multiple tumor types. TROP-2 expression is higher in HER2-negative breast tumors (HR+/HR-) and is associated with worse survival. Sacituzumab govitecan (SG) is a first-in-class TROP-2-directed ADC with an anti-TROP-2 antibody conjugated to SN-38, a topoisomerase inhibitor via a hydrolysable linker. This hydrolysable linker permits intracellular and extracellular release of the membrane permeable payload enabling the "bystander effect" contributing to the efficacy of this agent. There was significant improvement in progression free survival (PFS) and overall survival (OS) with SG versus chemotherapy in pretreated metastatic triple negative breast cancer (TNBC), resulting in regulatory approval. Common adverse events (AE) reported were neutropenia and diarrhea. SG also demonstrated clinical activity versus chemotherapy in a phase III trial of HR+/HER2-metastatic breast cancer (MBC) and is under evaluation in first-line metastatic and early stage TNBC as well. Datopotamab deruxtecan (Dato-DXd) is a TROP-2 ADC that differs from SG in that it has a cleavable tetrapeptide linker and a more potent topoisomerase inhibitor payload. This construct is highly stable in circulation with a longer half-life than SG, and undergoes cleavage in presence of intracellular lysosomal proteases. Dato-DXd demonstrated preliminary efficacy in unselected metastatic TNBC, with common AEs of low-grade nausea and stomatitis. Dato-DXd is being investigated in phase III studies in metastatic TNBC and HR+/HER2- MBC. These novel TROP-2 ADCs have the potential to deliver enhanced efficacy with reduced toxicity in MBC and possibly in early stage breast cancer (EBC).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".