A B7-H4–Targeting Antibody–Drug Conjugate Shows Antitumor Activity in PARPi and Platinum-Resistant Cancers with B7-H4 Expression
Bibliographic record
Abstract
PURPOSE: Platinum and PARP inhibitors (PARPi) demonstrate activity in breast and ovarian cancers, but drug resistance ultimately emerges. Here, we examine B7-H4 expression in primary and recurrent high-grade serous ovarian carcinoma (HGSOC) and the activity of a B7-H4-directed antibody-drug conjugate (B7-H4-ADC), using a pyrrolobenzodiazepine-dimer payload, in PARPi- and platinum-resistant HGSOC patient-derived xenograft (PDX) models. EXPERIMENTAL DESIGN: B7-H4 expression was quantified by flow cytometry and IHC. B7-H4-ADC efficacy was tested against multiple cell lines in vitro and PDX in vivo. The effect of B7-H4-ADC on cell cycle, DNA damage, and apoptosis was measured using flow cytometry. RESULTS: B7-H4 is overexpressed in 92% of HGSOC tumors at diagnosis (n = 12), persisted in recurrent matched samples after platinum treatment, and was expressed at similar levels across metastatic sites after acquired multi-drug resistance (n = 4). Treatment with B7-H4-ADC resulted in target-specific growth inhibition of multiple ovarian and breast cancer cell lines. In platinum- or PARPi-resistant ovarian cancer cells, B7-H4-ADC significantly decreased viability and colony formation while increasing cell-cycle arrest and DNA damage, ultimately leading to apoptosis. Single-dose B7-H4-ADC led to tumor regression in 65.5% of breast and ovarian PDX models (n = 29), with reduced activity in B7-H4 low or negative models. In PARPi and platinum-resistant HGSOC PDX models, scheduled B7-H4-ADC dosing led to sustained tumor regression and increased survival. CONCLUSIONS: These data support B7-H4 as an attractive ADC target for treatment of drug-resistant HGSOC and provide evidence for activity of an ADC with a DNA-damaging payload in this population. See related commentary by Veneziani et al., p. 1434.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".