Tumor-agnostic cancer therapy using antibodies targeting oncofetal chondroitin sulfate
Bibliographic record
Abstract
Molecular similarities between embryonic and malignant cells can be exploited to target tumors through specific signatures absent in healthy adult tissues. One such embryonic signature tumors express is oncofetal chondroitin sulfate (ofCS), which supports disease progression and dissemination in cancer. Here, we report the identification and characterization of phage display-derived antibody fragments recognizing two distinct ofCS epitopes. These antibody fragments show binding affinity to ofCS in the low nanomolar range across a broad selection of solid tumor types in vitro and in vivo with minimal binding to normal, inflamed, or benign tumor tissues. Anti-ofCS antibody drug conjugates and bispecific immune cell engagers based on these targeting moieties disrupt tumor progression in animal models of human and murine cancers. Thus, anti-ofCS antibody fragments hold promise for the development of broadly effective therapeutic and diagnostic applications targeting human malignancies. Cancer cells often acquire molecular patterns of fast-growing embryonic tissues to enable propagation and invasion, which distinguish tumour tissues from their healthy adult counterpart. Here authors develop antibody fragments which specifically target oncofetal chondroitin sulphate on the cancer cell surface and in the tumour stroma, with the antibodies achieving therapeutic effect in multiple mouse models in antibody drug conjugate and bispecific immune cell engager formats.
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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".