Therapeutic monoclonal antibodies target phenotypically-differing human breast cancer
Bibliographic record
Abstract
13510 Background: CD44 (an adhesion molecule and stem cell antigen), CD59 (a complement-inhibitory molecule), MCSP (an adhesion and cell-cell interactions), and Trop-2 (EpCam a related signaling molecule) represent a group of biologically-significant cancer proteins acting through distinct mechanisms. We have described Abs with in vitro and in vivo cancer suppressive activity to this group of targets. However, their effectiveness depends on the phenotype of malignant cells; cell response should correlate with expression of its Ag, and tumor cells represent a heterogeneous group of non-synchronous cells. The present study describes the efficacy of those antibodies in breast cancer models and the prevalence of their antigen targets in a survey of human breast cancer tissues. Methods: In vivo activity of antibodies ARH460–16–2 (anti-CD44), AR36A36.11.1 (anti-CD59), AR11BD-2E11–2 (anti-MCSP), and AR47A6.4.2 (anti-Trop-2) in estrogen-dependent and hormone sensitive xenograft models of human breast cancer was examined. In addition, distribution of the antigens in breast cancer was determined by immunohistochemistry using tumor tissue arrays of breast cancer sections from distinct patients. Results: Treatment of an established breast cancer model with ARH460–16–2 resulted in 51% median tumor xenograft suppression (p<0.05), as well as increased survival in an MDA-MB-231 (breast cancer) grafted model. 63% of human breast cancer sections expressed the CD44 antigen. Treatment with anti-CD59 antibody AR36A36.11.1 resulted in 68% xenograft tumor suppression (p<0.005). AR47A6.4.2 anti-Trop-2 antibody bound to 100% of human breast cancer sections tested, and showed efficacy in the estrogen- dependent MCF-7 breast cancer model. Anti-MCSP antibody AR11BD-2E11–2 demonstrated 80% tumor growth inhibition (p<0.001), increased survival in an estrogen-dependent model of breast cancer, and was found to stain 62% of breast cancer tissues examined. Conclusions: The heterogeneity of breast cancer cell phenotypes in in vitro and in vivo studies and variable composite cellular antigen targets is the basis for the therapeutic use of multiple antibodies, each with independent mechanisms of action, and offers a rationale for combined antibody therapy in selected patients. [Table: see text]
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".