Abstract 666: AB-3A4: A novel KAAG1-targeting antibody-drug conjugate is active in models of ovarian carcinoma, triple-negative breast cancer and castration-resistant prostate cancer
Bibliographic record
Abstract
Abstract Starting from total RNA prepared from late-stage serous ovarian tumors and low-malignancy potential tumors, a sensitive subtractive cloning technology called STAR was used to identify differentially expressed sequences that were up-regulated in the malignant tumors. This analysis led to the identification of KAAG1, an antigen that is specifically expressed in the vast majority of the ovarian tumors. Monoclonal antibodies (mAbs) that bind to human KAAG1 were generated and one of these, AB-3A4, interacted with KAAG1 expressed on the surface of several ovarian cancer, triple-negative breast cancer (TNBC) and prostate cancer cell lines. Further studies indicated that the AB-3A4/KAAG1 complex was rapidly internalized and co-localized with LAMP1, a marker of lysosomes, indicating that AB-3A4 has potential as an antibody-drug conjugate (ADC). Here we describe preclinical results of AB-3A4 conjugated to the cytotoxic agent monomethyl auristatin E (AB-3A4-vcMMAE). Binding studies with KAAG1-positive cell lines, such as the SKOV-3 (ovarian), MDA-MB-231 (TNBC) and PC-3 (prostate) showed that AB-3A4-vcMMAE interacted with its antigen in a similar manner to the unconjugated AB-3A4. In cytotoxicity assays, AB-3A4-vcMMAE killed cancer cell lines expressing KAAG1 with an IC50 value of 1 - 2 nM, whereas it displayed no specific potency in KAAG1-low or -negative cells. SKOV-3 cells were subcutaneously implanted in SCID mice and allowed to grow to a volume of 150 - 200 mm3 after which four injections of AB-3A4-vcMMAE at 5 mg/kg were administered over two weeks. Rapid and sustained tumor regression was observed for several weeks post-injection. In PC-3 xenografts, a single injection of AB-3A4-vcMMAE at 3 mg/kg was sufficient to cause significant tumor regression and this efficacy was found to be dose-dependent between doses of 1 and 10 mg/kg. In parallel treatment groups, no significant tumor shrinkage was observed with either a control IgG-vcMMAE or a mixture of unconjugated AB-3A4 and free MMAE at a drug-antibody ratio corresponding to that of AB-3A4-vcMMAE. Furthermore, xenografts of TNBC MDA-MB-231 cells were significantly regressed following a single injection of AB-3A4-vcMMAE with no recurrence after several weeks post-treatment. Finally, using an intraperitoneal orthotopic model of ovarian cancer, treatment with AB-3A4-vcMMAE significantly increased the overall survival of mice compared to mice treated with the control ADC. Collectively, our results demonstrate the potent efficacy of AB-3A4-vcMMAE in several disease models and underscore the potential of this novel agent in ovarian cancer, TNBC and castration-resistant prostate cancer, three areas for which very few targeted therapies exist. Citation Format: Gilles B. Tremblay, Anna Moraitis, Dominique Bédard, Adriana Orimoto, Martine Pagé, Anne-Marie Mes-Masson, Mario Filion. AB-3A4: A novel KAAG1-targeting antibody-drug conjugate is active in models of ovarian carcinoma, triple-negative breast cancer and castration-resistant prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 666. doi:10.1158/1538-7445.AM2014-666
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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) | 0.002 | 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".