Abstract IA13: Inhibiting the Wnt pathway with selective anti-Frizzled synthetic antibodies
Bibliographic record
Abstract
Abstract Secreted Wnt glycoproteins regulate intracellular signaling pathways important for embryonic development and adult tissue homeostasis. Misregulation of Wnt signalling is frequent in human cancers. Wnt ligands are recognized at the surface of cells by various receptor complexes that are differentially expressed and determine context-dependent cellular responses. In humans, 16 Wnt ligands interact directly with 10 Frizzled seven transmembrane proteins through their extracellular cysteine rich domain (CRD). Wnts also interact with various co-receptors, such as LRP5/6, ROR1/2, RYK, PTK7, which engage different downstream signaling pathways. While genetic analysis of mouse knockouts has revealed specific roles for different Wnt receptors and co-receptors during embryonic development, relatively little is known about their respective functions during adult tissue homeostasis or in the initiation and progression of human diseases such as cancer. There is a paucity of reagents allowing the selective and temporal pharmacological inhibition of Wnt receptors. We are employing phage-display technology to identify high affinity synthetic blocking monoclonal antibodies (mAbs) against all human Wnt receptors. To date we have isolated a collection of anti-receptor antibodies exhibiting unique selectivity and functionality profiles. Our lead anti-Frizzled mAbs targeting a subset of Frizzled proteins inhibit Wnt-promoted activation of the TopFlash reporter with low nanomolar potency. These mAbs also exhibit dose dependent anti-proliferative efficacies against various pancreatic cancer cell lines grown in vitro and as xenografts in mice. These reagents will be useful to probe the function of specific Wnt receptors in tissue homeostasis and human diseases and will lead to novel therapeutic agents for their treatment. Our lab is also employing whole genome CRISPR screening approach to identify genotype specific vulnerabilities in cancer cells. These efforts have identified specific Frizzled important for cancer cells proliferation and is guiding our antibody development program. Citation Format: Zachary Steinhart, Traver Hart, Sachdev Sidhu, Jason Moffat, Stephane Angers. Inhibiting the Wnt pathway with selective anti-Frizzled synthetic antibodies. [abstract]. In: Proceedings of the AACR Special Conference: Developmental Biology and Cancer; Nov 30-Dec 3, 2015; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(4_Suppl):Abstract nr IA13.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".