Abstract A016: Targeting calpain-1 and calpain-2 for prevention of breast cancer metastasis: In vivo insights and drug discovery approaches
Bibliographic record
Abstract
Abstract Calpain-1 and calpain-2 are heterodimeric calcium-dependent cysteine proteases composed of the catalytic subunits CAPN1 or CAPN2, respectively, and the common regulatory subunit CAPNS1. They are associated with cancer progression, metastasis, and treatment resistance in breast cancer. Here, we present novel insights into the therapeutic potential of calpain inhibition by combining genetic disruption and preclinical mouse tumor models and biosensor-based detection of calpain heterodimerization with ongoing efforts to discover small-molecule and peptide inhibitors. CRISPR-Cas9-mediated knockout of CAPN1, CAPN2, or CAPNS1 in MDA-MB-231 human triple-negative breast cancer (TNBC) cells revealed that disruption of both calpains, through CAPNS1 knockout, significantly reduced cell migration and inhibited spontaneous metastasis in a mouse orthotopic engraftment model by over 80%. Individual knockouts of CAPN1 or CAPN2 also reduced metastasis but to a lesser extent, highlighting the necessity of dual inhibition for optimal therapeutic effect. In parallel, we have been actively seeking small molecules and peptide inhibitors to target calpain through two approaches: inhibiting the PEF-PEF interaction that mediates heterodimerization using small molecules; and targeting the active site using a calpastatin (CAST)-based peptide. To identify small molecules capable of disrupting the PEF-PEF interaction, we conducted in silico screens of over 3.6 million compounds from several libraries. These compounds were evaluated based on their calculated binding affinity and potential to sterically hinder the conformational changes required for calpain activity. The CAST-based peptide inhibitor was designed based on the active site binding B-domain and tested on a purified calpain-2. These efforts lay the groundwork for novel therapeutic approaches targeting calpain-1 and calpain-2, which have emerged as promising targets for preventing metastasis in TNBC. We will present our progress in identifying and validating these small molecules and peptides for further development. Citation Format: Ivan Shapovalov, Pitambar Poudel, Shailesh K. Panday, Danielle Harper, Jung Yeon Min, Yan Gao, Kazem Nouri, Emil Alexov, Peter A. Greer. Targeting calpain-1 and calpain-2 for prevention of breast cancer metastasis: In vivo insights and drug discovery approaches. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A016
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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 | 0.000 | 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) | 0.000 | 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".