Abstract 3116: Designing a model system for the study of CD44-Ezrin interactions in breast cancer progression and drug resistance
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) has the highest risk of relapse and is thought to be the most aggressive subtype of BC. This subtype is known to express high levels of the cancer stem cell (CSC) marker CD44. The tumor-initiating properties of CSC's, as well as their resistance to chemotherapy, contribute to the difficulty in successfully treating TNBCs.CD44 is a transmembrane glycoprotein that mediates communication between cells and the extracellular matrix. In doing so, it acts as a central node in several cancer-related signaling pathways through interactions with binding partners such as Ezrin to promote tumor growth, invasion and survival. Furthermore, expression of CD44 is associated clinically with positive lymph node status, recurrence and poor overall survival in BC. We aimed to validate CD44's role in these cancer promoting properties and develop a genetically tractable and engraftable TNBC cell model to study CD44 in the context of interactions with the Ezrin adaptor protein. We first hypothesize that disrupting CD44 expression will attenuate the metastatic and drug resistant potential of TNBC cells. To address this, we utilized CRISPR-Cas9 gene editing to knockout (KO) CD44 expression in the MDA-MB-231 human TNBC cell line and then subjected CD44 KO cells, KO cells rescued with wild type CD44, or the parental MDA-MB-231 cells to a variety of in vitro assays to assess proliferative, migratory and drug resistant potential, as well as mouse lung metastatic seeding in vivo. We show that the transmembrane glycoprotein CD44 plays an important role in cancer cell proliferation, migration, and chemotherapeutic resistance in vitro as well as having an essential role in metastatic seeding in vivo in the MDA-MB-231 cell model of TNBC. Our results validate the utility of this CRISPR-Cas9 KO and rescue TNBC cell model for the study of CD44 involvement in drug resistance and metastasis. More importantly we have set the stage for further investigating CD44's role in the context of interactions with the Ezrin adaptor protein. Citation Format: Rayanna Jane Birtch, Peter Greer, Yan Gao, Victoria Hoskin. Designing a model system for the study of CD44-Ezrin interactions in breast cancer progression and drug resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3116.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".