Abstract 2210 To Die or Not to Die: Controlling Death in Breast and Ovarian Cancer Cells with a Novel Pathway Regulating Anticancer Drug
Bibliographic record
Abstract
Using both orthotopic mouse and patient-derived xenograft models, our non-competitive anticancer drug ErSO induces complete or near-complete regression of advanced ERα+ breast and ovarian cancers. Unlike most cancer drugs, ErSO kills cells via necrosis pathway, a type of cell death that releases Damage Associated Molecular Pattern (DAMPs) from lysed cells and subsequently activates an innate immune response. This ErSO-induced pathway can eradicate deadly metastatic cancers without the resistance seen in modern-day endocrine therapy such as Tamoxifen and Fulvestrant. Mechanistically, ErSO works through ERα to instigate lethal hyperactivation of the anticipatory Unfolded Protein Response (a-UPR), resulting in protein synthesis inhibition, ATP depletion, and cell swelling, followed by membrane rupture. However, the specifics of ErSO-induced cell death were elusive. From a genome-wide CRISPR/Cas9 screening with negative selection against ErSO, the little-studied guanine exchange factor (GEF) FGD3 was identified as a top target. We show that FGD3 knockout protects breast cancer cells from ErSO-induced cell death, which increases the chance of recurrence after removing the treatment. Interestingly, instead of regulating upstream signals in the necrosis pathway, FGD3 regulates actin reorganization to mediate membrane rupture. This indicates that such cytoskeleton regulation plays a critical role in life-death situations, and it becomes relevant to understanding drug sensitivity in relation to FGD3 protein levels in the future when cancer patients are treated with ErSO or its derivatives. This research was supported and funded by the School of Molecular and Cellular Biology Summer Undergraduate Research Fellowship at the University of Illinois at Urbana-Champaign.
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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) | 0.001 | 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".