Combined in vitro/in vivo genome-wide CRISPR screens in triple negative breast cancer identify cancer stemness regulators in paclitaxel resistance
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) is defined as lacking the expressions of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC patients exhibit relatively poor clinical outcomes due to lack of molecular markers for targeted therapies. As such chemotherapy often remains the only systemic treatment option for these patients. While chemotherapy can initially help shrink TNBC tumor size, patients eventually develop resistance to drug, leading to tumor recurrence. We report a combined in vitro/in vivo genome-wide CRISPR synthetic lethality screening approach in a relevant TNBC cell line model to identify several targets responsible for the chemotherapy drug, paclitaxel resistance. Computational analysis integrating in vitro and in vivo data identified a set of genes, for which specific loss-of-function deletion enhanced paclitaxel resistance in TNBC. We found that several of these genes (ATP8B3, FOXR2, FRG2, HIST1H4A) act as cancer stemness negative regulators. Finally, using in vivo orthotopic transplantation TNBC models we showed that FRG2 gene deletion reduced paclitaxel efficacy and promoted tumor metastasis, while increasing FRG2 expression by means of CRISPR activation efficiently sensitized TNBC tumors to paclitaxel treatment and inhibited their metastatic abilities. In summary, the combined in vitro/in vivo genome-wide CRISPR screening approach proved effective as a tool to identify novel regulators of paclitaxel resistance/sensitivity and highlight the FRG2 gene as a potential therapeutical target overcoming paclitaxel resistance in TNBC.
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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 | 0.001 |
| 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".