RNAi Screening in Primary Human Breast Biopsies Identifies Mitochondrial Glutamate Flux as A Metabolic Vulnerability in Locally Advanced Breast Cancer
Bibliographic record
Abstract
Abstract Background: Breast cancer (BC) is a leading cause of death in women[1]. Women with Locally Advanced Breast Cancer (LABC) have high risk disease with either large primary breast tumours and/or lymph node involvement. While neoadjuvant chemotherapy eradicates breast cancer in approximately one-third of cases prior to surgery, almost 70% of patients have residual disease and many will require additional chemotherapy post-operatively. Improving pre-operative efficacy of neoadjuvant systemic treatments while reducing their iatrogenicities are critical unmet needs. Methods: Here, we develop an RNA interference (RNAi) screening approach using conditionally reprogrammed primary LABC biopsies to identify genes of the mitochondrial Solute Ligand Carrier 25 (SLC25) family that support LABC cell viability. Results: We report that silencing SLC25A12, -A15, and -A18 genes, involved in glutamate and ornithine flux, augment 5-fluorouracil (5FU) cytotoxic effectiveness in LABC cells. Conclusions: Our data suggest glutamate metabolism may be a tumour-specific metabolic vulnerability in LABC. Furthermore, we demonstrate that RNAi screening in conditionally reprogrammed primary human breast cells can identify novel targets for the development of non-genotoxic BC treatments.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".