Molecular Characteristics and Therapeutic Vulnerabilities of Claudin-low Breast Cancers Derived from Cell Line Models
Bibliographic record
Abstract
BACKGROUND/AIM: Breast cancers constitute heterogeneous tumor groups and their categorization in subtypes based on the expression of the estrogen (ER), progesterone (PR) and HER2 receptors has advanced therapeutics. Claudin-low breast cancer has been proposed as an additional subtype which is mostly ER, PR and HER2 negative, but its identification has not led to corresponding specific treatments yet. MATERIALS AND METHODS: Breast cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) were assessed for mRNA suppression of claudins and mRNA expression of ER and ERBB2 (the gene encoding HER2). The set of identified claudin-low cell lines were compared with representative ER-/ERBB2- cell lines for associated molecular alterations, gene dependencies through CRISPR and microRNA arrays and in vitro drug sensitivities using the Genomics of Drug Sensitivity in Cancer (GDSC) project. RESULTS: Claudin-low cell lines display up-regulation of mRNA expression of epithelial to mesenchymal transition (EMT) regulators. Methylation sensitive genes are down-regulated in claudin-low lines compared with other cell lines, without associated up-regulation of DNA methyltransferases. Dependency screen microarrays reveal dependencies of claudin-low cell lines on components of the cytoskeleton but no consistent dependencies in known oncogenes or tumor suppressors. Potential drug sensitivities revealed in the drug screens included sensitivities to WNT pathway modulators, tyrosine kinase cascade inhibitors and BET inhibitors. On the other hand, claudin-low cell lines showed resistance to deacetylase inhibitors. CONCLUSION: Claudin-low cell line models duplicate features of claudin-low breast cancers and may serve as guides for identification of drugs worth exploring for further development.
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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 | 0.001 |
| 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".