Synthetic Lethal Approaches to Metastatic Breast Cancer Therapy
Bibliographic record
Abstract
Background Breast cancer is the most commonly diagnosed cancer in women and each year approximately 1.7 million new cases are detected globally. Metastatic breast cancers are the most aggressive form of this disease, with an average survival rate of less than 2 years. We recently discovered a metastasis suppressor, CREB3L1 (cAMP responsive element binding protein 3‐like protein), which is frequently missing from metastatic breast cancer cells. CREB3L1 is expressed ubiquitously in non‐cancerous human breast cells and restricts expression of genes that promote cell growth, angiogenesis, and migration. Loss of CREB3L1 expression can result in enhanced metastatic properties and our analysis indicates that it is a frequent event in high‐grade metastatic human breast tumors, thus highlighting a potential target for cancer therapy. Synthetic lethality is a promising approach for the development of targeted cancer therapies. The principle of synthetic lethality involves identifying a cancer cell‐specific defect and a second target or drug that triggers death only when both factors exist in the same cell. The synthetic lethality approach can target any gene differentially expressed between normal, cancer and metastatic cells, thus making CREB3L1 an ideal candidate. Objective Our aim is to identify synthetic lethal genes that are essential for cell survival in metastatic breast cancer cells that lack CREB3L1, but that are not essential in non‐metastatic breast cancer cells or normal cells. Methods Matched stable cell lines were generated in 5 metastatic breast cancer cell lines lacking CREB3L1 expression. Each cell line was transfected with HA‐tagged CREB3L1. Characterization was performed on each of the parental and transfected cell lines, comparing the extent of cell migration and anchorage‐independent growth. We identified several potential candidate synthetic lethal genes by performing microarray screens and using published gene essentiality data. Western blots were performed to determine if the candidate synthetic lethal proteins are expressed in each of the selected cell lines. To determine whether the candidate genes are selectively lethal in CREB3L1‐deficient cells, the impact of sh RNA‐mediated knockdowns were tested in the 5 cell lines ± HA‐CREB3L1 expression, as well as in a non‐tumorigenic breast cell line. Cell survival was determined at 2 time‐points. Results Re‐expression of CREB3L1 in the CREB3L1 −/− metastatic breast cancer cell lines repressed tumorigenic and migratory properties. Western blots verified that several of the candidate synthetic lethal proteins are expressed in all or most of the selected cell lines. Knockdowns for each target were successfully generated in a non‐tumorigenic breast cell line, confirming that the test genes are not essential for survival in non‐cancerous breast cells. Preliminary results have identified at least one gene that when down regulated specifically kills CREB3L1‐deficient cells, without harming normal CREB3L1‐expressing cells. Conclusion Future studies will test the promising candidates in mouse xenograft experiments using inhibitors to the gene products. Identifying inhibitors for targets that selectively kill cancer cells that are CREB3L1‐deficient will provide novel treatments for metastatic breast cancers. Support or Funding Information Funding for the project was provided by the Canadian Institutes of Health Research and the Saskatchewan Health Research Foundation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".