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Synthetic Lethal Approaches to Metastatic Breast Cancer Therapy

2016· article· en· W4389008082 on OpenAlexafffundabout
Shari Smith, Paul Mellor, Deborah H. Anderson

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchSaskatchewan Health Research Foundation
KeywordsBreast cancerMetastatic breast cancerMedicineCancer researchMetastasisCancerCancer cellAngiogenesisCellCA15-3OncologyInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.052
GPT teacher head0.254
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes3
Has abstractyes

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