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Record W7162000096 · doi:10.82308/23817

Characterizing the role of ER-alpha Y537S in metastatic breast cancer

2021· dissertation· en· W7162000096 on OpenAlexaboutno aff
Joshua Roccamo

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsnot available
Fundersnot available
KeywordsMetastatic breast cancerMutationBreast cancerMissense mutationMutantEstrogen receptorCancerEstrogen receptor alpha

Abstract

fetched live from OpenAlex

Among Canadian women, breast cancer is the second most prevalent and second most lethal cancer. Frequently implicated in this type of cancer are hormone-related pathways, including estrogen-stimulated pathways. The effects of estrogens are mediated mainly through two nuclear receptors. One receptor, called estrogen receptor ⍺ (ER⍺), has been implicated in breast cancer and is expressed in about 70% of cases. These ER+ breast cancers are often treated with endocrine therapy; however, many patients relapse on this therapy. A possible mechanism driving this resistance is missense mutation of ER⍺ in its ligand-binding domain (LBD). Within the LBD, the most commonly mutated residues are Y537 and D538. Mutation at these residues, which is rare in primary tumours but often detectable in metastases, results in the ligand-independent activation of ER⍺. Several different mutations have been found at the Y537 residue, including Y537S. Although the Y537S mutation has been widely reported in endocrine therapy-resistant metastatic breast cancer, this mutation remains poorly characterized. The goal of this project was to characterize the ERa Y541S mutation, especially as it relates to metastasis. We hypothesized that the Y537S mutation would be associated with increased metastasis and worse overall survival in the MIC mouse model. Previously, the MIC model was modified to introduce the conditional ER⍺ Y541S mutant receptor, the murine homolog of the human ERa Y537S mutant. Using these mice with the conditional ER⍺ Y541S mutation, it was shown that the mutant allele did not affect overall survival or metastatic burden. Following this, RNASeq showed a greater expression of estrogen-response genes in ER⍺ Y541S tumours than in wild-type ERa tumours; however, immunohistochemical staining showed no difference in subcellular localization of ER⍺ between the control and ER⍺ Y514S tumours, suggesting that the increased expression of estrogen-response genes in ER⍺ Y541S tumors was not due to increased nuclear localization. Immunohistochemical staining also showed that ER⍺ Y541S tumours expressed more cytokeratin 14, a basal cytokeratin associated with a poorer prognosis. Other cytokeratins, including luminal cytokeratins, were not shown to be differentially expressed between the ER⍺-mutant and ER⍺-wild-type tumours. Motivated by the association between ERa Y537S and metastasis, an invasion assay was performed and showed greater invasiveness in an ER⍺ Y541S cell line than in a control cell line. In contrast, both cell lines showed a similar ability to migrate. Overall, this project supports previous observations that the ERa Y537S confers advantageous traits to metastases but not to primary tumours. By recapitulating these features of human metastatic breast cancer, the MIC model used here appears to be an appropriate model for studying the ERa Y537S mutation. In this way, this paper contributes to an improved understanding of ERa Y537S. In future, efforts to further understand this mutation could lead to the development of new therapeutic strategies to combat endocrine therapy-resistant metastatic breast cancer

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.251
Teacher spread0.247 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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