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Record W1561900970 · doi:10.5772/22977

Tamoxifen Resistant Breast Cancer and Autophagy

2011· book-chapter· en· W1561900970 on OpenAlexaff
Arthur A. Grey, Nabil R. Adam, Sung Woo Kim

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTamoxifenBreast cancerMedicineEstrogenInternal medicineOncologyEstrogen receptorEndocrine systemAntiestrogenCancerEndocrinologyHormone

Abstract

fetched live from OpenAlex

Introduction1.1 Tamoxifen for the treatment of breast cancer Tamoxifen was originally developed with the hope of becoming a "morning after" contraceptive.During the 1960s, studies showed that tamoxifen and other anti-estrogens have a profound impact on the fertility of laboratory rats and it was believed that tamoxifen could elicit the same effects in humans.Coincidently, in humans, anti-estrogens were found to improve fertility by inducing ovulation (Jordan, 2006).The use of tamoxifen for the treatment of breast cancer became apparent when early in vitro and in vivo studies found that tamoxifen inhibited estradiol (E2) binding to estrogen receptors (ERs) in breast tissue (Jordan, 1976;Jordan & Dowse, 1976;Jordan & Koerner, 1975;Nicholson & Golder, 1975).The inhibition of ERs proved to be a significant finding as the estrogen-stimulated growth and the ovarian dependence of some breast cancers had been known since the later part of the eighteenth century (Beatson, 1896).Prior to the 1970s, and the successful development of drug-based endocrine therapies, attempts to reduce estrogen-stimulated growth of breast cancer include the surgical removal of the ovaries and/or pituitary and adrenal glands (Kennedy, 1965).Unfortunately, for years it was not known which breast tumors would respond favorably to ablative surgery, where only 30% of patients would receive a benefit (Jensen et al., 1971;Kennedy, 1965).By examining proteins from breast tumor biopsies, Jensen et al. (1971) showed that if the estrogen receptor is present, tumors will respond to ablative surgical treatment, but if absent, the tumors fail to respond.Fortunately today, endocrine-based surgical procedures for breast cancer are not necessary for most women as drug-based endocrine therapies have advanced with the use of estrogen antagonists and aromatase inhibitors.Estrogen antagonists, such as tamoxifen or fulvestrant competitively inhibit estrogen binding to ERs.Aromatase inhibitors prevent estrogen production, by inhibiting the aromatase class of enzymes, impeding the conversion of androgens to estrogens and thus reducing the bioavailability of estrogen hormones (Jordan, 1994;Mokbel, 2002).Currently tamoxifen is typically used as an adjuvant treatment option for early and advanced ER-positive (ER + ) breast cancer in pre-and post-menopausal women (Jordan, 1994).Adjuvant treatment with tamoxifen, has significantly improved disease-free survival and reduced the number of deaths from breast cancer (Early Breast Cancer Trialists' Collaborative, 2005).Tamoxifen may also be used in the neoadjuvant setting and as a www.intechopen.com How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following:

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2011
Admission routes1
Has abstractyes

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