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Record W2587349694 · doi:10.18192/uojm.v7i1.1563

Triple Negative Breast Cancer: A review of common therapeutic Targets and Current Treatment options.

2017· review· en· W2587349694 on OpenAlexaffvenue
Daud Akhtar, Ahsen Chaudhry

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineTriple-negative breast cancerCarboplatinBreast cancerOncologyInternal medicineCancerChemotherapyClinical trialBevacizumabAdjuvantCancer researchCisplatin

Abstract

fetched live from OpenAlex

Triple negative breast cancer (TNBC) is a subtype of breast cancer which lacks ER, PR, and HER2 expression. It is characterized by poor prognosis and resistance to standard treatment forms for breast cancer. Chemotherapy is still currently the core neo-adjuvant treatment option for patients with TNBC, although it has mixed levels of efficacy on overall survival and many serious side effects. Platinum- based therapies have been used to treat TNBC in conjunction with chemotherapy, but they are not a widely effective treatment due to the heterogeneity of TNBC. For this reason, other novel approaches, particularly those which target molecular components involved in TNBC pathogenesis, are being investigated. Angiogenesis inhibitors, which include monoclonal antibodies or small molecules that inhibit VEGF, have been shown to improve progression-free survival, but have not demonstrated an impact on overall survival. PARP enzyme inhibitors, when combined with chemotherapy and carboplatin for the treatment of TNBC, have demonstrated a significant reduction in risk progression and mortality. However, the majority of PARP inhibitors are still in trials and their effectiveness in clini- cal settings has yet to be determined. Additional proposed targets for directed therapy against TNBC include cell signalling pathways involving EGFR or PI3K. Overall, issues such as treatment resistance and side effects are important challenges that must be overcome in order to enable improvements in patient prognosis and clinical impact. RÉSUMÉ Le cancer du sein triple négatif (CSTN) est un sous-type de cancer du sein auquel il manque les récepteurs d’œstrogènes (ER), les récepteurs de progestérone (PR) et l’expression de HER2. Il est caractérisé par un pronostic défavorable et une résistance aux traite- ments standards du cancer du sein. À l’heure actuelle, la chimiothérapie est encore l’option principale de traitement néoadjuvant pour les patients ayant le CSTN, bien qu’elle ait des niveaux variés d’efficacité sur la survie globale, ainsi que de nombreux effets secondaires sérieux. Les thérapies à base de platine ont été utilisées pour traiter le CSTN en conjonction avec la chimiothérapie, mais elles ne sont pas très efficaces étant donné l’hétérogénéité du CSTN. En raison de cela, d’autres approches novatrices, particulièrement celles qui ciblent les composantes moléculaires impliquées dans la pathogenèse du CSTN, font actuellement l’objet d’enquêtes. Les inhibiteurs de l’angiogenèse, dont les anticorps monoclonaux ou les petites molécules inhibant le VEGF, ont démontré la capacité d’améliorer la survie sans progression de la maladie, mais n’ont pas démontré d’impact sur la survie globale. Les inhibiteurs d’enzymes PARP, lorsque combinés avec la chimiothérapie et le carboplatine pour le traitement du CSTN, ont démontré une réduction significative du risque de progression et de la mortalité. Toutefois, la majorité des inhibiteurs PARP subissent encore des essais et leur efficacité clinique reste à être déterminée. D’autres cibles suggérées pour la thérapie dirigée contre le CSTN incluent les voies de signalisation impliquant le EGFR ou le PI3K. Dans l’ensemble, des problèmes tels la résistance au traitement et les effets secondaires sont des défis importants qui doivent être surmontés afin de permettre des améliorations au niveau du pronostic du patient et de l’impact clinique.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.389
Teacher spread0.310 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2017
Admission routes2
Has abstractyes

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