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Record W3006656146 · doi:10.1186/s13058-020-1252-7

HER2 heterogeneity and resistance to anti-HER2 antibody-drug conjugates

2020· review· en· W3006656146 on OpenAlexaff
Alberto Ocaña, Eitan Amir, Atanasio Pandiella

Bibliographic record

VenueBreast Cancer Research · 2020
Typereview
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersInstituto de Salud Carlos IIICRIS Cancer Foundation
KeywordsTrastuzumabTrastuzumab emtansineAntibody-drug conjugateMedicineBreast cancerSurgical oncologyDrug resistanceDrug developmentDrugCancerComputational biologyOncologyPharmacologyInternal medicineAntibodyMonoclonal antibodyImmunologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: There has been substantial interest in HER2 intratumoral heterogeneity as an explanation for the development of resistance to anti-HER2 therapies in breast cancer, particularly to trastuzumab emtansine (T-DM1). METHODS: Through a literature-based approach, we discuss mechanisms of resistance to HER2-targeting antibody-drug conjugates (ADCs) in breast cancer. RESULTS: We describe results from clinical studies reporting the effect of anti-HER2 strategies particularly ADCs and their mechanistic effect. We review biological findings underlying HER2 heterogeneity and its implication in the development of novel anti-HER2 drugs including new ADCs in clinical development like trastuzumab deruxtecan (DS-8201). CONCLUSIONS: We suggest potential mechanisms to optimize these compounds and their future clinical implementation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.141
GPT teacher head0.509
Teacher spread0.369 · 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

Citations96
Published2020
Admission routes1
Has abstractyes

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