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Enregistrement W4322772140 · doi:10.1158/1538-7445.sabcs22-her2-15

Abstract HER2-15: HER2-15 Retrospective Study to Estimate the Prevalence and Describe the Clinicopathological Characteristics, Treatment Patterns, and Outcomes of HER2-Low Breast Cancer

2023· article· en· W4322772140 sur OpenAlexaff
Giuseppe Viale, Mark Basik, Naoki Niikura, Eriko Tokunaga, Sara Y. Brucker, Frédérique Penault‐Llorca, Naoki Hayashi, Joohyuk Sohn, Rita Sousa, Adam Brufsky, Ciara O’Brien, Fernando Schmitt, Gavin C. Higgins, Della Varghese, Gareth D. James, Akira Moh, Andrew Livingston, Victoria de Giorgio‐Miller

Notice bibliographique

RevueCancer Research · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueAdvanced Breast Cancer Therapies
Établissements canadiensJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineImmunohistochemistryConcordanceInternal medicineBreast cancerTrastuzumabOncologyMetastatic breast cancerCancer

Résumé

récupéré en direct d'OpenAlex

Abstract Background: About 60% of breast cancers (BCs) traditionally categorized as HER2 negative (HER2-neg; immunohistochemistry [IHC] 0, IHC 1+ or IHC 2+/in situ hybridization [ISH]–) express low levels of HER2 (HER2-low; IHC 1+ or IHC 2+/ISH–; Schettini, NPJ Breast Cancer 2021). In the phase 3 DESTINY-Breast04 trial (NCT03734029), trastuzumab deruxtecan (T-DXd) showed significantly longer progression-free survival and overall survival (OS) vs physician’s choice of chemotherapy in patients (pts) with HER2-low metastatic BC (mBC) who previously received chemotherapy (Modi, NEJM 2022). As HER2-low becomes a clinically relevant HER2 status among pts with BC, greater understanding of pts with HER2-low disease is needed, including identification of these pts using conventional IHC assays. Our objectives were to assess the prevalence of HER2-low among HER2-neg mBC based on rescored HER2 IHC slides, to describe characteristics of pts with HER2-low mBC, and to characterize concordance between historical HER2 scores and rescores. Methods: This global, multicenter, retrospective study (NCT04807595) included pts with confirmed HER2-neg (HER2 IHC 0, 1+, or 2+/ISH−) unresectable/mBC diagnosed from 2014 through 2017. HER2 IHC-stained slides were rescored after training on low-end expression scoring using Ventana 4B5 and other assays by local laboratories at 13 sites in 10 countries blinded to historical HER2 scores. BCs were categorized as HER2-low (IHC 1+ or IHC 2+/ISH−) or HER2 IHC 0 (IHC 0 or >0< 1+). Prevalence of HER2-low and concordance between historical HER2 scores and rescores were assessed. Demographics, clinicopathological characteristics, treatment patterns, and outcomes were examined via data from medical charts/health records. Results: HER2 rescores were obtained for 781 pts with HER2-neg mBC. HER2-low prevalence was 67.1% overall; 71.1% in hormone receptor (HR)–positive (HR+) and 52.5% in HR–negative (HR−) subgroups. There were no notable differences in characteristics (Table) or treatment patterns between pts with HER2-low and HER2 IHC 0. The most frequent therapies used in the first treatment in the metastatic setting were endocrine therapy (64.1%) for pts with HR+ mBC and chemotherapy (94.4%) for pts with HR− mBC. Among pts with HR+ mBC, 10.2% received cyclin-dependent kinase 4/6 inhibitors as part of their first treatment. There were no statistically significant differences in clinical outcomes between the HER2-low and HER2 IHC 0 groups within each HR subgroup. For pts with HR+ mBC, median time to first subsequent treatment was 10 and 8 months for the HER2-low and HER2 IHC 0 groups, respectively. Overall, concordance was 81.2% (kappa=0.582). Concordance between historical HER2 scores and rescores was 87.3% for HER2-low and 70.1% for HER2 IHC 0 samples. Conclusions: The prevalence of HER2-low (67.1%) among pts previously categorized as HER2-neg mBC in this study was similar to that of an earlier study (≈60%). No obvious differences in patient characteristics or clinical presentation were seen between pts with HER2-low and HER2 IHC 0 mBC. Overall percentage agreement between rescored and historical HER2 scores was 81.2%; agreement was numerically greater for HER2-low than HER2 IHC 0. As HER2-targeted therapies such as T-DXd for the treatment of pts with HER2-low BC are emerging, a greater understanding of pts with HER2-low expression who may benefit from these therapies is important. Citation Format: Giuseppe Viale, Mark Basik, Naoki Niikura, Eriko Tokunaga, Sara Brucker, Frédérique Penault-Llorca, Naoki Hayashi, Joo Hyuk Sohn, Rita Teixeira de Sousa, Adam M. Brufsky, Ciara S. O’Brien, Fernando Schmitt, Gavin Higgins, Della Varghese, Gareth D. James, Akira Moh, Andrew Livingston, Victoria de Giorgio-Miller. HER2-15 Retrospective Study to Estimate the Prevalence and Describe the Clinicopathological Characteristics, Treatment Patterns, and Outcomes of HER2-Low Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr HER2-15.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,046
Score d'incertitude au seuil0,694

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,094
Tête enseignante GPT0,473
Écart entre enseignants0,379 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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