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Enregistrement W4220673086 · doi:10.1158/1538-7445.sabcs21-p5-13-25

Abstract P5-13-25: <i>PIK3CA</i> registry: A noninterventional, descriptive, retrospective cohort study of <i>PIK3CA</i> mutations in patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC)

2022· article· en· W4220673086 sur OpenAlexaff
Pathmanathan Rajadurai, Т. Yu. Semiglazova, Alinta Hegmane, Fadi El Karak, Joanne Chiu, Sudeep Gupta, Hamdy A. Azim, Josef JEM Kitzen, Antoine Arnaud, Sina Haftchenary, Jiwen Wu, Lakshmi Menon-Singh, LaTonya Smith, Lyudmila Zhukova

Notice bibliographique

RevueCancer Research · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueAdvanced Breast Cancer Therapies
Établissements canadiensNovartis (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineFulvestrantInternal medicineOncologyPopulationMetastatic breast cancerCohortClinical endpointClinical trialBreast cancerCancerGynecologyEstrogen receptor

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: PIK3CA mutations (mut) occur in ~40% of patients (pts) with HR+, HER2- ABC, and lead to phosphatidylinositol 3-kinase (PI3K) pathway hyperactivation, endocrine resistance, and poor survival in advanced disease. Alpelisib, an α-selective PI3K inhibitor and degrader, demonstrated efficacy in combination with fulvestrant in the Phase III SOLAR-1 trial in pts with PIK3CA-mut HR+, HER2- ABC. Notably, treatment benefit was not seen in pts without PIK3CA-mut tumors. Expert guidelines now recommend testing for PIK3CA mut at advanced diagnosis; however, data on PIK3CA mut prevalence in a broader population outside of clinical trials are limited. This real-world study snapshot describes the global prevalence of PIK3CA mut across geographic areas in HR+, HER2- ABC. Methods: This noninterventional, retrospective cohort study of ~2,000 adults (≥18 years) in ~20 countries from Europe, Asia, Middle East (ME), and Latin America (LA) is assessing the frequency of PIK3CA mut in HR+, HER2- ABC. Key inclusion criteria are histologically/cytologically confirmed estrogen/progesterone receptor-positive and HER2- ABC with available fresh or archival tumor tissue. The primary endpoint is the percentage of pts with PIK3CA-mut tumors, specifying each hotspot. Key secondary endpoints include the percentage of pts with PIK3CA-mut tumors by geographic region, demographics by PIK3CA status, clinical characteristics, number of lines of treatment in the advanced setting, and time to subsequent treatment by PIK3CA status. Tumor tissue samples are assessed at a local laboratory, at a minimum, for PIK3CA mut in C420R, E542K, E545A/D/G/K, Q546E/R, and H1047L/R/Y. All statistical analyses are descriptive, and the prognostic role of PIK3CA mut will be evaluated in the final analysis. Results: As of data cut-off (03 May 2021), 1,361 pts were enrolled in the Full Analysis Set, 574 (42.2%) of whom have tumors harboring a PIK3CA mut. Table 1 summarizes demographics and baseline characteristics in the mut and non-mut cohorts. Polymerase chain reaction and next-generation sequencing were the common methods used to assess PIK3CA mut in 570 (41.9%) and 625 (45.9%) of pts, respectively. PIK3CA mut rates are generally consistent across regions (30.7-44.0%, Table 2). Table 2 shows sample types and most common biomarker muts. Conclusions: In this study, PIK3CA mut rates, 43.0% in Asia, 44.0% in Europe, 40.9% in LA, and 30.7% in ME, were consistent across regions and closely followed previous reports, supporting the prevalence of this mut outside the trial setting and in a more diverse real-world pt population. The most common PIK3CA muts found in this study were H1047R, E545K, and E542K, consistent with SOLAR-1. PIK3CA mut rates were comparable in primary vs metastatic samples, supporting the existing body of evidence that PIK3CA mut are truncal and can be tested on any available tissue. Further analysis, including treatment-related information, will be presented. Table 1.Demographics, baseline characteristics, and disease history (Full Analysis Set)Mutant PIK3CANon-mutant PIK3CAAll patientsn=574n=787N=1,361Median age (range) at early disease diagnosisa50.0 (28.0-85.0)51.0 (23.0-83.0)51.0 (23.0-85.0)Median age (range) at advanced disease diagnosis57.0 (26.0-89.0)55.5 (23.0-87.0)56.0 (23.0-89.0)Median age (range) at enrollment59.5 (27.0-89.0)59.0 (23.0-87.0)59.0 (23.0-89.0)Sex, n (%)Female566 (98.6)778 (98.9)1,344 (98.8)Male8 (1.4)8 (1.0)16 (1.2)Unknown01 (0.1)1 (0.1)Race, n (%)White294 (51.2)418 (53.1)712 (52.3)Asian183 (31.9)239 (30.4)422 (31.0)Black or African American5 (0.9)13 (1.7)18 (1.3)Multiple1 (0.2)0 (0.0)1 (0.1)Unknown91 (15.9)117 (14.9)208 (15.3)Menopausal status at advanced disease diagnosis, n (%)bMutant PIK3CANon-mutant PIK3CAAll patientsn=566n=778N=1,344Postmenopausal410 (72.4)554 (71.2)964 (71.7)Premenopausal146 (25.8)214 (27.5)360 (26.8)Stage at initial diagnosis, n (%)Mutant PIK3CANon-mutant PIK3CAAll patientsn=574n=787N=1,361Recurrent breast cancerc299 (52.1)414 (52.6)713 (52.4)De novo advanced breast cancerd265 (46.2)357 (45.4)622 (45.7)Unknown10 (1.7)16 (2.0)26 (1.9)Time from early diagnosis to advanced disease, n (%)&amp;lt;1 year32 (5.6)33 (4.2)65 (4.8)1 to &amp;lt;2 years25 (4.4)25 (3.2)50 (3.7)2 to &amp;lt;3 years29 (5.1)40 (5.1)69 (5.1)≥ 3 years149 (26.0)214 (27.2)363 (26.7)Extent of metastatic disease, n (%)Bone390 (67.9)456 (57.9)846 (62.2)Liver141 (24.6)204 (25.9)345 (25.3)Lung171 (29.8)245 (31.1)416 (30.6)Other127 (22.1)155 (19.7)282 (20.7)Number of metastatic sites, n (%)013 (2.3)21 (2.7)34 (2.5)1229 (39.9)324 (41.2)553 (40.6)&amp;gt;1332 (57.8)442 (56.2)774 (56.9)aCensored patients initially diagnosed as de novo advanced breast cancer.bMenopausal status is applicable only to female patients. Sites are provided the option to choose from 1) Able to bear children, 2) Post-menopausal, or 3) Sterile - of childbearing age.cStage 0-IIIA at initial diagnosis.dStage IIIB, IIIC, or IV at initial diagnosis. Table 2.PIK3CA mutation status by region and sample typeFrequency of mutant PIK3CA by regionMutant/Number of patients% (95% CI)All patients574/1,36142.2 (39.5-44.9)Asia193/44943.0 (38.4-47.7)Europe312/70944.0 (40.3-47.8)Latin America27/6640.9 (29.0-53.7)Middle East42/13730.7 (23.1-39.1)Mutant PIK3CANon-mutant PIK3CAAll patientsn=574n=787N=1,361Region, n (%)Asia193 (33.6)256 (32.5)449 (33.0)Europe312 (54.4)397 (50.4)709 (52.1)Latin America27 (4.7)39 (5.0)66 (4.8)Middle East42 (7.3)95 (12.1)137 (10.1)Tumor tissue type, n (%)Archival tumor536 (93.4)754 (95.8)1,290 (94.8)Newly obtained tumor sample38 (6.6)33 (4.2)71 (5.2)Source of tumor biopsy, n (%)Primary372 (64.8)496 (63.0)868 (63.8)Metastatic202 (35.2)291 (37.0)493 (36.2)Most common PIK3CA mutationsa, n (%); 95% CIbH1047R197 (34.3); 95% CI (30.4-38.4)0197 (14.5); 95% CI (12.6-16.5)E545K100 (17.4); 95% CI (14.4-20.8)0100 (7.3); 95% CI (6.0-8.9)E542K66 (11.5); 95% CI (9.0-14.4)066 (4.8); 95% CI (3.8-6.1)aIncludes patients with double or multiple mutations.b95% Confidence Interval (CI) is calculated using exact binomial method. Citation Format: Pathmanathan Rajadurai, Tatiana Semiglazova, Alinta Hegmane, Fadi El Karak, Joanne W Chiu, Sudeep Gupta, Hamdy A Azim, Josef JEM Kitzen, Antoine Arnaud, Sina Haftchenary, Jiwen Wu, Lakshmi Menon-Singh, LaTonya Smith, Lyudmila Zhukova. PIK3CA registry: A noninterventional, descriptive, retrospective cohort study of PIK3CA mutations in patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-13-25.

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 candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,019
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,029
Tête enseignante GPT0,344
Écart entre enseignants0,315 · 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.

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

Citations4
Publié2022
Routes d'admission1
Résumé présentoui

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