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Enregistrement W4221101062 · doi:10.1158/1538-7445.sabcs21-p3-09-18

Abstract P3-09-18: The association between genomic alterations and body mass index in patients with early breast cancer

2022· article· en· W4221101062 sur OpenAlexaff
Ha-Linh Nguyen, Tatjana Geukens, Marion Maetens, Karen Van Baelen, Maxim De Schepper, Sophia Leduc, Edoardo Isnaldi, Samuel Aparício, Åke Borg, Jane Brock, Annegien Broeks, Carlos Caldas, Andrew R. Green, Hazem Khout, Eyfjörð Jórunn, Stian Knappskog, Savitri Krishnamurthy, Sunil R. Lakhani, Anita Langerød, John W.M. Martens, Leigh C. Murphy, Serena Nik‐Zainal, Colin A. Purdie, Emad A. Rakha, Andrea L. Richardson, Anne Vincent‐Salomon, Peter T. Simpson, Christos Sotiriou, Paul N. Span, Benita Kiat Tee Tan, Alastair M. Thompson, Stefania Tommasi, Marc J. van de Vijver, Steven Van Laere, Alain Viari, Giuseppe Floris, Elia Biganzoli, François Richard, Christine Desmedt

Notice bibliographique

RevueCancer Research · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueCancer Risks and Factors
Établissements canadiensUniversity of ManitobaCancerCare Manitoba
Organismes subventionnairesnon disponible
Mots-clésMedicineBody mass indexBreast cancerInternal medicineOverweightLogistic regressionOncologyCancerCohort

Résumé

récupéré en direct d'OpenAlex

Abstract Background: High body mass index (BMI) is an established risk factor for developing breast cancer (BC), especially estrogen receptor (ER)-positive, and also has been associated with adverse survival. Still, patients with BC are currently treated independently of their BMI given limited understandings of the association between BC biology and patient adiposity. In this study, using retrospective data retrieved from two large BC studies, we aimed to identify genomic alterations of primary BC that are associated with BMI in the most common histological BC subtype - invasive carcinoma of no special type (NST). Patients, Data and Methods: Clinicopathological and genomic alteration data were retrieved from two study cohorts: METABRIC (Pereira et al. 2016) and ICGC (Nik-Zainal et al. 2016), with BMI recorded at the time of diagnosis and represented as either a continuous variable or a categorical variable of three categories - lean, overweight and obese. Stratification according to ER and HER2 status resulted in two focused subgroups: NST ER+/HER2- (n=392) and NST ER-/HER2- (n=152). Mutations classified as oncogenic using a set of predefined criteria were used to determine gene-level mutation status. Copy number alteration (CNA) calls were distinguished into three event types: amplification, hemizygous deletion and homozygous deletion. We used multivariable Firth’s logistic regression models with the presence of a genomic alteration as the response variable, BMI as the predicting variable of interest, and data cohort (METABRIC vs ICGC), age group (≤50 vs >50) and tumor grade (I & II vs III) as covariates, to assess the associations between BMI and recurrent gene-level genomic alterations, including gene mutations and CNAs. In a similar manner, we performed multivariable linear regression analysis, adjusting for age and tumor grade, to evaluate the associations of BMI with mutational signatures (MS) and tumor mutational burden in the ICGC NST subsets where these data are available. Results: Considering BMI as a categorical variable, we observed in the NST ER+/HER2- subgroup that PIK3CA was significantly less frequently mutated in obese compared to lean patients (33% vs 46%, odds ratio (OR) = 0.57 (95% confidence interval = (0.33, 0.97)), p = .039), while PTEN and TBX3 showed an increased frequency in overweight (6% vs 1%, OR = 4.14 (1.1, 22.34), p = .034) and obese (8% vs 1%, OR = 7.41 (1.82, 70.65), p = .008) patients, respectively. Regression analyses with BMI as a continuous variable revealed an increased prevalence of mutations in CDH1 and TBX3 genes as BMI increases by 1kg/m2 (OR = 1.14 (1.05, 1.24), p = .002, and OR = 1.13 (1.04, 1.22), p = .005, respectively) in patients with NST ER+/HER2- BC. No associations between BMI and oncogenic mutations was observed in the NST ER-/HER2- subgroup. Interrogation of gene-level CNAs in both subgroups demonstrated differences according to BMI in the prevalence of CNAs affecting a number of genes, many of which are known or have been presented with evidence to be involved in regulation of or regulated by hallmark pathways of BC, such as the MAPK/ERK, JAK/STAT and Wnt/β-catenin signaling pathways. We report a strong positive association between the single-base substitution signature 1 (SBS1), an age-correlated MS, and both continuous (coefficient (coef) = 18.3 (7.7, 28.9), p < .001) and categorical BMI (obese vs lean, coef = 336.3 (187.9, 484.8), p < .001) in the ICGC NST ER+/HER2- subgroup. Conclusion: This exploratory retrospective study suggests that the genomic profiles of primary BC may differ according to BMI. Clinical implications of these differences, especially the decreased prevalence of PIK3CA mutations in obese patients in the context of alpelisib, warrant further investigation. These results however indicate that patient adiposity should be taken into account in the era of personalized medicine. Citation Format: Ha-Linh Nguyen, Tatjana Geukens, Marion Maetens, Karen Van Baelen, Maxim De Schepper, Sophia Leduc, Edoardo Isnaldi, Sam Aparicio, Ake Borg, Jane Brock, Annegien Broeks, Carlos Caldas, Andrew Green, Hazem Khout, Eyfjörð Jórunn, Stian Knappskog, Savitri Krishnamurthy, Sunil Lakhani, Anita Langerod, John WM Martens, Leigh Murphy, Serena Nik-Zainal, Colin Purdie, Emad Rakha, Andrea Richardson, Anne Salomon, Peter Simpson, Christos Sotiriou, Paul Span, Benita Kiat-Tee Tan, Alastair Thompson, Stefania Tommasi, Marc Van de Vijver, Steven Van Laere, Alain Viari, Giuseppe Floris, Elia Biganzoli, François Richard, Christine Desmedt. The association between genomic alterations and body mass index in patients with early breast cancer [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 P3-09-18.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,004
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,043
Tête enseignante GPT0,366
Écart entre enseignants0,324 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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