Abstract B50: Breast cancer in the Caribbean – A six-country cohort
Notice bibliographique
Résumé
Abstract Introduction: In Low and Middle Income Countries (LMIC) non-communicable chronic diseases are now the leading cause of premature mortality. Breast cancer is the leading cause of cancer death in Caribbean women. A Caribbean wide study was launched to determine the demographics of breast cancer and the contribution of breast cancer gene mutations to its incidence. Methods: Breast and ovarian cancer patients were recruited from public and private clinics in the Cayman Islands, Jamaica, Barbados, Dominica, Trinidad & Tobago, Haiti and at the University of Miami/ Jackson Memorial Hospital between 2010 and January 2015. Study participants had to have at least 1 grandparent born in one of the participating countries. All women provided written consent. The study was approved by the ethics review board in all participating institutions and/or countries. A saliva sample was taken. A pedigree was drawn and demographic and clinical pathologic data was collected from 855 women and men. Summary statistics and t-tests and ANOVA were used to analyze study population characteristics. Results: Afro Caribbean women have a young age of onset of breast cancer mean age 49.1 yrs. Within the cohort, Trinidad & Tobago had significantly p<0.0001) younger participants - mean age 43 (19 - 80 yr, 95%CI 41.9-44.0); Barbados mean age 46.4 (p<0.05) compared to Cayman Islands (51.4), Dominica (52) and Haiti (51.1). The mean BMI for the cohort was 28.2: Barbados 27.5, Cayman lslands 30.3, Dominica 28.7, Haiti 25.9, Jamaica 28.2 and Trinidad & Tobago 27.4. Estrogen receptors were found in 62% (95%CI 51.6-72.6) of AC women although in Dominica and Haiti they were seldom done and all therapy was empirically guided. AC women had Her-2 overexpression in 19% (95%CI 10.7-) although it was higher in Haiti and Jamaica, 38% and 27%. Stage at presentation was skewed to stages III and IV in 5/6 countries studied (except in Cayman Islands). 30-40% of participants presented at an advance stage compared to their American counter parts where Stage III and IV account for 12%. In the cohort, women who were nulliparous had an earlier age at presentation of invasive breast cancer by 7 years (p<0.0001) compared to women with 1 or more children. Similarly 1 or more pregnancies, increased the age at invasive breast cancer diagnosis (p<0.0001). Women who underwent a TAH-BSO had a later age of onset of diagnosis by 5.1yrs (p=0.0001) Deleterious mutations in the breast cancer genes were a frequent cause of breast cancer in the Bahamas (28% of unselected patients) and Trinidad & Tobago (12% of unselected patients). 94% of breast cancers were detected by the patient. Conclusions: This population-based study provides information on the tumor stage and characteristics as well as risk factors such as genetic, weight and fertility factors that contribute to the incidence of breast cancer in AC women. AC women develop breast cancer at an earlier age than either European American (EA) or African American (AA) women (AC 49.1, AA 58, EA 62). 60% of AC women were diagnosed between the ages of 35-54 compared to 30% of AA women. Breast cancer in AC women presents at a higher stage than in AA or EA women (30-40% Stage III&IV vs 12%). Genetic factors play a large role in the etiology of breast cancer in the Bahamas and Trinidad & Tobago. Fertility factors have a direct effect on the age of onset of breast cancer. Mammography is not readily available and most cancers are detected by palpation. Financial constraints limit diagnostic capacity, treatment options and genetic testing. Breast cancer is the leading cause of cancer death in the Caribbean and as the countries in the Caribbean move rapidly from the third world into the first world, the breast cancer incidence will increase. Understanding the factors that contribute to the cause of breast cancer will enable the countries to strategy. Citation Format: Sophia HL George, Talia Donenberg, Mohammad Akbari, Cheryl Alexis, Gilian Wharfe, Hedda Dyer, Sook Yin, Theodore Turnquest, Jameel Ali, Vincent DeGennaro, Jr., Steven A. Narod, Judith Hurley. Breast cancer in the Caribbean – A six-country cohort. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr B50.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».