Abstract C008: Characterizing factors associated with early onset breast cancer to inform tailored strategies for cancer risk assessment, screening, and treatment
Notice bibliographique
Résumé
Abstract Background Early onset breast cancer (EOBC) diagnosed before age 50 is on the rise; there is a need to characterize associated factors to inform appropriate screening, risk reduction, and treatment. This retrospective study compared clinical, pathologic, family history (FH), and genetic factors between EOBC cohort (≤ 49 years) and late-onset breast cancer (LOBC, diagnosed ≥ 50 years) from a tertiary care cancer genetics and prevention program to gain insights into EOBC and inform strategies to reduce the burden of this disease. Methods We conducted a retrospective study of 4,461 female breast cancer patients seen from 2015–2023 at the Yale/Smilow Cancer Genetics & Prevention Program. Patients were stratified into EOBC (n=1,784) and LOBC (n=2,677). Clinical, pathologic, tumor marker, genetic, and FH data were collected from electronic medical records and family history software leveraging natural language processing (NLP) with cross-check for validity and accuracy. Continuous variables were summarized as medians with interquartile range. Categorical variables were summarized with means and percentages. Statistical comparisons used Chi-square tests for categorical variables and Wilcoxon rank sum tests for continuous variables. Results Median age of breast cancer for EOBC patients was 43 years (IQR 39–47), while median age at diagnosis for LOBC was 69 years (IQR 63–76, p <0.0001). EOBC patients had higher rates of BRCA1 (2.1% vs. 1.2%, p=0.0203) and BRCA2 mutations (pathogenic variants) (2.9% vs. 1.3%, p=0.0001), lower proportion of hormone receptor positive / HER2 negative tumors (36.7% vs. 46.4%, p <0.0001), and higher rate of triple positive subtype (3.4% vs. 2.3%, p=0.0366). Family history of breast cancer was less frequent in EOBC (80.0% vs. 96.9%, p <0.0001), though FH of ovarian, pancreatic, and prostate cancers were all higher in the EOBC cohort (p-values 0.0267, 0.0387, 0.0021 respectively). In a subset analysis (n=444), family history of breast cancer was associated with aggressive disease (N>0, T3/T4 disease) for patients with LOBC vs. EOBC (100% vs. 88.57%, p<0.0001). Conclusions Patients with EOBC may have a more notable family history for cancers beyond breast cancer, compared to the patients with LOBC. This finding deserves confirmation and may inform strategies to increase awareness of broad family history assessment, screening, and genetic testing. High penetrance gene mutations, particularly in BRCA1 and BRCA2, are more common in patients with EOBC as are more aggressive receptor subtypes of disease, which is consistent with current state of the field. These findings reinforce the need for tailored risk stratification, genetic counseling, and prevention strategies focused on younger women, especially to improve early detection and equity in outcomes. Further research is needed to uncover additional factors related to EOBC predisposition and aggressive biology to optimize clinical outcomes. Citation Format: Guannan Gong, Tanaya Shroff, Wei Cheng, Aparna Namboodiri, Laura Gross, Nancy A. Borstelmann, Veda N. Giri, Ellie Proussaloglou. Characterizing factors associated with early onset breast cancer to inform tailored strategies for cancer risk assessment, screening, and treatment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr C008.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
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 source (Gemma direct ou Codex distillé), 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 ».