Abstract C008: Characterizing factors associated with early onset breast cancer to inform tailored strategies for cancer risk assessment, screening, and treatment
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".