Abstract 5348: Gene alterations in breast tumors reveal significant association with patient and tumor characteristics
Bibliographic record
Abstract
Abstract Background: Profiling breast tumor gene alterations gives insight into the nature of the disease and will eventually lead to better diagnosis and personalized treatments. Ethnicity plays a role in the behavior of breast cancer etiology. In this study, we aimed to verify gene mutations in the context of single nucleotide variation (SNV) and copy number variations (CNV) in a patient group from The Sultanate of Oman. We correlated the gene alterations with age and tumor characteristics simply because breast cancer in this population happens at an earlier age with an aggressive nature compared with Western women. Materials and Methods: The DNA extracted from formalin-fixed paraffin-embedded FFPE tissues was analyzed using the Oncomine Comprehensive Assay Plus targeted cancer genes panel using a next-generation sequencing platform. The data was analyzed using the Oncomine informatics software, and statistical analysis was further employed to analyze the significant gene alterations with the patient and tumor characteristics. Results and discussion: 40 Patients' tumors were collected and classified based on patient age and tumor-specific parameters, including tumor size, grade, lymph node status, hormone receptor status, and molecular subtypes. Remarkably, SNVs in NTRK1 (p=0.003) and PIK3CA (p=0.008) were more associated with the tumors of older patients. SNVs in TP53 (p=0.007) and CBFB (p=0.037) exhibited significance with the molecular subtype of patients. Additionally, copy number amplifications in FGF 3, 4 and 19 were significant based on the tumor molecular subtype (P ≤0.05). TSC1 with SNV was exclusively mutated significantly in smaller tumor sizes (p=0.023). Besides, the CNVs (gain) in NBN (p=0.010) and RECQL4 (p=0.028) were also found in smaller tumors. Regarding tumor grade, five genes showed more CNV-gain in higher-grade tumors: BRIP1 (p=0.015), RPS6KB1(p=0.019), SOX9 (p=0.015), CHEK2 (p=0.013), and NOTCH1 (p=0.045). CDK12 copy number gain led to a significant decrease in the overall survival of the studied patients (p=0.048). Conclusion: Like the international distribution of BC mutations, TP53 and PIK3CA are still the most mutated in the studied cohort. The analysis indicated a few unique gene mutations in this study, such as NTRK1 and its mutations in older breast cancer patients. Further analysis of the transcriptome data will confirm the highlighted gene activity and correlation with the characteristics of the disease. Citation Format: Muna Al Dalali, Buthaina Al Amri, Noura Al Zeheimi, Raghad Al Busaidi, M Mazharul Islam, Marwa Al Riyami, Adil Al Jarrah, Yahya Tamimi, Cheryl Crozier, John Bartlett, Jane Bayani, Sirin A. Adham. Gene alterations in breast tumors reveal significant association with patient and tumor characteristics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5348.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".