Somatic mutations in Middle East and North Africa breast cancer patients: a systematic review
Bibliographic record
Abstract
BACKGROUND: Breast cancer presents with distinct clinical and molecular characteristics in the Middle East and North Africa (MENA) region, where women are diagnosed at younger ages and with more aggressive disease compared to Western populations. Despite the global burden, genomic studies of breast cancer in MENA remain underrepresented. This systematic review provides the first comprehensive analysis of somatic mutations in breast cancer patients across the MENA region. METHODS: Following PRISMA guidelines, we analyzed 44 studies encompassing 13 MENA countries, representing data from over 2500 breast cancer patients. Studies were rigorously assessed using the Newcastle-Ottawa Scale, with mutation data extracted, standardized, and classified according to pathogenicity using established databases. We employed multiple sequencing methodologies, including next-generation sequencing and targeted gene panels, to identify country-specific and region-wide mutation patterns. RESULTS: We identified 559 mutations across 104 genes, with TP53 (23.79%) and PIK3CA (10.19%) emerging as the most frequently altered genes, followed by significant mutations in BRCA1/2, ATM, ESR1, and PTEN. Nearly 43% of variants were classified as pathogenic/likely pathogenic, while 23% remained variants of uncertain significance. Missense mutations predominated (60.29%), followed by frameshift variants (13.06%) and stop-gained mutations (10.91%). We discovered distinctive country-specific mutation profiles, including unique alterations in KLF6 (Turkey) and IL-1β (Iraq), reflecting potential environmental and hereditary influences unique to MENA populations. Notably, all 11 PIK3CA hotspot mutations that predict sensitivity to alpelisib therapy were identified. CONCLUSIONS: This study reveals both shared and distinct somatic mutation patterns in MENA breast cancer patients compared to Western populations. The high prevalence of clinically actionable mutations, particularly in PIK3CA and DNA repair genes, presents immediate opportunities for implementing targeted therapies across the region. Our findings underscore the urgent need for establishing a MENA Breast Cancer Genomics Consortium to standardize sequencing protocols, develop locally validated gene panels, and create regional variant databases that capture the unique mutation spectrum of these populations. This comprehensive genomic landscape of breast cancer in the MENA region addresses a critical gap in global cancer genomics, ultimately improving outcomes for a historically underrepresented patient population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".