ArticleAsian Pacific journal of cancer prevention : APJCP2021
Tumor Mutation Burden Prediction Model in Egyptian Breast Cancer patients based on Next Generation Sequencing.
Article in Asian Pacific journal of cancer prevention : APJCP, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 9 citations in OpenAlex.
- Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis.Communications medicine · 2026Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Somatic Mutations Alter Interleukin Signaling Pathways in Grade II Invasive Breast Cancer Patients: An Egyptian Experience.Current issues in molecular biology · 2022Article
- Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements.Journal of global health · 2022Article
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to identify the tumor mutation burden (TMB) value in Egyptian breast cancer (BC) patients. Moreover, to find the best TMB prediction model based on the expression of estrogen (ER), progesterone (PR), human epidermal growth factor receptor 2 (HER-2), and proliferation index Ki-67.
methodsThe Ion AmpliSeq Comprehensive Cancer Panel was used to determine TMB value of 58 Egyptian BC tumor tissues. Different machine learning models were used to select the optimal classification model for prediction of TMB level according to patient's receptor status.
resultsThe measured TMB value was between 0 and 8.12/Mb. Positive expression of ER and PR was significantly associated with TMB ≤ 1.25 [(OR =0.35, 95% CI: 0.04-2.98), (OR = 0.17, 95% CI= 0.02-0.44)] respectively. Ki-67 expression positive was significantly associated with TMB >1.25 than those who were Ki-67 expression negative (OR = 9.33, 95% CI= 2.07-42.18). However, no significant differences were observed between HER2 positive and HER2 negative groups. The optimized logistic regression model was TMB = -27.5 -1.82 ER - 0.73 PR + 0.826 HER2 + 2.08 Ki-67.
conclusionOur findings revealed that TMB value can be predicted based on the expression level of ER, PR, HER-2, and Ki-67.
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