ArticleHead and neck pathology2015
Mutant allele tumor heterogeneity (MATH) and head and neck squamous cell carcinoma.
Article in Head and neck pathology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Single-cell and machine learning reveal ROS-associated heterogeneity in hepatocellular carcinoma for precision medicine.BMC cancer · 2026Article
- Conserved sphingolipid metabolism under transcriptomic diversity: a prognostic and therapeutic target in triple-negative breast cancer.Journal of translational medicine · 2025Article
- Molecular and Genetic Pathogenesis of Oral Cancer: A Basis for Customized Diagnosis and Treatment.Biology · 2025Review
- The American Joint Committee on Cancer (AJCC) Breast Cancer Staging, Eighth Edition, is more Reflective of Cancer Biology than the Seventh Edition.Annals of surgical oncology · 2025Article
- Integrated Analysis of Single-Cell RNA Sequencing and Machine Learning Reveals a T Cell-Specific PANoptosis Signature Predicting Prognosis and Immunotherapy in Prostate Cancer.Human mutation · 2025Article
- Exploring the role of ADAMTSL2 across multiple cancer types: A pan-cancer analysis and validated in colorectal cancer.Discover oncology · 2024Article
- On the Boundary of Exploratory Genomics and Translation in Sequential Glioblastoma.International journal of molecular sciences · 2024Article
- Genomic heterogeneity at baseline is associated with T790M resistance mutations in EGFR-mutated lung cancer treated with the first-/second-generation tyrosine kinase inhibitors.The journal of pathology. Clinical research · 2024Article
- Clonal architecture and evolutionary history of Waldenström's macroglobulinemia at the single-cell level.Disease models & mechanisms · 2023Article
- Extracellular sialyltransferase st6gal1 in breast tumor cell growth and invasiveness.Cancer gene therapy · 2022Article
- Article
- Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma.Frontiers in cell and developmental biology · 2022Article
- Quantifying smoking exposure, genomic correlates, and related risk of treatment failure in p16+ squamous cell carcinoma of the oropharynx.Laryngoscope investigative otolaryngology · 2021Article
- Article
- Epithelial-Mesenchymal Transition Associated with Head and Neck Squamous Cell Carcinomas: A Review.Cancers · 2021Review
- Article
- Liquid biopsy in NSCLC: a new challenge in radiation therapy.Exploration of targeted anti-tumor therapy · 2021Review
- Immune cytolytic activity is associated with reduced intra-tumoral genetic heterogeneity and with better clinical outcomes in triple negative breast cancer.American journal of cancer research · 2021Article
- Stromal score as a prognostic factor in primary gastric cancer and close association with tumor immune microenvironment.Cancer medicine · 2020Article
- Tumor Infiltrating Lymphocytes and Macrophages Improve Survival in Microsatellite Unstable Colorectal Cancer.Scientific reports · 2019Article
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Abstract
Intra-tumor heterogeneity, variation between individual tumor cells within a patient's tumor, is increasingly seen as a critical mechanism underlying treatment resistance and therapeutic failure. Despite this growing awareness, few methods to assess intra-tumor heterogeneity exist outside the research laboratory, especially in the absence of a known marker. Mutant allele tumor heterogeneity (MATH) is a novel, non-biased, quantitative method to assess genetic heterogeneity based on tumor next generation exome sequencing. The quantitative aspect of MATH has allowed it to be verified as an actionable biomarker in a retrospective HNSCC data set with available exome sequencing and clinical data. In addition, it was also capable of stratifying patient outcome after controlling for other high-risk features such as p53 mutation, HPV status, and advanced tumor stage. Future work will explore the predictive power of MATH in larger data sets such as The Cancer Genome Atlas and examine the underlying cellular mechanisms responsible for intra-tumor heterogeneity.
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