Original Article
Clinical relevance of mutant-allele tumor heterogeneity and lung adenocarcinoma
Abstract
Background: Intra-tumor heterogeneity (ITH) plays an important role in the progressing of lung adenocarcinoma (LUAD). We used a mutant-allele tumor heterogeneity (MATH) algorithm to measure ITH and the correlation between ITH and clinical parameters and its prognosis.
Methods: We assessed 230 LUAD patients from The Cancer Genome Atlas (TCGA). We calculated the MATH values from whole-exome sequencing data and further investigated their correlation with clinical characteristics.
Results: The patients were divided into low and high MATH groups. People with high MATH were more likely to be female, smoking and EGFR mutation. And a high MATH may predict a poor prognosis.
Conclusions: MATH is a new method for describing the internal heterogeneity of tumors in lung cancer. We used TCGA data to demonstrate that groups with higher MATH values are more likely to be female, smoker and EGFR mutations, and may have a poor prognosis.
Methods: We assessed 230 LUAD patients from The Cancer Genome Atlas (TCGA). We calculated the MATH values from whole-exome sequencing data and further investigated their correlation with clinical characteristics.
Results: The patients were divided into low and high MATH groups. People with high MATH were more likely to be female, smoking and EGFR mutation. And a high MATH may predict a poor prognosis.
Conclusions: MATH is a new method for describing the internal heterogeneity of tumors in lung cancer. We used TCGA data to demonstrate that groups with higher MATH values are more likely to be female, smoker and EGFR mutations, and may have a poor prognosis.
