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Mutant-allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence.

ABSTRACT: Intra-tumor heterogeneity (ITH) is one of the most important causes of therapy resistance, which eventually leads to the poor outcomes observed in patients with glioma. Mutant-allele tumor heterogeneity (MATH) values are based on whole-exon sequencing and precisely reflect genetic ITH. However, the significance of MATH values in predicting glioma recurrence remains unclear. Information of patients with glioma was obtained from The Cancer Genome Atlas database. The present study calculated the MATH value for each patient, analyzed the distributions of MATH values in different subtypes and investigated the rates of clinical recurrence in patients with different MATH values. Gene enrichment and Cox regression analyses were performed to determine which factors influenced recurrence. A nomogram table was established to predict 1-, 2- and 5-year recurrence probabilities. MATH values were increased in patients with glioma with the wild-type isocitrate dehydrogenase (NADP⁽⁺⁾) (IDH)1/2 (IDH-wt) gene (P=0.001) and glioblastoma (GBM; P=0.001). MATH values were negatively associated with the 2- and 5-year recurrence-free survival (RFS) rates in patients with glioma, particularly in the IDH1/2-wt and GBM cohorts (P=0.001 and P=0.017, respectively). Furthermore, glioma cases with different MATH levels had distinct patterns of gene mutation frequencies and gene expression enrichment. Finally, a nomogram table that contained MATH values could be used to accurately predict the probabilities of the 1-, 2- and 5-year RFS of patients with glioma. In conclusion, the MATH value of a patient may be an independent predictor that influences glioma recurrence. The nomogram model presented in the current study was an appropriate method to predict 1-, 2- and 5-year RFS probabilities in patients with glioma.

SUBMITTER: Wu P

PROVIDER: S-EPMC6865645 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Mutant-allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence.

Wu Pengfei P Yang Wei W Ma Jianxing J Zhang Jingyu J Liao Maojun M Xu Lunshan L Xu Minhui M Yi Liang L

Oncology letters 20191011 6

Intra-tumor heterogeneity (ITH) is one of the most important causes of therapy resistance, which eventually leads to the poor outcomes observed in patients with glioma. Mutant-allele tumor heterogeneity (MATH) values are based on whole-exon sequencing and precisely reflect genetic ITH. However, the significance of MATH values in predicting glioma recurrence remains unclear. Information of patients with glioma was obtained from The Cancer Genome Atlas database. The present study calculated the MA ...[more]

PMID: 31788085

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Project description:Background:Uterine corpus endometrial carcinoma (UCEC) is a clinically heterogeneous disease, and this heterogeneity is associated with tumor development, clinical characteristics, and prognostic outcomes. Mutant-allele tumor heterogeneity (MATH) is a novel, non-biased, quantitative measure to assess intra-tumor heterogeneity based on next-generation sequencing data. We aimed to explore the use of MATH as a measure for tumor heterogeneity and its prognostic role in UCEC patients. Methods:We calculated MATH scores from the available data of 560 UCEC patients from The Cancer Genome Atlas (TCGA) and investigated their correlations with clinical characteristics, genetic alterations, and overall survival. Predictive accuracy was quantified using the area under the receiver operating characteristic curve (AUC) and the index of concordance (C-index). Results:In total, 242 MATH scores were obtained from the UCEC cohort. MATH scores were significantly related to age, race, cancer type, clinical stage, histological grade, molecular type, targeted molecular therapy, and hormonal therapy. Furthermore, the genomic pattern on the basis of MATH scores showed that mutation rates of TP53 (tumor protein p53) and ARID1A (AT-rich interaction domain 1A) were independently associated with MATH scores. Correlation analysis revealed a significantly positive association of MATH scores with the fraction of somatic copy number alteration (SCNA). Importantly, a high MATH score was significantly associated with shorter overall survival [hazard ratio (HR), 2.342; 95% confidence interval (CI), 1.110-4.942]. Multivariate Cox regression combined with stratified analysis revealed that the MATH score is an independent prognostic factor in UCEC patients under 60 years old, and predictive quantification showed the MATH score had an AUC of 0.756 and a C-index of 0.845. Conclusions:Our results suggest that MATH, a practical and useful way to measure intra-tumor heterogeneity, may serve as a significant biomarker for the prognosis of patients with UCEC, enabling more accurate prediction of clinical outcomes.

Dataset Information

Mutant-allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence.

Publications

Mutant-allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence.

Similar Datasets

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