Limits...
Significant Prognostic Features and Patterns of Somatic TP53 Mutations in Human Cancers

View Article: PubMed Central - PubMed

ABSTRACT

TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 (p53) status has been a controversial issue. In this work, we aimed to characterize TP53 somatic mutations in tumor cells across multiple cancer types, primarily focusing on several less investigated features of the mutation spectra, and determine their prognostic implications. We performed an integrative study on the clinically annotated genomic data released by The Cancer Genome Atlas. Standard statistical methods, such as the Cox proportional hazards model and logistic regression, were used. This study resulted in several novel findings. They include the following: (1) similar to previously reported cases in breast cancer, the mutations in exons 1 to 4 of TP53 were more lethal than those in exons 5 to 9 for the patients with lung adenocarcinomas; (2) TP53 mutants tended to be negatively selected in mammalian evolution, but the evolutionary conservation had various clinical implications for different cancers; (3) conserved correlation patterns (ie, consistent co-occurrence or consistent mutual exclusivity) between TP53 mutations and the alterations in several other cancer genes (ie, PIK3CA, PTEN, KRAS, APC, CDKN2A, and ATM) were present in several cancers in which prognosis was associated with TP53 status and/or the mutational characteristics; (4) among TP53-mutated tumors, the total mutation burden in other driver genes was a predictive signature (P <.05, false discovery rate <0.11) for better patient survival outcome in several cancer types, including glioblastoma multiforme. Among these findings, the fourth is of special significance as it suggested the potential existence of epistatic interaction effects among the mutations in different cancer driver genes on clinical outcomes.

No MeSH data available.


Related in: MedlinePlus

The co-occurrence or mutual-exclusivity relationships between somatic TP53 mutations and the alterations in other 117 cancer driver genes over 11 cancer types. Ovarian serous cystadenocarcinoma (OV) was not included in this figure due to the lack of significant mutational relationships. The cell color, that is, green, red, or gray, indicates co-occurrence, mutual exclusivity, or the lack of association, respectively. The gene clusters, indicated by the color bar on the left side of the figure, are determined by a hierarchical clustering analysis (Manhattan distance and Ward method) with a sparse matrix (ie, M) as the input. In the matrix, rows and columns represent genes and cancer types, respectively. When the ith gene has a significant co-occurrence (or mutual exclusivity) relationship (P < .05) with TP53 in somatic mutation for jth cancer, the element mij of M is 1 (or −1); otherwise, it is 0. BLCA indicates bladder urothelial carcinoma; BRCA, breast invasive carcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; STAD, stomach adenocarcinoma; UCEC, uterine corpus endometrial carcinoma.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC5392013&req=5

f4-10.1177_1176935117691267: The co-occurrence or mutual-exclusivity relationships between somatic TP53 mutations and the alterations in other 117 cancer driver genes over 11 cancer types. Ovarian serous cystadenocarcinoma (OV) was not included in this figure due to the lack of significant mutational relationships. The cell color, that is, green, red, or gray, indicates co-occurrence, mutual exclusivity, or the lack of association, respectively. The gene clusters, indicated by the color bar on the left side of the figure, are determined by a hierarchical clustering analysis (Manhattan distance and Ward method) with a sparse matrix (ie, M) as the input. In the matrix, rows and columns represent genes and cancer types, respectively. When the ith gene has a significant co-occurrence (or mutual exclusivity) relationship (P < .05) with TP53 in somatic mutation for jth cancer, the element mij of M is 1 (or −1); otherwise, it is 0. BLCA indicates bladder urothelial carcinoma; BRCA, breast invasive carcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; STAD, stomach adenocarcinoma; UCEC, uterine corpus endometrial carcinoma.

Mentions: Survival analysis was performed using R package “survival.” The Kaplan-Meier survival curves were created by the function “survfit().” P values for the effect of TP53 status (or a mutation feature) on patient survival time were calculated by the function “coxph().” Benjamini-Hochberg false discovery rate (FDR) was calculated using the function “p.adjust()” in the R package “stats.” The mutational association (or relationship) between TP53 and another gene was measured by the Yule phi coefficient (a Pearson correlation applied to dichotomous data)17 between the numbered genotypes (1 and 0 were assigned to mutant and WT, respectively). The statistical significance was further evaluated with the P value calculated using a logistic regression model, in which TP53 genotype and the genotype of the paired gene were the independent variable and dependent variable, respectively. A co-occurrence (or mutual exclusivity) relationship was determined by P < .05 and r > 0 (or r < 0). The analysis was performed using the “corr()” and “glm()” function in the R package “stats.” The obtained results (ie, P values) are similar to the Fisher test that had been used by Kandoth et al.18 Hierarchical clustering analysis was conducted using the “hclust()” function in the R package “stats.” The detailed implementation is described in the “Results” section and the legend of Figure 4.


Significant Prognostic Features and Patterns of Somatic TP53 Mutations in Human Cancers
The co-occurrence or mutual-exclusivity relationships between somatic TP53 mutations and the alterations in other 117 cancer driver genes over 11 cancer types. Ovarian serous cystadenocarcinoma (OV) was not included in this figure due to the lack of significant mutational relationships. The cell color, that is, green, red, or gray, indicates co-occurrence, mutual exclusivity, or the lack of association, respectively. The gene clusters, indicated by the color bar on the left side of the figure, are determined by a hierarchical clustering analysis (Manhattan distance and Ward method) with a sparse matrix (ie, M) as the input. In the matrix, rows and columns represent genes and cancer types, respectively. When the ith gene has a significant co-occurrence (or mutual exclusivity) relationship (P < .05) with TP53 in somatic mutation for jth cancer, the element mij of M is 1 (or −1); otherwise, it is 0. BLCA indicates bladder urothelial carcinoma; BRCA, breast invasive carcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; STAD, stomach adenocarcinoma; UCEC, uterine corpus endometrial carcinoma.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5392013&req=5

f4-10.1177_1176935117691267: The co-occurrence or mutual-exclusivity relationships between somatic TP53 mutations and the alterations in other 117 cancer driver genes over 11 cancer types. Ovarian serous cystadenocarcinoma (OV) was not included in this figure due to the lack of significant mutational relationships. The cell color, that is, green, red, or gray, indicates co-occurrence, mutual exclusivity, or the lack of association, respectively. The gene clusters, indicated by the color bar on the left side of the figure, are determined by a hierarchical clustering analysis (Manhattan distance and Ward method) with a sparse matrix (ie, M) as the input. In the matrix, rows and columns represent genes and cancer types, respectively. When the ith gene has a significant co-occurrence (or mutual exclusivity) relationship (P < .05) with TP53 in somatic mutation for jth cancer, the element mij of M is 1 (or −1); otherwise, it is 0. BLCA indicates bladder urothelial carcinoma; BRCA, breast invasive carcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; STAD, stomach adenocarcinoma; UCEC, uterine corpus endometrial carcinoma.
Mentions: Survival analysis was performed using R package “survival.” The Kaplan-Meier survival curves were created by the function “survfit().” P values for the effect of TP53 status (or a mutation feature) on patient survival time were calculated by the function “coxph().” Benjamini-Hochberg false discovery rate (FDR) was calculated using the function “p.adjust()” in the R package “stats.” The mutational association (or relationship) between TP53 and another gene was measured by the Yule phi coefficient (a Pearson correlation applied to dichotomous data)17 between the numbered genotypes (1 and 0 were assigned to mutant and WT, respectively). The statistical significance was further evaluated with the P value calculated using a logistic regression model, in which TP53 genotype and the genotype of the paired gene were the independent variable and dependent variable, respectively. A co-occurrence (or mutual exclusivity) relationship was determined by P < .05 and r > 0 (or r < 0). The analysis was performed using the “corr()” and “glm()” function in the R package “stats.” The obtained results (ie, P values) are similar to the Fisher test that had been used by Kandoth et al.18 Hierarchical clustering analysis was conducted using the “hclust()” function in the R package “stats.” The detailed implementation is described in the “Results” section and the legend of Figure 4.

View Article: PubMed Central - PubMed

ABSTRACT

TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 (p53) status has been a controversial issue. In this work, we aimed to characterize TP53 somatic mutations in tumor cells across multiple cancer types, primarily focusing on several less investigated features of the mutation spectra, and determine their prognostic implications. We performed an integrative study on the clinically annotated genomic data released by The Cancer Genome Atlas. Standard statistical methods, such as the Cox proportional hazards model and logistic regression, were used. This study resulted in several novel findings. They include the following: (1) similar to previously reported cases in breast cancer, the mutations in exons 1 to 4 of TP53 were more lethal than those in exons 5 to 9 for the patients with lung adenocarcinomas; (2) TP53 mutants tended to be negatively selected in mammalian evolution, but the evolutionary conservation had various clinical implications for different cancers; (3) conserved correlation patterns (ie, consistent co-occurrence or consistent mutual exclusivity) between TP53 mutations and the alterations in several other cancer genes (ie, PIK3CA, PTEN, KRAS, APC, CDKN2A, and ATM) were present in several cancers in which prognosis was associated with TP53 status and/or the mutational characteristics; (4) among TP53-mutated tumors, the total mutation burden in other driver genes was a predictive signature (P &lt;.05, false discovery rate &lt;0.11) for better patient survival outcome in several cancer types, including glioblastoma multiforme. Among these findings, the fourth is of special significance as it suggested the potential existence of epistatic interaction effects among the mutations in different cancer driver genes on clinical outcomes.

No MeSH data available.


Related in: MedlinePlus