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Integrated genomic analysis identifies subclasses and prognosis signatures of kidney cancer.

Christinat Y, Krek W - Oncotarget (2015)

Bottom Line: Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome.Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Health Sciences, ETH Zurich, 8093 Zurich, Switzerland.

ABSTRACT

Purpose: To define robust miRNA-based molecular classifiers for human clear cell renal cell carcinoma (ccRCC) subgrouping and prognostication.

Experimental design: Multidimensional data of over 500 clear cell renal cell carcinoma (ccRCC) patients were retrieved from The Cancer Genome Atlas (TCGA) archive. Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.

Results: Our in silico analysis unveiled a novel ccRCC-specific 5-miRNA (miR-10b, miR-21, miR-143, miR-183, and miR-192) signature able, when combined with information from conventional TNM staging and the age of the patient, to prognosticate ccRCC outcome more accurately than known ccRCC miRNA signatures or TNM staging alone. Furthermore, our approach revealed the existence of 6 distinct subgroups of ccRCC characterized by discrete differences in overall survival, tumor stage, and mutational spectra in key ccRCC tumor suppressor genes. It also demonstrated that BAP1 mutations correlate with tumor progression rather than overall survival.

Conclusion: Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome. Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

No MeSH data available.


Related in: MedlinePlus

Comparison of miRisk5 with the TNM staging system and the Mayo Clinic score (SSIGN) on the validation cohort(A) Kaplan-Meier curves for each risk category. (B) Prognosis performance of the three methods. (C) Graphical visualization of confusion matrices for risk categories between miRisk5 and the two other clinical scores. Values are normalized column-wise by the respective total number of patients.
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Figure 3: Comparison of miRisk5 with the TNM staging system and the Mayo Clinic score (SSIGN) on the validation cohort(A) Kaplan-Meier curves for each risk category. (B) Prognosis performance of the three methods. (C) Graphical visualization of confusion matrices for risk categories between miRisk5 and the two other clinical scores. Values are normalized column-wise by the respective total number of patients.

Mentions: A continuous risk, as resulting from a Cox regression analysis, has a high discriminative power but is unpractical for clinical usage. Consequently, we summarized our composite risk into 5 categories to provide a usable tool, referred to as miRisk5 (see Supplementary Methods). Kaplan-Meier survival curves, as displayed in Fig. 3A, show that our summarized risk is able to span a wider range of patient outcomes than the TNM staging or the Mayo Clinic score (SSIGN), which is another well-established scoring system for RCC patients [28]. Both the TNM staging and the SSIGN score identify three different groups while miRisk5 clearly separates 5 different groups. Additionally, miRisk5 provides a substantial amelioration with respect to TNM staging (c-index of 0.65 vs. 0.59 for the TNM staging; Fig. 3B). Although it does not significantly outperform the SSIGN score in terms of prognosis, it does assign patients into different risk groups than the SSIGN or the TNM score (Fig. 3C).


Integrated genomic analysis identifies subclasses and prognosis signatures of kidney cancer.

Christinat Y, Krek W - Oncotarget (2015)

Comparison of miRisk5 with the TNM staging system and the Mayo Clinic score (SSIGN) on the validation cohort(A) Kaplan-Meier curves for each risk category. (B) Prognosis performance of the three methods. (C) Graphical visualization of confusion matrices for risk categories between miRisk5 and the two other clinical scores. Values are normalized column-wise by the respective total number of patients.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496372&req=5

Figure 3: Comparison of miRisk5 with the TNM staging system and the Mayo Clinic score (SSIGN) on the validation cohort(A) Kaplan-Meier curves for each risk category. (B) Prognosis performance of the three methods. (C) Graphical visualization of confusion matrices for risk categories between miRisk5 and the two other clinical scores. Values are normalized column-wise by the respective total number of patients.
Mentions: A continuous risk, as resulting from a Cox regression analysis, has a high discriminative power but is unpractical for clinical usage. Consequently, we summarized our composite risk into 5 categories to provide a usable tool, referred to as miRisk5 (see Supplementary Methods). Kaplan-Meier survival curves, as displayed in Fig. 3A, show that our summarized risk is able to span a wider range of patient outcomes than the TNM staging or the Mayo Clinic score (SSIGN), which is another well-established scoring system for RCC patients [28]. Both the TNM staging and the SSIGN score identify three different groups while miRisk5 clearly separates 5 different groups. Additionally, miRisk5 provides a substantial amelioration with respect to TNM staging (c-index of 0.65 vs. 0.59 for the TNM staging; Fig. 3B). Although it does not significantly outperform the SSIGN score in terms of prognosis, it does assign patients into different risk groups than the SSIGN or the TNM score (Fig. 3C).

Bottom Line: Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome.Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Health Sciences, ETH Zurich, 8093 Zurich, Switzerland.

ABSTRACT

Purpose: To define robust miRNA-based molecular classifiers for human clear cell renal cell carcinoma (ccRCC) subgrouping and prognostication.

Experimental design: Multidimensional data of over 500 clear cell renal cell carcinoma (ccRCC) patients were retrieved from The Cancer Genome Atlas (TCGA) archive. Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.

Results: Our in silico analysis unveiled a novel ccRCC-specific 5-miRNA (miR-10b, miR-21, miR-143, miR-183, and miR-192) signature able, when combined with information from conventional TNM staging and the age of the patient, to prognosticate ccRCC outcome more accurately than known ccRCC miRNA signatures or TNM staging alone. Furthermore, our approach revealed the existence of 6 distinct subgroups of ccRCC characterized by discrete differences in overall survival, tumor stage, and mutational spectra in key ccRCC tumor suppressor genes. It also demonstrated that BAP1 mutations correlate with tumor progression rather than overall survival.

Conclusion: Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome. Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

No MeSH data available.


Related in: MedlinePlus