Kidney Renal Papillary Cell Carcinoma: Correlation between gene mutation status and selected clinical features<BR>(primary solid tumor cohort)

Kidney Renal Papillary Cell Carcinoma: Correlation between gene mutation status and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 13 genes and 8 clinical features across 98 patients, one significant finding detected with Q value < 0.25.

IL32 mutation correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 13 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.


		Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
IL32	4 (4%)	94	1.58e-14 (1.21e-12)	0.362 (1.00)	0.304 (1.00)		0.78 (1.00)		0.223 (1.00)	0.676 (1.00)
MET	7 (7%)	91	0.69 (1.00)	0.887 (1.00)	1 (1.00)		1 (1.00)		0.806 (1.00)	0.894 (1.00)
NF2	5 (5%)	93	0.603 (1.00)	0.299 (1.00)	1 (1.00)		0.404 (1.00)		0.0317 (1.00)	0.0911 (1.00)
LGI4	4 (4%)	94	0.189 (1.00)	0.905 (1.00)	0.304 (1.00)		0.78 (1.00)		0.703 (1.00)	0.676 (1.00)
PCF11	7 (7%)	91	0.828 (1.00)	0.403 (1.00)	0.426 (1.00)		0.864 (1.00)	0.204 (1.00)	0.584 (1.00)	1 (1.00)
PARD6B	4 (4%)	94	0.419 (1.00)	0.74 (1.00)	0.304 (1.00)		0.173 (1.00)		1 (1.00)	0.571 (1.00)
CHCHD3	3 (3%)	95	0.997 (1.00)	0.757 (1.00)	0.549 (1.00)		1 (1.00)		1 (1.00)	1 (1.00)
KDM6A	5 (5%)	93		0.709 (1.00)	1 (1.00)		0.53 (1.00)		0.738 (1.00)	0.868 (1.00)
POMC	3 (3%)	95	0.371 (1.00)	0.44 (1.00)	1 (1.00)		0.223 (1.00)		0.407 (1.00)	0.258 (1.00)
SAV1	3 (3%)	95	0.69 (1.00)	0.717 (1.00)	0.234 (1.00)		0.699 (1.00)		1 (1.00)	0.354 (1.00)
ACTB	3 (3%)	95		0.184 (1.00)	0.549 (1.00)		1 (1.00)		0.0675 (1.00)	0.454 (1.00)
CD86	3 (3%)	95	0.743 (1.00)	0.545 (1.00)	0.234 (1.00)		1 (1.00)			1 (1.00)
SFRS2IP	5 (5%)	93	0.233 (1.00)	0.12 (1.00)	0.65 (1.00)		0.105 (1.00)	1 (1.00)	0.0847 (1.00)	0.324 (1.00)

'IL32 MUTATION STATUS' versus 'Time to Death'

P value = 1.58e-14 (logrank test), Q value = 1.2e-12

Table S1. Gene #2: 'IL32 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	91	14	0.0 - 182.7 (14.1)
IL32 MUTATED	4	1	3.6 - 7.9 (4.8)
IL32 WILD-TYPE	87	13	0.0 - 182.7 (15.5)

Figure S1. Get High-res Image Gene #2: 'IL32 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Mutation data file = KIRP-TP.mutsig.cluster.txt
Clinical data file = KIRP-TP.clin.merged.picked.txt
Number of patients = 98
Number of significantly mutated genes = 13
Number of selected clinical features = 8
Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[4] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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