Rectum Adenocarcinoma: Correlation between gene mutation status and selected clinical features<BR>(primary solid tumor cohort)

Rectum Adenocarcinoma: 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 16 genes and 10 clinical features across 69 patients, 2 significant findings detected with Q value < 0.25.

SMAD2 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
ERBB2 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 16 genes and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test
SMAD2	5 (7%)	64	0.497 (1.00)	0.834 (1.00)	1 (1.00)	1 (1.00)	0.525 (1.00)	0.342 (1.00)	1 (1.00)	0.536 (1.00)	1 (1.00)	0.000868 (0.137)
ERBB2	4 (6%)	65		0.273 (1.00)	0.127 (1.00)	1 (1.00)	0.416 (1.00)	0.453 (1.00)	1 (1.00)	0.662 (1.00)	1 (1.00)	0.000874 (0.137)
APC	57 (83%)	12	0.588 (1.00)	0.897 (1.00)	1 (1.00)	0.634 (1.00)	0.0753 (1.00)	0.813 (1.00)	0.362 (1.00)	0.0491 (1.00)	0.693 (1.00)	0.495 (1.00)
KRAS	38 (55%)	31	0.0279 (1.00)	0.534 (1.00)	0.329 (1.00)	0.27 (1.00)	0.0973 (1.00)	0.832 (1.00)	0.327 (1.00)	0.309 (1.00)	0.0405 (1.00)	0.34 (1.00)
TP53	45 (65%)	24	0.924 (1.00)	0.976 (1.00)	0.803 (1.00)	0.238 (1.00)	0.0498 (1.00)	0.935 (1.00)	0.73 (1.00)	0.297 (1.00)	0.624 (1.00)	0.476 (1.00)
SMAD4	8 (12%)	61	0.447 (1.00)	0.668 (1.00)	1 (1.00)	0.00581 (0.907)	1 (1.00)	0.645 (1.00)	0.327 (1.00)	0.594 (1.00)	1 (1.00)	0.586 (1.00)
KIAA1804	9 (13%)	60	0.447 (1.00)	0.24 (1.00)	1 (1.00)	1 (1.00)	0.707 (1.00)	1 (1.00)	1 (1.00)	0.957 (1.00)	1 (1.00)	0.0883 (1.00)
FBXW7	9 (13%)	60	0.497 (1.00)	0.92 (1.00)	0.722 (1.00)	1 (1.00)	0.579 (1.00)	0.199 (1.00)	0.338 (1.00)	0.341 (1.00)	0.631 (1.00)	0.721 (1.00)
NRAS	5 (7%)	64	0.116 (1.00)	0.0221 (1.00)	1 (1.00)	1 (1.00)	0.0521 (1.00)	0.53 (1.00)	0.555 (1.00)	0.887 (1.00)	1 (1.00)	0.0288 (1.00)
TCF7L2	7 (10%)	62	0.665 (1.00)	0.744 (1.00)	1 (1.00)	1 (1.00)	0.794 (1.00)	0.85 (1.00)	0.266 (1.00)	0.514 (1.00)	0.276 (1.00)	0.21 (1.00)
PIK3CA	7 (10%)	62	0.497 (1.00)	0.432 (1.00)	1 (1.00)	0.0348 (1.00)	1 (1.00)	0.62 (1.00)	0.582 (1.00)	0.63 (1.00)	0.628 (1.00)	0.0101 (1.00)
OPCML	6 (9%)	63	0.497 (1.00)	0.966 (1.00)	0.69 (1.00)	1 (1.00)	0.148 (1.00)	1 (1.00)	1 (1.00)	0.504 (1.00)	1 (1.00)	0.724 (1.00)
SPATA8	3 (4%)	66		0.194 (1.00)	0.0775 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.737 (1.00)	1 (1.00)	0.314 (1.00)
IL1RAPL2	5 (7%)	64	0.765 (1.00)	0.639 (1.00)	1 (1.00)	0.11 (1.00)	1 (1.00)	0.816 (1.00)	1 (1.00)	0.852 (1.00)	1 (1.00)	0.0153 (1.00)
FAM123B	6 (9%)	63	0.484 (1.00)	0.485 (1.00)	0.69 (1.00)	1 (1.00)	0.0463 (1.00)	1 (1.00)	0.207 (1.00)	0.0851 (1.00)	0.222 (1.00)	0.639 (1.00)
ZIM3	5 (7%)	64	0.069 (1.00)	0.235 (1.00)	1 (1.00)	0.493 (1.00)	0.366 (1.00)	0.53 (1.00)	1 (1.00)	0.666 (1.00)	0.0831 (1.00)	0.00803 (1.00)

'SMAD2 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000868 (t-test), Q value = 0.14

Table S1. Gene #11: 'SMAD2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	68	2.3 (5.5)
SMAD2 MUTATED	5	0.0 (0.0)
SMAD2 WILD-TYPE	63	2.5 (5.7)

Figure S1. Get High-res Image Gene #11: 'SMAD2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'ERBB2 MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000874 (t-test), Q value = 0.14

Table S2. Gene #13: 'ERBB2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	68	2.3 (5.5)
ERBB2 MUTATED	4	0.0 (0.0)
ERBB2 WILD-TYPE	64	2.5 (5.7)

Figure S2. Get High-res Image Gene #13: 'ERBB2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Mutation data file = READ-TP.mutsig.cluster.txt
Clinical data file = READ-TP.clin.merged.picked.txt
Number of patients = 69
Number of significantly mutated genes = 16
Number of selected clinical features = 10
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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