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 32 genes and 8 clinical features across 69 patients, no significant finding detected with Q value < 0.25.

No gene mutations related to clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 32 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE
	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
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)
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)
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)
SMAD4	8 (12%)	61	0.447 (1.00)	0.668 (1.00)	1 (1.00)	0.00581 (1.00)	1 (1.00)	0.645 (1.00)	0.327 (1.00)	0.594 (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)
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)
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)
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)
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)
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)
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)
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)
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)
CSMD1	9 (13%)	60	0.469 (1.00)	0.256 (1.00)	1 (1.00)	1 (1.00)	0.354 (1.00)	1 (1.00)	1 (1.00)	0.601 (1.00)
LRRTM2	4 (6%)	65	0.497 (1.00)	0.333 (1.00)	1 (1.00)	1 (1.00)	0.0132 (1.00)	0.453 (1.00)	1 (1.00)	0.275 (1.00)
GFRA1	5 (7%)	64	0.588 (1.00)	0.41 (1.00)	0.646 (1.00)	0.11 (1.00)	0.0194 (1.00)	0.816 (1.00)	1 (1.00)	0.0329 (1.00)
SGCB	4 (6%)	65	0.116 (1.00)	0.987 (1.00)	0.627 (1.00)	1 (1.00)	0.0484 (1.00)	1 (1.00)	0.474 (1.00)	0.0644 (1.00)
CCBP2	5 (7%)	64	0.588 (1.00)	0.516 (1.00)	0.646 (1.00)	0.493 (1.00)	0.141 (1.00)	0.342 (1.00)	1 (1.00)	0.316 (1.00)
LPHN3	6 (9%)	63	0.36 (1.00)	0.572 (1.00)	1 (1.00)	1 (1.00)	0.578 (1.00)	0.827 (1.00)	0.582 (1.00)	0.941 (1.00)
MAP2K3	4 (6%)	65		0.372 (1.00)	0.627 (1.00)	1 (1.00)	1 (1.00)	0.8 (1.00)	0.474 (1.00)	1 (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)
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)
PCDHA13	6 (9%)	63	0.668 (1.00)	0.314 (1.00)	0.69 (1.00)	0.561 (1.00)	1 (1.00)	0.827 (1.00)	1 (1.00)	0.776 (1.00)
C4BPA	5 (7%)	64	0.497 (1.00)	0.0565 (1.00)	0.646 (1.00)	1 (1.00)	0.366 (1.00)	0.342 (1.00)	1 (1.00)	0.536 (1.00)
CSMD3	9 (13%)	60	0.332 (1.00)	0.987 (1.00)	0.722 (1.00)	0.586 (1.00)	0.579 (1.00)	0.681 (1.00)	1 (1.00)	0.714 (1.00)
KCNS2	5 (7%)	64	0.765 (1.00)	0.489 (1.00)	0.159 (1.00)	1 (1.00)	0.141 (1.00)	0.182 (1.00)	1 (1.00)	0.102 (1.00)
CASP14	4 (6%)	65	0.765 (1.00)	0.901 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.0923 (1.00)	0.474 (1.00)	0.175 (1.00)
RBM10	5 (7%)	64	0.627 (1.00)	0.0772 (1.00)	1 (1.00)	0.493 (1.00)	1 (1.00)	0.0578 (1.00)	0.15 (1.00)	0.807 (1.00)
OSBPL6	5 (7%)	64		0.36 (1.00)	0.646 (1.00)	1 (1.00)	0.366 (1.00)	0.53 (1.00)	0.555 (1.00)	0.717 (1.00)
SLITRK1	5 (7%)	64	0.521 (1.00)	0.733 (1.00)	0.379 (1.00)	1 (1.00)	0.525 (1.00)	0.816 (1.00)	1 (1.00)	1 (1.00)
DKK4	3 (4%)	66	0.613 (1.00)	0.139 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.178 (1.00)	0.38 (1.00)	0.482 (1.00)
LIFR	5 (7%)	64	0.116 (1.00)	0.306 (1.00)	0.379 (1.00)	1 (1.00)	1 (1.00)	0.43 (1.00)	0.555 (1.00)	0.732 (1.00)

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 = 32
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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