Correlation between gene mutation status and selected clinical features

Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15B01BZ

Overview

Introduction

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

Summary

Testing the association between mutation status of 27 genes and 11 clinical features across 306 patients, 2 significant findings detected with Q value < 0.25.

CASP8 mutation correlated to 'AGE'.
NSD1 mutation correlated to 'PATHOLOGY.N.STAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 27 genes and 11 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	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	GENDER	RADIATIONS RADIATION REGIMENINDICATION	NUMBERPACKYEARSSMOKED	NUMBER OF LYMPH NODES	RACE	ETHNICITY
	nMutated (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Wilcoxon-test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
CASP8	27 (9%)	279	0.513 (1.00)	7.61e-06 (0.00225)	0.317 (1.00)	0.558 (1.00)	0.0856 (1.00)	0.00127 (0.375)	0.372 (1.00)	0.215 (1.00)	0.0416 (1.00)	0.416 (1.00)	0.351 (1.00)
NSD1	33 (11%)	273	0.0877 (1.00)	0.0865 (1.00)	0.0414 (1.00)	0.78 (1.00)	0.00065 (0.192)	0.301 (1.00)	0.301 (1.00)	0.0913 (1.00)	0.00301 (0.87)	0.381 (1.00)	0.0513 (1.00)
TP53	213 (70%)	93	0.0216 (1.00)	0.509 (1.00)	0.00931 (1.00)	0.00552 (1.00)	0.016 (1.00)	0.781 (1.00)	0.123 (1.00)	0.0635 (1.00)	0.00776 (1.00)	0.651 (1.00)	0.243 (1.00)
CDKN2A	65 (21%)	241	0.411 (1.00)	0.409 (1.00)	0.271 (1.00)	0.666 (1.00)	0.95 (1.00)	0.64 (1.00)	0.428 (1.00)	0.0107 (1.00)	0.785 (1.00)	0.779 (1.00)	0.197 (1.00)
FAT1	69 (23%)	237	0.571 (1.00)	0.00142 (0.417)	0.537 (1.00)	0.448 (1.00)	0.853 (1.00)	0.0677 (1.00)	0.758 (1.00)	0.341 (1.00)	0.295 (1.00)	0.19 (1.00)	0.0889 (1.00)
MLL2	53 (17%)	253	0.944 (1.00)	0.988 (1.00)	0.369 (1.00)	0.123 (1.00)	0.884 (1.00)	0.867 (1.00)	1 (1.00)	0.52 (1.00)	0.919 (1.00)	0.501 (1.00)	1 (1.00)
JUB	18 (6%)	288	0.00209 (0.609)	0.155 (1.00)	0.776 (1.00)	0.455 (1.00)	0.295 (1.00)	1 (1.00)	1 (1.00)	0.196 (1.00)	0.221 (1.00)	0.0911 (1.00)	0.57 (1.00)
NOTCH1	57 (19%)	249	0.758 (1.00)	0.144 (1.00)	0.836 (1.00)	0.752 (1.00)	0.935 (1.00)	0.188 (1.00)	0.405 (1.00)	0.0649 (1.00)	0.403 (1.00)	0.932 (1.00)	0.151 (1.00)
NFE2L2	17 (6%)	289	0.566 (1.00)	0.00939 (1.00)	0.695 (1.00)	0.773 (1.00)	1 (1.00)	0.261 (1.00)	0.256 (1.00)	0.965 (1.00)	0.677 (1.00)	0.179 (1.00)	0.524 (1.00)
HRAS	10 (3%)	296	0.213 (1.00)	0.293 (1.00)	0.161 (1.00)	0.24 (1.00)	0.0491 (1.00)	1 (1.00)	0.465 (1.00)	0.214 (1.00)	0.0534 (1.00)	0.678 (1.00)	1 (1.00)
ZNF750	13 (4%)	293	0.368 (1.00)	0.824 (1.00)	0.672 (1.00)	0.0594 (1.00)	0.748 (1.00)	0.123 (1.00)	0.525 (1.00)	0.677 (1.00)	0.357 (1.00)	0.327 (1.00)	1 (1.00)
RASA1	14 (5%)	292	0.968 (1.00)	0.942 (1.00)	0.907 (1.00)	0.522 (1.00)	1 (1.00)	0.364 (1.00)	0.534 (1.00)	0.694 (1.00)	0.608 (1.00)	0.532 (1.00)	0.499 (1.00)
HLA-A	9 (3%)	297	0.142 (1.00)	0.307 (1.00)	0.421 (1.00)	0.621 (1.00)	0.341 (1.00)	1 (1.00)	0.703 (1.00)	0.463 (1.00)	0.701 (1.00)	0.665 (1.00)	1 (1.00)
EPHA2	13 (4%)	293	0.999 (1.00)	0.675 (1.00)	0.886 (1.00)	0.95 (1.00)	0.599 (1.00)	0.199 (1.00)	1 (1.00)	0.0231 (1.00)	0.439 (1.00)	0.114 (1.00)	0.473 (1.00)
RAC1	9 (3%)	297	0.102 (1.00)	0.00213 (0.616)	0.379 (1.00)	0.41 (1.00)	0.215 (1.00)	0.0676 (1.00)	1 (1.00)	0.701 (1.00)	0.163 (1.00)	1 (1.00)	1 (1.00)
EP300	24 (8%)	282	0.713 (1.00)	0.427 (1.00)	0.59 (1.00)	0.927 (1.00)	0.696 (1.00)	0.483 (1.00)	0.634 (1.00)	0.413 (1.00)	0.95 (1.00)	0.271 (1.00)	0.296 (1.00)
TGFBR2	10 (3%)	296	0.694 (1.00)	0.0813 (1.00)	0.871 (1.00)	0.712 (1.00)	0.341 (1.00)	0.47 (1.00)	0.0678 (1.00)	0.12 (1.00)	0.485 (1.00)	0.122 (1.00)	1 (1.00)
PIK3CA	64 (21%)	242	0.183 (1.00)	0.79 (1.00)	0.434 (1.00)	0.74 (1.00)	0.465 (1.00)	0.114 (1.00)	1 (1.00)	0.996 (1.00)	0.462 (1.00)	0.0505 (1.00)	1 (1.00)
FBXW7	15 (5%)	291	0.536 (1.00)	0.143 (1.00)	0.36 (1.00)	0.635 (1.00)	0.294 (1.00)	0.372 (1.00)	0.553 (1.00)	0.218 (1.00)	0.568 (1.00)	0.158 (1.00)	0.499 (1.00)
RB1	10 (3%)	296	0.395 (1.00)	0.728 (1.00)	0.181 (1.00)	0.241 (1.00)	0.667 (1.00)	0.145 (1.00)	1 (1.00)	0.905 (1.00)	0.582 (1.00)	0.233 (1.00)	1 (1.00)
CTCF	11 (4%)	295	0.299 (1.00)	0.0422 (1.00)	0.635 (1.00)	0.934 (1.00)	0.661 (1.00)	0.181 (1.00)	0.49 (1.00)	0.489 (1.00)	0.186 (1.00)	0.265 (1.00)	1 (1.00)
KDM6A	8 (3%)	298	0.2 (1.00)	0.766 (1.00)	0.569 (1.00)	0.457 (1.00)	0.891 (1.00)	0.453 (1.00)	0.686 (1.00)	0.981 (1.00)	0.574 (1.00)	0.0752 (1.00)	1 (1.00)
ELF4	5 (2%)	301	0.933 (1.00)	0.712 (1.00)	0.0512 (1.00)	0.203 (1.00)	0.405 (1.00)	1 (1.00)	0.33 (1.00)	0.553 (1.00)	0.192 (1.00)	1 (1.00)	1 (1.00)
RHOA	4 (1%)	302	0.00641 (1.00)	0.765 (1.00)	0.638 (1.00)	0.225 (1.00)	0.407 (1.00)	0.303 (1.00)	0.576 (1.00)	0.475 (1.00)	0.953 (1.00)	1 (1.00)	1 (1.00)
HLA-B	8 (3%)	298	0.262 (1.00)	0.597 (1.00)	0.295 (1.00)	0.766 (1.00)	0.109 (1.00)	0.222 (1.00)	0.115 (1.00)	0.311 (1.00)	0.0923 (1.00)	1 (1.00)	1 (1.00)
PRSS1	7 (2%)	299	0.87 (1.00)	0.684 (1.00)	0.186 (1.00)	0.911 (1.00)	0.0921 (1.00)	0.00204 (0.595)	0.196 (1.00)		0.185 (1.00)	0.0957 (1.00)	1 (1.00)
GUCY2F	8 (3%)	298	0.168 (1.00)	0.966 (1.00)	0.403 (1.00)	0.309 (1.00)	0.67 (1.00)	1 (1.00)	0.44 (1.00)	0.704 (1.00)	0.293 (1.00)	0.121 (1.00)	1 (1.00)

'CASP8 MUTATION STATUS' versus 'AGE'

P value = 7.61e-06 (Wilcoxon-test), Q value = 0.0023

Table S1. Gene #3: 'CASP8 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	306	61.1 (12.1)
CASP8 MUTATED	27	71.1 (13.4)
CASP8 WILD-TYPE	279	60.2 (11.6)

Figure S1. Get High-res Image Gene #3: 'CASP8 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'NSD1 MUTATION STATUS' versus 'PATHOLOGY.N.STAGE'

P value = 0.00065 (Fisher's exact test), Q value = 0.19

Table S2. Gene #9: 'NSD1 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	99	33	99	5
NSD1 MUTATED	18	4	2	1
NSD1 WILD-TYPE	81	29	97	4

Figure S2. Get High-res Image Gene #9: 'NSD1 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

Methods & Data

Input

Mutation data file = transformed.cor.cli.txt
Clinical data file = HNSC-TP.merged_data.txt
Number of patients = 306
Number of significantly mutated genes = 27
Number of selected clinical features = 11
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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

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

[2] 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)

[3] 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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