Correlation between gene mutation status and selected clinical features

Overview

Introduction

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

Summary

Testing the association between mutation status of 17 genes and 5 clinical features across 201 patients, 3 significant findings detected with Q value < 0.25.

BRAF mutation correlated to 'HISTOLOGICAL.TYPE'.
NRAS mutation correlated to 'HISTOLOGICAL.TYPE'.
HRAS mutation correlated to 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 17 genes and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Clinical Features	AGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	RADIATIONEXPOSURE
	nMutated (%)	nWild-Type	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
BRAF	115 (57%)	86	0.445 (1.00)	0.517 (1.00)	4.71e-18 (4e-16)	0.0144 (1.00)	0.738 (1.00)
NRAS	16 (8%)	185	0.818 (1.00)	0.132 (1.00)	9.52e-06 (8e-04)	0.604 (1.00)	0.537 (1.00)
HRAS	10 (5%)	191	0.732 (1.00)	0.721 (1.00)	0.000153 (0.0127)	1 (1.00)	1 (1.00)
EIF1AX	3 (1%)	198	0.432 (1.00)	0.165 (1.00)	0.0366 (1.00)	1 (1.00)	1 (1.00)
EMG1	4 (2%)	197	0.686 (1.00)	0.574 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
TG	9 (4%)	192	0.359 (1.00)	1 (1.00)	0.0109 (0.89)	1 (1.00)	1 (1.00)
ZNF845	3 (1%)	198	0.0965 (1.00)	0.57 (1.00)	0.559 (1.00)	1 (1.00)	1 (1.00)
PPM1D	5 (2%)	196	0.545 (1.00)	1 (1.00)	0.842 (1.00)	1 (1.00)	1 (1.00)
ZNF799	3 (1%)	198	0.946 (1.00)	1 (1.00)	0.559 (1.00)	1 (1.00)	1 (1.00)
CCDC15	5 (2%)	196	0.139 (1.00)	1 (1.00)	0.121 (1.00)	1 (1.00)	1 (1.00)
COL5A3	4 (2%)	197	0.666 (1.00)	0.574 (1.00)	0.289 (1.00)	1 (1.00)	1 (1.00)
ZFHX3	4 (2%)	197	0.71 (1.00)	0.574 (1.00)	0.289 (1.00)	0.22 (1.00)	1 (1.00)
DNMT3A	4 (2%)	197	0.665 (1.00)	1 (1.00)	0.626 (1.00)	1 (1.00)	0.192 (1.00)
FAM155A	3 (1%)	198	0.174 (1.00)	1 (1.00)	0.165 (1.00)	1 (1.00)	1 (1.00)
KRAS	3 (1%)	198	0.888 (1.00)	0.57 (1.00)	0.0985 (1.00)	1 (1.00)	1 (1.00)
R3HDM2	3 (1%)	198	0.577 (1.00)	1 (1.00)	0.723 (1.00)	1 (1.00)	1 (1.00)
ARID1B	5 (2%)	196	0.52 (1.00)	1 (1.00)	0.219 (1.00)	1 (1.00)	1 (1.00)

'BRAF MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 4.71e-18 (Fisher's exact test), Q value = 4e-16

Table S1. Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	8	108	64	21
BRAF MUTATED	1	84	11	19
BRAF WILD-TYPE	7	24	53	2

Figure S1. Get High-res Image Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'NRAS MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 9.52e-06 (Fisher's exact test), Q value = 8e-04

Table S2. Gene #2: 'NRAS MUTATION STATUS' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	8	108	64	21
NRAS MUTATED	1	1	14	0
NRAS WILD-TYPE	7	107	50	21

Figure S2. Get High-res Image Gene #2: 'NRAS MUTATION STATUS' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'HRAS MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000153 (Fisher's exact test), Q value = 0.013

Table S3. Gene #3: 'HRAS MUTATION STATUS' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	8	108	64	21
HRAS MUTATED	1	0	9	0
HRAS WILD-TYPE	7	108	55	21

Figure S3. Get High-res Image Gene #3: 'HRAS MUTATION STATUS' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

Methods & Data

Input

Mutation data file = THCA-TP.mutsig.cluster.txt
Clinical data file = THCA-TP.clin.merged.picked.txt
Number of patients = 201
Number of significantly mutated genes = 17
Number of selected clinical features = 5
Exclude genes that fewer than K tumors have mutations, K = 3

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.

References

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

[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)