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 7 genes and 15 clinical features across 385 patients, 8 significant findings detected with Q value < 0.25.

NRAS mutation correlated to 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', and 'NUMBER.OF.LYMPH.NODES'.
BRAF mutation correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 15 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.


		Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	RADIATIONEXPOSURE	EXTRATHYROIDAL EXTENSION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	MULTIFOCALITY	TUMOR SIZE
	nMutated (%)	nWild-Type	logrank test	t-test	Chi-square 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	Fisher's exact test	Fisher's exact test	t-test	Fisher's exact test	t-test
BRAF	232 (60%)	153	0.806 (1.00)	0.848 (1.00)	0.00245 (0.236)	0.000849 (0.0832)	0.000905 (0.0877)	0.0672 (1.00)	0.552 (1.00)	5.43e-20 (5.59e-18)	0.0323 (1.00)	0.591 (1.00)	2.09e-07 (2.13e-05)	0.587 (1.00)	0.618 (1.00)	0.752 (1.00)	0.231 (1.00)
NRAS	32 (8%)	353	0.478 (1.00)	0.748 (1.00)	0.0436 (1.00)	0.0788 (1.00)	0.000413 (0.0409)	0.348 (1.00)	0.674 (1.00)	1.55e-05 (0.00156)	0.61 (1.00)	1 (1.00)	0.105 (1.00)	0.739 (1.00)	0.000113 (0.0113)	0.194 (1.00)	0.851 (1.00)
HRAS	13 (3%)	372	0.52 (1.00)	0.616 (1.00)	0.339 (1.00)	0.436 (1.00)	0.549 (1.00)	0.262 (1.00)	0.745 (1.00)	0.0147 (1.00)	1 (1.00)	1 (1.00)	0.681 (1.00)	0.137 (1.00)	0.51 (1.00)	0.779 (1.00)	0.933 (1.00)
OTUD4	5 (1%)	380	0.65 (1.00)	0.709 (1.00)	0.911 (1.00)	1 (1.00)	0.671 (1.00)	0.241 (1.00)	0.336 (1.00)	0.436 (1.00)	1 (1.00)	1 (1.00)	0.67 (1.00)	1 (1.00)	0.00629 (0.597)	0.667 (1.00)
EIF1AX	6 (2%)	379	0.0796 (1.00)	0.135 (1.00)	0.969 (1.00)	1 (1.00)	0.625 (1.00)	0.468 (1.00)	0.171 (1.00)	0.0922 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.255 (1.00)	0.771 (1.00)	0.667 (1.00)	0.672 (1.00)
NUP93	4 (1%)	381	0.907 (1.00)	0.391 (1.00)	0.794 (1.00)	0.731 (1.00)	0.349 (1.00)	0.653 (1.00)	1 (1.00)	0.0157 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.498 (1.00)	0.609 (1.00)	0.342 (1.00)	0.995 (1.00)
NLRP6	3 (1%)	382	0.655 (1.00)	0.0342 (1.00)	0.815 (1.00)	0.157 (1.00)	0.605 (1.00)	0.132 (1.00)	1 (1.00)	0.668 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.525 (1.00)	0.6 (1.00)

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

P value = 0.000413 (Fisher's exact test), Q value = 0.041

Table S1. Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	181	163
NRAS MUTATED	25	5
NRAS WILD-TYPE	156	158

Figure S1. Get High-res Image Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'NRAS MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 1.55e-05 (Fisher's exact test), Q value = 0.0016

Table S2. Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	6	267	83	29
NRAS MUTATED	0	13	19	0
NRAS WILD-TYPE	6	254	64	29

Figure S2. Get High-res Image Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.000113 (t-test), Q value = 0.011

Table S3. Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	302	3.3 (6.0)
NRAS MUTATED	25	1.0 (2.4)
NRAS WILD-TYPE	277	3.5 (6.2)

Figure S3. Get High-res Image Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'BRAF MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00245 (Chi-square test), Q value = 0.24

Table S4. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	220	43	83	2	30	5
BRAF MUTATED	129	17	59	0	23	3
BRAF WILD-TYPE	91	26	24	2	7	2

Figure S4. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'BRAF MUTATION STATUS' versus 'PATHOLOGY.T.STAGE'

P value = 0.000849 (Fisher's exact test), Q value = 0.083

Table S5. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	113	133	124	13
BRAF MUTATED	67	65	88	11
BRAF WILD-TYPE	46	68	36	2

Figure S5. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.000905 (Fisher's exact test), Q value = 0.088

Table S6. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	181	163
BRAF MUTATED	96	115
BRAF WILD-TYPE	85	48

Figure S6. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'BRAF MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 5.43e-20 (Fisher's exact test), Q value = 5.6e-18

Table S7. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	OTHER SPECIFY	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	6	267	83	29
BRAF MUTATED	3	187	15	27
BRAF WILD-TYPE	3	80	68	2

Figure S7. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'BRAF MUTATION STATUS' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.09e-07 (Fisher's exact test), Q value = 2.1e-05

Table S8. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	99	10	264	1
BRAF MUTATED	78	10	139	0
BRAF WILD-TYPE	21	0	125	1

Figure S8. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

Methods & Data

Input

Mutation data file = THCA-TP.mutsig.cluster.txt
Clinical data file = THCA-TP.clin.merged.picked.txt
Number of patients = 385
Number of significantly mutated genes = 7
Number of selected clinical features = 15
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] 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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[4] 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)

[5] 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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