Correlation between gene mutation status and molecular subtypes

Overview

Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 28 genes and 10 molecular subtypes across 293 patients, 10 significant findings detected with P value < 0.05 and Q value < 0.25.

PBRM1 mutation correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.
BAP1 mutation correlated to 'MRNASEQ_CHIERARCHICAL', 'MIRSEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.
SETD2 mutation correlated to 'METHLYATION_CNMF'.
MTOR mutation correlated to 'RPPA_CNMF' and 'RPPA_CHIERARCHICAL'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 28 genes and 10 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 10 significant findings detected.


		Clinical Features	MRNA CNMF	MRNA CHIERARCHICAL	CN CNMF	METHLYATION CNMF	RPPA CNMF	RPPA CHIERARCHICAL	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nMutated (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Chi-square test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
PBRM1	107 (37%)	186	0.0211 (1.00)	0.0735 (1.00)	0.000201 (0.0448)	0.000645 (0.143)	0.00738 (1.00)	0.799 (1.00)	0.000649 (0.143)	0.0822 (1.00)	0.0456 (1.00)	0.000141 (0.0318)
BAP1	27 (9%)	266	0.0212 (1.00)	0.0111 (1.00)	0.0883 (1.00)	0.00376 (0.82)	0.0259 (1.00)	0.774 (1.00)	0.00145 (0.317)	3.76e-08 (8.61e-06)	0.000192 (0.043)	9.92e-07 (0.000226)
MTOR	24 (8%)	269			0.631 (1.00)	0.043 (1.00)	8.32e-05 (0.0188)	0.000582 (0.129)	0.707 (1.00)	0.323 (1.00)	0.358 (1.00)	0.544 (1.00)
SETD2	34 (12%)	259	0.747 (1.00)	1 (1.00)	0.0992 (1.00)	8e-05 (0.0182)	0.249 (1.00)	0.21 (1.00)	0.338 (1.00)	0.655 (1.00)	0.0978 (1.00)	0.465 (1.00)
VHL	138 (47%)	155	0.0307 (1.00)	0.0514 (1.00)	0.582 (1.00)	0.397 (1.00)	0.885 (1.00)	0.307 (1.00)	0.00744 (1.00)	0.0654 (1.00)	0.311 (1.00)	0.156 (1.00)
SV2C	3 (1%)	290			0.169 (1.00)	0.0102 (1.00)	0.54 (1.00)	0.79 (1.00)			0.236 (1.00)	0.0191 (1.00)
KDM5C	18 (6%)	275			0.351 (1.00)	0.261 (1.00)	0.71 (1.00)	0.0388 (1.00)	0.105 (1.00)	0.671 (1.00)	0.0516 (1.00)	0.684 (1.00)
TP53	6 (2%)	287			0.875 (1.00)	0.535 (1.00)	0.506 (1.00)	0.427 (1.00)	0.481 (1.00)	0.245 (1.00)	0.868 (1.00)	0.0787 (1.00)
PTEN	9 (3%)	284	0.163 (1.00)	0.665 (1.00)	1 (1.00)	0.317 (1.00)	0.0869 (1.00)	0.606 (1.00)	0.00835 (1.00)	0.0437 (1.00)	0.412 (1.00)	0.661 (1.00)
EBPL	6 (2%)	287			0.769 (1.00)	0.797 (1.00)	0.477 (1.00)	0.414 (1.00)	0.318 (1.00)	0.174 (1.00)	0.2 (1.00)	0.313 (1.00)
PIK3CA	10 (3%)	283			0.319 (1.00)	0.205 (1.00)	0.445 (1.00)	1 (1.00)	0.914 (1.00)	0.586 (1.00)	0.84 (1.00)	0.404 (1.00)
TSPAN19	4 (1%)	289			0.473 (1.00)		0.06 (1.00)	0.248 (1.00)	0.391 (1.00)	0.454 (1.00)	0.274 (1.00)	0.0583 (1.00)
NBPF10	19 (6%)	274	0.537 (1.00)	0.602 (1.00)	0.594 (1.00)	0.872 (1.00)	0.0897 (1.00)	0.45 (1.00)	0.35 (1.00)	0.0484 (1.00)	0.647 (1.00)	0.921 (1.00)
TOR1A	3 (1%)	290			0.464 (1.00)		0.828 (1.00)	0.225 (1.00)	0.563 (1.00)	1 (1.00)	0.8 (1.00)	1 (1.00)
MUC4	41 (14%)	252	0.786 (1.00)	0.892 (1.00)	0.0839 (1.00)	0.044 (1.00)	0.0467 (1.00)	0.543 (1.00)	0.0476 (1.00)	0.114 (1.00)	0.0889 (1.00)	0.0622 (1.00)
UQCRFS1	3 (1%)	290			0.253 (1.00)				0.293 (1.00)	0.341 (1.00)	0.322 (1.00)	0.617 (1.00)
WDR52	9 (3%)	284			0.283 (1.00)	0.482 (1.00)	0.576 (1.00)	1 (1.00)	0.448 (1.00)	0.506 (1.00)	0.623 (1.00)	0.233 (1.00)
BAGE2	4 (1%)	289			0.816 (1.00)	0.117 (1.00)	0.635 (1.00)	0.0749 (1.00)	0.171 (1.00)	0.0914 (1.00)	0.0473 (1.00)	0.00644 (1.00)
CNTNAP4	9 (3%)	284			0.758 (1.00)	0.369 (1.00)	0.0932 (1.00)	0.513 (1.00)	0.815 (1.00)	0.625 (1.00)	0.908 (1.00)	1 (1.00)
CR1	10 (3%)	283			1 (1.00)	0.279 (1.00)	0.178 (1.00)	0.497 (1.00)	0.0978 (1.00)	0.483 (1.00)	0.602 (1.00)	0.574 (1.00)
STAG2	9 (3%)	284			0.0789 (1.00)	0.0119 (1.00)	0.468 (1.00)	0.412 (1.00)	0.355 (1.00)	0.13 (1.00)	0.0785 (1.00)	0.0676 (1.00)
MSN	4 (1%)	289			0.581 (1.00)	0.444 (1.00)	0.794 (1.00)	0.533 (1.00)	0.284 (1.00)	0.17 (1.00)	0.352 (1.00)	0.665 (1.00)
ABCB1	8 (3%)	285			0.333 (1.00)	0.608 (1.00)	0.0733 (1.00)	0.0793 (1.00)	0.265 (1.00)	0.161 (1.00)	0.268 (1.00)	0.0892 (1.00)
ADCY8	5 (2%)	288			0.527 (1.00)	0.106 (1.00)	0.953 (1.00)	1 (1.00)	0.848 (1.00)	1 (1.00)	0.621 (1.00)	0.358 (1.00)
NPNT	6 (2%)	287			0.667 (1.00)	0.502 (1.00)	0.18 (1.00)	1 (1.00)	0.333 (1.00)	0.382 (1.00)	0.581 (1.00)	0.756 (1.00)
OR5H1	3 (1%)	290			1 (1.00)		0.258 (1.00)	0.0476 (1.00)	0.768 (1.00)	0.721 (1.00)	0.602 (1.00)	0.101 (1.00)
SPAM1	5 (2%)	288			0.386 (1.00)		0.44 (1.00)	0.277 (1.00)	0.116 (1.00)	0.11 (1.00)	0.196 (1.00)	0.478 (1.00)
TPTE2	7 (2%)	286			0.549 (1.00)	0.63 (1.00)	0.609 (1.00)	0.694 (1.00)	0.876 (1.00)	0.873 (1.00)	0.157 (1.00)	0.608 (1.00)

'PBRM1 MUTATION STATUS' versus 'CN_CNMF'

P value = 0.000201 (Fisher's exact test), Q value = 0.045

Table S1. Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	73	130	89
PBRM1 MUTATED	36	53	18
PBRM1 WILD-TYPE	37	77	71

Figure S1. Get High-res Image Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

'PBRM1 MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 0.000645 (Fisher's exact test), Q value = 0.14

Table S2. Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	71	84	44
PBRM1 MUTATED	36	31	7
PBRM1 WILD-TYPE	35	53	37

Figure S2. Get High-res Image Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #4: 'METHLYATION_CNMF'

'PBRM1 MUTATION STATUS' versus 'MRNASEQ_CNMF'

P value = 0.000649 (Fisher's exact test), Q value = 0.14

Table S3. Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #7: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	124	110	46
PBRM1 MUTATED	58	34	8
PBRM1 WILD-TYPE	66	76	38

Figure S3. Get High-res Image Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #7: 'MRNASEQ_CNMF'

'PBRM1 MUTATION STATUS' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000141 (Fisher's exact test), Q value = 0.032

Table S4. Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #10: 'MIRSEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	14	85	189
PBRM1 MUTATED	2	18	84
PBRM1 WILD-TYPE	12	67	105

Figure S4. Get High-res Image Gene #3: 'PBRM1 MUTATION STATUS' versus Clinical Feature #10: 'MIRSEQ_CHIERARCHICAL'

'BAP1 MUTATION STATUS' versus 'MRNASEQ_CHIERARCHICAL'

P value = 3.76e-08 (Fisher's exact test), Q value = 8.6e-06

Table S5. Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #8: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	31	132	117
BAP1 MUTATED	1	1	25
BAP1 WILD-TYPE	30	131	92

Figure S5. Get High-res Image Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #8: 'MRNASEQ_CHIERARCHICAL'

'BAP1 MUTATION STATUS' versus 'MIRSEQ_CNMF'

P value = 0.000192 (Fisher's exact test), Q value = 0.043

Table S6. Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #9: 'MIRSEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	64	128	96
BAP1 MUTATED	7	3	17
BAP1 WILD-TYPE	57	125	79

Figure S6. Get High-res Image Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #9: 'MIRSEQ_CNMF'

'BAP1 MUTATION STATUS' versus 'MIRSEQ_CHIERARCHICAL'

P value = 9.92e-07 (Fisher's exact test), Q value = 0.00023

Table S7. Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #10: 'MIRSEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	14	85	189
BAP1 MUTATED	5	16	6
BAP1 WILD-TYPE	9	69	183

Figure S7. Get High-res Image Gene #4: 'BAP1 MUTATION STATUS' versus Clinical Feature #10: 'MIRSEQ_CHIERARCHICAL'

'SETD2 MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 8e-05 (Fisher's exact test), Q value = 0.018

Table S8. Gene #5: 'SETD2 MUTATION STATUS' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	71	84	44
SETD2 MUTATED	18	8	0
SETD2 WILD-TYPE	53	76	44

Figure S8. Get High-res Image Gene #5: 'SETD2 MUTATION STATUS' versus Clinical Feature #4: 'METHLYATION_CNMF'

'MTOR MUTATION STATUS' versus 'RPPA_CNMF'

P value = 8.32e-05 (Chi-square test), Q value = 0.019

Table S9. Gene #7: 'MTOR MUTATION STATUS' versus Clinical Feature #5: 'RPPA_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4	CLUS_5	CLUS_6
ALL	66	52	60	46	24	26
MTOR MUTATED	5	1	7	1	0	8
MTOR WILD-TYPE	61	51	53	45	24	18

Figure S9. Get High-res Image Gene #7: 'MTOR MUTATION STATUS' versus Clinical Feature #5: 'RPPA_CNMF'

'MTOR MUTATION STATUS' versus 'RPPA_CHIERARCHICAL'

P value = 0.000582 (Fisher's exact test), Q value = 0.13

Table S10. Gene #7: 'MTOR MUTATION STATUS' versus Clinical Feature #6: 'RPPA_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	117	105	52
MTOR MUTATED	2	11	9
MTOR WILD-TYPE	115	94	43

Figure S10. Get High-res Image Gene #7: 'MTOR MUTATION STATUS' versus Clinical Feature #6: 'RPPA_CHIERARCHICAL'

Methods & Data

Input

Mutation data file = KIRC-TP.mutsig.cluster.txt
Molecular subtypes file = KIRC-TP.transferedmergedcluster.txt
Number of patients = 293
Number of significantly mutated genes = 28
Number of Molecular subtypes = 10
Exclude genes that fewer than K tumors have mutations, K = 3

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

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

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] 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)

[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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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