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 16 genes and 7 molecular subtypes across 154 patients, 7 significant findings detected with P value < 0.05 and Q value < 0.25.

BRAF mutation correlated to 'MRNA_CNMF', 'MRNA_CHIERARCHICAL', and 'CN_CNMF'.
KRAS mutation correlated to 'MRNA_CHIERARCHICAL'.
TP53 mutation correlated to 'MRNA_CNMF' and 'CN_CNMF'.
FBXW7 mutation correlated to 'CN_CNMF'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 16 genes and 7 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 7 significant findings detected.


		Clinical Features	MRNA CNMF	MRNA CHIERARCHICAL	CN CNMF	RPPA CNMF	RPPA CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nMutated (%)	nWild-Type	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
BRAF	20 (13%)	134	3.29e-07 (3.36e-05)	4.37e-08 (4.5e-06)	9.33e-05 (0.00943)	0.722 (1.00)	0.926 (1.00)	0.462 (1.00)	1 (1.00)
TP53	74 (48%)	80	0.00226 (0.222)	0.013 (1.00)	9.19e-10 (9.56e-08)	0.271 (1.00)	0.38 (1.00)	0.62 (1.00)	1 (1.00)
KRAS	58 (38%)	96	0.0241 (1.00)	0.000638 (0.0638)	0.0552 (1.00)	0.72 (1.00)	0.752 (1.00)	0.626 (1.00)	0.283 (1.00)
FBXW7	29 (19%)	125	0.227 (1.00)	0.0892 (1.00)	0.00223 (0.221)	0.00343 (0.333)	0.233 (1.00)	1 (1.00)	0.474 (1.00)
APC	103 (67%)	51	0.171 (1.00)	0.0187 (1.00)	0.025 (1.00)	0.216 (1.00)	0.388 (1.00)	0.601 (1.00)	0.258 (1.00)
PIK3CA	26 (17%)	128	0.331 (1.00)	0.336 (1.00)	0.00617 (0.592)	0.119 (1.00)	0.332 (1.00)	0.514 (1.00)	0.416 (1.00)
NRAS	15 (10%)	139	0.0773 (1.00)	0.409 (1.00)	0.927 (1.00)	0.465 (1.00)	0.332 (1.00)	1 (1.00)	1 (1.00)
SMAD4	18 (12%)	136	0.304 (1.00)	0.58 (1.00)	0.0558 (1.00)	0.852 (1.00)	0.495 (1.00)	1 (1.00)	1 (1.00)
FAM123B	19 (12%)	135	0.208 (1.00)	0.00622 (0.592)	0.0228 (1.00)	0.286 (1.00)	0.422 (1.00)	0.425 (1.00)	0.338 (1.00)
SOX9	9 (6%)	145	0.235 (1.00)	0.214 (1.00)	0.223 (1.00)	0.231 (1.00)	0.461 (1.00)	1 (1.00)	1 (1.00)
ACVR2A	8 (5%)	146	0.045 (1.00)	0.296 (1.00)	0.0382 (1.00)	0.199 (1.00)	0.668 (1.00)	1 (1.00)	0.162 (1.00)
TNFRSF10C	6 (4%)	148	0.381 (1.00)	0.676 (1.00)	0.299 (1.00)	0.68 (1.00)	0.169 (1.00)	1 (1.00)	1 (1.00)
ACOT4	3 (2%)	151	0.665 (1.00)	0.391 (1.00)	0.462 (1.00)			1 (1.00)	1 (1.00)
SMAD2	10 (6%)	144	0.0995 (1.00)	0.19 (1.00)	0.0277 (1.00)	1 (1.00)	0.377 (1.00)	1 (1.00)	1 (1.00)
PCBP1	4 (3%)	150	0.33 (1.00)	0.552 (1.00)	0.44 (1.00)			1 (1.00)	1 (1.00)
GGT1	3 (2%)	151			0.23 (1.00)			1 (1.00)	1 (1.00)

'BRAF MUTATION STATUS' versus 'MRNA_CNMF'

P value = 3.29e-07 (Fisher's exact test), Q value = 3.4e-05

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

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	57	28	22
BRAF MUTATED	14	1	3	0
BRAF WILD-TYPE	20	56	25	22

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

'BRAF MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 4.37e-08 (Fisher's exact test), Q value = 4.5e-06

Table S2. Gene #3: 'BRAF MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	53	42	46
BRAF MUTATED	1	16	1
BRAF WILD-TYPE	52	26	45

Figure S2. Get High-res Image Gene #3: 'BRAF MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

'BRAF MUTATION STATUS' versus 'CN_CNMF'

P value = 9.33e-05 (Fisher's exact test), Q value = 0.0094

Table S3. Gene #3: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	72	64	16
BRAF MUTATED	2	17	1
BRAF WILD-TYPE	70	47	15

Figure S3. Get High-res Image Gene #3: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

'KRAS MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.000638 (Fisher's exact test), Q value = 0.064

Table S4. Gene #4: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	53	42	46
KRAS MUTATED	27	16	7
KRAS WILD-TYPE	26	26	39

Figure S4. Get High-res Image Gene #4: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

'TP53 MUTATION STATUS' versus 'MRNA_CNMF'

P value = 0.00226 (Fisher's exact test), Q value = 0.22

Table S5. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	34	57	28	22
TP53 MUTATED	12	39	9	10
TP53 WILD-TYPE	22	18	19	12

Figure S5. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

'TP53 MUTATION STATUS' versus 'CN_CNMF'

P value = 9.19e-10 (Fisher's exact test), Q value = 9.6e-08

Table S6. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	72	64	16
TP53 MUTATED	53	13	7
TP53 WILD-TYPE	19	51	9

Figure S6. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

'FBXW7 MUTATION STATUS' versus 'CN_CNMF'

P value = 0.00223 (Fisher's exact test), Q value = 0.22

Table S7. Gene #6: 'FBXW7 MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	72	64	16
FBXW7 MUTATED	6	20	3
FBXW7 WILD-TYPE	66	44	13

Figure S7. Get High-res Image Gene #6: 'FBXW7 MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

Methods & Data

Input

Mutation data file = COAD-TP.mutsig.cluster.txt
Molecular subtypes file = COAD-TP.transferedmergedcluster.txt
Number of patients = 154
Number of significantly mutated genes = 16
Number of Molecular subtypes = 7
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

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] 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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