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 3 genes and 7 molecular subtypes across 69 patients, 4 significant findings detected with P value < 0.05 and Q value < 0.25.

  • KRAS mutation correlated to 'MRNA_CHIERARCHICAL' and 'CN_CNMF'.

  • TP53 mutation correlated to 'MRNA_CNMF' and 'MRNA_CHIERARCHICAL'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 3 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, 4 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 Chi-square test Fisher's exact test Chi-square test Fisher's exact test Fisher's exact test
KRAS 38 (55%) 31 0.249
(1.00)
0.00641
(0.115)
0.00233
(0.0467)
0.83
(1.00)
0.817
(1.00)
0.292
(1.00)
0.0465
(0.79)
TP53 45 (65%) 24 0.000428
(0.00899)
0.00307
(0.0584)
0.21
(1.00)
0.16
(1.00)
0.603
(1.00)
0.812
(1.00)
0.288
(1.00)
APC 57 (83%) 12 0.568
(1.00)
0.849
(1.00)
0.0561
(0.897)
0.362
(1.00)
0.598
(1.00)
0.337
(1.00)
1
(1.00)
'KRAS MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.00641 (Fisher's exact test), Q value = 0.12

Table S1.  Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 23 26 15
KRAS MUTATED 16 8 11
KRAS WILD-TYPE 7 18 4

Figure S1.  Get High-res Image Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

'KRAS MUTATION STATUS' versus 'CN_CNMF'

P value = 0.00233 (Chi-square test), Q value = 0.047

Table S2.  Gene #2: 'KRAS MUTATION STATUS' versus Clinical Feature #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 1 15 17 25 10
KRAS MUTATED 1 3 7 19 8
KRAS WILD-TYPE 0 12 10 6 2

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

'TP53 MUTATION STATUS' versus 'MRNA_CNMF'

P value = 0.000428 (Fisher's exact test), Q value = 0.009

Table S3.  Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 24 21 19
TP53 MUTATED 8 15 17
TP53 WILD-TYPE 16 6 2

Figure S3.  Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'MRNA_CNMF'

'TP53 MUTATION STATUS' versus 'MRNA_CHIERARCHICAL'

P value = 0.00307 (Fisher's exact test), Q value = 0.058

Table S4.  Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 23 26 15
TP53 MUTATED 8 20 12
TP53 WILD-TYPE 15 6 3

Figure S4.  Get High-res Image Gene #3: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'MRNA_CHIERARCHICAL'

Methods & Data
Input
  • Mutation data file = READ-TP.mutsig.cluster.txt

  • Molecular subtypes file = READ-TP.transferedmergedcluster.txt

  • Number of patients = 69

  • Number of significantly mutated genes = 3

  • 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

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)