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 4 genes and 12 clinical features across 66 patients, 7 significant findings detected with Q value < 0.25.

  • TP53 mutation correlated to 'Time to Death' and 'PATHOLOGY_N_STAGE'.

  • PTEN mutation correlated to 'Time to Death' and 'PATHOLOGY_T_STAGE'.

  • PABPC1 mutation correlated to 'Time to Death',  'NEOPLASM_DISEASESTAGE', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between mutation status of 4 genes and 12 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.

Clinical
Features
Time
to
Death
YEARS
TO
BIRTH
NEOPLASM
DISEASESTAGE
PATHOLOGY
T
STAGE
PATHOLOGY
N
STAGE
PATHOLOGY
M
STAGE
GENDER KARNOFSKY
PERFORMANCE
SCORE
NUMBER
PACK
YEARS
SMOKED
YEAR
OF
TOBACCO
SMOKING
ONSET
RACE ETHNICITY
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Wilcoxon-test Wilcoxon-test Wilcoxon-test Fisher's exact test Fisher's exact test
PABPC1 7 (11%) 59 0.0126
(0.163)
0.252
(0.711)
0.00447
(0.163)
0.0469
(0.271)
0.0874
(0.381)
1
(1.00)
0.226
(0.677)
0.0136
(0.163)
1
(1.00)
TP53 22 (33%) 44 0.0214
(0.205)
0.935
(1.00)
0.306
(0.816)
0.369
(0.932)
0.036
(0.247)
0.524
(1.00)
1
(1.00)
0.394
(0.946)
0.885
(1.00)
0.195
(0.677)
1
(1.00)
PTEN 6 (9%) 60 0.00755
(0.163)
0.422
(0.964)
0.0508
(0.271)
0.0323
(0.247)
0.0874
(0.381)
1
(1.00)
0.217
(0.677)
0.216
(0.677)
0.213
(0.677)
URGCP 3 (5%) 63 0.504
(1.00)
0.841
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.0214 (logrank test), Q value = 0.21

Table S1.  Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 152.0 (65.2)
TP53 MUTATED 22 6 10.7 - 141.7 (55.7)
TP53 WILD-TYPE 43 3 1.0 - 152.0 (73.9)

Figure S1.  Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'PATHOLOGY_N_STAGE'

P value = 0.036 (Fisher's exact test), Q value = 0.25

Table S2.  Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 5
TP53 MUTATED 11 4
TP53 WILD-TYPE 29 1

Figure S2.  Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'PTEN MUTATION STATUS' versus 'Time to Death'

P value = 0.00755 (logrank test), Q value = 0.16

Table S3.  Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 152.0 (65.2)
PTEN MUTATED 6 3 16.7 - 90.5 (46.4)
PTEN WILD-TYPE 59 6 1.0 - 152.0 (71.4)

Figure S3.  Get High-res Image Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'PTEN MUTATION STATUS' versus 'PATHOLOGY_T_STAGE'

P value = 0.0323 (Fisher's exact test), Q value = 0.25

Table S4.  Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 21 25 20
PTEN MUTATED 2 0 4
PTEN WILD-TYPE 19 25 16

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

'PABPC1 MUTATION STATUS' versus 'Time to Death'

P value = 0.0126 (logrank test), Q value = 0.16

Table S5.  Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 152.0 (65.2)
PABPC1 MUTATED 7 3 1.0 - 123.1 (71.4)
PABPC1 WILD-TYPE 58 6 2.5 - 152.0 (64.6)

Figure S5.  Get High-res Image Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'PABPC1 MUTATION STATUS' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00447 (Fisher's exact test), Q value = 0.16

Table S6.  Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
PABPC1 MUTATED 3 0 1 3
PABPC1 WILD-TYPE 18 25 13 3

Figure S6.  Get High-res Image Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'PABPC1 MUTATION STATUS' versus 'RACE'

P value = 0.0136 (Fisher's exact test), Q value = 0.16

Table S7.  Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 58
PABPC1 MUTATED 2 0 5
PABPC1 WILD-TYPE 0 4 53

Figure S7.  Get High-res Image Gene #3: 'PABPC1 MUTATION STATUS' versus Clinical Feature #11: 'RACE'

Methods & Data
Input
  • Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline

  • Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/KICH-TP/15174156/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KICH-TP/15080874/KICH-TP.merged_data.txt

  • Number of patients = 66

  • Number of significantly mutated genes = 4

  • Number of selected clinical features = 12

  • 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

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] 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)