Correlation between gene mutation status and selected clinical features
Brain Lower Grade Glioma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C18C9TJV
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18C9TJV
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 18 genes and 6 clinical features across 210 patients, 9 significant findings detected with Q value < 0.25.

- ATRX mutation correlated to 'AGE' and 'HISTOLOGICAL.TYPE'.

- IDH1 mutation correlated to 'Time to Death'.

- TP53 mutation correlated to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- CIC mutation correlated to 'HISTOLOGICAL.TYPE'.

- FUBP1 mutation correlated to 'HISTOLOGICAL.TYPE'.

- NOTCH1 mutation correlated to 'HISTOLOGICAL.TYPE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 18 genes and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 9 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | HISTOLOGICAL TYPE | RADIATIONS RADIATION REGIMENINDICATION
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | Fisher's exact test | Fisher's exact test
TP53 | 109 (52%) | 101 | 0.149 (1.00) | 9.69e-07 (9.79e-05) | 0.211 (1.00) | 0.461 (1.00) | 2.41e-06 (0.000241) | 0.00202 (0.19)
ATRX | 91 (43%) | 119 | 0.108 (1.00) | 5.4e-06 (0.000535) | 0.483 (1.00) | 0.423 (1.00) | 0.000209 (0.02) | 0.0341 (1.00)
IDH1 | 158 (75%) | 52 | 1.76e-05 (0.00173) | 0.0117 (1.00) | 0.00992 (0.893) | 0.539 (1.00) | 0.00454 (0.422) | 0.419 (1.00)
CIC | 38 (18%) | 172 | 0.0549 (1.00) | 0.703 (1.00) | 0.718 (1.00) | 0.573 (1.00) | 8.82e-08 (9e-06) | 0.364 (1.00)
FUBP1 | 21 (10%) | 189 | 0.697 (1.00) | 0.0107 (0.956) | 1 (1.00) | 0.72 (1.00) | 0.000712 (0.0676) | 1 (1.00)
NOTCH1 | 18 (9%) | 192 | 0.834 (1.00) | 0.0152 (1.00) | 0.328 (1.00) | 0.125 (1.00) | 0.000119 (0.0116) | 0.809 (1.00)
IL32 | 8 (4%) | 202 | 0.539 (1.00) | 0.555 (1.00) | 0.732 (1.00) | 0.191 (1.00) | 1 (1.00) | 0.022 (1.00)
IDH2 | 8 (4%) | 202 | 0.48 (1.00) | 0.124 (1.00) | 0.732 (1.00) |  | 0.265 (1.00) | 1 (1.00)
PIK3CA | 19 (9%) | 191 | 0.872 (1.00) | 0.458 (1.00) | 0.631 (1.00) | 0.346 (1.00) | 0.104 (1.00) | 0.808 (1.00)
PIK3R1 | 13 (6%) | 197 | 0.2 (1.00) | 0.125 (1.00) | 0.397 (1.00) | 0.662 (1.00) | 0.254 (1.00) | 0.778 (1.00)
PTEN | 12 (6%) | 198 | 0.158 (1.00) | 0.0123 (1.00) | 0.373 (1.00) | 0.268 (1.00) | 0.00517 (0.471) | 1 (1.00)
CREBZF | 4 (2%) | 206 | 0.686 (1.00) | 0.217 (1.00) | 0.321 (1.00) | 0.288 (1.00) | 0.469 (1.00) | 0.641 (1.00)
PCDHAC2 | 14 (7%) | 196 | 0.148 (1.00) | 0.302 (1.00) | 0.162 (1.00) | 0.273 (1.00) | 0.0382 (1.00) | 0.00501 (0.461)
NOX4 | 5 (2%) | 205 | 0.457 (1.00) | 0.491 (1.00) | 1 (1.00) |  | 0.736 (1.00) | 0.652 (1.00)
ZNF57 | 6 (3%) | 204 | 0.239 (1.00) | 0.767 (1.00) | 0.408 (1.00) |  | 0.212 (1.00) | 1 (1.00)
EIF1AX | 3 (1%) | 207 | 0.385 (1.00) | 0.774 (1.00) | 0.258 (1.00) |  | 0.118 (1.00) | 0.573 (1.00)
DCP1B | 4 (2%) | 206 | 0.666 (1.00) | 0.0509 (1.00) | 0.633 (1.00) |  | 0.687 (1.00) | 1 (1.00)
NAB2 | 4 (2%) | 206 | 0.441 (1.00) | 0.532 (1.00) | 0.321 (1.00) |  | 0.0673 (1.00) | 0.641 (1.00)

'ATRX MUTATION STATUS' versus 'AGE'

P value = 5.4e-06 (t-test), Q value = 0.00053

Table S1. Gene #2: 'ATRX MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 210 | 43.0 (13.3)
ATRX MUTATED | 91 | 38.3 (12.0)
ATRX WILD-TYPE | 119 | 46.5 (13.3)

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

'ATRX MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000209 (Fisher's exact test), Q value = 0.02

Table S2. Gene #2: 'ATRX MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 65 | 57 | 87
ATRX MUTATED | 30 | 36 | 25
ATRX WILD-TYPE | 35 | 21 | 62

Figure S2. Get High-res Image Gene #2: 'ATRX MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'IDH1 MUTATION STATUS' versus 'Time to Death'

P value = 1.76e-05 (logrank test), Q value = 0.0017

Table S3. Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 210 | 53 | 0.0 - 211.2 (15.0)
IDH1 MUTATED | 158 | 33 | 0.0 - 182.3 (17.4)
IDH1 WILD-TYPE | 52 | 20 | 0.1 - 211.2 (10.0)

Figure S3. Get High-res Image Gene #4: 'IDH1 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'AGE'

P value = 9.69e-07 (t-test), Q value = 9.8e-05

Table S4. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 210 | 43.0 (13.3)
TP53 MUTATED | 109 | 38.7 (11.8)
TP53 WILD-TYPE | 101 | 47.6 (13.5)

Figure S4. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'TP53 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 2.41e-06 (Fisher's exact test), Q value = 0.00024

Table S5. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 65 | 57 | 87
TP53 MUTATED | 42 | 39 | 27
TP53 WILD-TYPE | 23 | 18 | 60

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

'TP53 MUTATION STATUS' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00202 (Fisher's exact test), Q value = 0.19

Table S6. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 88 | 122
TP53 MUTATED | 57 | 52
TP53 WILD-TYPE | 31 | 70

Figure S6. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CIC MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 8.82e-08 (Fisher's exact test), Q value = 9e-06

Table S7. Gene #6: 'CIC MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 65 | 57 | 87
CIC MUTATED | 2 | 5 | 31
CIC WILD-TYPE | 63 | 52 | 56

Figure S7. Get High-res Image Gene #6: 'CIC MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'FUBP1 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000712 (Fisher's exact test), Q value = 0.068

Table S8. Gene #7: 'FUBP1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 65 | 57 | 87
FUBP1 MUTATED | 2 | 2 | 17
FUBP1 WILD-TYPE | 63 | 55 | 70

Figure S8. Get High-res Image Gene #7: 'FUBP1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'NOTCH1 MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 0.000119 (Fisher's exact test), Q value = 0.012

Table S9. Gene #8: 'NOTCH1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 65 | 57 | 87
NOTCH1 MUTATED | 1 | 1 | 16
NOTCH1 WILD-TYPE | 64 | 56 | 71

Figure S9. Get High-res Image Gene #8: 'NOTCH1 MUTATION STATUS' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Methods & Data
Input

- Mutation data file = LGG-TP.mutsig.cluster.txt

- Clinical data file = LGG-TP.clin.merged.picked.txt

- Number of patients = 210

- Number of significantly mutated genes = 18

- Number of selected clinical features = 6

- 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

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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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] 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)
[4] 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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