Correlation between gene mutation status and selected clinical features
Thyroid Adenocarcinoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1J101JZ
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/C1J101JZ
Overview
Introduction

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

Summary

Testing the association between mutation status of 7 genes and 15 clinical features across 385 patients, 8 significant findings detected with Q value < 0.25.

- NRAS mutation correlated to 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', and 'NUMBER.OF.LYMPH.NODES'.

- BRAF mutation correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', and 'EXTRATHYROIDAL.EXTENSION'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 15 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.

|  | Clinical Features | Time to Death | AGE | NEOPLASM DISEASESTAGE | PATHOLOGY T STAGE | PATHOLOGY N STAGE | PATHOLOGY M STAGE | GENDER | HISTOLOGICAL TYPE | RADIATIONS RADIATION REGIMENINDICATION | RADIATIONEXPOSURE | EXTRATHYROIDAL EXTENSION | COMPLETENESS OF RESECTION | NUMBER OF LYMPH NODES | MULTIFOCALITY | TUMOR SIZE
| nMutated (%) | nWild-Type | logrank test | t-test | Chi-square 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 | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | Fisher's exact test | t-test
BRAF | 232 (60%) | 153 | 0.806 (1.00) | 0.848 (1.00) | 0.00245 (0.236) | 0.000849 (0.0832) | 0.000905 (0.0877) | 0.0672 (1.00) | 0.552 (1.00) | 5.43e-20 (5.59e-18) | 0.0323 (1.00) | 0.591 (1.00) | 2.09e-07 (2.13e-05) | 0.587 (1.00) | 0.618 (1.00) | 0.752 (1.00) | 0.231 (1.00)
NRAS | 32 (8%) | 353 | 0.478 (1.00) | 0.748 (1.00) | 0.0436 (1.00) | 0.0788 (1.00) | 0.000413 (0.0409) | 0.348 (1.00) | 0.674 (1.00) | 1.55e-05 (0.00156) | 0.61 (1.00) | 1 (1.00) | 0.105 (1.00) | 0.739 (1.00) | 0.000113 (0.0113) | 0.194 (1.00) | 0.851 (1.00)
HRAS | 13 (3%) | 372 | 0.52 (1.00) | 0.616 (1.00) | 0.339 (1.00) | 0.436 (1.00) | 0.549 (1.00) | 0.262 (1.00) | 0.745 (1.00) | 0.0147 (1.00) | 1 (1.00) | 1 (1.00) | 0.681 (1.00) | 0.137 (1.00) | 0.51 (1.00) | 0.779 (1.00) | 0.933 (1.00)
OTUD4 | 5 (1%) | 380 | 0.65 (1.00) | 0.709 (1.00) | 0.911 (1.00) | 1 (1.00) | 0.671 (1.00) | 0.241 (1.00) | 0.336 (1.00) | 0.436 (1.00) | 1 (1.00) | 1 (1.00) | 0.67 (1.00) | 1 (1.00) | 0.00629 (0.597) | 0.667 (1.00) |
EIF1AX | 6 (2%) | 379 | 0.0796 (1.00) | 0.135 (1.00) | 0.969 (1.00) | 1 (1.00) | 0.625 (1.00) | 0.468 (1.00) | 0.171 (1.00) | 0.0922 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.255 (1.00) | 0.771 (1.00) | 0.667 (1.00) | 0.672 (1.00)
NUP93 | 4 (1%) | 381 | 0.907 (1.00) | 0.391 (1.00) | 0.794 (1.00) | 0.731 (1.00) | 0.349 (1.00) | 0.653 (1.00) | 1 (1.00) | 0.0157 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.498 (1.00) | 0.609 (1.00) | 0.342 (1.00) | 0.995 (1.00)
NLRP6 | 3 (1%) | 382 | 0.655 (1.00) | 0.0342 (1.00) | 0.815 (1.00) | 0.157 (1.00) | 0.605 (1.00) | 0.132 (1.00) | 1 (1.00) | 0.668 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.525 (1.00) | 0.6 (1.00) |

'NRAS MUTATION STATUS' versus 'PATHOLOGY.N.STAGE'

P value = 0.000413 (Fisher's exact test), Q value = 0.041

Table S1. Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 181 | 163
NRAS MUTATED | 25 | 5
NRAS WILD-TYPE | 156 | 158

Figure S1. Get High-res Image Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'NRAS MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 1.55e-05 (Fisher's exact test), Q value = 0.0016

Table S2. Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 6 | 267 | 83 | 29
NRAS MUTATED | 0 | 13 | 19 | 0
NRAS WILD-TYPE | 6 | 254 | 64 | 29

Figure S2. Get High-res Image Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'NRAS MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000113 (t-test), Q value = 0.011

Table S3. Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 302 | 3.3 (6.0)
NRAS MUTATED | 25 | 1.0 (2.4)
NRAS WILD-TYPE | 277 | 3.5 (6.2)

Figure S3. Get High-res Image Gene #1: 'NRAS MUTATION STATUS' versus Clinical Feature #13: 'NUMBER.OF.LYMPH.NODES'

'BRAF MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00245 (Chi-square test), Q value = 0.24

Table S4. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients | STAGE I | STAGE II | STAGE III | STAGE IV | STAGE IVA | STAGE IVC
--- | --- | --- | --- | --- | --- | ---
ALL | 220 | 43 | 83 | 2 | 30 | 5
BRAF MUTATED | 129 | 17 | 59 | 0 | 23 | 3
BRAF WILD-TYPE | 91 | 26 | 24 | 2 | 7 | 2

Figure S4. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'BRAF MUTATION STATUS' versus 'PATHOLOGY.T.STAGE'

P value = 0.000849 (Fisher's exact test), Q value = 0.083

Table S5. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 113 | 133 | 124 | 13
BRAF MUTATED | 67 | 65 | 88 | 11
BRAF WILD-TYPE | 46 | 68 | 36 | 2

Figure S5. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'BRAF MUTATION STATUS' versus 'PATHOLOGY.N.STAGE'

P value = 0.000905 (Fisher's exact test), Q value = 0.088

Table S6. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients | 0 | 1
--- | --- | ---
ALL | 181 | 163
BRAF MUTATED | 96 | 115
BRAF WILD-TYPE | 85 | 48

Figure S6. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'BRAF MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 5.43e-20 (Fisher's exact test), Q value = 5.6e-18

Table S7. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients | OTHER SPECIFY | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 6 | 267 | 83 | 29
BRAF MUTATED | 3 | 187 | 15 | 27
BRAF WILD-TYPE | 3 | 80 | 68 | 2

Figure S7. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'BRAF MUTATION STATUS' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 2.09e-07 (Fisher's exact test), Q value = 2.1e-05

Table S8. Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

nPatients | MINIMAL (T3) | MODERATE/ADVANCED (T4A) | NONE | VERY ADVANCED (T4B)
--- | --- | --- | --- | ---
ALL | 99 | 10 | 264 | 1
BRAF MUTATED | 78 | 10 | 139 | 0
BRAF WILD-TYPE | 21 | 0 | 125 | 1

Figure S8. Get High-res Image Gene #2: 'BRAF MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL.EXTENSION'

Methods & Data
Input

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

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

- Number of patients = 385

- Number of significantly mutated genes = 7

- Number of selected clinical features = 15

- 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

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

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[4] 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)
[5] 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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