Correlation between gene mutation status and selected clinical features

Thyroid Adenocarcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N58KG1

Overview

Introduction

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

Summary

Testing the association between mutation status of 11 genes and 17 clinical features across 400 patients, 13 significant findings detected with Q value < 0.25.

BRAF mutation correlated to 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', and 'EXTRATHYROIDAL_EXTENSION'.
NRAS mutation correlated to 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
NUP93 mutation correlated to 'HISTOLOGICAL_TYPE'.
DNMT3A mutation correlated to 'Time to Death'.
ITGAL mutation correlated to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', and 'EXTRATHYROIDAL_EXTENSION'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 11 genes and 17 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 13 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	RADIATION EXPOSURE	EXTRATHYROIDAL EXTENSION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	MULTIFOCALITY	TUMOR SIZE	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	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Wilcoxon-test	Fisher's exact test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
BRAF	239 (60%)	161	0.956 (1.00)	0.335 (1.00)	0.00137 (0.0259)	0.00122 (0.0259)	0.000777 (0.0207)	1 (1.00)	0.245 (1.00)	1e-05 (0.000623)	0.0322 (0.376)	0.595 (1.00)	1e-05 (0.000623)	0.358 (1.00)	0.0239 (0.298)	1 (1.00)	0.234 (1.00)	0.917 (1.00)	0.437 (1.00)
NRAS	34 (8%)	366	0.474 (1.00)	0.548 (1.00)	0.0384 (0.399)	0.145 (0.82)	0.000139 (0.00519)	0.442 (1.00)	0.84 (1.00)	2e-05 (0.000935)	0.61 (1.00)	1 (1.00)	0.187 (0.921)	0.689 (1.00)	0.00152 (0.0259)	0.371 (1.00)	0.527 (1.00)	0.0986 (0.683)	0.0576 (0.47)
ITGAL	4 (1%)	396	0.0353 (0.388)	0.0164 (0.236)	0.00038 (0.0118)	0.143 (0.82)	0.348 (1.00)	1 (1.00)	0.0527 (0.47)	0.712 (1.00)	1 (1.00)	1 (1.00)	0.00146 (0.0259)	0.0967 (0.683)		0.627 (1.00)	0.443 (1.00)	1 (1.00)	0.263 (1.00)
NUP93	4 (1%)	396	0.883 (1.00)	0.51 (1.00)	0.547 (1.00)	0.734 (1.00)	0.348 (1.00)	1 (1.00)	1 (1.00)	0.014 (0.218)	1 (1.00)	1 (1.00)	1 (1.00)	0.517 (1.00)	0.411 (1.00)	0.34 (1.00)	0.998 (1.00)	0.199 (0.933)	1 (1.00)
DNMT3A	3 (1%)	397	0 (0)	0.136 (0.819)	0.0612 (0.477)	0.166 (0.88)	1 (1.00)	1 (1.00)	0.574 (1.00)	0.656 (1.00)	1 (1.00)	0.123 (0.768)	0.258 (1.00)	0.418 (1.00)	0.724 (1.00)	1 (1.00)		1 (1.00)	1 (1.00)
HRAS	14 (4%)	386	0.486 (1.00)	0.338 (1.00)	0.344 (1.00)	0.78 (1.00)	0.388 (1.00)	0.249 (1.00)	0.76 (1.00)	0.0217 (0.29)	1 (1.00)	1 (1.00)	0.688 (1.00)	0.2 (0.933)	0.686 (1.00)	0.792 (1.00)	0.716 (1.00)	0.107 (0.713)	0.615 (1.00)
EIF1AX	6 (2%)	394	0.0578 (0.47)	0.0562 (0.47)	0.92 (1.00)	1 (1.00)	0.625 (1.00)	1 (1.00)	0.176 (0.889)	0.0727 (0.543)	1 (1.00)	1 (1.00)	1 (1.00)	0.28 (1.00)	0.774 (1.00)	0.666 (1.00)	0.468 (1.00)		1 (1.00)
PPM1D	5 (1%)	395	0.852 (1.00)	0.158 (0.867)	0.231 (1.00)	0.288 (1.00)	1 (1.00)	1 (1.00)	0.605 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.111 (0.719)	0.353 (1.00)		1 (1.00)	0.516 (1.00)	1 (1.00)	1 (1.00)
KRAS	4 (1%)	396	0.869 (1.00)	0.459 (1.00)	1 (1.00)	0.206 (0.941)	0.625 (1.00)	1 (1.00)	0.576 (1.00)	0.471 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.419 (1.00)	0.281 (1.00)	1 (1.00)	0.749 (1.00)		1 (1.00)
DLC1	4 (1%)	396	0.0517 (0.47)	0.782 (1.00)	0.73 (1.00)	1 (1.00)	0.605 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.385 (1.00)	0.517 (1.00)		0.627 (1.00)	0.573 (1.00)	1 (1.00)	0.263 (1.00)
NLRP6	3 (1%)	397	0.624 (1.00)	0.437 (1.00)	0.788 (1.00)	0.169 (0.88)	0.605 (1.00)		1 (1.00)	0.653 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.359 (1.00)	0.598 (1.00)			1 (1.00)

'BRAF MUTATION STATUS' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00137 (Fisher's exact test), Q value = 0.026

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	230	45	83	2	32	6
BRAF MUTATED	131	19	59	0	25	4
BRAF WILD-TYPE	99	26	24	2	7	2

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

'BRAF MUTATION STATUS' versus 'PATHOLOGY_T_STAGE'

P value = 0.00122 (Fisher's exact test), Q value = 0.026

Table S2. Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	117	137	129	15
BRAF MUTATED	69	67	89	13
BRAF WILD-TYPE	48	70	40	2

Figure S2. Get High-res Image Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'BRAF MUTATION STATUS' versus 'PATHOLOGY_N_STAGE'

P value = 0.000777 (Fisher's exact test), Q value = 0.021

Table S3. Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	189	170
BRAF MUTATED	99	119
BRAF WILD-TYPE	90	51

Figure S3. Get High-res Image Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'BRAF MUTATION STATUS' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00062

Table S4. Gene #1: '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	281	83	30
BRAF MUTATED	3	193	15	28
BRAF WILD-TYPE	3	88	68	2

Figure S4. Get High-res Image Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'BRAF MUTATION STATUS' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 1e-05 (Fisher's exact test), Q value = 0.00062

Table S5. Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	103	11	272	1
BRAF MUTATED	80	11	143	0
BRAF WILD-TYPE	23	0	129	1

Figure S5. Get High-res Image Gene #1: 'BRAF MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

'NRAS MUTATION STATUS' versus 'PATHOLOGY_N_STAGE'

P value = 0.000139 (Fisher's exact test), Q value = 0.0052

Table S6. Gene #2: 'NRAS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	189	170
NRAS MUTATED	27	5
NRAS WILD-TYPE	162	165

Figure S6. Get High-res Image Gene #2: 'NRAS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'NRAS MUTATION STATUS' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00093

Table S7. Gene #2: '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	281	83	30
NRAS MUTATED	0	15	19	0
NRAS WILD-TYPE	6	266	64	30

Figure S7. Get High-res Image Gene #2: 'NRAS MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'NRAS MUTATION STATUS' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00152 (Wilcoxon-test), Q value = 0.026

Table S8. Gene #2: 'NRAS MUTATION STATUS' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	303	3.4 (6.0)
NRAS MUTATED	25	1.0 (2.4)
NRAS WILD-TYPE	278	3.6 (6.2)

Figure S8. Get High-res Image Gene #2: 'NRAS MUTATION STATUS' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'NUP93 MUTATION STATUS' versus 'HISTOLOGICAL_TYPE'

P value = 0.014 (Fisher's exact test), Q value = 0.22

Table S9. Gene #5: 'NUP93 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	281	83	30
NUP93 MUTATED	1	1	1	1
NUP93 WILD-TYPE	5	280	82	29

Figure S9. Get High-res Image Gene #5: 'NUP93 MUTATION STATUS' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S10. Gene #7: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	399	13	0.1 - 169.3 (21.0)
DNMT3A MUTATED	3	1	1.0 - 7.7 (6.8)
DNMT3A WILD-TYPE	396	12	0.1 - 169.3 (21.2)

Figure S10. Get High-res Image Gene #7: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'ITGAL MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.0164 (Wilcoxon-test), Q value = 0.24

Table S11. Gene #10: 'ITGAL MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	400	47.1 (15.7)
ITGAL MUTATED	4	64.8 (3.5)
ITGAL WILD-TYPE	396	46.9 (15.7)

Figure S11. Get High-res Image Gene #10: 'ITGAL MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'ITGAL MUTATION STATUS' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00038 (Fisher's exact test), Q value = 0.012

Table S12. Gene #10: 'ITGAL MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV	STAGE IVA	STAGE IVC
ALL	230	45	83	2	32	6
ITGAL MUTATED	0	1	0	1	2	0
ITGAL WILD-TYPE	230	44	83	1	30	6

Figure S12. Get High-res Image Gene #10: 'ITGAL MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'ITGAL MUTATION STATUS' versus 'EXTRATHYROIDAL_EXTENSION'

P value = 0.00146 (Fisher's exact test), Q value = 0.026

Table S13. Gene #10: 'ITGAL MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

nPatients	MINIMAL (T3)	MODERATE/ADVANCED (T4A)	NONE	VERY ADVANCED (T4B)
ALL	103	11	272	1
ITGAL MUTATED	0	1	2	1
ITGAL WILD-TYPE	103	10	270	0

Figure S13. Get High-res Image Gene #10: 'ITGAL MUTATION STATUS' versus Clinical Feature #11: 'EXTRATHYROIDAL_EXTENSION'

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/THCA-TP/15165022/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/THCA-TP/15092795/THCA-TP.merged_data.txt
Number of patients = 400
Number of significantly mutated genes = 11
Number of selected clinical features = 17
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.

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.

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)

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