Correlation between copy number variations of arm-level result and selected clinical features

Kidney Chromophobe (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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 (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1639N1P

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 59 arm-level events and 10 clinical features across 64 patients, 7 significant findings detected with Q value < 0.25.

5P GAIN MUTATION ANALYSIS cnv correlated to 'Time to Death'.
5Q GAIN MUTATION ANALYSIS cnv correlated to 'Time to Death'.
16P LOSS MUTATION ANALYSIS cnv correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.N.STAGE'.
16Q LOSS MUTATION ANALYSIS cnv correlated to 'Time to Death' and 'PATHOLOGY.N.STAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 59 arm-level events and 10 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	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBERPACKYEARSSMOKED	YEAROFTOBACCOSMOKINGONSET
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test	t-test	t-test
16P LOSS MUTATION ANALYSIS	4 (6%)	60	2.71e-10 (1.33e-07)	0.466 (1.00)	0.000464 (0.226)	0.00763 (1.00)	3.36e-05 (0.0164)		1 (1.00)
16Q LOSS MUTATION ANALYSIS	5 (8%)	59	2.35e-10 (1.15e-07)	0.199 (1.00)	0.00495 (1.00)	0.0371 (1.00)	0.000165 (0.0804)		0.64 (1.00)
5P GAIN MUTATION ANALYSIS	7 (11%)	57	0.00042 (0.205)	0.105 (1.00)	0.000926 (0.446)	0.278 (1.00)	0.0874 (1.00)	0.00903 (1.00)	0.695 (1.00)
5Q GAIN MUTATION ANALYSIS	7 (11%)	57	0.00042 (0.205)	0.105 (1.00)	0.000926 (0.446)	0.278 (1.00)	0.0874 (1.00)	0.00903 (1.00)	0.695 (1.00)
3P GAIN MUTATION ANALYSIS	8 (12%)	56	0.00254 (1.00)	0.0657 (1.00)	0.0146 (1.00)	0.46 (1.00)	0.0327 (1.00)	0.269 (1.00)	0.135 (1.00)
3Q GAIN MUTATION ANALYSIS	8 (12%)	56	0.00254 (1.00)	0.0657 (1.00)	0.0146 (1.00)	0.46 (1.00)	0.0327 (1.00)	0.269 (1.00)	0.135 (1.00)
4P GAIN MUTATION ANALYSIS	19 (30%)	45	0.26 (1.00)	0.575 (1.00)	0.141 (1.00)	0.941 (1.00)	1 (1.00)	0.127 (1.00)	0.577 (1.00)	0.863 (1.00)	0.073 (1.00)	0.0084 (1.00)
4Q GAIN MUTATION ANALYSIS	18 (28%)	46	0.26 (1.00)	0.902 (1.00)	0.144 (1.00)	1 (1.00)	1 (1.00)	0.127 (1.00)	0.776 (1.00)	0.863 (1.00)	0.073 (1.00)	0.0084 (1.00)
7P GAIN MUTATION ANALYSIS	23 (36%)	41	0.0188 (1.00)	0.264 (1.00)	0.106 (1.00)	0.547 (1.00)	0.155 (1.00)	0.107 (1.00)	0.181 (1.00)	0.863 (1.00)	0.551 (1.00)	0.0341 (1.00)
7Q GAIN MUTATION ANALYSIS	23 (36%)	41	0.0188 (1.00)	0.264 (1.00)	0.106 (1.00)	0.547 (1.00)	0.155 (1.00)	0.107 (1.00)	0.181 (1.00)	0.863 (1.00)	0.551 (1.00)	0.0341 (1.00)
8P GAIN MUTATION ANALYSIS	15 (23%)	49	0.606 (1.00)	0.202 (1.00)	0.613 (1.00)	0.661 (1.00)	0.639 (1.00)	0.482 (1.00)	0.368 (1.00)	0.863 (1.00)	0.83 (1.00)
8Q GAIN MUTATION ANALYSIS	16 (25%)	48	0.606 (1.00)	0.211 (1.00)	0.329 (1.00)	0.671 (1.00)	0.639 (1.00)	0.0699 (1.00)	0.243 (1.00)	0.863 (1.00)	0.83 (1.00)
9P GAIN MUTATION ANALYSIS	10 (16%)	54	0.404 (1.00)	0.958 (1.00)	0.0852 (1.00)	0.163 (1.00)	0.306 (1.00)	1 (1.00)	1 (1.00)		0.566 (1.00)
9Q GAIN MUTATION ANALYSIS	10 (16%)	54	0.404 (1.00)	0.958 (1.00)	0.0852 (1.00)	0.163 (1.00)	0.306 (1.00)	1 (1.00)	1 (1.00)		0.566 (1.00)
11P GAIN MUTATION ANALYSIS	14 (22%)	50	0.575 (1.00)	0.846 (1.00)	0.608 (1.00)	0.697 (1.00)	0.617 (1.00)	0.367 (1.00)	1 (1.00)		0.073 (1.00)	0.0084 (1.00)
11Q GAIN MUTATION ANALYSIS	15 (23%)	49	0.575 (1.00)	0.866 (1.00)	0.279 (1.00)	0.708 (1.00)	0.617 (1.00)	0.0529 (1.00)	0.765 (1.00)		0.073 (1.00)	0.0084 (1.00)
12P GAIN MUTATION ANALYSIS	17 (27%)	47	0.714 (1.00)	0.0319 (1.00)	0.385 (1.00)	0.881 (1.00)	1 (1.00)	0.0939 (1.00)	0.397 (1.00)	0.863 (1.00)	0.83 (1.00)
12Q GAIN MUTATION ANALYSIS	17 (27%)	47	0.167 (1.00)	0.0956 (1.00)	0.138 (1.00)	0.938 (1.00)	0.315 (1.00)	0.0939 (1.00)	0.397 (1.00)	0.863 (1.00)	0.473 (1.00)	0.0084 (1.00)
14Q GAIN MUTATION ANALYSIS	17 (27%)	47	0.806 (1.00)	0.276 (1.00)	0.256 (1.00)	0.356 (1.00)	0.651 (1.00)	0.671 (1.00)	0.778 (1.00)	0.863 (1.00)	0.473 (1.00)	0.0084 (1.00)
15Q GAIN MUTATION ANALYSIS	18 (28%)	46	0.344 (1.00)	0.821 (1.00)	0.743 (1.00)	0.735 (1.00)	1 (1.00)	0.695 (1.00)	1 (1.00)	0.863 (1.00)	0.368 (1.00)	0.161 (1.00)
16P GAIN MUTATION ANALYSIS	18 (28%)	46	0.501 (1.00)	0.693 (1.00)	0.495 (1.00)	0.444 (1.00)	0.641 (1.00)	0.695 (1.00)	1 (1.00)	0.863 (1.00)	0.073 (1.00)	0.0084 (1.00)
16Q GAIN MUTATION ANALYSIS	18 (28%)	46	0.501 (1.00)	0.693 (1.00)	0.495 (1.00)	0.444 (1.00)	0.641 (1.00)	0.695 (1.00)	1 (1.00)	0.863 (1.00)	0.073 (1.00)	0.0084 (1.00)
18P GAIN MUTATION ANALYSIS	14 (22%)	50	0.591 (1.00)	0.427 (1.00)	0.399 (1.00)	0.648 (1.00)	0.313 (1.00)	0.337 (1.00)	0.537 (1.00)		0.745 (1.00)
18Q GAIN MUTATION ANALYSIS	13 (20%)	51	0.699 (1.00)	0.755 (1.00)	0.497 (1.00)	0.339 (1.00)	0.313 (1.00)	0.337 (1.00)	0.544 (1.00)		0.745 (1.00)
19P GAIN MUTATION ANALYSIS	17 (27%)	47	0.000914 (0.442)	0.0575 (1.00)	0.0158 (1.00)	0.527 (1.00)	0.00246 (1.00)	0.0699 (1.00)	0.397 (1.00)		0.551 (1.00)	0.0341 (1.00)
19Q GAIN MUTATION ANALYSIS	16 (25%)	48	0.0137 (1.00)	0.0274 (1.00)	0.107 (1.00)	0.719 (1.00)	0.027 (1.00)	0.0366 (1.00)	0.561 (1.00)		0.368 (1.00)	0.161 (1.00)
20P GAIN MUTATION ANALYSIS	18 (28%)	46	0.353 (1.00)	0.261 (1.00)	0.428 (1.00)	0.941 (1.00)	0.35 (1.00)	0.695 (1.00)	0.776 (1.00)	0.863 (1.00)	0.473 (1.00)	0.0084 (1.00)
20Q GAIN MUTATION ANALYSIS	18 (28%)	46	0.288 (1.00)	0.219 (1.00)	0.144 (1.00)	1 (1.00)	0.33 (1.00)	0.695 (1.00)	0.776 (1.00)	0.863 (1.00)	0.073 (1.00)	0.0084 (1.00)
21Q GAIN MUTATION ANALYSIS	3 (5%)	61	0.604 (1.00)	0.853 (1.00)	1 (1.00)	1 (1.00)			0.555 (1.00)
22Q GAIN MUTATION ANALYSIS	17 (27%)	47	0.0287 (1.00)	0.733 (1.00)	0.521 (1.00)	0.527 (1.00)	0.166 (1.00)	0.34 (1.00)	0.397 (1.00)		0.551 (1.00)	0.0341 (1.00)
XQ GAIN MUTATION ANALYSIS	10 (16%)	54	0.335 (1.00)	0.159 (1.00)	0.191 (1.00)	0.756 (1.00)	1 (1.00)	0.167 (1.00)	0.292 (1.00)
1P LOSS MUTATION ANALYSIS	47 (73%)	17	0.447 (1.00)	0.0592 (1.00)	0.0376 (1.00)	0.148 (1.00)	1 (1.00)	0.0671 (1.00)	0.155 (1.00)	0.374 (1.00)	0.661 (1.00)	0.308 (1.00)
1Q LOSS MUTATION ANALYSIS	47 (73%)	17	0.816 (1.00)	0.063 (1.00)	0.0461 (1.00)	0.095 (1.00)	1 (1.00)	0.0964 (1.00)	0.155 (1.00)	0.374 (1.00)	0.164 (1.00)	0.308 (1.00)
2P LOSS MUTATION ANALYSIS	42 (66%)	22	0.488 (1.00)	0.0564 (1.00)	0.241 (1.00)	0.197 (1.00)	1 (1.00)	0.372 (1.00)	0.188 (1.00)	0.374 (1.00)	0.164 (1.00)	0.308 (1.00)
2Q LOSS MUTATION ANALYSIS	42 (66%)	22	0.488 (1.00)	0.0564 (1.00)	0.241 (1.00)	0.197 (1.00)	1 (1.00)	0.372 (1.00)	0.188 (1.00)	0.374 (1.00)	0.164 (1.00)	0.308 (1.00)
3P LOSS MUTATION ANALYSIS	7 (11%)	57	0.353 (1.00)	0.195 (1.00)	0.218 (1.00)	0.083 (1.00)	1 (1.00)	1 (1.00)	0.417 (1.00)
3Q LOSS MUTATION ANALYSIS	6 (9%)	58	0.409 (1.00)	0.37 (1.00)	0.337 (1.00)	0.175 (1.00)	1 (1.00)		0.199 (1.00)
5P LOSS MUTATION ANALYSIS	6 (9%)	58	0.617 (1.00)	0.000736 (0.357)	0.166 (1.00)	0.15 (1.00)	0.387 (1.00)	0.455 (1.00)	0.671 (1.00)
5Q LOSS MUTATION ANALYSIS	6 (9%)	58	0.617 (1.00)	0.000736 (0.357)	0.166 (1.00)	0.15 (1.00)	0.387 (1.00)	0.455 (1.00)	0.671 (1.00)
6P LOSS MUTATION ANALYSIS	46 (72%)	18	0.178 (1.00)	0.0745 (1.00)	0.146 (1.00)	0.0887 (1.00)	1 (1.00)	0.21 (1.00)	0.0972 (1.00)	0.374 (1.00)	0.164 (1.00)	0.308 (1.00)
6Q LOSS MUTATION ANALYSIS	47 (73%)	17	0.178 (1.00)	0.0843 (1.00)	0.149 (1.00)	0.095 (1.00)	0.571 (1.00)	0.21 (1.00)	0.155 (1.00)	0.374 (1.00)	0.164 (1.00)	0.308 (1.00)
8P LOSS MUTATION ANALYSIS	10 (16%)	54	0.233 (1.00)	0.888 (1.00)	0.582 (1.00)	0.316 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
8Q LOSS MUTATION ANALYSIS	9 (14%)	55	0.271 (1.00)	0.767 (1.00)	0.684 (1.00)	0.435 (1.00)	1 (1.00)	1 (1.00)	0.728 (1.00)
9P LOSS MUTATION ANALYSIS	6 (9%)	58	0.0342 (1.00)	0.16 (1.00)	0.581 (1.00)	0.424 (1.00)	0.387 (1.00)	1 (1.00)	0.391 (1.00)
9Q LOSS MUTATION ANALYSIS	7 (11%)	57	0.0882 (1.00)	0.341 (1.00)	0.29 (1.00)	0.237 (1.00)	0.461 (1.00)	1 (1.00)	0.695 (1.00)
10P LOSS MUTATION ANALYSIS	45 (70%)	19	0.192 (1.00)	0.432 (1.00)	0.422 (1.00)	0.313 (1.00)	1 (1.00)	0.0233 (1.00)	0.577 (1.00)	0.107 (1.00)
10Q LOSS MUTATION ANALYSIS	45 (70%)	19	0.192 (1.00)	0.432 (1.00)	0.422 (1.00)	0.313 (1.00)	1 (1.00)	0.0233 (1.00)	0.577 (1.00)	0.107 (1.00)
11P LOSS MUTATION ANALYSIS	7 (11%)	57	0.391 (1.00)	0.0226 (1.00)	0.0877 (1.00)	0.0502 (1.00)	0.461 (1.00)	1 (1.00)	0.695 (1.00)
11Q LOSS MUTATION ANALYSIS	7 (11%)	57	0.391 (1.00)	0.0226 (1.00)	0.0877 (1.00)	0.0502 (1.00)	0.461 (1.00)	1 (1.00)	0.695 (1.00)
13Q LOSS MUTATION ANALYSIS	40 (62%)	24	0.064 (1.00)	0.01 (1.00)	0.341 (1.00)	0.244 (1.00)	0.153 (1.00)	0.628 (1.00)	0.112 (1.00)	0.81 (1.00)	0.0556 (1.00)	1 (1.00)
17P LOSS MUTATION ANALYSIS	46 (72%)	18	0.764 (1.00)	0.0449 (1.00)	0.0305 (1.00)	0.0228 (1.00)	1 (1.00)	0.178 (1.00)	0.0972 (1.00)	0.374 (1.00)
17Q LOSS MUTATION ANALYSIS	46 (72%)	18	0.764 (1.00)	0.0449 (1.00)	0.0305 (1.00)	0.0228 (1.00)	1 (1.00)	0.178 (1.00)	0.0972 (1.00)	0.374 (1.00)
18P LOSS MUTATION ANALYSIS	6 (9%)	58	0.685 (1.00)	0.97 (1.00)	0.269 (1.00)	0.582 (1.00)	0.125 (1.00)	0.638 (1.00)	0.199 (1.00)
18Q LOSS MUTATION ANALYSIS	8 (12%)	56	0.18 (1.00)	0.595 (1.00)	0.0854 (1.00)	0.572 (1.00)	0.166 (1.00)	0.638 (1.00)	0.245 (1.00)
20P LOSS MUTATION ANALYSIS	4 (6%)	60	0.442 (1.00)	0.172 (1.00)	0.162 (1.00)	0.11 (1.00)	0.304 (1.00)	1 (1.00)	0.64 (1.00)
20Q LOSS MUTATION ANALYSIS	3 (5%)	61	0.313 (1.00)	0.423 (1.00)	0.0432 (1.00)	0.0274 (1.00)	0.304 (1.00)	1 (1.00)	1 (1.00)
21Q LOSS MUTATION ANALYSIS	32 (50%)	32	0.0913 (1.00)	0.897 (1.00)	0.717 (1.00)	0.546 (1.00)	0.0634 (1.00)	0.1 (1.00)	1 (1.00)	0.278 (1.00)	0.28 (1.00)	0.101 (1.00)
22Q LOSS MUTATION ANALYSIS	8 (12%)	56	0.18 (1.00)	0.542 (1.00)	0.12 (1.00)	0.716 (1.00)	0.166 (1.00)	0.342 (1.00)	0.048 (1.00)
XQ LOSS MUTATION ANALYSIS	40 (62%)	24	0.31 (1.00)	0.35 (1.00)	0.233 (1.00)	0.244 (1.00)	1 (1.00)	0.141 (1.00)	0.291 (1.00)	0.0791 (1.00)	0.0398 (1.00)	0.277 (1.00)

'5P GAIN MUTATION STATUS' versus 'Time to Death'

P value = 0.00042 (logrank test), Q value = 0.21

Table S1. Gene #5: '5P GAIN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
5P GAIN MUTATED	7	3	0.6 - 137.1 (28.1)
5P GAIN WILD-TYPE	56	4	2.5 - 151.9 (65.9)

Figure S1. Get High-res Image Gene #5: '5P GAIN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'5Q GAIN MUTATION STATUS' versus 'Time to Death'

P value = 0.00042 (logrank test), Q value = 0.21

Table S2. Gene #6: '5Q GAIN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
5Q GAIN MUTATED	7	3	0.6 - 137.1 (28.1)
5Q GAIN WILD-TYPE	56	4	2.5 - 151.9 (65.9)

Figure S2. Get High-res Image Gene #6: '5Q GAIN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'16P LOSS MUTATION STATUS' versus 'Time to Death'

P value = 2.71e-10 (logrank test), Q value = 1.3e-07

Table S3. Gene #49: '16P LOSS MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
16P LOSS MUTATED	4	3	0.6 - 30.2 (22.4)
16P LOSS WILD-TYPE	59	4	2.5 - 151.9 (66.5)

Figure S3. Get High-res Image Gene #49: '16P LOSS MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'16P LOSS MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000464 (Fisher's exact test), Q value = 0.23

Table S4. Gene #49: '16P LOSS MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	23	14	6
16P LOSS MUTATED	0	0	1	3
16P LOSS WILD-TYPE	21	23	13	3

Figure S4. Get High-res Image Gene #49: '16P LOSS MUTATION STATUS' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'16P LOSS MUTATION STATUS' versus 'PATHOLOGY.N.STAGE'

P value = 3.36e-05 (Fisher's exact test), Q value = 0.016

Table S5. Gene #49: '16P LOSS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
16P LOSS MUTATED	0	4
16P LOSS WILD-TYPE	40	1

Figure S5. Get High-res Image Gene #49: '16P LOSS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'16Q LOSS MUTATION STATUS' versus 'Time to Death'

P value = 2.35e-10 (logrank test), Q value = 1.2e-07

Table S6. Gene #50: '16Q LOSS MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
16Q LOSS MUTATED	5	4	0.6 - 52.3 (28.1)
16Q LOSS WILD-TYPE	58	3	2.5 - 151.9 (67.2)

Figure S6. Get High-res Image Gene #50: '16Q LOSS MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'16Q LOSS MUTATION STATUS' versus 'PATHOLOGY.N.STAGE'

P value = 0.000165 (Fisher's exact test), Q value = 0.08

Table S7. Gene #50: '16Q LOSS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
16Q LOSS MUTATED	1	4
16Q LOSS WILD-TYPE	39	1

Figure S7. Get High-res Image Gene #50: '16Q LOSS MUTATION STATUS' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = KICH-TP.clin.merged.picked.txt
Number of patients = 64
Number of significantly arm-level cnvs = 59
Number of selected clinical features = 10
Exclude regions 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.

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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