Brain Lower Grade Glioma: Correlation between copy number variations of arm-level result and selected clinical features<BR>(primary solid tumor cohort)

Brain Lower Grade Glioma: Correlation between copy number variations of arm-level result and selected clinical features
(primary solid tumor cohort)

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

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 50 arm-level results and 7 clinical features across 207 patients, 16 significant findings detected with Q value < 0.25.

19q gain cnv correlated to 'Time to Death'.
20p gain cnv correlated to 'Time to Death'.
20q gain cnv correlated to 'Time to Death'.
1p loss cnv correlated to 'HISTOLOGICAL.TYPE'.
3p loss cnv correlated to 'AGE'.
6p loss cnv correlated to 'Time to Death'.
10p loss cnv correlated to 'Time to Death', 'AGE', and 'HISTOLOGICCLASSIFICATION'.
10q loss cnv correlated to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'HISTOLOGICCLASSIFICATION'.
11q loss cnv correlated to 'Time to Death'.
19q loss cnv correlated to 'HISTOLOGICAL.TYPE'.
22q loss cnv correlated to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 50 arm-level results and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 16 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	HISTOLOGICCLASSIFICATION	RADIATIONS RADIATION REGIMENINDICATION
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test
10q loss	31 (15%)	176	1.88e-10 (6.29e-08)	3.06e-08 (1.02e-05)	0.0308 (1.00)	0.927 (1.00)	0.000389 (0.126)	7.47e-06 (0.00247)	1 (1.00)
10p loss	30 (14%)	177	8.93e-11 (3e-08)	6.05e-08 (2.02e-05)	0.0478 (1.00)	0.879 (1.00)	0.00083 (0.267)	1.48e-05 (0.00487)	1 (1.00)
19q gain	8 (4%)	199	0.000661 (0.214)	0.0102 (1.00)	1 (1.00)	0.26 (1.00)	0.595 (1.00)	0.0733 (1.00)	0.721 (1.00)
20p gain	17 (8%)	190	0.000116 (0.0379)	0.00665 (1.00)	0.0744 (1.00)	0.976 (1.00)	0.175 (1.00)	0.207 (1.00)	0.614 (1.00)
20q gain	15 (7%)	192	0.000335 (0.109)	0.0141 (1.00)	0.0627 (1.00)	0.893 (1.00)	0.16 (1.00)	0.179 (1.00)	1 (1.00)
1p loss	65 (31%)	142	0.124 (1.00)	0.0169 (1.00)	0.88 (1.00)	0.692 (1.00)	4.61e-19 (1.56e-16)	0.132 (1.00)	0.00165 (0.525)
3p loss	4 (2%)	203	0.917 (1.00)	1.04e-05 (0.00342)	0.636 (1.00)		0.688 (1.00)	0.127 (1.00)	0.369 (1.00)
6p loss	5 (2%)	202	5.29e-05 (0.0173)	0.806 (1.00)	1 (1.00)	0.861 (1.00)	0.449 (1.00)	0.379 (1.00)	1 (1.00)
11q loss	4 (2%)	203	0.000569 (0.184)	0.0656 (1.00)	0.316 (1.00)		0.214 (1.00)	0.127 (1.00)	0.621 (1.00)
19q loss	73 (35%)	134	0.186 (1.00)	0.0459 (1.00)	0.769 (1.00)	0.825 (1.00)	2.67e-14 (8.99e-12)	0.467 (1.00)	0.0295 (1.00)
22q loss	14 (7%)	193	3.89e-06 (0.00129)	0.452 (1.00)	0.28 (1.00)	0.187 (1.00)	0.00293 (0.912)	0.00373 (1.00)	0.284 (1.00)
1p gain	6 (3%)	201	0.00425 (1.00)	0.171 (1.00)	0.0864 (1.00)	0.14 (1.00)	0.0282 (1.00)	0.0325 (1.00)	1 (1.00)
1q gain	8 (4%)	199	0.252 (1.00)	0.00496 (1.00)	0.293 (1.00)	0.572 (1.00)	0.432 (1.00)	0.0733 (1.00)	0.721 (1.00)
6p gain	3 (1%)	204	0.506 (1.00)	0.761 (1.00)	0.578 (1.00)		0.0449 (1.00)	1 (1.00)	0.246 (1.00)
7p gain	36 (17%)	171	0.00841 (1.00)	0.0264 (1.00)	0.044 (1.00)	0.958 (1.00)	0.0204 (1.00)	0.0262 (1.00)	0.36 (1.00)
7q gain	49 (24%)	158	0.0113 (1.00)	0.00174 (0.55)	0.137 (1.00)	0.702 (1.00)	0.0623 (1.00)	0.325 (1.00)	0.514 (1.00)
8p gain	14 (7%)	193	0.731 (1.00)	0.618 (1.00)	0.403 (1.00)	0.987 (1.00)	0.159 (1.00)	1 (1.00)	0.165 (1.00)
8q gain	17 (8%)	190	0.941 (1.00)	0.271 (1.00)	0.613 (1.00)	0.348 (1.00)	0.0511 (1.00)	1 (1.00)	0.311 (1.00)
9p gain	4 (2%)	203	0.135 (1.00)	0.00595 (1.00)	0.316 (1.00)		0.817 (1.00)	0.627 (1.00)	1 (1.00)
9q gain	4 (2%)	203	0.043 (1.00)	0.0912 (1.00)	0.316 (1.00)	0.14 (1.00)	0.214 (1.00)	0.127 (1.00)	0.621 (1.00)
10p gain	21 (10%)	186	0.502 (1.00)	0.00632 (1.00)	0.0669 (1.00)	0.101 (1.00)	0.101 (1.00)	0.822 (1.00)	0.0635 (1.00)
11p gain	9 (4%)	198	0.511 (1.00)	0.277 (1.00)	1 (1.00)	0.722 (1.00)	0.469 (1.00)	0.514 (1.00)	1 (1.00)
11q gain	13 (6%)	194	0.69 (1.00)	0.185 (1.00)	0.564 (1.00)	0.605 (1.00)	0.875 (1.00)	0.576 (1.00)	0.568 (1.00)
12p gain	10 (5%)	197	0.465 (1.00)	0.00184 (0.581)	0.0458 (1.00)		0.758 (1.00)	0.0826 (1.00)	1 (1.00)
12q gain	4 (2%)	203	0.364 (1.00)	0.000869 (0.278)	0.136 (1.00)		1 (1.00)	0.0934 (1.00)	1 (1.00)
18p gain	4 (2%)	203	0.493 (1.00)	0.75 (1.00)	0.316 (1.00)		0.214 (1.00)	0.627 (1.00)	0.369 (1.00)
19p gain	10 (5%)	197	0.171 (1.00)	0.00203 (0.639)	1 (1.00)	0.0572 (1.00)	0.293 (1.00)	0.114 (1.00)	0.332 (1.00)
21q gain	3 (1%)	204	0.533 (1.00)	0.994 (1.00)	1 (1.00)		0.26 (1.00)	0.593 (1.00)	1 (1.00)
1q loss	9 (4%)	198	0.489 (1.00)	0.047 (1.00)	1 (1.00)	0.111 (1.00)	0.00112 (0.356)	0.735 (1.00)	0.748 (1.00)
2p loss	4 (2%)	203	0.054 (1.00)	0.675 (1.00)	0.636 (1.00)		0.688 (1.00)	0.627 (1.00)	0.621 (1.00)
2q loss	3 (1%)	204	0.539 (1.00)	0.189 (1.00)	0.261 (1.00)		0.108 (1.00)	1 (1.00)	0.621 (1.00)
3q loss	9 (4%)	198	0.554 (1.00)	0.857 (1.00)	0.735 (1.00)	0.217 (1.00)	0.387 (1.00)	0.514 (1.00)	1 (1.00)
4p loss	15 (7%)	192	0.143 (1.00)	0.252 (1.00)	0.279 (1.00)	0.563 (1.00)	0.0664 (1.00)	0.79 (1.00)	0.436 (1.00)
4q loss	24 (12%)	183	0.116 (1.00)	0.88 (1.00)	0.663 (1.00)	0.395 (1.00)	0.314 (1.00)	0.514 (1.00)	0.394 (1.00)
5p loss	11 (5%)	196	0.309 (1.00)	0.814 (1.00)	0.358 (1.00)	0.212 (1.00)	0.00272 (0.852)	0.757 (1.00)	0.538 (1.00)
5q loss	9 (4%)	198	0.0292 (1.00)	0.364 (1.00)	1 (1.00)	0.14 (1.00)	0.224 (1.00)	0.514 (1.00)	0.17 (1.00)
6q loss	19 (9%)	188	0.00251 (0.787)	0.134 (1.00)	0.467 (1.00)	0.194 (1.00)	0.025 (1.00)	0.14 (1.00)	0.0029 (0.905)
9p loss	36 (17%)	171	0.00408 (1.00)	0.126 (1.00)	0.855 (1.00)	0.595 (1.00)	0.0808 (1.00)	0.192 (1.00)	0.00544 (1.00)
9q loss	6 (3%)	201	0.851 (1.00)	0.116 (1.00)	0.406 (1.00)	0.615 (1.00)	0.579 (1.00)	1 (1.00)	0.445 (1.00)
11p loss	18 (9%)	189	0.131 (1.00)	0.111 (1.00)	0.621 (1.00)	0.797 (1.00)	0.00472 (1.00)	0.14 (1.00)	0.217 (1.00)
12q loss	8 (4%)	199	0.464 (1.00)	0.894 (1.00)	0.727 (1.00)	0.859 (1.00)	0.214 (1.00)	0.473 (1.00)	0.721 (1.00)
13q loss	27 (13%)	180	0.163 (1.00)	0.397 (1.00)	1 (1.00)	0.839 (1.00)	1 (1.00)	1 (1.00)	0.215 (1.00)
14q loss	22 (11%)	185	0.000835 (0.268)	0.0113 (1.00)	0.0432 (1.00)	0.5 (1.00)	0.0839 (1.00)	0.111 (1.00)	0.377 (1.00)
15q loss	12 (6%)	195	0.252 (1.00)	0.734 (1.00)	0.561 (1.00)	0.816 (1.00)	0.0384 (1.00)	0.388 (1.00)	0.0822 (1.00)
16q loss	6 (3%)	201	0.962 (1.00)	0.178 (1.00)	1 (1.00)		0.879 (1.00)	0.69 (1.00)	0.212 (1.00)
18p loss	14 (7%)	193	0.69 (1.00)	0.409 (1.00)	1 (1.00)	0.5 (1.00)	0.606 (1.00)	1 (1.00)	0.783 (1.00)
18q loss	15 (7%)	192	0.813 (1.00)	0.367 (1.00)	0.428 (1.00)	0.731 (1.00)	0.182 (1.00)	0.597 (1.00)	0.593 (1.00)
19p loss	9 (4%)	198	0.484 (1.00)	0.247 (1.00)	0.503 (1.00)	0.44 (1.00)	0.198 (1.00)	0.185 (1.00)	0.498 (1.00)
21q loss	7 (3%)	200	0.431 (1.00)	0.746 (1.00)	1 (1.00)	0.26 (1.00)	0.0666 (1.00)	1 (1.00)	1 (1.00)
Xq loss	5 (2%)	202	0.423 (1.00)	0.794 (1.00)	0.654 (1.00)		0.578 (1.00)	0.333 (1.00)	0.369 (1.00)

'19q gain mutation analysis' versus 'Time to Death'

P value = 0.000661 (logrank test), Q value = 0.21

Table S1. Gene #17: '19q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
19Q GAIN MUTATED	8	4	0.5 - 26.3 (14.5)
19Q GAIN WILD-TYPE	198	47	0.0 - 211.2 (13.4)

Figure S1. Get High-res Image Gene #17: '19q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'20p gain mutation analysis' versus 'Time to Death'

P value = 0.000116 (logrank test), Q value = 0.038

Table S2. Gene #18: '20p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
20P GAIN MUTATED	17	7	0.5 - 41.1 (15.3)
20P GAIN WILD-TYPE	189	44	0.0 - 211.2 (13.4)

Figure S2. Get High-res Image Gene #18: '20p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'20q gain mutation analysis' versus 'Time to Death'

P value = 0.000335 (logrank test), Q value = 0.11

Table S3. Gene #19: '20q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
20Q GAIN MUTATED	15	6	0.5 - 41.1 (12.4)
20Q GAIN WILD-TYPE	191	45	0.0 - 211.2 (13.4)

Figure S3. Get High-res Image Gene #19: '20q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'1p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 4.61e-19 (Fisher's exact test), Q value = 1.6e-16

Table S4. Gene #21: '1p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
1P LOSS MUTATED	2	6	57
1P LOSS WILD-TYPE	61	48	32

Figure S4. Get High-res Image Gene #21: '1p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'3p loss mutation analysis' versus 'AGE'

P value = 1.04e-05 (t-test), Q value = 0.0034

Table S5. Gene #25: '3p loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
3P LOSS MUTATED	4	31.0 (2.2)
3P LOSS WILD-TYPE	203	43.3 (13.4)

Figure S5. Get High-res Image Gene #25: '3p loss mutation analysis' versus Clinical Feature #2: 'AGE'

'6p loss mutation analysis' versus 'Time to Death'

P value = 5.29e-05 (logrank test), Q value = 0.017

Table S6. Gene #31: '6p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
6P LOSS MUTATED	5	2	3.0 - 13.4 (6.4)
6P LOSS WILD-TYPE	201	49	0.0 - 211.2 (14.4)

Figure S6. Get High-res Image Gene #31: '6p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10p loss mutation analysis' versus 'Time to Death'

P value = 8.93e-11 (logrank test), Q value = 3e-08

Table S7. Gene #35: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
10P LOSS MUTATED	30	17	0.1 - 134.3 (8.6)
10P LOSS WILD-TYPE	176	34	0.0 - 211.2 (14.6)

Figure S7. Get High-res Image Gene #35: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10p loss mutation analysis' versus 'AGE'

P value = 6.05e-08 (t-test), Q value = 2e-05

Table S8. Gene #35: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
10P LOSS MUTATED	30	54.9 (10.4)
10P LOSS WILD-TYPE	177	41.1 (12.9)

Figure S8. Get High-res Image Gene #35: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

'10p loss mutation analysis' versus 'HISTOLOGICCLASSIFICATION'

P value = 1.48e-05 (Fisher's exact test), Q value = 0.0049

Table S9. Gene #35: '10p loss mutation analysis' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'

nPatients	GRADE II	GRADE III
ALL	94	112
10P LOSS MUTATED	3	27
10P LOSS WILD-TYPE	91	85

Figure S9. Get High-res Image Gene #35: '10p loss mutation analysis' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'

'10q loss mutation analysis' versus 'Time to Death'

P value = 1.88e-10 (logrank test), Q value = 6.3e-08

Table S10. Gene #36: '10q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
10Q LOSS MUTATED	31	17	0.1 - 134.3 (8.8)
10Q LOSS WILD-TYPE	175	34	0.0 - 211.2 (14.7)

Figure S10. Get High-res Image Gene #36: '10q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10q loss mutation analysis' versus 'AGE'

P value = 3.06e-08 (t-test), Q value = 1e-05

Table S11. Gene #36: '10q loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
10Q LOSS MUTATED	31	54.8 (10.3)
10Q LOSS WILD-TYPE	176	41.0 (12.9)

Figure S11. Get High-res Image Gene #36: '10q loss mutation analysis' versus Clinical Feature #2: 'AGE'

'10q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 0.000389 (Fisher's exact test), Q value = 0.13

Table S12. Gene #36: '10q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
10Q LOSS MUTATED	19	6	6
10Q LOSS WILD-TYPE	44	48	83

Figure S12. Get High-res Image Gene #36: '10q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'10q loss mutation analysis' versus 'HISTOLOGICCLASSIFICATION'

P value = 7.47e-06 (Fisher's exact test), Q value = 0.0025

Table S13. Gene #36: '10q loss mutation analysis' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'

nPatients	GRADE II	GRADE III
ALL	94	112
10Q LOSS MUTATED	3	28
10Q LOSS WILD-TYPE	91	84

Figure S13. Get High-res Image Gene #36: '10q loss mutation analysis' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'

'11q loss mutation analysis' versus 'Time to Death'

P value = 0.000569 (logrank test), Q value = 0.18

Table S14. Gene #38: '11q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
11Q LOSS MUTATED	4	3	6.0 - 41.1 (13.2)
11Q LOSS WILD-TYPE	202	48	0.0 - 211.2 (13.4)

Figure S14. Get High-res Image Gene #38: '11q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'19q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 2.67e-14 (Fisher's exact test), Q value = 9e-12

Table S15. Gene #47: '19q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
19Q LOSS MUTATED	8	7	58
19Q LOSS WILD-TYPE	55	47	31

Figure S15. Get High-res Image Gene #47: '19q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'22q loss mutation analysis' versus 'Time to Death'

P value = 3.89e-06 (logrank test), Q value = 0.0013

Table S16. Gene #49: '22q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
22Q LOSS MUTATED	14	7	0.1 - 46.6 (11.9)
22Q LOSS WILD-TYPE	192	44	0.0 - 211.2 (13.9)

Figure S16. Get High-res Image Gene #49: '22q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = LGG-TP.clin.merged.picked.txt
Number of patients = 207
Number of significantly arm-level cnvs = 50
Number of selected clinical features = 7
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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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