Brain Lower Grade Glioma: Correlation between copy number variations of arm-level result and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 51 arm-level results and 7 clinical features across 144 patients, 13 significant findings detected with Q value < 0.25.

1p gain cnv correlated to 'Time to Death'.
7q gain cnv correlated to 'AGE'.
19q gain cnv correlated to 'Time to Death'.
1p loss cnv correlated to 'HISTOLOGICAL.TYPE' and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
1q loss cnv correlated to 'HISTOLOGICAL.TYPE'.
6q loss cnv correlated to 'HISTOLOGICAL.TYPE'.
10p loss cnv correlated to 'Time to Death' and 'AGE'.
10q loss cnv correlated to 'Time to Death' and 'AGE'.
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 51 arm-level results and 7 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	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
	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
1p loss	41 (28%)	103	0.113 (1.00)	0.0555 (1.00)	0.71 (1.00)	0.941 (1.00)	2.28e-14 (7.79e-12)	0.000133 (0.0442)	1 (1.00)
10p loss	22 (15%)	122	7.96e-10 (2.7e-07)	6.65e-06 (0.00224)	0.252 (1.00)	0.441 (1.00)	0.0238 (1.00)	0.481 (1.00)	0.648 (1.00)
10q loss	26 (18%)	118	4.78e-11 (1.62e-08)	9.08e-06 (0.00304)	0.126 (1.00)	0.507 (1.00)	0.0072 (1.00)	0.184 (1.00)	0.391 (1.00)
1p gain	5 (3%)	139	0.000243 (0.0807)	0.0217 (1.00)	0.652 (1.00)	0.465 (1.00)	0.0619 (1.00)	1 (1.00)	0.206 (1.00)
7q gain	43 (30%)	101	0.0523 (1.00)	0.000718 (0.236)	0.27 (1.00)	0.869 (1.00)	0.212 (1.00)	0.267 (1.00)	0.718 (1.00)
19q gain	6 (4%)	138	0.000433 (0.143)	0.0488 (1.00)	0.236 (1.00)	0.315 (1.00)	0.382 (1.00)	0.402 (1.00)	0.681 (1.00)
1q loss	23 (16%)	121	0.223 (1.00)	0.279 (1.00)	0.651 (1.00)	0.97 (1.00)	4.97e-06 (0.00168)	0.248 (1.00)	0.261 (1.00)
6q loss	18 (12%)	126	0.00686 (1.00)	0.487 (1.00)	0.312 (1.00)	0.475 (1.00)	8.27e-05 (0.0276)	0.00873 (1.00)	0.322 (1.00)
19q loss	52 (36%)	92	0.0218 (1.00)	0.451 (1.00)	0.862 (1.00)	0.785 (1.00)	1.45e-09 (4.92e-07)	0.00502 (1.00)	0.165 (1.00)
22q loss	10 (7%)	134	0.000265 (0.0876)	0.886 (1.00)	0.745 (1.00)	0.21 (1.00)	0.00569 (1.00)	0.524 (1.00)	0.53 (1.00)
1q gain	6 (4%)	138	0.142 (1.00)	0.0366 (1.00)	0.403 (1.00)	0.457 (1.00)	0.282 (1.00)	1 (1.00)	0.438 (1.00)
7p gain	30 (21%)	114	0.0504 (1.00)	0.0263 (1.00)	0.219 (1.00)	0.578 (1.00)	0.0263 (1.00)	0.0982 (1.00)	0.54 (1.00)
8p gain	13 (9%)	131	0.706 (1.00)	0.536 (1.00)	0.778 (1.00)	0.739 (1.00)	0.759 (1.00)	0.0751 (1.00)	0.397 (1.00)
8q gain	22 (15%)	122	0.941 (1.00)	0.472 (1.00)	0.641 (1.00)	0.69 (1.00)	0.295 (1.00)	0.239 (1.00)	1 (1.00)
9p gain	3 (2%)	141	0.125 (1.00)	0.0178 (1.00)	0.578 (1.00)		0.344 (1.00)	1 (1.00)	0.617 (1.00)
10p gain	17 (12%)	127	0.524 (1.00)	0.0038 (1.00)	0.3 (1.00)	0.336 (1.00)	0.0912 (1.00)	0.433 (1.00)	1 (1.00)
10q gain	3 (2%)	141	0.396 (1.00)	0.11 (1.00)	0.259 (1.00)		0.0548 (1.00)	1 (1.00)	0.117 (1.00)
11p gain	5 (3%)	139	0.216 (1.00)	0.779 (1.00)	1 (1.00)	0.44 (1.00)	0.297 (1.00)	1 (1.00)	0.679 (1.00)
11q gain	10 (7%)	134	0.535 (1.00)	0.586 (1.00)	0.328 (1.00)	0.269 (1.00)	0.842 (1.00)	1 (1.00)	1 (1.00)
12p gain	8 (6%)	136	0.757 (1.00)	0.00679 (1.00)	0.0104 (1.00)		0.59 (1.00)	1 (1.00)	0.275 (1.00)
12q gain	3 (2%)	141	0.387 (1.00)	0.00117 (0.384)	0.259 (1.00)		1 (1.00)	0.565 (1.00)	0.245 (1.00)
15q gain	3 (2%)	141	0.75 (1.00)	0.947 (1.00)	0.259 (1.00)	0.315 (1.00)	0.344 (1.00)	0.273 (1.00)	0.617 (1.00)
17p gain	3 (2%)	141	0.0416 (1.00)	0.976 (1.00)	0.259 (1.00)		0.614 (1.00)	1 (1.00)	1 (1.00)
17q gain	5 (3%)	139	0.0234 (1.00)	0.66 (1.00)	0.0702 (1.00)	0.205 (1.00)	1 (1.00)	0.648 (1.00)	1 (1.00)
18p gain	4 (3%)	140	0.0547 (1.00)	0.558 (1.00)	0.315 (1.00)		0.0169 (1.00)	1 (1.00)	1 (1.00)
19p gain	10 (7%)	134	0.0146 (1.00)	0.094 (1.00)	1 (1.00)	0.373 (1.00)	0.162 (1.00)	0.316 (1.00)	1 (1.00)
20p gain	10 (7%)	134	0.0277 (1.00)	0.173 (1.00)	0.745 (1.00)	0.7 (1.00)	0.338 (1.00)	0.316 (1.00)	0.53 (1.00)
20q gain	10 (7%)	134	0.00201 (0.651)	0.223 (1.00)	0.745 (1.00)	0.835 (1.00)	0.254 (1.00)	0.74 (1.00)	1 (1.00)
21q gain	3 (2%)	141	0.568 (1.00)	0.982 (1.00)	1 (1.00)		0.182 (1.00)	1 (1.00)	1 (1.00)
3p loss	4 (3%)	140	0.45 (1.00)	0.0281 (1.00)	1 (1.00)		0.126 (1.00)	0.648 (1.00)	0.363 (1.00)
3q loss	6 (4%)	138	0.983 (1.00)	0.83 (1.00)	0.236 (1.00)	0.886 (1.00)	0.282 (1.00)	1 (1.00)	1 (1.00)
4p loss	15 (10%)	129	0.347 (1.00)	0.0334 (1.00)	0.583 (1.00)	0.495 (1.00)	0.0737 (1.00)	0.283 (1.00)	0.181 (1.00)
4q loss	20 (14%)	124	0.147 (1.00)	0.0997 (1.00)	0.812 (1.00)	0.36 (1.00)	0.16 (1.00)	0.218 (1.00)	0.153 (1.00)
5p loss	12 (8%)	132	0.248 (1.00)	0.599 (1.00)	0.233 (1.00)	0.18 (1.00)	0.00163 (0.531)	0.361 (1.00)	0.56 (1.00)
5q loss	8 (6%)	136	0.0435 (1.00)	0.718 (1.00)	1 (1.00)	0.44 (1.00)	0.59 (1.00)	0.144 (1.00)	1 (1.00)
6p loss	3 (2%)	141	0.00216 (0.696)	0.791 (1.00)	1 (1.00)	0.536 (1.00)	0.614 (1.00)	0.273 (1.00)	1 (1.00)
8p loss	3 (2%)	141	0.746 (1.00)	0.00574 (1.00)	1 (1.00)		0.344 (1.00)	0.565 (1.00)	0.117 (1.00)
9p loss	35 (24%)	109	0.0247 (1.00)	0.0127 (1.00)	0.44 (1.00)	0.803 (1.00)	0.14 (1.00)	0.00139 (0.453)	0.081 (1.00)
9q loss	5 (3%)	139	0.149 (1.00)	0.0817 (1.00)	0.652 (1.00)		1 (1.00)	1 (1.00)	0.679 (1.00)
11p loss	15 (10%)	129	0.257 (1.00)	0.0261 (1.00)	1 (1.00)	0.969 (1.00)	0.0434 (1.00)	0.405 (1.00)	1 (1.00)
12p loss	3 (2%)	141	0.43 (1.00)	0.423 (1.00)	1 (1.00)		0.614 (1.00)	0.565 (1.00)	0.617 (1.00)
12q loss	6 (4%)	138	0.547 (1.00)	0.306 (1.00)	0.236 (1.00)		0.195 (1.00)	0.685 (1.00)	0.681 (1.00)
13q loss	19 (13%)	125	0.0624 (1.00)	0.152 (1.00)	1 (1.00)	0.432 (1.00)	0.491 (1.00)	0.217 (1.00)	0.225 (1.00)
14q loss	23 (16%)	121	0.00853 (1.00)	0.796 (1.00)	0.00223 (0.717)	0.608 (1.00)	0.0895 (1.00)	0.489 (1.00)	0.823 (1.00)
15q loss	10 (7%)	134	0.865 (1.00)	0.847 (1.00)	0.188 (1.00)		0.214 (1.00)	0.202 (1.00)	0.53 (1.00)
16p loss	3 (2%)	141	0.319 (1.00)	0.521 (1.00)	1 (1.00)		0.344 (1.00)	1 (1.00)	0.117 (1.00)
16q loss	5 (3%)	139	0.832 (1.00)	0.276 (1.00)	1 (1.00)		0.534 (1.00)	0.648 (1.00)	0.679 (1.00)
18p loss	10 (7%)	134	0.948 (1.00)	0.276 (1.00)	0.745 (1.00)	0.44 (1.00)	0.12 (1.00)	1 (1.00)	1 (1.00)
18q loss	13 (9%)	131	0.239 (1.00)	0.626 (1.00)	0.24 (1.00)	0.486 (1.00)	0.103 (1.00)	0.562 (1.00)	0.397 (1.00)
19p loss	28 (19%)	116	0.179 (1.00)	0.592 (1.00)	0.288 (1.00)	0.599 (1.00)	0.00132 (0.432)	0.285 (1.00)	0.0936 (1.00)
21q loss	5 (3%)	139	0.955 (1.00)	0.735 (1.00)	1 (1.00)		0.534 (1.00)	1 (1.00)	0.679 (1.00)

'1p gain mutation analysis' versus 'Time to Death'

P value = 0.000243 (logrank test), Q value = 0.081

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	144	48	0.0 - 211.2 (18.0)
1P GAIN MUTATED	5	3	7.7 - 15.0 (12.4)
1P GAIN WILD-TYPE	139	45	0.0 - 211.2 (18.9)

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

'7q gain mutation analysis' versus 'AGE'

P value = 0.000718 (t-test), Q value = 0.24

Table S2. Gene #4: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	144	42.7 (13.0)
7Q GAIN MUTATED	43	48.6 (13.6)
7Q GAIN WILD-TYPE	101	40.2 (11.9)

Figure S2. Get High-res Image Gene #4: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

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

P value = 0.000433 (logrank test), Q value = 0.14

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	144	48	0.0 - 211.2 (18.0)
19Q GAIN MUTATED	6	4	4.1 - 26.3 (17.2)
19Q GAIN WILD-TYPE	138	44	0.0 - 211.2 (18.4)

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

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

P value = 2.28e-14 (Fisher's exact test), Q value = 7.8e-12

Table S4. Gene #23: '1p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	50	35	58
1P LOSS MUTATED	2	2	37
1P LOSS WILD-TYPE	48	33	21

Figure S4. Get High-res Image Gene #23: '1p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'1p loss mutation analysis' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.000133 (Fisher's exact test), Q value = 0.044

Table S5. Gene #23: '1p loss mutation analysis' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	86	58
1P LOSS MUTATED	14	27
1P LOSS WILD-TYPE	72	31

Figure S5. Get High-res Image Gene #23: '1p loss mutation analysis' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 4.97e-06 (Fisher's exact test), Q value = 0.0017

Table S6. Gene #24: '1q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	50	35	58
1Q LOSS MUTATED	2	1	20
1Q LOSS WILD-TYPE	48	34	38

Figure S6. Get High-res Image Gene #24: '1q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'6q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 8.27e-05 (Fisher's exact test), Q value = 0.028

Table S7. Gene #32: '6q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	50	35	58
6Q LOSS MUTATED	12	5	0
6Q LOSS WILD-TYPE	38	30	58

Figure S7. Get High-res Image Gene #32: '6q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 7.96e-10 (logrank test), Q value = 2.7e-07

Table S8. Gene #36: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	144	48	0.0 - 211.2 (18.0)
10P LOSS MUTATED	22	16	4.1 - 134.3 (10.9)
10P LOSS WILD-TYPE	122	32	0.0 - 211.2 (21.0)

Figure S8. Get High-res Image Gene #36: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10p loss mutation analysis' versus 'AGE'

P value = 6.65e-06 (t-test), Q value = 0.0022

Table S9. Gene #36: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	144	42.7 (13.0)
10P LOSS MUTATED	22	54.6 (11.0)
10P LOSS WILD-TYPE	122	40.6 (12.2)

Figure S9. Get High-res Image Gene #36: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

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

P value = 4.78e-11 (logrank test), Q value = 1.6e-08

Table S10. Gene #37: '10q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	144	48	0.0 - 211.2 (18.0)
10Q LOSS MUTATED	26	18	4.1 - 134.3 (12.0)
10Q LOSS WILD-TYPE	118	30	0.0 - 211.2 (22.8)

Figure S10. Get High-res Image Gene #37: '10q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10q loss mutation analysis' versus 'AGE'

P value = 9.08e-06 (t-test), Q value = 0.003

Table S11. Gene #37: '10q loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	144	42.7 (13.0)
10Q LOSS MUTATED	26	53.2 (11.4)
10Q LOSS WILD-TYPE	118	40.4 (12.2)

Figure S11. Get High-res Image Gene #37: '10q loss mutation analysis' versus Clinical Feature #2: 'AGE'

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

P value = 1.45e-09 (Fisher's exact test), Q value = 4.9e-07

Table S12. Gene #49: '19q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	50	35	58
19Q LOSS MUTATED	7	6	39
19Q LOSS WILD-TYPE	43	29	19

Figure S12. Get High-res Image Gene #49: '19q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.000265 (logrank test), Q value = 0.088

Table S13. Gene #51: '22q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	144	48	0.0 - 211.2 (18.0)
22Q LOSS MUTATED	10	6	6.4 - 46.6 (16.8)
22Q LOSS WILD-TYPE	134	42	0.0 - 211.2 (19.0)

Figure S13. Get High-res Image Gene #51: '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.clin.merged.picked.txt
Number of patients = 144
Number of significantly arm-level cnvs = 51
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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