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.

- 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] 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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