Correlation between copy number variation genes (focal events) and selected clinical features
Mesothelioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
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 (2016): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15D8R8G
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

Testing the association between copy number variation 21 focal events and 11 clinical features across 87 patients, 15 significant findings detected with Q value < 0.25.

  • del_1p36.31 cnv correlated to 'Time to Death'.

  • del_2q35 cnv correlated to 'Time to Death' and 'GENDER'.

  • del_3p21.1 cnv correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

  • del_4q26 cnv correlated to 'Time to Death'.

  • del_4q34.3 cnv correlated to 'Time to Death'.

  • del_5q23.2 cnv correlated to 'Time to Death'.

  • del_9p21.3 cnv correlated to 'Time to Death'.

  • del_10p15.3 cnv correlated to 'Time to Death' and 'RADIATION_THERAPY'.

  • del_10q25.2 cnv correlated to 'Time to Death'.

  • del_14q32.31 cnv correlated to 'Time to Death'.

  • del_15q15.1 cnv correlated to 'Time to Death' and 'KARNOFSKY_PERFORMANCE_SCORE'.

  • del_16q24.1 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 21 focal events and 11 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 15 significant findings detected.

Clinical
Features
Time
to
Death
YEARS
TO
BIRTH
PATHOLOGIC
STAGE
PATHOLOGY
T
STAGE
PATHOLOGY
N
STAGE
PATHOLOGY
M
STAGE
GENDER RADIATION
THERAPY
KARNOFSKY
PERFORMANCE
SCORE
HISTOLOGICAL
TYPE
RESIDUAL
TUMOR
nCNV (%) 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 Wilcoxon-test Fisher's exact test Fisher's exact test
del 2q35 17 (20%) 70 0.00831
(0.174)
0.397
(0.882)
0.766
(1.00)
0.82
(1.00)
0.313
(0.797)
0.566
(0.932)
0.0129
(0.213)
0.376
(0.851)
0.655
(0.964)
0.319
(0.8)
del 10p15 3 28 (32%) 59 6.76e-07
(9.72e-05)
0.598
(0.946)
0.036
(0.397)
0.186
(0.711)
0.367
(0.851)
0.551
(0.932)
1
(1.00)
0.00408
(0.106)
0.058
(0.446)
0.102
(0.589)
0.211
(0.743)
del 15q15 1 28 (32%) 59 0.000648
(0.0374)
0.993
(1.00)
0.561
(0.932)
0.764
(1.00)
0.888
(1.00)
1
(1.00)
1
(1.00)
0.801
(1.00)
0.00382
(0.106)
0.191
(0.711)
0.42
(0.903)
del 1p36 31 33 (38%) 54 0.000204
(0.0157)
0.93
(1.00)
0.956
(1.00)
0.644
(0.959)
0.949
(1.00)
0.268
(0.764)
0.776
(1.00)
0.804
(1.00)
0.0751
(0.469)
0.935
(1.00)
0.818
(1.00)
del 3p21 1 48 (55%) 39 0.884
(1.00)
0.864
(1.00)
0.224
(0.743)
0.703
(0.99)
0.672
(0.971)
1
(1.00)
0.782
(1.00)
0.813
(1.00)
0.0102
(0.197)
0.179
(0.711)
0.824
(1.00)
del 4q26 38 (44%) 49 0.00112
(0.052)
0.566
(0.932)
0.388
(0.87)
0.106
(0.589)
0.447
(0.924)
0.251
(0.762)
0.28
(0.779)
0.477
(0.932)
0.0471
(0.409)
0.151
(0.648)
0.131
(0.633)
del 4q34 3 41 (47%) 46 0.00453
(0.106)
0.147
(0.648)
0.634
(0.953)
0.31
(0.797)
0.546
(0.932)
0.238
(0.743)
0.58
(0.932)
1
(1.00)
0.0471
(0.409)
0.304
(0.797)
0.0778
(0.473)
del 5q23 2 17 (20%) 70 0.00239
(0.0919)
0.249
(0.762)
1
(1.00)
0.52
(0.932)
1
(1.00)
0.462
(0.932)
0.179
(0.711)
0.228
(0.743)
0.0751
(0.469)
0.875
(1.00)
0.448
(0.924)
del 9p21 3 51 (59%) 36 8.42e-07
(9.72e-05)
0.41
(0.893)
0.765
(1.00)
0.447
(0.924)
0.668
(0.971)
0.0672
(0.469)
1
(1.00)
1
(1.00)
0.801
(1.00)
0.649
(0.962)
0.853
(1.00)
del 10q25 2 31 (36%) 56 0.00458
(0.106)
0.776
(1.00)
0.725
(1.00)
0.585
(0.932)
0.716
(1.00)
0.255
(0.764)
1
(1.00)
0.616
(0.953)
0.131
(0.633)
0.62
(0.953)
0.482
(0.932)
del 14q32 31 39 (45%) 48 0.0116
(0.206)
0.932
(1.00)
0.481
(0.932)
0.14
(0.647)
0.67
(0.971)
0.238
(0.743)
0.0503
(0.415)
1
(1.00)
0.0207
(0.266)
0.0266
(0.307)
0.126
(0.633)
del 16q24 1 22 (25%) 65 0.0142
(0.219)
0.721
(1.00)
0.626
(0.953)
0.969
(1.00)
0.224
(0.743)
0.545
(0.932)
0.107
(0.589)
0.58
(0.932)
0.181
(0.711)
0.263
(0.764)
del 1p21 3 37 (43%) 50 0.0265
(0.307)
0.298
(0.797)
0.314
(0.797)
0.128
(0.633)
0.355
(0.845)
1
(1.00)
0.581
(0.932)
0.23
(0.743)
0.152
(0.648)
0.967
(1.00)
0.964
(1.00)
del 6q22 1 41 (47%) 46 0.574
(0.932)
0.848
(1.00)
0.14
(0.647)
0.832
(1.00)
0.577
(0.932)
0.565
(0.932)
0.422
(0.903)
0.342
(0.831)
0.574
(0.932)
0.763
(1.00)
0.529
(0.932)
del 6q26 36 (41%) 51 0.223
(0.743)
0.87
(1.00)
0.236
(0.743)
0.816
(1.00)
0.0737
(0.469)
0.268
(0.764)
1
(1.00)
0.467
(0.932)
0.683
(0.971)
0.856
(1.00)
0.791
(1.00)
del 8p23 2 18 (21%) 69 0.426
(0.903)
0.294
(0.797)
0.335
(0.823)
0.16
(0.672)
0.755
(1.00)
1
(1.00)
0.506
(0.932)
0.566
(0.932)
0.0751
(0.469)
0.264
(0.764)
0.232
(0.743)
del 11q23 2 18 (21%) 69 0.0657
(0.469)
0.626
(0.953)
0.184
(0.711)
0.55
(0.932)
1
(1.00)
0.558
(0.932)
0.506
(0.932)
0.548
(0.932)
0.307
(0.797)
0.348
(0.837)
del 12p13 31 11 (13%) 76 0.0184
(0.266)
0.361
(0.85)
0.375
(0.851)
0.939
(1.00)
0.585
(0.932)
1
(1.00)
0.681
(0.971)
0.278
(0.779)
0.194
(0.711)
0.193
(0.711)
del 13q14 11 44 (51%) 43 0.0207
(0.266)
0.575
(0.932)
0.0466
(0.409)
0.62
(0.953)
0.0378
(0.397)
0.237
(0.743)
0.103
(0.589)
0.633
(0.953)
0.838
(1.00)
0.904
(1.00)
0.58
(0.932)
del 16p13 3 6 (7%) 81 0.753
(1.00)
0.0425
(0.409)
0.128
(0.633)
0.0478
(0.409)
0.583
(0.932)
1
(1.00)
0.304
(0.797)
1
(1.00)
0.0674
(0.469)
0.132
(0.633)
del 22q12 2 68 (78%) 19 0.0529
(0.421)
0.825
(1.00)
0.409
(0.893)
0.151
(0.648)
0.869
(1.00)
0.556
(0.932)
0.327
(0.813)
0.569
(0.932)
0.635
(0.953)
0.37
(0.851)
0.685
(0.971)
'del_1p36.31' versus 'Time to Death'

P value = 0.000204 (logrank test), Q value = 0.016

Table S1.  Gene #1: 'del_1p36.31' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 1(1P36.31) MUTATED 32 31 1.3 - 49.0 (12.2)
DEL PEAK 1(1P36.31) WILD-TYPE 53 42 0.7 - 91.7 (22.6)

Figure S1.  Get High-res Image Gene #1: 'del_1p36.31' versus Clinical Feature #1: 'Time to Death'

'del_2q35' versus 'Time to Death'

P value = 0.00831 (logrank test), Q value = 0.17

Table S2.  Gene #3: 'del_2q35' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 3(2Q35) MUTATED 17 17 1.9 - 29.1 (12.7)
DEL PEAK 3(2Q35) WILD-TYPE 68 56 0.7 - 91.7 (18.7)

Figure S2.  Get High-res Image Gene #3: 'del_2q35' versus Clinical Feature #1: 'Time to Death'

'del_2q35' versus 'GENDER'

P value = 0.0129 (Fisher's exact test), Q value = 0.21

Table S3.  Gene #3: 'del_2q35' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 71
DEL PEAK 3(2Q35) MUTATED 7 10
DEL PEAK 3(2Q35) WILD-TYPE 9 61

Figure S3.  Get High-res Image Gene #3: 'del_2q35' versus Clinical Feature #7: 'GENDER'

'del_3p21.1' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0102 (Wilcoxon-test), Q value = 0.2

Table S4.  Gene #4: 'del_3p21.1' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
DEL PEAK 4(3P21.1) MUTATED 9 93.3 (7.1)
DEL PEAK 4(3P21.1) WILD-TYPE 8 60.0 (39.3)

Figure S4.  Get High-res Image Gene #4: 'del_3p21.1' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'del_4q26' versus 'Time to Death'

P value = 0.00112 (logrank test), Q value = 0.052

Table S5.  Gene #5: 'del_4q26' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 5(4Q26) MUTATED 38 36 1.9 - 68.7 (13.5)
DEL PEAK 5(4Q26) WILD-TYPE 47 37 0.7 - 91.7 (23.6)

Figure S5.  Get High-res Image Gene #5: 'del_4q26' versus Clinical Feature #1: 'Time to Death'

'del_4q34.3' versus 'Time to Death'

P value = 0.00453 (logrank test), Q value = 0.11

Table S6.  Gene #6: 'del_4q34.3' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 6(4Q34.3) MUTATED 41 38 2.5 - 68.7 (13.6)
DEL PEAK 6(4Q34.3) WILD-TYPE 44 35 0.7 - 91.7 (23.8)

Figure S6.  Get High-res Image Gene #6: 'del_4q34.3' versus Clinical Feature #1: 'Time to Death'

'del_5q23.2' versus 'Time to Death'

P value = 0.00239 (logrank test), Q value = 0.092

Table S7.  Gene #7: 'del_5q23.2' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 7(5Q23.2) MUTATED 17 17 3.5 - 41.5 (11.9)
DEL PEAK 7(5Q23.2) WILD-TYPE 68 56 0.7 - 91.7 (20.1)

Figure S7.  Get High-res Image Gene #7: 'del_5q23.2' versus Clinical Feature #1: 'Time to Death'

'del_9p21.3' versus 'Time to Death'

P value = 8.42e-07 (logrank test), Q value = 9.7e-05

Table S8.  Gene #11: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 11(9P21.3) MUTATED 50 47 0.7 - 41.5 (13.0)
DEL PEAK 11(9P21.3) WILD-TYPE 35 26 2.8 - 91.7 (27.2)

Figure S8.  Get High-res Image Gene #11: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

'del_10p15.3' versus 'Time to Death'

P value = 6.76e-07 (logrank test), Q value = 9.7e-05

Table S9.  Gene #12: 'del_10p15.3' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 12(10P15.3) MUTATED 27 26 0.7 - 28.3 (10.8)
DEL PEAK 12(10P15.3) WILD-TYPE 58 47 1.3 - 91.7 (23.5)

Figure S9.  Get High-res Image Gene #12: 'del_10p15.3' versus Clinical Feature #1: 'Time to Death'

'del_10p15.3' versus 'RADIATION_THERAPY'

P value = 0.00408 (Fisher's exact test), Q value = 0.11

Table S10.  Gene #12: 'del_10p15.3' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 62 24
DEL PEAK 12(10P15.3) MUTATED 25 2
DEL PEAK 12(10P15.3) WILD-TYPE 37 22

Figure S10.  Get High-res Image Gene #12: 'del_10p15.3' versus Clinical Feature #8: 'RADIATION_THERAPY'

'del_10q25.2' versus 'Time to Death'

P value = 0.00458 (logrank test), Q value = 0.11

Table S11.  Gene #13: 'del_10q25.2' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 13(10Q25.2) MUTATED 30 28 0.7 - 55.1 (11.7)
DEL PEAK 13(10Q25.2) WILD-TYPE 55 45 1.3 - 91.7 (20.7)

Figure S11.  Get High-res Image Gene #13: 'del_10q25.2' versus Clinical Feature #1: 'Time to Death'

'del_14q32.31' versus 'Time to Death'

P value = 0.0116 (logrank test), Q value = 0.21

Table S12.  Gene #17: 'del_14q32.31' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 17(14Q32.31) MUTATED 38 35 1.3 - 77.6 (13.5)
DEL PEAK 17(14Q32.31) WILD-TYPE 47 38 0.7 - 91.7 (21.8)

Figure S12.  Get High-res Image Gene #17: 'del_14q32.31' versus Clinical Feature #1: 'Time to Death'

'del_15q15.1' versus 'Time to Death'

P value = 0.000648 (logrank test), Q value = 0.037

Table S13.  Gene #18: 'del_15q15.1' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 18(15Q15.1) MUTATED 27 26 0.7 - 41.5 (12.7)
DEL PEAK 18(15Q15.1) WILD-TYPE 58 47 1.9 - 91.7 (20.1)

Figure S13.  Get High-res Image Gene #18: 'del_15q15.1' versus Clinical Feature #1: 'Time to Death'

'del_15q15.1' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00382 (Wilcoxon-test), Q value = 0.11

Table S14.  Gene #18: 'del_15q15.1' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
DEL PEAK 18(15Q15.1) MUTATED 6 50.0 (41.0)
DEL PEAK 18(15Q15.1) WILD-TYPE 11 92.7 (6.5)

Figure S14.  Get High-res Image Gene #18: 'del_15q15.1' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'del_16q24.1' versus 'Time to Death'

P value = 0.0142 (logrank test), Q value = 0.22

Table S15.  Gene #20: 'del_16q24.1' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 73 0.7 - 91.7 (17.3)
DEL PEAK 20(16Q24.1) MUTATED 21 19 1.3 - 49.0 (14.7)
DEL PEAK 20(16Q24.1) WILD-TYPE 64 54 0.7 - 91.7 (21.3)

Figure S15.  Get High-res Image Gene #20: 'del_16q24.1' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input
  • Copy number data file = all_lesions.txt from GISTIC pipeline

  • Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/MESO-TP/22533663/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/MESO-TP/22507181/MESO-TP.merged_data.txt

  • Number of patients = 87

  • Number of significantly focal cnvs = 21

  • Number of selected clinical features = 11

  • 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)