Correlation between copy number variations of arm-level result and selected clinical features
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 63 arm-level events and 12 clinical features across 66 patients, 10 significant findings detected with Q value < 0.25.

  • 5p gain cnv correlated to 'NEOPLASM_DISEASESTAGE'.

  • 5q gain cnv correlated to 'NEOPLASM_DISEASESTAGE'.

  • 19p gain cnv correlated to 'PATHOLOGY_N_STAGE'.

  • 9p loss cnv correlated to 'Time to Death'.

  • 9q loss cnv correlated to 'Time to Death'.

  • 16p loss cnv correlated to 'Time to Death',  'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_N_STAGE'.

  • 16q loss 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 63 arm-level events and 12 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 10 significant findings detected.

Clinical
Features
Time
to
Death
YEARS
TO
BIRTH
NEOPLASM
DISEASESTAGE
PATHOLOGY
T
STAGE
PATHOLOGY
N
STAGE
PATHOLOGY
M
STAGE
GENDER KARNOFSKY
PERFORMANCE
SCORE
NUMBER
PACK
YEARS
SMOKED
YEAR
OF
TOBACCO
SMOKING
ONSET
RACE ETHNICITY
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 Wilcoxon-test Wilcoxon-test Wilcoxon-test Fisher's exact test Fisher's exact test
16p loss 4 (6%) 62 3.48e-09
(1.32e-06)
0.519
(1.00)
0.00035
(0.0529)
0.00605
(0.352)
3.36e-05
(0.00846)
0.11
(0.711)
0.639
(1.00)
1
(1.00)
1
(1.00)
16q loss 5 (8%) 61 2.01e-09
(1.32e-06)
0.221
(0.869)
0.00462
(0.318)
0.0489
(0.597)
0.000165
(0.0311)
0.11
(0.711)
0.641
(1.00)
1
(1.00)
1
(1.00)
5p gain 8 (12%) 58 0.00738
(0.372)
0.0661
(0.649)
0.00158
(0.172)
0.365
(0.995)
0.0874
(0.654)
0.00952
(0.379)
0.455
(1.00)
1
(1.00)
1
(1.00)
5q gain 8 (12%) 58 0.00738
(0.372)
0.0661
(0.649)
0.00159
(0.172)
0.362
(0.995)
0.0874
(0.654)
0.00952
(0.379)
0.455
(1.00)
1
(1.00)
1
(1.00)
19p gain 19 (29%) 47 0.0204
(0.531)
0.0291
(0.569)
0.0152
(0.443)
0.521
(1.00)
0.00246
(0.232)
0.0571
(0.617)
0.412
(1.00)
0.784
(1.00)
0.0606
(0.619)
0.256
(0.922)
0.29
(0.928)
9p loss 10 (15%) 56 0.00314
(0.237)
0.0116
(0.409)
1
(1.00)
1
(1.00)
0.529
(1.00)
1
(1.00)
0.508
(1.00)
1
(1.00)
0.535
(1.00)
9q loss 10 (15%) 56 0.00314
(0.237)
0.0116
(0.409)
1
(1.00)
1
(1.00)
0.529
(1.00)
1
(1.00)
0.508
(1.00)
1
(1.00)
0.535
(1.00)
3p gain 8 (12%) 58 0.00854
(0.379)
0.0837
(0.654)
0.0165
(0.446)
0.464
(1.00)
0.0327
(0.569)
0.213
(0.86)
0.128
(0.711)
0.293
(0.928)
1
(1.00)
3q gain 8 (12%) 58 0.00854
(0.379)
0.0837
(0.654)
0.016
(0.446)
0.466
(1.00)
0.0327
(0.569)
0.213
(0.86)
0.128
(0.711)
0.293
(0.928)
1
(1.00)
4p gain 24 (36%) 42 0.885
(1.00)
0.265
(0.928)
0.141
(0.711)
0.808
(1.00)
1
(1.00)
0.124
(0.711)
1
(1.00)
1
(1.00)
0.0467
(0.579)
0.0369
(0.569)
1
(1.00)
0.287
(0.928)
4q gain 24 (36%) 42 0.885
(1.00)
0.265
(0.928)
0.14
(0.711)
0.81
(1.00)
1
(1.00)
0.124
(0.711)
1
(1.00)
1
(1.00)
0.0467
(0.579)
0.0369
(0.569)
1
(1.00)
0.287
(0.928)
7p gain 24 (36%) 42 0.0281
(0.569)
0.172
(0.759)
0.36
(0.995)
0.69
(1.00)
0.636
(1.00)
0.124
(0.711)
0.195
(0.832)
1
(1.00)
0.784
(1.00)
0.0606
(0.619)
1
(1.00)
0.303
(0.928)
7q gain 24 (36%) 42 0.0281
(0.569)
0.172
(0.759)
0.362
(0.995)
0.691
(1.00)
0.636
(1.00)
0.124
(0.711)
0.195
(0.832)
1
(1.00)
0.784
(1.00)
0.0606
(0.619)
1
(1.00)
0.303
(0.928)
8p gain 17 (26%) 49 0.912
(1.00)
0.142
(0.711)
0.39
(1.00)
0.409
(1.00)
1
(1.00)
0.443
(1.00)
0.776
(1.00)
1
(1.00)
0.394
(1.00)
1
(1.00)
0.588
(1.00)
8q gain 18 (27%) 48 0.912
(1.00)
0.15
(0.716)
0.227
(0.882)
0.448
(1.00)
1
(1.00)
0.0714
(0.654)
0.577
(1.00)
1
(1.00)
0.394
(1.00)
1
(1.00)
0.588
(1.00)
9p gain 10 (15%) 56 0.636
(1.00)
0.979
(1.00)
0.121
(0.711)
0.195
(0.832)
0.306
(0.928)
1
(1.00)
1
(1.00)
0.759
(1.00)
0.373
(1)
0.535
(1.00)
9q gain 10 (15%) 56 0.636
(1.00)
0.979
(1.00)
0.122
(0.711)
0.194
(0.832)
0.306
(0.928)
1
(1.00)
1
(1.00)
0.759
(1.00)
0.376
(1)
0.535
(1.00)
10p gain 4 (6%) 62 0.556
(1.00)
0.34
(0.995)
0.269
(0.928)
0.451
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
11p gain 15 (23%) 51 0.952
(1.00)
0.812
(1.00)
0.74
(1.00)
0.813
(1.00)
0.617
(1.00)
0.443
(1.00)
1
(1.00)
0.0467
(0.579)
0.0369
(0.569)
0.563
(1.00)
0.0756
(0.654)
11q gain 15 (23%) 51 0.488
(1.00)
0.945
(1.00)
0.738
(1.00)
0.812
(1.00)
1
(1.00)
0.443
(1.00)
1
(1.00)
0.0467
(0.579)
0.0369
(0.569)
0.56
(1.00)
0.0756
(0.654)
12p gain 19 (29%) 47 0.676
(1.00)
0.0137
(0.415)
0.0769
(0.654)
0.316
(0.945)
1
(1.00)
0.0873
(0.654)
0.412
(1.00)
1
(1.00)
0.357
(0.995)
1
(1.00)
1
(1.00)
12q gain 20 (30%) 46 0.218
(0.862)
0.0696
(0.654)
0.111
(0.711)
0.709
(1.00)
0.33
(0.982)
0.0873
(0.654)
0.593
(1.00)
1
(1.00)
0.234
(0.882)
0.0369
(0.569)
0.807
(1.00)
1
(1.00)
14q gain 21 (32%) 45 0.816
(1.00)
0.144
(0.711)
0.0979
(0.698)
0.383
(1.00)
1
(1.00)
0.105
(0.711)
0.794
(1.00)
1
(1.00)
0.234
(0.882)
0.0369
(0.569)
1
(1.00)
0.609
(1.00)
15q gain 21 (32%) 45 0.338
(0.995)
0.283
(0.928)
0.41
(1.00)
0.48
(1.00)
0.35
(0.995)
0.105
(0.711)
0.794
(1.00)
1
(1.00)
1
(1.00)
0.233
(0.882)
0.816
(1.00)
0.124
(0.711)
16p gain 21 (32%) 45 0.217
(0.862)
0.995
(1.00)
0.476
(1.00)
0.404
(1.00)
0.635
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.0467
(0.579)
0.0369
(0.569)
1
(1.00)
0.124
(0.711)
16q gain 21 (32%) 45 0.217
(0.862)
0.995
(1.00)
0.478
(1.00)
0.405
(1.00)
0.635
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.0467
(0.579)
0.0369
(0.569)
1
(1.00)
0.124
(0.711)
18p gain 17 (26%) 49 0.878
(1.00)
0.278
(0.928)
0.292
(0.928)
0.435
(1.00)
0.303
(0.928)
0.4
(1.00)
0.776
(1.00)
0.298
(0.928)
1
(1.00)
0.57
(1.00)
18q gain 16 (24%) 50 0.358
(0.995)
0.525
(1.00)
0.312
(0.937)
0.202
(0.851)
0.303
(0.928)
0.4
(1.00)
1
(1.00)
0.298
(0.928)
1
(1.00)
0.305
(0.928)
19q gain 17 (26%) 49 0.11
(0.711)
0.0137
(0.415)
0.297
(0.928)
0.644
(1.00)
0.136
(0.711)
0.0444
(0.579)
0.776
(1.00)
1
(1.00)
0.233
(0.882)
0.596
(1.00)
0.29
(0.928)
20p gain 20 (30%) 46 0.816
(1.00)
0.128
(0.711)
0.366
(0.995)
0.791
(1.00)
0.35
(0.995)
0.524
(1.00)
0.593
(1.00)
1
(1.00)
0.234
(0.882)
0.0369
(0.569)
0.365
(0.995)
0.588
(1.00)
20q gain 21 (32%) 45 0.343
(0.995)
0.053
(0.606)
0.138
(0.711)
0.845
(1.00)
0.35
(0.995)
0.524
(1.00)
0.433
(1.00)
1
(1.00)
0.234
(0.882)
0.0369
(0.569)
0.367
(0.995)
0.609
(1.00)
21q gain 4 (6%) 62 0.495
(1.00)
0.591
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
22q gain 19 (29%) 47 0.171
(0.759)
0.415
(1.00)
0.495
(1.00)
0.52
(1.00)
0.166
(0.754)
0.524
(1.00)
0.412
(1.00)
0.784
(1.00)
0.0606
(0.619)
0.793
(1.00)
0.588
(1.00)
xp gain 7 (11%) 59 0.403
(1.00)
0.95
(1.00)
0.699
(1.00)
0.881
(1.00)
1
(1.00)
0.213
(0.86)
0.691
(1.00)
0.517
(1.00)
0.466
(1.00)
xq gain 6 (9%) 60 0.41
(1.00)
0.815
(1.00)
0.381
(1.00)
0.661
(1.00)
1
(1.00)
0.213
(0.86)
0.388
(1.00)
0.46
(1.00)
0.39
(1.00)
1p loss 53 (80%) 13 0.437
(1.00)
0.478
(1.00)
0.0289
(0.569)
0.042
(0.579)
1
(1.00)
0.162
(0.742)
0.534
(1.00)
0.438
(1.00)
0.609
(1.00)
1
(1.00)
1
(1.00)
1q loss 52 (79%) 14 0.815
(1.00)
0.456
(1.00)
0.00591
(0.352)
0.0136
(0.415)
0.529
(1.00)
0.213
(0.86)
0.367
(0.995)
0.438
(1.00)
1
(1.00)
1
(1.00)
2p loss 46 (70%) 20 0.929
(1.00)
0.241
(0.893)
0.144
(0.711)
0.114
(0.711)
1
(1.00)
0.4
(1.00)
0.284
(0.928)
0.438
(1.00)
1
(1.00)
1
(1.00)
2q loss 46 (70%) 20 0.929
(1.00)
0.241
(0.893)
0.145
(0.711)
0.114
(0.711)
1
(1.00)
0.4
(1.00)
0.284
(0.928)
0.438
(1.00)
1
(1.00)
1
(1.00)
3p loss 9 (14%) 57 0.196
(0.832)
0.0465
(0.579)
0.0762
(0.654)
0.0222
(0.56)
1
(1.00)
1
(1.00)
0.469
(1.00)
1
(1.00)
0.535
(1.00)
3q loss 8 (12%) 58 0.234
(0.882)
0.107
(0.711)
0.141
(0.711)
0.0525
(0.606)
1
(1.00)
1
(1.00)
0.256
(0.922)
1
(1.00)
0.535
(1.00)
5p loss 10 (15%) 56 0.762
(1.00)
0.642
(1.00)
0.134
(0.711)
0.083
(0.654)
1
(1.00)
1
(1.00)
1
(1.00)
0.155
(0.733)
0.0736
(0.654)
0.159
(0.735)
0.121
(0.711)
5q loss 10 (15%) 56 0.762
(1.00)
0.642
(1.00)
0.131
(0.711)
0.0853
(0.654)
1
(1.00)
1
(1.00)
1
(1.00)
0.155
(0.733)
0.0736
(0.654)
0.158
(0.733)
0.121
(0.711)
6p loss 51 (77%) 15 0.572
(1.00)
0.379
(1.00)
0.0818
(0.654)
0.055
(0.606)
0.568
(1.00)
0.356
(0.995)
0.244
(0.896)
0.438
(1.00)
1
(1.00)
1
(1.00)
6q loss 51 (77%) 15 0.572
(1.00)
0.379
(1.00)
0.0833
(0.654)
0.0552
(0.606)
0.568
(1.00)
0.356
(0.995)
0.244
(0.896)
0.438
(1.00)
1
(1.00)
1
(1.00)
8p loss 9 (14%) 57 0.214
(0.86)
0.779
(1.00)
0.742
(1.00)
0.44
(1.00)
1
(1.00)
1
(1.00)
0.727
(1.00)
0.614
(1.00)
0.566
(1.00)
8q loss 8 (12%) 58 0.255
(0.922)
0.575
(1.00)
0.701
(1.00)
0.41
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.567
(1.00)
1
(1.00)
10p loss 48 (73%) 18 0.126
(0.711)
0.762
(1.00)
0.542
(1.00)
0.395
(1.00)
1
(1.00)
0.31
(0.932)
1
(1.00)
0.146
(0.711)
0.26
(0.925)
1
(1.00)
1
(1.00)
10q loss 49 (74%) 17 0.144
(0.711)
0.953
(1.00)
0.347
(0.995)
0.208
(0.86)
1
(1.00)
0.31
(0.932)
1
(1.00)
0.146
(0.711)
0.26
(0.925)
1
(1.00)
1
(1.00)
11p loss 7 (11%) 59 0.304
(0.928)
0.0527
(0.606)
0.0962
(0.693)
0.0791
(0.654)
0.461
(1.00)
1
(1.00)
0.691
(1.00)
1
(1.00)
1
(1.00)
11q loss 7 (11%) 59 0.304
(0.928)
0.0527
(0.606)
0.0951
(0.693)
0.0791
(0.654)
0.461
(1.00)
1
(1.00)
0.691
(1.00)
1
(1.00)
1
(1.00)
13q loss 43 (65%) 23 0.548
(1.00)
0.0641
(0.646)
0.446
(1.00)
0.357
(0.995)
0.301
(0.928)
1
(1.00)
0.294
(0.928)
1
(1.00)
0.474
(1.00)
0.371
(1)
0.816
(1.00)
1
(1.00)
17p loss 50 (76%) 16 0.8
(1.00)
0.149
(0.715)
0.075
(0.654)
0.0425
(0.579)
0.568
(1.00)
0.356
(0.995)
0.158
(0.733)
0.438
(1.00)
1
(1.00)
1
(1.00)
17q loss 50 (76%) 16 0.8
(1.00)
0.149
(0.715)
0.0737
(0.654)
0.0404
(0.579)
0.568
(1.00)
0.356
(0.995)
0.158
(0.733)
0.438
(1.00)
1
(1.00)
1
(1.00)
18p loss 8 (12%) 58 0.964
(1.00)
0.455
(1.00)
0.446
(1.00)
0.9
(1.00)
0.211
(0.86)
1
(1.00)
0.256
(0.922)
0.294
(0.928)
1
(1.00)
18q loss 10 (15%) 56 0.443
(1.00)
0.343
(0.995)
0.14
(0.711)
0.835
(1.00)
0.258
(0.923)
1
(1.00)
0.295
(0.928)
0.375
(1)
1
(1.00)
19q loss 3 (5%) 63 0.0955
(0.693)
0.423
(1.00)
0.0119
(0.409)
0.191
(0.832)
0.0289
(0.569)
1
(1.00)
1
(1.00)
0.101
(0.711)
0.111
(0.711)
20p loss 4 (6%) 62 0.637
(1.00)
0.237
(0.886)
0.139
(0.711)
0.142
(0.711)
0.304
(0.928)
1
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
20q loss 3 (5%) 63 0.444
(1.00)
0.432
(1.00)
0.0409
(0.579)
0.0244
(0.569)
0.304
(0.928)
1
(1.00)
1
(1.00)
1
(1.00)
21q loss 35 (53%) 31 0.0345
(0.569)
0.375
(1)
0.906
(1.00)
0.784
(1.00)
0.0731
(0.654)
1
(1.00)
0.805
(1.00)
0.448
(1.00)
0.705
(1.00)
0.112
(0.711)
0.543
(1.00)
0.613
(1.00)
22q loss 8 (12%) 58 0.275
(0.928)
0.783
(1.00)
0.182
(0.798)
1
(1.00)
0.211
(0.86)
0.262
(0.925)
0.0553
(0.606)
0.566
(1.00)
0.39
(1.00)
xp loss 38 (58%) 28 0.848
(1.00)
0.0808
(0.654)
0.289
(0.928)
0.547
(1.00)
1
(1.00)
1
(1.00)
0.128
(0.711)
0.132
(0.711)
0.17
(0.759)
0.112
(0.711)
0.814
(1.00)
1
(1.00)
xq loss 39 (59%) 27 0.336
(0.995)
0.0923
(0.684)
0.304
(0.928)
0.361
(0.995)
1
(1.00)
1
(1.00)
0.136
(0.711)
0.132
(0.711)
0.17
(0.759)
0.233
(0.882)
0.817
(1.00)
1
(1.00)
'5p gain' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00158 (Fisher's exact test), Q value = 0.17

Table S1.  Gene #5: '5p gain' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
5P GAIN MUTATED 1 3 0 4
5P GAIN WILD-TYPE 20 22 14 2

Figure S1.  Get High-res Image Gene #5: '5p gain' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'5q gain' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00159 (Fisher's exact test), Q value = 0.17

Table S2.  Gene #6: '5q gain' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
5Q GAIN MUTATED 1 3 0 4
5Q GAIN WILD-TYPE 20 22 14 2

Figure S2.  Get High-res Image Gene #6: '5q gain' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'19p gain' versus 'PATHOLOGY_N_STAGE'

P value = 0.00246 (Fisher's exact test), Q value = 0.23

Table S3.  Gene #24: '19p gain' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 5
19P GAIN MUTATED 10 5
19P GAIN WILD-TYPE 30 0

Figure S3.  Get High-res Image Gene #24: '19p gain' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'9p loss' versus 'Time to Death'

P value = 0.00314 (logrank test), Q value = 0.24

Table S4.  Gene #44: '9p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 152.0 (65.2)
9P LOSS MUTATED 9 4 10.7 - 141.7 (57.2)
9P LOSS WILD-TYPE 56 5 1.0 - 152.0 (70.2)

Figure S4.  Get High-res Image Gene #44: '9p loss' versus Clinical Feature #1: 'Time to Death'

'9q loss' versus 'Time to Death'

P value = 0.00314 (logrank test), Q value = 0.24

Table S5.  Gene #45: '9q loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 152.0 (65.2)
9Q LOSS MUTATED 9 4 10.7 - 141.7 (57.2)
9Q LOSS WILD-TYPE 56 5 1.0 - 152.0 (70.2)

Figure S5.  Get High-res Image Gene #45: '9q loss' versus Clinical Feature #1: 'Time to Death'

'16p loss' versus 'Time to Death'

P value = 3.48e-09 (logrank test), Q value = 1.3e-06

Table S6.  Gene #51: '16p loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 152.0 (65.2)
16P LOSS MUTATED 4 3 1.0 - 30.2 (22.4)
16P LOSS WILD-TYPE 61 6 2.5 - 152.0 (71.4)

Figure S6.  Get High-res Image Gene #51: '16p loss' versus Clinical Feature #1: 'Time to Death'

'16p loss' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00035 (Fisher's exact test), Q value = 0.053

Table S7.  Gene #51: '16p loss' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
16P LOSS MUTATED 0 0 1 3
16P LOSS WILD-TYPE 21 25 13 3

Figure S7.  Get High-res Image Gene #51: '16p loss' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'16p loss' versus 'PATHOLOGY_N_STAGE'

P value = 3.36e-05 (Fisher's exact test), Q value = 0.0085

Table S8.  Gene #51: '16p loss' 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 S8.  Get High-res Image Gene #51: '16p loss' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'16q loss' versus 'Time to Death'

P value = 2.01e-09 (logrank test), Q value = 1.3e-06

Table S9.  Gene #52: '16q loss' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 152.0 (65.2)
16Q LOSS MUTATED 5 4 1.0 - 52.3 (28.1)
16Q LOSS WILD-TYPE 60 5 2.5 - 152.0 (72.7)

Figure S9.  Get High-res Image Gene #52: '16q loss' versus Clinical Feature #1: 'Time to Death'

'16q loss' versus 'PATHOLOGY_N_STAGE'

P value = 0.000165 (Fisher's exact test), Q value = 0.031

Table S10.  Gene #52: '16q loss' 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 S10.  Get High-res Image Gene #52: '16q loss' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

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

  • Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/KICH-TP/15084587/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KICH-TP/15080874/KICH-TP.merged_data.txt

  • Number of patients = 66

  • Number of significantly arm-level cnvs = 63

  • Number of selected clinical features = 12

  • 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

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.

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)