Kidney Renal Papillary Cell Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features
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 57 arm-level results and 8 clinical features across 101 patients, 7 significant findings detected with Q value < 0.25.

  • 1q gain cnv correlated to 'Time to Death'.

  • 6q gain cnv correlated to 'Time to Death'.

  • 17p gain cnv correlated to 'PATHOLOGY.T' and 'TUMOR.STAGE'.

  • 3q loss cnv correlated to 'Time to Death'.

  • 5p loss cnv correlated to 'Time to Death'.

  • 17p 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 57 arm-level results and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE
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 Fisher's exact test
17p gain 51 (50%) 50 0.222
(1.00) 		0.072
(1.00) 		0.0105
(1.00) 		0.201
(1.00) 		0.000345
(0.136) 		0.224
(1.00) 		0.035
(1.00) 		0.000381
(0.15)
1q gain 8 (8%) 93 9.91e-05
(0.0394) 		0.796
(1.00) 		0.259
(1.00) 		0.0027
(1.00) 		0.836
(1.00) 		0.102
(1.00) 		0.00716
(1.00)
6q gain 3 (3%) 98 5.18e-06
(0.00206) 		0.258
(1.00) 		0.55
(1.00) 		0.704
(1.00) 		0.00701
(1.00) 		0.0832
(1.00)
3q loss 3 (3%) 98 2.89e-06
(0.00115) 		0.324
(1.00) 		1
(1.00) 		0.0727
(1.00) 		0.506
(1.00) 		0.199
(1.00) 		0.103
(1.00)
5p loss 4 (4%) 97 0.000217
(0.0859) 		0.0657
(1.00) 		0.0926
(1.00) 		0.0151
(1.00) 		0.0879
(1.00) 		0.624
(1.00) 		0.103
(1.00)
17p loss 5 (5%) 96 2.02e-07
(8.08e-05) 		0.489
(1.00) 		0.0339
(1.00) 		0.154
(1.00) 		0.338
(1.00) 		0.0613
(1.00) 		0.0453
(1.00)
2p gain 11 (11%) 90 0.191
(1.00) 		0.351
(1.00) 		0.739
(1.00) 		0.54
(1.00) 		0.81
(1.00) 		0.222
(1.00) 		0.0436
(1.00) 		0.0372
(1.00)
2q gain 13 (13%) 88 0.35
(1.00) 		0.855
(1.00) 		0.544
(1.00) 		0.5
(1.00) 		0.919
(1.00) 		0.055
(1.00) 		0.0532
(1.00) 		0.0101
(1.00)
3p gain 24 (24%) 77 0.414
(1.00) 		0.93
(1.00) 		0.0827
(1.00) 		0.088
(1.00) 		0.0175
(1.00) 		1
(1.00) 		0.755
(1.00) 		0.232
(1.00)
3q gain 26 (26%) 75 0.288
(1.00) 		0.871
(1.00) 		0.0502
(1.00) 		0.208
(1.00) 		0.0509
(1.00) 		1
(1.00) 		0.707
(1.00) 		0.381
(1.00)
4p gain 4 (4%) 97 0.0702
(1.00) 		0.0414
(1.00) 		1
(1.00) 		0.349
(1.00) 		0.338
(1.00) 		0.028
(1.00) 		0.0832
(1.00)
4q gain 3 (3%) 98 0.694
(1.00) 		0.0405
(1.00) 		1
(1.00) 		0.371
(1.00)
5p gain 9 (9%) 92 0.76
(1.00) 		0.201
(1.00) 		0.459
(1.00) 		0.395
(1.00) 		0.0557
(1.00) 		0.643
(1.00) 		0.529
(1.00) 		0.0743
(1.00)
5q gain 9 (9%) 92 0.31
(1.00) 		0.307
(1.00) 		0.459
(1.00) 		0.395
(1.00) 		0.205
(1.00) 		0.643
(1.00) 		0.436
(1.00) 		0.305
(1.00)
6p gain 4 (4%) 97 0.0755
(1.00) 		0.362
(1.00) 		0.304
(1.00) 		0.172
(1.00) 		0.506
(1.00) 		0.00732
(1.00) 		0.08
(1.00)
7p gain 54 (53%) 47 0.0708
(1.00) 		0.925
(1.00) 		0.0337
(1.00) 		0.2
(1.00) 		0.002
(0.785) 		0.454
(1.00) 		0.117
(1.00) 		0.000645
(0.253)
7q gain 55 (54%) 46 0.0708
(1.00) 		0.925
(1.00) 		0.0852
(1.00) 		0.2
(1.00) 		0.00594
(1.00) 		0.454
(1.00) 		0.142
(1.00) 		0.00233
(0.905)
8p gain 5 (5%) 96 0.576
(1.00) 		0.242
(1.00) 		0.651
(1.00) 		0.0534
(1.00) 		0.561
(1.00) 		0.734
(1.00) 		0.166
(1.00)
8q gain 7 (7%) 94 0.00501
(1.00) 		0.624
(1.00) 		0.676
(1.00) 		0.0102
(1.00) 		0.205
(1.00) 		0.399
(1.00) 		0.00947
(1.00)
10p gain 3 (3%) 98 1
(1.00) 		0.371
(1.00)
12p gain 30 (30%) 71 0.991
(1.00) 		0.857
(1.00) 		0.0381
(1.00) 		0.26
(1.00) 		1
(1.00) 		0.408
(1.00) 		0.333
(1.00) 		0.406
(1.00)
12q gain 30 (30%) 71 0.991
(1.00) 		0.857
(1.00) 		0.0381
(1.00) 		0.26
(1.00) 		1
(1.00) 		0.408
(1.00) 		0.333
(1.00) 		0.406
(1.00)
13q gain 10 (10%) 91 0.717
(1.00) 		0.393
(1.00) 		1
(1.00) 		0.208
(1.00) 		0.798
(1.00) 		1
(1.00) 		0.931
(1.00)
16p gain 43 (43%) 58 0.537
(1.00) 		0.336
(1.00) 		0.00493
(1.00) 		0.531
(1.00) 		0.921
(1.00) 		0.366
(1.00) 		0.731
(1.00) 		0.824
(1.00)
16q gain 40 (40%) 61 0.103
(1.00) 		0.428
(1.00) 		0.0162
(1.00) 		0.264
(1.00) 		0.681
(1.00) 		0.186
(1.00) 		0.276
(1.00) 		0.793
(1.00)
17q gain 61 (60%) 40 0.768
(1.00) 		0.137
(1.00) 		0.19
(1.00) 		0.556
(1.00) 		0.125
(1.00) 		0.226
(1.00) 		0.0649
(1.00) 		0.169
(1.00)
18p gain 6 (6%) 95 0.195
(1.00) 		0.594
(1.00) 		0.662
(1.00) 		0.54
(1.00) 		0.856
(1.00) 		0.363
(1.00) 		0.469
(1.00)
18q gain 5 (5%) 96 0.609
(1.00) 		0.967
(1.00) 		1
(1.00) 		0.678
(1.00) 		1
(1.00) 		0.651
(1.00)
20p gain 30 (30%) 71 0.439
(1.00) 		0.031
(1.00) 		0.64
(1.00) 		0.639
(1.00) 		0.276
(1.00) 		1
(1.00) 		0.413
(1.00) 		0.49
(1.00)
20q gain 31 (31%) 70 0.439
(1.00) 		0.07
(1.00) 		0.49
(1.00) 		0.639
(1.00) 		0.385
(1.00) 		1
(1.00) 		0.693
(1.00) 		0.702
(1.00)
Xq gain 4 (4%) 97 0.416
(1.00) 		0.36
(1.00) 		0.0926
(1.00) 		0.783
(1.00) 		0.624
(1.00) 		0.366
(1.00)
1p loss 10 (10%) 91 0.721
(1.00) 		0.62
(1.00) 		1
(1.00) 		0.349
(1.00) 		0.891
(1.00) 		1
(1.00) 		0.393
(1.00)
1q loss 6 (6%) 95 0.646
(1.00) 		0.821
(1.00) 		1
(1.00) 		0.349
(1.00) 		0.715
(1.00) 		0.521
(1.00) 		0.651
(1.00)
3p loss 6 (6%) 95 0.211
(1.00) 		0.0512
(1.00) 		0.662
(1.00) 		0.0787
(1.00) 		0.55
(1.00) 		0.43
(1.00) 		0.0978
(1.00)
4p loss 8 (8%) 93 0.131
(1.00) 		0.13
(1.00) 		0.0117
(1.00) 		0.135
(1.00) 		0.761
(1.00) 		0.668
(1.00) 		0.0545
(1.00)
4q loss 8 (8%) 93 0.699
(1.00) 		0.476
(1.00) 		0.105
(1.00) 		0.385
(1.00) 		1
(1.00) 		0.668
(1.00) 		0.254
(1.00)
5q loss 4 (4%) 97 0.0275
(1.00) 		0.229
(1.00) 		0.0926
(1.00) 		0.172
(1.00) 		0.624
(1.00) 		0.573
(1.00)
6p loss 9 (9%) 92 0.0312
(1.00) 		0.992
(1.00) 		0.459
(1.00) 		0.26
(1.00) 		0.241
(1.00) 		0.204
(1.00) 		1
(1.00) 		0.345
(1.00)
6q loss 10 (10%) 91 0.279
(1.00) 		0.364
(1.00) 		0.069
(1.00) 		0.392
(1.00) 		0.138
(1.00) 		0.309
(1.00) 		0.689
(1.00) 		0.493
(1.00)
8p loss 3 (3%) 98 0.748
(1.00) 		0.402
(1.00) 		0.0298
(1.00) 		0.231
(1.00) 		1
(1.00) 		0.276
(1.00)
9p loss 12 (12%) 89 0.0576
(1.00) 		0.529
(1.00) 		0.0164
(1.00) 		0.424
(1.00) 		0.126
(1.00) 		0.836
(1.00) 		0.518
(1.00) 		0.286
(1.00)
9q loss 12 (12%) 89 0.0985
(1.00) 		0.501
(1.00) 		0.0481
(1.00) 		0.424
(1.00) 		0.0783
(1.00) 		0.836
(1.00) 		0.646
(1.00) 		0.436
(1.00)
10p loss 5 (5%) 96 0.129
(1.00) 		0.947
(1.00) 		0.651
(1.00) 		0.532
(1.00) 		0.134
(1.00) 		0.686
(1.00)
10q loss 5 (5%) 96 0.0067
(1.00) 		0.74
(1.00) 		1
(1.00) 		0.407
(1.00) 		0.506
(1.00) 		0.279
(1.00) 		0.575
(1.00)
11p loss 8 (8%) 93 0.00515
(1.00) 		0.0518
(1.00) 		0.706
(1.00) 		0.321
(1.00) 		0.529
(1.00) 		0.181
(1.00)
11q loss 9 (9%) 92 0.0224
(1.00) 		0.0674
(1.00) 		1
(1.00) 		0.0557
(1.00) 		0.561
(1.00) 		0.581
(1.00) 		0.0678
(1.00)
13q loss 9 (9%) 92 0.025
(1.00) 		0.424
(1.00) 		0.00414
(1.00) 		0.205
(1.00) 		0.488
(1.00) 		0.482
(1.00) 		0.275
(1.00)
14q loss 17 (17%) 84 0.974
(1.00) 		0.472
(1.00) 		0.397
(1.00) 		0.396
(1.00) 		0.608
(1.00) 		0.447
(1.00) 		0.437
(1.00) 		0.256
(1.00)
15q loss 10 (10%) 91 0.0655
(1.00) 		0.362
(1.00) 		0.721
(1.00) 		0.654
(1.00) 		0.138
(1.00) 		1
(1.00) 		1
(1.00) 		0.206
(1.00)
16q loss 3 (3%) 98 0.0298
(1.00) 		0.704
(1.00) 		0.624
(1.00) 		0.366
(1.00)
18p loss 15 (15%) 86 0.00216
(0.846) 		0.853
(1.00) 		1
(1.00) 		0.349
(1.00) 		0.11
(1.00) 		0.329
(1.00) 		0.697
(1.00) 		0.275
(1.00)
18q loss 16 (16%) 85 0.00216
(0.846) 		0.853
(1.00) 		0.572
(1.00) 		0.349
(1.00) 		0.0456
(1.00) 		0.329
(1.00) 		0.9
(1.00) 		0.128
(1.00)
19p loss 4 (4%) 97 0.646
(1.00) 		0.584
(1.00) 		1
(1.00) 		0.596
(1.00) 		0.624
(1.00) 		0.276
(1.00)
19q loss 3 (3%) 98 0.748
(1.00) 		0.879
(1.00) 		1
(1.00) 		0.371
(1.00)
21q loss 11 (11%) 90 0.151
(1.00) 		0.435
(1.00) 		0.318
(1.00) 		0.664
(1.00) 		1
(1.00) 		1
(1.00) 		0.206
(1.00)
22q loss 18 (18%) 83 0.704
(1.00) 		0.583
(1.00) 		0.0926
(1.00) 		0.457
(1.00) 		0.212
(1.00) 		0.596
(1.00) 		0.272
(1.00) 		0.219
(1.00)
Xq loss 3 (3%) 98 0.748
(1.00) 		0.919
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
'1q gain mutation analysis' versus 'Time to Death'

P value = 9.91e-05 (logrank test), Q value = 0.039

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

nPatients nDeath Duration Range (Median), Month
ALL 94 14 0.0 - 182.7 (13.9)
1Q GAIN MUTATED 7 2 0.7 - 25.4 (7.6)
1Q GAIN WILD-TYPE 87 12 0.0 - 182.7 (15.5)

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

'6q gain mutation analysis' versus 'Time to Death'

P value = 5.18e-06 (logrank test), Q value = 0.0021

Table S2.  Gene #11: '6q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 94 14 0.0 - 182.7 (13.9)
6Q GAIN MUTATED 3 2 7.9 - 13.6 (9.6)
6Q GAIN WILD-TYPE 91 12 0.0 - 182.7 (14.6)

Figure S2.  Get High-res Image Gene #11: '6q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'17p gain mutation analysis' versus 'PATHOLOGY.T'

P value = 0.000345 (Fisher's exact test), Q value = 0.14

Table S3.  Gene #22: '17p gain mutation analysis' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3+T4
ALL 56 13 32
17P GAIN MUTATED 35 9 7
17P GAIN WILD-TYPE 21 4 25

Figure S3.  Get High-res Image Gene #22: '17p gain mutation analysis' versus Clinical Feature #5: 'PATHOLOGY.T'

'17p gain mutation analysis' versus 'TUMOR.STAGE'

P value = 0.000381 (Fisher's exact test), Q value = 0.15

Table S4.  Gene #22: '17p gain mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 51 7 23 9
17P GAIN MUTATED 33 3 4 2
17P GAIN WILD-TYPE 18 4 19 7

Figure S4.  Get High-res Image Gene #22: '17p gain mutation analysis' versus Clinical Feature #8: 'TUMOR.STAGE'

'3q loss mutation analysis' versus 'Time to Death'

P value = 2.89e-06 (logrank test), Q value = 0.0012

Table S5.  Gene #32: '3q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 94 14 0.0 - 182.7 (13.9)
3Q LOSS MUTATED 3 2 3.7 - 21.6 (8.8)
3Q LOSS WILD-TYPE 91 12 0.0 - 182.7 (14.1)

Figure S5.  Get High-res Image Gene #32: '3q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'5p loss mutation analysis' versus 'Time to Death'

P value = 0.000217 (logrank test), Q value = 0.086

Table S6.  Gene #35: '5p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 94 14 0.0 - 182.7 (13.9)
5P LOSS MUTATED 4 2 0.0 - 22.9 (7.4)
5P LOSS WILD-TYPE 90 12 0.0 - 182.7 (14.4)

Figure S6.  Get High-res Image Gene #35: '5p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'17p loss mutation analysis' versus 'Time to Death'

P value = 2.02e-07 (logrank test), Q value = 8.1e-05

Table S7.  Gene #50: '17p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 94 14 0.0 - 182.7 (13.9)
17P LOSS MUTATED 4 2 0.2 - 11.1 (5.2)
17P LOSS WILD-TYPE 90 12 0.0 - 182.7 (15.1)

Figure S7.  Get High-res Image Gene #50: '17p 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 = KIRP-TP.clin.merged.picked.txt

  • Number of patients = 101

  • Number of significantly arm-level cnvs = 57

  • Number of selected clinical features = 8

  • 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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