Kidney Renal Papillary Cell Carcinoma: Correlation between copy number variations of arm-level result and molecular subtypes
(primary solid tumor cohort)
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 subtypes.

Summary

Testing the association between copy number variation 59 arm-level results and 8 molecular subtypes across 127 patients, 40 significant findings detected with Q value < 0.25.

- 1q gain cnv correlated to 'METHLYATION_CNMF'.

- 3p gain cnv correlated to 'CN_CNMF', 'MRNASEQ_CNMF', 'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_CHIERARCHICAL'.

- 3q gain cnv correlated to 'CN_CNMF', 'MRNASEQ_CNMF', 'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_CHIERARCHICAL'.

- 5p gain cnv correlated to 'CN_CNMF'.

- 5q gain cnv correlated to 'CN_CNMF'.

- 7p gain cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.

- 7q gain cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.

- 12p gain cnv correlated to 'CN_CNMF'.

- 12q gain cnv correlated to 'CN_CNMF'.

- 16p gain cnv correlated to 'CN_CNMF' and 'MRNASEQ_CHIERARCHICAL'.

- 16q gain cnv correlated to 'CN_CNMF', 'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_CHIERARCHICAL'.

- 17p gain cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.

- 17q gain cnv correlated to 'CN_CNMF'.

- 4p loss cnv correlated to 'CN_CNMF'.

- 5q loss cnv correlated to 'CN_CNMF'.

- 6p loss cnv correlated to 'CN_CNMF'.

- 9p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

- 9q loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

- 14q loss cnv correlated to 'CN_CNMF'.

- 22q loss cnv correlated to 'CN_CNMF'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 59 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 40 significant findings detected.

|  | Molecular subtypes | MRNA CNMF | MRNA CHIERARCHICAL | CN CNMF | METHLYATION CNMF | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL
| nCNV (%) | nWild-Type | Fisher's exact test | Fisher's exact test | Chi-square test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
3p gain | 29 (23%) | 98 | 0.302 (1.00) | 0.0559 (1.00) | 7.99e-15 (3.22e-12) | 0.0132 (1.00) | 0.000268 (0.0993) | 3.64e-07 (0.000144) | 0.0375 (1.00) | 1.22e-05 (0.0047)
3q gain | 33 (26%) | 94 | 0.302 (1.00) | 0.0559 (1.00) | 1.95e-12 (7.83e-10) | 0.00739 (1.00) | 3.65e-05 (0.0139) | 6.94e-06 (0.0027) | 0.00911 (1.00) | 0.000246 (0.0914)
7p gain | 67 (53%) | 60 | 0.633 (1.00) | 0.432 (1.00) | 2.69e-07 (0.000107) | 2.62e-06 (0.00103) | 5.65e-05 (0.0214) | 2.01e-05 (0.00771) | 0.011 (1.00) | 0.00896 (1.00)
7q gain | 68 (54%) | 59 | 0.633 (1.00) | 0.432 (1.00) | 5.79e-08 (2.31e-05) | 9.37e-06 (0.00363) | 0.000181 (0.068) | 3.74e-05 (0.0142) | 0.00419 (1.00) | 0.0113 (1.00)
17p gain | 66 (52%) | 61 | 0.633 (1.00) | 0.432 (1.00) | 9.52e-08 (3.78e-05) | 1.3e-06 (0.000513) | 3.49e-05 (0.0133) | 2.21e-06 (0.000869) | 0.129 (1.00) | 0.00197 (0.692)
16q gain | 58 (46%) | 69 | 0.302 (1.00) | 0.0559 (1.00) | 9.17e-06 (0.00356) | 0.0521 (1.00) | 0.00419 (1.00) | 1.46e-05 (0.00563) | 0.00179 (0.629) | 0.000223 (0.0835)
16p gain | 60 (47%) | 67 | 0.126 (1.00) | 0.0699 (1.00) | 4.19e-06 (0.00164) | 0.221 (1.00) | 0.00874 (1.00) | 0.000182 (0.0684) | 0.00268 (0.934) | 0.000706 (0.256)
9p loss | 15 (12%) | 112 | 1 (1.00) | 0.75 (1.00) | 0.000169 (0.0639) | 0.000318 (0.117) | 0.00782 (1.00) | 0.0678 (1.00) | 0.232 (1.00) | 0.587 (1.00)
9q loss | 17 (13%) | 110 | 1 (1.00) |  | 0.000445 (0.163) | 3.17e-05 (0.0121) | 0.0256 (1.00) | 0.131 (1.00) | 0.37 (1.00) | 0.254 (1.00)
1q gain | 9 (7%) | 118 | 0.438 (1.00) |  | 0.00553 (1.00) | 0.000353 (0.13) | 0.00232 (0.813) | 0.00827 (1.00) | 0.433 (1.00) | 0.145 (1.00)
5p gain | 14 (11%) | 113 | 0.175 (1.00) |  | 1.96e-08 (7.8e-06) | 0.0521 (1.00) | 0.156 (1.00) | 0.8 (1.00) | 0.697 (1.00) | 0.731 (1.00)
5q gain | 14 (11%) | 113 | 0.175 (1.00) |  | 2.18e-10 (8.74e-08) | 0.0521 (1.00) | 0.579 (1.00) | 0.8 (1.00) | 0.919 (1.00) | 0.301 (1.00)
12p gain | 40 (31%) | 87 | 1 (1.00) | 0.0909 (1.00) | 0.00055 (0.201) | 0.833 (1.00) | 0.161 (1.00) | 0.0648 (1.00) | 0.146 (1.00) | 0.0968 (1.00)
12q gain | 40 (31%) | 87 | 1 (1.00) | 0.0909 (1.00) | 0.00055 (0.201) | 0.833 (1.00) | 0.161 (1.00) | 0.0648 (1.00) | 0.146 (1.00) | 0.0968 (1.00)
17q gain | 77 (61%) | 50 | 0.308 (1.00) | 0.808 (1.00) | 5.4e-06 (0.00211) | 0.00104 (0.372) | 0.0144 (1.00) | 0.000926 (0.333) | 0.362 (1.00) | 0.156 (1.00)
4p loss | 11 (9%) | 116 | 1 (1.00) |  | 0.000247 (0.0917) | 0.00449 (1.00) | 0.0129 (1.00) | 0.18 (1.00) | 0.535 (1.00) | 1 (1.00)
5q loss | 6 (5%) | 121 |  |  | 1.96e-05 (0.00754) | 0.23 (1.00) | 0.41 (1.00) | 0.211 (1.00) | 0.236 (1.00) | 0.916 (1.00)
6p loss | 9 (7%) | 118 | 1 (1.00) |  | 0.00043 (0.158) | 0.443 (1.00) | 0.468 (1.00) | 1 (1.00) | 0.331 (1.00) | 0.539 (1.00)
14q loss | 23 (18%) | 104 | 1 (1.00) | 0.385 (1.00) | 1.54e-11 (6.18e-09) | 0.267 (1.00) | 0.135 (1.00) | 0.0977 (1.00) | 0.457 (1.00) | 0.968 (1.00)
22q loss | 24 (19%) | 103 | 1 (1.00) |  | 0.000233 (0.087) | 0.00271 (0.944) | 0.673 (1.00) | 0.0477 (1.00) | 0.186 (1.00) | 0.433 (1.00)
2p gain | 13 (10%) | 114 | 0.55 (1.00) | 0.75 (1.00) | 0.0607 (1.00) | 0.132 (1.00) | 0.585 (1.00) | 0.338 (1.00) | 0.869 (1.00) | 0.0669 (1.00)
2q gain | 15 (12%) | 112 | 0.55 (1.00) | 0.75 (1.00) | 0.0309 (1.00) | 0.354 (1.00) | 0.329 (1.00) | 0.205 (1.00) | 0.856 (1.00) | 0.174 (1.00)
4p gain | 4 (3%) | 123 | 0.175 (1.00) |  | 0.382 (1.00) |  | 0.457 (1.00) | 1 (1.00) | 0.683 (1.00) | 0.881 (1.00)
4q gain | 3 (2%) | 124 | 0.438 (1.00) |  | 0.572 (1.00) |  | 0.645 (1.00) | 0.602 (1.00) | 0.767 (1.00) | 0.618 (1.00)
6p gain | 4 (3%) | 123 | 0.438 (1.00) |  | 0.503 (1.00) | 0.44 (1.00) | 0.114 (1.00) | 0.677 (1.00) | 0.165 (1.00) | 0.374 (1.00)
6q gain | 3 (2%) | 124 | 0.438 (1.00) |  | 0.213 (1.00) |  | 0.41 (1.00) | 1 (1.00) | 0.231 (1.00) | 0.397 (1.00)
8p gain | 9 (7%) | 118 | 1 (1.00) |  | 0.318 (1.00) | 0.087 (1.00) | 0.732 (1.00) | 0.232 (1.00) | 0.617 (1.00) | 0.854 (1.00)
8q gain | 11 (9%) | 116 | 1 (1.00) |  | 0.00446 (1.00) | 0.0131 (1.00) | 0.243 (1.00) | 0.0683 (1.00) | 0.187 (1.00) | 0.767 (1.00)
10p gain | 4 (3%) | 123 | 1 (1.00) |  | 0.184 (1.00) |  | 0.645 (1.00) | 0.602 (1.00) | 0.189 (1.00) | 0.881 (1.00)
10q gain | 3 (2%) | 124 | 1 (1.00) |  | 0.393 (1.00) |  |  |  | 0.454 (1.00) | 0.341 (1.00)
13q gain | 14 (11%) | 113 | 1 (1.00) |  | 0.00662 (1.00) | 0.179 (1.00) | 0.324 (1.00) | 0.0445 (1.00) | 0.747 (1.00) | 0.0852 (1.00)
18p gain | 6 (5%) | 121 | 0.55 (1.00) | 0.141 (1.00) | 0.16 (1.00) | 0.329 (1.00) | 0.863 (1.00) | 1 (1.00) | 0.845 (1.00) | 0.877 (1.00)
18q gain | 4 (3%) | 123 | 0.438 (1.00) |  | 0.0227 (1.00) | 0.329 (1.00) | 0.77 (1.00) | 0.432 (1.00) | 0.91 (1.00) | 0.331 (1.00)
20p gain | 39 (31%) | 88 | 0.126 (1.00) | 0.162 (1.00) | 0.0185 (1.00) | 0.0743 (1.00) | 0.000722 (0.261) | 0.0152 (1.00) | 0.305 (1.00) | 0.125 (1.00)
20q gain | 40 (31%) | 87 | 0.315 (1.00) | 0.119 (1.00) | 0.0107 (1.00) | 0.142 (1.00) | 0.000813 (0.294) | 0.0381 (1.00) | 0.179 (1.00) | 0.371 (1.00)
21q gain | 5 (4%) | 122 | 0.438 (1.00) |  | 0.637 (1.00) | 0.468 (1.00) |  |  | 0.11 (1.00) | 0.0492 (1.00)
Xq gain | 5 (4%) | 122 | 1 (1.00) |  | 0.121 (1.00) | 0.137 (1.00) | 0.457 (1.00) | 1 (1.00) | 0.57 (1.00) | 0.881 (1.00)
1p loss | 14 (11%) | 113 | 1 (1.00) |  | 0.0256 (1.00) | 0.328 (1.00) | 0.611 (1.00) | 0.589 (1.00) | 0.0114 (1.00) | 0.0873 (1.00)
1q loss | 7 (6%) | 120 | 1 (1.00) |  | 0.0171 (1.00) | 0.193 (1.00) | 0.103 (1.00) | 0.432 (1.00) | 0.0359 (1.00) | 0.0124 (1.00)
3p loss | 8 (6%) | 119 | 0.438 (1.00) |  | 0.00138 (0.493) | 0.00142 (0.504) | 0.058 (1.00) | 0.0149 (1.00) | 0.0591 (1.00) | 0.221 (1.00)
3q loss | 3 (2%) | 124 |  |  | 0.248 (1.00) | 0.0335 (1.00) | 0.0288 (1.00) | 0.0762 (1.00) | 0.521 (1.00) | 0.72 (1.00)
4q loss | 12 (9%) | 115 | 1 (1.00) |  | 0.00104 (0.372) | 0.00153 (0.543) | 0.0999 (1.00) | 0.0683 (1.00) | 0.853 (1.00) | 0.698 (1.00)
5p loss | 5 (4%) | 122 |  |  | 0.00932 (1.00) | 0.246 (1.00) | 0.114 (1.00) | 0.0626 (1.00) | 0.236 (1.00) | 0.916 (1.00)
6q loss | 12 (9%) | 115 | 1 (1.00) |  | 0.00107 (0.382) | 0.066 (1.00) | 0.374 (1.00) | 0.558 (1.00) | 0.783 (1.00) | 0.693 (1.00)
8p loss | 3 (2%) | 124 | 1 (1.00) |  | 0.846 (1.00) |  |  |  | 1 (1.00) | 0.618 (1.00)
10p loss | 7 (6%) | 120 | 1 (1.00) |  | 0.0171 (1.00) | 0.056 (1.00) | 0.269 (1.00) | 0.18 (1.00) | 0.265 (1.00) | 0.675 (1.00)
10q loss | 8 (6%) | 119 | 0.438 (1.00) |  | 0.00696 (1.00) | 0.0362 (1.00) | 0.269 (1.00) | 0.0871 (1.00) | 0.617 (1.00) | 0.221 (1.00)
11p loss | 9 (7%) | 118 | 1 (1.00) |  | 0.167 (1.00) | 0.113 (1.00) | 0.269 (1.00) | 0.145 (1.00) | 1 (1.00) | 0.447 (1.00)
11q loss | 11 (9%) | 116 |  |  | 0.00512 (1.00) | 0.00354 (1.00) | 0.296 (1.00) | 0.0563 (1.00) | 0.968 (1.00) | 0.9 (1.00)
13q loss | 12 (9%) | 115 | 1 (1.00) |  | 0.019 (1.00) | 0.00153 (0.543) | 0.091 (1.00) | 0.403 (1.00) | 0.355 (1.00) | 0.234 (1.00)
15q loss | 11 (9%) | 116 | 1 (1.00) |  | 0.00407 (1.00) | 0.119 (1.00) | 0.579 (1.00) | 0.487 (1.00) | 0.573 (1.00) | 0.816 (1.00)
16q loss | 3 (2%) | 124 |  |  | 0.632 (1.00) | 0.139 (1.00) |  |  | 0.862 (1.00) | 0.286 (1.00)
17p loss | 5 (4%) | 122 | 0.438 (1.00) |  | 0.756 (1.00) | 0.0436 (1.00) | 0.0288 (1.00) | 0.0762 (1.00) | 0.219 (1.00) | 0.251 (1.00)
18p loss | 19 (15%) | 108 | 1 (1.00) |  | 0.0863 (1.00) | 0.0306 (1.00) | 0.0772 (1.00) | 0.0114 (1.00) | 0.158 (1.00) | 0.445 (1.00)
18q loss | 20 (16%) | 107 | 1 (1.00) |  | 0.209 (1.00) | 0.0155 (1.00) | 0.0392 (1.00) | 0.00363 (1.00) | 0.322 (1.00) | 0.373 (1.00)
19p loss | 4 (3%) | 123 |  |  | 0.0513 (1.00) | 1 (1.00) |  |  | 0.746 (1.00) | 0.331 (1.00)
19q loss | 4 (3%) | 123 |  |  | 0.0513 (1.00) | 1 (1.00) |  |  | 0.279 (1.00) | 0.618 (1.00)
21q loss | 15 (12%) | 112 | 0.55 (1.00) | 0.413 (1.00) | 0.504 (1.00) | 0.231 (1.00) | 0.246 (1.00) | 0.728 (1.00) | 0.219 (1.00) | 0.284 (1.00)
Xq loss | 3 (2%) | 124 | 1 (1.00) |  | 0.632 (1.00) |  |  |  | 0.767 (1.00) | 0.1 (1.00)

'1q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000353 (Fisher's exact test), Q value = 0.13

Table S1. Gene #1: '1q gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 53 | 31
1Q GAIN MUTATED | 1 | 0 | 7
1Q GAIN WILD-TYPE | 26 | 53 | 24

Figure S1. Get High-res Image Gene #1: '1q gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'3p gain mutation analysis' versus 'CN_CNMF'

P value = 7.99e-15 (Chi-square test), Q value = 3.2e-12

Table S2. Gene #4: '3p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
3P GAIN MUTATED | 1 | 3 | 1 | 5 | 19
3P GAIN WILD-TYPE | 23 | 41 | 12 | 21 | 1

Figure S2. Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'3p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000268 (Fisher's exact test), Q value = 0.099

Table S3. Gene #4: '3p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 24 | 25
3P GAIN MUTATED | 12 | 0 | 7
3P GAIN WILD-TYPE | 15 | 24 | 18

Figure S3. Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'3p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 3.64e-07 (Fisher's exact test), Q value = 0.00014

Table S4. Gene #4: '3p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 18 | 37 | 21
3P GAIN MUTATED | 0 | 19 | 0
3P GAIN WILD-TYPE | 18 | 18 | 21

Figure S4. Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'3p gain mutation analysis' versus 'MIRSEQ_CHIERARCHICAL'

P value = 1.22e-05 (Fisher's exact test), Q value = 0.0047

Table S5. Gene #4: '3p gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 27 | 45 | 12 | 33
3P GAIN MUTATED | 1 | 6 | 8 | 13
3P GAIN WILD-TYPE | 26 | 39 | 4 | 20

Figure S5. Get High-res Image Gene #4: '3p gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

'3q gain mutation analysis' versus 'CN_CNMF'

P value = 1.95e-12 (Chi-square test), Q value = 7.8e-10

Table S6. Gene #5: '3q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
3Q GAIN MUTATED | 2 | 4 | 2 | 6 | 19
3Q GAIN WILD-TYPE | 22 | 40 | 11 | 20 | 1

Figure S6. Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'3q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 3.65e-05 (Fisher's exact test), Q value = 0.014

Table S7. Gene #5: '3q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 24 | 25
3Q GAIN MUTATED | 14 | 0 | 8
3Q GAIN WILD-TYPE | 13 | 24 | 17

Figure S7. Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'3q gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 6.94e-06 (Fisher's exact test), Q value = 0.0027

Table S8. Gene #5: '3q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 18 | 37 | 21
3Q GAIN MUTATED | 1 | 20 | 1
3Q GAIN WILD-TYPE | 17 | 17 | 20

Figure S8. Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'3q gain mutation analysis' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000246 (Fisher's exact test), Q value = 0.091

Table S9. Gene #5: '3q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 27 | 45 | 12 | 33
3Q GAIN MUTATED | 4 | 6 | 8 | 14
3Q GAIN WILD-TYPE | 23 | 39 | 4 | 19

Figure S9. Get High-res Image Gene #5: '3q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

'5p gain mutation analysis' versus 'CN_CNMF'

P value = 1.96e-08 (Chi-square test), Q value = 7.8e-06

Table S10. Gene #8: '5p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
5P GAIN MUTATED | 1 | 0 | 0 | 12 | 1
5P GAIN WILD-TYPE | 23 | 44 | 13 | 14 | 19

Figure S10. Get High-res Image Gene #8: '5p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'5q gain mutation analysis' versus 'CN_CNMF'

P value = 2.18e-10 (Chi-square test), Q value = 8.7e-08

Table S11. Gene #9: '5q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
5Q GAIN MUTATED | 0 | 0 | 0 | 13 | 1
5Q GAIN WILD-TYPE | 24 | 44 | 13 | 13 | 19

Figure S11. Get High-res Image Gene #9: '5q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7p gain mutation analysis' versus 'CN_CNMF'

P value = 2.69e-07 (Chi-square test), Q value = 0.00011

Table S12. Gene #12: '7p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
7P GAIN MUTATED | 5 | 22 | 13 | 9 | 18
7P GAIN WILD-TYPE | 19 | 22 | 0 | 17 | 2

Figure S12. Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7p gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 2.62e-06 (Fisher's exact test), Q value = 0.001

Table S13. Gene #12: '7p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 53 | 31
7P GAIN MUTATED | 10 | 40 | 7
7P GAIN WILD-TYPE | 17 | 13 | 24

Figure S13. Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'7p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 5.65e-05 (Fisher's exact test), Q value = 0.021

Table S14. Gene #12: '7p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 24 | 25
7P GAIN MUTATED | 22 | 5 | 12
7P GAIN WILD-TYPE | 5 | 19 | 13

Figure S14. Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'7p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 2.01e-05 (Fisher's exact test), Q value = 0.0077

Table S15. Gene #12: '7p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 18 | 37 | 21
7P GAIN MUTATED | 5 | 29 | 5
7P GAIN WILD-TYPE | 13 | 8 | 16

Figure S15. Get High-res Image Gene #12: '7p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'7q gain mutation analysis' versus 'CN_CNMF'

P value = 5.79e-08 (Chi-square test), Q value = 2.3e-05

Table S16. Gene #13: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
7Q GAIN MUTATED | 5 | 22 | 13 | 9 | 19
7Q GAIN WILD-TYPE | 19 | 22 | 0 | 17 | 1

Figure S16. Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 9.37e-06 (Fisher's exact test), Q value = 0.0036

Table S17. Gene #13: '7q gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 53 | 31
7Q GAIN MUTATED | 10 | 40 | 8
7Q GAIN WILD-TYPE | 17 | 13 | 23

Figure S17. Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'7q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000181 (Fisher's exact test), Q value = 0.068

Table S18. Gene #13: '7q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 24 | 25
7Q GAIN MUTATED | 22 | 6 | 12
7Q GAIN WILD-TYPE | 5 | 18 | 13

Figure S18. Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'7q gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 3.74e-05 (Fisher's exact test), Q value = 0.014

Table S19. Gene #13: '7q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 18 | 37 | 21
7Q GAIN MUTATED | 6 | 29 | 5
7Q GAIN WILD-TYPE | 12 | 8 | 16

Figure S19. Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'12p gain mutation analysis' versus 'CN_CNMF'

P value = 0.00055 (Chi-square test), Q value = 0.2

Table S20. Gene #18: '12p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
12P GAIN MUTATED | 7 | 6 | 10 | 9 | 8
12P GAIN WILD-TYPE | 17 | 38 | 3 | 17 | 12

Figure S20. Get High-res Image Gene #18: '12p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'12q gain mutation analysis' versus 'CN_CNMF'

P value = 0.00055 (Chi-square test), Q value = 0.2

Table S21. Gene #19: '12q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
12Q GAIN MUTATED | 7 | 6 | 10 | 9 | 8
12Q GAIN WILD-TYPE | 17 | 38 | 3 | 17 | 12

Figure S21. Get High-res Image Gene #19: '12q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'16p gain mutation analysis' versus 'CN_CNMF'

P value = 4.19e-06 (Chi-square test), Q value = 0.0016

Table S22. Gene #21: '16p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
16P GAIN MUTATED | 6 | 16 | 3 | 16 | 19
16P GAIN WILD-TYPE | 18 | 28 | 10 | 10 | 1

Figure S22. Get High-res Image Gene #21: '16p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'16p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000182 (Fisher's exact test), Q value = 0.068

Table S23. Gene #21: '16p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 18 | 37 | 21
16P GAIN MUTATED | 2 | 24 | 6
16P GAIN WILD-TYPE | 16 | 13 | 15

Figure S23. Get High-res Image Gene #21: '16p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'16q gain mutation analysis' versus 'CN_CNMF'

P value = 9.17e-06 (Chi-square test), Q value = 0.0036

Table S24. Gene #22: '16q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
16Q GAIN MUTATED | 6 | 16 | 3 | 14 | 19
16Q GAIN WILD-TYPE | 18 | 28 | 10 | 12 | 1

Figure S24. Get High-res Image Gene #22: '16q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'16q gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 1.46e-05 (Fisher's exact test), Q value = 0.0056

Table S25. Gene #22: '16q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 18 | 37 | 21
16Q GAIN MUTATED | 2 | 24 | 3
16Q GAIN WILD-TYPE | 16 | 13 | 18

Figure S25. Get High-res Image Gene #22: '16q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'16q gain mutation analysis' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000223 (Fisher's exact test), Q value = 0.083

Table S26. Gene #22: '16q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 27 | 45 | 12 | 33
16Q GAIN MUTATED | 8 | 12 | 8 | 23
16Q GAIN WILD-TYPE | 19 | 33 | 4 | 10

Figure S26. Get High-res Image Gene #22: '16q gain mutation analysis' versus Clinical Feature #8: 'MIRSEQ_CHIERARCHICAL'

'17p gain mutation analysis' versus 'CN_CNMF'

P value = 9.52e-08 (Chi-square test), Q value = 3.8e-05

Table S27. Gene #23: '17p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
17P GAIN MUTATED | 6 | 19 | 13 | 9 | 19
17P GAIN WILD-TYPE | 18 | 25 | 0 | 17 | 1

Figure S27. Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'17p gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 1.3e-06 (Fisher's exact test), Q value = 0.00051

Table S28. Gene #23: '17p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 53 | 31
17P GAIN MUTATED | 9 | 40 | 7
17P GAIN WILD-TYPE | 18 | 13 | 24

Figure S28. Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'17p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 3.49e-05 (Fisher's exact test), Q value = 0.013

Table S29. Gene #23: '17p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 24 | 25
17P GAIN MUTATED | 20 | 3 | 10
17P GAIN WILD-TYPE | 7 | 21 | 15

Figure S29. Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #5: 'MRNASEQ_CNMF'

'17p gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 2.21e-06 (Fisher's exact test), Q value = 0.00087

Table S30. Gene #23: '17p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 18 | 37 | 21
17P GAIN MUTATED | 3 | 27 | 3
17P GAIN WILD-TYPE | 15 | 10 | 18

Figure S30. Get High-res Image Gene #23: '17p gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'17q gain mutation analysis' versus 'CN_CNMF'

P value = 5.4e-06 (Chi-square test), Q value = 0.0021

Table S31. Gene #24: '17q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
17Q GAIN MUTATED | 7 | 25 | 13 | 13 | 19
17Q GAIN WILD-TYPE | 17 | 19 | 0 | 13 | 1

Figure S31. Get High-res Image Gene #24: '17q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'4p loss mutation analysis' versus 'CN_CNMF'

P value = 0.000247 (Chi-square test), Q value = 0.092

Table S32. Gene #35: '4p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
4P LOSS MUTATED | 7 | 0 | 0 | 4 | 0
4P LOSS WILD-TYPE | 17 | 44 | 13 | 22 | 20

Figure S32. Get High-res Image Gene #35: '4p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'5q loss mutation analysis' versus 'CN_CNMF'

P value = 1.96e-05 (Chi-square test), Q value = 0.0075

Table S33. Gene #38: '5q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
5Q LOSS MUTATED | 6 | 0 | 0 | 0 | 0
5Q LOSS WILD-TYPE | 18 | 44 | 13 | 26 | 20

Figure S33. Get High-res Image Gene #38: '5q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'6p loss mutation analysis' versus 'CN_CNMF'

P value = 0.00043 (Chi-square test), Q value = 0.16

Table S34. Gene #39: '6p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
6P LOSS MUTATED | 1 | 0 | 0 | 7 | 1
6P LOSS WILD-TYPE | 23 | 44 | 13 | 19 | 19

Figure S34. Get High-res Image Gene #39: '6p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'9p loss mutation analysis' versus 'CN_CNMF'

P value = 0.000169 (Chi-square test), Q value = 0.064

Table S35. Gene #42: '9p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
9P LOSS MUTATED | 8 | 0 | 0 | 6 | 1
9P LOSS WILD-TYPE | 16 | 44 | 13 | 20 | 19

Figure S35. Get High-res Image Gene #42: '9p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'9p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000318 (Fisher's exact test), Q value = 0.12

Table S36. Gene #42: '9p loss mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 53 | 31
9P LOSS MUTATED | 5 | 0 | 7
9P LOSS WILD-TYPE | 22 | 53 | 24

Figure S36. Get High-res Image Gene #42: '9p loss mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'9q loss mutation analysis' versus 'CN_CNMF'

P value = 0.000445 (Chi-square test), Q value = 0.16

Table S37. Gene #43: '9q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
9Q LOSS MUTATED | 8 | 1 | 0 | 7 | 1
9Q LOSS WILD-TYPE | 16 | 43 | 13 | 19 | 19

Figure S37. Get High-res Image Gene #43: '9q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'9q loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 3.17e-05 (Fisher's exact test), Q value = 0.012

Table S38. Gene #43: '9q loss mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 27 | 53 | 31
9Q LOSS MUTATED | 6 | 0 | 9
9Q LOSS WILD-TYPE | 21 | 53 | 22

Figure S38. Get High-res Image Gene #43: '9q loss mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

'14q loss mutation analysis' versus 'CN_CNMF'

P value = 1.54e-11 (Chi-square test), Q value = 6.2e-09

Table S39. Gene #49: '14q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
14Q LOSS MUTATED | 16 | 0 | 0 | 7 | 0
14Q LOSS WILD-TYPE | 8 | 44 | 13 | 19 | 20

Figure S39. Get High-res Image Gene #49: '14q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'22q loss mutation analysis' versus 'CN_CNMF'

P value = 0.000233 (Chi-square test), Q value = 0.087

Table S40. Gene #58: '22q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 24 | 44 | 13 | 26 | 20
22Q LOSS MUTATED | 6 | 6 | 0 | 12 | 0
22Q LOSS WILD-TYPE | 18 | 38 | 13 | 14 | 20

Figure S40. Get High-res Image Gene #58: '22q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

Methods & Data
Input

- Mutation data file = broad_values_by_arm.mutsig.cluster.txt

- Molecular subtypes file = KIRP-TP.transferedmergedcluster.txt

- Number of patients = 127

- Number of significantly arm-level cnvs = 59

- Number of molecular subtypes = 8

- Exclude genes that fewer than K tumors have mutations, K = 3

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] 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)
[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[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)
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