Correlation between copy number variations of arm-level result and molecular subtypes
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1T43R8Q
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1T43R8Q
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 117 patients, 28 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'.

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

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

- 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'.

- 10p loss cnv correlated to 'CN_CNMF'.

- 14q 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, 28 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 | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
3p gain | 28 (24%) | 89 | 0.302 (1.00) | 0.0559 (1.00) | 2.1e-14 (8.44e-12) | 0.0297 (1.00) | 0.000268 (0.101) | 3.64e-07 (0.000144) | 0.0375 (1.00) | 1.22e-05 (0.00477)
3q gain | 32 (27%) | 85 | 0.302 (1.00) | 0.0559 (1.00) | 3.79e-10 (1.5e-07) | 0.0245 (1.00) | 3.65e-05 (0.0141) | 6.94e-06 (0.00273) | 0.00911 (1.00) | 0.000246 (0.0928)
7p gain | 64 (55%) | 53 | 0.633 (1.00) | 0.432 (1.00) | 3.47e-10 (1.38e-07) | 0.000334 (0.125) | 5.65e-05 (0.0217) | 2.01e-05 (0.00785) | 0.011 (1.00) | 0.00896 (1.00)
17p gain | 61 (52%) | 56 | 0.633 (1.00) | 0.432 (1.00) | 1.02e-14 (4.08e-12) | 5.33e-05 (0.0205) | 3.49e-05 (0.0135) | 2.21e-06 (0.000871) | 0.129 (1.00) | 0.00197 (0.713)
7q gain | 65 (56%) | 52 | 0.633 (1.00) | 0.432 (1.00) | 7.18e-11 (2.86e-08) | 0.0011 (0.405) | 0.000181 (0.0691) | 3.74e-05 (0.0144) | 0.00419 (1.00) | 0.0113 (1.00)
16q gain | 51 (44%) | 66 | 0.302 (1.00) | 0.0559 (1.00) | 2.25e-05 (0.00875) | 0.117 (1.00) | 0.00419 (1.00) | 1.46e-05 (0.00571) | 0.00179 (0.65) | 0.000223 (0.0846)
16p gain | 54 (46%) | 63 | 0.126 (1.00) | 0.0699 (1.00) | 0.000205 (0.0778) | 0.352 (1.00) | 0.00874 (1.00) | 0.000182 (0.0695) | 0.00268 (0.96) | 0.000706 (0.264)
1q gain | 8 (7%) | 109 | 0.438 (1.00) |  | 0.207 (1.00) | 0.000172 (0.0658) | 0.00232 (0.836) | 0.00827 (1.00) | 0.433 (1.00) | 0.145 (1.00)
17q gain | 71 (61%) | 46 | 0.308 (1.00) | 0.808 (1.00) | 1.32e-09 (5.23e-07) | 0.0143 (1.00) | 0.0144 (1.00) | 0.000926 (0.344) | 0.362 (1.00) | 0.156 (1.00)
10p loss | 6 (5%) | 111 | 1 (1.00) |  | 0.000295 (0.111) | 0.199 (1.00) | 0.269 (1.00) | 0.18 (1.00) | 0.265 (1.00) | 0.675 (1.00)
14q loss | 20 (17%) | 97 | 1 (1.00) | 0.385 (1.00) | 2.83e-11 (1.13e-08) | 0.39 (1.00) | 0.135 (1.00) | 0.0977 (1.00) | 0.457 (1.00) | 0.968 (1.00)
2p gain | 11 (9%) | 106 | 0.55 (1.00) | 0.75 (1.00) | 0.00302 (1.00) | 0.149 (1.00) | 0.585 (1.00) | 0.338 (1.00) | 0.869 (1.00) | 0.0669 (1.00)
2q gain | 13 (11%) | 104 | 0.55 (1.00) | 0.75 (1.00) | 0.00178 (0.65) | 0.286 (1.00) | 0.329 (1.00) | 0.205 (1.00) | 0.856 (1.00) | 0.174 (1.00)
4p gain | 4 (3%) | 113 | 0.175 (1.00) |  | 0.438 (1.00) |  | 0.457 (1.00) | 1 (1.00) | 0.683 (1.00) | 0.881 (1.00)
4q gain | 3 (3%) | 114 | 0.438 (1.00) |  | 0.777 (1.00) |  | 0.645 (1.00) | 0.602 (1.00) | 0.767 (1.00) | 0.618 (1.00)
5p gain | 11 (9%) | 106 | 0.175 (1.00) |  | 0.23 (1.00) | 0.18 (1.00) | 0.156 (1.00) | 0.8 (1.00) | 0.697 (1.00) | 0.731 (1.00)
5q gain | 11 (9%) | 106 | 0.175 (1.00) |  | 0.23 (1.00) | 0.18 (1.00) | 0.579 (1.00) | 0.8 (1.00) | 0.919 (1.00) | 0.301 (1.00)
6p gain | 4 (3%) | 113 | 0.438 (1.00) |  | 0.438 (1.00) | 0.456 (1.00) | 0.114 (1.00) | 0.677 (1.00) | 0.165 (1.00) | 0.374 (1.00)
6q gain | 3 (3%) | 114 | 0.438 (1.00) |  | 0.113 (1.00) |  | 0.41 (1.00) | 1 (1.00) | 0.231 (1.00) | 0.397 (1.00)
8p gain | 7 (6%) | 110 | 1 (1.00) |  | 0.72 (1.00) | 0.0776 (1.00) | 0.732 (1.00) | 0.232 (1.00) | 0.617 (1.00) | 0.854 (1.00)
8q gain | 9 (8%) | 108 | 1 (1.00) |  | 0.371 (1.00) | 0.0234 (1.00) | 0.243 (1.00) | 0.0683 (1.00) | 0.187 (1.00) | 0.767 (1.00)
10p gain | 4 (3%) | 113 | 1 (1.00) |  | 0.173 (1.00) |  | 0.645 (1.00) | 0.602 (1.00) | 0.189 (1.00) | 0.881 (1.00)
10q gain | 3 (3%) | 114 | 1 (1.00) |  | 0.31 (1.00) |  |  |  | 0.454 (1.00) | 0.341 (1.00)
12p gain | 36 (31%) | 81 | 1 (1.00) | 0.0909 (1.00) | 0.0345 (1.00) | 0.276 (1.00) | 0.161 (1.00) | 0.0648 (1.00) | 0.146 (1.00) | 0.0968 (1.00)
12q gain | 36 (31%) | 81 | 1 (1.00) | 0.0909 (1.00) | 0.0345 (1.00) | 0.276 (1.00) | 0.161 (1.00) | 0.0648 (1.00) | 0.146 (1.00) | 0.0968 (1.00)
13q gain | 14 (12%) | 103 | 1 (1.00) |  | 0.0454 (1.00) | 0.455 (1.00) | 0.324 (1.00) | 0.0445 (1.00) | 0.747 (1.00) | 0.0852 (1.00)
18p gain | 6 (5%) | 111 | 0.55 (1.00) | 0.141 (1.00) | 0.0489 (1.00) | 0.327 (1.00) | 0.863 (1.00) | 1 (1.00) | 0.845 (1.00) | 0.877 (1.00)
18q gain | 4 (3%) | 113 | 0.438 (1.00) |  | 0.00209 (0.753) | 0.327 (1.00) | 0.77 (1.00) | 0.432 (1.00) | 0.91 (1.00) | 0.331 (1.00)
20p gain | 37 (32%) | 80 | 0.126 (1.00) | 0.162 (1.00) | 0.00294 (1.00) | 0.106 (1.00) | 0.000722 (0.269) | 0.0152 (1.00) | 0.305 (1.00) | 0.125 (1.00)
20q gain | 38 (32%) | 79 | 0.315 (1.00) | 0.119 (1.00) | 0.00595 (1.00) | 0.293 (1.00) | 0.000813 (0.303) | 0.0381 (1.00) | 0.179 (1.00) | 0.371 (1.00)
21q gain | 4 (3%) | 113 | 0.438 (1.00) |  | 0.317 (1.00) |  |  |  | 0.11 (1.00) | 0.0492 (1.00)
Xq gain | 4 (3%) | 113 | 1 (1.00) |  | 0.113 (1.00) | 0.456 (1.00) | 0.457 (1.00) | 1 (1.00) | 0.57 (1.00) | 0.881 (1.00)
1p loss | 12 (10%) | 105 | 1 (1.00) |  | 0.119 (1.00) | 1 (1.00) | 0.611 (1.00) | 0.589 (1.00) | 0.0114 (1.00) | 0.0873 (1.00)
1q loss | 7 (6%) | 110 | 1 (1.00) |  | 0.0998 (1.00) | 0.198 (1.00) | 0.103 (1.00) | 0.432 (1.00) | 0.0359 (1.00) | 0.0124 (1.00)
3p loss | 7 (6%) | 110 | 0.438 (1.00) |  | 0.00335 (1.00) | 0.00274 (0.979) | 0.058 (1.00) | 0.0149 (1.00) | 0.0591 (1.00) | 0.221 (1.00)
3q loss | 3 (3%) | 114 |  |  | 0.0248 (1.00) | 0.0401 (1.00) | 0.0288 (1.00) | 0.0762 (1.00) | 0.521 (1.00) | 0.72 (1.00)
4p loss | 10 (9%) | 107 | 1 (1.00) |  | 0.0826 (1.00) | 0.00141 (0.518) | 0.00399 (1.00) | 0.0866 (1.00) | 0.853 (1.00) | 0.9 (1.00)
4q loss | 10 (9%) | 107 | 1 (1.00) |  | 0.0112 (1.00) | 0.00141 (0.518) | 0.0999 (1.00) | 0.0683 (1.00) | 0.853 (1.00) | 0.698 (1.00)
5p loss | 5 (4%) | 112 |  |  | 0.286 (1.00) | 0.434 (1.00) | 0.114 (1.00) | 0.0626 (1.00) | 0.236 (1.00) | 0.916 (1.00)
5q loss | 5 (4%) | 112 |  |  | 0.411 (1.00) | 0.434 (1.00) | 0.41 (1.00) | 0.211 (1.00) | 0.236 (1.00) | 0.916 (1.00)
6p loss | 9 (8%) | 108 | 1 (1.00) |  | 0.107 (1.00) | 0.568 (1.00) | 0.468 (1.00) | 1 (1.00) | 0.331 (1.00) | 0.539 (1.00)
6q loss | 11 (9%) | 106 | 1 (1.00) |  | 0.0822 (1.00) | 0.205 (1.00) | 0.374 (1.00) | 0.558 (1.00) | 0.783 (1.00) | 0.693 (1.00)
8p loss | 3 (3%) | 114 | 1 (1.00) |  | 0.143 (1.00) |  |  |  | 1 (1.00) | 0.618 (1.00)
9p loss | 14 (12%) | 103 | 1 (1.00) | 0.75 (1.00) | 0.0123 (1.00) | 0.00135 (0.495) | 0.00782 (1.00) | 0.0678 (1.00) | 0.232 (1.00) | 0.587 (1.00)
9q loss | 14 (12%) | 103 | 1 (1.00) |  | 0.0531 (1.00) | 0.00125 (0.463) | 0.0256 (1.00) | 0.131 (1.00) | 0.37 (1.00) | 0.254 (1.00)
10q loss | 7 (6%) | 110 | 0.438 (1.00) |  | 0.00335 (1.00) | 0.0868 (1.00) | 0.269 (1.00) | 0.0871 (1.00) | 0.617 (1.00) | 0.221 (1.00)
11p loss | 8 (7%) | 109 | 1 (1.00) |  | 0.0664 (1.00) | 0.0434 (1.00) | 0.269 (1.00) | 0.145 (1.00) | 1 (1.00) | 0.447 (1.00)
11q loss | 10 (9%) | 107 |  |  | 0.0362 (1.00) | 0.00652 (1.00) | 0.296 (1.00) | 0.0563 (1.00) | 0.968 (1.00) | 0.9 (1.00)
13q loss | 10 (9%) | 107 | 1 (1.00) |  | 0.178 (1.00) | 0.00141 (0.518) | 0.091 (1.00) | 0.403 (1.00) | 0.355 (1.00) | 0.234 (1.00)
15q loss | 10 (9%) | 107 | 1 (1.00) |  | 0.0362 (1.00) | 0.32 (1.00) | 0.579 (1.00) | 0.487 (1.00) | 0.573 (1.00) | 0.816 (1.00)
16q loss | 3 (3%) | 114 |  |  | 0.143 (1.00) | 0.0401 (1.00) |  |  | 0.862 (1.00) | 0.286 (1.00)
17p loss | 5 (4%) | 112 | 0.438 (1.00) |  | 0.411 (1.00) | 0.0106 (1.00) | 0.0288 (1.00) | 0.0762 (1.00) | 0.219 (1.00) | 0.251 (1.00)
18p loss | 17 (15%) | 100 | 1 (1.00) |  | 0.905 (1.00) | 0.0675 (1.00) | 0.0772 (1.00) | 0.0114 (1.00) | 0.158 (1.00) | 0.445 (1.00)
18q loss | 18 (15%) | 99 | 1 (1.00) |  | 0.91 (1.00) | 0.0318 (1.00) | 0.0392 (1.00) | 0.00363 (1.00) | 0.322 (1.00) | 0.373 (1.00)
19p loss | 4 (3%) | 113 |  |  | 0.0954 (1.00) | 1 (1.00) |  |  | 0.746 (1.00) | 0.331 (1.00)
19q loss | 3 (3%) | 114 |  |  | 0.143 (1.00) | 0.603 (1.00) |  |  | 0.279 (1.00) | 0.618 (1.00)
21q loss | 12 (10%) | 105 | 0.55 (1.00) | 0.413 (1.00) | 0.5 (1.00) | 0.355 (1.00) | 0.246 (1.00) | 0.728 (1.00) | 0.219 (1.00) | 0.284 (1.00)
22q loss | 21 (18%) | 96 | 1 (1.00) |  | 0.00916 (1.00) | 0.0971 (1.00) | 0.673 (1.00) | 0.0477 (1.00) | 0.186 (1.00) | 0.433 (1.00)
Xq loss | 3 (3%) | 114 | 1 (1.00) |  | 0.31 (1.00) |  |  |  | 0.767 (1.00) | 0.1 (1.00)

'1q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000172 (Fisher's exact test), Q value = 0.066

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

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 21 | 27 | 39
1Q GAIN MUTATED | 0 | 7 | 0
1Q GAIN WILD-TYPE | 21 | 20 | 39

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 = 2.1e-14 (Fisher's exact test), Q value = 8.4e-12

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

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
3P GAIN MUTATED | 21 | 2 | 0 | 5
3P GAIN WILD-TYPE | 3 | 18 | 28 | 40

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 = 0.1

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 = 0.0048

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 = 3.79e-10 (Fisher's exact test), Q value = 1.5e-07

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

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
3Q GAIN MUTATED | 20 | 4 | 2 | 6
3Q GAIN WILD-TYPE | 4 | 16 | 26 | 39

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 = 0.093

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'

'7p gain mutation analysis' versus 'CN_CNMF'

P value = 3.47e-10 (Fisher's exact test), Q value = 1.4e-07

Table S10. Gene #12: '7p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
7P GAIN MUTATED | 20 | 20 | 6 | 18
7P GAIN WILD-TYPE | 4 | 0 | 22 | 27

Figure S10. 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 = 0.000334 (Fisher's exact test), Q value = 0.13

Table S11. Gene #12: '7p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 21 | 27 | 39
7P GAIN MUTATED | 11 | 6 | 28
7P GAIN WILD-TYPE | 10 | 21 | 11

Figure S11. 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 = 0.022

Table S12. 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 S12. 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 = 0.0079

Table S13. 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 S13. 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 = 7.18e-11 (Fisher's exact test), Q value = 2.9e-08

Table S14. Gene #13: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
7Q GAIN MUTATED | 21 | 20 | 6 | 18
7Q GAIN WILD-TYPE | 3 | 0 | 22 | 27

Figure S14. Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.000181 (Fisher's exact test), Q value = 0.069

Table S15. 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 S15. 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 S16. 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 S16. Get High-res Image Gene #13: '7q gain mutation analysis' versus Clinical Feature #6: 'MRNASEQ_CHIERARCHICAL'

'16p gain mutation analysis' versus 'CN_CNMF'

P value = 0.000205 (Fisher's exact test), Q value = 0.078

Table S17. Gene #21: '16p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
16P GAIN MUTATED | 20 | 8 | 7 | 19
16P GAIN WILD-TYPE | 4 | 12 | 21 | 26

Figure S17. 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 = 0.069

Table S18. 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 S18. 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 = 2.25e-05 (Fisher's exact test), Q value = 0.0088

Table S19. Gene #22: '16q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
16Q GAIN MUTATED | 20 | 8 | 5 | 18
16Q GAIN WILD-TYPE | 4 | 12 | 23 | 27

Figure S19. 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 = 0.0057

Table S20. 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 S20. 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 = 0.085

Table S21. 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 S21. 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 = 1.02e-14 (Fisher's exact test), Q value = 4.1e-12

Table S22. Gene #23: '17p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
17P GAIN MUTATED | 22 | 20 | 3 | 16
17P GAIN WILD-TYPE | 2 | 0 | 25 | 29

Figure S22. 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 = 5.33e-05 (Fisher's exact test), Q value = 0.021

Table S23. Gene #23: '17p gain mutation analysis' versus Clinical Feature #4: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 21 | 27 | 39
17P GAIN MUTATED | 8 | 5 | 28
17P GAIN WILD-TYPE | 13 | 22 | 11

Figure S23. 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 = 0.014

Table S24. 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 S24. 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 S25. 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 S25. 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 = 1.32e-09 (Fisher's exact test), Q value = 5.2e-07

Table S26. Gene #24: '17q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
17Q GAIN MUTATED | 22 | 20 | 8 | 21
17Q GAIN WILD-TYPE | 2 | 0 | 20 | 24

Figure S26. Get High-res Image Gene #24: '17q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'10p loss mutation analysis' versus 'CN_CNMF'

P value = 0.000295 (Fisher's exact test), Q value = 0.11

Table S27. Gene #44: '10p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
10P LOSS MUTATED | 0 | 0 | 6 | 0
10P LOSS WILD-TYPE | 24 | 20 | 22 | 45

Figure S27. Get High-res Image Gene #44: '10p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'14q loss mutation analysis' versus 'CN_CNMF'

P value = 2.83e-11 (Fisher's exact test), Q value = 1.1e-08

Table S28. Gene #49: '14q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 24 | 20 | 28 | 45
14Q LOSS MUTATED | 3 | 0 | 17 | 0
14Q LOSS WILD-TYPE | 21 | 20 | 11 | 45

Figure S28. Get High-res Image Gene #49: '14q 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 = 117

- 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

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] 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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