Correlation between copy number variations of arm-level result and molecular subtypes

Bladder Urothelial Carcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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 (2013): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C19S1P1W

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 76 arm-level results and 10 molecular subtypes across 150 patients, 18 significant findings detected with Q value < 0.25.

1p gain cnv correlated to 'CN_CNMF'.
2p gain cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.
3p gain cnv correlated to 'CN_CNMF'.
3q gain cnv correlated to 'CN_CNMF'.
17q gain cnv correlated to 'CN_CNMF'.
18p gain cnv correlated to 'CN_CNMF'.
5q loss cnv correlated to 'CN_CNMF'.
6q loss cnv correlated to 'CN_CNMF'.
8p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.
9p loss cnv correlated to 'CN_CNMF' and 'MRNASEQ_CHIERARCHICAL'.
9q loss cnv correlated to 'MRNASEQ_CNMF' and 'MRNASEQ_CHIERARCHICAL'.
10q loss cnv correlated to 'CN_CNMF'.
16p loss cnv correlated to 'CN_CNMF'.
16q 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 76 arm-level results and 10 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 18 significant findings detected.


		Molecular subtypes	CN CNMF	METHLYATION CNMF	RPPA CNMF	RPPA CHIERARCHICAL	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRSEQ MATURE CNMF	MIRSEQ MATURE 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	Chi-square test	Fisher's exact test	Fisher's exact test
2p gain	0 (0%)	120	2.93e-05 (0.021)	2.97e-05 (0.0213)	0.214 (1.00)	0.637 (1.00)	0.0387 (1.00)	0.415 (1.00)	0.0854 (1.00)	0.256 (1.00)	0.251 (1.00)	0.0212 (1.00)
8p loss	0 (0%)	102	2.54e-08 (1.84e-05)	7.9e-06 (0.0057)	0.143 (1.00)	0.287 (1.00)	0.189 (1.00)	0.615 (1.00)	0.0147 (1.00)	0.142 (1.00)	0.171 (1.00)	0.014 (1.00)
9p loss	0 (0%)	107	5.15e-05 (0.037)	0.00144 (1.00)	0.00167 (1.00)	0.128 (1.00)	0.0064 (1.00)	0.000146 (0.104)	0.894 (1.00)	0.719 (1.00)	0.771 (1.00)	0.533 (1.00)
9q loss	0 (0%)	109	0.0374 (1.00)	0.00201 (1.00)	0.00121 (0.848)	0.00833 (1.00)	0.000214 (0.152)	1.56e-06 (0.00113)	0.917 (1.00)	0.732 (1.00)	0.863 (1.00)	0.845 (1.00)
1p gain	0 (0%)	133	1.69e-05 (0.0122)	0.0931 (1.00)	0.466 (1.00)	0.744 (1.00)	0.0246 (1.00)	0.332 (1.00)	0.235 (1.00)	0.664 (1.00)	0.389 (1.00)	0.717 (1.00)
3p gain	0 (0%)	122	0.000128 (0.091)	0.0281 (1.00)	0.0287 (1.00)	0.0146 (1.00)	0.0512 (1.00)	0.00123 (0.863)	0.0116 (1.00)	0.024 (1.00)	0.015 (1.00)	0.0357 (1.00)
3q gain	0 (0%)	108	5.44e-06 (0.00393)	0.124 (1.00)	1 (1.00)	0.142 (1.00)	0.229 (1.00)	0.161 (1.00)	0.163 (1.00)	0.0327 (1.00)	0.117 (1.00)	0.171 (1.00)
17q gain	0 (0%)	126	0.000205 (0.146)	0.00479 (1.00)	0.497 (1.00)	0.168 (1.00)	0.00789 (1.00)	0.0833 (1.00)	0.15 (1.00)	0.00307 (1.00)	0.0183 (1.00)	0.136 (1.00)
18p gain	0 (0%)	126	5.47e-05 (0.0392)	0.0645 (1.00)	0.656 (1.00)	0.497 (1.00)	0.00847 (1.00)	0.157 (1.00)	0.429 (1.00)	0.0764 (1.00)	0.394 (1.00)	0.0581 (1.00)
5q loss	0 (0%)	115	0.000103 (0.0738)	0.1 (1.00)	0.648 (1.00)	1 (1.00)	0.388 (1.00)	0.578 (1.00)	0.21 (1.00)	0.0212 (1.00)	0.553 (1.00)	0.567 (1.00)
6q loss	0 (0%)	121	0.000333 (0.235)	0.0115 (1.00)	0.247 (1.00)	0.802 (1.00)	0.286 (1.00)	0.138 (1.00)	0.596 (1.00)	0.492 (1.00)	0.892 (1.00)	1 (1.00)
10q loss	0 (0%)	120	0.000293 (0.208)	0.0775 (1.00)	0.921 (1.00)	0.843 (1.00)	0.631 (1.00)	0.0745 (1.00)	0.0297 (1.00)	0.278 (1.00)	0.0432 (1.00)	0.208 (1.00)
16p loss	0 (0%)	133	0.000115 (0.0824)	0.3 (1.00)	0.603 (1.00)	1 (1.00)	0.0212 (1.00)	0.421 (1.00)	0.109 (1.00)	0.0402 (1.00)	0.135 (1.00)	0.392 (1.00)
16q loss	0 (0%)	133	0.000115 (0.0824)	0.182 (1.00)	0.75 (1.00)	1 (1.00)	0.0246 (1.00)	0.633 (1.00)	0.674 (1.00)	0.12 (1.00)	0.345 (1.00)	0.235 (1.00)
1q gain	0 (0%)	117	0.0174 (1.00)	0.259 (1.00)	0.941 (1.00)	0.637 (1.00)	0.0916 (1.00)	0.559 (1.00)	0.514 (1.00)	0.515 (1.00)	0.328 (1.00)	0.455 (1.00)
2q gain	0 (0%)	139	0.0142 (1.00)	0.0922 (1.00)	0.69 (1.00)	0.605 (1.00)	0.0167 (1.00)	0.249 (1.00)	0.182 (1.00)	0.0863 (1.00)	0.414 (1.00)	0.0281 (1.00)
4p gain	0 (0%)	142	0.897 (1.00)	0.646 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.515 (1.00)	0.724 (1.00)	0.56 (1.00)	1 (1.00)
4q gain	0 (0%)	147	0.797 (1.00)	0.406 (1.00)			1 (1.00)	0.601 (1.00)	0.321 (1.00)	0.313 (1.00)	0.303 (1.00)	0.744 (1.00)
5p gain	0 (0%)	109	0.00236 (1.00)	0.513 (1.00)	0.0287 (1.00)	0.436 (1.00)	0.351 (1.00)	0.148 (1.00)	0.115 (1.00)	0.152 (1.00)	0.0605 (1.00)	0.0251 (1.00)
5q gain	0 (0%)	133	0.0605 (1.00)	0.0786 (1.00)	0.228 (1.00)	0.261 (1.00)	0.934 (1.00)	1 (1.00)	0.266 (1.00)	0.618 (1.00)	0.345 (1.00)	0.235 (1.00)
6p gain	0 (0%)	139	0.0943 (1.00)	0.178 (1.00)			0.571 (1.00)	1 (1.00)	0.216 (1.00)	0.0724 (1.00)	0.352 (1.00)	0.0391 (1.00)
6q gain	0 (0%)	144	0.0613 (1.00)	0.567 (1.00)			0.117 (1.00)	0.427 (1.00)	0.0256 (1.00)	0.17 (1.00)	0.0673 (1.00)	0.0129 (1.00)
7p gain	0 (0%)	105	0.00608 (1.00)	0.00262 (1.00)	0.835 (1.00)	0.211 (1.00)	0.0958 (1.00)	0.605 (1.00)	0.0132 (1.00)	0.486 (1.00)	0.0925 (1.00)	0.282 (1.00)
7q gain	0 (0%)	109	0.00391 (1.00)	0.0534 (1.00)	1 (1.00)	0.258 (1.00)	0.171 (1.00)	0.478 (1.00)	0.394 (1.00)	0.66 (1.00)	0.838 (1.00)	0.903 (1.00)
8p gain	0 (0%)	135	0.0172 (1.00)	0.00456 (1.00)	0.192 (1.00)	0.252 (1.00)	0.178 (1.00)	0.256 (1.00)	0.831 (1.00)	0.391 (1.00)	0.59 (1.00)	0.272 (1.00)
8q gain	0 (0%)	106	0.00286 (1.00)	0.0574 (1.00)	0.623 (1.00)	0.459 (1.00)	0.13 (1.00)	0.372 (1.00)	0.846 (1.00)	0.789 (1.00)	0.819 (1.00)	0.472 (1.00)
9p gain	0 (0%)	135	0.0489 (1.00)	0.491 (1.00)	0.189 (1.00)	1 (1.00)	0.0114 (1.00)	0.0103 (1.00)	0.175 (1.00)	0.515 (1.00)	0.042 (1.00)	0.272 (1.00)
9q gain	0 (0%)	138	1 (1.00)	0.239 (1.00)	0.259 (1.00)	0.366 (1.00)	0.038 (1.00)	0.0122 (1.00)	1 (1.00)	0.86 (1.00)	1 (1.00)	0.764 (1.00)
10p gain	0 (0%)	123	0.000642 (0.453)	0.00203 (1.00)	0.382 (1.00)	0.017 (1.00)	0.869 (1.00)	0.236 (1.00)	0.348 (1.00)	0.0916 (1.00)	0.169 (1.00)	0.384 (1.00)
10q gain	0 (0%)	143	0.363 (1.00)	0.211 (1.00)	0.512 (1.00)	0.294 (1.00)	0.695 (1.00)	0.241 (1.00)	0.686 (1.00)	0.724 (1.00)	0.606 (1.00)	1 (1.00)
11p gain	0 (0%)	143	0.0106 (1.00)	0.867 (1.00)			0.163 (1.00)	0.323 (1.00)	0.536 (1.00)	0.483 (1.00)	0.0871 (1.00)	0.491 (1.00)
11q gain	0 (0%)	141	0.327 (1.00)	0.398 (1.00)			0.198 (1.00)	0.216 (1.00)	0.405 (1.00)	0.742 (1.00)	0.393 (1.00)	1 (1.00)
12p gain	0 (0%)	124	0.0184 (1.00)	0.452 (1.00)	0.163 (1.00)	0.478 (1.00)	0.381 (1.00)	0.0415 (1.00)	0.528 (1.00)	0.258 (1.00)	0.102 (1.00)	0.0157 (1.00)
12q gain	0 (0%)	132	0.617 (1.00)	0.293 (1.00)	0.419 (1.00)	0.551 (1.00)	0.167 (1.00)	0.0623 (1.00)	0.144 (1.00)	0.199 (1.00)	0.00822 (1.00)	0.132 (1.00)
13q gain	0 (0%)	126	0.106 (1.00)	0.824 (1.00)	0.0318 (1.00)	0.0844 (1.00)	0.0215 (1.00)	0.384 (1.00)	0.124 (1.00)	0.436 (1.00)	0.592 (1.00)	1 (1.00)
14q gain	0 (0%)	139	0.281 (1.00)	1 (1.00)	0.206 (1.00)	0.576 (1.00)	0.162 (1.00)	0.0576 (1.00)	0.521 (1.00)	0.163 (1.00)	0.155 (1.00)	0.411 (1.00)
15q gain	0 (0%)	145	1 (1.00)	0.0355 (1.00)	0.296 (1.00)	0.328 (1.00)	0.11 (1.00)	0.115 (1.00)	1 (1.00)	0.859 (1.00)	1 (1.00)	0.151 (1.00)
16p gain	0 (0%)	141	0.0288 (1.00)	0.869 (1.00)	0.638 (1.00)	1 (1.00)	0.447 (1.00)	0.668 (1.00)	0.619 (1.00)	0.504 (1.00)	0.601 (1.00)	0.492 (1.00)
16q gain	0 (0%)	137	0.0049 (1.00)	0.295 (1.00)	1 (1.00)	1 (1.00)	0.0755 (1.00)	0.225 (1.00)	0.814 (1.00)	0.812 (1.00)	0.933 (1.00)	0.785 (1.00)
17p gain	0 (0%)	142	0.366 (1.00)	0.567 (1.00)			0.271 (1.00)	0.327 (1.00)	1 (1.00)	0.419 (1.00)	0.56 (1.00)	0.229 (1.00)
18q gain	0 (0%)	141	0.218 (1.00)	0.598 (1.00)	0.465 (1.00)	0.0836 (1.00)	0.165 (1.00)	0.115 (1.00)	0.619 (1.00)	0.929 (1.00)	0.906 (1.00)	0.63 (1.00)
19p gain	0 (0%)	137	0.707 (1.00)	0.0217 (1.00)			0.0966 (1.00)	0.181 (1.00)	0.651 (1.00)	0.584 (1.00)	0.0928 (1.00)	0.33 (1.00)
19q gain	0 (0%)	123	0.0305 (1.00)	0.316 (1.00)	0.633 (1.00)	1 (1.00)	0.105 (1.00)	0.454 (1.00)	0.763 (1.00)	0.742 (1.00)	0.338 (1.00)	0.458 (1.00)
20p gain	0 (0%)	96	0.0224 (1.00)	0.11 (1.00)	0.415 (1.00)	0.805 (1.00)	0.47 (1.00)	0.38 (1.00)	0.46 (1.00)	0.163 (1.00)	0.354 (1.00)	0.917 (1.00)
20q gain	0 (0%)	90	0.0268 (1.00)	0.0559 (1.00)	0.439 (1.00)	0.494 (1.00)	0.0782 (1.00)	0.392 (1.00)	0.0233 (1.00)	0.0563 (1.00)	0.0114 (1.00)	0.342 (1.00)
21q gain	0 (0%)	123	0.0152 (1.00)	0.000899 (0.634)	0.598 (1.00)	0.539 (1.00)	0.264 (1.00)	0.109 (1.00)	0.244 (1.00)	0.38 (1.00)	0.215 (1.00)	0.0316 (1.00)
22q gain	0 (0%)	138	0.0983 (1.00)	0.184 (1.00)	0.536 (1.00)	0.572 (1.00)	0.414 (1.00)	0.499 (1.00)	0.0166 (1.00)	0.233 (1.00)	0.0154 (1.00)	0.088 (1.00)
Xq gain	0 (0%)	144	0.0233 (1.00)	0.845 (1.00)			0.117 (1.00)	0.866 (1.00)	0.765 (1.00)	0.371 (1.00)	0.574 (1.00)	1 (1.00)
1p loss	0 (0%)	146	0.197 (1.00)	0.0602 (1.00)			0.294 (1.00)	0.421 (1.00)	0.352 (1.00)	0.359 (1.00)	0.414 (1.00)	0.362 (1.00)
2p loss	0 (0%)	142	0.411 (1.00)	0.0195 (1.00)			0.0951 (1.00)	0.149 (1.00)	1 (1.00)	0.652 (1.00)	0.56 (1.00)	0.876 (1.00)
2q loss	0 (0%)	133	0.0108 (1.00)	0.53 (1.00)	1 (1.00)	0.294 (1.00)	0.198 (1.00)	0.498 (1.00)	1 (1.00)	0.815 (1.00)	0.707 (1.00)	0.876 (1.00)
3p loss	0 (0%)	141	0.615 (1.00)	0.793 (1.00)	0.345 (1.00)	0.467 (1.00)	0.0224 (1.00)	0.0783 (1.00)	0.22 (1.00)	0.622 (1.00)	0.323 (1.00)	0.492 (1.00)
4p loss	0 (0%)	121	0.01 (1.00)	0.158 (1.00)	0.853 (1.00)	0.767 (1.00)	0.667 (1.00)	0.684 (1.00)	0.534 (1.00)	0.379 (1.00)	0.285 (1.00)	0.807 (1.00)
4q loss	0 (0%)	123	0.0197 (1.00)	0.699 (1.00)	0.922 (1.00)	1 (1.00)	0.525 (1.00)	0.922 (1.00)	0.539 (1.00)	0.632 (1.00)	0.191 (1.00)	0.763 (1.00)
5p loss	0 (0%)	136	0.00353 (1.00)	0.175 (1.00)	0.424 (1.00)	1 (1.00)	0.476 (1.00)	0.0992 (1.00)	0.163 (1.00)	0.163 (1.00)	0.193 (1.00)	0.414 (1.00)
6p loss	0 (0%)	130	0.124 (1.00)	0.106 (1.00)	0.233 (1.00)	0.456 (1.00)	0.561 (1.00)	0.813 (1.00)	0.782 (1.00)	0.131 (1.00)	0.32 (1.00)	0.794 (1.00)
7q loss	0 (0%)	147	0.453 (1.00)	0.347 (1.00)			0.547 (1.00)	0.313 (1.00)	0.16 (1.00)	0.839 (1.00)	0.135 (1.00)	0.361 (1.00)
8q loss	0 (0%)	144	0.317 (1.00)	0.0883 (1.00)			0.242 (1.00)	0.144 (1.00)	0.232 (1.00)	0.0352 (1.00)	0.091 (1.00)	0.0219 (1.00)
10p loss	0 (0%)	131	0.0116 (1.00)	0.934 (1.00)	0.189 (1.00)	0.528 (1.00)	0.128 (1.00)	0.164 (1.00)	0.258 (1.00)	0.265 (1.00)	0.244 (1.00)	0.616 (1.00)
11p loss	0 (0%)	103	0.000951 (0.67)	0.00107 (0.75)	0.253 (1.00)	0.756 (1.00)	0.0322 (1.00)	0.00576 (1.00)	0.115 (1.00)	0.959 (1.00)	0.738 (1.00)	0.754 (1.00)
11q loss	0 (0%)	116	0.0151 (1.00)	0.0303 (1.00)	0.12 (1.00)	0.427 (1.00)	0.0179 (1.00)	0.179 (1.00)	0.25 (1.00)	0.44 (1.00)	0.443 (1.00)	0.655 (1.00)
12p loss	0 (0%)	144	0.765 (1.00)	0.201 (1.00)			0.271 (1.00)	0.255 (1.00)	0.577 (1.00)	0.444 (1.00)	0.859 (1.00)	1 (1.00)
12q loss	0 (0%)	143	0.477 (1.00)	0.867 (1.00)			0.412 (1.00)	0.418 (1.00)	0.611 (1.00)	0.026 (1.00)	0.677 (1.00)	0.742 (1.00)
13q loss	0 (0%)	130	0.102 (1.00)	0.363 (1.00)	0.104 (1.00)	0.141 (1.00)	0.137 (1.00)	0.277 (1.00)	0.644 (1.00)	0.185 (1.00)	0.859 (1.00)	0.63 (1.00)
14q loss	0 (0%)	125	0.0732 (1.00)	0.0376 (1.00)	0.818 (1.00)	0.317 (1.00)	0.37 (1.00)	0.471 (1.00)	0.0312 (1.00)	0.224 (1.00)	0.147 (1.00)	0.507 (1.00)
15q loss	0 (0%)	131	0.175 (1.00)	0.107 (1.00)	0.121 (1.00)	0.411 (1.00)	0.0495 (1.00)	0.199 (1.00)	0.103 (1.00)	0.0171 (1.00)	0.342 (1.00)	0.0434 (1.00)
17p loss	0 (0%)	107	0.637 (1.00)	0.105 (1.00)	0.541 (1.00)	0.805 (1.00)	0.0216 (1.00)	0.339 (1.00)	0.0961 (1.00)	0.375 (1.00)	0.0211 (1.00)	0.113 (1.00)
17q loss	0 (0%)	144	0.565 (1.00)	0.0349 (1.00)			0.437 (1.00)	0.306 (1.00)	0.267 (1.00)	0.536 (1.00)	0.335 (1.00)	0.614 (1.00)
18p loss	0 (0%)	129	0.0593 (1.00)	0.0401 (1.00)	0.573 (1.00)	0.456 (1.00)	0.845 (1.00)	0.109 (1.00)	0.513 (1.00)	0.19 (1.00)	0.0183 (1.00)	0.045 (1.00)
18q loss	0 (0%)	113	0.0654 (1.00)	0.526 (1.00)	0.876 (1.00)	0.0947 (1.00)	0.519 (1.00)	0.0513 (1.00)	0.224 (1.00)	0.155 (1.00)	0.853 (1.00)	0.519 (1.00)
19p loss	0 (0%)	141	0.0531 (1.00)	0.933 (1.00)	0.252 (1.00)	0.14 (1.00)	0.687 (1.00)	0.548 (1.00)	0.441 (1.00)	0.05 (1.00)	0.291 (1.00)	0.109 (1.00)
19q loss	0 (0%)	146	0.106 (1.00)	0.694 (1.00)			0.833 (1.00)	0.832 (1.00)	0.147 (1.00)	0.121 (1.00)	0.136 (1.00)	0.362 (1.00)
20p loss	0 (0%)	143	0.241 (1.00)	0.0498 (1.00)			0.791 (1.00)	0.122 (1.00)	1 (1.00)	0.589 (1.00)	0.677 (1.00)	0.742 (1.00)
21q loss	0 (0%)	134	0.661 (1.00)	0.224 (1.00)	0.466 (1.00)	0.216 (1.00)	0.386 (1.00)	0.616 (1.00)	0.944 (1.00)	0.704 (1.00)	0.74 (1.00)	0.811 (1.00)
22q loss	0 (0%)	121	0.00238 (1.00)	0.722 (1.00)	0.0706 (1.00)	0.126 (1.00)	0.00765 (1.00)	0.575 (1.00)	0.693 (1.00)	0.0209 (1.00)	0.356 (1.00)	0.958 (1.00)
Xq loss	0 (0%)	147	0.155 (1.00)	0.0542 (1.00)			0.476 (1.00)	0.601 (1.00)	0.46 (1.00)	0.548 (1.00)	1 (1.00)	0.744 (1.00)

'1p gain' versus 'CN_CNMF'

P value = 1.69e-05 (Fisher's exact test), Q value = 0.012

Table S1. Gene #1: '1p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
1P GAIN CNV	10	0	7
1P GAIN WILD-TYPE	35	69	29

Figure S1. Get High-res Image Gene #1: '1p gain' versus Molecular Subtype #1: 'CN_CNMF'

'2p gain' versus 'CN_CNMF'

P value = 2.93e-05 (Fisher's exact test), Q value = 0.021

Table S2. Gene #3: '2p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
2P GAIN CNV	18	4	8
2P GAIN WILD-TYPE	27	65	28

Figure S2. Get High-res Image Gene #3: '2p gain' versus Molecular Subtype #1: 'CN_CNMF'

'2p gain' versus 'METHLYATION_CNMF'

P value = 2.97e-05 (Fisher's exact test), Q value = 0.021

Table S3. Gene #3: '2p gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	37	49	40	24
2P GAIN CNV	18	6	5	1
2P GAIN WILD-TYPE	19	43	35	23

Figure S3. Get High-res Image Gene #3: '2p gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'3p gain' versus 'CN_CNMF'

P value = 0.000128 (Fisher's exact test), Q value = 0.091

Table S4. Gene #5: '3p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
3P GAIN CNV	18	6	4
3P GAIN WILD-TYPE	27	63	32

Figure S4. Get High-res Image Gene #5: '3p gain' versus Molecular Subtype #1: 'CN_CNMF'

'3q gain' versus 'CN_CNMF'

P value = 5.44e-06 (Fisher's exact test), Q value = 0.0039

Table S5. Gene #6: '3q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
3Q GAIN CNV	25	9	8
3Q GAIN WILD-TYPE	20	60	28

Figure S5. Get High-res Image Gene #6: '3q gain' versus Molecular Subtype #1: 'CN_CNMF'

'17q gain' versus 'CN_CNMF'

P value = 0.000205 (Fisher's exact test), Q value = 0.15

Table S6. Gene #31: '17q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
17Q GAIN CNV	16	5	3
17Q GAIN WILD-TYPE	29	64	33

Figure S6. Get High-res Image Gene #31: '17q gain' versus Molecular Subtype #1: 'CN_CNMF'

'18p gain' versus 'CN_CNMF'

P value = 5.47e-05 (Fisher's exact test), Q value = 0.039

Table S7. Gene #32: '18p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
18P GAIN CNV	11	2	11
18P GAIN WILD-TYPE	34	67	25

Figure S7. Get High-res Image Gene #32: '18p gain' versus Molecular Subtype #1: 'CN_CNMF'

'5q loss' versus 'CN_CNMF'

P value = 0.000103 (Fisher's exact test), Q value = 0.074

Table S8. Gene #48: '5q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
5Q LOSS CNV	19	6	10
5Q LOSS WILD-TYPE	26	63	26

Figure S8. Get High-res Image Gene #48: '5q loss' versus Molecular Subtype #1: 'CN_CNMF'

'6q loss' versus 'CN_CNMF'

P value = 0.000333 (Fisher's exact test), Q value = 0.24

Table S9. Gene #50: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
6Q LOSS CNV	18	7	4
6Q LOSS WILD-TYPE	27	62	32

Figure S9. Get High-res Image Gene #50: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

'8p loss' versus 'CN_CNMF'

P value = 2.54e-08 (Fisher's exact test), Q value = 1.8e-05

Table S10. Gene #52: '8p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
8P LOSS CNV	18	7	23
8P LOSS WILD-TYPE	27	62	13

Figure S10. Get High-res Image Gene #52: '8p loss' versus Molecular Subtype #1: 'CN_CNMF'

'8p loss' versus 'METHLYATION_CNMF'

P value = 7.9e-06 (Fisher's exact test), Q value = 0.0057

Table S11. Gene #52: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	37	49	40	24
8P LOSS CNV	24	14	5	5
8P LOSS WILD-TYPE	13	35	35	19

Figure S11. Get High-res Image Gene #52: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'9p loss' versus 'CN_CNMF'

P value = 5.15e-05 (Fisher's exact test), Q value = 0.037

Table S12. Gene #54: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
9P LOSS CNV	15	27	1
9P LOSS WILD-TYPE	30	42	35

Figure S12. Get High-res Image Gene #54: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

'9p loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000146 (Fisher's exact test), Q value = 0.1

Table S13. Gene #54: '9p loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	30	55	63
9P LOSS CNV	11	24	7
9P LOSS WILD-TYPE	19	31	56

Figure S13. Get High-res Image Gene #54: '9p loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'9q loss' versus 'MRNASEQ_CNMF'

P value = 0.000214 (Fisher's exact test), Q value = 0.15

Table S14. Gene #55: '9q loss' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	55	23	45	25
9Q LOSS CNV	26	3	5	6
9Q LOSS WILD-TYPE	29	20	40	19

Figure S14. Get High-res Image Gene #55: '9q loss' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'9q loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 1.56e-06 (Fisher's exact test), Q value = 0.0011

Table S15. Gene #55: '9q loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	30	55	63
9Q LOSS CNV	13	23	4
9Q LOSS WILD-TYPE	17	32	59

Figure S15. Get High-res Image Gene #55: '9q loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'10q loss' versus 'CN_CNMF'

P value = 0.000293 (Fisher's exact test), Q value = 0.21

Table S16. Gene #57: '10q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
10Q LOSS CNV	17	5	8
10Q LOSS WILD-TYPE	28	64	28

Figure S16. Get High-res Image Gene #57: '10q loss' versus Molecular Subtype #1: 'CN_CNMF'

'16p loss' versus 'CN_CNMF'

P value = 0.000115 (Fisher's exact test), Q value = 0.082

Table S17. Gene #65: '16p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
16P LOSS CNV	6	1	10
16P LOSS WILD-TYPE	39	68	26

Figure S17. Get High-res Image Gene #65: '16p loss' versus Molecular Subtype #1: 'CN_CNMF'

'16q loss' versus 'CN_CNMF'

P value = 0.000115 (Fisher's exact test), Q value = 0.082

Table S18. Gene #66: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	45	69	36
16Q LOSS CNV	6	1	10
16Q LOSS WILD-TYPE	39	68	26

Figure S18. Get High-res Image Gene #66: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Molecular subtypes file = BLCA-TP.transferedmergedcluster.txt
Number of patients = 150
Number of significantly arm-level cnvs = 76
Number of molecular subtypes = 10
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.

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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