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

Sarcoma (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

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 (2014): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1B856KX

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and molecular subtypes.

Summary

Testing the association between copy number variation 80 arm-level events and 8 molecular subtypes across 127 patients, 14 significant findings detected with P value < 0.05 and Q value < 0.25.

19p gain cnv correlated to 'CN_CNMF'.
2p loss cnv correlated to 'METHLYATION_CNMF', 'MRNASEQ_CNMF', and 'MIRSEQ_MATURE_CHIERARCHICAL'.
10p loss cnv correlated to 'METHLYATION_CNMF' and 'MRNASEQ_CNMF'.
10q loss cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CNMF', and 'MRNASEQ_CHIERARCHICAL'.
16q loss cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', and 'MIRSEQ_CNMF'.
xq loss cnv correlated to 'METHLYATION_CNMF'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 80 arm-level events and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 14 significant findings detected.


		Clinical Features	CN CNMF	METHLYATION CNMF	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	Fisher's exact test
10q loss	66 (52%)	61	6.97e-08 (4.46e-05)	4.82e-07 (0.000307)	3.03e-07 (0.000193)	3.52e-05 (0.0223)	0.00277 (1.00)	0.00231 (1.00)	0.00123 (0.748)	0.000543 (0.337)
2p loss	39 (31%)	88	0.000511 (0.318)	0.000114 (0.072)	8.53e-05 (0.0539)	0.000478 (0.298)	0.00313 (1.00)	0.00217 (1.00)	0.00523 (1.00)	0.000119 (0.0749)
16q loss	64 (50%)	63	1.87e-05 (0.0118)	3.35e-06 (0.00213)	0.000961 (0.592)	0.00153 (0.923)	0.000212 (0.133)	0.00427 (1.00)	0.00651 (1.00)	0.0011 (0.672)
10p loss	56 (44%)	71	0.00146 (0.886)	0.000171 (0.107)	0.000257 (0.161)	0.0234 (1.00)	0.0527 (1.00)	0.0898 (1.00)	0.225 (1.00)	0.107 (1.00)
19p gain	36 (28%)	91	1.65e-05 (0.0105)	0.139 (1.00)	0.606 (1.00)	0.353 (1.00)	0.45 (1.00)	0.515 (1.00)	0.757 (1.00)	0.755 (1.00)
xq loss	48 (38%)	79	0.0321 (1.00)	2.95e-07 (0.000189)	0.0547 (1.00)	0.00136 (0.83)	0.325 (1.00)	0.251 (1.00)	0.115 (1.00)	0.0783 (1.00)
1p gain	27 (21%)	100	0.000662 (0.411)	0.00737 (1.00)	0.146 (1.00)	0.671 (1.00)	0.302 (1.00)	1 (1.00)	0.552 (1.00)	0.212 (1.00)
1q gain	28 (22%)	99	0.000789 (0.488)	0.06 (1.00)	0.0544 (1.00)	0.0856 (1.00)	0.383 (1.00)	0.222 (1.00)	0.21 (1.00)	0.0193 (1.00)
2p gain	10 (8%)	117	0.058 (1.00)	0.00772 (1.00)	0.174 (1.00)	0.079 (1.00)	0.0947 (1.00)	0.308 (1.00)	0.0434 (1.00)	0.168 (1.00)
2q gain	7 (6%)	120	0.148 (1.00)	0.127 (1.00)	0.484 (1.00)	0.205 (1.00)	0.184 (1.00)	0.514 (1.00)	0.151 (1.00)	0.308 (1.00)
3p gain	12 (9%)	115	0.00957 (1.00)	0.347 (1.00)	0.0608 (1.00)	0.146 (1.00)	0.17 (1.00)	0.356 (1.00)	0.407 (1.00)	0.00443 (1.00)
3q gain	11 (9%)	116	0.0196 (1.00)	0.924 (1.00)	0.0943 (1.00)	0.38 (1.00)	0.542 (1.00)	0.438 (1.00)	0.307 (1.00)	0.00817 (1.00)
4p gain	25 (20%)	102	0.0798 (1.00)	0.12 (1.00)	0.461 (1.00)	1 (1.00)	0.32 (1.00)	0.227 (1.00)	0.424 (1.00)	0.548 (1.00)
4q gain	20 (16%)	107	0.555 (1.00)	0.501 (1.00)	0.863 (1.00)	1 (1.00)	0.967 (1.00)	0.424 (1.00)	0.681 (1.00)	1 (1.00)
5p gain	42 (33%)	85	0.00102 (0.629)	0.327 (1.00)	0.0695 (1.00)	1 (1.00)	0.155 (1.00)	0.617 (1.00)	0.0453 (1.00)	0.128 (1.00)
5q gain	34 (27%)	93	0.0386 (1.00)	0.337 (1.00)	0.285 (1.00)	1 (1.00)	0.187 (1.00)	0.673 (1.00)	0.209 (1.00)	0.708 (1.00)
6p gain	24 (19%)	103	0.0178 (1.00)	0.000405 (0.253)	0.034 (1.00)	0.0265 (1.00)	0.0378 (1.00)	0.511 (1.00)	0.0158 (1.00)	0.548 (1.00)
6q gain	24 (19%)	103	0.0132 (1.00)	0.00337 (1.00)	0.0391 (1.00)	0.0161 (1.00)	0.327 (1.00)	0.0932 (1.00)	0.0346 (1.00)	0.196 (1.00)
7p gain	34 (27%)	93	0.0371 (1.00)	0.337 (1.00)	0.0727 (1.00)	0.353 (1.00)	0.772 (1.00)	0.696 (1.00)	0.688 (1.00)	1 (1.00)
7q gain	29 (23%)	98	0.0636 (1.00)	0.555 (1.00)	0.554 (1.00)	0.189 (1.00)	0.516 (1.00)	0.465 (1.00)	0.209 (1.00)	1 (1.00)
8p gain	27 (21%)	100	0.158 (1.00)	0.935 (1.00)	0.336 (1.00)	1 (1.00)	0.354 (1.00)	0.33 (1.00)	0.623 (1.00)	0.461 (1.00)
8q gain	33 (26%)	94	0.0643 (1.00)	0.333 (1.00)	0.0387 (1.00)	0.79 (1.00)	0.101 (1.00)	0.329 (1.00)	0.472 (1.00)	0.109 (1.00)
9p gain	24 (19%)	103	0.184 (1.00)	0.0203 (1.00)	0.23 (1.00)	0.249 (1.00)	0.15 (1.00)	0.626 (1.00)	0.266 (1.00)	0.441 (1.00)
9q gain	30 (24%)	97	0.0378 (1.00)	0.0978 (1.00)	0.274 (1.00)	0.0925 (1.00)	0.267 (1.00)	0.648 (1.00)	0.0595 (1.00)	0.428 (1.00)
10p gain	12 (9%)	115	0.0697 (1.00)	0.218 (1.00)	0.712 (1.00)	0.459 (1.00)	0.241 (1.00)	0.292 (1.00)	0.294 (1.00)	0.332 (1.00)
10q gain	6 (5%)	121	0.487 (1.00)	0.0859 (1.00)	0.305 (1.00)	0.185 (1.00)	0.223 (1.00)	0.64 (1.00)	0.0406 (1.00)	1 (1.00)
11p gain	8 (6%)	119	0.23 (1.00)	0.661 (1.00)	0.15 (1.00)	1 (1.00)	0.183 (1.00)	0.218 (1.00)	0.283 (1.00)	0.255 (1.00)
11q gain	8 (6%)	119	0.65 (1.00)	0.468 (1.00)	0.228 (1.00)	0.616 (1.00)	0.266 (1.00)	0.292 (1.00)	0.626 (1.00)	0.332 (1.00)
12p gain	15 (12%)	112	0.608 (1.00)	0.396 (1.00)	0.703 (1.00)	0.165 (1.00)	0.041 (1.00)	1 (1.00)	0.228 (1.00)	0.671 (1.00)
12q gain	9 (7%)	118	0.829 (1.00)	0.566 (1.00)	0.384 (1.00)	0.799 (1.00)	0.331 (1.00)	1 (1.00)	0.772 (1.00)	0.728 (1.00)
13q gain	6 (5%)	121	0.375 (1.00)	0.24 (1.00)	0.112 (1.00)	0.0475 (1.00)	0.523 (1.00)	0.0106 (1.00)	0.0984 (1.00)	0.155 (1.00)
14q gain	24 (19%)	103	0.184 (1.00)	0.105 (1.00)	0.317 (1.00)	0.78 (1.00)	0.481 (1.00)	1 (1.00)	0.895 (1.00)	0.693 (1.00)
15q gain	32 (25%)	95	0.00415 (1.00)	0.436 (1.00)	0.00681 (1.00)	0.0408 (1.00)	0.0498 (1.00)	0.0292 (1.00)	0.0683 (1.00)	0.00529 (1.00)
16p gain	18 (14%)	109	0.023 (1.00)	0.627 (1.00)	0.724 (1.00)	1 (1.00)	0.68 (1.00)	0.206 (1.00)	1 (1.00)	0.47 (1.00)
16q gain	6 (5%)	121	0.0732 (1.00)	0.281 (1.00)	0.861 (1.00)	0.484 (1.00)	0.83 (1.00)	1 (1.00)	0.491 (1.00)	0.685 (1.00)
17p gain	26 (20%)	101	0.00137 (0.834)	0.0206 (1.00)	0.513 (1.00)	0.444 (1.00)	0.369 (1.00)	0.269 (1.00)	0.255 (1.00)	0.178 (1.00)
17q gain	23 (18%)	104	0.0733 (1.00)	0.0501 (1.00)	0.173 (1.00)	0.616 (1.00)	0.461 (1.00)	0.319 (1.00)	1 (1.00)	0.239 (1.00)
18p gain	20 (16%)	107	0.00836 (1.00)	0.024 (1.00)	0.826 (1.00)	0.82 (1.00)	0.398 (1.00)	0.424 (1.00)	0.908 (1.00)	0.225 (1.00)
18q gain	18 (14%)	109	0.226 (1.00)	0.307 (1.00)	0.67 (1.00)	1 (1.00)	0.763 (1.00)	0.371 (1.00)	1 (1.00)	1 (1.00)
19q gain	26 (20%)	101	0.000402 (0.251)	0.369 (1.00)	0.813 (1.00)	0.379 (1.00)	0.434 (1.00)	0.826 (1.00)	0.192 (1.00)	1 (1.00)
20p gain	32 (25%)	95	0.128 (1.00)	0.192 (1.00)	0.732 (1.00)	0.539 (1.00)	0.569 (1.00)	0.695 (1.00)	0.306 (1.00)	0.733 (1.00)
20q gain	40 (31%)	87	0.0964 (1.00)	0.00914 (1.00)	1 (1.00)	0.212 (1.00)	0.221 (1.00)	1 (1.00)	0.0986 (1.00)	0.451 (1.00)
21q gain	31 (24%)	96	0.00839 (1.00)	0.00155 (0.934)	0.407 (1.00)	0.279 (1.00)	0.2 (1.00)	0.85 (1.00)	0.287 (1.00)	0.275 (1.00)
22q gain	30 (24%)	97	0.0214 (1.00)	0.836 (1.00)	0.428 (1.00)	0.392 (1.00)	0.201 (1.00)	0.228 (1.00)	0.139 (1.00)	0.322 (1.00)
xq gain	15 (12%)	112	0.00767 (1.00)	0.0636 (1.00)	0.827 (1.00)	1 (1.00)	0.0737 (1.00)	1 (1.00)	0.124 (1.00)	1 (1.00)
1p loss	15 (12%)	112	0.485 (1.00)	0.362 (1.00)	0.0509 (1.00)	0.849 (1.00)	0.477 (1.00)	0.348 (1.00)	0.748 (1.00)	1 (1.00)
1q loss	15 (12%)	112	0.404 (1.00)	0.242 (1.00)	0.683 (1.00)	0.818 (1.00)	0.876 (1.00)	0.693 (1.00)	0.733 (1.00)	0.528 (1.00)
2q loss	31 (24%)	96	0.0516 (1.00)	0.315 (1.00)	0.684 (1.00)	0.721 (1.00)	0.712 (1.00)	0.469 (1.00)	0.829 (1.00)	0.109 (1.00)
3p loss	25 (20%)	102	0.0113 (1.00)	0.00496 (1.00)	0.00529 (1.00)	0.00308 (1.00)	0.0447 (1.00)	0.00838 (1.00)	0.0042 (1.00)	0.00273 (1.00)
3q loss	30 (24%)	97	0.0273 (1.00)	0.00276 (1.00)	0.0103 (1.00)	0.00252 (1.00)	0.131 (1.00)	0.00642 (1.00)	0.00993 (1.00)	0.002 (1.00)
4p loss	21 (17%)	106	0.0219 (1.00)	0.00294 (1.00)	0.132 (1.00)	0.387 (1.00)	0.159 (1.00)	0.589 (1.00)	0.895 (1.00)	0.618 (1.00)
4q loss	22 (17%)	105	0.0109 (1.00)	0.00116 (0.709)	0.0779 (1.00)	0.855 (1.00)	0.264 (1.00)	0.826 (1.00)	1 (1.00)	1 (1.00)
5p loss	12 (9%)	115	0.86 (1.00)	0.236 (1.00)	0.207 (1.00)	0.332 (1.00)	0.865 (1.00)	0.312 (1.00)	0.529 (1.00)	0.189 (1.00)
5q loss	15 (12%)	112	0.269 (1.00)	0.274 (1.00)	0.0465 (1.00)	0.27 (1.00)	1 (1.00)	0.366 (1.00)	0.589 (1.00)	0.388 (1.00)
6p loss	27 (21%)	100	0.407 (1.00)	0.0147 (1.00)	0.0799 (1.00)	0.0516 (1.00)	0.0843 (1.00)	0.33 (1.00)	0.123 (1.00)	0.461 (1.00)
6q loss	17 (13%)	110	0.0692 (1.00)	0.00626 (1.00)	0.0411 (1.00)	0.175 (1.00)	0.0536 (1.00)	0.299 (1.00)	0.282 (1.00)	0.25 (1.00)
7p loss	20 (16%)	107	0.289 (1.00)	0.0361 (1.00)	0.119 (1.00)	0.387 (1.00)	0.486 (1.00)	1 (1.00)	0.878 (1.00)	0.195 (1.00)
7q loss	19 (15%)	108	0.292 (1.00)	0.184 (1.00)	0.119 (1.00)	0.387 (1.00)	0.846 (1.00)	1 (1.00)	0.641 (1.00)	0.195 (1.00)
8p loss	26 (20%)	101	0.818 (1.00)	0.157 (1.00)	0.245 (1.00)	0.357 (1.00)	0.231 (1.00)	0.00634 (1.00)	0.551 (1.00)	0.309 (1.00)
8q loss	16 (13%)	111	0.219 (1.00)	0.587 (1.00)	0.549 (1.00)	0.418 (1.00)	0.78 (1.00)	0.115 (1.00)	0.589 (1.00)	0.388 (1.00)
9p loss	39 (31%)	88	0.0975 (1.00)	0.327 (1.00)	0.876 (1.00)	0.0984 (1.00)	0.127 (1.00)	0.255 (1.00)	0.0954 (1.00)	0.238 (1.00)
9q loss	26 (20%)	101	0.351 (1.00)	0.174 (1.00)	0.432 (1.00)	0.379 (1.00)	0.149 (1.00)	0.551 (1.00)	0.118 (1.00)	0.178 (1.00)
11p loss	45 (35%)	82	0.000796 (0.492)	0.00393 (1.00)	0.549 (1.00)	0.144 (1.00)	0.821 (1.00)	0.211 (1.00)	0.378 (1.00)	0.184 (1.00)
11q loss	39 (31%)	88	0.00105 (0.646)	0.00815 (1.00)	0.632 (1.00)	0.0648 (1.00)	0.186 (1.00)	0.056 (1.00)	0.0366 (1.00)	0.0446 (1.00)
12p loss	28 (22%)	99	0.054 (1.00)	0.00703 (1.00)	0.828 (1.00)	0.821 (1.00)	0.571 (1.00)	0.312 (1.00)	0.742 (1.00)	0.579 (1.00)
12q loss	27 (21%)	100	0.0061 (1.00)	0.00737 (1.00)	1 (1.00)	0.678 (1.00)	0.969 (1.00)	0.356 (1.00)	0.667 (1.00)	0.592 (1.00)
13q loss	65 (51%)	62	0.00042 (0.262)	0.00186 (1.00)	0.66 (1.00)	0.167 (1.00)	0.78 (1.00)	0.87 (1.00)	0.791 (1.00)	1 (1.00)
14q loss	37 (29%)	90	0.456 (1.00)	0.689 (1.00)	0.00683 (1.00)	0.367 (1.00)	0.185 (1.00)	0.886 (1.00)	0.528 (1.00)	0.409 (1.00)
15q loss	20 (16%)	107	0.0801 (1.00)	0.0386 (1.00)	0.0199 (1.00)	0.0668 (1.00)	0.0149 (1.00)	0.0625 (1.00)	0.0666 (1.00)	0.0944 (1.00)
16p loss	35 (28%)	92	0.00788 (1.00)	0.000816 (0.503)	0.709 (1.00)	0.531 (1.00)	0.283 (1.00)	1 (1.00)	0.762 (1.00)	0.305 (1.00)
17p loss	25 (20%)	102	0.163 (1.00)	0.319 (1.00)	0.317 (1.00)	0.732 (1.00)	0.438 (1.00)	0.62 (1.00)	0.303 (1.00)	0.426 (1.00)
17q loss	23 (18%)	104	0.00119 (0.73)	0.258 (1.00)	0.00427 (1.00)	0.11 (1.00)	0.089 (1.00)	0.0956 (1.00)	0.185 (1.00)	0.0371 (1.00)
18p loss	30 (24%)	97	0.257 (1.00)	0.0464 (1.00)	0.408 (1.00)	0.721 (1.00)	0.891 (1.00)	1 (1.00)	0.951 (1.00)	1 (1.00)
18q loss	32 (25%)	95	0.0133 (1.00)	0.0134 (1.00)	0.835 (1.00)	0.599 (1.00)	0.775 (1.00)	0.842 (1.00)	0.642 (1.00)	0.833 (1.00)
19p loss	9 (7%)	118	0.0427 (1.00)	0.0568 (1.00)	0.484 (1.00)	1 (1.00)	0.941 (1.00)	0.754 (1.00)	1 (1.00)	1 (1.00)
19q loss	17 (13%)	110	0.173 (1.00)	0.00841 (1.00)	0.939 (1.00)	0.76 (1.00)	0.846 (1.00)	0.653 (1.00)	0.641 (1.00)	0.713 (1.00)
20p loss	22 (17%)	105	0.344 (1.00)	0.241 (1.00)	0.234 (1.00)	0.858 (1.00)	0.728 (1.00)	0.272 (1.00)	0.821 (1.00)	0.683 (1.00)
20q loss	10 (8%)	117	0.0279 (1.00)	0.772 (1.00)	0.275 (1.00)	0.653 (1.00)	0.42 (1.00)	0.0427 (1.00)	0.772 (1.00)	0.308 (1.00)
21q loss	24 (19%)	103	0.073 (1.00)	0.173 (1.00)	0.371 (1.00)	0.379 (1.00)	0.0659 (1.00)	0.0935 (1.00)	0.416 (1.00)	0.386 (1.00)
22q loss	38 (30%)	89	0.0145 (1.00)	0.00146 (0.887)	0.0333 (1.00)	0.227 (1.00)	0.724 (1.00)	0.866 (1.00)	0.747 (1.00)	0.736 (1.00)

'19p gain' versus 'CN_CNMF'

P value = 1.65e-05 (Fisher's exact test), Q value = 0.011

Table S1. Gene #34: '19p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	33	55	39
19P GAIN MUTATED	8	26	2
19P GAIN WILD-TYPE	25	29	37

Figure S1. Get High-res Image Gene #34: '19p gain' versus Molecular Subtype #1: 'CN_CNMF'

'2p loss' versus 'METHLYATION_CNMF'

P value = 0.000114 (Fisher's exact test), Q value = 0.072

Table S2. Gene #43: '2p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	34	43	50
2P LOSS MUTATED	4	9	26
2P LOSS WILD-TYPE	30	34	24

Figure S2. Get High-res Image Gene #43: '2p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'2p loss' versus 'MRNASEQ_CNMF'

P value = 8.53e-05 (Fisher's exact test), Q value = 0.054

Table S3. Gene #43: '2p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	41	30	32
2P LOSS MUTATED	8	7	21
2P LOSS WILD-TYPE	33	23	11

Figure S3. Get High-res Image Gene #43: '2p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'2p loss' versus 'MIRSEQ_MATURE_CHIERARCHICAL'

P value = 0.000119 (Fisher's exact test), Q value = 0.075

Table S4. Gene #43: '2p loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	1	46	50
2P LOSS MUTATED	1	6	24
2P LOSS WILD-TYPE	0	40	26

Figure S4. Get High-res Image Gene #43: '2p loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

'10p loss' versus 'METHLYATION_CNMF'

P value = 0.000171 (Fisher's exact test), Q value = 0.11

Table S5. Gene #59: '10p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	34	43	50
10P LOSS MUTATED	5	23	28
10P LOSS WILD-TYPE	29	20	22

Figure S5. Get High-res Image Gene #59: '10p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'10p loss' versus 'MRNASEQ_CNMF'

P value = 0.000257 (Fisher's exact test), Q value = 0.16

Table S6. Gene #59: '10p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	41	30	32
10P LOSS MUTATED	8	14	21
10P LOSS WILD-TYPE	33	16	11

Figure S6. Get High-res Image Gene #59: '10p loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'10q loss' versus 'CN_CNMF'

P value = 6.97e-08 (Fisher's exact test), Q value = 4.5e-05

Table S7. Gene #60: '10q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	33	55	39
10Q LOSS MUTATED	5	42	19
10Q LOSS WILD-TYPE	28	13	20

Figure S7. Get High-res Image Gene #60: '10q loss' versus Molecular Subtype #1: 'CN_CNMF'

'10q loss' versus 'METHLYATION_CNMF'

P value = 4.82e-07 (Fisher's exact test), Q value = 0.00031

Table S8. Gene #60: '10q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	34	43	50
10Q LOSS MUTATED	5	25	36
10Q LOSS WILD-TYPE	29	18	14

Figure S8. Get High-res Image Gene #60: '10q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'10q loss' versus 'MRNASEQ_CNMF'

P value = 3.03e-07 (Fisher's exact test), Q value = 0.00019

Table S9. Gene #60: '10q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	41	30	32
10Q LOSS MUTATED	9	17	27
10Q LOSS WILD-TYPE	32	13	5

Figure S9. Get High-res Image Gene #60: '10q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'10q loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 3.52e-05 (Fisher's exact test), Q value = 0.022

Table S10. Gene #60: '10q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	46	53	4
10Q LOSS MUTATED	34	19	0
10Q LOSS WILD-TYPE	12	34	4

Figure S10. Get High-res Image Gene #60: '10q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'16q loss' versus 'CN_CNMF'

P value = 1.87e-05 (Fisher's exact test), Q value = 0.012

Table S11. Gene #69: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	33	55	39
16Q LOSS MUTATED	8	40	16
16Q LOSS WILD-TYPE	25	15	23

Figure S11. Get High-res Image Gene #69: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

'16q loss' versus 'METHLYATION_CNMF'

P value = 3.35e-06 (Fisher's exact test), Q value = 0.0021

Table S12. Gene #69: '16q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	34	43	50
16Q LOSS MUTATED	5	26	33
16Q LOSS WILD-TYPE	29	17	17

Figure S12. Get High-res Image Gene #69: '16q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'16q loss' versus 'MIRSEQ_CNMF'

P value = 0.000212 (Fisher's exact test), Q value = 0.13

Table S13. Gene #69: '16q loss' versus Molecular Subtype #5: 'MIRSEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	10	40	14	33
16Q LOSS MUTATED	6	13	4	26
16Q LOSS WILD-TYPE	4	27	10	7

Figure S13. Get High-res Image Gene #69: '16q loss' versus Molecular Subtype #5: 'MIRSEQ_CNMF'

'xq loss' versus 'METHLYATION_CNMF'

P value = 2.95e-07 (Fisher's exact test), Q value = 0.00019

Table S14. Gene #80: 'xq loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	34	43	50
XQ LOSS MUTATED	2	15	31
XQ LOSS WILD-TYPE	32	28	19

Figure S14. Get High-res Image Gene #80: 'xq loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Molecular subtypes file = SARC-TP.transferedmergedcluster.txt
Number of patients = 127
Number of significantly arm-level cnvs = 80
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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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