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

Pancreatic Adenocarcinoma (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/C15719HV

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 68 arm-level events and 8 molecular subtypes across 81 patients, 29 significant findings detected with P value < 0.05 and Q value < 0.25.

8q gain cnv correlated to 'MRNASEQ_CNMF' and 'MRNASEQ_CHIERARCHICAL'.
12p gain cnv correlated to 'CN_CNMF'.
18p gain cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.
20p gain cnv correlated to 'CN_CNMF'.
1p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.
6p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.
6q loss cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CNMF', 'MRNASEQ_CHIERARCHICAL', 'MIRSEQ_CNMF', 'MIRSEQ_CHIERARCHICAL', 'MIRSEQ_MATURE_CNMF', and 'MIRSEQ_MATURE_CHIERARCHICAL'.
9p loss cnv correlated to 'CN_CNMF'.
10p loss cnv correlated to 'MIRSEQ_MATURE_CNMF'.
15q loss cnv correlated to 'METHLYATION_CNMF', 'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_MATURE_CNMF'.
17p loss cnv correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CHIERARCHICAL', 'MIRSEQ_MATURE_CNMF', and 'MIRSEQ_MATURE_CHIERARCHICAL'.
21q 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 68 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, 29 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
6q loss	34 (42%)	47	0.000219 (0.108)	2.68e-07 (0.000136)	0.000314 (0.154)	4.62e-07 (0.000235)	0.000209 (0.104)	3.46e-05 (0.0174)	5.31e-05 (0.0267)	0.00017 (0.0845)
17p loss	33 (41%)	48	4.59e-09 (2.34e-06)	2.13e-11 (1.09e-08)	0.00264 (1.00)	0.000184 (0.0915)	0.00066 (0.315)	0.000921 (0.43)	6.19e-05 (0.031)	0.000372 (0.181)
15q loss	11 (14%)	70	0.00302 (1.00)	0.000229 (0.112)	0.00233 (1.00)	0.000315 (0.154)	0.00192 (0.885)	0.000862 (0.406)	0.000212 (0.105)	0.000554 (0.267)
8q gain	21 (26%)	60	0.00678 (1.00)	0.000993 (0.462)	4.42e-05 (0.0222)	8.83e-05 (0.044)	0.0013 (0.602)	0.00275 (1.00)	0.00146 (0.673)	0.00143 (0.659)
18p gain	10 (12%)	71	2.78e-05 (0.014)	0.000421 (0.204)	0.0191 (1.00)	0.0092 (1.00)	0.0225 (1.00)	0.0131 (1.00)	0.00463 (1.00)	0.00874 (1.00)
1p loss	11 (14%)	70	0.000478 (0.231)	0.000313 (0.153)	0.577 (1.00)	0.533 (1.00)	0.318 (1.00)	0.699 (1.00)	0.591 (1.00)	0.702 (1.00)
6p loss	28 (35%)	53	8.48e-05 (0.0423)	9e-06 (0.00456)	0.000763 (0.362)	0.00708 (1.00)	0.0178 (1.00)	0.000783 (0.37)	0.00087 (0.408)	0.00311 (1.00)
12p gain	8 (10%)	73	0.000411 (0.199)	0.0145 (1.00)	0.0449 (1.00)	0.0355 (1.00)	0.0225 (1.00)	0.138 (1.00)	0.0383 (1.00)	0.092 (1.00)
20p gain	13 (16%)	68	0.000349 (0.17)	0.00365 (1.00)	0.0587 (1.00)	0.0562 (1.00)	0.0436 (1.00)	0.149 (1.00)	0.0607 (1.00)	0.122 (1.00)
9p loss	34 (42%)	47	7.88e-05 (0.0394)	0.0377 (1.00)	0.277 (1.00)	0.0455 (1.00)	0.178 (1.00)	0.431 (1.00)	0.244 (1.00)	0.367 (1.00)
10p loss	13 (16%)	68	0.00815 (1.00)	0.12 (1.00)	0.019 (1.00)	0.000786 (0.371)	0.00192 (0.885)	0.000862 (0.406)	0.000212 (0.105)	0.000554 (0.267)
21q loss	28 (35%)	53	1.79e-06 (0.000905)	0.00533 (1.00)	0.135 (1.00)	0.0162 (1.00)	0.0269 (1.00)	0.224 (1.00)	0.0741 (1.00)	0.16 (1.00)
1p gain	6 (7%)	75	0.869 (1.00)	0.867 (1.00)	0.913 (1.00)	0.782 (1.00)	1 (1.00)	0.84 (1.00)	0.834 (1.00)	0.838 (1.00)
1q gain	19 (23%)	62	0.246 (1.00)	0.0722 (1.00)	0.0602 (1.00)	0.117 (1.00)	0.203 (1.00)	0.861 (1.00)	0.673 (1.00)	0.687 (1.00)
2p gain	6 (7%)	75	0.0236 (1.00)	0.0433 (1.00)	0.0449 (1.00)	0.0156 (1.00)	0.107 (1.00)	0.407 (1.00)	0.138 (1.00)	0.271 (1.00)
2q gain	7 (9%)	74	0.00803 (1.00)	0.0177 (1.00)	0.0191 (1.00)	0.0092 (1.00)	0.0375 (1.00)	0.226 (1.00)	0.0564 (1.00)	0.166 (1.00)
3p gain	4 (5%)	77	0.256 (1.00)	0.153 (1.00)	0.26 (1.00)	0.122 (1.00)	0.289 (1.00)	1 (1.00)	0.385 (1.00)	1 (1.00)
3q gain	9 (11%)	72	0.116 (1.00)	0.00375 (1.00)	0.0587 (1.00)	0.0562 (1.00)	0.108 (1.00)	0.255 (1.00)	0.151 (1.00)	0.195 (1.00)
4p gain	3 (4%)	78	0.606 (1.00)	0.603 (1.00)	0.885 (1.00)	0.683 (1.00)	1 (1.00)	0.772 (1.00)	0.571 (1.00)	0.789 (1.00)
4q gain	3 (4%)	78	0.606 (1.00)	0.603 (1.00)	0.885 (1.00)	0.683 (1.00)	1 (1.00)	0.772 (1.00)	0.571 (1.00)	0.789 (1.00)
5p gain	9 (11%)	72	0.0755 (1.00)	0.102 (1.00)	0.314 (1.00)	0.195 (1.00)	0.305 (1.00)	0.74 (1.00)	0.461 (1.00)	0.643 (1.00)
5q gain	5 (6%)	76	0.708 (1.00)	0.437 (1.00)	0.577 (1.00)	0.533 (1.00)	0.811 (1.00)	0.39 (1.00)	0.509 (1.00)	0.327 (1.00)
7p gain	19 (23%)	62	0.0639 (1.00)	0.0053 (1.00)	0.0703 (1.00)	0.0619 (1.00)	0.034 (1.00)	0.117 (1.00)	0.037 (1.00)	0.0921 (1.00)
7q gain	17 (21%)	64	0.159 (1.00)	0.0114 (1.00)	0.0703 (1.00)	0.0619 (1.00)	0.034 (1.00)	0.117 (1.00)	0.037 (1.00)	0.0921 (1.00)
8p gain	10 (12%)	71	0.115 (1.00)	0.0467 (1.00)	0.0244 (1.00)	0.0185 (1.00)	0.108 (1.00)	0.255 (1.00)	0.151 (1.00)	0.195 (1.00)
9p gain	3 (4%)	78	0.606 (1.00)	0.328 (1.00)	0.26 (1.00)	0.122 (1.00)	0.289 (1.00)	0.289 (1.00)	0.206 (1.00)	0.301 (1.00)
9q gain	4 (5%)	77	0.256 (1.00)	0.115 (1.00)	0.26 (1.00)	0.122 (1.00)	0.289 (1.00)	0.289 (1.00)	0.206 (1.00)	0.301 (1.00)
10q gain	4 (5%)	77	0.0169 (1.00)	0.0301 (1.00)
11p gain	7 (9%)	74	0.106 (1.00)	0.0274 (1.00)	0.244 (1.00)	0.432 (1.00)	0.642 (1.00)	0.122 (1.00)	0.54 (1.00)	0.107 (1.00)
11q gain	7 (9%)	74	0.106 (1.00)	0.24 (1.00)	0.133 (1.00)	0.119 (1.00)	0.305 (1.00)	0.0982 (1.00)	0.161 (1.00)	0.0825 (1.00)
12q gain	6 (7%)	75	0.00761 (1.00)	0.0533 (1.00)	0.123 (1.00)	0.0814 (1.00)	0.0375 (1.00)	0.226 (1.00)	0.0564 (1.00)	0.166 (1.00)
13q gain	6 (7%)	75	1 (1.00)	0.29 (1.00)	1 (1.00)	0.744 (1.00)	0.509 (1.00)	0.0551 (1.00)	0.165 (1.00)	0.066 (1.00)
14q gain	9 (11%)	72	0.0755 (1.00)	0.476 (1.00)	0.234 (1.00)	0.245 (1.00)	0.706 (1.00)	0.714 (1.00)	0.8 (1.00)	0.65 (1.00)
15q gain	6 (7%)	75	0.3 (1.00)	0.195 (1.00)	0.484 (1.00)	1 (1.00)	0.151 (1.00)	0.578 (1.00)	1 (1.00)	0.501 (1.00)
16p gain	8 (10%)	73	0.00279 (1.00)	0.0145 (1.00)	0.133 (1.00)	0.119 (1.00)	0.161 (1.00)	0.138 (1.00)	0.0383 (1.00)	0.092 (1.00)
16q gain	9 (11%)	72	0.0025 (1.00)	0.00499 (1.00)	0.0244 (1.00)	0.049 (1.00)	0.108 (1.00)	0.0614 (1.00)	0.0138 (1.00)	0.0413 (1.00)
17q gain	4 (5%)	77	0.256 (1.00)	0.115 (1.00)	0.123 (1.00)	0.0588 (1.00)	0.232 (1.00)	0.126 (1.00)	0.0916 (1.00)	0.133 (1.00)
18q gain	3 (4%)	78	0.107 (1.00)
19p gain	4 (5%)	77	0.0739 (1.00)	0.448 (1.00)	0.577 (1.00)	0.533 (1.00)	0.394 (1.00)	0.661 (1.00)	0.509 (1.00)	0.531 (1.00)
19q gain	9 (11%)	72	0.259 (1.00)	0.476 (1.00)	0.234 (1.00)	0.245 (1.00)	0.396 (1.00)	0.316 (1.00)	0.553 (1.00)	0.894 (1.00)
20q gain	17 (21%)	64	0.00101 (0.47)	0.0461 (1.00)	0.036 (1.00)	0.0209 (1.00)	0.0421 (1.00)	0.118 (1.00)	0.0179 (1.00)	0.0926 (1.00)
22q gain	4 (5%)	77	0.0494 (1.00)	0.0474 (1.00)	0.26 (1.00)	0.122 (1.00)	0.289 (1.00)	1 (1.00)	0.385 (1.00)	1 (1.00)
xq gain	3 (4%)	78	1 (1.00)	1 (1.00)
1q loss	3 (4%)	78	0.0114 (1.00)	0.104 (1.00)
2p loss	6 (7%)	75	0.0986 (1.00)	0.224 (1.00)	1 (1.00)	0.782 (1.00)	0.848 (1.00)	0.74 (1.00)	0.461 (1.00)	0.643 (1.00)
3p loss	12 (15%)	69	0.000913 (0.427)	0.12 (1.00)	0.234 (1.00)	0.406 (1.00)	0.306 (1.00)	0.339 (1.00)	0.196 (1.00)	0.343 (1.00)
3q loss	6 (7%)	75	0.0236 (1.00)	0.262 (1.00)	0.577 (1.00)	0.533 (1.00)	0.394 (1.00)	0.661 (1.00)	0.509 (1.00)	0.531 (1.00)
4p loss	9 (11%)	72	0.0121 (1.00)	0.102 (1.00)	0.495 (1.00)	0.119 (1.00)	0.252 (1.00)	0.226 (1.00)	0.0564 (1.00)	0.166 (1.00)
4q loss	8 (10%)	73	0.031 (1.00)	0.00941 (1.00)	0.667 (1.00)	0.195 (1.00)	0.571 (1.00)	0.195 (1.00)	0.312 (1.00)	0.206 (1.00)
5p loss	3 (4%)	78	0.0114 (1.00)	0.328 (1.00)
5q loss	7 (9%)	74	0.000732 (0.349)	0.0274 (1.00)	0.26 (1.00)	0.122 (1.00)	0.107 (1.00)	0.407 (1.00)	0.138 (1.00)	0.271 (1.00)
8p loss	11 (14%)	70	0.0308 (1.00)	0.0353 (1.00)	0.0587 (1.00)	0.0562 (1.00)	0.0436 (1.00)	0.00989 (1.00)	0.0181 (1.00)	0.00739 (1.00)
9q loss	20 (25%)	61	0.21 (1.00)	0.55 (1.00)	0.678 (1.00)	0.694 (1.00)	0.355 (1.00)	0.527 (1.00)	0.754 (1.00)	0.699 (1.00)
10q loss	12 (15%)	69	0.000581 (0.279)	0.18 (1.00)	0.0696 (1.00)	0.00666 (1.00)	0.00844 (1.00)	0.00408 (1.00)	0.000611 (0.293)	0.00269 (1.00)
11p loss	6 (7%)	75	0.0339 (1.00)	0.867 (1.00)	0.0257 (1.00)	0.615 (1.00)	0.00536 (1.00)	0.117 (1.00)	0.248 (1.00)	0.129 (1.00)
11q loss	6 (7%)	75	0.3 (1.00)	0.755 (1.00)	0.173 (1.00)	0.436 (1.00)	0.299 (1.00)	0.523 (1.00)	0.658 (1.00)	0.671 (1.00)
12p loss	8 (10%)	73	0.031 (1.00)	0.152 (1.00)	0.0449 (1.00)	0.0156 (1.00)	0.107 (1.00)	0.0693 (1.00)	0.0357 (1.00)	0.0447 (1.00)
12q loss	10 (12%)	71	0.000738 (0.351)	0.0636 (1.00)	0.0587 (1.00)	0.0562 (1.00)	0.111 (1.00)	0.138 (1.00)	0.0383 (1.00)	0.092 (1.00)
13q loss	10 (12%)	71	0.000738 (0.351)	0.281 (1.00)	0.252 (1.00)	0.0743 (1.00)	0.0478 (1.00)	0.0614 (1.00)	0.0138 (1.00)	0.0413 (1.00)
14q loss	4 (5%)	77	1 (1.00)	0.0301 (1.00)			1 (1.00)	0.414 (1.00)	1 (1.00)	0.427 (1.00)
17q loss	11 (14%)	70	0.000967 (0.45)	0.0253 (1.00)	0.32 (1.00)	0.374 (1.00)	0.419 (1.00)	0.694 (1.00)	0.203 (1.00)	0.615 (1.00)
18p loss	29 (36%)	52	0.503 (1.00)	0.0727 (1.00)	0.575 (1.00)	0.849 (1.00)	0.809 (1.00)	1 (1.00)	0.407 (1.00)	0.863 (1.00)
18q loss	44 (54%)	37	0.0035 (1.00)	0.00213 (0.977)	0.225 (1.00)	0.193 (1.00)	0.205 (1.00)	0.0802 (1.00)	0.062 (1.00)	0.144 (1.00)
19p loss	7 (9%)	74	0.0711 (1.00)	0.24 (1.00)	0.102 (1.00)	0.0092 (1.00)	0.0225 (1.00)	0.138 (1.00)	0.0383 (1.00)	0.092 (1.00)
19q loss	5 (6%)	76	0.261 (1.00)	0.522 (1.00)	0.321 (1.00)	0.0588 (1.00)	0.107 (1.00)	0.0693 (1.00)	0.0357 (1.00)	0.0447 (1.00)
20p loss	7 (9%)	74	0.438 (1.00)	0.129 (1.00)	0.667 (1.00)	0.257 (1.00)	0.318 (1.00)	0.407 (1.00)	0.312 (1.00)	0.271 (1.00)
22q loss	16 (20%)	65	0.00353 (1.00)	0.0685 (1.00)	0.234 (1.00)	0.406 (1.00)	0.396 (1.00)	0.561 (1.00)	0.215 (1.00)	0.453 (1.00)
xq loss	5 (6%)	76	0.00408 (1.00)	0.437 (1.00)	0.366 (1.00)	1 (1.00)	0.116 (1.00)	0.58 (1.00)	0.0739 (1.00)	0.587 (1.00)

'8q gain' versus 'MRNASEQ_CNMF'

P value = 4.42e-05 (Fisher's exact test), Q value = 0.022

Table S1. Gene #14: '8q gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	21	12	12	10
8Q GAIN MUTATED	13	0	2	0
8Q GAIN WILD-TYPE	8	12	10	10

Figure S1. Get High-res Image Gene #14: '8q gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'8q gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 8.83e-05 (Fisher's exact test), Q value = 0.044

Table S2. Gene #14: '8q gain' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	5	22	28
8Q GAIN MUTATED	0	13	2
8Q GAIN WILD-TYPE	5	9	26

Figure S2. Get High-res Image Gene #14: '8q gain' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'12p gain' versus 'CN_CNMF'

P value = 0.000411 (Fisher's exact test), Q value = 0.2

Table S3. Gene #20: '12p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
12P GAIN MUTATED	2	0	6
12P GAIN WILD-TYPE	18	42	13

Figure S3. Get High-res Image Gene #20: '12p gain' versus Molecular Subtype #1: 'CN_CNMF'

'18p gain' versus 'CN_CNMF'

P value = 2.78e-05 (Fisher's exact test), Q value = 0.014

Table S4. Gene #28: '18p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
18P GAIN MUTATED	8	0	2
18P GAIN WILD-TYPE	12	42	17

Figure S4. Get High-res Image Gene #28: '18p gain' versus Molecular Subtype #1: 'CN_CNMF'

'18p gain' versus 'METHLYATION_CNMF'

P value = 0.000421 (Fisher's exact test), Q value = 0.2

Table S5. Gene #28: '18p gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	27	34	19
18P GAIN MUTATED	8	0	1
18P GAIN WILD-TYPE	19	34	18

Figure S5. Get High-res Image Gene #28: '18p gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'20p gain' versus 'CN_CNMF'

P value = 0.000349 (Fisher's exact test), Q value = 0.17

Table S6. Gene #32: '20p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
20P GAIN MUTATED	1	3	9
20P GAIN WILD-TYPE	19	39	10

Figure S6. Get High-res Image Gene #32: '20p gain' versus Molecular Subtype #1: 'CN_CNMF'

'1p loss' versus 'CN_CNMF'

P value = 0.000478 (Fisher's exact test), Q value = 0.23

Table S7. Gene #36: '1p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
1P LOSS MUTATED	1	2	8
1P LOSS WILD-TYPE	19	40	11

Figure S7. Get High-res Image Gene #36: '1p loss' versus Molecular Subtype #1: 'CN_CNMF'

'1p loss' versus 'METHLYATION_CNMF'

P value = 0.000313 (Fisher's exact test), Q value = 0.15

Table S8. Gene #36: '1p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	27	34	19
1P LOSS MUTATED	4	0	7
1P LOSS WILD-TYPE	23	34	12

Figure S8. Get High-res Image Gene #36: '1p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'6p loss' versus 'CN_CNMF'

P value = 8.48e-05 (Fisher's exact test), Q value = 0.042

Table S9. Gene #45: '6p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
6P LOSS MUTATED	9	6	13
6P LOSS WILD-TYPE	11	36	6

Figure S9. Get High-res Image Gene #45: '6p loss' versus Molecular Subtype #1: 'CN_CNMF'

'6p loss' versus 'METHLYATION_CNMF'

P value = 9e-06 (Fisher's exact test), Q value = 0.0046

Table S10. Gene #45: '6p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	27	34	19
6P LOSS MUTATED	15	2	10
6P LOSS WILD-TYPE	12	32	9

Figure S10. Get High-res Image Gene #45: '6p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'6q loss' versus 'CN_CNMF'

P value = 0.000219 (Fisher's exact test), Q value = 0.11

Table S11. Gene #46: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
6Q LOSS MUTATED	11	9	14
6Q LOSS WILD-TYPE	9	33	5

Figure S11. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

'6q loss' versus 'METHLYATION_CNMF'

P value = 2.68e-07 (Fisher's exact test), Q value = 0.00014

Table S12. Gene #46: '6q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	27	34	19
6Q LOSS MUTATED	20	3	10
6Q LOSS WILD-TYPE	7	31	9

Figure S12. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'6q loss' versus 'MRNASEQ_CNMF'

P value = 0.000314 (Fisher's exact test), Q value = 0.15

Table S13. Gene #46: '6q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	21	12	12	10
6Q LOSS MUTATED	16	2	2	2
6Q LOSS WILD-TYPE	5	10	10	8

Figure S13. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'6q loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 4.62e-07 (Fisher's exact test), Q value = 0.00023

Table S14. Gene #46: '6q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	5	22	28
6Q LOSS MUTATED	0	18	4
6Q LOSS WILD-TYPE	5	4	24

Figure S14. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'6q loss' versus 'MIRSEQ_CNMF'

P value = 0.000209 (Fisher's exact test), Q value = 0.1

Table S15. Gene #46: '6q loss' versus Molecular Subtype #5: 'MIRSEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	29	11	18
6Q LOSS MUTATED	19	2	2
6Q LOSS WILD-TYPE	10	9	16

Figure S15. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #5: 'MIRSEQ_CNMF'

'6q loss' versus 'MIRSEQ_CHIERARCHICAL'

P value = 3.46e-05 (Fisher's exact test), Q value = 0.017

Table S16. Gene #46: '6q loss' versus Molecular Subtype #6: 'MIRSEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	11	20	27
6Q LOSS MUTATED	1	3	19
6Q LOSS WILD-TYPE	10	17	8

Figure S16. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #6: 'MIRSEQ_CHIERARCHICAL'

'6q loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 5.31e-05 (Fisher's exact test), Q value = 0.027

Table S17. Gene #46: '6q loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	25	10	23
6Q LOSS MUTATED	18	1	4
6Q LOSS WILD-TYPE	7	9	19

Figure S17. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

'6q loss' versus 'MIRSEQ_MATURE_CHIERARCHICAL'

P value = 0.00017 (Fisher's exact test), Q value = 0.084

Table S18. Gene #46: '6q loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	12	26	20
6Q LOSS MUTATED	2	18	3
6Q LOSS WILD-TYPE	10	8	17

Figure S18. Get High-res Image Gene #46: '6q loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

'9p loss' versus 'CN_CNMF'

P value = 7.88e-05 (Fisher's exact test), Q value = 0.039

Table S19. Gene #48: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
9P LOSS MUTATED	10	9	15
9P LOSS WILD-TYPE	10	33	4

Figure S19. Get High-res Image Gene #48: '9p loss' versus Molecular Subtype #1: 'CN_CNMF'

'10p loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000212 (Fisher's exact test), Q value = 0.1

Table S20. Gene #50: '10p loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	25	10	23
10P LOSS MUTATED	10	0	0
10P LOSS WILD-TYPE	15	10	23

Figure S20. Get High-res Image Gene #50: '10p loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

'15q loss' versus 'METHLYATION_CNMF'

P value = 0.000229 (Fisher's exact test), Q value = 0.11

Table S21. Gene #58: '15q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	27	34	19
15Q LOSS MUTATED	9	0	2
15Q LOSS WILD-TYPE	18	34	17

Figure S21. Get High-res Image Gene #58: '15q loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'15q loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000315 (Fisher's exact test), Q value = 0.15

Table S22. Gene #58: '15q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	5	22	28
15Q LOSS MUTATED	1	9	0
15Q LOSS WILD-TYPE	4	13	28

Figure S22. Get High-res Image Gene #58: '15q loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'15q loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000212 (Fisher's exact test), Q value = 0.1

Table S23. Gene #58: '15q loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	25	10	23
15Q LOSS MUTATED	10	0	0
15Q LOSS WILD-TYPE	15	10	23

Figure S23. Get High-res Image Gene #58: '15q loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

'17p loss' versus 'CN_CNMF'

P value = 4.59e-09 (Fisher's exact test), Q value = 2.3e-06

Table S24. Gene #59: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
17P LOSS MUTATED	14	4	15
17P LOSS WILD-TYPE	6	38	4

Figure S24. Get High-res Image Gene #59: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

'17p loss' versus 'METHLYATION_CNMF'

P value = 2.13e-11 (Fisher's exact test), Q value = 1.1e-08

Table S25. Gene #59: '17p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	27	34	19
17P LOSS MUTATED	23	1	9
17P LOSS WILD-TYPE	4	33	10

Figure S25. Get High-res Image Gene #59: '17p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'17p loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000184 (Fisher's exact test), Q value = 0.091

Table S26. Gene #59: '17p loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	5	22	28
17P LOSS MUTATED	1	15	4
17P LOSS WILD-TYPE	4	7	24

Figure S26. Get High-res Image Gene #59: '17p loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'17p loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 6.19e-05 (Fisher's exact test), Q value = 0.031

Table S27. Gene #59: '17p loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	25	10	23
17P LOSS MUTATED	17	3	2
17P LOSS WILD-TYPE	8	7	21

Figure S27. Get High-res Image Gene #59: '17p loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

'17p loss' versus 'MIRSEQ_MATURE_CHIERARCHICAL'

P value = 0.000372 (Fisher's exact test), Q value = 0.18

Table S28. Gene #59: '17p loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	12	26	20
17P LOSS MUTATED	1	17	4
17P LOSS WILD-TYPE	11	9	16

Figure S28. Get High-res Image Gene #59: '17p loss' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

'21q loss' versus 'CN_CNMF'

P value = 1.79e-06 (Fisher's exact test), Q value = 0.00091

Table S29. Gene #66: '21q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	20	42	19
21Q LOSS MUTATED	11	4	13
21Q LOSS WILD-TYPE	9	38	6

Figure S29. Get High-res Image Gene #66: '21q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Molecular subtypes file = PAAD-TP.transferedmergedcluster.txt
Number of patients = 81
Number of significantly arm-level cnvs = 68
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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