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

Liver Hepatocellular Carcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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/C1FB514N

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 71 arm-level results and 6 molecular subtypes across 97 patients, 3 significant findings detected with Q value < 0.25.

8q gain cnv correlated to 'CN_CNMF'.
19q gain cnv correlated to 'MRNASEQ_CHIERARCHICAL'.
16q 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 71 arm-level results and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.


		Molecular subtypes	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
8q gain	45 (46%)	52	4.7e-09 (1.86e-06)	0.75 (1.00)	1 (1.00)	0.807 (1.00)	0.287 (1.00)	0.302 (1.00)
19q gain	10 (10%)	87	0.489 (1.00)	0.00543 (1.00)	0.0445 (1.00)	0.000615 (0.242)	0.242 (1.00)	0.395 (1.00)
16q loss	27 (28%)	70	0.00429 (1.00)	0.000233 (0.0919)	0.708 (1.00)	0.529 (1.00)	0.0707 (1.00)	0.00246 (0.959)
1p gain	10 (10%)	87	0.54 (1.00)	0.392 (1.00)	1 (1.00)	0.677 (1.00)	0.611 (1.00)	0.558 (1.00)
1q gain	51 (53%)	46	0.00386 (1.00)	0.0464 (1.00)	0.732 (1.00)	0.312 (1.00)	0.463 (1.00)	0.625 (1.00)
2p gain	8 (8%)	89	0.586 (1.00)	0.137 (1.00)	0.601 (1.00)	0.484 (1.00)	0.0104 (1.00)	0.793 (1.00)
2q gain	7 (7%)	90	0.618 (1.00)	0.0561 (1.00)	0.227 (1.00)	0.156 (1.00)	0.0228 (1.00)	0.84 (1.00)
3p gain	7 (7%)	90	0.377 (1.00)	0.758 (1.00)	1 (1.00)		0.272 (1.00)	0.714 (1.00)
3q gain	7 (7%)	90	0.377 (1.00)	0.758 (1.00)	1 (1.00)		0.272 (1.00)	0.714 (1.00)
4p gain	6 (6%)	91	0.766 (1.00)	0.101 (1.00)	0.103 (1.00)	0.00507 (1.00)	0.319 (1.00)	0.714 (1.00)
5p gain	28 (29%)	69	0.0381 (1.00)	0.247 (1.00)	1 (1.00)	0.562 (1.00)	0.624 (1.00)	0.564 (1.00)
5q gain	20 (21%)	77	0.129 (1.00)	0.523 (1.00)	1 (1.00)	0.674 (1.00)	0.36 (1.00)	0.81 (1.00)
6p gain	18 (19%)	79	0.00474 (1.00)	0.00692 (1.00)	1 (1.00)	1 (1.00)	0.238 (1.00)	0.919 (1.00)
6q gain	11 (11%)	86	0.0806 (1.00)	0.0922 (1.00)	0.601 (1.00)	0.484 (1.00)	0.397 (1.00)	0.726 (1.00)
7p gain	26 (27%)	71	0.0589 (1.00)	0.195 (1.00)	0.141 (1.00)	0.212 (1.00)	0.105 (1.00)	0.188 (1.00)
7q gain	26 (27%)	71	0.103 (1.00)	0.00982 (1.00)	0.259 (1.00)	0.318 (1.00)	0.31 (1.00)	0.192 (1.00)
8p gain	14 (14%)	83	0.338 (1.00)	0.458 (1.00)	0.656 (1.00)	0.519 (1.00)	0.676 (1.00)	0.359 (1.00)
9p gain	4 (4%)	93	0.201 (1.00)	0.571 (1.00)	1 (1.00)		0.172 (1.00)	0.564 (1.00)
9q gain	3 (3%)	94	0.633 (1.00)	0.797 (1.00)	1 (1.00)		0.29 (1.00)	1 (1.00)
10p gain	7 (7%)	90	0.433 (1.00)	0.553 (1.00)	0.103 (1.00)	0.0373 (1.00)	0.668 (1.00)	0.594 (1.00)
10q gain	4 (4%)	93	0.258 (1.00)	0.444 (1.00)	1 (1.00)		0.172 (1.00)	0.491 (1.00)
11q gain	3 (3%)	94	0.192 (1.00)	0.325 (1.00)			0.767 (1.00)	1 (1.00)
12p gain	4 (4%)	93	0.201 (1.00)	0.688 (1.00)	1 (1.00)		0.826 (1.00)	1 (1.00)
12q gain	5 (5%)	92	0.0736 (1.00)	0.85 (1.00)	1 (1.00)		0.71 (1.00)	1 (1.00)
13q gain	5 (5%)	92	0.0508 (1.00)	0.595 (1.00)	1 (1.00)		0.845 (1.00)	0.603 (1.00)
14q gain	5 (5%)	92	0.374 (1.00)	0.36 (1.00)			1 (1.00)	0.921 (1.00)
15q gain	5 (5%)	92	0.315 (1.00)	0.264 (1.00)	1 (1.00)		0.71 (1.00)	0.781 (1.00)
16p gain	4 (4%)	93	0.201 (1.00)	0.571 (1.00)	0.485 (1.00)		0.519 (1.00)	0.564 (1.00)
17p gain	5 (5%)	92	0.315 (1.00)	0.0992 (1.00)	1 (1.00)		0.71 (1.00)	0.781 (1.00)
17q gain	20 (21%)	77	0.0406 (1.00)	0.947 (1.00)	0.225 (1.00)	0.0144 (1.00)	0.673 (1.00)	0.886 (1.00)
18p gain	3 (3%)	94	0.192 (1.00)	0.444 (1.00)	0.485 (1.00)		0.767 (1.00)	1 (1.00)
18q gain	4 (4%)	93	0.258 (1.00)	0.144 (1.00)	0.485 (1.00)		0.519 (1.00)	1 (1.00)
19p gain	9 (9%)	88	0.223 (1.00)	0.0144 (1.00)	0.103 (1.00)	0.00507 (1.00)	0.0431 (1.00)	0.615 (1.00)
20p gain	20 (21%)	77	0.376 (1.00)	0.427 (1.00)	0.0854 (1.00)	0.0458 (1.00)	0.743 (1.00)	0.946 (1.00)
20q gain	21 (22%)	76	0.263 (1.00)	0.399 (1.00)	0.225 (1.00)	0.137 (1.00)	0.57 (1.00)	0.932 (1.00)
21q gain	6 (6%)	91	0.316 (1.00)	0.254 (1.00)	1 (1.00)		1 (1.00)	0.547 (1.00)
22q gain	8 (8%)	89	0.238 (1.00)	0.35 (1.00)	0.601 (1.00)	0.275 (1.00)	0.541 (1.00)	0.42 (1.00)
Xq gain	5 (5%)	92	0.126 (1.00)	0.08 (1.00)	0.227 (1.00)	0.27 (1.00)	0.198 (1.00)	0.13 (1.00)
1p loss	15 (15%)	82	0.496 (1.00)	0.0422 (1.00)	1 (1.00)	0.038 (1.00)	0.293 (1.00)	0.723 (1.00)
1q loss	5 (5%)	92	0.315 (1.00)	0.188 (1.00)	1 (1.00)		0.489 (1.00)	0.848 (1.00)
2p loss	3 (3%)	94	0.385 (1.00)	0.603 (1.00)	1 (1.00)		0.41 (1.00)	0.481 (1.00)
2q loss	4 (4%)	93	0.144 (1.00)	0.458 (1.00)	1 (1.00)		0.311 (1.00)	0.272 (1.00)
3p loss	8 (8%)	89	0.181 (1.00)	0.147 (1.00)	0.103 (1.00)	0.0373 (1.00)	0.268 (1.00)	0.56 (1.00)
3q loss	3 (3%)	94	0.192 (1.00)	0.0353 (1.00)	0.227 (1.00)	0.0585 (1.00)	1 (1.00)	1 (1.00)
4p loss	9 (9%)	88	0.021 (1.00)	0.492 (1.00)	1 (1.00)	0.562 (1.00)	1 (1.00)	0.486 (1.00)
4q loss	18 (19%)	79	0.00105 (0.413)	0.591 (1.00)	1 (1.00)	1 (1.00)	0.535 (1.00)	0.552 (1.00)
5q loss	5 (5%)	92	0.0736 (1.00)	0.571 (1.00)	0.485 (1.00)		0.255 (1.00)	0.921 (1.00)
6q loss	20 (21%)	77	0.513 (1.00)	1 (1.00)	0.688 (1.00)	0.65 (1.00)	0.743 (1.00)	0.615 (1.00)
7p loss	5 (5%)	92	0.0508 (1.00)	0.85 (1.00)			0.845 (1.00)	0.848 (1.00)
7q loss	7 (7%)	90	0.0337 (1.00)	1 (1.00)	1 (1.00)		0.761 (1.00)	0.145 (1.00)
8p loss	41 (42%)	56	0.00184 (0.718)	0.641 (1.00)	1 (1.00)	0.716 (1.00)	0.601 (1.00)	0.133 (1.00)
8q loss	6 (6%)	91	0.0015 (0.589)	0.472 (1.00)	1 (1.00)		0.319 (1.00)	0.714 (1.00)
9p loss	20 (21%)	77	0.465 (1.00)	0.85 (1.00)	1 (1.00)	0.333 (1.00)	0.399 (1.00)	0.673 (1.00)
9q loss	19 (20%)	78	0.499 (1.00)	0.427 (1.00)	1 (1.00)	0.196 (1.00)	0.697 (1.00)	0.942 (1.00)
10p loss	6 (6%)	91	0.204 (1.00)	1 (1.00)	1 (1.00)	0.562 (1.00)	0.854 (1.00)	0.766 (1.00)
10q loss	17 (18%)	80	0.0534 (1.00)	0.255 (1.00)	0.118 (1.00)	0.0512 (1.00)	0.255 (1.00)	0.102 (1.00)
11p loss	6 (6%)	91	0.316 (1.00)	0.553 (1.00)	0.601 (1.00)	0.484 (1.00)	0.854 (1.00)	0.766 (1.00)
11q loss	8 (8%)	89	0.238 (1.00)	1 (1.00)	0.0184 (1.00)	0.00466 (1.00)	0.346 (1.00)	0.402 (1.00)
12p loss	8 (8%)	89	0.337 (1.00)	0.00863 (1.00)			0.463 (1.00)	0.245 (1.00)
13q loss	30 (31%)	67	0.0219 (1.00)	0.115 (1.00)	0.728 (1.00)	0.475 (1.00)	0.0619 (1.00)	0.506 (1.00)
14q loss	26 (27%)	71	0.0114 (1.00)	0.508 (1.00)	0.141 (1.00)	0.072 (1.00)	0.713 (1.00)	0.592 (1.00)
15q loss	9 (9%)	88	0.00523 (1.00)	0.492 (1.00)	1 (1.00)	1 (1.00)	0.469 (1.00)	0.506 (1.00)
16p loss	20 (21%)	77	0.0776 (1.00)	0.0149 (1.00)	1 (1.00)	0.729 (1.00)	0.0542 (1.00)	0.002 (0.779)
17p loss	41 (42%)	56	0.00899 (1.00)	0.018 (1.00)	0.494 (1.00)	0.236 (1.00)	0.152 (1.00)	0.0428 (1.00)
17q loss	6 (6%)	91	0.766 (1.00)	0.078 (1.00)	1 (1.00)	0.406 (1.00)	0.854 (1.00)	1 (1.00)
18p loss	10 (10%)	87	0.191 (1.00)	0.029 (1.00)	0.601 (1.00)	0.275 (1.00)	0.911 (1.00)	0.665 (1.00)
18q loss	11 (11%)	86	0.0245 (1.00)	0.0938 (1.00)	0.601 (1.00)	0.275 (1.00)	0.837 (1.00)	0.518 (1.00)
19p loss	5 (5%)	92	0.52 (1.00)	0.198 (1.00)	1 (1.00)		1 (1.00)	0.452 (1.00)
20p loss	5 (5%)	92	0.734 (1.00)	0.595 (1.00)	1 (1.00)		0.026 (1.00)	0.288 (1.00)
21q loss	16 (16%)	81	0.357 (1.00)	0.563 (1.00)	1 (1.00)	0.519 (1.00)	0.298 (1.00)	0.0897 (1.00)
22q loss	10 (10%)	87	0.374 (1.00)	0.473 (1.00)	0.335 (1.00)	0.288 (1.00)	0.911 (1.00)	0.627 (1.00)

'8q gain mutation analysis' versus 'CN_CNMF'

P value = 4.7e-09 (Fisher's exact test), Q value = 1.9e-06

Table S1. Gene #15: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	28	36	33
8Q GAIN MUTATED	26	8	11
8Q GAIN WILD-TYPE	2	28	22

Figure S1. Get High-res Image Gene #15: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'19q gain mutation analysis' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000615 (Fisher's exact test), Q value = 0.24

Table S2. Gene #32: '19q gain mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	11	6	17
19Q GAIN MUTATED	1	4	0
19Q GAIN WILD-TYPE	10	2	17

Figure S2. Get High-res Image Gene #32: '19q gain mutation analysis' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

'16q loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000233 (Fisher's exact test), Q value = 0.092

Table S3. Gene #63: '16q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	23	28	45
16Q LOSS MUTATED	1	15	10
16Q LOSS WILD-TYPE	22	13	35

Figure S3. Get High-res Image Gene #63: '16q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Molecular subtypes file = LIHC-TP.transferedmergedcluster.txt
Number of patients = 97
Number of significantly arm-level cnvs = 71
Number of molecular subtypes = 6
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.

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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