Liver Hepatocellular Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features<BR>(primary solid tumor cohort)

Liver Hepatocellular Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 61 arm-level results and 3 clinical features across 61 patients, 2 significant findings detected with Q value < 0.25.

18p loss cnv correlated to 'AGE'.
18q loss cnv correlated to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 61 arm-level results and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test
18p loss	7 (11%)	54	0.0213 (1.00)	0.000571 (0.101)	0.695 (1.00)
18q loss	9 (15%)	52	0.2 (1.00)	0.00109 (0.191)	0.71 (1.00)
1p gain	8 (13%)	53	0.807 (1.00)	0.237 (1.00)	1 (1.00)
1q gain	33 (54%)	28	0.71 (1.00)	0.73 (1.00)	0.423 (1.00)
2p gain	8 (13%)	53	0.738 (1.00)	0.115 (1.00)	0.124 (1.00)
2q gain	7 (11%)	54	0.867 (1.00)	0.227 (1.00)	0.087 (1.00)
4p gain	4 (7%)	57	0.931 (1.00)	0.455 (1.00)	0.129 (1.00)
5p gain	18 (30%)	43	0.304 (1.00)	0.227 (1.00)	1 (1.00)
5q gain	13 (21%)	48	0.197 (1.00)	0.201 (1.00)	1 (1.00)
6p gain	12 (20%)	49	0.123 (1.00)	0.79 (1.00)	0.509 (1.00)
6q gain	8 (13%)	53	0.00574 (1.00)	0.506 (1.00)	0.699 (1.00)
7p gain	17 (28%)	44	0.816 (1.00)	0.615 (1.00)	0.373 (1.00)
7q gain	18 (30%)	43	0.828 (1.00)	0.601 (1.00)	0.397 (1.00)
8p gain	11 (18%)	50	0.112 (1.00)	0.577 (1.00)	0.299 (1.00)
8q gain	31 (51%)	30	0.271 (1.00)	0.594 (1.00)	0.6 (1.00)
9p gain	3 (5%)	58		0.822 (1.00)	0.293 (1.00)
9q gain	3 (5%)	58		0.822 (1.00)	0.293 (1.00)
10p gain	5 (8%)	56	0.166 (1.00)	0.39 (1.00)	0.341 (1.00)
12q gain	3 (5%)	58		0.894 (1.00)	0.293 (1.00)
15q gain	5 (8%)	56	0.814 (1.00)	0.328 (1.00)	0.341 (1.00)
16p gain	3 (5%)	58	0.0077 (1.00)	0.149 (1.00)	1 (1.00)
17p gain	3 (5%)	58	0.467 (1.00)	0.711 (1.00)	0.293 (1.00)
17q gain	16 (26%)	45	0.0844 (1.00)	0.876 (1.00)	0.548 (1.00)
18q gain	3 (5%)	58	0.937 (1.00)	0.619 (1.00)	1 (1.00)
19p gain	5 (8%)	56	0.759 (1.00)	0.62 (1.00)	0.0524 (1.00)
19q gain	7 (11%)	54	0.795 (1.00)	0.822 (1.00)	0.087 (1.00)
20p gain	12 (20%)	49	0.238 (1.00)	0.0865 (1.00)	0.322 (1.00)
20q gain	13 (21%)	48	0.361 (1.00)	0.059 (1.00)	0.517 (1.00)
21q gain	4 (7%)	57	0.696 (1.00)	0.613 (1.00)	0.129 (1.00)
22q gain	6 (10%)	55	0.16 (1.00)	0.068 (1.00)	0.658 (1.00)
Xq gain	4 (7%)	57	0.228 (1.00)	0.223 (1.00)	1 (1.00)
1p loss	11 (18%)	50	0.689 (1.00)	0.511 (1.00)	0.504 (1.00)
1q loss	3 (5%)	58	0.862 (1.00)	0.134 (1.00)	1 (1.00)
2p loss	3 (5%)	58			0.293 (1.00)
2q loss	4 (7%)	57		0.0102 (1.00)	0.615 (1.00)
3p loss	5 (8%)	56	0.721 (1.00)	0.763 (1.00)	0.341 (1.00)
3q loss	3 (5%)	58	0.628 (1.00)	0.237 (1.00)	0.293 (1.00)
4p loss	9 (15%)	52	0.508 (1.00)	0.864 (1.00)	1 (1.00)
4q loss	15 (25%)	46	0.515 (1.00)	0.779 (1.00)	1 (1.00)
5q loss	4 (7%)	57	0.00925 (1.00)	0.953 (1.00)	1 (1.00)
6q loss	10 (16%)	51	0.258 (1.00)	0.944 (1.00)	0.473 (1.00)
7p loss	5 (8%)	56	0.489 (1.00)	0.661 (1.00)	0.0524 (1.00)
7q loss	7 (11%)	54	0.935 (1.00)	0.429 (1.00)	0.24 (1.00)
8p loss	28 (46%)	33	0.439 (1.00)	0.262 (1.00)	0.423 (1.00)
8q loss	5 (8%)	56	0.582 (1.00)	0.855 (1.00)	0.341 (1.00)
9p loss	15 (25%)	46	0.77 (1.00)	0.266 (1.00)	1 (1.00)
9q loss	13 (21%)	48	0.611 (1.00)	0.27 (1.00)	0.753 (1.00)
10p loss	3 (5%)	58	0.7 (1.00)	0.0302 (1.00)	0.547 (1.00)
10q loss	12 (20%)	49	0.514 (1.00)	0.588 (1.00)	0.742 (1.00)
11p loss	5 (8%)	56	0.691 (1.00)	0.279 (1.00)	0.341 (1.00)
11q loss	8 (13%)	53	0.244 (1.00)	0.375 (1.00)	1 (1.00)
12p loss	3 (5%)	58	0.489 (1.00)	0.509 (1.00)	1 (1.00)
13q loss	22 (36%)	39	0.212 (1.00)	0.162 (1.00)	1 (1.00)
14q loss	21 (34%)	40	0.64 (1.00)	0.549 (1.00)	1 (1.00)
15q loss	7 (11%)	54	0.924 (1.00)	0.363 (1.00)	1 (1.00)
16p loss	11 (18%)	50	0.39 (1.00)	0.89 (1.00)	1 (1.00)
16q loss	19 (31%)	42	0.902 (1.00)	0.743 (1.00)	0.571 (1.00)
17p loss	25 (41%)	36	0.245 (1.00)	0.372 (1.00)	0.787 (1.00)
19p loss	4 (7%)	57	0.23 (1.00)	0.474 (1.00)	0.287 (1.00)
21q loss	9 (15%)	52	0.0736 (1.00)	0.76 (1.00)	0.0203 (1.00)
22q loss	9 (15%)	52	0.624 (1.00)	0.696 (1.00)	0.0596 (1.00)

'18p loss mutation analysis' versus 'AGE'

P value = 0.000571 (t-test), Q value = 0.1

Table S1. Gene #57: '18p loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	56	61.8 (14.0)
18P LOSS MUTATED	7	73.6 (6.4)
18P LOSS WILD-TYPE	49	60.1 (14.0)

Figure S1. Get High-res Image Gene #57: '18p loss mutation analysis' versus Clinical Feature #2: 'AGE'

'18q loss mutation analysis' versus 'AGE'

P value = 0.00109 (t-test), Q value = 0.19

Table S2. Gene #58: '18q loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	56	61.8 (14.0)
18Q LOSS MUTATED	9	71.6 (6.9)
18Q LOSS WILD-TYPE	47	60.0 (14.3)

Figure S2. Get High-res Image Gene #58: '18q loss mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = LIHC-TP.clin.merged.picked.txt
Number of patients = 61
Number of significantly arm-level cnvs = 61
Number of selected clinical features = 3
Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R

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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] 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)

[4] 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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