Correlation between copy number variations of arm-level result and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Liver Hepatocellular Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C14X55SW

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 65 arm-level results and 4 clinical features across 72 patients, no significant finding detected with Q value < 0.25.

No arm-level cnvs related to clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 65 arm-level results and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	Time to Death	AGE	GENDER	COMPLETENESS OF RESECTION
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test
1p gain	0 (0%)	64	0.507 (1.00)	0.0942 (1.00)	1 (1.00)	0.0654 (1.00)
1q gain	0 (0%)	33	0.995 (1.00)	0.886 (1.00)	0.225 (1.00)	0.956 (1.00)
2p gain	0 (0%)	64	0.349 (1.00)	0.077 (1.00)	0.116 (1.00)	0.147 (1.00)
2q gain	0 (0%)	65	0.483 (1.00)	0.163 (1.00)	0.045 (1.00)	0.437 (1.00)
3p gain	0 (0%)	69	0.87 (1.00)	0.0359 (1.00)	1 (1.00)	0.591 (1.00)
3q gain	0 (0%)	69	0.87 (1.00)	0.0359 (1.00)	1 (1.00)	0.591 (1.00)
4p gain	0 (0%)	66	0.786 (1.00)	0.117 (1.00)	0.412 (1.00)	1 (1.00)
5p gain	0 (0%)	50	0.387 (1.00)	0.236 (1.00)	0.794 (1.00)	0.821 (1.00)
5q gain	0 (0%)	57	0.442 (1.00)	0.12 (1.00)	1 (1.00)	0.692 (1.00)
6p gain	0 (0%)	59	0.127 (1.00)	0.84 (1.00)	0.521 (1.00)	0.0566 (1.00)
6q gain	0 (0%)	63	0.0769 (1.00)	0.298 (1.00)	0.482 (1.00)	0.0352 (1.00)
7p gain	0 (0%)	53	0.772 (1.00)	0.774 (1.00)	0.575 (1.00)	0.793 (1.00)
7q gain	0 (0%)	52	0.415 (1.00)	0.693 (1.00)	0.589 (1.00)	0.793 (1.00)
8p gain	0 (0%)	61	0.268 (1.00)	0.758 (1.00)	0.309 (1.00)	0.088 (1.00)
8q gain	0 (0%)	38	0.614 (1.00)	0.528 (1.00)	0.46 (1.00)	0.839 (1.00)
9p gain	0 (0%)	69		0.935 (1.00)	0.275 (1.00)	0.591 (1.00)
9q gain	0 (0%)	69		0.935 (1.00)	0.275 (1.00)	0.591 (1.00)
10p gain	0 (0%)	66	0.0729 (1.00)	0.864 (1.00)	0.412 (1.00)	0.501 (1.00)
12q gain	0 (0%)	69		0.999 (1.00)	0.275 (1.00)	0.591 (1.00)
15q gain	0 (0%)	67	0.511 (1.00)	0.263 (1.00)	0.334 (1.00)	0.781 (1.00)
16p gain	0 (0%)	69	0.00316 (0.8)	0.123 (1.00)	1 (1.00)	1 (1.00)
17p gain	0 (0%)	69	0.288 (1.00)	0.643 (1.00)	0.275 (1.00)	0.591 (1.00)
17q gain	0 (0%)	55	0.112 (1.00)	0.524 (1.00)	0.568 (1.00)	0.793 (1.00)
18p gain	0 (0%)	69	0.87 (1.00)	0.172 (1.00)	1 (1.00)	0.591 (1.00)
18q gain	0 (0%)	68	0.522 (1.00)	0.45 (1.00)	1 (1.00)	0.7 (1.00)
19p gain	0 (0%)	67	0.431 (1.00)	0.649 (1.00)	0.0463 (1.00)	1 (1.00)
19q gain	0 (0%)	65	0.572 (1.00)	0.888 (1.00)	0.045 (1.00)	0.552 (1.00)
20p gain	0 (0%)	59	0.0785 (1.00)	0.235 (1.00)	0.353 (1.00)	0.0886 (1.00)
20q gain	0 (0%)	58	0.139 (1.00)	0.173 (1.00)	0.539 (1.00)	0.0284 (1.00)
21q gain	0 (0%)	68	0.415 (1.00)	0.631 (1.00)	0.117 (1.00)	1 (1.00)
22q gain	0 (0%)	64	0.134 (1.00)	0.318 (1.00)	0.436 (1.00)	0.844 (1.00)
Xq gain	0 (0%)	68	0.123 (1.00)	0.0956 (1.00)	1 (1.00)	0.7 (1.00)
1p loss	0 (0%)	58	0.888 (1.00)	0.761 (1.00)	0.539 (1.00)	1 (1.00)
1q loss	0 (0%)	67	0.851 (1.00)	0.514 (1.00)	1 (1.00)	1 (1.00)
2p loss	0 (0%)	69			0.275 (1.00)	0.591 (1.00)
2q loss	0 (0%)	68		0.00352 (0.886)	0.606 (1.00)	0.188 (1.00)
3p loss	0 (0%)	65	0.866 (1.00)	0.455 (1.00)	0.688 (1.00)	0.844 (1.00)
3q loss	0 (0%)	69	0.674 (1.00)	0.314 (1.00)	0.275 (1.00)	1 (1.00)
4p loss	0 (0%)	63	0.257 (1.00)	0.967 (1.00)	1 (1.00)	1 (1.00)
4q loss	0 (0%)	56	0.504 (1.00)	0.83 (1.00)	1 (1.00)	0.915 (1.00)
5q loss	0 (0%)	68	0.00285 (0.724)	0.952 (1.00)	1 (1.00)	0.29 (1.00)
6q loss	0 (0%)	60	0.428 (1.00)	0.712 (1.00)	0.741 (1.00)	0.39 (1.00)
7p loss	0 (0%)	67	0.523 (1.00)	0.768 (1.00)	0.0463 (1.00)	0.781 (1.00)
7q loss	0 (0%)	65	0.656 (1.00)	0.538 (1.00)	0.227 (1.00)	0.3 (1.00)
8p loss	0 (0%)	42	0.252 (1.00)	0.0495 (1.00)	0.218 (1.00)	0.949 (1.00)
8q loss	0 (0%)	67	0.394 (1.00)	0.721 (1.00)	0.334 (1.00)	0.399 (1.00)
9p loss	0 (0%)	55	0.937 (1.00)	0.742 (1.00)	0.773 (1.00)	0.519 (1.00)
9q loss	0 (0%)	57	0.646 (1.00)	0.829 (1.00)	0.553 (1.00)	0.573 (1.00)
10p loss	0 (0%)	69	0.574 (1.00)	0.026 (1.00)	0.547 (1.00)	0.182 (1.00)
10q loss	0 (0%)	59	0.237 (1.00)	0.578 (1.00)	0.757 (1.00)	0.33 (1.00)
11p loss	0 (0%)	66	0.643 (1.00)	0.276 (1.00)	0.412 (1.00)	1 (1.00)
11q loss	0 (0%)	64	0.618 (1.00)	0.33 (1.00)	1 (1.00)	0.03 (1.00)
12p loss	0 (0%)	68	0.323 (1.00)	0.174 (1.00)	0.606 (1.00)	0.7 (1.00)
13q loss	0 (0%)	48	0.397 (1.00)	0.138 (1.00)	1 (1.00)	0.203 (1.00)
14q loss	0 (0%)	48	0.563 (1.00)	0.662 (1.00)	0.795 (1.00)	0.706 (1.00)
15q loss	0 (0%)	64	0.803 (1.00)	0.414 (1.00)	0.705 (1.00)	0.844 (1.00)
16p loss	0 (0%)	57	0.84 (1.00)	0.631 (1.00)	1 (1.00)	0.00976 (1.00)
16q loss	0 (0%)	49	0.891 (1.00)	0.415 (1.00)	0.606 (1.00)	0.17 (1.00)
17p loss	0 (0%)	42	0.247 (1.00)	0.321 (1.00)	1 (1.00)	0.406 (1.00)
17q loss	0 (0%)	69	0.818 (1.00)	0.0104 (1.00)	0.275 (1.00)	1 (1.00)
18p loss	0 (0%)	64	0.00678 (1.00)	0.124 (1.00)	1 (1.00)	1 (1.00)
18q loss	0 (0%)	62	0.0875 (1.00)	0.0802 (1.00)	0.73 (1.00)	1 (1.00)
19p loss	0 (0%)	67	0.33 (1.00)	0.305 (1.00)	0.156 (1.00)	0.269 (1.00)
21q loss	0 (0%)	62	0.0284 (1.00)	0.9 (1.00)	0.012 (1.00)	0.3 (1.00)
22q loss	0 (0%)	63	0.484 (1.00)	0.591 (1.00)	0.0565 (1.00)	0.0521 (1.00)

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 = 72
Number of significantly arm-level cnvs = 65
Number of selected clinical features = 4
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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