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

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 28 arm-level results and 6 clinical features across 179 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 28 arm-level results and 6 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	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
1q gain	5 (3%)	174	1 (1.00)	0.242 (1.00)	0.624 (1.00)	0.371 (1.00)	0.338 (1.00)	1 (1.00)
4p gain	4 (2%)	175	0.0143 (1.00)	0.118 (1.00)	1 (1.00)	0.798 (1.00)	0.28 (1.00)	1 (1.00)
4q gain	4 (2%)	175	0.0143 (1.00)	0.118 (1.00)	1 (1.00)	0.798 (1.00)	0.28 (1.00)	1 (1.00)
5p gain	7 (4%)	172	0.0143 (1.00)	0.0758 (1.00)	1 (1.00)	0.516 (1.00)	0.44 (1.00)	1 (1.00)
5q gain	7 (4%)	172	0.0143 (1.00)	0.0758 (1.00)	1 (1.00)	0.516 (1.00)	0.44 (1.00)	1 (1.00)
7p gain	8 (4%)	171	1 (1.00)	0.164 (1.00)	1 (1.00)	0.683 (1.00)	1 (1.00)	1 (1.00)
7q gain	10 (6%)	169	1 (1.00)	0.0967 (1.00)	0.727 (1.00)	0.21 (1.00)	1 (1.00)	1 (1.00)
12p gain	7 (4%)	172	1 (1.00)	0.36 (1.00)	0.675 (1.00)	0.516 (1.00)	1 (1.00)	1 (1.00)
12q gain	7 (4%)	172	1 (1.00)	0.36 (1.00)	0.675 (1.00)	0.516 (1.00)	1 (1.00)	1 (1.00)
14q gain	4 (2%)	175	1 (1.00)	0.55 (1.00)	0.579 (1.00)	0.798 (1.00)	1 (1.00)	1 (1.00)
16p gain	7 (4%)	172	1 (1.00)	0.509 (1.00)	0.194 (1.00)	0.429 (1.00)	1 (1.00)	1 (1.00)
16q gain	5 (3%)	174	1 (1.00)	0.344 (1.00)	0.323 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
17p gain	6 (3%)	173	1 (1.00)	0.54 (1.00)	0.185 (1.00)	0.77 (1.00)	1 (1.00)	1 (1.00)
17q gain	7 (4%)	172	1 (1.00)	0.751 (1.00)	0.194 (1.00)	0.805 (1.00)	1 (1.00)	1 (1.00)
19q gain	3 (2%)	176	0.0143 (1.00)	0.017 (1.00)	1 (1.00)	0.56 (1.00)	0.218 (1.00)	1 (1.00)
20p gain	3 (2%)	176	1 (1.00)	0.59 (1.00)	0.559 (1.00)	0.56 (1.00)	1 (1.00)	1 (1.00)
20q gain	3 (2%)	176	1 (1.00)	0.59 (1.00)	0.559 (1.00)	0.56 (1.00)	1 (1.00)	1 (1.00)
2p loss	5 (3%)	174	1 (1.00)	0.389 (1.00)	1 (1.00)	0.504 (1.00)	1 (1.00)	1 (1.00)
2q loss	4 (2%)	175	1 (1.00)	0.0894 (1.00)	1 (1.00)	0.283 (1.00)	1 (1.00)	1 (1.00)
9q loss	4 (2%)	175	1 (1.00)	0.0782 (1.00)	1 (1.00)	0.221 (1.00)	0.28 (1.00)	1 (1.00)
11p loss	3 (2%)	176	0.0143 (1.00)	0.101 (1.00)	0.196 (1.00)	0.56 (1.00)	0.218 (1.00)	1 (1.00)
11q loss	4 (2%)	175	0.0143 (1.00)	0.0581 (1.00)	0.0705 (1.00)	0.283 (1.00)	0.28 (1.00)	1 (1.00)
13q loss	6 (3%)	173	0.0143 (1.00)	0.241 (1.00)	0.355 (1.00)	0.0308 (1.00)	0.391 (1.00)	1 (1.00)
17p loss	3 (2%)	176	1 (1.00)	0.852 (1.00)	0.559 (1.00)	0.56 (1.00)	0.0164 (1.00)	1 (1.00)
18p loss	3 (2%)	176	1 (1.00)	0.948 (1.00)	0.559 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
18q loss	3 (2%)	176	1 (1.00)	0.948 (1.00)	0.559 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
21q loss	3 (2%)	176	1 (1.00)	0.0283 (1.00)	0.196 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
22q loss	28 (16%)	151	1 (1.00)	0.751 (1.00)	0.653 (1.00)	0.0969 (1.00)	0.131 (1.00)	1 (1.00)

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = THCA.clin.merged.picked.txt
Number of patients = 179
Number of significantly arm-level cnvs = 28
Number of selected clinical features = 6
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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