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 5 clinical features across 166 patients, 4 significant findings detected with Q value < 0.25.

4p gain cnv correlated to 'Time to Death'.
4q gain cnv correlated to 'Time to Death'.
11p loss cnv correlated to 'Time to Death'.
11q loss cnv correlated to 'Time to Death'.

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 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	NEOADJUVANT THERAPY
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test
4p gain	3 (2%)	163	0.00014 (0.0193)	0.122 (1.00)	1 (1.00)	0.756 (1.00)	1 (1.00)
4q gain	3 (2%)	163	0.00014 (0.0193)	0.122 (1.00)	1 (1.00)	0.756 (1.00)	1 (1.00)
11p loss	3 (2%)	163	4.32e-08 (6.05e-06)	0.102 (1.00)	0.193 (1.00)	0.756 (1.00)	1 (1.00)
11q loss	4 (2%)	162	4.32e-08 (6.05e-06)	0.0592 (1.00)	0.0691 (1.00)	0.307 (1.00)	1 (1.00)
1q gain	5 (3%)	161	0.855 (1.00)	0.193 (1.00)	0.622 (1.00)	0.323 (1.00)	1 (1.00)
5p gain	6 (4%)	160	0.00937 (1.00)	0.0846 (1.00)	1 (1.00)	0.339 (1.00)	1 (1.00)
5q gain	6 (4%)	160	0.00937 (1.00)	0.0846 (1.00)	1 (1.00)	0.339 (1.00)	1 (1.00)
7p gain	6 (4%)	160	0.743 (1.00)	0.111 (1.00)	1 (1.00)	0.339 (1.00)	1 (1.00)
7q gain	8 (5%)	158	0.743 (1.00)	0.0596 (1.00)	0.443 (1.00)	0.117 (1.00)	1 (1.00)
12p gain	6 (4%)	160	0.743 (1.00)	0.403 (1.00)	1 (1.00)	0.339 (1.00)	1 (1.00)
12q gain	6 (4%)	160	0.743 (1.00)	0.403 (1.00)	1 (1.00)	0.339 (1.00)	1 (1.00)
14q gain	3 (2%)	163	0.793 (1.00)	0.629 (1.00)	0.559 (1.00)	0.756 (1.00)	1 (1.00)
16p gain	6 (4%)	160	0.793 (1.00)	0.568 (1.00)	0.185 (1.00)	0.595 (1.00)	1 (1.00)
16q gain	4 (2%)	162	0.793 (1.00)	0.402 (1.00)	0.578 (1.00)	0.832 (1.00)	1 (1.00)
17p gain	6 (4%)	160	0.743 (1.00)	0.555 (1.00)	0.185 (1.00)	0.736 (1.00)	1 (1.00)
17q gain	7 (4%)	159	0.743 (1.00)	0.773 (1.00)	0.193 (1.00)	0.848 (1.00)	1 (1.00)
20p gain	4 (2%)	162	0.743 (1.00)	0.43 (1.00)	0.578 (1.00)	0.252 (1.00)	1 (1.00)
20q gain	3 (2%)	163	0.743 (1.00)	0.6 (1.00)	0.559 (1.00)	0.756 (1.00)	1 (1.00)
2p loss	5 (3%)	161	1 (1.00)	0.405 (1.00)	1 (1.00)	0.581 (1.00)	1 (1.00)
2q loss	4 (2%)	162	1 (1.00)	0.0928 (1.00)	1 (1.00)	0.307 (1.00)	1 (1.00)
9q loss	4 (2%)	162	1 (1.00)	0.08 (1.00)	1 (1.00)	0.502 (1.00)	1 (1.00)
10q loss	3 (2%)	163	0.855 (1.00)	0.388 (1.00)	0.193 (1.00)	0.129 (1.00)	1 (1.00)
13q loss	7 (4%)	159	0.00225 (0.306)	0.517 (1.00)	0.405 (1.00)	0.168 (1.00)	1 (1.00)
17p loss	3 (2%)	163	0.793 (1.00)	0.871 (1.00)	0.559 (1.00)	0.756 (1.00)	1 (1.00)
18p loss	3 (2%)	163	1 (1.00)	0.927 (1.00)	0.559 (1.00)	1 (1.00)	1 (1.00)
18q loss	3 (2%)	163	1 (1.00)	0.927 (1.00)	0.559 (1.00)	1 (1.00)	1 (1.00)
21q loss	3 (2%)	163	1 (1.00)	0.0278 (1.00)	0.193 (1.00)	1 (1.00)	1 (1.00)
22q loss	25 (15%)	141	0.855 (1.00)	0.949 (1.00)	1 (1.00)	0.293 (1.00)	1 (1.00)

'4p gain mutation analysis' versus 'Time to Death'

P value = 0.00014 (logrank test), Q value = 0.019

Table S1. Gene #2: '4p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	166	1	0.1 - 66.1 (8.1)
4P GAIN MUTATED	3	1	6.9 - 39.8 (30.7)
4P GAIN WILD-TYPE	163	0	0.1 - 66.1 (8.1)

Figure S1. Get High-res Image Gene #2: '4p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'4q gain mutation analysis' versus 'Time to Death'

P value = 0.00014 (logrank test), Q value = 0.019

Table S2. Gene #3: '4q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	166	1	0.1 - 66.1 (8.1)
4Q GAIN MUTATED	3	1	6.9 - 39.8 (30.7)
4Q GAIN WILD-TYPE	163	0	0.1 - 66.1 (8.1)

Figure S2. Get High-res Image Gene #3: '4q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'11p loss mutation analysis' versus 'Time to Death'

P value = 4.32e-08 (logrank test), Q value = 6e-06

Table S3. Gene #21: '11p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	166	1	0.1 - 66.1 (8.1)
11P LOSS MUTATED	3	1	0.4 - 30.7 (6.9)
11P LOSS WILD-TYPE	163	0	0.1 - 66.1 (8.1)

Figure S3. Get High-res Image Gene #21: '11p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'11q loss mutation analysis' versus 'Time to Death'

P value = 4.32e-08 (logrank test), Q value = 6e-06

Table S4. Gene #22: '11q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	166	1	0.1 - 66.1 (8.1)
11Q LOSS MUTATED	4	1	0.4 - 30.7 (9.3)
11Q LOSS WILD-TYPE	162	0	0.1 - 66.1 (8.1)

Figure S4. Get High-res Image Gene #22: '11q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = THCA.clin.merged.picked.txt
Number of patients = 166
Number of significantly arm-level cnvs = 28
Number of selected clinical features = 5
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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