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 29 arm-level results and 6 clinical features across 206 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 29 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 | 6 (3%) | 200 | 1 (1.00) | 0.422 (1.00) | 0.649 (1.00) | 0.264 (1.00) | 0.348 (1.00) | 1 (1.00)
4p gain | 4 (2%) | 202 | 0.0143 (1.00) | 0.118 (1.00) | 1 (1.00) | 0.793 (1.00) | 0.247 (1.00) | 1 (1.00)
4q gain | 4 (2%) | 202 | 0.0143 (1.00) | 0.118 (1.00) | 1 (1.00) | 0.793 (1.00) | 0.247 (1.00) | 1 (1.00)
5p gain | 7 (3%) | 199 | 0.0143 (1.00) | 0.0757 (1.00) | 1 (1.00) | 0.51 (1.00) | 0.394 (1.00) | 1 (1.00)
5q gain | 7 (3%) | 199 | 0.0143 (1.00) | 0.0757 (1.00) | 1 (1.00) | 0.51 (1.00) | 0.394 (1.00) | 1 (1.00)
7p gain | 9 (4%) | 197 | 1 (1.00) | 0.0797 (1.00) | 1 (1.00) | 0.393 (1.00) | 1 (1.00) | 1 (1.00)
7q gain | 11 (5%) | 195 | 1 (1.00) | 0.0451 (1.00) | 0.732 (1.00) | 0.136 (1.00) | 1 (1.00) | 1 (1.00)
12p gain | 7 (3%) | 199 | 1 (1.00) | 0.356 (1.00) | 0.68 (1.00) | 0.51 (1.00) | 1 (1.00) | 1 (1.00)
12q gain | 7 (3%) | 199 | 1 (1.00) | 0.356 (1.00) | 0.68 (1.00) | 0.51 (1.00) | 1 (1.00) | 1 (1.00)
14q gain | 4 (2%) | 202 | 1 (1.00) | 0.545 (1.00) | 0.574 (1.00) | 0.793 (1.00) | 1 (1.00) | 1 (1.00)
16p gain | 7 (3%) | 199 | 1 (1.00) | 0.501 (1.00) | 0.194 (1.00) | 0.427 (1.00) | 1 (1.00) | 1 (1.00)
16q gain | 5 (2%) | 201 | 1 (1.00) | 0.34 (1.00) | 0.331 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
17p gain | 6 (3%) | 200 | 1 (1.00) | 0.536 (1.00) | 0.342 (1.00) | 0.582 (1.00) | 1 (1.00) | 1 (1.00)
17q gain | 7 (3%) | 199 | 1 (1.00) | 0.745 (1.00) | 0.194 (1.00) | 0.802 (1.00) | 1 (1.00) | 1 (1.00)
19q gain | 3 (1%) | 203 | 0.0143 (1.00) | 0.0175 (1.00) | 1 (1.00) | 0.55 (1.00) | 0.191 (1.00) | 1 (1.00)
20p gain | 3 (1%) | 203 | 1 (1.00) | 0.587 (1.00) | 0.571 (1.00) | 0.55 (1.00) | 1 (1.00) | 1 (1.00)
20q gain | 3 (1%) | 203 | 1 (1.00) | 0.587 (1.00) | 0.571 (1.00) | 0.55 (1.00) | 1 (1.00) | 1 (1.00)
2p loss | 6 (3%) | 200 | 1 (1.00) | 0.194 (1.00) | 1 (1.00) | 0.367 (1.00) | 1 (1.00) | 1 (1.00)
2q loss | 5 (2%) | 201 | 1 (1.00) | 0.0266 (1.00) | 1 (1.00) | 0.157 (1.00) | 1 (1.00) | 1 (1.00)
3q loss | 3 (1%) | 203 | 1 (1.00) | 0.202 (1.00) | 1 (1.00) | 0.0914 (1.00) | 1 (1.00) | 1 (1.00)
9q loss | 4 (2%) | 202 | 1 (1.00) | 0.0782 (1.00) | 1 (1.00) | 0.218 (1.00) | 0.247 (1.00) | 1 (1.00)
11p loss | 4 (2%) | 202 | 0.0143 (1.00) | 0.0299 (1.00) | 0.273 (1.00) | 0.275 (1.00) | 0.247 (1.00) | 1 (1.00)
11q loss | 5 (2%) | 201 | 0.0143 (1.00) | 0.0178 (1.00) | 0.109 (1.00) | 0.157 (1.00) | 0.299 (1.00) | 1 (1.00)
13q loss | 7 (3%) | 199 | 0.0143 (1.00) | 0.131 (1.00) | 0.377 (1.00) | 0.026 (1.00) | 0.394 (1.00) | 1 (1.00)
17p loss | 3 (1%) | 203 | 1 (1.00) | 0.846 (1.00) | 0.571 (1.00) | 0.55 (1.00) | 0.0124 (1.00) | 1 (1.00)
18p loss | 3 (1%) | 203 | 1 (1.00) | 0.955 (1.00) | 0.571 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
18q loss | 3 (1%) | 203 | 1 (1.00) | 0.955 (1.00) | 0.571 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
21q loss | 4 (2%) | 202 | 1 (1.00) | 0.00416 (0.724) | 0.273 (1.00) | 0.793 (1.00) | 1 (1.00) | 1 (1.00)
22q loss | 30 (15%) | 176 | 1 (1.00) | 0.625 (1.00) | 0.652 (1.00) | 0.0622 (1.00) | 0.23 (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 = 206

- Number of significantly arm-level cnvs = 29

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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