Correlation between copy number variations of arm-level result and selected clinical features
Adrenocortical Carcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C19W0CVK
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19W0CVK
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 34 arm-level events and 5 clinical features across 10 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 34 arm-level events and 5 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 | NEOPLASM DISEASESTAGE | PATHOLOGY T STAGE | GENDER
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | NULL | Fisher's exact test
4p gain | 5 (50%) | 5 | 0.0784 (1.00) | 0.971 (1.00) | 0.524 (1.00) |  | 0.206 (1.00)
4q gain | 5 (50%) | 5 | 0.0784 (1.00) | 0.971 (1.00) | 0.524 (1.00) |  | 0.206 (1.00)
5p gain | 5 (50%) | 5 | 0.0784 (1.00) | 0.971 (1.00) | 0.524 (1.00) |  | 0.206 (1.00)
5q gain | 4 (40%) | 6 | 0.514 (1.00) | 0.994 (1.00) | 0.714 (1.00) |  | 0.524 (1.00)
7p gain | 3 (30%) | 7 | 0.163 (1.00) | 0.136 (1.00) | 0.714 (1.00) |  | 1 (1.00)
7q gain | 3 (30%) | 7 | 0.163 (1.00) | 0.136 (1.00) | 0.714 (1.00) |  | 1 (1.00)
8p gain | 4 (40%) | 6 | 0.28 (1.00) | 0.734 (1.00) | 1 (1.00) |  | 1 (1.00)
8q gain | 5 (50%) | 5 | 0.28 (1.00) | 0.658 (1.00) | 1 (1.00) |  | 1 (1.00)
10q gain | 3 (30%) | 7 | 0.596 (1.00) | 0.456 (1.00) |  |  | 1 (1.00)
12p gain | 6 (60%) | 4 | 0.378 (1.00) | 0.487 (1.00) | 0.381 (1.00) |  | 0.524 (1.00)
12q gain | 5 (50%) | 5 | 0.0784 (1.00) | 0.971 (1.00) | 0.524 (1.00) |  | 0.206 (1.00)
16p gain | 5 (50%) | 5 | 0.0784 (1.00) | 0.971 (1.00) | 0.524 (1.00) |  | 0.206 (1.00)
16q gain | 5 (50%) | 5 | 0.0784 (1.00) | 0.971 (1.00) | 0.524 (1.00) |  | 0.206 (1.00)
19p gain | 7 (70%) | 3 | 0.87 (1.00) | 0.956 (1.00) | 1 (1.00) |  | 1 (1.00)
19q gain | 6 (60%) | 4 | 0.467 (1.00) | 0.507 (1.00) | 1 (1.00) |  | 0.524 (1.00)
20p gain | 3 (30%) | 7 | 0.175 (1.00) | 0.191 (1.00) |  |  | 0.167 (1.00)
20q gain | 5 (50%) | 5 | 0.0784 (1.00) | 0.971 (1.00) | 0.524 (1.00) |  | 0.206 (1.00)
21q gain | 5 (50%) | 5 | 0.799 (1.00) | 0.528 (1.00) | 1 (1.00) |  | 1 (1.00)
1p loss | 5 (50%) | 5 | 0.799 (1.00) | 0.287 (1.00) | 1 (1.00) |  | 1 (1.00)
1q loss | 4 (40%) | 6 | 0.899 (1.00) | 0.113 (1.00) | 0.714 (1.00) |  | 1 (1.00)
3p loss | 3 (30%) | 7 | 0.0793 (1.00) | 0.423 (1.00) |  |  | 1 (1.00)
8p loss | 3 (30%) | 7 | 0.87 (1.00) | 0.0886 (1.00) | 0.5 (1.00) |  | 1 (1.00)
9p loss | 3 (30%) | 7 | 0.596 (1.00) | 0.561 (1.00) |  |  | 1 (1.00)
9q loss | 3 (30%) | 7 | 0.596 (1.00) | 0.561 (1.00) |  |  | 1 (1.00)
11p loss | 5 (50%) | 5 | 0.899 (1.00) | 0.769 (1.00) | 0.381 (1.00) |  | 1 (1.00)
11q loss | 5 (50%) | 5 | 0.899 (1.00) | 0.769 (1.00) | 0.381 (1.00) |  | 1 (1.00)
13q loss | 5 (50%) | 5 | 0.381 (1.00) | 0.883 (1.00) | 1 (1.00) |  | 1 (1.00)
15q loss | 4 (40%) | 6 | 0.28 (1.00) | 0.587 (1.00) | 1 (1.00) |  | 0.524 (1.00)
17p loss | 5 (50%) | 5 | 0.043 (1.00) | 0.797 (1.00) | 0.19 (1.00) |  | 1 (1.00)
17q loss | 3 (30%) | 7 | 0.497 (1.00) | 0.399 (1.00) |  |  | 1 (1.00)
18p loss | 4 (40%) | 6 | 0.987 (1.00) | 0.617 (1.00) | 0.714 (1.00) |  | 0.524 (1.00)
18q loss | 4 (40%) | 6 | 0.987 (1.00) | 0.617 (1.00) | 0.714 (1.00) |  | 0.524 (1.00)
22q loss | 6 (60%) | 4 | 0.994 (1.00) | 0.872 (1.00) | 1 (1.00) |  | 0.524 (1.00)
xq loss | 3 (30%) | 7 | 0.149 (1.00) | 0.0324 (1.00) | 0.143 (1.00) |  | 1 (1.00)

Methods & Data
Input

- Copy number data file = transformed.cor.cli.txt

- Clinical data file = ACC-TP.merged_data.txt

- Number of patients = 10

- Number of significantly arm-level cnvs = 34

- Number of selected clinical features = 5

- Exclude regions 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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- 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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