Correlation between copy number variations of arm-level result and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
15 July 2014 | analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1N878JV
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/C1N878JV
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 30 arm-level events and 5 clinical features across 191 patients, 2 significant findings detected with Q value < 0.25.

- 5q loss cnv correlated to 'Time to Death'.

- 18q 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 30 arm-level events and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER | RACE | ETHNICITY
| nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test
5q loss | 6 (3%) | 185 | 0.000704 (0.101) | 0.0519 (1.00) | 0.0325 (1.00) | 0.416 (1.00) | 1 (1.00)
18q loss | 4 (2%) | 187 | 0.000836 (0.119) | 0.0121 (1.00) | 0.127 (1.00) | 1 (1.00) | 1 (1.00)
1p gain | 3 (2%) | 188 |  | 0.115 (1.00) | 0.592 (1.00) | 1 (1.00) | 1 (1.00)
4p gain | 4 (2%) | 187 | 0.583 (1.00) | 0.328 (1.00) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
4q gain | 4 (2%) | 187 | 0.583 (1.00) | 0.328 (1.00) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
8p gain | 22 (12%) | 169 | 0.514 (1.00) | 0.112 (1.00) | 0.0734 (1.00) | 1 (1.00) | 0.313 (1.00)
8q gain | 23 (12%) | 168 | 0.56 (1.00) | 0.167 (1.00) | 0.0723 (1.00) | 1 (1.00) | 0.325 (1.00)
10q gain | 3 (2%) | 188 |  | 0.654 (1.00) | 0.252 (1.00) | 0.233 (1.00) | 1 (1.00)
11p gain | 4 (2%) | 187 |  | 0.173 (1.00) | 1 (1.00) | 1 (1.00) | 0.0628 (1.00)
11q gain | 7 (4%) | 184 | 0.566 (1.00) | 0.047 (1.00) | 1 (1.00) | 1 (1.00) | 0.108 (1.00)
13q gain | 6 (3%) | 185 | 0.826 (1.00) | 0.12 (1.00) | 0.69 (1.00) | 1 (1.00) | 1 (1.00)
17q gain | 3 (2%) | 188 | 0.737 (1.00) | 0.113 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
19p gain | 5 (3%) | 186 | 0.741 (1.00) | 0.715 (1.00) | 0.378 (1.00) | 1 (1.00) | 1 (1.00)
19q gain | 5 (3%) | 186 | 0.741 (1.00) | 0.715 (1.00) | 0.378 (1.00) | 1 (1.00) | 1 (1.00)
21q gain | 8 (4%) | 183 | 0.0186 (1.00) | 0.401 (1.00) | 0.0732 (1.00) | 0.186 (1.00) | 1 (1.00)
22q gain | 9 (5%) | 182 | 0.728 (1.00) | 0.0629 (1.00) | 0.513 (1.00) | 1 (1.00) | 1 (1.00)
xq gain | 3 (2%) | 188 |  | 0.108 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
3p loss | 3 (2%) | 188 |  | 0.0208 (1.00) | 0.252 (1.00) | 1 (1.00) | 1 (1.00)
3q loss | 3 (2%) | 188 |  | 0.0208 (1.00) | 0.252 (1.00) | 1 (1.00) | 1 (1.00)
7p loss | 17 (9%) | 174 | 0.0328 (1.00) | 0.458 (1.00) | 0.802 (1.00) | 1 (1.00) | 1 (1.00)
7q loss | 20 (10%) | 171 | 0.015 (1.00) | 0.513 (1.00) | 1 (1.00) | 0.718 (1.00) | 1 (1.00)
12p loss | 4 (2%) | 187 |  | 0.869 (1.00) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
15q loss | 4 (2%) | 187 | 0.679 (1.00) | 0.677 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
16q loss | 4 (2%) | 187 | 0.0589 (1.00) | 0.388 (1.00) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
17p loss | 13 (7%) | 178 | 0.109 (1.00) | 0.765 (1.00) | 0.147 (1.00) | 0.655 (1.00) | 1 (1.00)
17q loss | 7 (4%) | 184 | 0.403 (1.00) | 0.196 (1.00) | 0.458 (1.00) | 1 (1.00) | 1 (1.00)
18p loss | 5 (3%) | 186 | 0.00203 (0.287) | 0.367 (1.00) | 0.0642 (1.00) | 0.362 (1.00) | 1 (1.00)
19p loss | 4 (2%) | 187 | 0.0991 (1.00) | 0.0472 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
19q loss | 4 (2%) | 187 | 0.0991 (1.00) | 0.0472 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
xq loss | 5 (3%) | 186 | 0.181 (1.00) | 0.0283 (1.00) | 0.0642 (1.00) | 1 (1.00) | 1 (1.00)

'5q loss' versus 'Time to Death'

P value = 0.000704 (logrank test), Q value = 0.1

Table S1. Gene #18: '5q loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
5Q LOSS MUTATED | 6 | 6 | 1.0 - 12.0 (7.0)
5Q LOSS WILD-TYPE | 162 | 100 | 0.9 - 94.1 (12.5)

Figure S1. Get High-res Image Gene #18: '5q loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'Time to Death'

P value = 0.000836 (logrank test), Q value = 0.12

Table S2. Gene #27: '18q loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
18Q LOSS MUTATED | 4 | 4 | 1.0 - 10.0 (4.5)
18Q LOSS WILD-TYPE | 164 | 102 | 0.9 - 94.1 (12.0)

Figure S2. Get High-res Image Gene #27: '18q loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

- Clinical data file = LAML-TB.merged_data.txt

- Number of patients = 191

- Number of significantly arm-level cnvs = 30

- 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

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] 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)
[3] 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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