Correlation between copy number variations of arm-level result and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1VX0F49
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/C1VX0F49
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 3 clinical features across 191 patients, 6 significant findings detected with Q value < 0.25.

- 3p loss cnv correlated to 'AGE'.

- 3q loss cnv correlated to 'AGE'.

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

- 18p loss cnv correlated to 'Time to Death'.

- 18q loss cnv correlated to 'Time to Death' and 'AGE'.

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 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 6 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test
18q loss | 4 (2%) | 187 | 0.000836 (0.0686) | 0.003 (0.234) | 0.127 (1.00)
3p loss | 3 (2%) | 188 |  | 0.00241 (0.193) | 0.252 (1.00)
3q loss | 3 (2%) | 188 |  | 0.00241 (0.193) | 0.252 (1.00)
5q loss | 6 (3%) | 185 | 0.000704 (0.0584) | 0.0603 (1.00) | 0.0325 (1.00)
18p loss | 5 (3%) | 186 | 0.00203 (0.165) | 0.355 (1.00) | 0.0642 (1.00)
1p gain | 3 (2%) | 188 |  | 0.0914 (1.00) | 0.592 (1.00)
4p gain | 4 (2%) | 187 | 0.583 (1.00) | 0.425 (1.00) | 0.627 (1.00)
4q gain | 4 (2%) | 187 | 0.583 (1.00) | 0.425 (1.00) | 0.627 (1.00)
8p gain | 22 (12%) | 169 | 0.514 (1.00) | 0.0726 (1.00) | 0.0734 (1.00)
8q gain | 23 (12%) | 168 | 0.56 (1.00) | 0.107 (1.00) | 0.0723 (1.00)
10q gain | 3 (2%) | 188 |  | 0.947 (1.00) | 0.252 (1.00)
11p gain | 4 (2%) | 187 |  | 0.189 (1.00) | 1 (1.00)
11q gain | 7 (4%) | 184 | 0.566 (1.00) | 0.0155 (1.00) | 1 (1.00)
13q gain | 6 (3%) | 185 | 0.826 (1.00) | 0.144 (1.00) | 0.69 (1.00)
17q gain | 3 (2%) | 188 | 0.737 (1.00) | 0.29 (1.00) | 1 (1.00)
19p gain | 5 (3%) | 186 | 0.741 (1.00) | 0.701 (1.00) | 0.378 (1.00)
19q gain | 5 (3%) | 186 | 0.741 (1.00) | 0.701 (1.00) | 0.378 (1.00)
21q gain | 8 (4%) | 183 | 0.0186 (1.00) | 0.617 (1.00) | 0.0732 (1.00)
22q gain | 9 (5%) | 182 | 0.728 (1.00) | 0.034 (1.00) | 0.513 (1.00)
xq gain | 3 (2%) | 188 |  | 0.0896 (1.00) | 1 (1.00)
7p loss | 17 (9%) | 174 | 0.0328 (1.00) | 0.172 (1.00) | 0.802 (1.00)
7q loss | 20 (10%) | 171 | 0.015 (1.00) | 0.182 (1.00) | 1 (1.00)
12p loss | 4 (2%) | 187 |  | 0.751 (1.00) | 0.627 (1.00)
15q loss | 4 (2%) | 187 | 0.679 (1.00) | 0.278 (1.00) | 1 (1.00)
16q loss | 4 (2%) | 187 | 0.0589 (1.00) | 0.365 (1.00) | 0.627 (1.00)
17p loss | 13 (7%) | 178 | 0.109 (1.00) | 0.785 (1.00) | 0.147 (1.00)
17q loss | 7 (4%) | 184 | 0.403 (1.00) | 0.165 (1.00) | 0.458 (1.00)
19p loss | 4 (2%) | 187 | 0.0991 (1.00) | 0.0522 (1.00) | 1 (1.00)
19q loss | 4 (2%) | 187 | 0.0991 (1.00) | 0.0522 (1.00) | 1 (1.00)
xq loss | 5 (3%) | 186 | 0.181 (1.00) | 0.0197 (1.00) | 0.0642 (1.00)

'3p loss' versus 'AGE'

P value = 0.00241 (t-test), Q value = 0.19

Table S1. Gene #16: '3p loss' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
3P LOSS MUTATED | 3 | 74.0 (3.6)
3P LOSS WILD-TYPE | 188 | 54.9 (16.0)

Figure S1. Get High-res Image Gene #16: '3p loss' versus Clinical Feature #2: 'AGE'

'3q loss' versus 'AGE'

P value = 0.00241 (t-test), Q value = 0.19

Table S2. Gene #17: '3q loss' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
3Q LOSS MUTATED | 3 | 74.0 (3.6)
3Q LOSS WILD-TYPE | 188 | 54.9 (16.0)

Figure S2. Get High-res Image Gene #17: '3q loss' versus Clinical Feature #2: 'AGE'

'5q loss' versus 'Time to Death'

P value = 0.000704 (logrank test), Q value = 0.058

Table S3. 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 S3. Get High-res Image Gene #18: '5q loss' versus Clinical Feature #1: 'Time to Death'

'18p loss' versus 'Time to Death'

P value = 0.00203 (logrank test), Q value = 0.16

Table S4. Gene #26: '18p loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
18P LOSS MUTATED | 5 | 5 | 1.0 - 12.0 (7.0)
18P LOSS WILD-TYPE | 163 | 101 | 0.9 - 94.1 (12.0)

Figure S4. Get High-res Image Gene #26: '18p loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'Time to Death'

P value = 0.000836 (logrank test), Q value = 0.069

Table S5. 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 S5. Get High-res Image Gene #27: '18q loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'AGE'

P value = 0.003 (t-test), Q value = 0.23

Table S6. Gene #27: '18q loss' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
18Q LOSS MUTATED | 4 | 72.8 (5.4)
18Q LOSS WILD-TYPE | 187 | 54.9 (16.0)

Figure S6. Get High-res Image Gene #27: '18q loss' versus Clinical Feature #2: 'AGE'

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 = 3

- 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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