Correlation between copy number variations of arm-level result and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
21 April 2013 | analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1RX991S
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood cohort) - 21 April 2013: Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RX991S
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 22 arm-level results and 3 clinical features across 191 patients, 4 significant findings detected with Q value < 0.25.

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

- 7q loss cnv correlated to 'Time to Death'.

- 18p 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 22 arm-level results and 3 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
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test
5q loss | 0 (0%) | 185 | 0.00073 (0.0438) | 0.0601 (1.00) | 0.0325 (1.00)
7q loss | 0 (0%) | 173 | 0.00315 (0.183) | 0.325 (1.00) | 0.805 (1.00)
18p loss | 0 (0%) | 186 | 0.00202 (0.119) | 0.355 (1.00) | 0.0642 (1.00)
18q loss | 0 (0%) | 188 | 0.000113 (0.00689) | 0.0297 (1.00) | 0.252 (1.00)
1p gain | 0 (0%) | 188 |  | 0.0913 (1.00) | 0.592 (1.00)
4p gain | 0 (0%) | 187 | 0.587 (1.00) | 0.425 (1.00) | 0.627 (1.00)
4q gain | 0 (0%) | 187 | 0.587 (1.00) | 0.425 (1.00) | 0.627 (1.00)
8p gain | 0 (0%) | 171 | 0.48 (1.00) | 0.119 (1.00) | 0.0597 (1.00)
8q gain | 0 (0%) | 170 | 0.529 (1.00) | 0.168 (1.00) | 0.0382 (1.00)
10q gain | 0 (0%) | 188 |  | 0.947 (1.00) | 0.252 (1.00)
11p gain | 0 (0%) | 187 |  | 0.188 (1.00) | 1 (1.00)
11q gain | 0 (0%) | 184 | 0.57 (1.00) | 0.0154 (0.834) | 1 (1.00)
13q gain | 0 (0%) | 188 |  | 0.511 (1.00) | 0.592 (1.00)
17q gain | 0 (0%) | 188 | 0.729 (1.00) | 0.29 (1.00) | 1 (1.00)
19p gain | 0 (0%) | 186 | 0.347 (1.00) | 0.789 (1.00) | 0.0642 (1.00)
19q gain | 0 (0%) | 186 | 0.347 (1.00) | 0.789 (1.00) | 0.0642 (1.00)
21q gain | 0 (0%) | 183 | 0.0186 (0.966) | 0.616 (1.00) | 0.0732 (1.00)
22q gain | 0 (0%) | 183 | 0.887 (1.00) | 0.00598 (0.341) | 0.73 (1.00)
7p loss | 0 (0%) | 176 | 0.00716 (0.401) | 0.325 (1.00) | 1 (1.00)
12p loss | 0 (0%) | 188 |  | 0.514 (1.00) | 1 (1.00)
17p loss | 0 (0%) | 181 | 0.376 (1.00) | 0.308 (1.00) | 0.114 (1.00)
17q loss | 0 (0%) | 186 | 0.0166 (0.882) | 0.0075 (0.412) | 0.378 (1.00)

'5q loss' versus 'Time to Death'

P value = 0.00073 (logrank test), Q value = 0.044

Table S1. Gene #15: '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 CNV | 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 #15: '5q loss' versus Clinical Feature #1: 'Time to Death'

'7q loss' versus 'Time to Death'

P value = 0.00315 (logrank test), Q value = 0.18

Table S2. Gene #17: '7q loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
7Q LOSS CNV | 15 | 13 | 1.0 - 40.0 (9.0)
7Q LOSS WILD-TYPE | 153 | 93 | 0.9 - 94.1 (13.9)

Figure S2. Get High-res Image Gene #17: '7q loss' versus Clinical Feature #1: 'Time to Death'

'18p loss' versus 'Time to Death'

P value = 0.00202 (logrank test), Q value = 0.12

Table S3. Gene #21: '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 CNV | 5 | 5 | 1.0 - 12.0 (7.0)
18P LOSS WILD-TYPE | 163 | 101 | 0.9 - 94.1 (12.0)

Figure S3. Get High-res Image Gene #21: '18p loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'Time to Death'

P value = 0.000113 (logrank test), Q value = 0.0069

Table S4. Gene #22: '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 CNV | 3 | 3 | 1.0 - 7.0 (2.0)
18Q LOSS WILD-TYPE | 165 | 103 | 0.9 - 94.1 (12.0)

Figure S4. Get High-res Image Gene #22: '18q loss' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

- Mutation data file = broad_values_by_arm.mutsig.cluster.txt

- Clinical data file = LAML-TB.clin.merged.picked.txt

- Number of patients = 191

- Number of significantly arm-level cnvs = 22

- Number of selected clinical features = 3

- 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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