Correlation between copy number variation genes (focal) 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/C1N58J9C
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 variation genes (focal) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N58J9C
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

Testing the association between copy number variation 20 arm-level results and 3 clinical features across 191 patients, 6 significant findings detected with Q value < 0.25.

- Amp Peak 6(21q22.2) cnv correlated to 'Time to Death'.

- Del Peak 2(3p13) cnv correlated to 'Time to Death'.

- Del Peak 3(3q26.31) cnv correlated to 'AGE'.

- Del Peak 4(5q31.2) cnv correlated to 'Time to Death'.

- Del Peak 10(12p13.2) cnv correlated to 'Time to Death'.

- Del Peak 11(12q21.33) cnv correlated to 'AGE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 20 arm-level results 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
Amp Peak 6(21q22 2) | 0 (0%) | 177 | 0.000742 (0.0415) | 0.0657 (1.00) | 0.0922 (1.00)
Del Peak 2(3p13) | 0 (0%) | 182 | 4.2e-05 (0.00239) | 0.16 (1.00) | 0.185 (1.00)
Del Peak 3(3q26 31) | 0 (0%) | 188 |  | 0.00241 (0.13) | 0.252 (1.00)
Del Peak 4(5q31 2) | 0 (0%) | 173 | 0.00346 (0.183) | 0.00583 (0.297) | 0.0464 (1.00)
Del Peak 10(12p13 2) | 0 (0%) | 181 | 0.00107 (0.059) | 0.581 (1.00) | 0.351 (1.00)
Del Peak 11(12q21 33) | 0 (0%) | 188 |  | 8.84e-17 (5.13e-15) | 0.252 (1.00)
Amp Peak 1(1p33) | 0 (0%) | 184 | 0.34 (1.00) | 0.0372 (1.00) | 1 (1.00)
Amp Peak 2(1q43) | 0 (0%) | 184 | 0.737 (1.00) | 0.0234 (1.00) | 1 (1.00)
Amp Peak 3(11q23 3) | 0 (0%) | 174 | 0.117 (1.00) | 0.0106 (0.53) | 0.45 (1.00)
Amp Peak 4(13q31 3) | 0 (0%) | 184 | 0.929 (1.00) | 0.0714 (1.00) | 1 (1.00)
Amp Peak 5(20q11 21) | 0 (0%) | 188 | 0.116 (1.00) | 0.372 (1.00) | 0.252 (1.00)
Del Peak 5(7p12 1) | 0 (0%) | 175 | 0.075 (1.00) | 0.21 (1.00) | 0.604 (1.00)
Del Peak 6(7q32 3) | 0 (0%) | 168 | 0.0282 (1.00) | 0.0883 (1.00) | 0.656 (1.00)
Del Peak 7(7q34) | 0 (0%) | 167 | 0.0706 (1.00) | 0.0807 (1.00) | 0.512 (1.00)
Del Peak 9(9q21 32) | 0 (0%) | 186 | 0.899 (1.00) | 0.744 (1.00) | 0.378 (1.00)
Del Peak 12(16q23 1) | 0 (0%) | 182 | 0.126 (1.00) | 0.11 (1.00) | 0.513 (1.00)
Del Peak 13(17p13 2) | 0 (0%) | 176 | 0.0431 (1.00) | 0.226 (1.00) | 0.0565 (1.00)
Del Peak 14(17q11 2) | 0 (0%) | 178 | 0.0303 (1.00) | 0.547 (1.00) | 0.775 (1.00)
Del Peak 15(18p11 21) | 0 (0%) | 182 | 0.00548 (0.285) | 0.175 (1.00) | 0.185 (1.00)
Del Peak 16(20q13 13) | 0 (0%) | 187 | 0.0395 (1.00) | 0.113 (1.00) | 0.627 (1.00)

'Amp Peak 6(21q22.2)' versus 'Time to Death'

P value = 0.000742 (logrank test), Q value = 0.042

Table S1. Gene #6: 'Amp Peak 6(21q22.2)' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
AMP PEAK 6(21Q22.2) CNV | 12 | 10 | 1.0 - 24.0 (5.4)
AMP PEAK 6(21Q22.2) WILD-TYPE | 156 | 96 | 0.9 - 94.1 (12.5)

Figure S1. Get High-res Image Gene #6: 'Amp Peak 6(21q22.2)' versus Clinical Feature #1: 'Time to Death'

'Del Peak 2(3p13)' versus 'Time to Death'

P value = 4.2e-05 (logrank test), Q value = 0.0024

Table S2. Gene #7: 'Del Peak 2(3p13)' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
DEL PEAK 2(3P13) CNV | 8 | 7 | 1.0 - 14.0 (2.0)
DEL PEAK 2(3P13) WILD-TYPE | 160 | 99 | 0.9 - 94.1 (12.5)

Figure S2. Get High-res Image Gene #7: 'Del Peak 2(3p13)' versus Clinical Feature #1: 'Time to Death'

'Del Peak 3(3q26.31)' versus 'AGE'

P value = 0.00241 (t-test), Q value = 0.13

Table S3. Gene #8: 'Del Peak 3(3q26.31)' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
DEL PEAK 3(3Q26.31) CNV | 3 | 74.0 (3.6)
DEL PEAK 3(3Q26.31) WILD-TYPE | 188 | 54.9 (16.0)

Figure S3. Get High-res Image Gene #8: 'Del Peak 3(3q26.31)' versus Clinical Feature #2: 'AGE'

'Del Peak 4(5q31.2)' versus 'Time to Death'

P value = 0.00346 (logrank test), Q value = 0.18

Table S4. Gene #9: 'Del Peak 4(5q31.2)' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
DEL PEAK 4(5Q31.2) CNV | 17 | 15 | 1.0 - 73.0 (10.0)
DEL PEAK 4(5Q31.2) WILD-TYPE | 151 | 91 | 0.9 - 94.1 (12.9)

Figure S4. Get High-res Image Gene #9: 'Del Peak 4(5q31.2)' versus Clinical Feature #1: 'Time to Death'

'Del Peak 10(12p13.2)' versus 'Time to Death'

P value = 0.00107 (logrank test), Q value = 0.059

Table S5. Gene #14: 'Del Peak 10(12p13.2)' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 168 | 106 | 0.9 - 94.1 (12.0)
DEL PEAK 10(12P13.2) CNV | 8 | 8 | 1.0 - 22.1 (7.0)
DEL PEAK 10(12P13.2) WILD-TYPE | 160 | 98 | 0.9 - 94.1 (12.5)

Figure S5. Get High-res Image Gene #14: 'Del Peak 10(12p13.2)' versus Clinical Feature #1: 'Time to Death'

'Del Peak 11(12q21.33)' versus 'AGE'

P value = 8.84e-17 (t-test), Q value = 5.1e-15

Table S6. Gene #15: 'Del Peak 11(12q21.33)' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
DEL PEAK 11(12Q21.33) CNV | 3 | 72.0 (1.0)
DEL PEAK 11(12Q21.33) WILD-TYPE | 188 | 55.0 (16.0)

Figure S6. Get High-res Image Gene #15: 'Del Peak 11(12q21.33)' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

- Mutation data file = all_lesions.conf_99.cnv.cluster.txt

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

- Number of patients = 191

- Number of significantly arm-level cnvs = 20

- 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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