Acute Myeloid Leukemia: Correlation between copy number variation genes (focal) and selected clinical features
(primary blood tumor (peripheral) cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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) | 14 (7%) | 177 | 0.000742 (0.0415) | 0.0657 (1.00) | 0.0922 (1.00)
Del Peak 2(3p13) | 9 (5%) | 182 | 4.2e-05 (0.00239) | 0.16 (1.00) | 0.185 (1.00)
Del Peak 3(3q26 31) | 3 (2%) | 188 |  | 0.00241 (0.13) | 0.252 (1.00)
Del Peak 4(5q31 2) | 18 (9%) | 173 | 0.00346 (0.183) | 0.00583 (0.297) | 0.0464 (1.00)
Del Peak 10(12p13 2) | 10 (5%) | 181 | 0.00107 (0.059) | 0.581 (1.00) | 0.351 (1.00)
Del Peak 11(12q21 33) | 3 (2%) | 188 |  | 8.84e-17 (5.13e-15) | 0.252 (1.00)
Amp Peak 1(1p33) | 7 (4%) | 184 | 0.34 (1.00) | 0.0372 (1.00) | 1 (1.00)
Amp Peak 2(1q43) | 7 (4%) | 184 | 0.737 (1.00) | 0.0234 (1.00) | 1 (1.00)
Amp Peak 3(11q23 3) | 17 (9%) | 174 | 0.117 (1.00) | 0.0106 (0.53) | 0.45 (1.00)
Amp Peak 4(13q31 3) | 7 (4%) | 184 | 0.929 (1.00) | 0.0714 (1.00) | 1 (1.00)
Amp Peak 5(20q11 21) | 3 (2%) | 188 | 0.116 (1.00) | 0.372 (1.00) | 0.252 (1.00)
Del Peak 5(7p12 1) | 16 (8%) | 175 | 0.075 (1.00) | 0.21 (1.00) | 0.604 (1.00)
Del Peak 6(7q32 3) | 23 (12%) | 168 | 0.0282 (1.00) | 0.0883 (1.00) | 0.656 (1.00)
Del Peak 7(7q34) | 24 (13%) | 167 | 0.0706 (1.00) | 0.0807 (1.00) | 0.512 (1.00)
Del Peak 9(9q21 32) | 5 (3%) | 186 | 0.899 (1.00) | 0.744 (1.00) | 0.378 (1.00)
Del Peak 12(16q23 1) | 9 (5%) | 182 | 0.126 (1.00) | 0.11 (1.00) | 0.513 (1.00)
Del Peak 13(17p13 2) | 15 (8%) | 176 | 0.0431 (1.00) | 0.226 (1.00) | 0.0565 (1.00)
Del Peak 14(17q11 2) | 13 (7%) | 178 | 0.0303 (1.00) | 0.547 (1.00) | 0.775 (1.00)
Del Peak 15(18p11 21) | 9 (5%) | 182 | 0.00548 (0.285) | 0.175 (1.00) | 0.185 (1.00)
Del Peak 16(20q13 13) | 4 (2%) | 187 | 0.0395 (1.00) | 0.113 (1.00) | 0.627 (1.00)

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

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

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

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

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

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

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

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

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
DEL PEAK 3(3Q26.31) MUTATED | 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) mutation analysis' versus Clinical Feature #2: 'AGE'

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

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

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

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

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

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

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

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

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
DEL PEAK 11(12Q21.33) MUTATED | 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) mutation analysis' 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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