Correlation between copy number variation genes (focal events) and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C14Q7SB6
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C14Q7SB6
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 focal events and 3 clinical features across 191 patients, 7 significant findings detected with Q value < 0.25.

- AMP PEAK 6(21Q22.2) MUTATION ANALYSIS cnv correlated to 'Time to Death'.

- DEL PEAK 2(3P13) MUTATION ANALYSIS cnv correlated to 'Time to Death'.

- DEL PEAK 3(3Q26.31) MUTATION ANALYSIS cnv correlated to 'AGE'.

- DEL PEAK 4(5Q31.2) MUTATION ANALYSIS cnv correlated to 'Time to Death'.

- DEL PEAK 10(12P13.2) MUTATION ANALYSIS cnv correlated to 'Time to Death'.

- DEL PEAK 11(12Q21.33) MUTATION ANALYSIS cnv correlated to 'AGE'.

- DEL PEAK 15(18P11.21) MUTATION ANALYSIS 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 20 focal events and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 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) MUTATION ANALYSIS | 14 (7%) | 177 | 0.000578 (0.0324) | 0.0657 (1.00) | 0.0922 (1.00)
DEL PEAK 2(3P13) MUTATION ANALYSIS | 9 (5%) | 182 | 3.23e-05 (0.00184) | 0.16 (1.00) | 0.185 (1.00)
DEL PEAK 3(3Q26 31) MUTATION ANALYSIS | 3 (2%) | 188 |  | 0.00241 (0.13) | 0.252 (1.00)
DEL PEAK 4(5Q31 2) MUTATION ANALYSIS | 18 (9%) | 173 | 0.00293 (0.155) | 0.00583 (0.297) | 0.0464 (1.00)
DEL PEAK 10(12P13 2) MUTATION ANALYSIS | 10 (5%) | 181 | 0.000939 (0.0516) | 0.581 (1.00) | 0.351 (1.00)
DEL PEAK 11(12Q21 33) MUTATION ANALYSIS | 3 (2%) | 188 |  | 8.84e-17 (5.13e-15) | 0.252 (1.00)
DEL PEAK 15(18P11 21) MUTATION ANALYSIS | 9 (5%) | 182 | 0.00476 (0.248) | 0.175 (1.00) | 0.185 (1.00)
AMP PEAK 1(1P33) MUTATION ANALYSIS | 7 (4%) | 184 | 0.328 (1.00) | 0.0372 (1.00) | 1 (1.00)
AMP PEAK 2(1Q43) MUTATION ANALYSIS | 7 (4%) | 184 | 0.724 (1.00) | 0.0234 (1.00) | 1 (1.00)
AMP PEAK 3(11Q23 3) MUTATION ANALYSIS | 17 (9%) | 174 | 0.106 (1.00) | 0.0106 (0.53) | 0.45 (1.00)
AMP PEAK 4(13Q31 3) MUTATION ANALYSIS | 7 (4%) | 184 | 0.953 (1.00) | 0.0714 (1.00) | 1 (1.00)
AMP PEAK 5(20Q11 21) MUTATION ANALYSIS | 3 (2%) | 188 | 0.108 (1.00) | 0.372 (1.00) | 0.252 (1.00)
DEL PEAK 5(7P12 1) MUTATION ANALYSIS | 16 (8%) | 175 | 0.147 (1.00) | 0.21 (1.00) | 0.604 (1.00)
DEL PEAK 6(7Q32 3) MUTATION ANALYSIS | 23 (12%) | 168 | 0.0552 (1.00) | 0.0883 (1.00) | 0.656 (1.00)
DEL PEAK 7(7Q34) MUTATION ANALYSIS | 24 (13%) | 167 | 0.122 (1.00) | 0.0807 (1.00) | 0.512 (1.00)
DEL PEAK 9(9Q21 32) MUTATION ANALYSIS | 5 (3%) | 186 | 0.918 (1.00) | 0.744 (1.00) | 0.378 (1.00)
DEL PEAK 12(16Q23 1) MUTATION ANALYSIS | 9 (5%) | 182 | 0.12 (1.00) | 0.11 (1.00) | 0.513 (1.00)
DEL PEAK 13(17P13 2) MUTATION ANALYSIS | 15 (8%) | 176 | 0.038 (1.00) | 0.226 (1.00) | 0.0565 (1.00)
DEL PEAK 14(17Q11 2) MUTATION ANALYSIS | 13 (7%) | 178 | 0.0268 (1.00) | 0.547 (1.00) | 0.775 (1.00)
DEL PEAK 16(20Q13 13) MUTATION ANALYSIS | 4 (2%) | 187 | 0.0373 (1.00) | 0.113 (1.00) | 0.627 (1.00)

'AMP PEAK 6(21Q22.2) MUTATION STATUS' versus 'Time to Death'

P value = 0.000578 (logrank test), Q value = 0.032

Table S1. Gene #6: 'AMP PEAK 6(21Q22.2) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 166 | 104 | 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 | 154 | 94 | 0.9 - 94.1 (12.5)

Figure S1. Get High-res Image Gene #6: 'AMP PEAK 6(21Q22.2) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'DEL PEAK 2(3P13) MUTATION STATUS' versus 'Time to Death'

P value = 3.23e-05 (logrank test), Q value = 0.0018

Table S2. Gene #7: 'DEL PEAK 2(3P13) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 166 | 104 | 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 | 158 | 97 | 0.9 - 94.1 (12.5)

Figure S2. Get High-res Image Gene #7: 'DEL PEAK 2(3P13) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'DEL PEAK 3(3Q26.31) MUTATION STATUS' versus 'AGE'

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

Table S3. Gene #8: 'DEL PEAK 3(3Q26.31) MUTATION STATUS' 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 STATUS' versus Clinical Feature #2: 'AGE'

'DEL PEAK 4(5Q31.2) MUTATION STATUS' versus 'Time to Death'

P value = 0.00293 (logrank test), Q value = 0.16

Table S4. Gene #9: 'DEL PEAK 4(5Q31.2) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 166 | 104 | 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 | 149 | 89 | 0.9 - 94.1 (12.9)

Figure S4. Get High-res Image Gene #9: 'DEL PEAK 4(5Q31.2) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'DEL PEAK 10(12P13.2) MUTATION STATUS' versus 'Time to Death'

P value = 0.000939 (logrank test), Q value = 0.052

Table S5. Gene #14: 'DEL PEAK 10(12P13.2) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 166 | 104 | 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 | 158 | 96 | 0.9 - 94.1 (12.5)

Figure S5. Get High-res Image Gene #14: 'DEL PEAK 10(12P13.2) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'DEL PEAK 11(12Q21.33) MUTATION STATUS' versus 'AGE'

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

Table S6. Gene #15: 'DEL PEAK 11(12Q21.33) MUTATION STATUS' 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 STATUS' versus Clinical Feature #2: 'AGE'

'DEL PEAK 15(18P11.21) MUTATION STATUS' versus 'Time to Death'

P value = 0.00476 (logrank test), Q value = 0.25

Table S7. Gene #19: 'DEL PEAK 15(18P11.21) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 166 | 104 | 0.9 - 94.1 (12.0)
DEL PEAK 15(18P11.21) MUTATED | 8 | 8 | 1.0 - 17.0 (9.5)
DEL PEAK 15(18P11.21) WILD-TYPE | 158 | 96 | 0.9 - 94.1 (12.0)

Figure S7. Get High-res Image Gene #19: 'DEL PEAK 15(18P11.21) MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

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

- Number of patients = 191

- Number of significantly focal 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

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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle