Correlation between copy number variation genes (focal) and selected clinical features

Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

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) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1S46Q0J

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.

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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