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

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

15 January 2014 | analyses__2014_01_15

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 (2014): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GH9GCQ

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 21 focal events and 3 clinical features across 191 patients, 6 significant findings detected with Q value < 0.25.

amp_21q22.2 cnv correlated to 'Time to Death'.
del_3p13 cnv correlated to 'Time to Death'.
del_3q26.31 cnv correlated to 'AGE'.
del_5q31.2 cnv correlated to 'Time to Death'.
del_12p13.2 cnv correlated to 'Time to Death'.
del_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 21 focal events 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 21q22 2	14 (7%)	177	0.000742 (0.0437)	0.0657 (1.00)	0.0922 (1.00)
del 3p13	9 (5%)	182	4.2e-05 (0.00252)	0.16 (1.00)	0.185 (1.00)
del 3q26 31	3 (2%)	188		0.00241 (0.137)	0.252 (1.00)
del 5q31 2	18 (9%)	173	0.00346 (0.194)	0.00583 (0.315)	0.0464 (1.00)
del 12p13 2	10 (5%)	181	0.00107 (0.0622)	0.581 (1.00)	0.351 (1.00)
del 12q21 33	3 (2%)	188		8.84e-17 (5.39e-15)	0.252 (1.00)
amp 1p33	7 (4%)	184	0.34 (1.00)	0.0372 (1.00)	1 (1.00)
amp 1q43	7 (4%)	184	0.737 (1.00)	0.0234 (1.00)	1 (1.00)
amp 11q23 3	17 (9%)	174	0.117 (1.00)	0.0106 (0.562)	0.45 (1.00)
amp 13q31 3	7 (4%)	184	0.929 (1.00)	0.0714 (1.00)	1 (1.00)
amp 19p13 2	6 (3%)	185	0.977 (1.00)	0.99 (1.00)	0.223 (1.00)
amp 20q11 21	3 (2%)	188	0.116 (1.00)	0.372 (1.00)	0.252 (1.00)
del 7p12 1	16 (8%)	175	0.075 (1.00)	0.21 (1.00)	0.604 (1.00)
del 7q32 3	23 (12%)	168	0.0282 (1.00)	0.0883 (1.00)	0.656 (1.00)
del 7q34	24 (13%)	167	0.0706 (1.00)	0.0807 (1.00)	0.512 (1.00)
del 9q21 32	5 (3%)	186	0.899 (1.00)	0.744 (1.00)	0.378 (1.00)
del 16q23 1	9 (5%)	182	0.126 (1.00)	0.11 (1.00)	0.513 (1.00)
del 17p13 2	15 (8%)	176	0.0431 (1.00)	0.226 (1.00)	0.0565 (1.00)
del 17q11 2	13 (7%)	178	0.0303 (1.00)	0.547 (1.00)	0.775 (1.00)
del 18p11 21	9 (5%)	182	0.00548 (0.302)	0.175 (1.00)	0.185 (1.00)
del 20q13 13	4 (2%)	187	0.0395 (1.00)	0.113 (1.00)	0.627 (1.00)

'amp_21q22.2' versus 'Time to Death'

P value = 0.000742 (logrank test), Q value = 0.044

Table S1. Gene #7: 'amp_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 7(21Q22.2) MUTATED	12	10	1.0 - 24.0 (5.4)
AMP PEAK 7(21Q22.2) WILD-TYPE	156	96	0.9 - 94.1 (12.5)

Figure S1. Get High-res Image Gene #7: 'amp_21q22.2' versus Clinical Feature #1: 'Time to Death'

'del_3p13' versus 'Time to Death'

P value = 4.2e-05 (logrank test), Q value = 0.0025

Table S2. Gene #8: 'del_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) 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 #8: 'del_3p13' versus Clinical Feature #1: 'Time to Death'

'del_3q26.31' versus 'AGE'

P value = 0.00241 (t-test), Q value = 0.14

Table S3. Gene #9: 'del_3q26.31' 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 #9: 'del_3q26.31' versus Clinical Feature #2: 'AGE'

'del_5q31.2' versus 'Time to Death'

P value = 0.00346 (logrank test), Q value = 0.19

Table S4. Gene #10: 'del_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) 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 #10: 'del_5q31.2' versus Clinical Feature #1: 'Time to Death'

'del_12p13.2' versus 'Time to Death'

P value = 0.00107 (logrank test), Q value = 0.062

Table S5. Gene #15: 'del_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) 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 #15: 'del_12p13.2' versus Clinical Feature #1: 'Time to Death'

'del_12q21.33' versus 'AGE'

P value = 8.84e-17 (t-test), Q value = 5.4e-15

Table S6. Gene #16: 'del_12q21.33' 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 #16: 'del_12q21.33' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = LAML-TB.merged_data.txt
Number of patients = 191
Number of significantly focal cnvs = 21
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.

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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