Correlation between copy number variations of arm-level result and selected clinical features

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

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12R3R3V

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 32 arm-level events and 5 clinical features across 191 patients, 8 significant findings detected with Q value < 0.25.

10q gain cnv correlated to 'Time to Death'.
3p loss cnv correlated to 'Time to Death'.
3q loss cnv correlated to 'Time to Death'.
5q loss cnv correlated to 'Time to Death'.
12p loss cnv correlated to 'Time to Death'.
18p loss cnv correlated to 'Time to Death'.
18q loss cnv correlated to 'Time to Death' and 'YEARS_TO_BIRTH'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 32 arm-level events and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	GENDER	RACE	ETHNICITY
	nCNV (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test
18q loss	4 (2%)	187	0.00664 (0.177)	0.0121 (0.241)	0.127 (0.521)	1 (1.00)	1 (1.00)
10q gain	3 (2%)	188	0.00577 (0.177)	0.654 (1.00)	0.252 (0.823)	0.233 (0.809)	1 (1.00)
3p loss	3 (2%)	188	0.000763 (0.061)	0.0208 (0.3)	0.252 (0.823)	1 (1.00)	1 (1.00)
3q loss	3 (2%)	188	0.000763 (0.061)	0.0208 (0.3)	0.252 (0.823)	1 (1.00)	1 (1.00)
5q loss	6 (3%)	185	0.00532 (0.177)	0.0519 (0.463)	0.0325 (0.346)	0.415 (1.00)	1 (1.00)
12p loss	4 (2%)	187	0.00166 (0.0886)	0.869 (1.00)	0.627 (1.00)	1 (1.00)	1 (1.00)
18p loss	5 (3%)	186	0.011 (0.241)	0.367 (1.00)	0.0642 (0.463)	0.361 (1.00)	1 (1.00)
1p gain	3 (2%)	188		0.115 (0.513)	0.592 (1.00)	1 (1.00)	1 (1.00)
4p gain	4 (2%)	187	0.506 (1.00)	0.328 (0.972)	0.627 (1.00)	1 (1.00)	1 (1.00)
4q gain	4 (2%)	187	0.506 (1.00)	0.328 (0.972)	0.627 (1.00)	1 (1.00)	1 (1.00)
8p gain	22 (12%)	169	0.412 (1.00)	0.112 (0.513)	0.0734 (0.463)	1 (1.00)	0.313 (0.972)
8q gain	23 (12%)	168	0.464 (1.00)	0.167 (0.621)	0.0723 (0.463)	1 (1.00)	0.325 (0.972)
11p gain	5 (3%)	186	0.792 (1.00)	0.123 (0.519)	1 (1.00)	1 (1.00)	0.0781 (0.463)
11q gain	7 (4%)	184	0.37 (1.00)	0.047 (0.463)	1 (1.00)	1 (1.00)	0.108 (0.513)
13q gain	6 (3%)	185	0.582 (1.00)	0.12 (0.519)	0.69 (1.00)	1 (1.00)	1 (1.00)
17q gain	3 (2%)	188	0.856 (1.00)	0.113 (0.513)	1 (1.00)	1 (1.00)	1 (1.00)
19p gain	5 (3%)	186	0.91 (1.00)	0.715 (1.00)	0.378 (1.00)	1 (1.00)	1 (1.00)
19q gain	5 (3%)	186	0.91 (1.00)	0.715 (1.00)	0.378 (1.00)	1 (1.00)	1 (1.00)
21q gain	8 (4%)	183	0.0661 (0.463)	0.401 (1.00)	0.0732 (0.463)	0.185 (0.672)	1 (1.00)
22q gain	9 (5%)	182	0.564 (1.00)	0.0629 (0.463)	0.513 (1.00)	1 (1.00)	1 (1.00)
xp gain	3 (2%)	188	0.452 (1.00)	0.108 (0.513)	1 (1.00)	1 (1.00)	1 (1.00)
xq gain	3 (2%)	188	0.452 (1.00)	0.108 (0.513)	1 (1.00)	1 (1.00)	1 (1.00)
7p loss	17 (9%)	174	0.0916 (0.505)	0.458 (1.00)	0.802 (1.00)	1 (1.00)	1 (1.00)
7q loss	20 (10%)	171	0.0225 (0.3)	0.513 (1.00)	1 (1.00)	0.721 (1.00)	1 (1.00)
15q loss	4 (2%)	187	0.8 (1.00)	0.677 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
16q loss	4 (2%)	187	0.0189 (0.3)	0.388 (1.00)	0.627 (1.00)	1 (1.00)	1 (1.00)
17p loss	13 (7%)	178	0.112 (0.513)	0.765 (1.00)	0.147 (0.574)	0.654 (1.00)	1 (1.00)
17q loss	7 (4%)	184	0.274 (0.878)	0.196 (0.697)	0.458 (1.00)	1 (1.00)	1 (1.00)
19p loss	3 (2%)	188		0.151 (0.574)	0.592 (1.00)	1 (1.00)	1 (1.00)
19q loss	3 (2%)	188		0.151 (0.574)	0.592 (1.00)	1 (1.00)	1 (1.00)
xp loss	5 (3%)	186	0.081 (0.463)	0.0283 (0.323)	0.0642 (0.463)	1 (1.00)	1 (1.00)
xq loss	5 (3%)	186	0.081 (0.463)	0.0283 (0.323)	0.0642 (0.463)	1 (1.00)	1 (1.00)

'10q gain' versus 'Time to Death'

P value = 0.00577 (logrank test), Q value = 0.18

Table S1. Gene #6: '10q gain' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
10Q GAIN MUTATED	3	2	0.0 - 2.0 (1.9)
10Q GAIN WILD-TYPE	175	114	0.0 - 94.1 (12.0)

Figure S1. Get High-res Image Gene #6: '10q gain' versus Clinical Feature #1: 'Time to Death'

'3p loss' versus 'Time to Death'

P value = 0.000763 (logrank test), Q value = 0.061

Table S2. Gene #17: '3p loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
3P LOSS MUTATED	3	3	0.0 - 7.0 (1.0)
3P LOSS WILD-TYPE	175	113	0.0 - 94.1 (12.0)

Figure S2. Get High-res Image Gene #17: '3p loss' versus Clinical Feature #1: 'Time to Death'

'3q loss' versus 'Time to Death'

P value = 0.000763 (logrank test), Q value = 0.061

Table S3. Gene #18: '3q loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
3Q LOSS MUTATED	3	3	0.0 - 7.0 (1.0)
3Q LOSS WILD-TYPE	175	113	0.0 - 94.1 (12.0)

Figure S3. Get High-res Image Gene #18: '3q loss' versus Clinical Feature #1: 'Time to Death'

'5q loss' versus 'Time to Death'

P value = 0.00532 (logrank test), Q value = 0.18

Table S4. Gene #19: '5q loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
5Q LOSS MUTATED	6	6	1.0 - 12.0 (7.0)
5Q LOSS WILD-TYPE	172	110	0.0 - 94.1 (12.0)

Figure S4. Get High-res Image Gene #19: '5q loss' versus Clinical Feature #1: 'Time to Death'

'12p loss' versus 'Time to Death'

P value = 0.00166 (logrank test), Q value = 0.089

Table S5. Gene #22: '12p loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
12P LOSS MUTATED	3	3	0.0 - 7.0 (4.0)
12P LOSS WILD-TYPE	175	113	0.0 - 94.1 (12.0)

Figure S5. Get High-res Image Gene #22: '12p loss' versus Clinical Feature #1: 'Time to Death'

'18p loss' versus 'Time to Death'

P value = 0.011 (logrank test), Q value = 0.24

Table S6. Gene #27: '18p loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
18P LOSS MUTATED	5	5	1.0 - 12.0 (7.0)
18P LOSS WILD-TYPE	173	111	0.0 - 94.1 (12.0)

Figure S6. Get High-res Image Gene #27: '18p loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'Time to Death'

P value = 0.00664 (logrank test), Q value = 0.18

Table S7. Gene #28: '18q loss' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
18Q LOSS MUTATED	4	4	1.0 - 10.0 (4.5)
18Q LOSS WILD-TYPE	174	112	0.0 - 94.1 (12.0)

Figure S7. Get High-res Image Gene #28: '18q loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'YEARS_TO_BIRTH'

P value = 0.0121 (Wilcoxon-test), Q value = 0.24

Table S8. Gene #28: '18q loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	191	55.2 (16.1)
18Q LOSS MUTATED	4	72.8 (5.4)
18Q LOSS WILD-TYPE	187	54.9 (16.0)

Figure S8. Get High-res Image Gene #28: '18q loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

Methods & Data

Input

Copy number data file = broad_values_by_arm.txt from GISTIC pipeline
Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/LAML-TB/22529564/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/22506504/LAML-TB.merged_data.txt
Number of patients = 191
Number of significantly arm-level cnvs = 32
Number of selected clinical features = 5
Exclude regions that fewer than K tumors have mutations, K = 3

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

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

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] 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)

[2] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[3] 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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