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

Mesothelioma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

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 (2015): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15D8R37

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 11 clinical features across 87 patients, 7 significant findings detected with Q value < 0.25.

del_1p36.31 cnv correlated to 'Time to Death'.
del_4q26 cnv correlated to 'Time to Death'.
del_5q23.2 cnv correlated to 'Time to Death'.
del_9p21.3 cnv correlated to 'Time to Death'.
del_10p15.3 cnv correlated to 'Time to Death'.
del_15q15.1 cnv correlated to 'Time to Death' and 'KARNOFSKY_PERFORMANCE_SCORE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 21 focal events and 11 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	YEARS TO BIRTH	PATHOLOGIC STAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	RADIATION THERAPY	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RESIDUAL TUMOR
	nCNV (%)	nWild-Type	logrank test	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Wilcoxon-test	Fisher's exact test	Fisher's exact test
del 15q15 1	28 (32%)	59	0.00146 (0.0842)	0.993 (1.00)	0.56 (0.971)	0.763 (1.00)	0.887 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)	0.00382 (0.147)	0.187 (0.676)	0.42 (0.903)
del 1p36 31	33 (38%)	54	0.000626 (0.0482)	0.93 (1.00)	0.955 (1.00)	0.644 (0.992)	0.949 (1.00)	0.268 (0.773)	0.776 (1.00)	1 (1.00)	0.0751 (0.491)	0.934 (1.00)	0.82 (1.00)
del 4q26	38 (44%)	49	0.00377 (0.147)	0.566 (0.971)	0.387 (0.886)	0.105 (0.617)	0.449 (0.935)	0.251 (0.773)	0.28 (0.798)	0.812 (1.00)	0.0471 (0.451)	0.147 (0.625)	0.13 (0.617)
del 5q23 2	17 (20%)	70	0.00741 (0.244)	0.249 (0.773)	1 (1.00)	0.518 (0.971)	1 (1.00)	0.462 (0.952)	0.179 (0.676)	0.138 (0.625)	0.0751 (0.491)	0.875 (1.00)	0.447 (0.935)
del 9p21 3	51 (59%)	36	6.74e-06 (0.000778)	0.41 (0.898)	0.768 (1.00)	0.447 (0.935)	0.668 (0.999)	0.0672 (0.491)	1 (1.00)	1 (1.00)	0.801 (1.00)	0.651 (0.995)	0.853 (1.00)
del 10p15 3	28 (32%)	59	5.65e-06 (0.000778)	0.598 (0.985)	0.0366 (0.445)	0.185 (0.676)	0.37 (0.862)	0.551 (0.971)	1 (1.00)	0.0191 (0.364)	0.058 (0.491)	0.102 (0.617)	0.212 (0.733)
del 1p21 3	37 (43%)	50	0.0495 (0.451)	0.298 (0.799)	0.314 (0.799)	0.13 (0.617)	0.357 (0.859)	1 (1.00)	0.581 (0.971)	0.629 (0.985)	0.152 (0.625)	0.965 (1.00)	0.966 (1.00)
del 2q35	17 (20%)	70	0.027 (0.367)	0.397 (0.889)	0.765 (1.00)	0.818 (1.00)	0.315 (0.799)	0.566 (0.971)	0.0129 (0.285)	0.566 (0.971)		0.657 (0.998)	0.319 (0.801)
del 3p21 1	48 (55%)	39	0.775 (1.00)	0.864 (1.00)	0.225 (0.754)	0.705 (1.00)	0.673 (0.999)	1 (1.00)	0.782 (1.00)	0.632 (0.985)	0.0102 (0.285)	0.179 (0.676)	0.824 (1.00)
del 4q34 3	41 (47%)	46	0.0132 (0.285)	0.147 (0.625)	0.631 (0.985)	0.314 (0.799)	0.546 (0.971)	0.238 (0.754)	0.58 (0.971)	1 (1.00)	0.0471 (0.451)	0.304 (0.799)	0.0765 (0.491)
del 6q22 1	41 (47%)	46	0.49 (0.971)	0.848 (1.00)	0.142 (0.625)	0.83 (1.00)	0.579 (0.971)	0.565 (0.971)	0.422 (0.903)	0.814 (1.00)	0.574 (0.971)	0.764 (1.00)	0.531 (0.971)
del 6q26	36 (41%)	51	0.131 (0.617)	0.87 (1.00)	0.238 (0.754)	0.818 (1.00)	0.0744 (0.491)	0.268 (0.773)	1 (1.00)	0.634 (0.985)	0.683 (0.999)	0.858 (1.00)	0.79 (1.00)
del 8p23 2	18 (21%)	69	0.284 (0.799)	0.294 (0.799)	0.331 (0.814)	0.16 (0.649)	0.754 (1.00)	1 (1.00)	0.506 (0.971)	0.397 (0.889)	0.0751 (0.491)	0.265 (0.773)	0.23 (0.754)
del 10q25 2	31 (36%)	56	0.0136 (0.285)	0.776 (1.00)	0.729 (1.00)	0.584 (0.971)	0.714 (1.00)	0.255 (0.773)	1 (1.00)	0.803 (1.00)	0.131 (0.617)	0.622 (0.985)	0.481 (0.971)
del 11q23 2	18 (21%)	69	0.0671 (0.491)	0.626 (0.985)	0.185 (0.676)	0.55 (0.971)	1 (1.00)	0.558 (0.971)	0.506 (0.971)	0.551 (0.971)		0.31 (0.799)	0.344 (0.836)
del 12p13 31	11 (13%)	76	0.022 (0.364)	0.361 (0.859)	0.373 (0.862)	0.94 (1.00)	0.58 (0.971)	1 (1.00)	0.681 (0.999)	0.297 (0.799)		0.194 (0.681)	0.195 (0.681)
del 13q14 11	43 (49%)	44	0.0507 (0.451)	0.535 (0.971)	0.122 (0.617)	0.818 (1.00)	0.0598 (0.491)	0.107 (0.617)	0.166 (0.66)	0.636 (0.985)	0.838 (1.00)	1 (1.00)	0.755 (1.00)
del 14q32 31	39 (45%)	48	0.0295 (0.378)	0.932 (1.00)	0.483 (0.971)	0.138 (0.625)	0.673 (0.999)	0.238 (0.754)	0.0503 (0.451)	1 (1.00)	0.0207 (0.364)	0.0268 (0.367)	0.125 (0.617)
del 16p13 3	6 (7%)	81	0.777 (1.00)	0.0425 (0.451)	0.129 (0.617)	0.0471 (0.451)	0.583 (0.971)	1 (1.00)	0.304 (0.799)	1 (1.00)		0.0674 (0.491)	0.131 (0.617)
del 16q24 1	22 (25%)	65	0.0251 (0.367)	0.721 (1.00)	0.624 (0.985)	0.97 (1.00)	0.225 (0.754)	0.545 (0.971)	0.107 (0.617)	1 (1.00)		0.183 (0.676)	0.261 (0.773)
del 22q12 2	68 (78%)	19	0.111 (0.617)	0.825 (1.00)	0.412 (0.898)	0.151 (0.625)	0.867 (1.00)	0.556 (0.971)	0.327 (0.813)	0.4 (0.889)	0.635 (0.985)	0.37 (0.862)	0.684 (0.999)

'del_1p36.31' versus 'Time to Death'

P value = 0.000626 (logrank test), Q value = 0.048

Table S1. Gene #1: 'del_1p36.31' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	86	71	0.7 - 91.7 (16.9)
DEL PEAK 1(1P36.31) MUTATED	32	30	1.3 - 49.0 (12.2)
DEL PEAK 1(1P36.31) WILD-TYPE	54	41	0.7 - 91.7 (21.7)

Figure S1. Get High-res Image Gene #1: 'del_1p36.31' versus Clinical Feature #1: 'Time to Death'

'del_4q26' versus 'Time to Death'

P value = 0.00377 (logrank test), Q value = 0.15

Table S2. Gene #5: 'del_4q26' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	86	71	0.7 - 91.7 (16.9)
DEL PEAK 5(4Q26) MUTATED	38	34	1.9 - 62.3 (13.5)
DEL PEAK 5(4Q26) WILD-TYPE	48	37	0.7 - 91.7 (23.5)

Figure S2. Get High-res Image Gene #5: 'del_4q26' versus Clinical Feature #1: 'Time to Death'

'del_5q23.2' versus 'Time to Death'

P value = 0.00741 (logrank test), Q value = 0.24

Table S3. Gene #7: 'del_5q23.2' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	86	71	0.7 - 91.7 (16.9)
DEL PEAK 7(5Q23.2) MUTATED	17	16	3.5 - 41.5 (11.9)
DEL PEAK 7(5Q23.2) WILD-TYPE	69	55	0.7 - 91.7 (19.4)

Figure S3. Get High-res Image Gene #7: 'del_5q23.2' versus Clinical Feature #1: 'Time to Death'

'del_9p21.3' versus 'Time to Death'

P value = 6.74e-06 (logrank test), Q value = 0.00078

Table S4. Gene #11: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	86	71	0.7 - 91.7 (16.9)
DEL PEAK 11(9P21.3) MUTATED	50	45	0.7 - 41.5 (13.0)
DEL PEAK 11(9P21.3) WILD-TYPE	36	26	1.6 - 91.7 (25.4)

Figure S4. Get High-res Image Gene #11: 'del_9p21.3' versus Clinical Feature #1: 'Time to Death'

'del_10p15.3' versus 'Time to Death'

P value = 5.65e-06 (logrank test), Q value = 0.00078

Table S5. Gene #12: 'del_10p15.3' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	86	71	0.7 - 91.7 (16.9)
DEL PEAK 12(10P15.3) MUTATED	27	24	0.7 - 27.7 (10.8)
DEL PEAK 12(10P15.3) WILD-TYPE	59	47	1.3 - 91.7 (23.3)

Figure S5. Get High-res Image Gene #12: 'del_10p15.3' versus Clinical Feature #1: 'Time to Death'

'del_15q15.1' versus 'Time to Death'

P value = 0.00146 (logrank test), Q value = 0.084

Table S6. Gene #18: 'del_15q15.1' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	86	71	0.7 - 91.7 (16.9)
DEL PEAK 18(15Q15.1) MUTATED	27	25	0.7 - 41.5 (12.7)
DEL PEAK 18(15Q15.1) WILD-TYPE	59	46	1.6 - 91.7 (19.4)

Figure S6. Get High-res Image Gene #18: 'del_15q15.1' versus Clinical Feature #1: 'Time to Death'

'del_15q15.1' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00382 (Wilcoxon-test), Q value = 0.15

Table S7. Gene #18: 'del_15q15.1' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	17	77.6 (31.5)
DEL PEAK 18(15Q15.1) MUTATED	6	50.0 (41.0)
DEL PEAK 18(15Q15.1) WILD-TYPE	11	92.7 (6.5)

Figure S7. Get High-res Image Gene #18: 'del_15q15.1' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

Methods & Data

Input

Copy number data file = all_lesions.txt from GISTIC pipeline
Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/MESO-TP/19781625/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/MESO-TP/19775385/MESO-TP.merged_data.txt
Number of patients = 87
Number of significantly focal cnvs = 21
Number of selected clinical features = 11
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

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] 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)

[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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