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

Cholangiocarcinoma (Primary solid tumor)

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 variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1HH6JFM

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 10 clinical features across 36 patients, no significant finding detected with Q value < 0.25.

No focal cnvs related to clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 20 focal events and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	Time to Death	YEARS TO BIRTH	PATHOLOGIC STAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RESIDUAL TUMOR	RACE
	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	Fisher's exact test	Fisher's exact test
amp 1q22	24 (67%)	12	0.267 (0.837)	0.491 (0.922)	0.0145 (0.556)	0.00649 (0.457)	0.631 (0.967)	1 (1.00)	0.481 (0.922)	0.197 (0.804)	0.0878 (0.675)	1 (1.00)
amp 11q13 3	8 (22%)	28	0.031 (0.626)	0.128 (0.761)	0.37 (0.852)	0.563 (0.966)	0.241 (0.837)	0.282 (0.837)	0.709 (1.00)	0.539 (0.945)	0.127 (0.761)	0.0865 (0.675)
amp 12q13 2	10 (28%)	26	0.148 (0.761)	0.986 (1.00)	1 (1.00)	0.396 (0.891)	1 (1.00)	1 (1.00)	0.285 (0.837)	1 (1.00)	0.819 (1.00)	0.00667 (0.457)
del 1p36 21	30 (83%)	6	0.973 (1.00)	1 (1.00)	0.181 (0.772)	0.0711 (0.651)	1 (1.00)	1 (1.00)	0.672 (1.00)	0.0196 (0.56)	0.748 (1.00)	0.186 (0.773)
del 3p25 3	28 (78%)	8	0.289 (0.837)	0.864 (1.00)	0.0842 (0.675)	0.00685 (0.457)	0.0619 (0.651)	0.282 (0.837)	0.709 (1.00)	0.0716 (0.651)	1 (1.00)	0.479 (0.922)
del 3p13	28 (78%)	8	0.489 (0.922)	0.253 (0.837)	0.0846 (0.675)	0.115 (0.761)	0.241 (0.837)	0.0519 (0.651)	0.422 (0.917)	0.0522 (0.651)	1 (1.00)	0.481 (0.922)
del 4q34 3	23 (64%)	13	0.719 (1.00)	0.152 (0.761)	0.443 (0.922)	0.05 (0.651)	0.35 (0.837)	1 (1.00)	0.301 (0.837)	0.29 (0.837)	0.334 (0.837)	0.475 (0.922)
del 5q13 3	8 (22%)	28	0.39 (0.886)	0.939 (1.00)	0.333 (0.837)	1 (1.00)	0.173 (0.769)	0.0519 (0.651)	0.422 (0.917)	0.0143 (0.556)	0.803 (1.00)	0.304 (0.837)
del 6q13	21 (58%)	15	0.836 (1.00)	0.748 (1.00)	0.452 (0.922)	0.459 (0.922)	0.636 (0.967)	1 (1.00)	0.741 (1.00)	0.408 (0.907)	0.836 (1.00)	0.777 (1.00)
del 9p21 3	23 (64%)	13	0.99 (1.00)	0.934 (1.00)	0.459 (0.922)	0.309 (0.837)	0.35 (0.837)	0.36 (0.837)	0.169 (0.768)	0.811 (1.00)	0.209 (0.835)	0.596 (0.966)
del 9q21 11	19 (53%)	17	0.532 (0.945)	0.0167 (0.556)	0.567 (0.966)	0.475 (0.922)	0.636 (0.967)	0.152 (0.761)	1 (1.00)	0.146 (0.761)	0.861 (1.00)	0.232 (0.837)
del 10q26 12	12 (33%)	24	0.813 (1.00)	0.591 (0.966)	0.471 (0.922)	1 (1.00)	0.296 (0.837)	0.643 (0.967)	0.729 (1.00)	0.351 (0.837)	0.256 (0.837)	0.788 (1.00)
del 11q25	13 (36%)	23	0.0259 (0.626)	0.348 (0.837)	0.959 (1.00)	0.242 (0.837)	1 (1.00)	1 (1.00)	0.493 (0.922)	0.29 (0.837)	0.599 (0.966)	1 (1.00)
del 12q24 13	8 (22%)	28	0.472 (0.922)	0.505 (0.927)	0.056 (0.651)	0.484 (0.922)	0.0619 (0.651)	0.0313 (0.626)	0.257 (0.837)	0.538 (0.945)	0.178 (0.772)	1 (1.00)
del 13q21 32	16 (44%)	20	0.261 (0.837)	0.356 (0.837)	0.318 (0.837)	0.244 (0.837)	0.344 (0.837)	0.0574 (0.651)	1 (1.00)	0.0712 (0.651)	1 (1.00)	0.612 (0.967)
del 14q22 1	14 (39%)	22	0.572 (0.966)	0.685 (1.00)	0.502 (0.927)	0.796 (1.00)	0.147 (0.761)	0.63 (0.967)	1 (1.00)	0.51 (0.927)	1 (1.00)	1 (1.00)
del 14q32 11	20 (56%)	16	0.164 (0.761)	0.962 (1.00)	0.358 (0.837)	0.329 (0.837)	0.148 (0.761)	0.639 (0.967)	0.737 (1.00)	0.0989 (0.695)	0.598 (0.966)	0.781 (1.00)
del 14q32 13	20 (56%)	16	0.164 (0.761)	0.962 (1.00)	0.356 (0.837)	0.328 (0.837)	0.148 (0.761)	0.639 (0.967)	0.737 (1.00)	0.101 (0.695)	0.598 (0.966)	0.784 (1.00)
del 14q32 33	20 (56%)	16	0.164 (0.761)	0.962 (1.00)	0.358 (0.837)	0.329 (0.837)	0.148 (0.761)	0.639 (0.967)	0.737 (1.00)	0.1 (0.695)	0.596 (0.966)	0.78 (1.00)
del 16q23 1	7 (19%)	29	0.529 (0.945)	0.889 (1.00)	0.823 (1.00)	1 (1.00)	1 (1.00)	0.559 (0.966)	1 (1.00)	0.715 (1.00)	0.0573 (0.651)	0.686 (1.00)

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/CHOL-TP/22519029/transformed.cor.cli.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/CHOL-TP/22489349/CHOL-TP.merged_data.txt
Number of patients = 36
Number of significantly focal cnvs = 20
Number of selected clinical features = 10
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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