Cervical Squamous Cell Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

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 35 arm-level results and 4 clinical features across 26 patients, no significant finding detected with Q value < 0.25.

No arm-level 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 35 arm-level results and 4 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	AGE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test
1p gain	5 (19%)	21	0.617 (1.00)	0.0415 (1.00)	1 (1.00)	1 (1.00)
1q gain	10 (38%)	16	0.414 (1.00)	0.00733 (1.00)	0.625 (1.00)	0.625 (1.00)
3p gain	3 (12%)	23	1 (1.00)	0.742 (1.00)	0.408 (1.00)	0.408 (1.00)
3q gain	15 (58%)	11	1 (1.00)	0.426 (1.00)	0.614 (1.00)	0.614 (1.00)
5p gain	8 (31%)	18	0.617 (1.00)	0.438 (1.00)	0.563 (1.00)	1 (1.00)
6p gain	3 (12%)	23	1 (1.00)	0.745 (1.00)	1 (1.00)	1 (1.00)
8p gain	3 (12%)	23	1 (1.00)	0.561 (1.00)	0.408 (1.00)	0.408 (1.00)
8q gain	5 (19%)	21	0.617 (1.00)	0.633 (1.00)	1 (1.00)	0.155 (1.00)
10p gain	3 (12%)	23	0.0455 (1.00)	0.332 (1.00)	1 (1.00)	1 (1.00)
12p gain	5 (19%)	21	1 (1.00)	0.485 (1.00)	1 (1.00)	1 (1.00)
12q gain	3 (12%)	23	1 (1.00)	0.723 (1.00)	0.408 (1.00)	0.408 (1.00)
15q gain	3 (12%)	23	1 (1.00)	0.818 (1.00)	1 (1.00)	1 (1.00)
16p gain	4 (15%)	22	0.617 (1.00)	0.944 (1.00)	0.511 (1.00)	0.511 (1.00)
16q gain	3 (12%)	23	0.617 (1.00)	0.165 (1.00)	0.408 (1.00)	0.408 (1.00)
18p gain	3 (12%)	23	0.617 (1.00)	0.336 (1.00)	0.0523 (1.00)	0.0523 (1.00)
20p gain	8 (31%)	18	1 (1.00)	0.818 (1.00)	0.563 (1.00)	0.563 (1.00)
20q gain	9 (35%)	17	1 (1.00)	0.923 (1.00)	1 (1.00)	1 (1.00)
22q gain	4 (15%)	22	0.617 (1.00)	0.275 (1.00)	0.511 (1.00)	0.511 (1.00)
3p loss	8 (31%)	18	0.414 (1.00)	0.978 (1.00)	1 (1.00)	1 (1.00)
4p loss	10 (38%)	16	0.414 (1.00)	0.666 (1.00)	0.625 (1.00)	1 (1.00)
4q loss	3 (12%)	23	0.617 (1.00)	0.43 (1.00)	0.408 (1.00)	1 (1.00)
5q loss	9 (35%)	17	0.414 (1.00)	0.157 (1.00)	0.263 (1.00)	1 (1.00)
8p loss	7 (27%)	19	0.414 (1.00)	0.257 (1.00)	1 (1.00)	1 (1.00)
10p loss	5 (19%)	21	0.617 (1.00)	0.563 (1.00)	0.155 (1.00)	1 (1.00)
10q loss	5 (19%)	21	0.0455 (1.00)	0.582 (1.00)	0.155 (1.00)	1 (1.00)
11p loss	5 (19%)	21	0.414 (1.00)	0.229 (1.00)	0.155 (1.00)	1 (1.00)
11q loss	5 (19%)	21	0.414 (1.00)	0.933 (1.00)	0.155 (1.00)	1 (1.00)
12p loss	4 (15%)	22	0.0455 (1.00)	0.077 (1.00)	1 (1.00)	1 (1.00)
13q loss	8 (31%)	18	0.414 (1.00)	0.907 (1.00)	0.0721 (1.00)	0.563 (1.00)
17p loss	7 (27%)	19	0.414 (1.00)	0.451 (1.00)	0.287 (1.00)	1 (1.00)
17q loss	3 (12%)	23	0.221 (1.00)	0.383 (1.00)	0.408 (1.00)	0.408 (1.00)
18p loss	3 (12%)	23	0.617 (1.00)	0.726 (1.00)	0.408 (1.00)	0.408 (1.00)
18q loss	4 (15%)	22	0.414 (1.00)	0.491 (1.00)	0.511 (1.00)	0.511 (1.00)
20p loss	3 (12%)	23	0.617 (1.00)	0.332 (1.00)	1 (1.00)	1 (1.00)
21q loss	6 (23%)	20	0.617 (1.00)	0.92 (1.00)	0.542 (1.00)	0.542 (1.00)

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = CESC.clin.merged.picked.txt
Number of patients = 26
Number of significantly arm-level cnvs = 35
Number of selected clinical features = 4
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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