Skin Cutaneous Melanoma: Correlation between copy number variation genes (focal) and selected clinical features<BR>(WT cohort)

Skin Cutaneous Melanoma: Correlation between copy number variation genes (focal) and selected clinical features
(WT cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

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 arm-level results and 7 clinical features across 23 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 20 arm-level results and 7 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	PRIMARY SITE OF DISEASE	GENDER	LYMPH NODE METASTASIS	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Chi-square test	t-test	Chi-square test
Amp Peak 1(1p12)	9 (39%)	14	0.591 (1.00)	0.681 (1.00)	0.355 (1.00)	0.0361 (1.00)	0.627 (1.00)		0.638 (1.00)
Amp Peak 2(1q44)	11 (48%)	12	0.201 (1.00)	0.298 (1.00)	0.211 (1.00)	1 (1.00)	0.15 (1.00)		0.625 (1.00)
Amp Peak 3(4q12)	8 (35%)	15	0.608 (1.00)	0.246 (1.00)	0.318 (1.00)	1 (1.00)	0.263 (1.00)		0.233 (1.00)
Amp Peak 4(5p15 33)	7 (30%)	16	0.248 (1.00)	0.0206 (1.00)	0.816 (1.00)	1 (1.00)	0.829 (1.00)		0.103 (1.00)
Amp Peak 5(5q35 3)	5 (22%)	18	0.361 (1.00)	0.931 (1.00)	0.14 (1.00)	0.317 (1.00)	0.732 (1.00)		0.573 (1.00)
Amp Peak 6(8q11 21)	12 (52%)	11	0.705 (1.00)	0.322 (1.00)	1 (1.00)	0.414 (1.00)	0.0315 (1.00)		0.0424 (1.00)
Amp Peak 7(11q13 3)	5 (22%)	18	0.603 (1.00)	0.0406 (1.00)	1 (1.00)	1 (1.00)	0.629 (1.00)		0.525 (1.00)
Amp Peak 8(11q13 3)	5 (22%)	18	0.603 (1.00)	0.0406 (1.00)	1 (1.00)	1 (1.00)	0.629 (1.00)		0.525 (1.00)
Amp Peak 9(12q14 1)	10 (43%)	13	0.265 (1.00)	0.0127 (1.00)	1 (1.00)	1 (1.00)	0.404 (1.00)		0.103 (1.00)
Amp Peak 10(17q25 3)	10 (43%)	13	0.872 (1.00)	0.4 (1.00)	0.126 (1.00)	0.68 (1.00)	0.641 (1.00)		0.342 (1.00)
Amp Peak 11(22q13 1)	12 (52%)	11	0.324 (1.00)	0.53 (1.00)	0.334 (1.00)	0.684 (1.00)	0.37 (1.00)		0.151 (1.00)
Amp Peak 12(Xq28)	8 (35%)	15	0.632 (1.00)	0.00453 (0.544)	0.584 (1.00)	0.667 (1.00)	0.843 (1.00)		0.0986 (1.00)
Del Peak 1(2q37 3)	7 (30%)	16	0.627 (1.00)	0.285 (1.00)	1 (1.00)	0.371 (1.00)	0.0615 (1.00)		0.927 (1.00)
Del Peak 2(4q34 3)	7 (30%)	16	0.77 (1.00)	0.515 (1.00)	0.458 (1.00)	0.371 (1.00)	0.843 (1.00)		0.635 (1.00)
Del Peak 3(5p15 31)	5 (22%)	18	0.518 (1.00)	0.887 (1.00)	0.805 (1.00)	1 (1.00)	0.607 (1.00)		0.194 (1.00)
Del Peak 4(9p21 3)	14 (61%)	9	0.807 (1.00)	0.676 (1.00)	0.502 (1.00)	0.68 (1.00)	0.588 (1.00)		0.841 (1.00)
Del Peak 5(10q26 3)	13 (57%)	10	0.865 (1.00)	0.885 (1.00)	0.843 (1.00)	0.414 (1.00)	0.153 (1.00)		0.61 (1.00)
Del Peak 6(11q23 3)	14 (61%)	9	0.707 (1.00)	0.367 (1.00)	1 (1.00)	0.214 (1.00)	0.166 (1.00)		0.352 (1.00)
Del Peak 7(15q14)	9 (39%)	14	0.248 (1.00)	0.44 (1.00)	0.688 (1.00)	0.4 (1.00)	0.691 (1.00)		0.841 (1.00)
Del Peak 8(16p13 3)	4 (17%)	19	0.741 (1.00)	0.0114 (1.00)	0.753 (1.00)	0.59 (1.00)	0.772 (1.00)		0.0533 (1.00)

Methods & Data

Input

Mutation data file = all_lesions.conf_99.cnv.cluster.txt
Clinical data file = SKCM-WT.clin.merged.picked.txt
Number of patients = 23
Number of significantly arm-level cnvs = 20
Number of selected clinical features = 7
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[5] 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)

Made with Nozzle