Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features<BR>(NRAS_Hotspot

Skin Cutaneous Melanoma: Correlation between copy number variations of arm-level result and selected clinical features
(NRAS_Hotspot_Mutants cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 58 arm-level results and 7 clinical features across 45 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 58 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
1p gain	7 (16%)	38		0.665 (1.00)	0.844 (1.00)	0.686 (1.00)	0.834 (1.00)		0.964 (1.00)
1q gain	24 (53%)	21	0.673 (1.00)	0.864 (1.00)	0.696 (1.00)	1 (1.00)	0.779 (1.00)		0.949 (1.00)
2p gain	7 (16%)	38	0.596 (1.00)	0.582 (1.00)	0.199 (1.00)	0.225 (1.00)	0.0523 (1.00)		0.339 (1.00)
2q gain	7 (16%)	38	0.596 (1.00)	0.582 (1.00)	0.199 (1.00)	0.225 (1.00)	0.0523 (1.00)		0.339 (1.00)
3p gain	5 (11%)	40	0.623 (1.00)	0.589 (1.00)	0.103 (1.00)	1 (1.00)	0.0394 (1.00)		0.597 (1.00)
3q gain	5 (11%)	40		0.626 (1.00)	0.103 (1.00)	1 (1.00)	0.0548 (1.00)		0.798 (1.00)
4p gain	3 (7%)	42		0.221 (1.00)	0.174 (1.00)	1 (1.00)	0.0303 (1.00)		0.674 (1.00)
4q gain	3 (7%)	42		0.221 (1.00)	0.174 (1.00)	1 (1.00)	0.0303 (1.00)		0.674 (1.00)
6p gain	21 (47%)	24	0.188 (1.00)	0.237 (1.00)	0.837 (1.00)	0.369 (1.00)	0.476 (1.00)		0.642 (1.00)
7p gain	9 (20%)	36	0.519 (1.00)	0.0189 (1.00)	0.562 (1.00)	1 (1.00)	0.295 (1.00)		0.674 (1.00)
7q gain	7 (16%)	38	0.45 (1.00)	0.317 (1.00)	0.248 (1.00)	0.225 (1.00)	0.0127 (1.00)		0.182 (1.00)
8p gain	11 (24%)	34	0.0843 (1.00)	0.683 (1.00)	0.0547 (1.00)	0.72 (1.00)	0.198 (1.00)		0.785 (1.00)
8q gain	16 (36%)	29	0.407 (1.00)	0.392 (1.00)	0.102 (1.00)	0.752 (1.00)	0.563 (1.00)		0.946 (1.00)
11p gain	4 (9%)	41		0.868 (1.00)	0.802 (1.00)	0.281 (1.00)	0.0784 (1.00)		0.564 (1.00)
11q gain	4 (9%)	41		0.868 (1.00)	0.802 (1.00)	0.281 (1.00)	0.0784 (1.00)		0.564 (1.00)
12p gain	3 (7%)	42		0.821 (1.00)	1 (1.00)	0.0395 (1.00)	0.00871 (1.00)		0.936 (1.00)
13q gain	16 (36%)	29	0.888 (1.00)	0.924 (1.00)	0.115 (1.00)	1 (1.00)	0.651 (1.00)		0.36 (1.00)
14q gain	4 (9%)	41		0.14 (1.00)	0.802 (1.00)	1 (1.00)	0.88 (1.00)		0.693 (1.00)
15q gain	6 (13%)	39		0.807 (1.00)	0.236 (1.00)	0.65 (1.00)	0.0904 (1.00)		0.798 (1.00)
16p gain	6 (13%)	39	0.286 (1.00)	0.962 (1.00)	0.011 (1.00)	0.65 (1.00)	0.761 (1.00)		0.732 (1.00)
16q gain	5 (11%)	40		0.642 (1.00)	0.0365 (1.00)	0.33 (1.00)	0.798 (1.00)		0.798 (1.00)
17p gain	3 (7%)	42		0.734 (1.00)	0.174 (1.00)	0.0395 (1.00)	0.00871 (1.00)		0.674 (1.00)
17q gain	7 (16%)	38	0.138 (1.00)	0.308 (1.00)	0.6 (1.00)	0.0788 (1.00)	0.00436 (1.00)		0.389 (1.00)
18p gain	4 (9%)	41	0.967 (1.00)	0.782 (1.00)	0.306 (1.00)	1 (1.00)	0.103 (1.00)		0.0546 (1.00)
18q gain	4 (9%)	41	0.967 (1.00)	0.782 (1.00)	0.306 (1.00)	1 (1.00)	0.103 (1.00)		0.0546 (1.00)
19p gain	5 (11%)	40		0.141 (1.00)	0.178 (1.00)	0.33 (1.00)	0.773 (1.00)		0.608 (1.00)
19q gain	6 (13%)	39	0.578 (1.00)	0.0752 (1.00)	0.595 (1.00)	0.166 (1.00)	0.331 (1.00)		0.111 (1.00)
20p gain	10 (22%)	35	0.521 (1.00)	0.507 (1.00)	0.036 (1.00)	0.455 (1.00)	0.541 (1.00)		0.949 (1.00)
20q gain	13 (29%)	32	0.474 (1.00)	0.241 (1.00)	0.16 (1.00)	0.494 (1.00)	0.569 (1.00)		0.808 (1.00)
21q gain	7 (16%)	38	0.909 (1.00)	0.171 (1.00)	0.00295 (0.978)	0.225 (1.00)	0.202 (1.00)		0.546 (1.00)
22q gain	10 (22%)	35	0.298 (1.00)	0.569 (1.00)	0.271 (1.00)	0.455 (1.00)	0.0213 (1.00)		0.0523 (1.00)
4p loss	5 (11%)	40		0.976 (1.00)	0.426 (1.00)	0.00358 (1.00)	0.865 (1.00)		0.798 (1.00)
4q loss	6 (13%)	39	0.604 (1.00)	0.61 (1.00)	0.819 (1.00)	0.0165 (1.00)	0.497 (1.00)		0.389 (1.00)
5p loss	10 (22%)	35	0.745 (1.00)	0.00444 (1.00)	0.209 (1.00)	0.455 (1.00)	0.459 (1.00)		0.399 (1.00)
5q loss	12 (27%)	33	0.952 (1.00)	0.325 (1.00)	0.0674 (1.00)	1 (1.00)	0.211 (1.00)		0.339 (1.00)
6p loss	6 (13%)	39	0.616 (1.00)	0.369 (1.00)	0.0778 (1.00)	1 (1.00)	0.761 (1.00)		0.0919 (1.00)
6q loss	22 (49%)	23	0.652 (1.00)	0.785 (1.00)	0.765 (1.00)	0.221 (1.00)	0.734 (1.00)		0.451 (1.00)
8p loss	8 (18%)	37	0.196 (1.00)	0.999 (1.00)	0.307 (1.00)	1 (1.00)	0.479 (1.00)		0.182 (1.00)
9p loss	28 (62%)	17	0.961 (1.00)	0.0245 (1.00)	0.621 (1.00)	1 (1.00)	0.208 (1.00)		0.568 (1.00)
9q loss	22 (49%)	23	0.0293 (1.00)	0.00997 (1.00)	0.914 (1.00)	1 (1.00)	0.496 (1.00)		0.327 (1.00)
10p loss	19 (42%)	26	0.0958 (1.00)	0.171 (1.00)	0.291 (1.00)	0.212 (1.00)	0.0268 (1.00)		0.497 (1.00)
10q loss	20 (44%)	25	0.131 (1.00)	0.423 (1.00)	0.0962 (1.00)	0.348 (1.00)	0.0297 (1.00)		0.728 (1.00)
11p loss	15 (33%)	30	0.95 (1.00)	0.101 (1.00)	0.359 (1.00)	0.105 (1.00)	0.223 (1.00)		0.154 (1.00)
11q loss	16 (36%)	29	0.41 (1.00)	0.369 (1.00)	0.409 (1.00)	0.518 (1.00)	0.728 (1.00)		0.234 (1.00)
12p loss	6 (13%)	39	0.0911 (1.00)	0.955 (1.00)	0.819 (1.00)	0.399 (1.00)	0.832 (1.00)		0.157 (1.00)
12q loss	9 (20%)	36	0.399 (1.00)	0.93 (1.00)	0.562 (1.00)	1 (1.00)	0.812 (1.00)		0.384 (1.00)
13q loss	4 (9%)	41	0.228 (1.00)	0.516 (1.00)	1 (1.00)	0.608 (1.00)	0.367 (1.00)		0.246 (1.00)
14q loss	12 (27%)	33	0.721 (1.00)	0.984 (1.00)	0.891 (1.00)	1 (1.00)	0.416 (1.00)		0.674 (1.00)
15q loss	4 (9%)	41	0.816 (1.00)	0.573 (1.00)	0.144 (1.00)	1 (1.00)	0.254 (1.00)		0.773 (1.00)
16q loss	8 (18%)	37	0.0702 (1.00)	0.438 (1.00)	1 (1.00)	0.691 (1.00)	0.3 (1.00)		0.126 (1.00)
17p loss	15 (33%)	30	0.957 (1.00)	0.158 (1.00)	0.816 (1.00)	0.189 (1.00)	0.401 (1.00)		0.766 (1.00)
17q loss	3 (7%)	42		0.925 (1.00)	0.316 (1.00)	1 (1.00)	0.545 (1.00)		0.154 (1.00)
18p loss	9 (20%)	36	0.759 (1.00)	0.0648 (1.00)	0.488 (1.00)	0.7 (1.00)	0.344 (1.00)		0.461 (1.00)
18q loss	8 (18%)	37	0.759 (1.00)	0.0887 (1.00)	0.307 (1.00)	1 (1.00)	0.214 (1.00)		0.344 (1.00)
19p loss	6 (13%)	39	0.739 (1.00)	0.448 (1.00)	1 (1.00)	0.166 (1.00)	0.526 (1.00)		0.339 (1.00)
19q loss	4 (9%)	41	0.444 (1.00)	0.0678 (1.00)	0.444 (1.00)	0.608 (1.00)	0.223 (1.00)		0.693 (1.00)
21q loss	4 (9%)	41		0.583 (1.00)	0.619 (1.00)	1 (1.00)	0.799 (1.00)		0.608 (1.00)
22q loss	5 (11%)	40		0.706 (1.00)	0.522 (1.00)	1 (1.00)	0.763 (1.00)		0.242 (1.00)

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = SKCM-NRAS_Hotspot_Mutants.clin.merged.picked.txt
Number of patients = 45
Number of significantly arm-level cnvs = 58
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)

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