Correlation between copy number variations of arm-level result and selected clinical features
Kidney Chromophobe (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
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 (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1SJ1HMC
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 43 arm-level results and 7 clinical features across 25 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 43 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 GENDER DISTANT
METASTASIS
LYMPH
NODE
METASTASIS
TUMOR
STAGECODE
NEOPLASM
DISEASESTAGE
nCNV (%) nWild-Type logrank test t-test Fisher's exact test Fisher's exact test Fisher's exact test t-test Fisher's exact test
3p gain 0 (0%) 22 0.107
(1.00)
0.23
(1.00)
0.169
(1.00)
0.624
(1.00)
3q gain 0 (0%) 22 0.107
(1.00)
0.23
(1.00)
0.169
(1.00)
0.624
(1.00)
4p gain 0 (0%) 15 0.486
(1.00)
0.93
(1.00)
0.414
(1.00)
0.657
(1.00)
0.326
(1.00)
0.479
(1.00)
4q gain 0 (0%) 16 0.486
(1.00)
0.443
(1.00)
0.677
(1.00)
0.657
(1.00)
0.381
(1.00)
0.529
(1.00)
7p gain 0 (0%) 16 0.595
(1.00)
0.461
(1.00)
0.208
(1.00)
1
(1.00)
0.192
(1.00)
1
(1.00)
7q gain 0 (0%) 16 0.595
(1.00)
0.461
(1.00)
0.208
(1.00)
1
(1.00)
0.192
(1.00)
1
(1.00)
8p gain 0 (0%) 17 0.0231
(1.00)
0.145
(1.00)
0.0421
(1.00)
1
(1.00)
0.00468
(1.00)
0.54
(1.00)
8q gain 0 (0%) 16 0.0231
(1.00)
0.168
(1.00)
0.033
(1.00)
0.657
(1.00)
0.025
(1.00)
0.0981
(1.00)
9p gain 0 (0%) 21 0.397
(1.00)
0.264
(1.00)
0.604
(1.00)
0.101
(1.00)
0.32
(1.00)
9q gain 0 (0%) 21 0.397
(1.00)
0.264
(1.00)
0.604
(1.00)
0.101
(1.00)
0.32
(1.00)
11p gain 0 (0%) 17 0.486
(1.00)
0.478
(1.00)
0.234
(1.00)
1
(1.00)
0.167
(1.00)
0.846
(1.00)
11q gain 0 (0%) 16 0.486
(1.00)
0.528
(1.00)
0.208
(1.00)
0.657
(1.00)
0.381
(1.00)
0.529
(1.00)
12p gain 0 (0%) 18 0.397
(1.00)
0.361
(1.00)
0.09
(1.00)
1
(1.00)
0.137
(1.00)
1
(1.00)
12q gain 0 (0%) 19 0.533
(1.00)
0.701
(1.00)
0.18
(1.00)
1
(1.00)
0.2
(1.00)
1
(1.00)
14q gain 0 (0%) 16 0.595
(1.00)
0.461
(1.00)
0.208
(1.00)
1
(1.00)
0.192
(1.00)
1
(1.00)
15q gain 0 (0%) 17 0.595
(1.00)
0.313
(1.00)
0.234
(1.00)
1
(1.00)
0.342
(1.00)
1
(1.00)
16p gain 0 (0%) 17 0.533
(1.00)
0.603
(1.00)
1
(1.00)
1
(1.00)
0.342
(1.00)
0.846
(1.00)
16q gain 0 (0%) 17 0.533
(1.00)
0.603
(1.00)
1
(1.00)
1
(1.00)
0.342
(1.00)
0.846
(1.00)
18p gain 0 (0%) 17 0.595
(1.00)
0.651
(1.00)
0.234
(1.00)
1
(1.00)
0.074
(1.00)
1
(1.00)
18q gain 0 (0%) 17 0.595
(1.00)
0.651
(1.00)
0.234
(1.00)
1
(1.00)
0.074
(1.00)
1
(1.00)
19p gain 0 (0%) 19 0.533
(1.00)
0.625
(1.00)
0.661
(1.00)
1
(1.00)
0.806
(1.00)
0.611
(1.00)
19q gain 0 (0%) 20 0.533
(1.00)
0.486
(1.00)
0.341
(1.00)
1
(1.00)
0.582
(1.00)
0.884
(1.00)
20p gain 0 (0%) 18 0.421
(1.00)
0.518
(1.00)
0.407
(1.00)
0.231
(1.00)
0.544
(1.00)
20q gain 0 (0%) 18 0.421
(1.00)
0.518
(1.00)
0.407
(1.00)
0.231
(1.00)
0.544
(1.00)
22q gain 0 (0%) 18 0.195
(1.00)
0.717
(1.00)
0.09
(1.00)
0.679
(1.00)
0.215
(1.00)
Xq gain 0 (0%) 20 0.674
(1.00)
0.536
(1.00)
0.0464
(1.00)
0.582
(1.00)
0.884
(1.00)
1p loss 0 (0%) 11 0.486
(1.00)
0.903
(1.00)
0.0419
(1.00)
1
(1.00)
0.838
(1.00)
0.295
(1.00)
1q loss 0 (0%) 11 0.486
(1.00)
0.903
(1.00)
0.0419
(1.00)
1
(1.00)
0.838
(1.00)
0.295
(1.00)
2p loss 0 (0%) 12 0.379
(1.00)
0.234
(1.00)
0.428
(1.00)
1
(1.00)
1
(1.00)
0.104
(1.00)
2q loss 0 (0%) 12 0.379
(1.00)
0.234
(1.00)
0.428
(1.00)
1
(1.00)
1
(1.00)
0.104
(1.00)
3p loss 0 (0%) 21 0.533
(1.00)
0.277
(1.00)
1
(1.00)
1
(1.00)
0.854
(1.00)
3q loss 0 (0%) 21 0.533
(1.00)
0.277
(1.00)
1
(1.00)
1
(1.00)
0.854
(1.00)
6p loss 0 (0%) 10 0.482
(1.00)
0.69
(1.00)
0.0992
(1.00)
1
(1.00)
0.709
(1.00)
0.105
(1.00)
6q loss 0 (0%) 10 0.482
(1.00)
0.69
(1.00)
0.0992
(1.00)
1
(1.00)
0.709
(1.00)
0.105
(1.00)
8p loss 0 (0%) 22 0.674
(1.00)
0.146
(1.00)
1
(1.00)
0.101
(1.00)
0.499
(1.00)
10p loss 0 (0%) 13 0.379
(1.00)
0.44
(1.00)
0.238
(1.00)
0.307
(1.00)
0.802
(1.00)
10q loss 0 (0%) 13 0.379
(1.00)
0.44
(1.00)
0.238
(1.00)
0.307
(1.00)
0.802
(1.00)
13q loss 0 (0%) 12 0.379
(1.00)
0.689
(1.00)
0.111
(1.00)
1
(1.00)
0.695
(1.00)
0.104
(1.00)
17p loss 0 (0%) 10 0.482
(1.00)
0.69
(1.00)
0.0992
(1.00)
1
(1.00)
0.709
(1.00)
0.105
(1.00)
17q loss 0 (0%) 10 0.482
(1.00)
0.69
(1.00)
0.0992
(1.00)
1
(1.00)
0.709
(1.00)
0.105
(1.00)
18q loss 0 (0%) 22 0.421
(1.00)
0.454
(1.00)
0.565
(1.00)
0.407
(1.00)
0.317
(1.00)
21q loss 0 (0%) 16 0.138
(1.00)
0.336
(1.00)
0.434
(1.00)
0.381
(1.00)
1
(1.00)
Xq loss 0 (0%) 16 0.165
(1.00)
0.981
(1.00)
0.115
(1.00)
0.702
(1.00)
0.0845
(1.00)
Methods & Data
Input
  • Mutation data file = broad_values_by_arm.mutsig.cluster.txt

  • Clinical data file = KICH-TP.clin.merged.picked.txt

  • Number of patients = 25

  • Number of significantly arm-level cnvs = 43

  • 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

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)