Correlation between copy number variations of arm-level result and selected clinical features
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 50 arm-level events and 6 clinical features across 160 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 50 arm-level events and 6 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 PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		COMPLETENESS
OF
RESECTION 		NUMBER
OF
LYMPH
NODES
nCNV (%) nWild-Type logrank test t-test Fisher's exact test Fisher's exact test Fisher's exact test t-test
1P GAIN MUTATION ANALYSIS 4 (2%) 156 100
(1.00) 		0.937
(1.00) 		0.0341
(1.00) 		0.366
(1.00) 		1
(1.00) 		0.849
(1.00)
1Q GAIN MUTATION ANALYSIS 7 (4%) 153 100
(1.00) 		0.996
(1.00) 		0.0944
(1.00) 		0.553
(1.00) 		0.682
(1.00) 		0.717
(1.00)
3P GAIN MUTATION ANALYSIS 12 (8%) 148 100
(1.00) 		0.0107
(1.00) 		0.00486
(1.00) 		1
(1.00) 		0.776
(1.00) 		0.248
(1.00)
3Q GAIN MUTATION ANALYSIS 16 (10%) 144 100
(1.00) 		0.00344
(0.986) 		0.0058
(1.00) 		0.68
(1.00) 		0.517
(1.00) 		0.488
(1.00)
4P GAIN MUTATION ANALYSIS 4 (2%) 156 100
(1.00) 		0.0834
(1.00) 		0.673
(1.00) 		1
(1.00) 		0.613
(1.00) 		0.00152
(0.45)
4Q GAIN MUTATION ANALYSIS 3 (2%) 157 100
(1.00) 		0.247
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.00152
(0.45)
5P GAIN MUTATION ANALYSIS 3 (2%) 157 100
(1.00) 		0.728
(1.00) 		0.322
(1.00) 		0.288
(1.00) 		0.16
(1.00) 		0.722
(1.00)
7P GAIN MUTATION ANALYSIS 30 (19%) 130 100
(1.00) 		0.257
(1.00) 		0.182
(1.00) 		0.487
(1.00) 		0.121
(1.00) 		0.312
(1.00)
7Q GAIN MUTATION ANALYSIS 27 (17%) 133 100
(1.00) 		0.472
(1.00) 		0.206
(1.00) 		1
(1.00) 		0.15
(1.00) 		0.138
(1.00)
8P GAIN MUTATION ANALYSIS 19 (12%) 141 100
(1.00) 		0.275
(1.00) 		0.301
(1.00) 		0.121
(1.00) 		1
(1.00) 		0.908
(1.00)
8Q GAIN MUTATION ANALYSIS 30 (19%) 130 100
(1.00) 		0.905
(1.00) 		0.439
(1.00) 		0.498
(1.00) 		0.366
(1.00) 		0.491
(1.00)
9P GAIN MUTATION ANALYSIS 7 (4%) 153 100
(1.00) 		0.217
(1.00) 		0.571
(1.00) 		0.0266
(1.00) 		0.649
(1.00) 		0.24
(1.00)
9Q GAIN MUTATION ANALYSIS 14 (9%) 146 100
(1.00) 		0.244
(1.00) 		0.137
(1.00) 		0.00535
(1.00) 		0.283
(1.00) 		0.156
(1.00)
10P GAIN MUTATION ANALYSIS 6 (4%) 154 100
(1.00) 		0.671
(1.00) 		0.746
(1.00) 		1
(1.00) 		0.083
(1.00) 		0.00152
(0.45)
10Q GAIN MUTATION ANALYSIS 7 (4%) 153 100
(1.00) 		0.56
(1.00) 		0.571
(1.00) 		0.497
(1.00) 		0.0783
(1.00) 		0.441
(1.00)
11P GAIN MUTATION ANALYSIS 5 (3%) 155 100
(1.00) 		0.664
(1.00) 		0.473
(1.00) 		0.435
(1.00) 		0.344
(1.00) 		0.995
(1.00)
11Q GAIN MUTATION ANALYSIS 7 (4%) 153 100
(1.00) 		0.967
(1.00) 		0.0944
(1.00) 		0.162
(1.00) 		0.266
(1.00) 		0.652
(1.00)
16P GAIN MUTATION ANALYSIS 8 (5%) 152 100
(1.00) 		0.573
(1.00) 		0.0247
(1.00) 		0.553
(1.00) 		0.325
(1.00) 		0.717
(1.00)
16Q GAIN MUTATION ANALYSIS 3 (2%) 157 100
(1.00) 		0.148
(1.00) 		0.322
(1.00) 		1
(1.00) 		1
(1.00)
18P GAIN MUTATION ANALYSIS 6 (4%) 154 100
(1.00) 		0.312
(1.00) 		0.362
(1.00) 		0.0878
(1.00) 		1
(1.00) 		0.45
(1.00)
20Q GAIN MUTATION ANALYSIS 5 (3%) 155 100
(1.00) 		0.0647
(1.00) 		0.0258
(1.00) 		1
(1.00) 		0.344
(1.00) 		0.00152
(0.45)
21Q GAIN MUTATION ANALYSIS 3 (2%) 157 100
(1.00) 		0.0824
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.00152
(0.45)
1P LOSS MUTATION ANALYSIS 3 (2%) 157 100
(1.00) 		0.621
(1.00) 		0.322
(1.00) 		1
(1.00) 		0.532
(1.00) 		0.00152
(0.45)
5P LOSS MUTATION ANALYSIS 4 (2%) 156 100
(1.00) 		0.448
(1.00) 		0.0341
(1.00) 		0.0561
(1.00) 		1
(1.00) 		0.365
(1.00)
5Q LOSS MUTATION ANALYSIS 5 (3%) 155 100
(1.00) 		0.468
(1.00) 		0.0258
(1.00) 		0.0878
(1.00) 		1
(1.00) 		0.359
(1.00)
6P LOSS MUTATION ANALYSIS 5 (3%) 155 100
(1.00) 		0.0618
(1.00) 		0.473
(1.00) 		0.0878
(1.00) 		0.613
(1.00) 		0.273
(1.00)
6Q LOSS MUTATION ANALYSIS 10 (6%) 150 100
(1.00) 		0.538
(1.00) 		0.348
(1.00) 		0.0744
(1.00) 		0.739
(1.00) 		0.179
(1.00)
8P LOSS MUTATION ANALYSIS 53 (33%) 107 100
(1.00) 		0.156
(1.00) 		0.04
(1.00) 		0.565
(1.00) 		0.175
(1.00) 		0.143
(1.00)
8Q LOSS MUTATION ANALYSIS 10 (6%) 150 100
(1.00) 		0.0526
(1.00) 		0.0305
(1.00) 		0.0744
(1.00) 		0.537
(1.00) 		0.157
(1.00)
9P LOSS MUTATION ANALYSIS 6 (4%) 154 100
(1.00) 		0.889
(1.00) 		0.102
(1.00) 		0.0163
(1.00) 		1
(1.00) 		0.205
(1.00)
10P LOSS MUTATION ANALYSIS 10 (6%) 150 100
(1.00) 		0.53
(1.00) 		0.00136
(0.406) 		0.0744
(1.00) 		0.537
(1.00) 		0.304
(1.00)
10Q LOSS MUTATION ANALYSIS 12 (8%) 148 100
(1.00) 		0.945
(1.00) 		0.0611
(1.00) 		0.025
(1.00) 		0.776
(1.00) 		0.262
(1.00)
12P LOSS MUTATION ANALYSIS 14 (9%) 146 100
(1.00) 		0.849
(1.00) 		0.539
(1.00) 		0.00779
(1.00) 		0.478
(1.00) 		0.178
(1.00)
12Q LOSS MUTATION ANALYSIS 7 (4%) 153 100
(1.00) 		0.529
(1.00) 		0.24
(1.00) 		0.0266
(1.00) 		1
(1.00) 		0.29
(1.00)
13Q LOSS MUTATION ANALYSIS 25 (16%) 135 100
(1.00) 		0.102
(1.00) 		0.788
(1.00) 		0.0143
(1.00) 		1
(1.00) 		0.169
(1.00)
14Q LOSS MUTATION ANALYSIS 8 (5%) 152 100
(1.00) 		0.282
(1.00) 		0.0247
(1.00) 		0.203
(1.00) 		0.266
(1.00) 		0.509
(1.00)
15Q LOSS MUTATION ANALYSIS 10 (6%) 150 100
(1.00) 		0.806
(1.00) 		0.00366
(1.00) 		0.00127
(0.38) 		1
(1.00) 		0.0595
(1.00)
16P LOSS MUTATION ANALYSIS 13 (8%) 147 100
(1.00) 		0.73
(1.00) 		0.376
(1.00) 		1
(1.00) 		0.228
(1.00) 		0.248
(1.00)
16Q LOSS MUTATION ANALYSIS 34 (21%) 126 100
(1.00) 		0.312
(1.00) 		0.28
(1.00) 		0.331
(1.00) 		0.649
(1.00) 		0.368
(1.00)
17P LOSS MUTATION ANALYSIS 25 (16%) 135 100
(1.00) 		0.726
(1.00) 		0.0741
(1.00) 		0.136
(1.00) 		1
(1.00) 		0.199
(1.00)
17Q LOSS MUTATION ANALYSIS 5 (3%) 155 100
(1.00) 		0.765
(1.00) 		0.0258
(1.00) 		0.435
(1.00) 		0.344
(1.00) 		0.995
(1.00)
18P LOSS MUTATION ANALYSIS 23 (14%) 137 100
(1.00) 		0.262
(1.00) 		0.124
(1.00) 		0.254
(1.00) 		0.606
(1.00) 		0.198
(1.00)
18Q LOSS MUTATION ANALYSIS 33 (21%) 127 100
(1.00) 		0.324
(1.00) 		0.226
(1.00) 		0.0834
(1.00) 		0.398
(1.00) 		0.251
(1.00)
19P LOSS MUTATION ANALYSIS 3 (2%) 157 100
(1.00) 		0.0335
(1.00) 		0.625
(1.00) 		1
(1.00) 		1
(1.00) 		0.00152
(0.45)
19Q LOSS MUTATION ANALYSIS 4 (2%) 156 100
(1.00) 		0.00278
(0.802) 		0.107
(1.00) 		0.366
(1.00) 		1
(1.00) 		0.849
(1.00)
20P LOSS MUTATION ANALYSIS 6 (4%) 154 100
(1.00) 		0.72
(1.00) 		0.028
(1.00) 		0.435
(1.00) 		0.344
(1.00) 		0.632
(1.00)
20Q LOSS MUTATION ANALYSIS 3 (2%) 157 100
(1.00) 		0.581
(1.00) 		0.0474
(1.00) 		1
(1.00) 		0.16
(1.00) 		0.00152
(0.45)
21Q LOSS MUTATION ANALYSIS 8 (5%) 152 100
(1.00) 		0.688
(1.00) 		0.175
(1.00) 		0.00248
(0.717) 		1
(1.00) 		0.102
(1.00)
22Q LOSS MUTATION ANALYSIS 14 (9%) 146 100
(1.00) 		0.662
(1.00) 		0.00747
(1.00) 		0.0442
(1.00) 		0.618
(1.00) 		0.297
(1.00)
XQ LOSS MUTATION ANALYSIS 7 (4%) 153 100
(1.00) 		0.913
(1.00) 		0.308
(1.00) 		0.162
(1.00) 		1
(1.00) 		0.298
(1.00)
Methods & Data
Input

  • Copy number data file = transformed.cor.cli.txt

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

  • Number of patients = 160

  • Number of significantly arm-level cnvs = 50

  • Number of selected clinical features = 6

  • Exclude regions 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.

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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