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 subtypes identified by 22 different clustering approaches and 3 clinical features across 146 patients, no significant finding detected with Q value < 0.25.

  • 2 subtypes identified in current cancer cohort by '1q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '3p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '3q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '7p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '7q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '13q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '17p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '20p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '22q loss mutation analysis'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 22 different clustering approaches and 3 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
Statistical Tests logrank test t-test Fisher's exact test
1q gain 1
(1.00) 		0.299
(1.00) 		1
(1.00)
3p gain 1
(1.00) 		0.646
(1.00) 		1
(1.00)
3q gain 1
(1.00) 		0.329
(1.00) 		1
(1.00)
7p gain 1
(1.00) 		0.0153
(1.00) 		1
(1.00)
7q gain 1
(1.00) 		0.0682
(1.00) 		1
(1.00)
8p gain 1
(1.00) 		0.537
(1.00) 		1
(1.00)
8q gain 1
(1.00) 		0.603
(1.00) 		1
(1.00)
9q gain 1
(1.00) 		0.276
(1.00) 		1
(1.00)
5q loss 1
(1.00) 		0.214
(1.00) 		1
(1.00)
6q loss 1
(1.00) 		0.258
(1.00) 		1
(1.00)
8p loss 1
(1.00) 		0.0476
(1.00) 		0.327
(1.00)
8q loss 1
(1.00) 		0.635
(1.00) 		1
(1.00)
10p loss 1
(1.00) 		0.91
(1.00) 		1
(1.00)
10q loss 1
(1.00) 		0.396
(1.00) 		1
(1.00)
12p loss 1
(1.00) 		0.674
(1.00) 		1
(1.00)
13q loss 1
(1.00) 		0.82
(1.00) 		1
(1.00)
16q loss 1
(1.00) 		0.146
(1.00) 		1
(1.00)
17p loss 1
(1.00) 		0.529
(1.00) 		1
(1.00)
18p loss 1
(1.00) 		0.621
(1.00) 		1
(1.00)
18q loss 1
(1.00) 		0.3
(1.00) 		1
(1.00)
20p loss 1
(1.00) 		0.451
(1.00) 		0.131
(1.00)
22q loss 1
(1.00) 		0.274
(1.00) 		0.162
(1.00)
Clustering Approach #1: '1q gain mutation analysis'

Table S1.  Get Full Table Description of clustering approach #1: '1q gain mutation analysis'

Cluster Labels 1Q GAIN MUTATED 1Q GAIN WILD-TYPE
Number of samples 4 142
Clustering Approach #2: '3p gain mutation analysis'

Table S2.  Get Full Table Description of clustering approach #2: '3p gain mutation analysis'

Cluster Labels 3P GAIN MUTATED 3P GAIN WILD-TYPE
Number of samples 4 142
Clustering Approach #3: '3q gain mutation analysis'

Table S3.  Get Full Table Description of clustering approach #3: '3q gain mutation analysis'

Cluster Labels 3Q GAIN MUTATED 3Q GAIN WILD-TYPE
Number of samples 6 140
Clustering Approach #4: '7p gain mutation analysis'

Table S4.  Get Full Table Description of clustering approach #4: '7p gain mutation analysis'

Cluster Labels 7P GAIN MUTATED 7P GAIN WILD-TYPE
Number of samples 15 131
Clustering Approach #5: '7q gain mutation analysis'

Table S5.  Get Full Table Description of clustering approach #5: '7q gain mutation analysis'

Cluster Labels 7Q GAIN MUTATED 7Q GAIN WILD-TYPE
Number of samples 12 134
Clustering Approach #6: '8p gain mutation analysis'

Table S6.  Get Full Table Description of clustering approach #6: '8p gain mutation analysis'

Cluster Labels 8P GAIN MUTATED 8P GAIN WILD-TYPE
Number of samples 6 140
Clustering Approach #7: '8q gain mutation analysis'

Table S7.  Get Full Table Description of clustering approach #7: '8q gain mutation analysis'

Cluster Labels 8Q GAIN MUTATED 8Q GAIN WILD-TYPE
Number of samples 13 133
Clustering Approach #8: '9q gain mutation analysis'

Table S8.  Get Full Table Description of clustering approach #8: '9q gain mutation analysis'

Cluster Labels 9Q GAIN MUTATED 9Q GAIN WILD-TYPE
Number of samples 3 143
Clustering Approach #9: '5q loss mutation analysis'

Table S9.  Get Full Table Description of clustering approach #9: '5q loss mutation analysis'

Cluster Labels 5Q LOSS MUTATED 5Q LOSS WILD-TYPE
Number of samples 3 143
Clustering Approach #10: '6q loss mutation analysis'

Table S10.  Get Full Table Description of clustering approach #10: '6q loss mutation analysis'

Cluster Labels 6Q LOSS MUTATED 6Q LOSS WILD-TYPE
Number of samples 7 139
Clustering Approach #11: '8p loss mutation analysis'

Table S11.  Get Full Table Description of clustering approach #11: '8p loss mutation analysis'

Cluster Labels 8P LOSS MUTATED 8P LOSS WILD-TYPE
Number of samples 38 108
Clustering Approach #12: '8q loss mutation analysis'

Table S12.  Get Full Table Description of clustering approach #12: '8q loss mutation analysis'

Cluster Labels 8Q LOSS MUTATED 8Q LOSS WILD-TYPE
Number of samples 4 142
Clustering Approach #13: '10p loss mutation analysis'

Table S13.  Get Full Table Description of clustering approach #13: '10p loss mutation analysis'

Cluster Labels 10P LOSS MUTATED 10P LOSS WILD-TYPE
Number of samples 5 141
Clustering Approach #14: '10q loss mutation analysis'

Table S14.  Get Full Table Description of clustering approach #14: '10q loss mutation analysis'

Cluster Labels 10Q LOSS MUTATED 10Q LOSS WILD-TYPE
Number of samples 4 142
Clustering Approach #15: '12p loss mutation analysis'

Table S15.  Get Full Table Description of clustering approach #15: '12p loss mutation analysis'

Cluster Labels 12P LOSS MUTATED 12P LOSS WILD-TYPE
Number of samples 7 139
Clustering Approach #16: '13q loss mutation analysis'

Table S16.  Get Full Table Description of clustering approach #16: '13q loss mutation analysis'

Cluster Labels 13Q LOSS MUTATED 13Q LOSS WILD-TYPE
Number of samples 11 135
Clustering Approach #17: '16q loss mutation analysis'

Table S17.  Get Full Table Description of clustering approach #17: '16q loss mutation analysis'

Cluster Labels 16Q LOSS MUTATED 16Q LOSS WILD-TYPE
Number of samples 18 128
Clustering Approach #18: '17p loss mutation analysis'

Table S18.  Get Full Table Description of clustering approach #18: '17p loss mutation analysis'

Cluster Labels 17P LOSS MUTATED 17P LOSS WILD-TYPE
Number of samples 17 129
Clustering Approach #19: '18p loss mutation analysis'

Table S19.  Get Full Table Description of clustering approach #19: '18p loss mutation analysis'

Cluster Labels 18P LOSS MUTATED 18P LOSS WILD-TYPE
Number of samples 14 132
Clustering Approach #20: '18q loss mutation analysis'

Table S20.  Get Full Table Description of clustering approach #20: '18q loss mutation analysis'

Cluster Labels 18Q LOSS MUTATED 18Q LOSS WILD-TYPE
Number of samples 19 127
Clustering Approach #21: '20p loss mutation analysis'

Table S21.  Get Full Table Description of clustering approach #21: '20p loss mutation analysis'

Cluster Labels 20P LOSS MUTATED 20P LOSS WILD-TYPE
Number of samples 4 142
Clustering Approach #22: '22q loss mutation analysis'

Table S22.  Get Full Table Description of clustering approach #22: '22q loss mutation analysis'

Cluster Labels 22Q LOSS MUTATED 22Q LOSS WILD-TYPE
Number of samples 5 141
Methods & Data
Input

  • Cluster data file = broad_values_by_arm.mutsig.cluster.txt

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

  • Number of patients = 146

  • Number of clustering approaches = 22

  • Number of selected clinical features = 3

  • Exclude small clusters that include fewer than K patients, 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 two tumor subtypes using 't.test' function in R

Fisher's exact test

For binary clinical features, 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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