Correlation between copy number variation genes (focal) and selected clinical features

Overview

Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

Testing the association between subtypes identified by 22 different clustering approaches and 8 clinical features across 103 patients, 2 significant findings detected with Q value < 0.25.

2 subtypes identified in current cancer cohort by 'Amp Peak 1(2q32.1) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 2(3p14.1) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 3(5q35.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 4(6p12.3) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 5(7q22.1) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 6(17q25.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 7(19p13.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 8(19q13.11) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 1(1p36.31) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 2(2p11.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 3(2q37.3) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 4(3p22.1) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 5(4q31.22) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 6(5q35.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 7(6q22.31) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 8(9p21.3) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 9(14q11.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 10(14q23.3) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 11(14q32.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 12(19q13.42) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 13(Xq21.31) mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by 'Del Peak 14(Xq28) mutation analysis'. These subtypes correlate to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 22 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE
Statistical Tests	logrank test	t-test	Fisher's exact test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
Amp Peak 1(2q32 1)	0.602 (1.00)	0.388 (1.00)	0.793 (1.00)	0.5 (1.00)	0.134 (1.00)	0.508 (1.00)	0.0518 (1.00)	0.065 (1.00)
Amp Peak 2(3p14 1)	0.601 (1.00)	0.73 (1.00)	0.238 (1.00)	0.141 (1.00)	0.0823 (1.00)	1 (1.00)	0.651 (1.00)	0.265 (1.00)
Amp Peak 3(5q35 2)	0.885 (1.00)	0.979 (1.00)	1 (1.00)	0.395 (1.00)	0.206 (1.00)	1 (1.00)	0.668 (1.00)	0.337 (1.00)
Amp Peak 4(6p12 3)	0.57 (1.00)	0.349 (1.00)	1 (1.00)		0.164 (1.00)	0.506 (1.00)	0.217 (1.00)	0.262 (1.00)
Amp Peak 5(7q22 1)	0.0626 (1.00)	0.784 (1.00)	0.135 (1.00)	0.2 (1.00)	0.00703 (1.00)	0.686 (1.00)	0.39 (1.00)	0.00168 (0.276)
Amp Peak 6(17q25 2)	0.547 (1.00)	0.0893 (1.00)	0.0692 (1.00)	0.556 (1.00)	0.356 (1.00)	0.783 (1.00)	0.018 (1.00)	0.704 (1.00)
Amp Peak 7(19p13 2)	0.999 (1.00)	0.0722 (1.00)	1 (1.00)	0.144 (1.00)	0.0769 (1.00)	1 (1.00)	1 (1.00)	0.741 (1.00)
Amp Peak 8(19q13 11)	0.374 (1.00)	0.261 (1.00)	1 (1.00)		0.777 (1.00)	1 (1.00)	0.715 (1.00)	0.807 (1.00)
Del Peak 1(1p36 31)	0.236 (1.00)	0.636 (1.00)	0.616 (1.00)	0.396 (1.00)	0.125 (1.00)	0.263 (1.00)	0.0666 (1.00)	0.107 (1.00)
Del Peak 2(2p11 2)	0.357 (1.00)	0.716 (1.00)	1 (1.00)		0.526 (1.00)		1 (1.00)	0.561 (1.00)
Del Peak 3(2q37 3)	0.652 (1.00)	0.338 (1.00)	0.324 (1.00)		0.00909 (1.00)		0.32 (1.00)	0.0239 (1.00)
Del Peak 4(3p22 1)	0.211 (1.00)	0.0512 (1.00)	0.661 (1.00)		0.0728 (1.00)	0.55 (1.00)	0.342 (1.00)	0.0746 (1.00)
Del Peak 5(4q31 22)	0.13 (1.00)	0.281 (1.00)	0.324 (1.00)		0.221 (1.00)	0.359 (1.00)	0.0927 (1.00)	0.262 (1.00)
Del Peak 6(5q35 2)	0.61 (1.00)	0.113 (1.00)	0.265 (1.00)		0.0124 (1.00)	0.138 (1.00)	0.307 (1.00)	0.0445 (1.00)
Del Peak 7(6q22 31)	0.0444 (1.00)	0.911 (1.00)	0.0506 (1.00)	0.392 (1.00)	0.0277 (1.00)	0.836 (1.00)	0.548 (1.00)	0.153 (1.00)
Del Peak 8(9p21 3)	0.00552 (0.895)	0.326 (1.00)	0.0389 (1.00)	0.424 (1.00)	0.0033 (0.538)	0.311 (1.00)	0.0361 (1.00)	0.0102 (1.00)
Del Peak 9(14q11 2)	0.252 (1.00)	0.96 (1.00)	0.802 (1.00)	0.457 (1.00)	0.284 (1.00)	0.663 (1.00)	0.145 (1.00)	0.101 (1.00)
Del Peak 10(14q23 3)	0.231 (1.00)	0.326 (1.00)	0.616 (1.00)	0.396 (1.00)	0.197 (1.00)	0.239 (1.00)	0.0769 (1.00)	0.0771 (1.00)
Del Peak 11(14q32 2)	0.231 (1.00)	0.296 (1.00)	1 (1.00)	0.396 (1.00)	0.435 (1.00)	0.239 (1.00)	0.0421 (1.00)	0.0655 (1.00)
Del Peak 12(19q13 42)	0.386 (1.00)	0.617 (1.00)	1 (1.00)		0.377 (1.00)	0.681 (1.00)	1 (1.00)	0.408 (1.00)
Del Peak 13(Xq21 31)	0.000103 (0.017)	0.209 (1.00)	0.234 (1.00)	0.424 (1.00)	0.0409 (1.00)	0.312 (1.00)	0.17 (1.00)	0.155 (1.00)
Del Peak 14(Xq28)	4.26e-05 (0.00707)	0.282 (1.00)	0.135 (1.00)	0.475 (1.00)	0.3 (1.00)	0.329 (1.00)	0.496 (1.00)	0.337 (1.00)

Clustering Approach #1: 'Amp Peak 1(2q32.1) mutation analysis'

Table S1. Description of clustering approach #1: 'Amp Peak 1(2q32.1) mutation analysis'

Cluster Labels	AMP PEAK 1(2Q32.1) MUTATED	AMP PEAK 1(2Q32.1) WILD-TYPE
Number of samples	20	83

Clustering Approach #2: 'Amp Peak 2(3p14.1) mutation analysis'

Table S2. Description of clustering approach #2: 'Amp Peak 2(3p14.1) mutation analysis'

Cluster Labels	AMP PEAK 2(3P14.1) MUTATED	AMP PEAK 2(3P14.1) WILD-TYPE
Number of samples	27	76

Clustering Approach #3: 'Amp Peak 3(5q35.2) mutation analysis'

Table S3. Description of clustering approach #3: 'Amp Peak 3(5q35.2) mutation analysis'

Cluster Labels	AMP PEAK 3(5Q35.2) MUTATED	AMP PEAK 3(5Q35.2) WILD-TYPE
Number of samples	14	89

Clustering Approach #4: 'Amp Peak 4(6p12.3) mutation analysis'

Table S4. Description of clustering approach #4: 'Amp Peak 4(6p12.3) mutation analysis'

Cluster Labels	AMP PEAK 4(6P12.3) MUTATED	AMP PEAK 4(6P12.3) WILD-TYPE
Number of samples	4	99

Clustering Approach #5: 'Amp Peak 5(7q22.1) mutation analysis'

Table S5. Description of clustering approach #5: 'Amp Peak 5(7q22.1) mutation analysis'

Cluster Labels	AMP PEAK 5(7Q22.1) MUTATED	AMP PEAK 5(7Q22.1) WILD-TYPE
Number of samples	59	44

Clustering Approach #6: 'Amp Peak 6(17q25.2) mutation analysis'

Table S6. Description of clustering approach #6: 'Amp Peak 6(17q25.2) mutation analysis'

Cluster Labels	AMP PEAK 6(17Q25.2) MUTATED	AMP PEAK 6(17Q25.2) WILD-TYPE
Number of samples	70	33

Clustering Approach #7: 'Amp Peak 7(19p13.2) mutation analysis'

Table S7. Description of clustering approach #7: 'Amp Peak 7(19p13.2) mutation analysis'

Cluster Labels	AMP PEAK 7(19P13.2) MUTATED	AMP PEAK 7(19P13.2) WILD-TYPE
Number of samples	13	90

Clustering Approach #8: 'Amp Peak 8(19q13.11) mutation analysis'

Table S8. Description of clustering approach #8: 'Amp Peak 8(19q13.11) mutation analysis'

Cluster Labels	AMP PEAK 8(19Q13.11) MUTATED	AMP PEAK 8(19Q13.11) WILD-TYPE
Number of samples	4	99

Clustering Approach #9: 'Del Peak 1(1p36.31) mutation analysis'

Table S9. Description of clustering approach #9: 'Del Peak 1(1p36.31) mutation analysis'

Cluster Labels	DEL PEAK 1(1P36.31) MUTATED	DEL PEAK 1(1P36.31) WILD-TYPE
Number of samples	22	81

Clustering Approach #10: 'Del Peak 2(2p11.2) mutation analysis'

Table S10. Description of clustering approach #10: 'Del Peak 2(2p11.2) mutation analysis'

Cluster Labels	DEL PEAK 2(2P11.2) MUTATED	DEL PEAK 2(2P11.2) WILD-TYPE
Number of samples	3	100

Clustering Approach #11: 'Del Peak 3(2q37.3) mutation analysis'

Table S11. Description of clustering approach #11: 'Del Peak 3(2q37.3) mutation analysis'

Cluster Labels	DEL PEAK 3(2Q37.3) MUTATED	DEL PEAK 3(2Q37.3) WILD-TYPE
Number of samples	5	98

Clustering Approach #12: 'Del Peak 4(3p22.1) mutation analysis'

Table S12. Description of clustering approach #12: 'Del Peak 4(3p22.1) mutation analysis'

Cluster Labels	DEL PEAK 4(3P22.1) MUTATED	DEL PEAK 4(3P22.1) WILD-TYPE
Number of samples	6	97

Clustering Approach #13: 'Del Peak 5(4q31.22) mutation analysis'

Table S13. Description of clustering approach #13: 'Del Peak 5(4q31.22) mutation analysis'

Cluster Labels	DEL PEAK 5(4Q31.22) MUTATED	DEL PEAK 5(4Q31.22) WILD-TYPE
Number of samples	11	92

Clustering Approach #14: 'Del Peak 6(5q35.2) mutation analysis'

Table S14. Description of clustering approach #14: 'Del Peak 6(5q35.2) mutation analysis'

Cluster Labels	DEL PEAK 6(5Q35.2) MUTATED	DEL PEAK 6(5Q35.2) WILD-TYPE
Number of samples	8	95

Clustering Approach #15: 'Del Peak 7(6q22.31) mutation analysis'

Table S15. Description of clustering approach #15: 'Del Peak 7(6q22.31) mutation analysis'

Cluster Labels	DEL PEAK 7(6Q22.31) MUTATED	DEL PEAK 7(6Q22.31) WILD-TYPE
Number of samples	12	91

Clustering Approach #16: 'Del Peak 8(9p21.3) mutation analysis'

Table S16. Description of clustering approach #16: 'Del Peak 8(9p21.3) mutation analysis'

Cluster Labels	DEL PEAK 8(9P21.3) MUTATED	DEL PEAK 8(9P21.3) WILD-TYPE
Number of samples	16	87

Clustering Approach #17: 'Del Peak 9(14q11.2) mutation analysis'

Table S17. Description of clustering approach #17: 'Del Peak 9(14q11.2) mutation analysis'

Cluster Labels	DEL PEAK 9(14Q11.2) MUTATED	DEL PEAK 9(14Q11.2) WILD-TYPE
Number of samples	23	80

Clustering Approach #18: 'Del Peak 10(14q23.3) mutation analysis'

Table S18. Description of clustering approach #18: 'Del Peak 10(14q23.3) mutation analysis'

Cluster Labels	DEL PEAK 10(14Q23.3) MUTATED	DEL PEAK 10(14Q23.3) WILD-TYPE
Number of samples	22	81

Clustering Approach #19: 'Del Peak 11(14q32.2) mutation analysis'

Table S19. Description of clustering approach #19: 'Del Peak 11(14q32.2) mutation analysis'

Cluster Labels	DEL PEAK 11(14Q32.2) MUTATED	DEL PEAK 11(14Q32.2) WILD-TYPE
Number of samples	22	81

Clustering Approach #20: 'Del Peak 12(19q13.42) mutation analysis'

Table S20. Description of clustering approach #20: 'Del Peak 12(19q13.42) mutation analysis'

Cluster Labels	DEL PEAK 12(19Q13.42) MUTATED	DEL PEAK 12(19Q13.42) WILD-TYPE
Number of samples	8	95

Clustering Approach #21: 'Del Peak 13(Xq21.31) mutation analysis'

Table S21. Description of clustering approach #21: 'Del Peak 13(Xq21.31) mutation analysis'

Cluster Labels	DEL PEAK 13(XQ21.31) MUTATED	DEL PEAK 13(XQ21.31) WILD-TYPE
Number of samples	15	88

'Del Peak 13(Xq21.31) mutation analysis' versus 'Time to Death'

P value = 0.000103 (logrank test), Q value = 0.017

Table S22. Clustering Approach #21: 'Del Peak 13(Xq21.31) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	96	14	0.0 - 182.7 (13.7)
DEL PEAK 13(XQ21.31) MUTATED	14	5	0.0 - 79.8 (9.2)
DEL PEAK 13(XQ21.31) WILD-TYPE	82	9	0.0 - 182.7 (15.1)

Figure S1. Get High-res Image Clustering Approach #21: 'Del Peak 13(Xq21.31) mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #22: 'Del Peak 14(Xq28) mutation analysis'

Table S23. Description of clustering approach #22: 'Del Peak 14(Xq28) mutation analysis'

Cluster Labels	DEL PEAK 14(XQ28) MUTATED	DEL PEAK 14(XQ28) WILD-TYPE
Number of samples	14	89

'Del Peak 14(Xq28) mutation analysis' versus 'Time to Death'

P value = 4.26e-05 (logrank test), Q value = 0.0071

Table S24. Clustering Approach #22: 'Del Peak 14(Xq28) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	96	14	0.0 - 182.7 (13.7)
DEL PEAK 14(XQ28) MUTATED	13	4	0.0 - 22.9 (9.6)
DEL PEAK 14(XQ28) WILD-TYPE	83	10	0.0 - 182.7 (15.5)

Figure S2. Get High-res Image Clustering Approach #22: 'Del Peak 14(Xq28) mutation analysis' versus Clinical Feature #1: 'Time to Death'

Methods & Data

Input

Cluster data file = all_lesions.conf_99.cnv.cluster.txt
Clinical data file = KIRP-TP.clin.merged.picked.txt
Number of patients = 103
Number of clustering approaches = 22
Number of selected clinical features = 8
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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