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

Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)

22 February 2013 | analyses__2013_02_22

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 variation genes (focal) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1TX3CK4

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 20 different clustering approaches and 3 clinical features across 191 patients, 6 significant findings detected with Q value < 0.25.

2 subtypes identified in current cancer cohort by 'Amp Peak 1(1p33) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 2(1q43) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 3(11q23.3) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 4(13q31.3) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 5(20q11.21) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Amp Peak 6(21q22.2) mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by 'Del Peak 2(3p13) mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by 'Del Peak 3(3q26.31) mutation analysis'. These subtypes correlate to 'AGE'.
2 subtypes identified in current cancer cohort by 'Del Peak 4(5q31.2) mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by 'Del Peak 5(7p12.1) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 6(7q32.3) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 7(7q34) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 9(9q21.32) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 10(12p13.2) mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by 'Del Peak 11(12q21.33) mutation analysis'. These subtypes correlate to 'AGE'.
2 subtypes identified in current cancer cohort by 'Del Peak 12(16q23.1) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 13(17p13.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 14(17q11.2) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 15(18p11.21) mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'Del Peak 16(20q13.13) 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 20 different clustering approaches and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 6 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER
Statistical Tests	logrank test	t-test	Fisher's exact test
Amp Peak 1(1p33)	0.34 (1.00)	0.0372 (1.00)	1 (1.00)
Amp Peak 2(1q43)	0.737 (1.00)	0.0234 (1.00)	1 (1.00)
Amp Peak 3(11q23 3)	0.117 (1.00)	0.0106 (0.53)	0.45 (1.00)
Amp Peak 4(13q31 3)	0.929 (1.00)	0.0714 (1.00)	1 (1.00)
Amp Peak 5(20q11 21)	0.116 (1.00)	0.372 (1.00)	0.252 (1.00)
Amp Peak 6(21q22 2)	0.000742 (0.0415)	0.0657 (1.00)	0.0922 (1.00)
Del Peak 2(3p13)	4.2e-05 (0.00239)	0.16 (1.00)	0.185 (1.00)
Del Peak 3(3q26 31)		0.00241 (0.13)	0.252 (1.00)
Del Peak 4(5q31 2)	0.00346 (0.183)	0.00583 (0.297)	0.0464 (1.00)
Del Peak 5(7p12 1)	0.075 (1.00)	0.21 (1.00)	0.604 (1.00)
Del Peak 6(7q32 3)	0.0282 (1.00)	0.0883 (1.00)	0.656 (1.00)
Del Peak 7(7q34)	0.0706 (1.00)	0.0807 (1.00)	0.512 (1.00)
Del Peak 9(9q21 32)	0.899 (1.00)	0.744 (1.00)	0.378 (1.00)
Del Peak 10(12p13 2)	0.00107 (0.059)	0.581 (1.00)	0.351 (1.00)
Del Peak 11(12q21 33)		8.84e-17 (5.13e-15)	0.252 (1.00)
Del Peak 12(16q23 1)	0.126 (1.00)	0.11 (1.00)	0.513 (1.00)
Del Peak 13(17p13 2)	0.0431 (1.00)	0.226 (1.00)	0.0565 (1.00)
Del Peak 14(17q11 2)	0.0303 (1.00)	0.547 (1.00)	0.775 (1.00)
Del Peak 15(18p11 21)	0.00548 (0.285)	0.175 (1.00)	0.185 (1.00)
Del Peak 16(20q13 13)	0.0395 (1.00)	0.113 (1.00)	0.627 (1.00)

Clustering Approach #1: 'Amp Peak 1(1p33) mutation analysis'

Table S1. Description of clustering approach #1: 'Amp Peak 1(1p33) mutation analysis'

Cluster Labels	AMP PEAK 1(1P33) MUTATED	AMP PEAK 1(1P33) WILD-TYPE
Number of samples	7	184

Clustering Approach #2: 'Amp Peak 2(1q43) mutation analysis'

Table S2. Description of clustering approach #2: 'Amp Peak 2(1q43) mutation analysis'

Cluster Labels	AMP PEAK 2(1Q43) MUTATED	AMP PEAK 2(1Q43) WILD-TYPE
Number of samples	7	184

Clustering Approach #3: 'Amp Peak 3(11q23.3) mutation analysis'

Table S3. Description of clustering approach #3: 'Amp Peak 3(11q23.3) mutation analysis'

Cluster Labels	AMP PEAK 3(11Q23.3) MUTATED	AMP PEAK 3(11Q23.3) WILD-TYPE
Number of samples	17	174

Clustering Approach #4: 'Amp Peak 4(13q31.3) mutation analysis'

Table S4. Description of clustering approach #4: 'Amp Peak 4(13q31.3) mutation analysis'

Cluster Labels	AMP PEAK 4(13Q31.3) MUTATED	AMP PEAK 4(13Q31.3) WILD-TYPE
Number of samples	7	184

Clustering Approach #5: 'Amp Peak 5(20q11.21) mutation analysis'

Table S5. Description of clustering approach #5: 'Amp Peak 5(20q11.21) mutation analysis'

Cluster Labels	AMP PEAK 5(20Q11.21) MUTATED	AMP PEAK 5(20Q11.21) WILD-TYPE
Number of samples	3	188

Clustering Approach #6: 'Amp Peak 6(21q22.2) mutation analysis'

Table S6. Description of clustering approach #6: 'Amp Peak 6(21q22.2) mutation analysis'

Cluster Labels	AMP PEAK 6(21Q22.2) MUTATED	AMP PEAK 6(21Q22.2) WILD-TYPE
Number of samples	14	177

'Amp Peak 6(21q22.2) mutation analysis' versus 'Time to Death'

P value = 0.000742 (logrank test), Q value = 0.042

Table S7. Clustering Approach #6: 'Amp Peak 6(21q22.2) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	168	106	0.9 - 94.1 (12.0)
AMP PEAK 6(21Q22.2) MUTATED	12	10	1.0 - 24.0 (5.4)
AMP PEAK 6(21Q22.2) WILD-TYPE	156	96	0.9 - 94.1 (12.5)

Figure S1. Get High-res Image Clustering Approach #6: 'Amp Peak 6(21q22.2) mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #7: 'Del Peak 2(3p13) mutation analysis'

Table S8. Description of clustering approach #7: 'Del Peak 2(3p13) mutation analysis'

Cluster Labels	DEL PEAK 2(3P13) MUTATED	DEL PEAK 2(3P13) WILD-TYPE
Number of samples	9	182

'Del Peak 2(3p13) mutation analysis' versus 'Time to Death'

P value = 4.2e-05 (logrank test), Q value = 0.0024

Table S9. Clustering Approach #7: 'Del Peak 2(3p13) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	168	106	0.9 - 94.1 (12.0)
DEL PEAK 2(3P13) MUTATED	8	7	1.0 - 14.0 (2.0)
DEL PEAK 2(3P13) WILD-TYPE	160	99	0.9 - 94.1 (12.5)

Figure S2. Get High-res Image Clustering Approach #7: 'Del Peak 2(3p13) mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #8: 'Del Peak 3(3q26.31) mutation analysis'

Table S10. Description of clustering approach #8: 'Del Peak 3(3q26.31) mutation analysis'

Cluster Labels	DEL PEAK 3(3Q26.31) MUTATED	DEL PEAK 3(3Q26.31) WILD-TYPE
Number of samples	3	188

'Del Peak 3(3q26.31) mutation analysis' versus 'AGE'

P value = 0.00241 (t-test), Q value = 0.13

Table S11. Clustering Approach #8: 'Del Peak 3(3q26.31) mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	191	55.2 (16.1)
DEL PEAK 3(3Q26.31) MUTATED	3	74.0 (3.6)
DEL PEAK 3(3Q26.31) WILD-TYPE	188	54.9 (16.0)

Figure S3. Get High-res Image Clustering Approach #8: 'Del Peak 3(3q26.31) mutation analysis' versus Clinical Feature #2: 'AGE'

Clustering Approach #9: 'Del Peak 4(5q31.2) mutation analysis'

Table S12. Description of clustering approach #9: 'Del Peak 4(5q31.2) mutation analysis'

Cluster Labels	DEL PEAK 4(5Q31.2) MUTATED	DEL PEAK 4(5Q31.2) WILD-TYPE
Number of samples	18	173

'Del Peak 4(5q31.2) mutation analysis' versus 'Time to Death'

P value = 0.00346 (logrank test), Q value = 0.18

Table S13. Clustering Approach #9: 'Del Peak 4(5q31.2) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	168	106	0.9 - 94.1 (12.0)
DEL PEAK 4(5Q31.2) MUTATED	17	15	1.0 - 73.0 (10.0)
DEL PEAK 4(5Q31.2) WILD-TYPE	151	91	0.9 - 94.1 (12.9)

Figure S4. Get High-res Image Clustering Approach #9: 'Del Peak 4(5q31.2) mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #10: 'Del Peak 5(7p12.1) mutation analysis'

Table S14. Description of clustering approach #10: 'Del Peak 5(7p12.1) mutation analysis'

Cluster Labels	DEL PEAK 5(7P12.1) MUTATED	DEL PEAK 5(7P12.1) WILD-TYPE
Number of samples	16	175

Clustering Approach #11: 'Del Peak 6(7q32.3) mutation analysis'

Table S15. Description of clustering approach #11: 'Del Peak 6(7q32.3) mutation analysis'

Cluster Labels	DEL PEAK 6(7Q32.3) MUTATED	DEL PEAK 6(7Q32.3) WILD-TYPE
Number of samples	23	168

Clustering Approach #12: 'Del Peak 7(7q34) mutation analysis'

Table S16. Description of clustering approach #12: 'Del Peak 7(7q34) mutation analysis'

Cluster Labels	DEL PEAK 7(7Q34) MUTATED	DEL PEAK 7(7Q34) WILD-TYPE
Number of samples	24	167

Clustering Approach #13: 'Del Peak 9(9q21.32) mutation analysis'

Table S17. Description of clustering approach #13: 'Del Peak 9(9q21.32) mutation analysis'

Cluster Labels	DEL PEAK 9(9Q21.32) MUTATED	DEL PEAK 9(9Q21.32) WILD-TYPE
Number of samples	5	186

Clustering Approach #14: 'Del Peak 10(12p13.2) mutation analysis'

Table S18. Description of clustering approach #14: 'Del Peak 10(12p13.2) mutation analysis'

Cluster Labels	DEL PEAK 10(12P13.2) MUTATED	DEL PEAK 10(12P13.2) WILD-TYPE
Number of samples	10	181

'Del Peak 10(12p13.2) mutation analysis' versus 'Time to Death'

P value = 0.00107 (logrank test), Q value = 0.059

Table S19. Clustering Approach #14: 'Del Peak 10(12p13.2) mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	168	106	0.9 - 94.1 (12.0)
DEL PEAK 10(12P13.2) MUTATED	8	8	1.0 - 22.1 (7.0)
DEL PEAK 10(12P13.2) WILD-TYPE	160	98	0.9 - 94.1 (12.5)

Figure S5. Get High-res Image Clustering Approach #14: 'Del Peak 10(12p13.2) mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #15: 'Del Peak 11(12q21.33) mutation analysis'

Table S20. Description of clustering approach #15: 'Del Peak 11(12q21.33) mutation analysis'

Cluster Labels	DEL PEAK 11(12Q21.33) MUTATED	DEL PEAK 11(12Q21.33) WILD-TYPE
Number of samples	3	188

'Del Peak 11(12q21.33) mutation analysis' versus 'AGE'

P value = 8.84e-17 (t-test), Q value = 5.1e-15

Table S21. Clustering Approach #15: 'Del Peak 11(12q21.33) mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	191	55.2 (16.1)
DEL PEAK 11(12Q21.33) MUTATED	3	72.0 (1.0)
DEL PEAK 11(12Q21.33) WILD-TYPE	188	55.0 (16.0)

Figure S6. Get High-res Image Clustering Approach #15: 'Del Peak 11(12q21.33) mutation analysis' versus Clinical Feature #2: 'AGE'

Clustering Approach #16: 'Del Peak 12(16q23.1) mutation analysis'

Table S22. Description of clustering approach #16: 'Del Peak 12(16q23.1) mutation analysis'

Cluster Labels	DEL PEAK 12(16Q23.1) MUTATED	DEL PEAK 12(16Q23.1) WILD-TYPE
Number of samples	9	182

Clustering Approach #17: 'Del Peak 13(17p13.2) mutation analysis'

Table S23. Description of clustering approach #17: 'Del Peak 13(17p13.2) mutation analysis'

Cluster Labels	DEL PEAK 13(17P13.2) MUTATED	DEL PEAK 13(17P13.2) WILD-TYPE
Number of samples	15	176

Clustering Approach #18: 'Del Peak 14(17q11.2) mutation analysis'

Table S24. Description of clustering approach #18: 'Del Peak 14(17q11.2) mutation analysis'

Cluster Labels	DEL PEAK 14(17Q11.2) MUTATED	DEL PEAK 14(17Q11.2) WILD-TYPE
Number of samples	13	178

Clustering Approach #19: 'Del Peak 15(18p11.21) mutation analysis'

Table S25. Description of clustering approach #19: 'Del Peak 15(18p11.21) mutation analysis'

Cluster Labels	DEL PEAK 15(18P11.21) MUTATED	DEL PEAK 15(18P11.21) WILD-TYPE
Number of samples	9	182

Clustering Approach #20: 'Del Peak 16(20q13.13) mutation analysis'

Table S26. Description of clustering approach #20: 'Del Peak 16(20q13.13) mutation analysis'

Cluster Labels	DEL PEAK 16(20Q13.13) MUTATED	DEL PEAK 16(20Q13.13) WILD-TYPE
Number of samples	4	187

Methods & Data

Input

Cluster data file = all_lesions.conf_99.cnv.cluster.txt
Clinical data file = LAML-TB.clin.merged.picked.txt
Number of patients = 191
Number of clustering approaches = 20
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.

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)

Made with Nozzle