Correlation between copy number variations of arm-level result and selected clinical features

Brain Lower Grade Glioma (Primary solid tumor)

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 variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QZ285J

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 50 different clustering approaches and 6 clinical features across 207 patients, 17 significant findings detected with Q value < 0.25.

2 subtypes identified in current cancer cohort by '1p gain mutation analysis'. These subtypes do not correlate to any clinical features.
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 '6p 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 '9p 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 '10p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '11p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '11q gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '12p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '12q gain mutation analysis'. These subtypes correlate to 'AGE'.
2 subtypes identified in current cancer cohort by '18p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '19p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '19q gain mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by '20p gain mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by '20q gain mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by '21q gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '1p loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
2 subtypes identified in current cancer cohort by '1q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '2p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '2q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '3p loss mutation analysis'. These subtypes correlate to 'AGE'.
2 subtypes identified in current cancer cohort by '3q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '4p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '4q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '5p loss 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 '6p loss mutation analysis'. These subtypes correlate to 'Time to Death'.
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 '9p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '9q 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 correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
2 subtypes identified in current cancer cohort by '10q loss mutation analysis'. These subtypes correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
2 subtypes identified in current cancer cohort by '11p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '11q loss mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by '12q 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 '14q loss mutation analysis'. These subtypes correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by '15q 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 '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 '19p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '19q loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
2 subtypes identified in current cancer cohort by '21q 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 correlate to 'Time to Death'.
2 subtypes identified in current cancer cohort by 'Xq 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 50 different clustering approaches and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 17 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	t-test	Fisher's exact test	t-test	Chi-square test	Fisher's exact test
1p gain	0.00425 (1.00)	0.171 (1.00)	0.0864 (1.00)	0.14 (1.00)	0.0162 (1.00)	1 (1.00)
1q gain	0.252 (1.00)	0.00496 (1.00)	0.293 (1.00)	0.572 (1.00)	0.43 (1.00)	0.721 (1.00)
6p gain	0.506 (1.00)	0.761 (1.00)	0.578 (1.00)		0.0316 (1.00)	0.246 (1.00)
7p gain	0.00841 (1.00)	0.0264 (1.00)	0.044 (1.00)	0.958 (1.00)	0.0179 (1.00)	0.36 (1.00)
7q gain	0.0113 (1.00)	0.00174 (0.469)	0.137 (1.00)	0.702 (1.00)	0.063 (1.00)	0.514 (1.00)
8p gain	0.731 (1.00)	0.618 (1.00)	0.403 (1.00)	0.987 (1.00)	0.171 (1.00)	0.165 (1.00)
8q gain	0.941 (1.00)	0.271 (1.00)	0.613 (1.00)	0.348 (1.00)	0.0523 (1.00)	0.311 (1.00)
9p gain	0.135 (1.00)	0.00595 (1.00)	0.316 (1.00)		0.664 (1.00)	1 (1.00)
9q gain	0.043 (1.00)	0.0912 (1.00)	0.316 (1.00)	0.14 (1.00)	0.134 (1.00)	0.621 (1.00)
10p gain	0.502 (1.00)	0.00632 (1.00)	0.0669 (1.00)	0.101 (1.00)	0.102 (1.00)	0.0635 (1.00)
11p gain	0.511 (1.00)	0.277 (1.00)	1 (1.00)	0.722 (1.00)	0.431 (1.00)	1 (1.00)
11q gain	0.69 (1.00)	0.185 (1.00)	0.564 (1.00)	0.605 (1.00)	0.821 (1.00)	0.568 (1.00)
12p gain	0.465 (1.00)	0.00184 (0.495)	0.0458 (1.00)		0.812 (1.00)	1 (1.00)
12q gain	0.364 (1.00)	0.000869 (0.236)	0.136 (1.00)		0.935 (1.00)	1 (1.00)
18p gain	0.493 (1.00)	0.75 (1.00)	0.316 (1.00)		0.134 (1.00)	0.369 (1.00)
19p gain	0.171 (1.00)	0.00203 (0.544)	1 (1.00)	0.0572 (1.00)	0.305 (1.00)	0.332 (1.00)
19q gain	0.000661 (0.181)	0.0102 (1.00)	1 (1.00)	0.26 (1.00)	0.556 (1.00)	0.721 (1.00)
20p gain	0.000116 (0.0324)	0.00665 (1.00)	0.0744 (1.00)	0.976 (1.00)	0.185 (1.00)	0.614 (1.00)
20q gain	0.000335 (0.0929)	0.0141 (1.00)	0.0627 (1.00)	0.893 (1.00)	0.135 (1.00)	1 (1.00)
21q gain	0.533 (1.00)	0.994 (1.00)	1 (1.00)		0.135 (1.00)	1 (1.00)
1p loss	0.124 (1.00)	0.0169 (1.00)	0.88 (1.00)	0.692 (1.00)	1.52e-17 (4.39e-15)	0.00165 (0.447)
1q loss	0.489 (1.00)	0.047 (1.00)	1 (1.00)	0.111 (1.00)	0.00206 (0.55)	0.748 (1.00)
2p loss	0.054 (1.00)	0.675 (1.00)	0.636 (1.00)		0.539 (1.00)	0.621 (1.00)
2q loss	0.539 (1.00)	0.189 (1.00)	0.261 (1.00)		0.2 (1.00)	0.621 (1.00)
3p loss	0.917 (1.00)	1.04e-05 (0.00291)	0.636 (1.00)		0.539 (1.00)	0.369 (1.00)
3q loss	0.554 (1.00)	0.857 (1.00)	0.735 (1.00)	0.217 (1.00)	0.421 (1.00)	1 (1.00)
4p loss	0.143 (1.00)	0.252 (1.00)	0.279 (1.00)	0.563 (1.00)	0.0785 (1.00)	0.436 (1.00)
4q loss	0.116 (1.00)	0.88 (1.00)	0.663 (1.00)	0.395 (1.00)	0.264 (1.00)	0.394 (1.00)
5p loss	0.309 (1.00)	0.814 (1.00)	0.358 (1.00)	0.212 (1.00)	0.012 (1.00)	0.538 (1.00)
5q loss	0.0292 (1.00)	0.364 (1.00)	1 (1.00)	0.14 (1.00)	0.23 (1.00)	0.17 (1.00)
6p loss	5.29e-05 (0.0148)	0.806 (1.00)	1 (1.00)	0.861 (1.00)	0.339 (1.00)	1 (1.00)
6q loss	0.00251 (0.664)	0.134 (1.00)	0.467 (1.00)	0.194 (1.00)	0.0266 (1.00)	0.0029 (0.766)
9p loss	0.00408 (1.00)	0.126 (1.00)	0.855 (1.00)	0.595 (1.00)	0.0724 (1.00)	0.00544 (1.00)
9q loss	0.851 (1.00)	0.116 (1.00)	0.406 (1.00)	0.615 (1.00)	0.498 (1.00)	0.445 (1.00)
10p loss	8.93e-11 (2.55e-08)	6.05e-08 (1.71e-05)	0.0478 (1.00)	0.879 (1.00)	0.000602 (0.166)	1 (1.00)
10q loss	1.88e-10 (5.35e-08)	3.06e-08 (8.68e-06)	0.0308 (1.00)	0.927 (1.00)	0.000235 (0.0654)	1 (1.00)
11p loss	0.131 (1.00)	0.111 (1.00)	0.621 (1.00)	0.797 (1.00)	0.00233 (0.62)	0.217 (1.00)
11q loss	0.000569 (0.157)	0.0656 (1.00)	0.316 (1.00)		0.134 (1.00)	0.621 (1.00)
12q loss	0.464 (1.00)	0.894 (1.00)	0.727 (1.00)	0.859 (1.00)	0.135 (1.00)	0.721 (1.00)
13q loss	0.163 (1.00)	0.397 (1.00)	1 (1.00)	0.839 (1.00)	0.989 (1.00)	0.215 (1.00)
14q loss	0.000835 (0.228)	0.0113 (1.00)	0.0432 (1.00)	0.5 (1.00)	0.0662 (1.00)	0.377 (1.00)
15q loss	0.252 (1.00)	0.734 (1.00)	0.561 (1.00)	0.816 (1.00)	0.0397 (1.00)	0.0822 (1.00)
16q loss	0.962 (1.00)	0.178 (1.00)	1 (1.00)		0.872 (1.00)	0.212 (1.00)
18p loss	0.69 (1.00)	0.409 (1.00)	1 (1.00)	0.5 (1.00)	0.544 (1.00)	0.783 (1.00)
18q loss	0.813 (1.00)	0.367 (1.00)	0.428 (1.00)	0.731 (1.00)	0.144 (1.00)	0.593 (1.00)
19p loss	0.484 (1.00)	0.247 (1.00)	0.503 (1.00)	0.44 (1.00)	0.184 (1.00)	0.498 (1.00)
19q loss	0.186 (1.00)	0.0459 (1.00)	0.769 (1.00)	0.825 (1.00)	7.13e-14 (2.05e-11)	0.0295 (1.00)
21q loss	0.431 (1.00)	0.746 (1.00)	1 (1.00)	0.26 (1.00)	0.0564 (1.00)	1 (1.00)
22q loss	3.89e-06 (0.0011)	0.452 (1.00)	0.28 (1.00)	0.187 (1.00)	0.00631 (1.00)	0.284 (1.00)
Xq loss	0.423 (1.00)	0.794 (1.00)	0.654 (1.00)		0.446 (1.00)	0.369 (1.00)

Clustering Approach #1: '1p gain mutation analysis'

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

Cluster Labels	1P GAIN MUTATED	1P GAIN WILD-TYPE
Number of samples	6	201

Clustering Approach #2: '1q gain mutation analysis'

Table S2. Get Full Table Description of clustering approach #2: '1q gain mutation analysis'

Cluster Labels	1Q GAIN MUTATED	1Q GAIN WILD-TYPE
Number of samples	8	199

Clustering Approach #3: '6p gain mutation analysis'

Table S3. Get Full Table Description of clustering approach #3: '6p gain mutation analysis'

Cluster Labels	6P GAIN MUTATED	6P GAIN WILD-TYPE
Number of samples	3	204

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	36	171

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	49	158

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	14	193

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	17	190

Clustering Approach #8: '9p gain mutation analysis'

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

Cluster Labels	9P GAIN MUTATED	9P GAIN WILD-TYPE
Number of samples	4	203

Clustering Approach #9: '9q gain mutation analysis'

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

Cluster Labels	9Q GAIN MUTATED	9Q GAIN WILD-TYPE
Number of samples	4	203

Clustering Approach #10: '10p gain mutation analysis'

Table S10. Get Full Table Description of clustering approach #10: '10p gain mutation analysis'

Cluster Labels	10P GAIN MUTATED	10P GAIN WILD-TYPE
Number of samples	21	186

Clustering Approach #11: '11p gain mutation analysis'

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

Cluster Labels	11P GAIN MUTATED	11P GAIN WILD-TYPE
Number of samples	9	198

Clustering Approach #12: '11q gain mutation analysis'

Table S12. Get Full Table Description of clustering approach #12: '11q gain mutation analysis'

Cluster Labels	11Q GAIN MUTATED	11Q GAIN WILD-TYPE
Number of samples	13	194

Clustering Approach #13: '12p gain mutation analysis'

Table S13. Get Full Table Description of clustering approach #13: '12p gain mutation analysis'

Cluster Labels	12P GAIN MUTATED	12P GAIN WILD-TYPE
Number of samples	10	197

Clustering Approach #14: '12q gain mutation analysis'

Table S14. Get Full Table Description of clustering approach #14: '12q gain mutation analysis'

Cluster Labels	12Q GAIN MUTATED	12Q GAIN WILD-TYPE
Number of samples	4	203

'12q gain mutation analysis' versus 'AGE'

P value = 0.000869 (t-test), Q value = 0.24

Table S15. Clustering Approach #14: '12q gain mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
12Q GAIN MUTATED	4	29.2 (3.5)
12Q GAIN WILD-TYPE	203	43.4 (13.4)

Figure S1. Get High-res Image Clustering Approach #14: '12q gain mutation analysis' versus Clinical Feature #2: 'AGE'

Clustering Approach #15: '18p gain mutation analysis'

Table S16. Get Full Table Description of clustering approach #15: '18p gain mutation analysis'

Cluster Labels	18P GAIN MUTATED	18P GAIN WILD-TYPE
Number of samples	4	203

Clustering Approach #16: '19p gain mutation analysis'

Table S17. Get Full Table Description of clustering approach #16: '19p gain mutation analysis'

Cluster Labels	19P GAIN MUTATED	19P GAIN WILD-TYPE
Number of samples	10	197

Clustering Approach #17: '19q gain mutation analysis'

Table S18. Get Full Table Description of clustering approach #17: '19q gain mutation analysis'

Cluster Labels	19Q GAIN MUTATED	19Q GAIN WILD-TYPE
Number of samples	8	199

'19q gain mutation analysis' versus 'Time to Death'

P value = 0.000661 (logrank test), Q value = 0.18

Table S19. Clustering Approach #17: '19q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
19Q GAIN MUTATED	8	4	0.5 - 26.3 (14.5)
19Q GAIN WILD-TYPE	198	47	0.0 - 211.2 (13.4)

Figure S2. Get High-res Image Clustering Approach #17: '19q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #18: '20p gain mutation analysis'

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

Cluster Labels	20P GAIN MUTATED	20P GAIN WILD-TYPE
Number of samples	17	190

'20p gain mutation analysis' versus 'Time to Death'

P value = 0.000116 (logrank test), Q value = 0.032

Table S21. Clustering Approach #18: '20p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
20P GAIN MUTATED	17	7	0.5 - 41.1 (15.3)
20P GAIN WILD-TYPE	189	44	0.0 - 211.2 (13.4)

Figure S3. Get High-res Image Clustering Approach #18: '20p gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #19: '20q gain mutation analysis'

Table S22. Get Full Table Description of clustering approach #19: '20q gain mutation analysis'

Cluster Labels	20Q GAIN MUTATED	20Q GAIN WILD-TYPE
Number of samples	15	192

'20q gain mutation analysis' versus 'Time to Death'

P value = 0.000335 (logrank test), Q value = 0.093

Table S23. Clustering Approach #19: '20q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
20Q GAIN MUTATED	15	6	0.5 - 41.1 (12.4)
20Q GAIN WILD-TYPE	191	45	0.0 - 211.2 (13.4)

Figure S4. Get High-res Image Clustering Approach #19: '20q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #20: '21q gain mutation analysis'

Table S24. Get Full Table Description of clustering approach #20: '21q gain mutation analysis'

Cluster Labels	21Q GAIN MUTATED	21Q GAIN WILD-TYPE
Number of samples	3	204

Clustering Approach #21: '1p loss mutation analysis'

Table S25. Get Full Table Description of clustering approach #21: '1p loss mutation analysis'

Cluster Labels	1P LOSS MUTATED	1P LOSS WILD-TYPE
Number of samples	65	142

'1p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 1.52e-17 (Chi-square test), Q value = 4.4e-15

Table S26. Clustering Approach #21: '1p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
1P LOSS MUTATED	2	6	57
1P LOSS WILD-TYPE	61	48	32

Figure S5. Get High-res Image Clustering Approach #21: '1p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #22: '1q loss mutation analysis'

Table S27. Get Full Table Description of clustering approach #22: '1q loss mutation analysis'

Cluster Labels	1Q LOSS MUTATED	1Q LOSS WILD-TYPE
Number of samples	9	198

Clustering Approach #23: '2p loss mutation analysis'

Table S28. Get Full Table Description of clustering approach #23: '2p loss mutation analysis'

Cluster Labels	2P LOSS MUTATED	2P LOSS WILD-TYPE
Number of samples	4	203

Clustering Approach #24: '2q loss mutation analysis'

Table S29. Get Full Table Description of clustering approach #24: '2q loss mutation analysis'

Cluster Labels	2Q LOSS MUTATED	2Q LOSS WILD-TYPE
Number of samples	3	204

Clustering Approach #25: '3p loss mutation analysis'

Table S30. Get Full Table Description of clustering approach #25: '3p loss mutation analysis'

Cluster Labels	3P LOSS MUTATED	3P LOSS WILD-TYPE
Number of samples	4	203

'3p loss mutation analysis' versus 'AGE'

P value = 1.04e-05 (t-test), Q value = 0.0029

Table S31. Clustering Approach #25: '3p loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
3P LOSS MUTATED	4	31.0 (2.2)
3P LOSS WILD-TYPE	203	43.3 (13.4)

Figure S6. Get High-res Image Clustering Approach #25: '3p loss mutation analysis' versus Clinical Feature #2: 'AGE'

Clustering Approach #26: '3q loss mutation analysis'

Table S32. Get Full Table Description of clustering approach #26: '3q loss mutation analysis'

Cluster Labels	3Q LOSS MUTATED	3Q LOSS WILD-TYPE
Number of samples	9	198

Clustering Approach #27: '4p loss mutation analysis'

Table S33. Get Full Table Description of clustering approach #27: '4p loss mutation analysis'

Cluster Labels	4P LOSS MUTATED	4P LOSS WILD-TYPE
Number of samples	15	192

Clustering Approach #28: '4q loss mutation analysis'

Table S34. Get Full Table Description of clustering approach #28: '4q loss mutation analysis'

Cluster Labels	4Q LOSS MUTATED	4Q LOSS WILD-TYPE
Number of samples	24	183

Clustering Approach #29: '5p loss mutation analysis'

Table S35. Get Full Table Description of clustering approach #29: '5p loss mutation analysis'

Cluster Labels	5P LOSS MUTATED	5P LOSS WILD-TYPE
Number of samples	11	196

Clustering Approach #30: '5q loss mutation analysis'

Table S36. Get Full Table Description of clustering approach #30: '5q loss mutation analysis'

Cluster Labels	5Q LOSS MUTATED	5Q LOSS WILD-TYPE
Number of samples	9	198

Clustering Approach #31: '6p loss mutation analysis'

Table S37. Get Full Table Description of clustering approach #31: '6p loss mutation analysis'

Cluster Labels	6P LOSS MUTATED	6P LOSS WILD-TYPE
Number of samples	5	202

'6p loss mutation analysis' versus 'Time to Death'

P value = 5.29e-05 (logrank test), Q value = 0.015

Table S38. Clustering Approach #31: '6p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
6P LOSS MUTATED	5	2	3.0 - 13.4 (6.4)
6P LOSS WILD-TYPE	201	49	0.0 - 211.2 (14.4)

Figure S7. Get High-res Image Clustering Approach #31: '6p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #32: '6q loss mutation analysis'

Table S39. Get Full Table Description of clustering approach #32: '6q loss mutation analysis'

Cluster Labels	6Q LOSS MUTATED	6Q LOSS WILD-TYPE
Number of samples	19	188

Clustering Approach #33: '9p loss mutation analysis'

Table S40. Get Full Table Description of clustering approach #33: '9p loss mutation analysis'

Cluster Labels	9P LOSS MUTATED	9P LOSS WILD-TYPE
Number of samples	36	171

Clustering Approach #34: '9q loss mutation analysis'

Table S41. Get Full Table Description of clustering approach #34: '9q loss mutation analysis'

Cluster Labels	9Q LOSS MUTATED	9Q LOSS WILD-TYPE
Number of samples	6	201

Clustering Approach #35: '10p loss mutation analysis'

Table S42. Get Full Table Description of clustering approach #35: '10p loss mutation analysis'

Cluster Labels	10P LOSS MUTATED	10P LOSS WILD-TYPE
Number of samples	30	177

'10p loss mutation analysis' versus 'Time to Death'

P value = 8.93e-11 (logrank test), Q value = 2.6e-08

Table S43. Clustering Approach #35: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
10P LOSS MUTATED	30	17	0.1 - 134.3 (8.6)
10P LOSS WILD-TYPE	176	34	0.0 - 211.2 (14.6)

Figure S8. Get High-res Image Clustering Approach #35: '10p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10p loss mutation analysis' versus 'AGE'

P value = 6.05e-08 (t-test), Q value = 1.7e-05

Table S44. Clustering Approach #35: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
10P LOSS MUTATED	30	54.9 (10.4)
10P LOSS WILD-TYPE	177	41.1 (12.9)

Figure S9. Get High-res Image Clustering Approach #35: '10p loss mutation analysis' versus Clinical Feature #2: 'AGE'

'10p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 0.000602 (Chi-square test), Q value = 0.17

Table S45. Clustering Approach #35: '10p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
10P LOSS MUTATED	18	6	6
10P LOSS WILD-TYPE	45	48	83

Figure S10. Get High-res Image Clustering Approach #35: '10p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #36: '10q loss mutation analysis'

Table S46. Get Full Table Description of clustering approach #36: '10q loss mutation analysis'

Cluster Labels	10Q LOSS MUTATED	10Q LOSS WILD-TYPE
Number of samples	31	176

'10q loss mutation analysis' versus 'Time to Death'

P value = 1.88e-10 (logrank test), Q value = 5.3e-08

Table S47. Clustering Approach #36: '10q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
10Q LOSS MUTATED	31	17	0.1 - 134.3 (8.8)
10Q LOSS WILD-TYPE	175	34	0.0 - 211.2 (14.7)

Figure S11. Get High-res Image Clustering Approach #36: '10q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

'10q loss mutation analysis' versus 'AGE'

P value = 3.06e-08 (t-test), Q value = 8.7e-06

Table S48. Clustering Approach #36: '10q loss mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
10Q LOSS MUTATED	31	54.8 (10.3)
10Q LOSS WILD-TYPE	176	41.0 (12.9)

Figure S12. Get High-res Image Clustering Approach #36: '10q loss mutation analysis' versus Clinical Feature #2: 'AGE'

'10q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 0.000235 (Chi-square test), Q value = 0.065

Table S49. Clustering Approach #36: '10q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
10Q LOSS MUTATED	19	6	6
10Q LOSS WILD-TYPE	44	48	83

Figure S13. Get High-res Image Clustering Approach #36: '10q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #37: '11p loss mutation analysis'

Table S50. Get Full Table Description of clustering approach #37: '11p loss mutation analysis'

Cluster Labels	11P LOSS MUTATED	11P LOSS WILD-TYPE
Number of samples	18	189

Clustering Approach #38: '11q loss mutation analysis'

Table S51. Get Full Table Description of clustering approach #38: '11q loss mutation analysis'

Cluster Labels	11Q LOSS MUTATED	11Q LOSS WILD-TYPE
Number of samples	4	203

'11q loss mutation analysis' versus 'Time to Death'

P value = 0.000569 (logrank test), Q value = 0.16

Table S52. Clustering Approach #38: '11q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
11Q LOSS MUTATED	4	3	6.0 - 41.1 (13.2)
11Q LOSS WILD-TYPE	202	48	0.0 - 211.2 (13.4)

Figure S14. Get High-res Image Clustering Approach #38: '11q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #39: '12q loss mutation analysis'

Table S53. Get Full Table Description of clustering approach #39: '12q loss mutation analysis'

Cluster Labels	12Q LOSS MUTATED	12Q LOSS WILD-TYPE
Number of samples	8	199

Clustering Approach #40: '13q loss mutation analysis'

Table S54. Get Full Table Description of clustering approach #40: '13q loss mutation analysis'

Cluster Labels	13Q LOSS MUTATED	13Q LOSS WILD-TYPE
Number of samples	27	180

Clustering Approach #41: '14q loss mutation analysis'

Table S55. Get Full Table Description of clustering approach #41: '14q loss mutation analysis'

Cluster Labels	14Q LOSS MUTATED	14Q LOSS WILD-TYPE
Number of samples	22	185

'14q loss mutation analysis' versus 'Time to Death'

P value = 0.000835 (logrank test), Q value = 0.23

Table S56. Clustering Approach #41: '14q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
14Q LOSS MUTATED	22	10	4.1 - 80.0 (9.2)
14Q LOSS WILD-TYPE	184	41	0.0 - 211.2 (13.9)

Figure S15. Get High-res Image Clustering Approach #41: '14q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #42: '15q loss mutation analysis'

Table S57. Get Full Table Description of clustering approach #42: '15q loss mutation analysis'

Cluster Labels	15Q LOSS MUTATED	15Q LOSS WILD-TYPE
Number of samples	12	195

Clustering Approach #43: '16q loss mutation analysis'

Table S58. Get Full Table Description of clustering approach #43: '16q loss mutation analysis'

Cluster Labels	16Q LOSS MUTATED	16Q LOSS WILD-TYPE
Number of samples	6	201

Clustering Approach #44: '18p loss mutation analysis'

Table S59. Get Full Table Description of clustering approach #44: '18p loss mutation analysis'

Cluster Labels	18P LOSS MUTATED	18P LOSS WILD-TYPE
Number of samples	14	193

Clustering Approach #45: '18q loss mutation analysis'

Table S60. Get Full Table Description of clustering approach #45: '18q loss mutation analysis'

Cluster Labels	18Q LOSS MUTATED	18Q LOSS WILD-TYPE
Number of samples	15	192

Clustering Approach #46: '19p loss mutation analysis'

Table S61. Get Full Table Description of clustering approach #46: '19p loss mutation analysis'

Cluster Labels	19P LOSS MUTATED	19P LOSS WILD-TYPE
Number of samples	9	198

Clustering Approach #47: '19q loss mutation analysis'

Table S62. Get Full Table Description of clustering approach #47: '19q loss mutation analysis'

Cluster Labels	19Q LOSS MUTATED	19Q LOSS WILD-TYPE
Number of samples	73	134

'19q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 7.13e-14 (Chi-square test), Q value = 2e-11

Table S63. Clustering Approach #47: '19q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
19Q LOSS MUTATED	8	7	58
19Q LOSS WILD-TYPE	55	47	31

Figure S16. Get High-res Image Clustering Approach #47: '19q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #48: '21q loss mutation analysis'

Table S64. Get Full Table Description of clustering approach #48: '21q loss mutation analysis'

Cluster Labels	21Q LOSS MUTATED	21Q LOSS WILD-TYPE
Number of samples	7	200

Clustering Approach #49: '22q loss mutation analysis'

Table S65. Get Full Table Description of clustering approach #49: '22q loss mutation analysis'

Cluster Labels	22Q LOSS MUTATED	22Q LOSS WILD-TYPE
Number of samples	14	193

'22q loss mutation analysis' versus 'Time to Death'

P value = 3.89e-06 (logrank test), Q value = 0.0011

Table S66. Clustering Approach #49: '22q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
22Q LOSS MUTATED	14	7	0.1 - 46.6 (11.9)
22Q LOSS WILD-TYPE	192	44	0.0 - 211.2 (13.9)

Figure S17. Get High-res Image Clustering Approach #49: '22q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #50: 'Xq loss mutation analysis'

Table S67. Get Full Table Description of clustering approach #50: 'Xq loss mutation analysis'

Cluster Labels	XQ LOSS MUTATED	XQ LOSS WILD-TYPE
Number of samples	5	202

Methods & Data

Input

Cluster data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = LGG-TP.clin.merged.picked.txt
Number of patients = 207
Number of clustering approaches = 50
Number of selected clinical features = 6
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[5] 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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