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
doi:10.7908/C1QZ285J
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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