Correlation between copy number variations of arm-level result and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C17942WB
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/C17942WB
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 58 different clustering approaches and 8 clinical features across 103 patients, 8 significant findings detected with Q value < 0.25.

- 2 subtypes identified in current cancer cohort by '1q gain mutation analysis'. These subtypes correlate to 'Time to Death'.

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

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

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

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

- 2 subtypes identified in current cancer cohort by '4p gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '4q gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '5p gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '5q 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 correlate to 'PATHOLOGICSPREAD(M)'.

- 2 subtypes identified in current cancer cohort by '6q gain mutation analysis'. These subtypes correlate to 'Time to Death' and 'PATHOLOGICSPREAD(M)'.

- 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 '10p gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '10q 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 do not correlate to any clinical features.

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

- 2 subtypes identified in current cancer cohort by '16p gain mutation analysis'. These subtypes do not correlate to any clinical features.

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

- 2 subtypes identified in current cancer cohort by '17p gain mutation analysis'. These subtypes correlate to 'PATHOLOGY.T'.

- 2 subtypes identified in current cancer cohort by '17q gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 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 '18q gain mutation analysis'. These subtypes do not correlate to any clinical features.

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

- 2 subtypes identified in current cancer cohort by '20q gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by 'Xq 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 do not correlate to any clinical features.

- 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 '3p loss mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '3q loss mutation analysis'. These subtypes correlate to 'Time to Death'.

- 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 correlate to 'Time to Death'.

- 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 do not correlate to any clinical features.

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

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

- 2 subtypes identified in current cancer cohort by '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 do not correlate to any clinical features.

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

- 2 subtypes identified in current cancer cohort by '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 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 do not correlate to any clinical features.

- 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 '17p loss mutation analysis'. These subtypes correlate to 'Time to Death'.

- 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 do not correlate to any clinical features.

- 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 do not correlate to any clinical features.

- 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 58 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 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 | Chi-square test | Chi-square test | Chi-square test | Chi-square test
1q gain | 9.91e-05 (0.04) | 0.793 (1.00) | 0.265 (1.00) |  | 0.00155 (0.614) | 0.884 (1.00) | 0.0333 (1.00) | 0.00501 (1.00)
2p gain | 0.191 (1.00) | 0.348 (1.00) | 0.742 (1.00) | 0.54 (1.00) | 0.833 (1.00) | 0.361 (1.00) | 0.0389 (1.00) | 0.0553 (1.00)
2q gain | 0.35 (1.00) | 0.859 (1.00) | 0.751 (1.00) | 0.5 (1.00) | 0.948 (1.00) | 0.0575 (1.00) | 0.0698 (1.00) | 0.0103 (1.00)
3p gain | 0.414 (1.00) | 0.937 (1.00) | 0.0822 (1.00) | 0.088 (1.00) | 0.0237 (1.00) | 0.806 (1.00) | 0.496 (1.00) | 0.278 (1.00)
3q gain | 0.288 (1.00) | 0.877 (1.00) | 0.0507 (1.00) | 0.208 (1.00) | 0.0514 (1.00) | 0.661 (1.00) | 0.464 (1.00) | 0.394 (1.00)
4p gain | 0.0702 (1.00) | 0.0414 (1.00) | 1 (1.00) |  | 0.425 (1.00) | 0.426 (1.00) | 0.0291 (1.00) | 0.00894 (1.00)
4q gain | 0.694 (1.00) | 0.0406 (1.00) | 1 (1.00) |  | 0.51 (1.00) |  |  |
5p gain | 0.76 (1.00) | 0.377 (1.00) | 0.723 (1.00) | 0.395 (1.00) | 0.11 (1.00) | 0.731 (1.00) | 0.589 (1.00) | 0.182 (1.00)
5q gain | 0.31 (1.00) | 0.603 (1.00) | 0.723 (1.00) | 0.395 (1.00) | 0.394 (1.00) | 0.731 (1.00) | 0.714 (1.00) | 0.405 (1.00)
6p gain | 0.0755 (1.00) | 0.361 (1.00) | 0.303 (1.00) |  | 0.147 (1.00) | 0.368 (1.00) | 0.000177 (0.0713) | 0.0459 (1.00)
6q gain | 5.18e-06 (0.0021) | 0.258 (1.00) | 0.549 (1.00) |  | 0.379 (1.00) |  | 8.32e-06 (0.00336) | 0.00894 (1.00)
7p gain | 0.0708 (1.00) | 0.918 (1.00) | 0.0592 (1.00) | 0.2 (1.00) | 0.0025 (0.985) | 0.437 (1.00) | 0.112 (1.00) | 0.00142 (0.566)
7q gain | 0.0708 (1.00) | 0.918 (1.00) | 0.137 (1.00) | 0.2 (1.00) | 0.00651 (1.00) | 0.437 (1.00) | 0.134 (1.00) | 0.00399 (1.00)
8p gain | 0.576 (1.00) | 0.243 (1.00) | 0.654 (1.00) |  | 0.0512 (1.00) | 0.661 (1.00) | 0.557 (1.00) | 0.183 (1.00)
8q gain | 0.00501 (1.00) | 0.627 (1.00) | 0.677 (1.00) |  | 0.00519 (1.00) | 0.152 (1.00) | 0.523 (1.00) | 0.0059 (1.00)
10p gain | 0.488 (1.00) | 0.804 (1.00) | 1 (1.00) |  | 0.747 (1.00) |  | 0.894 (1.00) |
10q gain | 0.488 (1.00) | 0.804 (1.00) | 0.549 (1.00) |  | 0.355 (1.00) |  |  |
12p gain | 0.991 (1.00) | 0.677 (1.00) | 0.037 (1.00) | 0.26 (1.00) | 0.958 (1.00) | 0.451 (1.00) | 0.284 (1.00) | 0.348 (1.00)
12q gain | 0.991 (1.00) | 0.677 (1.00) | 0.037 (1.00) | 0.26 (1.00) | 0.958 (1.00) | 0.451 (1.00) | 0.284 (1.00) | 0.348 (1.00)
13q gain | 0.717 (1.00) | 0.231 (1.00) | 0.742 (1.00) | 0.208 (1.00) | 0.504 (1.00) |  | 0.714 (1.00) | 0.481 (1.00)
16p gain | 0.537 (1.00) | 0.459 (1.00) | 0.0108 (1.00) | 0.531 (1.00) | 0.942 (1.00) | 0.286 (1.00) | 0.614 (1.00) | 0.769 (1.00)
16q gain | 0.103 (1.00) | 0.573 (1.00) | 0.032 (1.00) | 0.264 (1.00) | 0.714 (1.00) | 0.187 (1.00) | 0.208 (1.00) | 0.739 (1.00)
17p gain | 0.222 (1.00) | 0.0472 (1.00) | 0.0208 (1.00) | 0.201 (1.00) | 0.000449 (0.179) | 0.177 (1.00) | 0.0403 (1.00) | 0.000843 (0.336)
17q gain | 0.768 (1.00) | 0.0952 (1.00) | 0.281 (1.00) | 0.556 (1.00) | 0.122 (1.00) | 0.178 (1.00) | 0.098 (1.00) | 0.189 (1.00)
18p gain | 0.195 (1.00) | 0.592 (1.00) | 0.661 (1.00) | 0.54 (1.00) | 0.933 (1.00) |  | 0.277 (1.00) | 0.742 (1.00)
18q gain | 0.609 (1.00) | 0.568 (1.00) | 1 (1.00) |  | 0.659 (1.00) |  | 0.894 (1.00) | 0.814 (1.00)
20p gain | 0.439 (1.00) | 0.018 (1.00) | 0.819 (1.00) | 0.639 (1.00) | 0.266 (1.00) | 0.905 (1.00) | 0.368 (1.00) | 0.515 (1.00)
20q gain | 0.439 (1.00) | 0.0434 (1.00) | 0.652 (1.00) | 0.639 (1.00) | 0.381 (1.00) | 0.905 (1.00) | 0.73 (1.00) | 0.718 (1.00)
Xq gain | 0.416 (1.00) | 0.359 (1.00) | 0.0956 (1.00) |  | 0.596 (1.00) |  | 0.549 (1.00) | 0.494 (1.00)
1p loss | 0.721 (1.00) | 0.421 (1.00) | 1 (1.00) | 0.349 (1.00) | 0.887 (1.00) |  | 0.729 (1.00) | 0.512 (1.00)
1q loss | 0.646 (1.00) | 0.819 (1.00) | 1 (1.00) | 0.349 (1.00) | 0.73 (1.00) |  | 0.523 (1.00) | 0.65 (1.00)
3p loss | 0.211 (1.00) | 0.0512 (1.00) | 0.661 (1.00) |  | 0.108 (1.00) | 0.43 (1.00) | 0.441 (1.00) | 0.214 (1.00)
3q loss | 2.89e-06 (0.00117) | 0.324 (1.00) | 1 (1.00) |  | 0.0325 (1.00) | 0.368 (1.00) | 0.0864 (1.00) | 0.115 (1.00)
4p loss | 0.131 (1.00) | 0.13 (1.00) | 0.0126 (1.00) |  | 0.118 (1.00) | 0.591 (1.00) | 0.627 (1.00) | 0.0531 (1.00)
4q loss | 0.699 (1.00) | 0.475 (1.00) | 0.107 (1.00) |  | 0.462 (1.00) | 0.806 (1.00) | 0.627 (1.00) | 0.268 (1.00)
5p loss | 0.000217 (0.087) | 0.0657 (1.00) | 0.0956 (1.00) |  | 0.00988 (1.00) | 0.103 (1.00) | 0.549 (1.00) | 0.115 (1.00)
5q loss | 0.0275 (1.00) | 0.229 (1.00) | 0.0956 (1.00) |  | 0.147 (1.00) |  | 0.549 (1.00) | 0.39 (1.00)
6p loss | 0.0312 (1.00) | 0.989 (1.00) | 0.463 (1.00) | 0.26 (1.00) | 0.183 (1.00) | 0.292 (1.00) | 0.777 (1.00) | 0.316 (1.00)
6q loss | 0.279 (1.00) | 0.367 (1.00) | 0.0713 (1.00) | 0.392 (1.00) | 0.0861 (1.00) | 0.301 (1.00) | 0.397 (1.00) | 0.46 (1.00)
8p loss | 0.748 (1.00) | 0.401 (1.00) | 0.0309 (1.00) |  | 0.355 (1.00) |  | 0.894 (1.00) | 0.282 (1.00)
9p loss | 0.0576 (1.00) | 0.527 (1.00) | 0.0168 (1.00) | 0.424 (1.00) | 0.0944 (1.00) | 0.884 (1.00) | 0.744 (1.00) | 0.3 (1.00)
9q loss | 0.0985 (1.00) | 0.5 (1.00) | 0.0506 (1.00) | 0.424 (1.00) | 0.0645 (1.00) | 0.884 (1.00) | 0.838 (1.00) | 0.421 (1.00)
10p loss | 0.129 (1.00) | 0.949 (1.00) | 0.654 (1.00) |  | 0.737 (1.00) |  | 0.136 (1.00) | 0.717 (1.00)
10q loss | 0.0067 (1.00) | 0.739 (1.00) | 1 (1.00) |  | 0.312 (1.00) | 0.368 (1.00) | 0.297 (1.00) | 0.631 (1.00)
11p loss | 0.00515 (1.00) | 0.0523 (1.00) | 0.709 (1.00) |  | 0.425 (1.00) |  | 0.627 (1.00) | 0.244 (1.00)
11q loss | 0.0224 (1.00) | 0.0682 (1.00) | 1 (1.00) |  | 0.0479 (1.00) | 0.661 (1.00) | 0.714 (1.00) | 0.144 (1.00)
13q loss | 0.025 (1.00) | 0.424 (1.00) | 0.00451 (1.00) |  | 0.241 (1.00) | 0.667 (1.00) | 0.423 (1.00) | 0.439 (1.00)
14q loss | 0.974 (1.00) | 0.341 (1.00) | 0.58 (1.00) | 0.396 (1.00) | 0.789 (1.00) | 0.387 (1.00) | 0.471 (1.00) | 0.332 (1.00)
15q loss | 0.0655 (1.00) | 0.362 (1.00) | 0.723 (1.00) | 0.654 (1.00) | 0.0861 (1.00) | 0.667 (1.00) | 0.699 (1.00) | 0.124 (1.00)
16q loss |  |  | 0.0309 (1.00) |  | 0.379 (1.00) |  | 0.549 (1.00) | 0.494 (1.00)
17p loss | 2.02e-07 (8.22e-05) | 0.488 (1.00) | 0.0354 (1.00) |  | 0.238 (1.00) | 0.426 (1.00) | 0.125 (1.00) | 0.0615 (1.00)
18p loss | 0.00216 (0.856) | 0.85 (1.00) | 1 (1.00) | 0.349 (1.00) | 0.108 (1.00) | 0.43 (1.00) | 0.905 (1.00) | 0.291 (1.00)
18q loss | 0.00216 (0.856) | 0.85 (1.00) | 0.771 (1.00) | 0.349 (1.00) | 0.0503 (1.00) | 0.43 (1.00) | 0.945 (1.00) | 0.131 (1.00)
19p loss | 0.646 (1.00) | 0.585 (1.00) | 1 (1.00) |  | 0.747 (1.00) |  | 0.549 (1.00) | 0.282 (1.00)
19q loss | 0.748 (1.00) | 0.88 (1.00) | 1 (1.00) |  | 0.51 (1.00) |  |  |
21q loss | 0.151 (1.00) | 0.438 (1.00) | 0.324 (1.00) |  | 0.549 (1.00) | 0.806 (1.00) | 0.688 (1.00) | 0.124 (1.00)
22q loss | 0.704 (1.00) | 0.748 (1.00) | 0.172 (1.00) | 0.457 (1.00) | 0.19 (1.00) | 0.691 (1.00) | 0.306 (1.00) | 0.353 (1.00)
Xq loss | 0.748 (1.00) | 0.921 (1.00) | 1 (1.00) |  | 0.797 (1.00) |  |  | 0.88 (1.00)

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

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

Cluster Labels | 1Q GAIN MUTATED | 1Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 8 | 95

'1q gain mutation analysis' versus 'Time to Death'

P value = 9.91e-05 (logrank test), Q value = 0.04

Table S2. Clustering Approach #1: '1q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
1Q GAIN MUTATED | 7 | 2 | 0.7 - 25.4 (7.6)
1Q GAIN WILD-TYPE | 89 | 12 | 0.0 - 182.7 (14.6)

Figure S1. Get High-res Image Clustering Approach #1: '1q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #2: '2p gain mutation analysis'

Table S3. Get Full Table Description of clustering approach #2: '2p gain mutation analysis'

Cluster Labels | 2P GAIN MUTATED | 2P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 11 | 92

Clustering Approach #3: '2q gain mutation analysis'

Table S4. Get Full Table Description of clustering approach #3: '2q gain mutation analysis'

Cluster Labels | 2Q GAIN MUTATED | 2Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 13 | 90

Clustering Approach #4: '3p gain mutation analysis'

Table S5. Get Full Table Description of clustering approach #4: '3p gain mutation analysis'

Cluster Labels | 3P GAIN MUTATED | 3P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 24 | 79

Clustering Approach #5: '3q gain mutation analysis'

Table S6. Get Full Table Description of clustering approach #5: '3q gain mutation analysis'

Cluster Labels | 3Q GAIN MUTATED | 3Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 26 | 77

Clustering Approach #6: '4p gain mutation analysis'

Table S7. Get Full Table Description of clustering approach #6: '4p gain mutation analysis'

Cluster Labels | 4P GAIN MUTATED | 4P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

Clustering Approach #7: '4q gain mutation analysis'

Table S8. Get Full Table Description of clustering approach #7: '4q gain mutation analysis'

Cluster Labels | 4Q GAIN MUTATED | 4Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

Clustering Approach #8: '5p gain mutation analysis'

Table S9. Get Full Table Description of clustering approach #8: '5p gain mutation analysis'

Cluster Labels | 5P GAIN MUTATED | 5P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 10 | 93

Clustering Approach #9: '5q gain mutation analysis'

Table S10. Get Full Table Description of clustering approach #9: '5q gain mutation analysis'

Cluster Labels | 5Q GAIN MUTATED | 5Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 10 | 93

Clustering Approach #10: '6p gain mutation analysis'

Table S11. Get Full Table Description of clustering approach #10: '6p gain mutation analysis'

Cluster Labels | 6P GAIN MUTATED | 6P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

'6p gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000177 (Chi-square test), Q value = 0.071

Table S12. Clustering Approach #10: '6p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 35
6P GAIN MUTATED | 2 | 2 | 0
6P GAIN WILD-TYPE | 52 | 3 | 35

Figure S2. Get High-res Image Clustering Approach #10: '6p gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #11: '6q gain mutation analysis'

Table S13. Get Full Table Description of clustering approach #11: '6q gain mutation analysis'

Cluster Labels | 6Q GAIN MUTATED | 6Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

'6q gain mutation analysis' versus 'Time to Death'

P value = 5.18e-06 (logrank test), Q value = 0.0021

Table S14. Clustering Approach #11: '6q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
6Q GAIN MUTATED | 3 | 2 | 7.9 - 13.6 (9.6)
6Q GAIN WILD-TYPE | 93 | 12 | 0.0 - 182.7 (14.1)

Figure S3. Get High-res Image Clustering Approach #11: '6q gain mutation analysis' versus Clinical Feature #1: 'Time to Death'

'6q gain mutation analysis' versus 'PATHOLOGICSPREAD(M)'

P value = 8.32e-06 (Chi-square test), Q value = 0.0034

Table S15. Clustering Approach #11: '6q gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 35
6Q GAIN MUTATED | 1 | 2 | 0
6Q GAIN WILD-TYPE | 53 | 3 | 35

Figure S4. Get High-res Image Clustering Approach #11: '6q gain mutation analysis' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #12: '7p gain mutation analysis'

Table S16. Get Full Table Description of clustering approach #12: '7p gain mutation analysis'

Cluster Labels | 7P GAIN MUTATED | 7P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 55 | 48

Clustering Approach #13: '7q gain mutation analysis'

Table S17. Get Full Table Description of clustering approach #13: '7q gain mutation analysis'

Cluster Labels | 7Q GAIN MUTATED | 7Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 56 | 47

Clustering Approach #14: '8p gain mutation analysis'

Table S18. Get Full Table Description of clustering approach #14: '8p gain mutation analysis'

Cluster Labels | 8P GAIN MUTATED | 8P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 5 | 98

Clustering Approach #15: '8q gain mutation analysis'

Table S19. Get Full Table Description of clustering approach #15: '8q gain mutation analysis'

Cluster Labels | 8Q GAIN MUTATED | 8Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 7 | 96

Clustering Approach #16: '10p gain mutation analysis'

Table S20. Get Full Table Description of clustering approach #16: '10p gain mutation analysis'

Cluster Labels | 10P GAIN MUTATED | 10P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

Clustering Approach #17: '10q gain mutation analysis'

Table S21. Get Full Table Description of clustering approach #17: '10q gain mutation analysis'

Cluster Labels | 10Q GAIN MUTATED | 10Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

Clustering Approach #18: '12p gain mutation analysis'

Table S22. Get Full Table Description of clustering approach #18: '12p gain mutation analysis'

Cluster Labels | 12P GAIN MUTATED | 12P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 31 | 72

Clustering Approach #19: '12q gain mutation analysis'

Table S23. Get Full Table Description of clustering approach #19: '12q gain mutation analysis'

Cluster Labels | 12Q GAIN MUTATED | 12Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 31 | 72

Clustering Approach #20: '13q gain mutation analysis'

Table S24. Get Full Table Description of clustering approach #20: '13q gain mutation analysis'

Cluster Labels | 13Q GAIN MUTATED | 13Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 11 | 92

Clustering Approach #21: '16p gain mutation analysis'

Table S25. Get Full Table Description of clustering approach #21: '16p gain mutation analysis'

Cluster Labels | 16P GAIN MUTATED | 16P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 44 | 59

Clustering Approach #22: '16q gain mutation analysis'

Table S26. Get Full Table Description of clustering approach #22: '16q gain mutation analysis'

Cluster Labels | 16Q GAIN MUTATED | 16Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 41 | 62

Clustering Approach #23: '17p gain mutation analysis'

Table S27. Get Full Table Description of clustering approach #23: '17p gain mutation analysis'

Cluster Labels | 17P GAIN MUTATED | 17P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 52 | 51

'17p gain mutation analysis' versus 'PATHOLOGY.T'

P value = 0.000449 (Chi-square test), Q value = 0.18

Table S28. Clustering Approach #23: '17p gain mutation analysis' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 58 | 13 | 32
17P GAIN MUTATED | 36 | 9 | 7
17P GAIN WILD-TYPE | 22 | 4 | 25

Figure S5. Get High-res Image Clustering Approach #23: '17p gain mutation analysis' versus Clinical Feature #5: 'PATHOLOGY.T'

Clustering Approach #24: '17q gain mutation analysis'

Table S29. Get Full Table Description of clustering approach #24: '17q gain mutation analysis'

Cluster Labels | 17Q GAIN MUTATED | 17Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 62 | 41

Clustering Approach #25: '18p gain mutation analysis'

Table S30. Get Full Table Description of clustering approach #25: '18p gain mutation analysis'

Cluster Labels | 18P GAIN MUTATED | 18P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 6 | 97

Clustering Approach #26: '18q gain mutation analysis'

Table S31. Get Full Table Description of clustering approach #26: '18q gain mutation analysis'

Cluster Labels | 18Q GAIN MUTATED | 18Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

Clustering Approach #27: '20p gain mutation analysis'

Table S32. Get Full Table Description of clustering approach #27: '20p gain mutation analysis'

Cluster Labels | 20P GAIN MUTATED | 20P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 31 | 72

Clustering Approach #28: '20q gain mutation analysis'

Table S33. Get Full Table Description of clustering approach #28: '20q gain mutation analysis'

Cluster Labels | 20Q GAIN MUTATED | 20Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 32 | 71

Clustering Approach #29: 'Xq gain mutation analysis'

Table S34. Get Full Table Description of clustering approach #29: 'Xq gain mutation analysis'

Cluster Labels | XQ GAIN MUTATED | XQ GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

Clustering Approach #30: '1p loss mutation analysis'

Table S35. Get Full Table Description of clustering approach #30: '1p loss mutation analysis'

Cluster Labels | 1P LOSS MUTATED | 1P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 11 | 92

Clustering Approach #31: '1q loss mutation analysis'

Table S36. Get Full Table Description of clustering approach #31: '1q loss mutation analysis'

Cluster Labels | 1Q LOSS MUTATED | 1Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 6 | 97

Clustering Approach #32: '3p loss mutation analysis'

Table S37. Get Full Table Description of clustering approach #32: '3p loss mutation analysis'

Cluster Labels | 3P LOSS MUTATED | 3P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 6 | 97

Clustering Approach #33: '3q loss mutation analysis'

Table S38. Get Full Table Description of clustering approach #33: '3q loss mutation analysis'

Cluster Labels | 3Q LOSS MUTATED | 3Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

'3q loss mutation analysis' versus 'Time to Death'

P value = 2.89e-06 (logrank test), Q value = 0.0012

Table S39. Clustering Approach #33: '3q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
3Q LOSS MUTATED | 3 | 2 | 3.7 - 21.6 (8.8)
3Q LOSS WILD-TYPE | 93 | 12 | 0.0 - 182.7 (13.7)

Figure S6. Get High-res Image Clustering Approach #33: '3q loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #34: '4p loss mutation analysis'

Table S40. Get Full Table Description of clustering approach #34: '4p loss mutation analysis'

Cluster Labels | 4P LOSS MUTATED | 4P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 8 | 95

Clustering Approach #35: '4q loss mutation analysis'

Table S41. Get Full Table Description of clustering approach #35: '4q loss mutation analysis'

Cluster Labels | 4Q LOSS MUTATED | 4Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 8 | 95

Clustering Approach #36: '5p loss mutation analysis'

Table S42. Get Full Table Description of clustering approach #36: '5p loss mutation analysis'

Cluster Labels | 5P LOSS MUTATED | 5P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

'5p loss mutation analysis' versus 'Time to Death'

P value = 0.000217 (logrank test), Q value = 0.087

Table S43. Clustering Approach #36: '5p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
5P LOSS MUTATED | 4 | 2 | 0.0 - 22.9 (7.4)
5P LOSS WILD-TYPE | 92 | 12 | 0.0 - 182.7 (13.9)

Figure S7. Get High-res Image Clustering Approach #36: '5p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #37: '5q loss mutation analysis'

Table S44. Get Full Table Description of clustering approach #37: '5q loss mutation analysis'

Cluster Labels | 5Q LOSS MUTATED | 5Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

Clustering Approach #38: '6p loss mutation analysis'

Table S45. Get Full Table Description of clustering approach #38: '6p loss mutation analysis'

Cluster Labels | 6P LOSS MUTATED | 6P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 9 | 94

Clustering Approach #39: '6q loss mutation analysis'

Table S46. Get Full Table Description of clustering approach #39: '6q loss mutation analysis'

Cluster Labels | 6Q LOSS MUTATED | 6Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 10 | 93

Clustering Approach #40: '8p loss mutation analysis'

Table S47. Get Full Table Description of clustering approach #40: '8p loss mutation analysis'

Cluster Labels | 8P LOSS MUTATED | 8P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

Clustering Approach #41: '9p loss mutation analysis'

Table S48. Get Full Table Description of clustering approach #41: '9p loss mutation analysis'

Cluster Labels | 9P LOSS MUTATED | 9P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 12 | 91

Clustering Approach #42: '9q loss mutation analysis'

Table S49. Get Full Table Description of clustering approach #42: '9q loss mutation analysis'

Cluster Labels | 9Q LOSS MUTATED | 9Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 12 | 91

Clustering Approach #43: '10p loss mutation analysis'

Table S50. Get Full Table Description of clustering approach #43: '10p loss mutation analysis'

Cluster Labels | 10P LOSS MUTATED | 10P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 5 | 98

Clustering Approach #44: '10q loss mutation analysis'

Table S51. Get Full Table Description of clustering approach #44: '10q loss mutation analysis'

Cluster Labels | 10Q LOSS MUTATED | 10Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 5 | 98

Clustering Approach #45: '11p loss mutation analysis'

Table S52. Get Full Table Description of clustering approach #45: '11p loss mutation analysis'

Cluster Labels | 11P LOSS MUTATED | 11P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 8 | 95

Clustering Approach #46: '11q loss mutation analysis'

Table S53. Get Full Table Description of clustering approach #46: '11q loss mutation analysis'

Cluster Labels | 11Q LOSS MUTATED | 11Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 9 | 94

Clustering Approach #47: '13q loss mutation analysis'

Table S54. Get Full Table Description of clustering approach #47: '13q loss mutation analysis'

Cluster Labels | 13Q LOSS MUTATED | 13Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 9 | 94

Clustering Approach #48: '14q loss mutation analysis'

Table S55. Get Full Table Description of clustering approach #48: '14q loss mutation analysis'

Cluster Labels | 14Q LOSS MUTATED | 14Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 18 | 85

Clustering Approach #49: '15q loss mutation analysis'

Table S56. Get Full Table Description of clustering approach #49: '15q loss mutation analysis'

Cluster Labels | 15Q LOSS MUTATED | 15Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 10 | 93

Clustering Approach #50: '16q loss mutation analysis'

Table S57. Get Full Table Description of clustering approach #50: '16q loss mutation analysis'

Cluster Labels | 16Q LOSS MUTATED | 16Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

Clustering Approach #51: '17p loss mutation analysis'

Table S58. Get Full Table Description of clustering approach #51: '17p loss mutation analysis'

Cluster Labels | 17P LOSS MUTATED | 17P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 5 | 98

'17p loss mutation analysis' versus 'Time to Death'

P value = 2.02e-07 (logrank test), Q value = 8.2e-05

Table S59. Clustering Approach #51: '17p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
17P LOSS MUTATED | 4 | 2 | 0.2 - 11.1 (5.2)
17P LOSS WILD-TYPE | 92 | 12 | 0.0 - 182.7 (14.4)

Figure S8. Get High-res Image Clustering Approach #51: '17p loss mutation analysis' versus Clinical Feature #1: 'Time to Death'

Clustering Approach #52: '18p loss mutation analysis'

Table S60. Get Full Table Description of clustering approach #52: '18p loss mutation analysis'

Cluster Labels | 18P LOSS MUTATED | 18P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 15 | 88

Clustering Approach #53: '18q loss mutation analysis'

Table S61. Get Full Table Description of clustering approach #53: '18q loss mutation analysis'

Cluster Labels | 18Q LOSS MUTATED | 18Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 16 | 87

Clustering Approach #54: '19p loss mutation analysis'

Table S62. Get Full Table Description of clustering approach #54: '19p loss mutation analysis'

Cluster Labels | 19P LOSS MUTATED | 19P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 4 | 99

Clustering Approach #55: '19q loss mutation analysis'

Table S63. Get Full Table Description of clustering approach #55: '19q loss mutation analysis'

Cluster Labels | 19Q LOSS MUTATED | 19Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

Clustering Approach #56: '21q loss mutation analysis'

Table S64. Get Full Table Description of clustering approach #56: '21q loss mutation analysis'

Cluster Labels | 21Q LOSS MUTATED | 21Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 11 | 92

Clustering Approach #57: '22q loss mutation analysis'

Table S65. Get Full Table Description of clustering approach #57: '22q loss mutation analysis'

Cluster Labels | 22Q LOSS MUTATED | 22Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 19 | 84

Clustering Approach #58: 'Xq loss mutation analysis'

Table S66. Get Full Table Description of clustering approach #58: 'Xq loss mutation analysis'

Cluster Labels | XQ LOSS MUTATED | XQ LOSS WILD-TYPE
--- | --- | ---
Number of samples | 3 | 100

Methods & Data
Input

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

- Clinical data file = KIRP-TP.clin.merged.picked.txt

- Number of patients = 103

- Number of clustering approaches = 58

- 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

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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