Correlation between copy number variations of arm-level result and selected clinical features
Cervical Squamous Cell Carcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1X34VNV
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/C1X34VNV
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 44 different clustering approaches and 11 clinical features across 34 patients, 2 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 '2p 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 '5p 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 '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 '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 '14q gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '15q gain mutation analysis'. These subtypes correlate to 'TOBACCOSMOKINGHISTORYINDICATOR'.

- 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 '18p 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 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- 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 '21q gain mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '22q gain 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 '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 '5q loss mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '7p loss mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '7q 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 '12p 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 '17p loss mutation analysis'. These subtypes do not correlate to any clinical features.

- 2 subtypes identified in current cancer cohort by '17q 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 '21q 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 44 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.

Clinical Features | Time to Death | AGE | HISTOLOGICAL TYPE | RADIATIONS RADIATION REGIMENINDICATION | NUMBERPACKYEARSSMOKED | STOPPEDSMOKINGYEAR | TOBACCOSMOKINGHISTORYINDICATOR | DISTANT METASTASIS | LYMPH NODE METASTASIS | NUMBER OF LYMPH NODES | TUMOR STAGECODE
Statistical Tests | logrank test | t-test | Chi-square test | Fisher's exact test | t-test | t-test | t-test | Fisher's exact test | Fisher's exact test | t-test | t-test
1p gain | 0.814 (1.00) | 0.0637 (1.00) | 0.136 (1.00) | 1 (1.00) |  |  | 0.528 (1.00) | 0.339 (1.00) | 1 (1.00) | 0.917 (1.00) |
1q gain | 0.553 (1.00) | 0.129 (1.00) | 0.217 (1.00) | 0.276 (1.00) | 0.969 (1.00) |  | 0.862 (1.00) | 0.106 (1.00) | 1 (1.00) | 0.463 (1.00) |
2p gain | 0.0598 (1.00) | 0.717 (1.00) | 0.732 (1.00) | 0.201 (1.00) |  |  | 0.769 (1.00) | 0.532 (1.00) | 1 (1.00) |  |
3p gain | 0.688 (1.00) | 0.791 (1.00) | 0.0653 (1.00) | 1 (1.00) |  |  | 0.589 (1.00) | 0.22 (1.00) | 0.537 (1.00) | 0.414 (1.00) |
3q gain | 0.113 (1.00) | 0.132 (1.00) | 0.326 (1.00) | 1 (1.00) | 0.268 (1.00) |  | 0.404 (1.00) | 1 (1.00) | 0.696 (1.00) | 0.565 (1.00) |
5p gain | 0.667 (1.00) | 0.495 (1.00) | 0.314 (1.00) | 1 (1.00) | 0.4 (1.00) |  | 0.822 (1.00) | 0.158 (1.00) | 0.372 (1.00) | 0.164 (1.00) |
6p gain | 0.221 (1.00) | 0.191 (1.00) | 0.389 (1.00) | 0.328 (1.00) |  |  | 0.235 (1.00) | 0.633 (1.00) | 1 (1.00) | 0.8 (1.00) |
7q gain | 0.268 (1.00) | 0.273 (1.00) | 0.191 (1.00) | 0.0666 (1.00) |  |  | 0.321 (1.00) | 0.568 (1.00) | 0.611 (1.00) | 0.556 (1.00) |
8p gain | 0.747 (1.00) | 0.294 (1.00) | 0.868 (1.00) | 1 (1.00) |  |  | 0.888 (1.00) | 0.28 (1.00) | 1 (1.00) | 0.443 (1.00) |
8q gain | 0.547 (1.00) | 0.357 (1.00) | 0.629 (1.00) | 1 (1.00) |  |  | 0.645 (1.00) | 1 (1.00) | 1 (1.00) | 0.704 (1.00) |
10p gain | 0.283 (1.00) | 0.459 (1.00) | 0.732 (1.00) | 0.539 (1.00) |  |  | 0.294 (1.00) | 1 (1.00) | 0.537 (1.00) | 0.414 (1.00) |
12p gain | 0.418 (1.00) | 0.366 (1.00) | 0.0392 (1.00) | 1 (1.00) |  |  | 0.218 (1.00) | 0.0559 (1.00) | 1 (1.00) | 0.917 (1.00) |
12q gain | 0.469 (1.00) | 0.575 (1.00) | 0.191 (1.00) | 0.564 (1.00) |  |  | 0.321 (1.00) | 0.076 (1.00) | 0.611 (1.00) | 0.556 (1.00) |
14q gain | 0.158 (1.00) | 0.82 (1.00) | 0.732 (1.00) | 1 (1.00) |  |  | 0.339 (1.00) | 1 (1.00) | 0.537 (1.00) | 0.593 (1.00) |
15q gain | 0.745 (1.00) | 0.771 (1.00) | 0.732 (1.00) | 0.539 (1.00) |  |  | 9.97e-05 (0.0364) | 1 (1.00) | 0.279 (1.00) | 0.00565 (1.00) |
16p gain | 0.745 (1.00) | 0.818 (1.00) | 0.191 (1.00) | 1 (1.00) |  |  | 0.769 (1.00) | 1 (1.00) | 0.537 (1.00) | 0.414 (1.00) |
16q gain |  | 0.205 (1.00) | 0.0653 (1.00) | 1 (1.00) |  |  |  | 1 (1.00) | 1 (1.00) |  |
18p gain | 0.355 (1.00) | 0.304 (1.00) | 0.0653 (1.00) | 0.201 (1.00) |  |  | 0.401 (1.00) | 1 (1.00) | 0.537 (1.00) | 0.593 (1.00) |
19q gain | 0.104 (1.00) | 0.87 (1.00) | 0.89 (1.00) | 0.000156 (0.0568) | 0.985 (1.00) |  | 0.439 (1.00) | 1 (1.00) | 0.611 (1.00) | 0.914 (1.00) |
20p gain | 0.119 (1.00) | 0.993 (1.00) | 0.178 (1.00) | 0.692 (1.00) | 0.0279 (1.00) |  | 0.72 (1.00) | 0.675 (1.00) | 0.425 (1.00) | 0.519 (1.00) |
20q gain | 0.00689 (1.00) | 0.332 (1.00) | 0.389 (1.00) | 0.714 (1.00) | 0.0279 (1.00) |  | 0.517 (1.00) | 0.675 (1.00) | 1 (1.00) | 0.872 (1.00) |
21q gain | 0.158 (1.00) | 0.0898 (1.00) | 0.732 (1.00) | 0.201 (1.00) |  |  | 0.589 (1.00) | 1 (1.00) | 0.537 (1.00) | 0.593 (1.00) |
22q gain | 0.355 (1.00) | 0.167 (1.00) | 0.348 (1.00) | 0.618 (1.00) |  |  | 0.671 (1.00) | 1 (1.00) | 0.611 (1.00) | 0.556 (1.00) |
3p loss | 0.403 (1.00) | 0.23 (1.00) | 0.672 (1.00) | 1 (1.00) | 0.193 (1.00) |  | 0.805 (1.00) | 1 (1.00) | 0.372 (1.00) | 0.247 (1.00) |
4p loss | 0.808 (1.00) | 0.317 (1.00) | 0.615 (1.00) | 0.704 (1.00) | 0.265 (1.00) |  | 0.883 (1.00) | 0.226 (1.00) | 1 (1.00) | 0.269 (1.00) |
4q loss | 0.221 (1.00) | 0.796 (1.00) | 0.909 (1.00) | 0.138 (1.00) | 0.937 (1.00) |  | 0.943 (1.00) | 0.568 (1.00) | 0.126 (1.00) | 0.181 (1.00) |
5q loss | 0.277 (1.00) | 0.119 (1.00) | 0.159 (1.00) | 0.683 (1.00) | 0.814 (1.00) |  | 0.792 (1.00) | 0.371 (1.00) | 1 (1.00) | 0.489 (1.00) |
7p loss | 0.838 (1.00) | 0.116 (1.00) | 0.556 (1.00) | 0.0666 (1.00) | 0.977 (1.00) |  | 0.502 (1.00) | 0.568 (1.00) | 0.611 (1.00) | 0.411 (1.00) |
7q loss | 0.00503 (1.00) | 0.391 (1.00) | 0.556 (1.00) | 0.0666 (1.00) | 0.907 (1.00) |  | 0.222 (1.00) | 0.532 (1.00) | 0.537 (1.00) | 0.383 (1.00) |
8p loss | 0.272 (1.00) | 0.708 (1.00) | 0.581 (1.00) | 0.431 (1.00) |  |  | 0.387 (1.00) | 1 (1.00) | 1 (1.00) | 0.704 (1.00) |
9p loss | 0.0398 (1.00) | 0.742 (1.00) | 0.556 (1.00) | 0.564 (1.00) |  |  | 0.345 (1.00) | 0.532 (1.00) | 1 (1.00) | 0.718 (1.00) |
9q loss | 0.0487 (1.00) | 0.353 (1.00) | 0.653 (1.00) | 1 (1.00) |  |  | 0.248 (1.00) | 0.532 (1.00) | 1 (1.00) | 0.718 (1.00) |
10p loss | 0.118 (1.00) | 0.553 (1.00) | 0.835 (1.00) | 0.157 (1.00) | 0.52 (1.00) |  | 0.167 (1.00) | 0.076 (1.00) | 0.0472 (1.00) | 0.175 (1.00) |
10q loss | 0.0684 (1.00) | 0.704 (1.00) | 0.89 (1.00) | 0.328 (1.00) | 0.77 (1.00) |  | 0.0717 (1.00) | 0.287 (1.00) | 0.0156 (1.00) | 0.111 (1.00) |
11p loss | 0.418 (1.00) | 0.535 (1.00) | 0.835 (1.00) | 0.394 (1.00) |  |  | 0.119 (1.00) | 0.158 (1.00) | 1 (1.00) | 0.277 (1.00) |
11q loss | 0.838 (1.00) | 0.767 (1.00) | 0.835 (1.00) | 0.157 (1.00) | 0.977 (1.00) |  | 0.666 (1.00) | 0.339 (1.00) | 0.641 (1.00) | 0.241 (1.00) |
12p loss | 0.439 (1.00) | 0.0155 (1.00) | 0.389 (1.00) | 0.644 (1.00) |  |  | 0.524 (1.00) | 1 (1.00) | 0.626 (1.00) | 0.261 (1.00) |
13q loss | 0.317 (1.00) | 0.911 (1.00) | 0.721 (1.00) | 0.666 (1.00) |  |  | 0.723 (1.00) | 0.642 (1.00) | 1 (1.00) | 0.277 (1.00) |
17p loss | 0.219 (1.00) | 0.249 (1.00) | 0.581 (1.00) | 0.431 (1.00) | 0.568 (1.00) |  | 0.662 (1.00) | 1 (1.00) | 1 (1.00) | 0.322 (1.00) |
17q loss | 0.825 (1.00) | 0.529 (1.00) | 0.653 (1.00) | 1 (1.00) |  |  | 0.861 (1.00) | 0.532 (1.00) | 1 (1.00) | 0.718 (1.00) |
18p loss | 0.74 (1.00) | 0.478 (1.00) | 0.0506 (1.00) | 0.0477 (1.00) | 0.0279 (1.00) |  | 0.235 (1.00) | 1 (1.00) | 0.626 (1.00) | 0.261 (1.00) |
18q loss | 0.875 (1.00) | 0.653 (1.00) | 0.0653 (1.00) | 0.157 (1.00) | 0.0279 (1.00) |  | 0.119 (1.00) | 0.633 (1.00) | 0.372 (1.00) | 0.154 (1.00) |
19p loss | 0.34 (1.00) | 0.457 (1.00) | 0.556 (1.00) | 0.0666 (1.00) |  |  | 0.693 (1.00) | 1 (1.00) | 1 (1.00) |  |
21q loss | 0.376 (1.00) | 0.966 (1.00) | 0.89 (1.00) | 0.148 (1.00) |  |  | 0.0676 (1.00) | 1 (1.00) | 0.372 (1.00) | 0.672 (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 | 7 | 27

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 | 15 | 19

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

Table S3. Get Full Table Description of clustering approach #3: '2p gain mutation analysis'

Cluster Labels | 2P GAIN MUTATED | 2P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

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

Table S4. Get Full Table Description of clustering approach #4: '3p gain mutation analysis'

Cluster Labels | 3P GAIN MUTATED | 3P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

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

Table S5. Get Full Table Description of clustering approach #5: '3q gain mutation analysis'

Cluster Labels | 3Q GAIN MUTATED | 3Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 21 | 13

Clustering Approach #6: '5p gain mutation analysis'

Table S6. Get Full Table Description of clustering approach #6: '5p gain mutation analysis'

Cluster Labels | 5P GAIN MUTATED | 5P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 9 | 25

Clustering Approach #7: '6p gain mutation analysis'

Table S7. Get Full Table Description of clustering approach #7: '6p gain mutation analysis'

Cluster Labels | 6P GAIN MUTATED | 6P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 6 | 28

Clustering Approach #8: '7q gain mutation analysis'

Table S8. Get Full Table Description of clustering approach #8: '7q gain mutation analysis'

Cluster Labels | 7Q GAIN MUTATED | 7Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

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

Table S9. Get Full Table Description of clustering approach #9: '8p gain mutation analysis'

Cluster Labels | 8P GAIN MUTATED | 8P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

Clustering Approach #10: '8q gain mutation analysis'

Table S10. Get Full Table Description of clustering approach #10: '8q gain mutation analysis'

Cluster Labels | 8Q GAIN MUTATED | 8Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 7 | 27

Clustering Approach #11: '10p gain mutation analysis'

Table S11. Get Full Table Description of clustering approach #11: '10p gain mutation analysis'

Cluster Labels | 10P GAIN MUTATED | 10P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

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

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

Cluster Labels | 12P GAIN MUTATED | 12P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 6 | 28

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

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

Cluster Labels | 12Q GAIN MUTATED | 12Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

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

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

Cluster Labels | 14Q GAIN MUTATED | 14Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

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

Table S15. Get Full Table Description of clustering approach #15: '15q gain mutation analysis'

Cluster Labels | 15Q GAIN MUTATED | 15Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

'15q gain mutation analysis' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 9.97e-05 (t-test), Q value = 0.036

Table S16. Clustering Approach #15: '15q gain mutation analysis' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 33 | 1.8 (1.1)
15Q GAIN MUTATED | 3 | 1.0 (0.0)
15Q GAIN WILD-TYPE | 30 | 1.9 (1.1)

Figure S1. Get High-res Image Clustering Approach #15: '15q gain mutation analysis' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

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

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

Cluster Labels | 16P GAIN MUTATED | 16P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

Clustering Approach #17: '16q gain mutation analysis'

Table S18. Get Full Table Description of clustering approach #17: '16q gain mutation analysis'

Cluster Labels | 16Q GAIN MUTATED | 16Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

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

Table S19. Get Full Table Description of clustering approach #18: '18p gain mutation analysis'

Cluster Labels | 18P GAIN MUTATED | 18P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

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

Table S20. Get Full Table Description of clustering approach #19: '19q gain mutation analysis'

Cluster Labels | 19Q GAIN MUTATED | 19Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 6 | 28

'19q gain mutation analysis' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.000156 (Fisher's exact test), Q value = 0.057

Table S21. Clustering Approach #19: '19q gain mutation analysis' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 10 | 24
19Q GAIN MUTATED | 6 | 0
19Q GAIN WILD-TYPE | 4 | 24

Figure S2. Get High-res Image Clustering Approach #19: '19q gain mutation analysis' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Cluster Labels | 20P GAIN MUTATED | 20P GAIN WILD-TYPE
--- | --- | ---
Number of samples | 11 | 23

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

Table S23. Get Full Table Description of clustering approach #21: '20q gain mutation analysis'

Cluster Labels | 20Q GAIN MUTATED | 20Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 12 | 22

Clustering Approach #22: '21q gain mutation analysis'

Table S24. Get Full Table Description of clustering approach #22: '21q gain mutation analysis'

Cluster Labels | 21Q GAIN MUTATED | 21Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

Clustering Approach #23: '22q gain mutation analysis'

Table S25. Get Full Table Description of clustering approach #23: '22q gain mutation analysis'

Cluster Labels | 22Q GAIN MUTATED | 22Q GAIN WILD-TYPE
--- | --- | ---
Number of samples | 5 | 29

Clustering Approach #24: '3p loss mutation analysis'

Table S26. Get Full Table Description of clustering approach #24: '3p loss mutation analysis'

Cluster Labels | 3P LOSS MUTATED | 3P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 9 | 25

Clustering Approach #25: '4p loss mutation analysis'

Table S27. Get Full Table Description of clustering approach #25: '4p loss mutation analysis'

Cluster Labels | 4P LOSS MUTATED | 4P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 14 | 20

Clustering Approach #26: '4q loss mutation analysis'

Table S28. Get Full Table Description of clustering approach #26: '4q loss mutation analysis'

Cluster Labels | 4Q LOSS MUTATED | 4Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 5 | 29

Clustering Approach #27: '5q loss mutation analysis'

Table S29. Get Full Table Description of clustering approach #27: '5q loss mutation analysis'

Cluster Labels | 5Q LOSS MUTATED | 5Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 10 | 24

Clustering Approach #28: '7p loss mutation analysis'

Table S30. Get Full Table Description of clustering approach #28: '7p loss mutation analysis'

Cluster Labels | 7P LOSS MUTATED | 7P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

Clustering Approach #29: '7q loss mutation analysis'

Table S31. Get Full Table Description of clustering approach #29: '7q loss mutation analysis'

Cluster Labels | 7Q LOSS MUTATED | 7Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

Clustering Approach #30: '8p loss mutation analysis'

Table S32. Get Full Table Description of clustering approach #30: '8p loss mutation analysis'

Cluster Labels | 8P LOSS MUTATED | 8P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 10 | 24

Clustering Approach #31: '9p loss mutation analysis'

Table S33. Get Full Table Description of clustering approach #31: '9p loss mutation analysis'

Cluster Labels | 9P LOSS MUTATED | 9P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

Clustering Approach #32: '9q loss mutation analysis'

Table S34. Get Full Table Description of clustering approach #32: '9q loss mutation analysis'

Cluster Labels | 9Q LOSS MUTATED | 9Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

Clustering Approach #33: '10p loss mutation analysis'

Table S35. Get Full Table Description of clustering approach #33: '10p loss mutation analysis'

Cluster Labels | 10P LOSS MUTATED | 10P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 7 | 27

Clustering Approach #34: '10q loss mutation analysis'

Table S36. Get Full Table Description of clustering approach #34: '10q loss mutation analysis'

Cluster Labels | 10Q LOSS MUTATED | 10Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 6 | 28

Clustering Approach #35: '11p loss mutation analysis'

Table S37. Get Full Table Description of clustering approach #35: '11p loss mutation analysis'

Cluster Labels | 11P LOSS MUTATED | 11P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 7 | 27

Clustering Approach #36: '11q loss mutation analysis'

Table S38. Get Full Table Description of clustering approach #36: '11q loss mutation analysis'

Cluster Labels | 11Q LOSS MUTATED | 11Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 7 | 27

Clustering Approach #37: '12p loss mutation analysis'

Table S39. Get Full Table Description of clustering approach #37: '12p loss mutation analysis'

Cluster Labels | 12P LOSS MUTATED | 12P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 6 | 28

Clustering Approach #38: '13q loss mutation analysis'

Table S40. Get Full Table Description of clustering approach #38: '13q loss mutation analysis'

Cluster Labels | 13Q LOSS MUTATED | 13Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 8 | 26

Clustering Approach #39: '17p loss mutation analysis'

Table S41. Get Full Table Description of clustering approach #39: '17p loss mutation analysis'

Cluster Labels | 17P LOSS MUTATED | 17P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 10 | 24

Clustering Approach #40: '17q loss mutation analysis'

Table S42. Get Full Table Description of clustering approach #40: '17q loss mutation analysis'

Cluster Labels | 17Q LOSS MUTATED | 17Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 3 | 31

Clustering Approach #41: '18p loss mutation analysis'

Table S43. Get Full Table Description of clustering approach #41: '18p loss mutation analysis'

Cluster Labels | 18P LOSS MUTATED | 18P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 6 | 28

Clustering Approach #42: '18q loss mutation analysis'

Table S44. Get Full Table Description of clustering approach #42: '18q loss mutation analysis'

Cluster Labels | 18Q LOSS MUTATED | 18Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 7 | 27

Clustering Approach #43: '19p loss mutation analysis'

Table S45. Get Full Table Description of clustering approach #43: '19p loss mutation analysis'

Cluster Labels | 19P LOSS MUTATED | 19P LOSS WILD-TYPE
--- | --- | ---
Number of samples | 4 | 30

Clustering Approach #44: '21q loss mutation analysis'

Table S46. Get Full Table Description of clustering approach #44: '21q loss mutation analysis'

Cluster Labels | 21Q LOSS MUTATED | 21Q LOSS WILD-TYPE
--- | --- | ---
Number of samples | 6 | 28

Methods & Data
Input

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

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

- Number of patients = 34

- Number of clustering approaches = 44

- Number of selected clinical features = 11

- 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

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

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- 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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[4] 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)
[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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