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

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.

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)

Made with Nozzle