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

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.

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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