Correlation between aggregated molecular cancer subtypes and selected clinical features

Bladder Urothelial Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1HT2MX9

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 11 clinical features across 198 patients, 6 significant findings detected with P value < 0.05 and Q value < 0.25.

8 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.N.STAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'PATHOLOGY.N.STAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE' and 'PATHOLOGY.T.STAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. 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 10 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 6 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.25 (1.00)	0.872 (1.00)	0.179 (1.00)	0.105 (1.00)	0.617 (1.00)	0.528 (1.00)	0.666 (1.00)	0.538 (1.00)	0.386 (1.00)	0.419 (1.00)
AGE	ANOVA	0.9 (1.00)	0.167 (1.00)	0.323 (1.00)	0.242 (1.00)	0.156 (1.00)	0.0683 (1.00)	0.0192 (1.00)	0.187 (1.00)	0.0169 (1.00)	0.029 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.00761 (0.769)	0.0181 (1.00)	0.227 (1.00)	0.133 (1.00)	0.0119 (1.00)	3.04e-05 (0.00335)	0.0132 (1.00)	0.472 (1.00)	0.00159 (0.17)	0.00534 (0.545)
PATHOLOGY T STAGE	Chi-square test	0.362 (1.00)	0.134 (1.00)	0.594 (1.00)	0.129 (1.00)	0.0034 (0.353)	0.000538 (0.0586)	0.035 (1.00)	0.647 (1.00)	0.00207 (0.218)	0.00966 (0.966)
PATHOLOGY N STAGE	Chi-square test	0.00183 (0.194)	0.199 (1.00)	0.512 (1.00)	0.915 (1.00)	0.643 (1.00)	0.000942 (0.102)	0.0391 (1.00)	0.399 (1.00)	0.0795 (1.00)	0.0648 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.307 (1.00)	0.12 (1.00)	0.102 (1.00)	0.21 (1.00)	0.0231 (1.00)	0.0203 (1.00)	0.288 (1.00)	0.837 (1.00)	0.33 (1.00)	0.516 (1.00)
GENDER	Fisher's exact test	0.0254 (1.00)	0.102 (1.00)	0.545 (1.00)	0.132 (1.00)	0.226 (1.00)	0.0281 (1.00)	0.00374 (0.385)	0.147 (1.00)	0.0295 (1.00)	0.0765 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA	0.0673 (1.00)	0.01 (0.993)	0.336 (1.00)	0.441 (1.00)	0.695 (1.00)	0.394 (1.00)	0.46 (1.00)	0.0912 (1.00)	0.627 (1.00)	0.347 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.398 (1.00)	0.266 (1.00)	0.111 (1.00)	0.0975 (1.00)	0.495 (1.00)	0.327 (1.00)	0.208 (1.00)	0.71 (1.00)	0.39 (1.00)	0.613 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.0368 (1.00)	0.12 (1.00)	0.183 (1.00)	0.448 (1.00)	0.841 (1.00)	0.0247 (1.00)	0.207 (1.00)	0.462 (1.00)	0.0512 (1.00)	0.0645 (1.00)
GLEASON SCORE	ANOVA	0.788 (1.00)	0.169 (1.00)	0.304 (1.00)	0.288 (1.00)	0.61 (1.00)	0.997 (1.00)	0.397 (1.00)	0.918 (1.00)	0.538 (1.00)	0.657 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	10	68	16	12	32	27	19	11

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.25 (logrank test), Q value = 1

Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	188	56	0.1 - 140.8 (8.3)
subtype1	9	4	2.7 - 40.1 (10.6)
subtype2	65	17	0.1 - 140.8 (8.8)
subtype3	16	7	2.0 - 50.3 (7.3)
subtype4	12	6	2.1 - 48.0 (15.3)
subtype5	31	4	0.4 - 64.2 (7.8)
subtype6	27	8	0.5 - 130.9 (11.9)
subtype7	17	8	1.8 - 87.3 (6.7)
subtype8	11	2	1.8 - 123.8 (6.9)

Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.9 (ANOVA), Q value = 1

Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	194	67.5 (10.7)
subtype1	9	67.7 (10.4)
subtype2	68	66.4 (11.0)
subtype3	16	65.9 (11.2)
subtype4	12	67.4 (10.7)
subtype5	32	68.4 (10.2)
subtype6	27	67.3 (10.2)
subtype7	19	69.5 (12.2)
subtype8	11	70.6 (10.5)

Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00761 (Chi-square test), Q value = 0.77

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	65	66	58
subtype1	1	5	0	3
subtype2	1	23	30	12
subtype3	0	6	4	6
subtype4	0	3	1	8
subtype5	0	13	12	6
subtype6	0	9	8	10
subtype7	0	2	6	11
subtype8	0	4	5	2

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.362 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	90	28
subtype1	6	0	1
subtype2	21	33	10
subtype3	5	9	1
subtype4	3	7	2
subtype5	10	16	2
subtype6	7	11	5
subtype7	5	9	5
subtype8	2	5	2

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00183 (Chi-square test), Q value = 0.19

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	120	18	33	7
subtype1	2	1	3	0
subtype2	55	3	7	2
subtype3	8	2	3	2
subtype4	4	3	5	0
subtype5	22	2	1	0
subtype6	13	3	5	3
subtype7	8	4	7	0
subtype8	8	0	2	0

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.307 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	103	5	86
subtype1	5	0	5
subtype2	40	2	26
subtype3	7	0	9
subtype4	9	1	2
subtype5	15	1	16
subtype6	13	0	13
subtype7	12	0	7
subtype8	2	1	8

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0254 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	147
subtype1	1	9
subtype2	16	52
subtype3	2	14
subtype4	6	6
subtype5	14	18
subtype6	3	24
subtype7	4	15
subtype8	2	9

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0673 (ANOVA), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	54	78.7 (15.8)
subtype1	2	85.0 (7.1)
subtype2	14	69.3 (17.7)
subtype3	8	80.0 (17.7)
subtype4	3	90.0 (0.0)
subtype5	11	75.5 (16.9)
subtype6	6	90.0 (6.3)
subtype7	8	82.5 (11.6)
subtype8	2	85.0 (7.1)

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.398 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	114	37.4 (25.3)
subtype1	7	19.6 (19.2)
subtype2	40	35.8 (28.2)
subtype3	10	44.9 (22.7)
subtype4	7	41.4 (32.2)
subtype5	16	40.4 (25.1)
subtype6	14	43.9 (25.8)
subtype7	15	30.9 (14.1)
subtype8	5	46.0 (26.1)

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0368 (ANOVA), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	6	1.2 (1.6)
subtype2	46	0.9 (1.9)
subtype3	14	3.9 (6.5)
subtype4	11	1.1 (0.9)
subtype5	21	0.6 (1.8)
subtype6	21	1.1 (1.5)
subtype7	17	2.8 (3.9)
subtype8	9	2.4 (6.0)

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.788 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	2	7.5 (0.7)
subtype2	13	6.2 (0.4)
subtype3	4	6.2 (0.5)
subtype4	1	7.0 (NA)
subtype5	7	6.3 (0.5)
subtype6	7	6.6 (1.1)
subtype7	6	6.5 (0.5)
subtype8	2	6.5 (0.7)

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S13. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	75	83	40

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.872 (logrank test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	191	58	0.1 - 140.8 (8.2)
subtype1	71	21	0.1 - 130.9 (6.7)
subtype2	82	26	0.4 - 140.8 (10.6)
subtype3	38	11	0.1 - 64.6 (7.0)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.167 (ANOVA), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	197	67.5 (10.7)
subtype1	74	67.1 (10.9)
subtype2	83	69.0 (10.1)
subtype3	40	65.2 (11.0)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0181 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	66	67	59
subtype1	2	26	21	24
subtype2	0	22	39	21
subtype3	0	18	7	14

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.134 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	91	29
subtype1	27	32	9
subtype2	18	46	13
subtype3	14	13	7

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.199 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	122	18	34	7
subtype1	43	9	13	2
subtype2	58	8	10	4
subtype3	21	1	11	1

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.12 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	106	5	86
subtype1	40	2	32
subtype2	39	1	43
subtype3	27	2	11

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.102 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	149
subtype1	15	60
subtype2	27	56
subtype3	7	33

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.01 (ANOVA), Q value = 0.99

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	55	78.9 (15.7)
subtype1	27	81.9 (13.0)
subtype2	15	83.3 (14.0)
subtype3	13	67.7 (18.3)

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.266 (ANOVA), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	115	37.7 (25.4)
subtype1	45	37.2 (25.7)
subtype2	52	40.9 (26.9)
subtype3	18	29.6 (18.8)

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.12 (ANOVA), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	54	1.7 (3.8)
subtype2	68	1.0 (2.4)
subtype3	23	2.6 (4.1)

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'GLEASON_SCORE'

P value = 0.169 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	13	6.2 (0.4)
subtype2	20	6.5 (0.8)
subtype3	9	6.7 (0.7)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S25. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	36	28	36	5	20

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.179 (logrank test), Q value = 1

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	47	0.1 - 140.8 (8.9)
subtype1	31	6	0.1 - 130.9 (7.7)
subtype2	27	11	1.8 - 140.8 (7.3)
subtype3	36	17	0.4 - 123.8 (12.5)
subtype4	5	3	3.1 - 11.9 (5.7)
subtype5	20	10	2.1 - 61.9 (10.1)

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.323 (ANOVA), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	124	67.2 (10.6)
subtype1	35	65.3 (10.8)
subtype2	28	69.8 (9.4)
subtype3	36	68.7 (10.6)
subtype4	5	64.4 (11.4)
subtype5	20	65.0 (11.0)

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.227 (Chi-square test), Q value = 1

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	35	42	43
subtype1	1	11	9	12
subtype2	0	5	8	15
subtype3	0	8	16	11
subtype4	0	1	3	1
subtype5	0	10	6	4

Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.594 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	27	62	19
subtype1	10	15	4
subtype2	6	14	8
subtype3	8	21	5
subtype4	0	3	1
subtype5	3	9	1

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.512 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	70	12	24	6
subtype1	19	1	9	2
subtype2	13	3	9	2
subtype3	23	5	4	2
subtype4	4	1	0	0
subtype5	11	2	2	0

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.102 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	72	5	47
subtype1	24	2	9
subtype2	10	2	16
subtype3	25	0	11
subtype4	4	0	1
subtype5	9	1	10

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.545 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	92
subtype1	7	29
subtype2	10	18
subtype3	8	28
subtype4	2	3
subtype5	6	14

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.336 (ANOVA), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	79.1 (15.8)
subtype1	12	75.0 (17.8)
subtype2	2	80.0 (14.1)
subtype3	10	77.0 (20.0)
subtype4	1	90.0 (NA)
subtype5	10	85.0 (7.1)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.111 (ANOVA), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	77	35.7 (21.4)
subtype1	23	30.5 (20.7)
subtype2	17	45.8 (22.4)
subtype3	22	36.8 (20.3)
subtype4	2	35.5 (6.4)
subtype5	13	30.1 (22.2)

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.183 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	88	1.9 (3.8)
subtype1	21	1.9 (3.1)
subtype2	25	3.3 (5.5)
subtype3	27	0.9 (2.4)
subtype4	4	0.2 (0.5)
subtype5	11	1.4 (3.6)

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.304 (ANOVA), Q value = 1

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	23	6.3 (0.5)
subtype1	4	6.5 (0.6)
subtype2	8	6.1 (0.4)
subtype3	8	6.2 (0.5)
subtype5	3	6.7 (0.6)

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S37. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	41	18	23	43

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.105 (logrank test), Q value = 1

Table S38. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	47	0.1 - 140.8 (8.9)
subtype1	36	8	0.1 - 112.4 (10.9)
subtype2	18	7	2.1 - 61.9 (9.0)
subtype3	23	12	0.4 - 130.9 (15.0)
subtype4	42	20	1.8 - 140.8 (7.8)

Figure S34. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.242 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	124	67.2 (10.6)
subtype1	40	65.1 (10.9)
subtype2	18	65.4 (9.3)
subtype3	23	69.0 (11.7)
subtype4	43	69.1 (9.9)

Figure S35. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.133 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	35	42	43
subtype1	1	14	12	11
subtype2	0	9	5	4
subtype3	0	4	11	7
subtype4	0	8	14	21

Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.129 (Chi-square test), Q value = 1

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	27	62	19
subtype1	14	15	6
subtype2	2	6	1
subtype3	3	17	2
subtype4	8	24	10

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.915 (Chi-square test), Q value = 1

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	70	12	24	6
subtype1	23	4	7	1
subtype2	9	0	2	1
subtype3	15	3	4	1
subtype4	23	5	11	3

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.21 (Chi-square test), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	72	5	47
subtype1	29	1	10
subtype2	8	2	8
subtype3	13	0	10
subtype4	22	2	19

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.132 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	92
subtype1	6	35
subtype2	5	13
subtype3	6	17
subtype4	16	27

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.441 (ANOVA), Q value = 1

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	79.1 (15.8)
subtype1	13	76.9 (17.5)
subtype2	10	86.0 (7.0)
subtype3	7	77.1 (18.0)
subtype4	5	74.0 (20.7)

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0975 (ANOVA), Q value = 1

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	77	35.7 (21.4)
subtype1	26	31.0 (19.0)
subtype2	12	29.5 (21.8)
subtype3	14	34.7 (19.0)
subtype4	25	44.2 (23.3)

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.448 (ANOVA), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	88	1.9 (3.8)
subtype1	24	1.5 (2.7)
subtype2	8	1.8 (4.2)
subtype3	19	0.9 (1.9)
subtype4	37	2.6 (5.0)

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 0.288 (ANOVA), Q value = 1

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	23	6.3 (0.5)
subtype1	3	6.7 (0.6)
subtype2	2	6.5 (0.7)
subtype3	7	6.3 (0.5)
subtype4	11	6.2 (0.4)

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S49. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	84	57	57

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.617 (logrank test), Q value = 1

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	191	58	0.1 - 140.8 (8.2)
subtype1	78	17	0.1 - 112.4 (5.9)
subtype2	56	21	1.2 - 140.8 (12.4)
subtype3	57	20	0.4 - 87.3 (8.9)

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.156 (ANOVA), Q value = 1

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	197	67.5 (10.7)
subtype1	83	65.8 (11.4)
subtype2	57	69.0 (9.3)
subtype3	57	68.5 (10.6)

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0119 (Chi-square test), Q value = 1

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	66	67	59
subtype1	2	38	21	20
subtype2	0	11	23	23
subtype3	0	17	23	16

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0034 (Chi-square test), Q value = 0.35

Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	91	29
subtype1	34	30	7
subtype2	11	36	9
subtype3	14	25	13

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.643 (Chi-square test), Q value = 1

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	122	18	34	7
subtype1	51	6	14	1
subtype2	34	6	13	3
subtype3	37	6	7	3

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0231 (Chi-square test), Q value = 1

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	106	5	86
subtype1	54	2	27
subtype2	23	3	31
subtype3	29	0	28

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.226 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	149
subtype1	16	68
subtype2	15	42
subtype3	18	39

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.695 (ANOVA), Q value = 1

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	55	78.9 (15.7)
subtype1	31	77.4 (15.7)
subtype2	9	82.2 (13.0)
subtype3	15	80.0 (17.7)

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.495 (ANOVA), Q value = 1

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	115	37.7 (25.4)
subtype1	46	35.4 (25.7)
subtype2	36	36.7 (29.0)
subtype3	33	42.1 (20.8)

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.841 (ANOVA), Q value = 1

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	49	1.5 (2.8)
subtype2	51	1.7 (3.3)
subtype3	45	1.3 (3.7)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.61 (ANOVA), Q value = 1

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	14	6.4 (0.6)
subtype2	18	6.3 (0.5)
subtype3	10	6.6 (1.0)

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S61. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	84	39	75

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.528 (logrank test), Q value = 1

Table S62. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	191	58	0.1 - 140.8 (8.2)
subtype1	83	30	0.4 - 140.8 (8.9)
subtype2	39	13	0.5 - 130.9 (13.4)
subtype3	69	15	0.1 - 67.4 (6.4)

Figure S56. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0683 (ANOVA), Q value = 1

Table S63. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	197	67.5 (10.7)
subtype1	84	68.0 (10.8)
subtype2	39	70.3 (9.1)
subtype3	74	65.5 (11.0)

Figure S57. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 3.04e-05 (Chi-square test), Q value = 0.0033

Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	66	67	59
subtype1	0	22	39	22
subtype2	0	8	8	22
subtype3	2	36	20	15

Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.000538 (Chi-square test), Q value = 0.059

Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	91	29
subtype1	18	43	17
subtype2	7	23	7
subtype3	34	25	5

Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.000942 (Chi-square test), Q value = 0.1

Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	122	18	34	7
subtype1	57	9	8	5
subtype2	18	3	17	1
subtype3	47	6	9	1

Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0203 (Chi-square test), Q value = 1

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	106	5	86
subtype1	37	1	46
subtype2	19	2	18
subtype3	50	2	22

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0281 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	149
subtype1	27	57
subtype2	11	28
subtype3	11	64

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.394 (ANOVA), Q value = 1

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	55	78.9 (15.7)
subtype1	20	81.5 (16.3)
subtype2	7	82.9 (12.5)
subtype3	28	76.1 (15.9)

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.327 (ANOVA), Q value = 1

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	115	37.7 (25.4)
subtype1	52	39.0 (22.1)
subtype2	19	43.6 (35.7)
subtype3	44	33.7 (23.9)

Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0247 (ANOVA), Q value = 1

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	69	1.0 (3.1)
subtype2	33	2.8 (3.9)
subtype3	43	1.3 (2.8)

Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 0.997 (ANOVA), Q value = 1

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	16	6.4 (0.8)
subtype2	12	6.4 (0.5)
subtype3	14	6.4 (0.6)

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S73. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	48	89	60

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.666 (logrank test), Q value = 1

Table S74. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	190	58	0.1 - 140.8 (8.2)
subtype1	44	13	0.1 - 130.9 (6.4)
subtype2	86	23	0.1 - 64.6 (8.1)
subtype3	60	22	0.4 - 140.8 (12.1)

Figure S67. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0192 (ANOVA), Q value = 1

Table S75. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	196	67.4 (10.7)
subtype1	47	69.2 (11.5)
subtype2	89	65.1 (10.6)
subtype3	60	69.5 (9.5)

Figure S68. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0132 (Chi-square test), Q value = 1

Table S76. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	66	66	59
subtype1	1	11	16	19
subtype2	1	42	27	18
subtype3	0	13	23	22

Figure S69. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.035 (Chi-square test), Q value = 1

Table S77. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	91	28
subtype1	14	22	9
subtype2	34	33	9
subtype3	11	36	10

Figure S70. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0391 (Chi-square test), Q value = 1

Table S78. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	121	18	34	7
subtype1	26	7	9	3
subtype2	58	7	8	4
subtype3	37	4	17	0

Figure S71. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.288 (Chi-square test), Q value = 1

Table S79. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	106	5	85
subtype1	25	1	21
subtype2	55	2	32
subtype3	26	2	32

Figure S72. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00374 (Fisher's exact test), Q value = 0.39

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	149
subtype1	7	41
subtype2	17	72
subtype3	24	36

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.46 (ANOVA), Q value = 1

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	55	78.9 (15.7)
subtype1	10	84.0 (8.4)
subtype2	37	78.4 (16.4)
subtype3	8	75.0 (19.3)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.208 (ANOVA), Q value = 1

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	114	37.8 (25.5)
subtype1	25	36.4 (27.0)
subtype2	58	34.7 (19.2)
subtype3	31	44.7 (33.2)

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.207 (ANOVA), Q value = 1

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	38	2.1 (4.3)
subtype2	55	0.9 (2.3)
subtype3	52	1.7 (3.3)

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'GLEASON_SCORE'

P value = 0.397 (ANOVA), Q value = 1

Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	14	6.4 (0.6)
subtype2	15	6.3 (0.5)
subtype3	13	6.6 (0.9)

Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S85. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	137	12	48

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.538 (logrank test), Q value = 1

Table S86. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	190	58	0.1 - 140.8 (8.2)
subtype1	134	39	0.1 - 140.8 (9.3)
subtype2	12	5	2.1 - 46.8 (9.7)
subtype3	44	14	0.1 - 130.9 (6.4)

Figure S78. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.187 (ANOVA), Q value = 1

Table S87. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	196	67.4 (10.7)
subtype1	137	66.6 (10.5)
subtype2	12	71.2 (9.8)
subtype3	47	69.0 (11.1)

Figure S79. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.472 (Chi-square test), Q value = 1

Table S88. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	66	66	59
subtype1	1	52	45	36
subtype2	0	3	4	5
subtype3	1	11	17	18

Figure S80. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.647 (Chi-square test), Q value = 1

Table S89. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	91	28
subtype1	43	60	19
subtype2	2	8	1
subtype3	14	23	8

Figure S81. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.399 (Chi-square test), Q value = 1

Table S90. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	121	18	34	7
subtype1	87	9	23	4
subtype2	8	1	3	0
subtype3	26	8	8	3

Figure S82. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.837 (Chi-square test), Q value = 1

Table S91. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	106	5	85
subtype1	75	4	58
subtype2	5	0	7
subtype3	26	1	20

Figure S83. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.147 (Fisher's exact test), Q value = 1

Table S92. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	149
subtype1	39	98
subtype2	2	10
subtype3	7	41

Figure S84. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0912 (ANOVA), Q value = 1

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	55	78.9 (15.7)
subtype1	43	77.4 (17.1)
subtype2	1	90.0 (NA)
subtype3	11	83.6 (8.1)

Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.71 (ANOVA), Q value = 1

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	114	37.8 (25.5)
subtype1	81	38.3 (25.9)
subtype2	7	30.0 (17.1)
subtype3	26	38.4 (26.6)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.462 (ANOVA), Q value = 1

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	97	1.3 (2.8)
subtype2	11	1.7 (2.9)
subtype3	37	2.1 (4.3)

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE'

P value = 0.918 (ANOVA), Q value = 1

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	25	6.4 (0.7)
subtype2	6	6.5 (0.5)
subtype3	11	6.4 (0.7)

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S97. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	40	82	75

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.386 (logrank test), Q value = 1

Table S98. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	190	58	0.1 - 140.8 (8.2)
subtype1	36	12	0.1 - 130.9 (6.6)
subtype2	79	19	0.1 - 67.4 (7.8)
subtype3	75	27	0.4 - 140.8 (10.6)

Figure S89. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0169 (ANOVA), Q value = 1

Table S99. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	196	67.4 (10.7)
subtype1	39	69.0 (12.3)
subtype2	82	64.9 (10.4)
subtype3	75	69.4 (9.5)

Figure S90. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00159 (Chi-square test), Q value = 0.17

Table S100. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	66	66	59
subtype1	1	7	13	18
subtype2	1	41	23	16
subtype3	0	18	30	25

Figure S91. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00207 (Chi-square test), Q value = 0.22

Table S101. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	91	28
subtype1	11	19	9
subtype2	34	26	8
subtype3	14	46	11

Figure S92. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0795 (Chi-square test), Q value = 1

Table S102. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	121	18	34	7
subtype1	19	7	9	3
subtype2	53	6	8	2
subtype3	49	5	17	2

Figure S93. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.33 (Chi-square test), Q value = 1

Table S103. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	106	5	85
subtype1	22	1	17
subtype2	49	3	29
subtype3	35	1	39

Figure S94. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0295 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	149
subtype1	6	34
subtype2	16	66
subtype3	26	49

Figure S95. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.627 (ANOVA), Q value = 1

Table S105. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	55	78.9 (15.7)
subtype1	8	83.8 (9.2)
subtype2	33	78.5 (15.6)
subtype3	14	77.1 (19.0)

Figure S96. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.39 (ANOVA), Q value = 1

Table S106. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	114	37.8 (25.5)
subtype1	21	36.7 (28.0)
subtype2	53	34.9 (20.6)
subtype3	40	42.2 (29.9)

Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0512 (ANOVA), Q value = 1

Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	32	2.5 (4.6)
subtype2	49	0.7 (1.6)
subtype3	64	1.6 (3.3)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.538 (ANOVA), Q value = 1

Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	11	6.5 (0.7)
subtype2	12	6.2 (0.5)
subtype3	19	6.5 (0.8)

Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S109. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	57	104	36

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.419 (logrank test), Q value = 1

Table S110. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	190	58	0.1 - 140.8 (8.2)
subtype1	54	12	0.1 - 64.6 (6.7)
subtype2	104	36	0.4 - 140.8 (10.6)
subtype3	32	10	0.1 - 130.9 (5.0)

Figure S100. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.029 (ANOVA), Q value = 1

Table S111. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	196	67.4 (10.7)
subtype1	57	64.3 (10.4)
subtype2	104	68.6 (10.2)
subtype3	35	69.2 (11.6)

Figure S101. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00534 (Chi-square test), Q value = 0.54

Table S112. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	66	66	59
subtype1	1	30	15	10
subtype2	0	27	41	34
subtype3	1	9	10	15

Figure S102. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00966 (Chi-square test), Q value = 0.97

Table S113. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	59	91	28
subtype1	25	19	4
subtype2	22	57	18
subtype3	12	15	6

Figure S103. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0648 (Chi-square test), Q value = 1

Table S114. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	121	18	34	7
subtype1	38	4	4	2
subtype2	66	7	23	3
subtype3	17	7	7	2

Figure S104. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.516 (Chi-square test), Q value = 1

Table S115. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	106	5	85
subtype1	33	2	22
subtype2	51	2	51
subtype3	22	1	12

Figure S105. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0765 (Fisher's exact test), Q value = 1

Table S116. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	149
subtype1	11	46
subtype2	32	72
subtype3	5	31

Figure S106. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.347 (ANOVA), Q value = 1

Table S117. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	55	78.9 (15.7)
subtype1	26	75.8 (16.3)
subtype2	21	81.0 (16.7)
subtype3	8	83.8 (9.2)

Figure S107. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.613 (ANOVA), Q value = 1

Table S118. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	114	37.8 (25.5)
subtype1	38	35.5 (22.2)
subtype2	57	40.2 (28.4)
subtype3	19	35.2 (23.0)

Figure S108. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0645 (ANOVA), Q value = 1

Table S119. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	1.5 (3.3)
subtype1	32	0.7 (1.7)
subtype2	88	1.5 (3.0)
subtype3	25	2.7 (5.0)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 0.657 (ANOVA), Q value = 1

Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	42	6.4 (0.7)
subtype1	11	6.3 (0.5)
subtype2	24	6.5 (0.7)
subtype3	7	6.4 (0.8)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

Methods & Data

Input

Cluster data file = BLCA-TP.mergedcluster.txt
Clinical data file = BLCA-TP.merged_data.txt
Number of patients = 198
Number of clustering approaches = 10
Number of selected clinical features = 11
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

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

[6] 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)

[7] 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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