Correlation between aggregated molecular cancer subtypes and selected clinical features

Prostate Adenocarcinoma (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

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/C128064Q

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 14 clinical features across 185 patients, 18 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'GLEASON_SCORE_COMBINED', 'GLEASON_SCORE_PRIMARY', and 'GLEASON_SCORE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', 'GLEASON_SCORE_PRIMARY', and 'PSA_RESULT_PREOP'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'GLEASON_SCORE_PRIMARY', 'GLEASON_SCORE', and 'DAYS_TO_PREOP_PSA'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'GLEASON_SCORE_PRIMARY', 'GLEASON_SCORE', and 'DAYS_TO_PREOP_PSA'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GLEASON_SCORE_PRIMARY'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GLEASON_SCORE_PRIMARY'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PSA_VALUE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PSA_VALUE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
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 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 18 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	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)
AGE	ANOVA	0.0339 (1.00)	0.000685 (0.0869)	0.0163 (1.00)	0.0225 (1.00)	0.746 (1.00)	0.0273 (1.00)	0.861 (1.00)	0.821 (1.00)	0.347 (1.00)	0.327 (1.00)
PATHOLOGY T STAGE	Chi-square test	7.79e-05 (0.0107)	0.0104 (1.00)	0.0197 (1.00)	0.0961 (1.00)	0.558 (1.00)	0.0835 (1.00)	0.098 (1.00)	0.0185 (1.00)	0.0666 (1.00)	0.0579 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.00159 (0.197)	0.0242 (1.00)	0.0347 (1.00)	0.0535 (1.00)	0.162 (1.00)	0.0998 (1.00)	0.138 (1.00)	0.0439 (1.00)	0.566 (1.00)	0.249 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.224 (1.00)	0.134 (1.00)	0.208 (1.00)	0.0366 (1.00)	0.681 (1.00)	0.703 (1.00)	0.397 (1.00)	0.96 (1.00)	0.614 (1.00)	0.535 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.00422 (0.502)	0.044 (1.00)	0.0431 (1.00)	0.0417 (1.00)	0.278 (1.00)	0.135 (1.00)	0.114 (1.00)	0.0282 (1.00)	0.67 (1.00)	0.0867 (1.00)
GLEASON SCORE COMBINED	ANOVA	7.53e-05 (0.0104)	0.00451 (0.532)	0.00291 (0.355)	0.00475 (0.556)	0.0236 (1.00)	0.195 (1.00)	0.226 (1.00)	0.0716 (1.00)	0.152 (1.00)	0.622 (1.00)
GLEASON SCORE PRIMARY	ANOVA	2.3e-05 (0.0032)	0.000516 (0.0666)	0.000154 (0.0208)	0.000279 (0.0371)	0.000484 (0.0629)	0.000568 (0.0727)	0.0799 (1.00)	0.148 (1.00)	0.238 (1.00)	0.475 (1.00)
GLEASON SCORE SECONDARY	ANOVA	0.0743 (1.00)	0.719 (1.00)	0.799 (1.00)	0.418 (1.00)	0.878 (1.00)	0.724 (1.00)	0.116 (1.00)	0.238 (1.00)	0.581 (1.00)	0.737 (1.00)
GLEASON SCORE	ANOVA	6.82e-06 (0.000955)	0.00392 (0.47)	0.000466 (0.061)	0.000393 (0.0518)	0.0468 (1.00)	0.151 (1.00)	0.118 (1.00)	0.136 (1.00)	0.0787 (1.00)	0.365 (1.00)
PSA RESULT PREOP	ANOVA	0.0109 (1.00)	0.000199 (0.0267)	0.0239 (1.00)	0.131 (1.00)	0.106 (1.00)	0.0179 (1.00)	0.0775 (1.00)	0.317 (1.00)	0.0907 (1.00)	0.0293 (1.00)
DAYS TO PREOP PSA	ANOVA	0.479 (1.00)	0.603 (1.00)	0.000766 (0.0965)	0.000107 (0.0146)	0.84 (1.00)	0.266 (1.00)	0.131 (1.00)	0.0184 (1.00)	0.0174 (1.00)	0.023 (1.00)
PSA VALUE	ANOVA	0.144 (1.00)	0.0479 (1.00)	0.13 (1.00)	0.296 (1.00)	0.0643 (1.00)	0.00623 (0.722)	0.00124 (0.154)	0.00172 (0.211)	0.0086 (0.989)	0.00349 (0.423)
DAYS TO PSA	ANOVA	0.361 (1.00)	0.386 (1.00)	0.231 (1.00)	0.0204 (1.00)	0.433 (1.00)	0.603 (1.00)	0.22 (1.00)	0.099 (1.00)	0.0295 (1.00)	0.0892 (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
Number of samples	31	98	55

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

P value = 100 (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	184	1	0.3 - 66.1 (17.8)
subtype1	31	0	0.3 - 61.1 (9.4)
subtype2	98	0	1.0 - 66.0 (17.4)
subtype3	55	1	0.8 - 66.1 (25.1)

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 = 0.0339 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	182	60.2 (7.0)
subtype1	31	61.5 (7.8)
subtype2	96	59.0 (7.1)
subtype3	55	61.7 (6.1)

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 'PATHOLOGY.T.STAGE'

P value = 7.79e-05 (Chi-square test), Q value = 0.011

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	80	97	5
subtype1	20	10	1
subtype2	50	45	1
subtype3	10	42	3

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.00159 (Fisher's exact test), Q value = 0.2

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	144	17
subtype1	25	2
subtype2	80	3
subtype3	39	12

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.224 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	134	37	3
subtype1	19	9	0
subtype2	78	14	2
subtype3	37	14	1

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.00422 (ANOVA), Q value = 0.5

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	159	0.2 (0.7)
subtype1	27	0.1 (0.3)
subtype2	81	0.1 (0.4)
subtype3	51	0.5 (1.1)

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 7.53e-05 (ANOVA), Q value = 0.01

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

	nPatients	Mean (Std.Dev)
ALL	184	7.3 (0.8)
subtype1	31	7.3 (0.7)
subtype2	98	7.1 (0.7)
subtype3	55	7.6 (0.9)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 2.3e-05 (ANOVA), Q value = 0.0032

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

	nPatients	Mean (Std.Dev)
ALL	184	3.5 (0.6)
subtype1	31	3.3 (0.5)
subtype2	98	3.4 (0.5)
subtype3	55	3.8 (0.6)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.0743 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	184	3.8 (0.6)
subtype1	31	4.0 (0.5)
subtype2	98	3.7 (0.6)
subtype3	55	3.9 (0.7)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE'

P value = 6.82e-06 (ANOVA), Q value = 0.00096

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	184	7.3 (0.8)
subtype1	31	7.3 (0.7)
subtype2	98	7.1 (0.7)
subtype3	55	7.7 (0.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.0109 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	182	10.4 (10.2)
subtype1	30	10.6 (8.0)
subtype2	97	8.4 (6.1)
subtype3	55	13.6 (15.3)

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

'Copy Number Ratio CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

P value = 0.479 (ANOVA), Q value = 1

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	179	-2.1 (99.8)
subtype1	29	-11.2 (50.0)
subtype2	96	6.4 (123.6)
subtype3	54	-12.1 (66.9)

Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'Copy Number Ratio CNMF subtypes' versus 'PSA_VALUE'

P value = 0.144 (ANOVA), Q value = 1

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	162	1.4 (4.5)
subtype1	26	1.4 (3.6)
subtype2	89	0.9 (3.0)
subtype3	47	2.5 (6.6)

Figure S13. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'Copy Number Ratio CNMF subtypes' versus 'DAYS_TO_PSA'

P value = 0.361 (ANOVA), Q value = 1

Table S15. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	167	537.3 (497.7)
subtype1	27	434.0 (437.7)
subtype2	92	532.5 (483.0)
subtype3	48	604.5 (553.7)

Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #2: 'METHLYATION CNMF'

Table S16. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	52	63	70

'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	185	1	0.3 - 66.1 (17.5)
subtype1	52	0	0.3 - 66.0 (16.0)
subtype2	63	0	1.1 - 66.0 (17.3)
subtype3	70	1	1.0 - 66.1 (20.8)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000685 (ANOVA), Q value = 0.087

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	183	60.3 (7.0)
subtype1	51	62.5 (6.6)
subtype2	62	57.7 (6.9)
subtype3	70	60.9 (6.8)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.0104 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	80	98	5
subtype1	24	25	3
subtype2	35	26	0
subtype3	21	47	2

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.0242 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	145	17
subtype1	43	5
subtype2	49	1
subtype3	53	11

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

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

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	135	37	3
subtype1	33	16	0
subtype2	49	9	2
subtype3	53	12	1

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.044 (ANOVA), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	160	0.2 (0.7)
subtype1	48	0.1 (0.5)
subtype2	48	0.0 (0.1)
subtype3	64	0.3 (1.0)

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'GLEASON_SCORE_COMBINED'

P value = 0.00451 (ANOVA), Q value = 0.53

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	185	7.3 (0.8)
subtype1	52	7.5 (0.9)
subtype2	63	7.0 (0.5)
subtype3	70	7.3 (0.8)

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'METHLYATION CNMF' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.000516 (ANOVA), Q value = 0.067

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	185	3.5 (0.6)
subtype1	52	3.7 (0.6)
subtype2	63	3.3 (0.4)
subtype3	70	3.5 (0.6)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'METHLYATION CNMF' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.719 (ANOVA), Q value = 1

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	185	3.8 (0.6)
subtype1	52	3.8 (0.7)
subtype2	63	3.8 (0.5)
subtype3	70	3.8 (0.6)

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'METHLYATION CNMF' versus 'GLEASON_SCORE'

P value = 0.00392 (ANOVA), Q value = 0.47

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	185	7.3 (0.8)
subtype1	52	7.6 (1.0)
subtype2	63	7.1 (0.5)
subtype3	70	7.3 (0.8)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'GLEASON_SCORE'

'METHLYATION CNMF' versus 'PSA_RESULT_PREOP'

P value = 0.000199 (ANOVA), Q value = 0.027

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	183	10.3 (10.2)
subtype1	52	14.4 (15.2)
subtype2	63	6.7 (3.4)
subtype3	68	10.6 (8.4)

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'METHLYATION CNMF' versus 'DAYS_TO_PREOP_PSA'

P value = 0.603 (ANOVA), Q value = 1

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	180	-1.5 (99.7)
subtype1	52	7.7 (160.5)
subtype2	61	-11.1 (55.7)
subtype3	67	0.1 (64.1)

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'METHLYATION CNMF' versus 'PSA_VALUE'

P value = 0.0479 (ANOVA), Q value = 1

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	163	1.5 (4.5)
subtype1	42	1.8 (4.4)
subtype2	55	0.3 (1.4)
subtype3	66	2.2 (5.8)

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'PSA_VALUE'

'METHLYATION CNMF' versus 'DAYS_TO_PSA'

P value = 0.386 (ANOVA), Q value = 1

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	168	534.6 (497.4)
subtype1	45	619.8 (598.6)
subtype2	57	487.1 (404.5)
subtype3	66	517.6 (494.9)

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S31. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	41	50	52

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	143	1	0.3 - 66.0 (23.5)
subtype1	41	0	1.0 - 48.0 (19.6)
subtype2	50	0	0.3 - 65.9 (22.2)
subtype3	52	1	0.9 - 66.0 (27.0)

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0163 (ANOVA), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	142	60.1 (7.1)
subtype1	41	62.7 (6.3)
subtype2	49	58.6 (7.7)
subtype3	52	59.6 (6.6)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0197 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	55	82	5
subtype1	18	22	0
subtype2	25	24	1
subtype3	12	36	4

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.0347 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	114	14
subtype1	37	4
subtype2	40	1
subtype3	37	9

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.208 (Chi-square test), Q value = 1

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	102	30	3
subtype1	34	5	1
subtype2	37	9	1
subtype3	31	16	1

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0431 (ANOVA), Q value = 1

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	128	0.2 (0.7)
subtype1	41	0.1 (0.5)
subtype2	41	0.0 (0.2)
subtype3	46	0.4 (1.1)

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.00291 (ANOVA), Q value = 0.35

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	143	7.3 (0.8)
subtype1	41	7.1 (0.5)
subtype2	50	7.2 (0.8)
subtype3	52	7.6 (1.0)

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'RPPA CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.000154 (ANOVA), Q value = 0.021

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	143	3.5 (0.6)
subtype1	41	3.3 (0.5)
subtype2	50	3.4 (0.5)
subtype3	52	3.8 (0.6)

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'RPPA CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.799 (ANOVA), Q value = 1

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	143	3.8 (0.6)
subtype1	41	3.8 (0.5)
subtype2	50	3.8 (0.6)
subtype3	52	3.8 (0.7)

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'RPPA CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.000466 (ANOVA), Q value = 0.061

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	143	7.4 (0.9)
subtype1	41	7.1 (0.6)
subtype2	50	7.2 (0.8)
subtype3	52	7.7 (1.0)

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

'RPPA CNMF subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.0239 (ANOVA), Q value = 1

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	141	10.5 (9.9)
subtype1	40	7.8 (4.3)
subtype2	49	9.6 (12.3)
subtype3	52	13.3 (10.0)

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'RPPA CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

P value = 0.000766 (ANOVA), Q value = 0.096

Table S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	138	-3.0 (55.9)
subtype1	38	25.1 (46.5)
subtype2	48	-18.2 (62.9)
subtype3	52	-9.5 (48.5)

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'RPPA CNMF subtypes' versus 'PSA_VALUE'

P value = 0.13 (ANOVA), Q value = 1

Table S44. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	133	1.5 (4.6)
subtype1	40	1.0 (2.8)
subtype2	45	0.9 (3.6)
subtype3	48	2.6 (6.3)

Figure S41. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'RPPA CNMF subtypes' versus 'DAYS_TO_PSA'

P value = 0.231 (ANOVA), Q value = 1

Table S45. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	134	547.4 (474.6)
subtype1	40	458.8 (339.8)
subtype2	45	635.0 (553.3)
subtype3	49	539.3 (485.7)

Figure S42. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S46. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	50	53	40

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	143	1	0.3 - 66.0 (23.5)
subtype1	50	0	1.0 - 48.0 (14.8)
subtype2	53	1	0.9 - 66.0 (27.0)
subtype3	40	0	0.3 - 65.9 (27.6)

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0225 (ANOVA), Q value = 1

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	142	60.1 (7.1)
subtype1	50	62.1 (6.7)
subtype2	53	59.8 (6.7)
subtype3	39	58.0 (7.6)

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0961 (Chi-square test), Q value = 1

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	55	82	5
subtype1	24	25	0
subtype2	15	34	4
subtype3	16	23	1

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.0535 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	114	14
subtype1	45	4
subtype2	36	9
subtype3	33	1

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0366 (Chi-square test), Q value = 1

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	102	30	3
subtype1	44	5	0
subtype2	30	16	2
subtype3	28	9	1

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0417 (ANOVA), Q value = 1

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	128	0.2 (0.7)
subtype1	49	0.1 (0.5)
subtype2	45	0.4 (1.1)
subtype3	34	0.0 (0.2)

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.00475 (ANOVA), Q value = 0.56

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	143	7.3 (0.8)
subtype1	50	7.0 (0.5)
subtype2	53	7.6 (0.9)
subtype3	40	7.3 (0.9)

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.000279 (ANOVA), Q value = 0.037

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	143	3.5 (0.6)
subtype1	50	3.3 (0.5)
subtype2	53	3.8 (0.6)
subtype3	40	3.4 (0.5)

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.418 (ANOVA), Q value = 1

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	143	3.8 (0.6)
subtype1	50	3.7 (0.5)
subtype2	53	3.8 (0.7)
subtype3	40	3.9 (0.7)

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 0.000393 (ANOVA), Q value = 0.052

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	143	7.4 (0.9)
subtype1	50	7.0 (0.5)
subtype2	53	7.7 (1.0)
subtype3	40	7.3 (0.9)

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

'RPPA cHierClus subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.131 (ANOVA), Q value = 1

Table S57. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	141	10.5 (9.9)
subtype1	49	8.3 (5.9)
subtype2	53	12.3 (9.2)
subtype3	39	10.7 (13.9)

Figure S53. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'RPPA cHierClus subtypes' versus 'DAYS_TO_PREOP_PSA'

P value = 0.000107 (ANOVA), Q value = 0.015

Table S58. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	138	-3.0 (55.9)
subtype1	47	23.4 (45.4)
subtype2	53	-10.8 (47.1)
subtype3	38	-24.9 (66.6)

Figure S54. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'RPPA cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.296 (ANOVA), Q value = 1

Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	133	1.5 (4.6)
subtype1	49	1.4 (3.7)
subtype2	49	2.3 (6.0)
subtype3	35	0.7 (3.3)

Figure S55. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'RPPA cHierClus subtypes' versus 'DAYS_TO_PSA'

P value = 0.0204 (ANOVA), Q value = 1

Table S60. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	134	547.4 (474.6)
subtype1	49	443.6 (335.6)
subtype2	50	521.3 (482.8)
subtype3	35	730.0 (579.0)

Figure S56. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S61. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	38	63	46	38

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	185	1	0.3 - 66.1 (17.5)
subtype1	38	0	0.8 - 66.0 (16.0)
subtype2	63	0	0.3 - 58.2 (19.6)
subtype3	46	1	0.9 - 66.1 (16.7)
subtype4	38	0	1.1 - 66.0 (19.4)

Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.746 (ANOVA), Q value = 1

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	183	60.3 (7.0)
subtype1	37	61.2 (7.8)
subtype2	62	60.4 (6.8)
subtype3	46	60.1 (6.6)
subtype4	38	59.4 (7.2)

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.558 (Chi-square test), Q value = 1

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	80	98	5
subtype1	18	19	1
subtype2	27	34	2
subtype3	15	30	1
subtype4	20	15	1

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.162 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	145	17
subtype1	29	2
subtype2	53	6
subtype3	33	8
subtype4	30	1

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.681 (Chi-square test), Q value = 1

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	135	37	3
subtype1	26	11	0
subtype2	50	9	1
subtype3	33	9	1
subtype4	26	8	1

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.278 (ANOVA), Q value = 1

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	160	0.2 (0.7)
subtype1	31	0.2 (1.1)
subtype2	59	0.1 (0.5)
subtype3	41	0.3 (0.8)
subtype4	29	0.0 (0.2)

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.0236 (ANOVA), Q value = 1

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	185	7.3 (0.8)
subtype1	38	7.5 (1.0)
subtype2	63	7.1 (0.6)
subtype3	46	7.4 (0.9)
subtype4	38	7.1 (0.6)

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.000484 (ANOVA), Q value = 0.063

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	185	3.5 (0.6)
subtype1	38	3.7 (0.6)
subtype2	63	3.3 (0.5)
subtype3	46	3.7 (0.6)
subtype4	38	3.3 (0.5)

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.878 (ANOVA), Q value = 1

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	185	3.8 (0.6)
subtype1	38	3.8 (0.8)
subtype2	63	3.8 (0.5)
subtype3	46	3.7 (0.6)
subtype4	38	3.8 (0.6)

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.0468 (ANOVA), Q value = 1

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	185	7.3 (0.8)
subtype1	38	7.5 (1.0)
subtype2	63	7.1 (0.6)
subtype3	46	7.5 (0.9)
subtype4	38	7.2 (0.6)

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

'RNAseq CNMF subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.106 (ANOVA), Q value = 1

Table S72. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	183	10.3 (10.2)
subtype1	38	13.0 (12.2)
subtype2	63	10.6 (11.9)
subtype3	44	10.3 (9.2)
subtype4	38	7.3 (3.8)

Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'RNAseq CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

P value = 0.84 (ANOVA), Q value = 1

Table S73. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	180	-1.5 (99.7)
subtype1	38	-3.2 (195.8)
subtype2	62	4.0 (51.7)
subtype3	43	2.6 (44.2)
subtype4	37	-13.9 (55.0)

Figure S68. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'RNAseq CNMF subtypes' versus 'PSA_VALUE'

P value = 0.0643 (ANOVA), Q value = 1

Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	163	1.5 (4.5)
subtype1	31	1.4 (4.3)
subtype2	57	1.0 (2.9)
subtype3	42	3.0 (6.9)
subtype4	33	0.4 (1.9)

Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'RNAseq CNMF subtypes' versus 'DAYS_TO_PSA'

P value = 0.433 (ANOVA), Q value = 1

Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	168	534.6 (497.4)
subtype1	34	640.8 (667.0)
subtype2	57	465.1 (395.2)
subtype3	42	523.6 (512.5)
subtype4	35	558.0 (435.4)

Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S76. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	69	61	46	9

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	185	1	0.3 - 66.1 (17.5)
subtype1	69	0	0.3 - 66.0 (19.6)
subtype2	61	1	0.8 - 66.1 (16.8)
subtype3	46	0	1.0 - 66.0 (20.3)
subtype4	9	0	1.2 - 49.8 (4.0)

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0273 (ANOVA), Q value = 1

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	183	60.3 (7.0)
subtype1	68	59.0 (6.7)
subtype2	61	60.7 (6.8)
subtype3	45	60.4 (7.2)
subtype4	9	66.2 (6.9)

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0835 (Chi-square test), Q value = 1

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	80	98	5
subtype1	32	34	1
subtype2	17	41	3
subtype3	26	19	1
subtype4	5	4	0

Figure S73. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.0998 (Fisher's exact test), Q value = 1

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	145	17
subtype1	57	3
subtype2	43	10
subtype3	36	4
subtype4	9	0

Figure S74. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.703 (Chi-square test), Q value = 1

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	135	37	3
subtype1	52	11	2
subtype2	43	13	1
subtype3	32	12	0
subtype4	8	1	0

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.135 (ANOVA), Q value = 1

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	160	0.2 (0.7)
subtype1	58	0.1 (0.2)
subtype2	53	0.3 (0.8)
subtype3	40	0.2 (1.0)
subtype4	9	0.0 (0.0)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.195 (ANOVA), Q value = 1

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	185	7.3 (0.8)
subtype1	69	7.1 (0.6)
subtype2	61	7.4 (0.9)
subtype3	46	7.3 (0.8)
subtype4	9	7.1 (0.3)

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.000568 (ANOVA), Q value = 0.073

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	185	3.5 (0.6)
subtype1	69	3.3 (0.4)
subtype2	61	3.7 (0.7)
subtype3	46	3.5 (0.5)
subtype4	9	3.3 (0.5)

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.724 (ANOVA), Q value = 1

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	185	3.8 (0.6)
subtype1	69	3.9 (0.5)
subtype2	61	3.7 (0.7)
subtype3	46	3.8 (0.6)
subtype4	9	3.8 (0.7)

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 0.151 (ANOVA), Q value = 1

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	185	7.3 (0.8)
subtype1	69	7.2 (0.6)
subtype2	61	7.5 (1.0)
subtype3	46	7.4 (0.9)
subtype4	9	7.1 (0.3)

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

'RNAseq cHierClus subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.0179 (ANOVA), Q value = 1

Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	183	10.3 (10.2)
subtype1	69	7.7 (4.6)
subtype2	59	13.1 (15.1)
subtype3	46	11.3 (8.4)
subtype4	9	7.7 (5.4)

Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'RNAseq cHierClus subtypes' versus 'DAYS_TO_PREOP_PSA'

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

Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	180	-1.5 (99.7)
subtype1	67	0.7 (53.1)
subtype2	58	-2.6 (57.4)
subtype3	46	8.6 (171.5)
subtype4	9	-63.3 (73.7)

Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'RNAseq cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.00623 (ANOVA), Q value = 0.72

Table S89. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	163	1.5 (4.5)
subtype1	62	0.5 (2.0)
subtype2	55	2.7 (6.4)
subtype3	40	0.7 (2.5)
subtype4	6	5.1 (8.0)

Figure S83. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'RNAseq cHierClus subtypes' versus 'DAYS_TO_PSA'

P value = 0.603 (ANOVA), Q value = 1

Table S90. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	168	534.6 (497.4)
subtype1	64	494.0 (403.6)
subtype2	55	559.0 (555.6)
subtype3	43	589.1 (552.7)
subtype4	6	353.5 (468.7)

Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S91. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	66	21	10	15	67

'MIRSEQ CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S92. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	1	0.3 - 66.1 (18.2)
subtype1	66	0	0.9 - 65.9 (29.7)
subtype2	21	0	4.2 - 66.1 (27.2)
subtype3	10	0	1.7 - 64.1 (23.8)
subtype4	15	0	0.8 - 27.1 (4.0)
subtype5	67	1	0.3 - 66.0 (6.3)

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.861 (ANOVA), Q value = 1

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	177	60.2 (7.0)
subtype1	66	60.9 (6.6)
subtype2	21	59.2 (7.3)
subtype3	10	59.6 (7.8)
subtype4	15	59.5 (8.7)
subtype5	65	60.1 (6.9)

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.098 (Chi-square test), Q value = 1

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	77	96	4
subtype1	23	40	3
subtype2	10	9	0
subtype3	6	3	1
subtype4	4	11	0
subtype5	34	33	0

Figure S87. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.138 (Chi-square test), Q value = 1

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	143	16
subtype1	51	11
subtype2	17	1
subtype3	9	0
subtype4	13	1
subtype5	53	3

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.397 (Chi-square test), Q value = 1

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	132	34	3
subtype1	50	16	0
subtype2	15	4	1
subtype3	6	4	0
subtype4	11	2	0
subtype5	50	8	2

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.114 (ANOVA), Q value = 1

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	157	0.2 (0.7)
subtype1	62	0.4 (1.0)
subtype2	18	0.1 (0.2)
subtype3	8	0.0 (0.0)
subtype4	13	0.1 (0.3)
subtype5	56	0.1 (0.3)

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'GLEASON_SCORE_COMBINED'

P value = 0.226 (ANOVA), Q value = 1

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	66	7.4 (0.8)
subtype2	21	7.1 (0.5)
subtype3	10	7.5 (1.1)
subtype4	15	7.4 (0.9)
subtype5	67	7.1 (0.7)

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'MIRSEQ CNMF' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.0799 (ANOVA), Q value = 1

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.5 (0.6)
subtype1	66	3.6 (0.6)
subtype2	21	3.2 (0.4)
subtype3	10	3.3 (0.7)
subtype4	15	3.7 (0.6)
subtype5	67	3.4 (0.5)

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'MIRSEQ CNMF' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.116 (ANOVA), Q value = 1

Table S100. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.8 (0.6)
subtype1	66	3.8 (0.7)
subtype2	21	3.9 (0.4)
subtype3	10	4.2 (0.4)
subtype4	15	3.7 (0.7)
subtype5	67	3.7 (0.6)

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'MIRSEQ CNMF' versus 'GLEASON_SCORE'

P value = 0.118 (ANOVA), Q value = 1

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	66	7.4 (0.8)
subtype2	21	7.2 (0.5)
subtype3	10	7.7 (1.2)
subtype4	15	7.5 (0.8)
subtype5	67	7.1 (0.7)

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'GLEASON_SCORE'

'MIRSEQ CNMF' versus 'PSA_RESULT_PREOP'

P value = 0.0775 (ANOVA), Q value = 1

Table S102. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	177	10.4 (10.3)
subtype1	64	12.3 (12.8)
subtype2	21	7.5 (4.5)
subtype3	10	8.3 (6.5)
subtype4	15	14.5 (17.1)
subtype5	67	8.7 (6.4)

Figure S95. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'MIRSEQ CNMF' versus 'DAYS_TO_PREOP_PSA'

P value = 0.131 (ANOVA), Q value = 1

Table S103. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	174	-2.9 (100.9)
subtype1	63	18.8 (47.3)
subtype2	20	1.9 (57.4)
subtype3	10	-28.1 (93.4)
subtype4	14	-49.9 (37.8)
subtype5	67	-11.3 (145.2)

Figure S96. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'MIRSEQ CNMF' versus 'PSA_VALUE'

P value = 0.00124 (ANOVA), Q value = 0.15

Table S104. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	162	1.5 (4.5)
subtype1	63	2.3 (5.9)
subtype2	21	0.1 (0.1)
subtype3	10	0.1 (0.0)
subtype4	9	6.2 (8.3)
subtype5	59	0.6 (1.8)

Figure S97. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'PSA_VALUE'

'MIRSEQ CNMF' versus 'DAYS_TO_PSA'

P value = 0.22 (ANOVA), Q value = 1

Table S105. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	162	540.1 (502.5)
subtype1	63	579.1 (404.1)
subtype2	21	600.7 (443.4)
subtype3	10	570.1 (531.9)
subtype4	10	184.5 (197.2)
subtype5	58	532.0 (624.8)

Figure S98. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S106. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	68	96	15

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S107. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	1	0.3 - 66.1 (18.2)
subtype1	68	0	0.9 - 66.1 (29.4)
subtype2	96	1	0.3 - 66.0 (14.8)
subtype3	15	0	0.8 - 40.0 (5.3)

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.821 (ANOVA), Q value = 1

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	177	60.2 (7.0)
subtype1	68	60.5 (6.9)
subtype2	94	59.9 (7.0)
subtype3	15	60.8 (8.1)

Figure S100. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

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

P value = 0.0185 (Chi-square test), Q value = 1

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	77	96	4
subtype1	25	42	0
subtype2	49	42	4
subtype3	3	12	0

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.0439 (Fisher's exact test), Q value = 1

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	143	16
subtype1	53	11
subtype2	77	4
subtype3	13	1

Figure S102. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.96 (Chi-square test), Q value = 1

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	132	34	3
subtype1	53	14	1
subtype2	68	18	2
subtype3	11	2	0

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0282 (ANOVA), Q value = 1

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	157	0.2 (0.7)
subtype1	63	0.4 (1.0)
subtype2	81	0.1 (0.3)
subtype3	13	0.1 (0.3)

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_COMBINED'

P value = 0.0716 (ANOVA), Q value = 1

Table S113. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	68	7.4 (0.8)
subtype2	96	7.1 (0.7)
subtype3	15	7.5 (0.8)

Figure S105. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.148 (ANOVA), Q value = 1

Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.5 (0.6)
subtype1	68	3.5 (0.6)
subtype2	96	3.4 (0.5)
subtype3	15	3.7 (0.6)

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.238 (ANOVA), Q value = 1

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.8 (0.6)
subtype1	68	3.9 (0.6)
subtype2	96	3.7 (0.6)
subtype3	15	3.8 (0.7)

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE'

P value = 0.136 (ANOVA), Q value = 1

Table S116. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	68	7.4 (0.8)
subtype2	96	7.2 (0.7)
subtype3	15	7.5 (0.8)

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'GLEASON_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'PSA_RESULT_PREOP'

P value = 0.317 (ANOVA), Q value = 1

Table S117. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	177	10.4 (10.3)
subtype1	66	10.8 (7.9)
subtype2	96	9.5 (10.3)
subtype3	15	13.7 (17.4)

Figure S109. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'MIRSEQ CHIERARCHICAL' versus 'DAYS_TO_PREOP_PSA'

P value = 0.0184 (ANOVA), Q value = 1

Table S118. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	174	-2.9 (100.9)
subtype1	65	19.0 (43.4)
subtype2	95	-9.6 (127.6)
subtype3	14	-59.6 (48.2)

Figure S110. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'MIRSEQ CHIERARCHICAL' versus 'PSA_VALUE'

P value = 0.00172 (ANOVA), Q value = 0.21

Table S119. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	162	1.5 (4.5)
subtype1	65	2.0 (5.6)
subtype2	87	0.6 (2.2)
subtype3	10	5.6 (8.1)

Figure S111. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'PSA_VALUE'

'MIRSEQ CHIERARCHICAL' versus 'DAYS_TO_PSA'

P value = 0.099 (ANOVA), Q value = 1

Table S120. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	162	540.1 (502.5)
subtype1	65	593.4 (425.9)
subtype2	86	538.0 (564.9)
subtype3	11	241.5 (275.5)

Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S121. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	82	79	18

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

P value = 100 (logrank test), Q value = 1

Table S122. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	1	0.3 - 66.1 (18.2)
subtype1	82	0	0.9 - 66.1 (30.5)
subtype2	79	1	0.3 - 66.0 (9.5)
subtype3	18	0	0.8 - 27.1 (4.8)

Figure S113. 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 = 0.347 (ANOVA), Q value = 1

Table S123. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	177	60.2 (7.0)
subtype1	82	61.0 (6.6)
subtype2	77	59.5 (7.2)
subtype3	18	59.4 (8.2)

Figure S114. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0666 (Chi-square test), Q value = 1

Table S124. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	77	96	4
subtype1	31	46	4
subtype2	41	38	0
subtype3	5	12	0

Figure S115. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.566 (Fisher's exact test), Q value = 1

Table S125. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	143	16
subtype1	66	10
subtype2	61	5
subtype3	16	1

Figure S116. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.614 (Chi-square test), Q value = 1

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	132	34	3
subtype1	61	20	1
subtype2	58	12	2
subtype3	13	2	0

Figure S117. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.67 (ANOVA), Q value = 1

Table S127. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	157	0.2 (0.7)
subtype1	75	0.2 (0.7)
subtype2	66	0.2 (0.8)
subtype3	16	0.1 (0.2)

Figure S118. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.152 (ANOVA), Q value = 1

Table S128. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	82	7.3 (0.8)
subtype2	79	7.2 (0.7)
subtype3	18	7.5 (0.9)

Figure S119. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.238 (ANOVA), Q value = 1

Table S129. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.5 (0.6)
subtype1	82	3.5 (0.6)
subtype2	79	3.4 (0.5)
subtype3	18	3.7 (0.6)

Figure S120. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.581 (ANOVA), Q value = 1

Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.8 (0.6)
subtype1	82	3.8 (0.6)
subtype2	79	3.7 (0.6)
subtype3	18	3.8 (0.7)

Figure S121. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.0787 (ANOVA), Q value = 1

Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	82	7.4 (0.8)
subtype2	79	7.2 (0.7)
subtype3	18	7.6 (0.8)

Figure S122. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

'MIRseq Mature CNMF subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.0907 (ANOVA), Q value = 1

Table S132. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	177	10.4 (10.3)
subtype1	80	11.7 (11.7)
subtype2	79	8.5 (6.2)
subtype3	18	12.6 (16.1)

Figure S123. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'MIRseq Mature CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

P value = 0.0174 (ANOVA), Q value = 1

Table S133. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	174	-2.9 (100.9)
subtype1	79	17.8 (47.6)
subtype2	79	-13.7 (136.9)
subtype3	16	-52.0 (53.2)

Figure S124. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'MIRseq Mature CNMF subtypes' versus 'PSA_VALUE'

P value = 0.0086 (ANOVA), Q value = 0.99

Table S134. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	162	1.5 (4.5)
subtype1	80	1.8 (5.3)
subtype2	70	0.6 (1.7)
subtype3	12	4.7 (7.6)

Figure S125. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'MIRseq Mature CNMF subtypes' versus 'DAYS_TO_PSA'

P value = 0.0295 (ANOVA), Q value = 1

Table S135. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	162	540.1 (502.5)
subtype1	80	597.7 (416.8)
subtype2	69	537.2 (603.0)
subtype3	13	200.8 (192.2)

Figure S126. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S136. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	80	81	18

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

P value = 100 (logrank test), Q value = 1

Table S137. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	1	0.3 - 66.1 (18.2)
subtype1	80	0	0.9 - 66.0 (30.3)
subtype2	81	1	0.3 - 66.1 (9.4)
subtype3	18	0	0.8 - 41.6 (8.2)

Figure S127. 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 = 0.327 (ANOVA), Q value = 1

Table S138. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	177	60.2 (7.0)
subtype1	80	61.0 (6.7)
subtype2	79	59.4 (7.1)
subtype3	18	60.5 (8.3)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0579 (Chi-square test), Q value = 1

Table S139. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	77	96	4
subtype1	29	46	4
subtype2	42	38	0
subtype3	6	12	0

Figure S129. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

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

P value = 0.249 (Fisher's exact test), Q value = 1

Table S140. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	143	16
subtype1	65	10
subtype2	64	4
subtype3	14	2

Figure S130. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.535 (Chi-square test), Q value = 1

Table S141. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	132	34	3
subtype1	59	20	1
subtype2	59	12	2
subtype3	14	2	0

Figure S131. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0867 (ANOVA), Q value = 1

Table S142. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	157	0.2 (0.7)
subtype1	75	0.2 (0.7)
subtype2	68	0.1 (0.3)
subtype3	14	0.5 (1.6)

Figure S132. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.622 (ANOVA), Q value = 1

Table S143. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	80	7.3 (0.8)
subtype2	81	7.2 (0.7)
subtype3	18	7.3 (0.8)

Figure S133. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GLEASON_SCORE_COMBINED'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.475 (ANOVA), Q value = 1

Table S144. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.5 (0.6)
subtype1	80	3.5 (0.6)
subtype2	81	3.4 (0.5)
subtype3	18	3.6 (0.6)

Figure S134. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_PRIMARY'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.737 (ANOVA), Q value = 1

Table S145. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

	nPatients	Mean (Std.Dev)
ALL	179	3.8 (0.6)
subtype1	80	3.8 (0.6)
subtype2	81	3.8 (0.6)
subtype3	18	3.7 (0.7)

Figure S135. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_SECONDARY'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 0.365 (ANOVA), Q value = 1

Table S146. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

	nPatients	Mean (Std.Dev)
ALL	179	7.3 (0.8)
subtype1	80	7.4 (0.8)
subtype2	81	7.2 (0.7)
subtype3	18	7.4 (0.8)

Figure S136. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.0293 (ANOVA), Q value = 1

Table S147. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

	nPatients	Mean (Std.Dev)
ALL	177	10.4 (10.3)
subtype1	78	11.8 (11.8)
subtype2	81	8.2 (6.1)
subtype3	18	13.8 (15.8)

Figure S137. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'PSA_RESULT_PREOP'

'MIRseq Mature cHierClus subtypes' versus 'DAYS_TO_PREOP_PSA'

P value = 0.023 (ANOVA), Q value = 1

Table S148. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

	nPatients	Mean (Std.Dev)
ALL	174	-2.9 (100.9)
subtype1	77	19.0 (47.7)
subtype2	80	-15.8 (137.2)
subtype3	17	-42.0 (40.8)

Figure S138. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'DAYS_TO_PREOP_PSA'

'MIRseq Mature cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.00349 (ANOVA), Q value = 0.42

Table S149. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

	nPatients	Mean (Std.Dev)
ALL	162	1.5 (4.5)
subtype1	78	1.8 (5.4)
subtype2	72	0.5 (1.7)
subtype3	12	5.0 (7.5)

Figure S139. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'MIRseq Mature cHierClus subtypes' versus 'DAYS_TO_PSA'

P value = 0.0892 (ANOVA), Q value = 1

Table S150. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

	nPatients	Mean (Std.Dev)
ALL	162	540.1 (502.5)
subtype1	78	604.4 (414.6)
subtype2	71	516.4 (598.4)
subtype3	13	283.7 (307.8)

Figure S140. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'DAYS_TO_PSA'

Methods & Data

Input

Cluster data file = PRAD-TP.mergedcluster.txt
Clinical data file = PRAD-TP.merged_data.txt
Number of patients = 185
Number of clustering approaches = 10
Number of selected clinical features = 14
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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