Correlation between aggregated molecular cancer subtypes and selected clinical features

Prostate Adenocarcinoma (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/C18P5Z55

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 206 patients, 22 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', 'NUMBER.OF.LYMPH.NODES', 'GLEASON_SCORE_COMBINED', 'GLEASON_SCORE_PRIMARY', and 'GLEASON_SCORE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GLEASON_SCORE_PRIMARY', 'GLEASON_SCORE', and 'PSA_RESULT_PREOP'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'GLEASON_SCORE_COMBINED', '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 3 subtypes that correlate to 'GLEASON_SCORE_PRIMARY' and 'PSA_RESULT_PREOP'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GLEASON_SCORE_PRIMARY'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.T.STAGE' and 'PSA_VALUE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE'.

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, 22 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	0.164 (1.00)	0.00243 (0.284)	0.0202 (1.00)	0.025 (1.00)	0.297 (1.00)	0.263 (1.00)	0.627 (1.00)	0.257 (1.00)	0.122 (1.00)	0.318 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.00113 (0.136)	0.0097 (1.00)	0.0155 (1.00)	0.0912 (1.00)	0.0317 (1.00)	0.00369 (0.417)	0.0949 (1.00)	0.000144 (0.0187)	0.151 (1.00)	1.03e-05 (0.00141)
PATHOLOGY N STAGE	Fisher's exact test	0.000133 (0.0175)	0.0163 (1.00)	0.0429 (1.00)	0.0539 (1.00)	0.00227 (0.268)	0.00815 (0.913)	0.0427 (1.00)	0.172 (1.00)	0.284 (1.00)	0.514 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.672 (1.00)	0.192 (1.00)	0.143 (1.00)	0.0211 (1.00)	0.399 (1.00)	0.441 (1.00)	0.318 (1.00)	0.844 (1.00)	0.773 (1.00)	0.6 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.000268 (0.0343)	0.0376 (1.00)	0.0455 (1.00)	0.044 (1.00)	0.0448 (1.00)	0.0561 (1.00)	0.0387 (1.00)	0.0235 (1.00)	0.232 (1.00)	0.427 (1.00)
GLEASON SCORE COMBINED	ANOVA	4.41e-08 (6.13e-06)	0.00265 (0.305)	0.00152 (0.181)	0.00265 (0.305)	0.0283 (1.00)	0.0143 (1.00)	0.175 (1.00)	0.0099 (1.00)	0.712 (1.00)	0.338 (1.00)
GLEASON SCORE PRIMARY	ANOVA	1.29e-07 (1.77e-05)	0.00032 (0.0407)	7.15e-05 (0.00958)	0.000136 (0.0178)	0.00083 (0.101)	1.94e-05 (0.00263)	0.0127 (1.00)	0.144 (1.00)	0.703 (1.00)	0.949 (1.00)
GLEASON SCORE SECONDARY	ANOVA	0.0612 (1.00)	0.827 (1.00)	0.772 (1.00)	0.361 (1.00)	0.214 (1.00)	0.183 (1.00)	0.561 (1.00)	0.277 (1.00)	0.853 (1.00)	0.366 (1.00)
GLEASON SCORE	ANOVA	1.64e-09 (2.29e-07)	0.001 (0.121)	0.000364 (0.0456)	0.000342 (0.043)	0.0189 (1.00)	0.00251 (0.292)	0.178 (1.00)	0.064 (1.00)	0.579 (1.00)	0.494 (1.00)
PSA RESULT PREOP	ANOVA	0.0279 (1.00)	5.66e-05 (0.00765)	0.0214 (1.00)	0.122 (1.00)	0.00046 (0.057)	0.0111 (1.00)	0.0342 (1.00)	0.0143 (1.00)	0.1 (1.00)	0.225 (1.00)
DAYS TO PREOP PSA	ANOVA	0.601 (1.00)	0.693 (1.00)	0.000664 (0.0817)	9.36e-05 (0.0125)	0.887 (1.00)	0.73 (1.00)	0.241 (1.00)	0.803 (1.00)	0.0514 (1.00)	0.015 (1.00)
PSA VALUE	ANOVA	0.253 (1.00)	0.0369 (1.00)	0.135 (1.00)	0.306 (1.00)	0.0718 (1.00)	0.0326 (1.00)	0.0275 (1.00)	0.000224 (0.0288)	0.167 (1.00)	0.0744 (1.00)
DAYS TO PSA	ANOVA	0.808 (1.00)	0.382 (1.00)	0.378 (1.00)	0.0429 (1.00)	0.0457 (1.00)	0.377 (1.00)	0.997 (1.00)	0.255 (1.00)	0.619 (1.00)	0.707 (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	30	117	58

'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	205	1	0.3 - 66.1 (19.6)
subtype1	30	0	0.3 - 64.5 (11.9)
subtype2	117	0	0.8 - 66.0 (19.8)
subtype3	58	1	0.7 - 66.1 (26.8)

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 = 0.164 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	203	60.2 (6.9)
subtype1	30	60.9 (7.3)
subtype2	115	59.5 (6.9)
subtype3	58	61.5 (6.5)

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 'PATHOLOGY.T.STAGE'

P value = 0.00113 (Chi-square test), Q value = 0.14

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

nPatients	T2	T3	T4
ALL	92	106	5
subtype1	18	12	0
subtype2	61	51	3
subtype3	13	43	2

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 = 0.000133 (Fisher's exact test), Q value = 0.018

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

nPatients	0	1
ALL	155	17
subtype1	24	2
subtype2	89	2
subtype3	42	13

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 = 0.672 (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	152	38	5
subtype1	21	8	1
subtype2	91	18	3
subtype3	40	12	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 = 0.000268 (ANOVA), Q value = 0.034

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

	nPatients	Mean (Std.Dev)
ALL	170	0.2 (0.7)
subtype1	26	0.1 (0.3)
subtype2	89	0.0 (0.1)
subtype3	55	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 = 4.41e-08 (ANOVA), Q value = 6.1e-06

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

	nPatients	Mean (Std.Dev)
ALL	205	7.2 (0.8)
subtype1	30	7.2 (0.7)
subtype2	117	7.0 (0.6)
subtype3	58	7.7 (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 = 1.29e-07 (ANOVA), Q value = 1.8e-05

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

	nPatients	Mean (Std.Dev)
ALL	205	3.5 (0.6)
subtype1	30	3.3 (0.5)
subtype2	117	3.3 (0.5)
subtype3	58	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 = 0.0612 (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	205	3.8 (0.6)
subtype1	30	3.9 (0.6)
subtype2	117	3.7 (0.6)
subtype3	58	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 = 1.64e-09 (ANOVA), Q value = 2.3e-07

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

	nPatients	Mean (Std.Dev)
ALL	205	7.3 (0.8)
subtype1	30	7.3 (0.6)
subtype2	117	7.0 (0.6)
subtype3	58	7.8 (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 = 0.0279 (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	203	10.3 (10.4)
subtype1	29	9.3 (7.8)
subtype2	116	9.0 (9.4)
subtype3	58	13.3 (12.5)

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 = 0.601 (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	199	-3.9 (96.2)
subtype1	29	-13.0 (55.2)
subtype2	114	2.0 (116.4)
subtype3	56	-11.3 (61.4)

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 = 0.253 (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	176	1.3 (4.3)
subtype1	24	1.5 (3.8)
subtype2	105	0.9 (3.1)
subtype3	47	2.2 (6.4)

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 = 0.808 (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	181	577.6 (545.5)
subtype1	26	514.8 (565.6)
subtype2	107	583.3 (549.6)
subtype3	48	599.0 (534.5)

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	59	71	76

'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	206	1	0.3 - 66.1 (19.6)
subtype1	59	0	0.3 - 66.0 (18.1)
subtype2	71	0	1.1 - 66.0 (20.6)
subtype3	76	1	1.0 - 66.1 (19.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00243 (ANOVA), Q value = 0.28

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

	nPatients	Mean (Std.Dev)
ALL	204	60.3 (6.9)
subtype1	58	62.2 (6.5)
subtype2	70	58.1 (6.8)
subtype3	76	60.8 (6.9)

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

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

P value = 0.0097 (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	92	107	5
subtype1	28	28	3
subtype2	40	29	0
subtype3	24	50	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 = 0.0163 (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	156	17
subtype1	46	5
subtype2	54	1
subtype3	56	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 = 0.192 (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	153	38	5
subtype1	38	17	1
subtype2	56	10	2
subtype3	59	11	2

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 = 0.0376 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	171	0.2 (0.7)
subtype1	51	0.1 (0.5)
subtype2	53	0.0 (0.1)
subtype3	67	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 = 0.00265 (ANOVA), Q value = 0.3

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

	nPatients	Mean (Std.Dev)
ALL	206	7.2 (0.8)
subtype1	59	7.5 (0.9)
subtype2	71	7.0 (0.5)
subtype3	76	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 = 0.00032 (ANOVA), Q value = 0.041

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

	nPatients	Mean (Std.Dev)
ALL	206	3.5 (0.6)
subtype1	59	3.7 (0.6)
subtype2	71	3.3 (0.4)
subtype3	76	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 = 0.827 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	206	3.8 (0.6)
subtype1	59	3.8 (0.7)
subtype2	71	3.7 (0.5)
subtype3	76	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 = 0.001 (ANOVA), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	206	7.3 (0.8)
subtype1	59	7.5 (0.9)
subtype2	71	7.0 (0.5)
subtype3	76	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 = 5.66e-05 (ANOVA), Q value = 0.0076

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

	nPatients	Mean (Std.Dev)
ALL	204	10.3 (10.3)
subtype1	59	14.5 (15.4)
subtype2	71	6.6 (3.3)
subtype3	74	10.4 (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 = 0.693 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	-3.5 (96.2)
subtype1	59	4.1 (152.3)
subtype2	69	-10.6 (60.2)
subtype3	72	-2.8 (58.5)

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 = 0.0369 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	177	1.4 (4.3)
subtype1	46	1.6 (4.2)
subtype2	62	0.3 (1.4)
subtype3	69	2.2 (5.7)

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 = 0.382 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	182	574.9 (545.2)
subtype1	49	663.7 (625.4)
subtype2	64	561.6 (502.4)
subtype3	69	524.2 (522.5)

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	42	50	54

'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	146	1	0.3 - 66.0 (24.6)
subtype1	42	0	0.7 - 64.5 (21.3)
subtype2	50	0	0.3 - 65.9 (23.3)
subtype3	54	1	0.8 - 66.0 (27.9)

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 = 0.0202 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	145	60.3 (7.1)
subtype1	42	62.7 (6.3)
subtype2	49	58.6 (7.7)
subtype3	54	60.0 (6.8)

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 = 0.0155 (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	85	5
subtype1	18	23	0
subtype2	25	24	1
subtype3	12	38	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 = 0.0429 (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	116	14
subtype1	38	4
subtype2	40	1
subtype3	38	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 = 0.143 (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	106	29	3
subtype1	36	4	1
subtype2	37	9	1
subtype3	33	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 = 0.0455 (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	130	0.2 (0.7)
subtype1	42	0.1 (0.5)
subtype2	41	0.0 (0.2)
subtype3	47	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 = 0.00152 (ANOVA), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	146	7.3 (0.8)
subtype1	42	7.1 (0.5)
subtype2	50	7.2 (0.8)
subtype3	54	7.6 (0.9)

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 = 7.15e-05 (ANOVA), Q value = 0.0096

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

	nPatients	Mean (Std.Dev)
ALL	146	3.5 (0.6)
subtype1	42	3.3 (0.5)
subtype2	50	3.4 (0.5)
subtype3	54	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 = 0.772 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	146	3.8 (0.6)
subtype1	42	3.7 (0.5)
subtype2	50	3.8 (0.6)
subtype3	54	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 = 0.000364 (ANOVA), Q value = 0.046

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

	nPatients	Mean (Std.Dev)
ALL	146	7.4 (0.9)
subtype1	42	7.1 (0.6)
subtype2	50	7.2 (0.8)
subtype3	54	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 = 0.0214 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	144	10.8 (10.5)
subtype1	41	8.3 (5.0)
subtype2	49	9.6 (12.3)
subtype3	54	13.9 (11.2)

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 = 0.000664 (ANOVA), Q value = 0.082

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

	nPatients	Mean (Std.Dev)
ALL	141	-2.4 (55.6)
subtype1	39	25.3 (45.7)
subtype2	48	-18.1 (63.3)
subtype3	54	-8.3 (48.0)

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 = 0.135 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	135	1.5 (4.6)
subtype1	41	1.0 (2.8)
subtype2	45	0.9 (3.6)
subtype3	49	2.5 (6.2)

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 = 0.378 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	136	555.0 (487.8)
subtype1	41	495.8 (410.7)
subtype2	45	635.8 (552.7)
subtype3	50	530.8 (483.8)

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	51	55	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	146	1	0.3 - 66.0 (24.6)
subtype1	51	0	0.7 - 64.5 (17.5)
subtype2	55	1	0.8 - 66.0 (27.2)
subtype3	40	0	0.3 - 65.9 (28.3)

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 = 0.025 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	145	60.3 (7.1)
subtype1	51	62.1 (6.6)
subtype2	55	60.2 (6.9)
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 = 0.0912 (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	85	5
subtype1	24	26	0
subtype2	15	36	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 = 0.0539 (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	116	14
subtype1	46	4
subtype2	37	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 = 0.0211 (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	106	29	3
subtype1	46	4	0
subtype2	32	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 = 0.044 (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	130	0.2 (0.7)
subtype1	50	0.1 (0.5)
subtype2	46	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 = 0.00265 (ANOVA), Q value = 0.3

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

	nPatients	Mean (Std.Dev)
ALL	146	7.3 (0.8)
subtype1	51	7.0 (0.5)
subtype2	55	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 = 0.000136 (ANOVA), Q value = 0.018

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

	nPatients	Mean (Std.Dev)
ALL	146	3.5 (0.6)
subtype1	51	3.3 (0.5)
subtype2	55	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 = 0.361 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	146	3.8 (0.6)
subtype1	51	3.7 (0.5)
subtype2	55	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 = 0.000342 (ANOVA), Q value = 0.043

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

	nPatients	Mean (Std.Dev)
ALL	146	7.4 (0.9)
subtype1	51	7.1 (0.5)
subtype2	55	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 = 0.122 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	144	10.8 (10.5)
subtype1	50	8.7 (6.3)
subtype2	55	12.9 (10.5)
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 = 9.36e-05 (ANOVA), Q value = 0.012

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

	nPatients	Mean (Std.Dev)
ALL	141	-2.4 (55.6)
subtype1	48	23.7 (44.7)
subtype2	55	-9.7 (46.8)
subtype3	38	-24.7 (67.1)

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 = 0.306 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	135	1.5 (4.6)
subtype1	50	1.4 (3.6)
subtype2	50	2.2 (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 = 0.0429 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	136	555.0 (487.8)
subtype1	50	474.5 (396.2)
subtype2	51	513.5 (480.7)
subtype3	35	730.2 (578.9)

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
Number of samples	64	72	70

'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	206	1	0.3 - 66.1 (19.6)
subtype1	64	0	0.8 - 66.0 (18.2)
subtype2	72	0	0.3 - 66.0 (18.1)
subtype3	70	1	0.9 - 66.1 (22.5)

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 = 0.297 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	204	60.3 (6.9)
subtype1	63	60.7 (6.7)
subtype2	71	59.3 (6.9)
subtype3	70	60.9 (7.1)

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 = 0.0317 (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	92	107	5
subtype1	32	30	2
subtype2	39	30	1
subtype3	21	47	2

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 = 0.00227 (Fisher's exact test), Q value = 0.27

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

nPatients	0	1
ALL	156	17
subtype1	48	2
subtype2	58	2
subtype3	50	13

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 = 0.399 (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	153	38	5
subtype1	44	17	2
subtype2	54	11	2
subtype3	55	10	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 = 0.0448 (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	171	0.2 (0.7)
subtype1	50	0.1 (0.9)
subtype2	58	0.0 (0.2)
subtype3	63	0.3 (0.8)

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 = 0.0283 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	206	7.2 (0.8)
subtype1	64	7.2 (0.8)
subtype2	72	7.1 (0.6)
subtype3	70	7.4 (0.9)

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 = 0.00083 (ANOVA), Q value = 0.1

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

	nPatients	Mean (Std.Dev)
ALL	206	3.5 (0.6)
subtype1	64	3.6 (0.6)
subtype2	72	3.3 (0.4)
subtype3	70	3.6 (0.6)

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 = 0.214 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	206	3.8 (0.6)
subtype1	64	3.7 (0.7)
subtype2	72	3.8 (0.5)
subtype3	70	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 = 0.0189 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	206	7.3 (0.8)
subtype1	64	7.3 (0.8)
subtype2	72	7.1 (0.6)
subtype3	70	7.5 (0.9)

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 = 0.00046 (ANOVA), Q value = 0.057

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

	nPatients	Mean (Std.Dev)
ALL	204	10.3 (10.3)
subtype1	64	13.8 (15.4)
subtype2	72	7.0 (3.7)
subtype3	68	10.4 (8.0)

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 = 0.887 (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	200	-3.5 (96.2)
subtype1	63	-6.9 (152.4)
subtype2	71	-4.6 (58.9)
subtype3	66	1.1 (48.9)

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 = 0.0718 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	177	1.4 (4.3)
subtype1	48	1.0 (3.5)
subtype2	65	0.7 (2.4)
subtype3	64	2.3 (6.0)

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 = 0.0457 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	182	574.9 (545.2)
subtype1	51	732.3 (633.8)
subtype2	67	536.0 (517.3)
subtype3	64	490.2 (475.4)

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
Number of samples	60	76	70

'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	206	1	0.3 - 66.1 (19.6)
subtype1	60	0	1.0 - 66.0 (17.7)
subtype2	76	0	0.3 - 66.0 (21.6)
subtype3	70	1	0.8 - 66.1 (14.8)

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 = 0.263 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	204	60.3 (6.9)
subtype1	59	60.9 (7.2)
subtype2	75	59.2 (6.6)
subtype3	70	60.9 (7.0)

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 = 0.00369 (Chi-square test), Q value = 0.42

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

nPatients	T2	T3	T4
ALL	92	107	5
subtype1	34	25	1
subtype2	38	36	0
subtype3	20	46	4

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 = 0.00815 (Fisher's exact test), Q value = 0.91

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

nPatients	0	1
ALL	156	17
subtype1	45	2
subtype2	63	3
subtype3	48	12

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 = 0.441 (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	153	38	5
subtype1	43	15	1
subtype2	60	9	2
subtype3	50	14	2

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 = 0.0561 (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	171	0.2 (0.7)
subtype1	47	0.1 (0.9)
subtype2	64	0.0 (0.2)
subtype3	60	0.3 (0.8)

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 = 0.0143 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	206	7.2 (0.8)
subtype1	60	7.2 (0.7)
subtype2	76	7.1 (0.6)
subtype3	70	7.4 (0.9)

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 = 1.94e-05 (ANOVA), Q value = 0.0026

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

	nPatients	Mean (Std.Dev)
ALL	206	3.5 (0.6)
subtype1	60	3.5 (0.6)
subtype2	76	3.2 (0.5)
subtype3	70	3.7 (0.6)

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 = 0.183 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	206	3.8 (0.6)
subtype1	60	3.6 (0.7)
subtype2	76	3.8 (0.5)
subtype3	70	3.8 (0.6)

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 = 0.00251 (ANOVA), Q value = 0.29

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

	nPatients	Mean (Std.Dev)
ALL	206	7.3 (0.8)
subtype1	60	7.2 (0.7)
subtype2	76	7.1 (0.6)
subtype3	70	7.5 (1.0)

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 = 0.0111 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	204	10.3 (10.3)
subtype1	60	11.8 (13.2)
subtype2	76	7.5 (4.4)
subtype3	68	12.0 (11.5)

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 = 0.73 (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	200	-3.5 (96.2)
subtype1	60	-10.0 (155.7)
subtype2	74	3.1 (51.5)
subtype3	66	-4.8 (57.0)

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 = 0.0326 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	177	1.4 (4.3)
subtype1	47	1.2 (3.8)
subtype2	69	0.5 (1.9)
subtype3	61	2.4 (6.1)

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 = 0.377 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	182	574.9 (545.2)
subtype1	50	665.6 (617.6)
subtype2	71	550.8 (502.3)
subtype3	61	528.6 (530.2)

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	63	32	68	14	25

'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	202	1	0.3 - 66.1 (19.7)
subtype1	63	0	0.9 - 65.9 (29.0)
subtype2	32	0	1.1 - 66.1 (22.8)
subtype3	68	1	0.3 - 66.0 (10.0)
subtype4	14	0	1.7 - 64.1 (23.8)
subtype5	25	0	0.8 - 65.9 (9.0)

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

'MIRSEQ CNMF' versus 'AGE'

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

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

	nPatients	Mean (Std.Dev)
ALL	200	60.3 (6.9)
subtype1	63	61.2 (6.7)
subtype2	32	59.0 (7.1)
subtype3	66	60.1 (6.9)
subtype4	14	59.2 (6.6)
subtype5	25	60.5 (7.7)

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

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

P value = 0.0949 (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	90	106	4
subtype1	23	37	3
subtype2	14	16	0
subtype3	33	35	0
subtype4	10	3	1
subtype5	10	15	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 = 0.0427 (Chi-square test), Q value = 1

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

nPatients	0	1
ALL	155	16
subtype1	47	11
subtype2	24	1
subtype3	54	3
subtype4	11	0
subtype5	19	1

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 = 0.318 (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	151	36	5
subtype1	47	16	0
subtype2	25	3	2
subtype3	51	8	2
subtype4	10	4	0
subtype5	18	5	1

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 = 0.0387 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	169	0.2 (0.7)
subtype1	58	0.4 (1.1)
subtype2	24	0.0 (0.2)
subtype3	57	0.1 (0.3)
subtype4	10	0.0 (0.0)
subtype5	20	0.1 (0.2)

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 = 0.175 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	7.2 (0.7)
subtype1	63	7.4 (0.8)
subtype2	32	7.1 (0.4)
subtype3	68	7.1 (0.7)
subtype4	14	7.2 (1.1)
subtype5	25	7.3 (0.9)

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 = 0.0127 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	3.5 (0.6)
subtype1	63	3.6 (0.6)
subtype2	32	3.2 (0.4)
subtype3	68	3.4 (0.5)
subtype4	14	3.3 (0.6)
subtype5	25	3.5 (0.6)

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 = 0.561 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	3.8 (0.6)
subtype1	63	3.8 (0.7)
subtype2	32	3.8 (0.4)
subtype3	68	3.7 (0.6)
subtype4	14	3.9 (0.6)
subtype5	25	3.8 (0.7)

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 = 0.178 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	7.3 (0.8)
subtype1	63	7.4 (0.8)
subtype2	32	7.1 (0.5)
subtype3	68	7.1 (0.7)
subtype4	14	7.3 (1.2)
subtype5	25	7.4 (0.8)

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 = 0.0342 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	10.2 (10.4)
subtype1	61	13.4 (13.9)
subtype2	32	7.3 (3.5)
subtype3	68	9.0 (6.7)
subtype4	14	7.8 (5.6)
subtype5	25	11.1 (14.3)

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 = 0.241 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	196	-4.6 (96.7)
subtype1	61	16.3 (41.7)
subtype2	30	-4.4 (70.6)
subtype3	68	-9.7 (144.5)
subtype4	14	-28.7 (80.3)
subtype5	23	-30.0 (43.1)

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 = 0.0275 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	176	1.4 (4.3)
subtype1	59	2.4 (6.1)
subtype2	29	0.5 (2.0)
subtype3	60	0.6 (1.8)
subtype4	14	0.1 (0.0)
subtype5	14	3.3 (7.0)

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 = 0.997 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	178	576.9 (549.1)
subtype1	59	585.3 (424.9)
subtype2	29	576.7 (495.7)
subtype3	59	565.2 (643.5)
subtype4	14	619.1 (553.5)
subtype5	17	554.1 (705.9)

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	4
Number of samples	37	34	71	60

'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	202	1	0.3 - 66.1 (19.7)
subtype1	37	0	0.3 - 66.0 (28.2)
subtype2	34	0	0.8 - 52.9 (9.4)
subtype3	71	0	1.0 - 66.1 (29.1)
subtype4	60	1	0.7 - 66.0 (9.6)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.257 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	60.3 (6.9)
subtype1	36	59.2 (7.2)
subtype2	34	62.3 (7.4)
subtype3	71	59.8 (6.5)
subtype4	59	60.3 (6.9)

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

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

P value = 0.000144 (Chi-square test), Q value = 0.019

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

nPatients	T2	T3	T4
ALL	90	106	4
subtype1	22	11	4
subtype2	11	23	0
subtype3	28	42	0
subtype4	29	30	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 = 0.172 (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	155	16
subtype1	26	2
subtype2	22	5
subtype3	56	7
subtype4	51	2

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 = 0.844 (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	151	36	5
subtype1	27	9	0
subtype2	25	7	1
subtype3	57	11	2
subtype4	42	9	2

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 = 0.0235 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	169	0.2 (0.7)
subtype1	28	0.1 (0.3)
subtype2	27	0.5 (1.4)
subtype3	61	0.2 (0.5)
subtype4	53	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 = 0.0099 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	7.2 (0.7)
subtype1	37	7.0 (0.7)
subtype2	34	7.6 (0.9)
subtype3	71	7.2 (0.7)
subtype4	60	7.2 (0.6)

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 = 0.144 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	3.5 (0.6)
subtype1	37	3.4 (0.5)
subtype2	34	3.6 (0.7)
subtype3	71	3.4 (0.5)
subtype4	60	3.5 (0.5)

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 = 0.277 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	3.8 (0.6)
subtype1	37	3.7 (0.6)
subtype2	34	3.9 (0.6)
subtype3	71	3.7 (0.6)
subtype4	60	3.8 (0.6)

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 = 0.064 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	7.3 (0.8)
subtype1	37	7.1 (0.9)
subtype2	34	7.6 (0.9)
subtype3	71	7.2 (0.7)
subtype4	60	7.2 (0.7)

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 = 0.0143 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	10.2 (10.4)
subtype1	37	12.1 (14.9)
subtype2	34	14.5 (15.1)
subtype3	69	9.0 (6.4)
subtype4	60	8.1 (5.8)

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 = 0.803 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	196	-4.6 (96.7)
subtype1	37	-12.8 (61.0)
subtype2	32	-8.7 (47.5)
subtype3	68	4.6 (56.1)
subtype4	59	-7.7 (155.3)

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 = 0.000224 (ANOVA), Q value = 0.029

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

	nPatients	Mean (Std.Dev)
ALL	176	1.4 (4.3)
subtype1	36	0.1 (0.2)
subtype2	20	5.1 (9.6)
subtype3	67	1.2 (3.4)
subtype4	53	0.9 (2.7)

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 = 0.255 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	178	576.9 (549.1)
subtype1	36	672.7 (580.1)
subtype2	23	439.6 (441.5)
subtype3	67	629.3 (525.4)
subtype4	52	503.7 (591.7)

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	4	5
Number of samples	80	12	73	26	11

'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	202	1	0.3 - 66.1 (19.7)
subtype1	80	0	0.9 - 66.0 (30.3)
subtype2	12	0	1.1 - 56.5 (18.0)
subtype3	73	1	0.3 - 66.1 (10.3)
subtype4	26	0	0.8 - 66.0 (7.9)
subtype5	11	0	1.2 - 50.7 (6.8)

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 = 0.122 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	60.3 (6.9)
subtype1	80	61.1 (6.8)
subtype2	12	56.0 (6.8)
subtype3	71	59.6 (7.0)
subtype4	26	61.5 (6.7)
subtype5	11	60.9 (7.3)

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 = 0.151 (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	90	106	4
subtype1	29	46	4
subtype2	8	4	0
subtype3	37	36	0
subtype4	10	16	0
subtype5	6	4	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 = 0.284 (Chi-square test), Q value = 1

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

nPatients	0	1
ALL	155	16
subtype1	64	11
subtype2	8	0
subtype3	58	4
subtype4	18	1
subtype5	7	0

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 = 0.773 (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	151	36	5
subtype1	61	18	1
subtype2	9	2	0
subtype3	54	10	2
subtype4	19	5	1
subtype5	8	1	1

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 = 0.232 (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	169	0.2 (0.7)
subtype1	75	0.3 (0.9)
subtype2	7	0.0 (0.0)
subtype3	62	0.1 (0.3)
subtype4	19	0.1 (0.2)
subtype5	6	0.0 (0.0)

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 = 0.712 (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	202	7.2 (0.7)
subtype1	80	7.3 (0.7)
subtype2	12	7.2 (1.2)
subtype3	73	7.1 (0.7)
subtype4	26	7.3 (0.8)
subtype5	11	7.2 (0.6)

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 = 0.703 (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	202	3.5 (0.6)
subtype1	80	3.5 (0.6)
subtype2	12	3.3 (0.7)
subtype3	73	3.4 (0.5)
subtype4	26	3.6 (0.6)
subtype5	11	3.4 (0.5)

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 = 0.853 (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	202	3.8 (0.6)
subtype1	80	3.8 (0.6)
subtype2	12	3.8 (0.7)
subtype3	73	3.7 (0.6)
subtype4	26	3.8 (0.7)
subtype5	11	3.8 (0.6)

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 = 0.579 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	7.3 (0.8)
subtype1	80	7.3 (0.8)
subtype2	12	7.2 (1.1)
subtype3	73	7.2 (0.7)
subtype4	26	7.4 (0.8)
subtype5	11	7.2 (0.6)

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 = 0.1 (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	200	10.2 (10.4)
subtype1	78	11.7 (11.8)
subtype2	12	7.4 (5.8)
subtype3	73	8.9 (6.5)
subtype4	26	13.0 (15.9)
subtype5	11	5.6 (3.3)

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 = 0.0514 (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	196	-4.6 (96.7)
subtype1	77	18.3 (47.1)
subtype2	12	-5.0 (47.2)
subtype3	73	-14.9 (142.0)
subtype4	24	-22.5 (46.4)
subtype5	10	-60.8 (67.6)

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 = 0.167 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	176	1.4 (4.3)
subtype1	78	1.9 (5.4)
subtype2	11	0.1 (0.0)
subtype3	64	0.6 (1.8)
subtype4	15	3.0 (6.8)
subtype5	8	1.4 (3.8)

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 = 0.619 (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	178	576.9 (549.1)
subtype1	78	609.2 (414.7)
subtype2	12	450.7 (428.0)
subtype3	63	566.5 (652.1)
subtype4	17	661.9 (795.8)
subtype5	8	352.5 (338.4)

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	4
Number of samples	67	28	64	43

'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	202	1	0.3 - 66.1 (19.7)
subtype1	67	0	0.9 - 66.0 (29.0)
subtype2	28	0	1.1 - 66.0 (29.6)
subtype3	64	1	0.3 - 66.1 (9.6)
subtype4	43	0	0.8 - 66.0 (7.7)

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 = 0.318 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	200	60.3 (6.9)
subtype1	67	61.1 (6.9)
subtype2	27	58.1 (6.6)
subtype3	63	60.4 (7.0)
subtype4	43	60.2 (7.0)

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 = 1.03e-05 (Chi-square test), Q value = 0.0014

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

nPatients	T2	T3	T4
ALL	90	106	4
subtype1	23	43	0
subtype2	16	8	4
subtype3	33	31	0
subtype4	18	24	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 = 0.514 (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	155	16
subtype1	54	9
subtype2	19	1
subtype3	53	4
subtype4	29	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 = 0.6 (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	151	36	5
subtype1	53	13	1
subtype2	20	8	0
subtype3	47	8	2
subtype4	31	7	2

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 = 0.427 (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	169	0.2 (0.7)
subtype1	63	0.3 (0.7)
subtype2	20	0.1 (0.2)
subtype3	57	0.1 (0.3)
subtype4	29	0.2 (1.1)

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 = 0.338 (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	202	7.2 (0.7)
subtype1	67	7.3 (0.8)
subtype2	28	7.1 (0.8)
subtype3	64	7.2 (0.6)
subtype4	43	7.2 (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 = 0.949 (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	202	3.5 (0.6)
subtype1	67	3.5 (0.6)
subtype2	28	3.4 (0.6)
subtype3	64	3.5 (0.5)
subtype4	43	3.4 (0.5)

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 = 0.366 (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	202	3.8 (0.6)
subtype1	67	3.9 (0.6)
subtype2	28	3.6 (0.6)
subtype3	64	3.7 (0.6)
subtype4	43	3.8 (0.6)

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 = 0.494 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	202	7.3 (0.8)
subtype1	67	7.4 (0.8)
subtype2	28	7.2 (1.0)
subtype3	64	7.2 (0.7)
subtype4	43	7.3 (0.7)

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 = 0.225 (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	200	10.2 (10.4)
subtype1	65	11.3 (9.5)
subtype2	28	12.9 (16.7)
subtype3	64	8.8 (6.0)
subtype4	43	9.1 (11.4)

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 = 0.015 (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	196	-4.6 (96.7)
subtype1	64	23.9 (42.2)
subtype2	28	-17.4 (68.5)
subtype3	64	-7.8 (146.9)
subtype4	40	-35.9 (57.3)

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 = 0.0744 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	176	1.4 (4.3)
subtype1	64	2.2 (5.9)
subtype2	28	0.1 (0.2)
subtype3	55	0.7 (1.9)
subtype4	29	2.1 (5.3)

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 = 0.707 (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	178	576.9 (549.1)
subtype1	64	586.8 (425.2)
subtype2	28	657.8 (516.7)
subtype3	54	573.9 (643.3)
subtype4	32	491.2 (632.7)

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 = 206
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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