Correlation between aggregated molecular cancer subtypes and selected clinical features
Prostate Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C198863G
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C198863G
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 8 different clustering approaches and 14 clinical features across 488 patients, 67 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 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'COMPLETENESS_OF_RESECTION',  'NUMBER_OF_LYMPH_NODES',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE_SECONDARY',  'GLEASON_SCORE',  'PSA_RESULT_PREOP', and 'PSA_VALUE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'COMPLETENESS_OF_RESECTION',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE', and 'PSA_RESULT_PREOP'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY_T_STAGE',  'NUMBER_OF_LYMPH_NODES',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE',  'PSA_RESULT_PREOP', and 'PSA_VALUE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'COMPLETENESS_OF_RESECTION',  'NUMBER_OF_LYMPH_NODES',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE',  'PSA_RESULT_PREOP', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'NUMBER_OF_LYMPH_NODES',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE_SECONDARY',  'GLEASON_SCORE', and 'PSA_VALUE'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'COMPLETENESS_OF_RESECTION',  'NUMBER_OF_LYMPH_NODES',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE_SECONDARY', and 'GLEASON_SCORE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'COMPLETENESS_OF_RESECTION',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE_SECONDARY', and 'GLEASON_SCORE'.

  • 6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'COMPLETENESS_OF_RESECTION',  'NUMBER_OF_LYMPH_NODES',  'GLEASON_SCORE_COMBINED',  'GLEASON_SCORE_PRIMARY',  'GLEASON_SCORE_SECONDARY', and 'GLEASON_SCORE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 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, 67 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.907
(0.923) 		0.8
(0.834) 		0.375
(0.451) 		0.8
(0.834) 		0.614
(0.702) 		0.823
(0.846) 		0.721
(0.784) 		0.942
(0.95)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.00146
(0.00379) 		0.00197
(0.00501) 		0.144
(0.209) 		0.0169
(0.0343) 		0.132
(0.196) 		0.0401
(0.069) 		0.00962
(0.0203) 		0.0334
(0.0604)
PATHOLOGY T STAGE Fisher's exact test 1e-05
(5.89e-05) 		0.0001
(0.000373) 		0.00014
(0.000506) 		0.00364
(0.00867) 		0.00864
(0.0186) 		2e-05
(9.33e-05) 		0.00767
(0.0168) 		2e-05
(9.33e-05)
PATHOLOGY N STAGE Fisher's exact test 1e-05
(5.89e-05) 		0.472
(0.557) 		0.0936
(0.146) 		0.0584
(0.0963) 		0.00636
(0.0142) 		0.00025
(0.000821) 		0.296
(0.381) 		2e-05
(9.33e-05)
HISTOLOGICAL TYPE Fisher's exact test 0.181
(0.253) 		0.675
(0.756) 		0.35
(0.43) 		0.229
(0.312) 		0.105
(0.159) 		0.782
(0.834) 		0.151
(0.216) 		0.627
(0.71)
COMPLETENESS OF RESECTION Fisher's exact test 2e-05
(9.33e-05) 		0.0319
(0.0595) 		0.104
(0.159) 		0.0247
(0.0478) 		0.172
(0.244) 		9e-05
(0.00035) 		0.0008
(0.00224) 		0.00043
(0.0013)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 4.25e-08
(4.77e-07) 		0.346
(0.43) 		0.0416
(0.0705) 		0.04
(0.069) 		0.00375
(0.00876) 		3.2e-05
(0.000141) 		0.344
(0.43) 		4.35e-07
(4.42e-06)
GLEASON SCORE COMBINED Kruskal-Wallis (anova) 2.87e-18
(1.61e-16) 		0.000257
(0.000821) 		0.0217
(0.0434) 		0.000174
(0.00061) 		4.68e-06
(3.67e-05) 		7.95e-12
(2.22e-10) 		3.49e-06
(3.01e-05) 		1.43e-10
(2.28e-09)
GLEASON SCORE PRIMARY Kruskal-Wallis (anova) 8.52e-18
(3.18e-16) 		5e-05
(0.000207) 		0.00264
(0.00642) 		8.75e-06
(5.76e-05) 		9.07e-05
(0.00035) 		1.07e-08
(1.33e-07) 		1.42e-05
(7.93e-05) 		7.2e-09
(1.01e-07)
GLEASON SCORE SECONDARY Kruskal-Wallis (anova) 3.28e-05
(0.000141) 		0.266
(0.355) 		0.0627
(0.102) 		0.25
(0.338) 		0.00559
(0.0128) 		0.000515
(0.00148) 		0.0121
(0.0251) 		0.000405
(0.00126)
GLEASON SCORE Kruskal-Wallis (anova) 2.48e-22
(2.78e-20) 		0.000493
(0.00145) 		0.000835
(0.00228) 		8.31e-06
(5.76e-05) 		4.92e-06
(3.67e-05) 		7.15e-11
(1.6e-09) 		9.56e-07
(8.93e-06) 		1.27e-10
(2.28e-09)
PSA RESULT PREOP Kruskal-Wallis (anova) 0.00213
(0.00531) 		0.000214
(0.000728) 		0.00117
(0.00311) 		0.0312
(0.0593) 		0.0918
(0.145) 		0.294
(0.381) 		0.295
(0.381) 		0.134
(0.197)
PSA VALUE Kruskal-Wallis (anova) 0.0326
(0.0598) 		0.466
(0.555) 		0.0433
(0.0723) 		0.554
(0.646) 		0.0389
(0.069) 		0.684
(0.758) 		0.0734
(0.117) 		0.363
(0.442)
RACE Fisher's exact test 0.564
(0.652) 		1
(1.00) 		0.337
(0.429) 		0.0226
(0.0445) 		0.215
(0.297) 		0.708
(0.777) 		0.805
(0.834) 		0.804
(0.834)
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 269 128 85
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.907 (logrank test), Q value = 0.92

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

nPatients nDeath Duration Range (Median), Month
ALL 481 8 0.7 - 165.2 (26.7)
subtype1 268 4 0.7 - 165.2 (26.2)
subtype2 128 3 0.8 - 115.9 (28.3)
subtype3 85 1 1.0 - 97.4 (25.5)

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 'YEARS_TO_BIRTH'

P value = 0.00146 (Kruskal-Wallis (anova)), Q value = 0.0038

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

nPatients Mean (Std.Dev)
ALL 471 60.9 (6.8)
subtype1 263 60.1 (6.8)
subtype2 125 62.7 (6.2)
subtype3 83 60.7 (7.4)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.9e-05

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

nPatients T2 T3 T4
ALL 188 281 10
subtype1 129 136 2
subtype2 15 106 7
subtype3 44 39 1

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 = 1e-05 (Fisher's exact test), Q value = 5.9e-05

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

nPatients 0 1
ALL 338 75
subtype1 197 26
subtype2 80 41
subtype3 61 8

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 'HISTOLOGICAL_TYPE'

P value = 0.181 (Fisher's exact test), Q value = 0.25

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

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 14 468
subtype1 9 260
subtype2 5 123
subtype3 0 85

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 2e-05 (Fisher's exact test), Q value = 9.3e-05

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

nPatients R0 R1 R2 RX
ALL 310 138 5 15
subtype1 198 53 3 7
subtype2 60 61 1 4
subtype3 52 24 1 4

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 4.25e-08 (Kruskal-Wallis (anova)), Q value = 4.8e-07

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

nPatients Mean (Std.Dev)
ALL 395 0.4 (1.4)
subtype1 213 0.2 (1.1)
subtype2 114 1.0 (1.9)
subtype3 68 0.1 (0.3)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 2.87e-18 (Kruskal-Wallis (anova)), Q value = 1.6e-16

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

nPatients Mean (Std.Dev)
ALL 482 7.6 (1.0)
subtype1 269 7.3 (0.9)
subtype2 128 8.2 (1.0)
subtype3 85 7.4 (0.9)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 8.52e-18 (Kruskal-Wallis (anova)), Q value = 3.2e-16

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

nPatients Mean (Std.Dev)
ALL 482 3.7 (0.7)
subtype1 269 3.5 (0.6)
subtype2 128 4.1 (0.6)
subtype3 85 3.5 (0.6)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 3.28e-05 (Kruskal-Wallis (anova)), Q value = 0.00014

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

nPatients Mean (Std.Dev)
ALL 482 3.9 (0.7)
subtype1 269 3.8 (0.7)
subtype2 128 4.1 (0.7)
subtype3 85 3.8 (0.6)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE'

P value = 2.48e-22 (Kruskal-Wallis (anova)), Q value = 2.8e-20

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

nPatients Mean (Std.Dev)
ALL 482 7.6 (1.0)
subtype1 269 7.3 (0.9)
subtype2 128 8.4 (0.9)
subtype3 85 7.4 (0.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.00213 (Kruskal-Wallis (anova)), Q value = 0.0053

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

nPatients Mean (Std.Dev)
ALL 479 10.8 (11.7)
subtype1 267 9.8 (11.5)
subtype2 128 13.3 (12.7)
subtype3 84 10.2 (9.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PSA_VALUE'

P value = 0.0326 (Kruskal-Wallis (anova)), Q value = 0.06

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

nPatients Mean (Std.Dev)
ALL 426 1.1 (4.1)
subtype1 242 0.7 (3.0)
subtype2 109 2.2 (6.5)
subtype3 75 0.6 (2.2)

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 'RACE'

P value = 0.564 (Fisher's exact test), Q value = 0.65

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 146
subtype1 2 3 90
subtype2 0 1 29
subtype3 0 3 27

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 160 164 164
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.8 (logrank test), Q value = 0.83

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

nPatients nDeath Duration Range (Median), Month
ALL 487 8 0.7 - 165.2 (26.4)
subtype1 160 1 1.0 - 97.4 (24.8)
subtype2 164 3 0.7 - 122.2 (28.7)
subtype3 163 4 1.0 - 165.2 (25.9)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00197 (Kruskal-Wallis (anova)), Q value = 0.005

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

nPatients Mean (Std.Dev)
ALL 477 60.9 (6.8)
subtype1 156 59.8 (6.9)
subtype2 160 62.4 (6.9)
subtype3 161 60.5 (6.4)

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-04 (Fisher's exact test), Q value = 0.00037

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T2 T3 T4
ALL 189 286 10
subtype1 81 76 0
subtype2 57 100 7
subtype3 51 110 3

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 = 0.472 (Fisher's exact test), Q value = 0.56

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 341 77
subtype1 114 21
subtype2 116 26
subtype3 111 30

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.675 (Fisher's exact test), Q value = 0.76

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 15 473
subtype1 4 156
subtype2 4 160
subtype3 7 157

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

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0319 (Fisher's exact test), Q value = 0.06

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 313 141 5 15
subtype1 113 37 1 4
subtype2 90 63 1 5
subtype3 110 41 3 6

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.346 (Kruskal-Wallis (anova)), Q value = 0.43

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

nPatients Mean (Std.Dev)
ALL 400 0.4 (1.4)
subtype1 128 0.3 (0.8)
subtype2 136 0.4 (1.4)
subtype3 136 0.6 (1.8)

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

'METHLYATION CNMF' versus 'GLEASON_SCORE_COMBINED'

P value = 0.000257 (Kruskal-Wallis (anova)), Q value = 0.00082

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

nPatients Mean (Std.Dev)
ALL 488 7.6 (1.0)
subtype1 160 7.4 (1.0)
subtype2 164 7.8 (1.0)
subtype3 164 7.5 (1.0)

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

'METHLYATION CNMF' versus 'GLEASON_SCORE_PRIMARY'

P value = 5e-05 (Kruskal-Wallis (anova)), Q value = 0.00021

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

nPatients Mean (Std.Dev)
ALL 488 3.7 (0.7)
subtype1 160 3.6 (0.7)
subtype2 164 3.9 (0.7)
subtype3 164 3.6 (0.6)

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

'METHLYATION CNMF' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.266 (Kruskal-Wallis (anova)), Q value = 0.36

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

nPatients Mean (Std.Dev)
ALL 488 3.9 (0.7)
subtype1 160 3.8 (0.6)
subtype2 164 3.9 (0.7)
subtype3 164 3.9 (0.7)

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

'METHLYATION CNMF' versus 'GLEASON_SCORE'

P value = 0.000493 (Kruskal-Wallis (anova)), Q value = 0.0015

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

nPatients Mean (Std.Dev)
ALL 488 7.6 (1.0)
subtype1 160 7.4 (1.0)
subtype2 164 7.8 (1.0)
subtype3 164 7.6 (1.0)

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

'METHLYATION CNMF' versus 'PSA_RESULT_PREOP'

P value = 0.000214 (Kruskal-Wallis (anova)), Q value = 0.00073

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

nPatients Mean (Std.Dev)
ALL 485 10.8 (11.7)
subtype1 160 9.0 (10.1)
subtype2 163 13.8 (15.2)
subtype3 162 9.7 (8.0)

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

'METHLYATION CNMF' versus 'PSA_VALUE'

P value = 0.466 (Kruskal-Wallis (anova)), Q value = 0.56

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

nPatients Mean (Std.Dev)
ALL 432 1.1 (4.1)
subtype1 145 0.7 (3.2)
subtype2 139 1.2 (3.9)
subtype3 148 1.3 (5.0)

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

'METHLYATION CNMF' versus 'RACE'

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

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 147
subtype1 1 2 53
subtype2 0 2 37
subtype3 1 3 57

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 145 173 169
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.375 (logrank test), Q value = 0.45

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

nPatients nDeath Duration Range (Median), Month
ALL 486 8 0.7 - 165.2 (26.5)
subtype1 145 3 0.7 - 97.4 (24.0)
subtype2 173 2 0.8 - 122.2 (28.2)
subtype3 168 3 0.9 - 165.2 (26.4)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.144 (Kruskal-Wallis (anova)), Q value = 0.21

Table S33.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 476 60.9 (6.8)
subtype1 141 60.1 (7.2)
subtype2 168 61.8 (6.9)
subtype3 167 60.8 (6.4)

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.00051

Table S34.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T2 T3 T4
ALL 188 286 10
subtype1 73 69 1
subtype2 70 98 4
subtype3 45 119 5

Figure S31.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0936 (Fisher's exact test), Q value = 0.15

Table S35.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 340 77
subtype1 105 17
subtype2 120 24
subtype3 115 36

Figure S32.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.35 (Fisher's exact test), Q value = 0.43

Table S36.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 15 472
subtype1 3 142
subtype2 4 169
subtype3 8 161

Figure S33.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.104 (Fisher's exact test), Q value = 0.16

Table S37.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 312 141 5 15
subtype1 102 32 0 5
subtype2 101 62 2 4
subtype3 109 47 3 6

Figure S34.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0416 (Kruskal-Wallis (anova)), Q value = 0.071

Table S38.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 399 0.4 (1.4)
subtype1 117 0.2 (0.6)
subtype2 139 0.4 (1.1)
subtype3 143 0.7 (1.9)

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.0217 (Kruskal-Wallis (anova)), Q value = 0.043

Table S39.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

nPatients Mean (Std.Dev)
ALL 487 7.6 (1.0)
subtype1 145 7.4 (0.9)
subtype2 173 7.6 (1.1)
subtype3 169 7.7 (1.0)

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 0.00264 (Kruskal-Wallis (anova)), Q value = 0.0064

Table S40.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

nPatients Mean (Std.Dev)
ALL 487 3.7 (0.7)
subtype1 145 3.5 (0.7)
subtype2 173 3.8 (0.7)
subtype3 169 3.7 (0.6)

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.0627 (Kruskal-Wallis (anova)), Q value = 0.1

Table S41.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

nPatients Mean (Std.Dev)
ALL 487 3.9 (0.7)
subtype1 145 3.8 (0.6)
subtype2 173 3.8 (0.7)
subtype3 169 4.0 (0.7)

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE'

P value = 0.000835 (Kruskal-Wallis (anova)), Q value = 0.0023

Table S42.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

nPatients Mean (Std.Dev)
ALL 487 7.6 (1.0)
subtype1 145 7.3 (0.8)
subtype2 173 7.7 (1.1)
subtype3 169 7.8 (1.0)

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

'RNAseq CNMF subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.00117 (Kruskal-Wallis (anova)), Q value = 0.0031

Table S43.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

nPatients Mean (Std.Dev)
ALL 484 10.9 (11.7)
subtype1 145 8.9 (11.3)
subtype2 172 13.6 (14.4)
subtype3 167 9.7 (8.0)

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

'RNAseq CNMF subtypes' versus 'PSA_VALUE'

P value = 0.0433 (Kruskal-Wallis (anova)), Q value = 0.072

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

nPatients Mean (Std.Dev)
ALL 431 1.1 (4.1)
subtype1 133 0.7 (3.3)
subtype2 147 1.0 (3.6)
subtype3 151 1.4 (5.1)

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.337 (Fisher's exact test), Q value = 0.43

Table S45.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 147
subtype1 2 1 51
subtype2 0 3 40
subtype3 0 3 56

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 173 203 111
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.8 (logrank test), Q value = 0.83

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

nPatients nDeath Duration Range (Median), Month
ALL 486 8 0.7 - 165.2 (26.5)
subtype1 173 3 0.7 - 122.2 (25.1)
subtype2 203 2 0.8 - 114.4 (27.9)
subtype3 110 3 1.0 - 165.2 (26.2)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0169 (Kruskal-Wallis (anova)), Q value = 0.034

Table S48.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 476 60.9 (6.8)
subtype1 168 60.1 (6.8)
subtype2 199 62.1 (6.8)
subtype3 109 60.1 (6.5)

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00364 (Fisher's exact test), Q value = 0.0087

Table S49.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T2 T3 T4
ALL 188 286 10
subtype1 85 84 2
subtype2 70 126 6
subtype3 33 76 2

Figure S45.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0584 (Fisher's exact test), Q value = 0.096

Table S50.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 340 77
subtype1 127 18
subtype2 136 37
subtype3 77 22

Figure S46.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.229 (Fisher's exact test), Q value = 0.31

Table S51.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 15 472
subtype1 3 170
subtype2 6 197
subtype3 6 105

Figure S47.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0247 (Fisher's exact test), Q value = 0.048

Table S52.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 312 141 5 15
subtype1 121 38 0 6
subtype2 124 70 3 3
subtype3 67 33 2 6

Figure S48.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.04 (Kruskal-Wallis (anova)), Q value = 0.069

Table S53.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 399 0.4 (1.4)
subtype1 137 0.2 (0.5)
subtype2 168 0.5 (1.4)
subtype3 94 0.7 (2.0)

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 0.000174 (Kruskal-Wallis (anova)), Q value = 0.00061

Table S54.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

nPatients Mean (Std.Dev)
ALL 487 7.6 (1.0)
subtype1 173 7.3 (0.9)
subtype2 203 7.8 (1.1)
subtype3 111 7.6 (1.0)

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 8.75e-06 (Kruskal-Wallis (anova)), Q value = 5.8e-05

Table S55.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

nPatients Mean (Std.Dev)
ALL 487 3.7 (0.7)
subtype1 173 3.5 (0.7)
subtype2 203 3.9 (0.7)
subtype3 111 3.7 (0.6)

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.25 (Kruskal-Wallis (anova)), Q value = 0.34

Table S56.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

nPatients Mean (Std.Dev)
ALL 487 3.9 (0.7)
subtype1 173 3.8 (0.6)
subtype2 203 3.9 (0.7)
subtype3 111 3.9 (0.7)

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 8.31e-06 (Kruskal-Wallis (anova)), Q value = 5.8e-05

Table S57.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

nPatients Mean (Std.Dev)
ALL 487 7.6 (1.0)
subtype1 173 7.3 (0.9)
subtype2 203 7.8 (1.1)
subtype3 111 7.7 (1.0)

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

'RNAseq cHierClus subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.0312 (Kruskal-Wallis (anova)), Q value = 0.059

Table S58.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

nPatients Mean (Std.Dev)
ALL 484 10.9 (11.7)
subtype1 173 9.5 (11.8)
subtype2 202 12.3 (12.8)
subtype3 109 10.3 (9.0)

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

'RNAseq cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.554 (Kruskal-Wallis (anova)), Q value = 0.65

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

nPatients Mean (Std.Dev)
ALL 431 1.1 (4.1)
subtype1 156 0.8 (3.2)
subtype2 176 1.0 (3.4)
subtype3 99 1.8 (6.1)

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0226 (Fisher's exact test), Q value = 0.045

Table S60.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 147
subtype1 2 0 65
subtype2 0 5 46
subtype3 0 2 36

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3
Number of samples 157 145 182
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.614 (logrank test), Q value = 0.7

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

nPatients nDeath Duration Range (Median), Month
ALL 483 8 0.7 - 165.2 (26.3)
subtype1 157 3 1.0 - 115.9 (23.9)
subtype2 145 1 0.7 - 115.1 (26.7)
subtype3 181 4 0.8 - 165.2 (28.9)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.132 (Kruskal-Wallis (anova)), Q value = 0.2

Table S63.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 473 60.9 (6.8)
subtype1 154 60.2 (6.5)
subtype2 142 60.7 (7.3)
subtype3 177 61.8 (6.7)

Figure S58.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00864 (Fisher's exact test), Q value = 0.019

Table S64.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T2 T3 T4
ALL 188 284 9
subtype1 64 89 2
subtype2 69 71 4
subtype3 55 124 3

Figure S59.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00636 (Fisher's exact test), Q value = 0.014

Table S65.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 339 76
subtype1 110 26
subtype2 105 11
subtype3 124 39

Figure S60.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.105 (Fisher's exact test), Q value = 0.16

Table S66.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 15 469
subtype1 7 150
subtype2 1 144
subtype3 7 175

Figure S61.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.172 (Fisher's exact test), Q value = 0.24

Table S67.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 310 140 5 15
subtype1 108 38 0 6
subtype2 93 41 3 2
subtype3 109 61 2 7

Figure S62.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00375 (Kruskal-Wallis (anova)), Q value = 0.0088

Table S68.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 397 0.4 (1.4)
subtype1 126 0.3 (1.1)
subtype2 114 0.2 (0.7)
subtype3 157 0.7 (1.8)

Figure S63.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'GLEASON_SCORE_COMBINED'

P value = 4.68e-06 (Kruskal-Wallis (anova)), Q value = 3.7e-05

Table S69.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

nPatients Mean (Std.Dev)
ALL 484 7.6 (1.0)
subtype1 157 7.5 (1.0)
subtype2 145 7.3 (1.0)
subtype3 182 7.8 (1.0)

Figure S64.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

'MIRSEQ CNMF' versus 'GLEASON_SCORE_PRIMARY'

P value = 9.07e-05 (Kruskal-Wallis (anova)), Q value = 0.00035

Table S70.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

nPatients Mean (Std.Dev)
ALL 484 3.7 (0.7)
subtype1 157 3.6 (0.6)
subtype2 145 3.6 (0.7)
subtype3 182 3.8 (0.7)

Figure S65.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

'MIRSEQ CNMF' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.00559 (Kruskal-Wallis (anova)), Q value = 0.013

Table S71.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

nPatients Mean (Std.Dev)
ALL 484 3.9 (0.7)
subtype1 157 3.9 (0.7)
subtype2 145 3.7 (0.7)
subtype3 182 3.9 (0.7)

Figure S66.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

'MIRSEQ CNMF' versus 'GLEASON_SCORE'

P value = 4.92e-06 (Kruskal-Wallis (anova)), Q value = 3.7e-05

Table S72.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'GLEASON_SCORE'

nPatients Mean (Std.Dev)
ALL 484 7.6 (1.0)
subtype1 157 7.5 (0.9)
subtype2 145 7.3 (1.0)
subtype3 182 7.9 (1.0)

Figure S67.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'GLEASON_SCORE'

'MIRSEQ CNMF' versus 'PSA_RESULT_PREOP'

P value = 0.0918 (Kruskal-Wallis (anova)), Q value = 0.14

Table S73.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

nPatients Mean (Std.Dev)
ALL 481 10.9 (11.7)
subtype1 156 10.0 (12.4)
subtype2 144 11.3 (13.0)
subtype3 181 11.2 (10.0)

Figure S68.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

'MIRSEQ CNMF' versus 'PSA_VALUE'

P value = 0.0389 (Kruskal-Wallis (anova)), Q value = 0.069

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

nPatients Mean (Std.Dev)
ALL 430 1.1 (4.1)
subtype1 145 1.0 (3.9)
subtype2 127 0.7 (3.9)
subtype3 158 1.4 (4.5)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.215 (Fisher's exact test), Q value = 0.3

Table S75.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 146
subtype1 1 0 39
subtype2 1 3 69
subtype3 0 4 38

Figure S70.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S76.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 147 97 59 125 56
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.823 (logrank test), Q value = 0.85

Table S77.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 483 8 0.7 - 165.2 (26.3)
subtype1 147 2 1.0 - 115.9 (25.5)
subtype2 97 2 2.2 - 115.1 (28.2)
subtype3 59 2 1.0 - 140.2 (24.0)
subtype4 125 1 0.8 - 165.2 (28.2)
subtype5 55 1 0.7 - 111.4 (25.7)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.0401 (Kruskal-Wallis (anova)), Q value = 0.069

Table S78.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 473 60.9 (6.8)
subtype1 144 60.5 (6.6)
subtype2 94 60.6 (6.7)
subtype3 59 59.6 (6.4)
subtype4 122 62.6 (6.8)
subtype5 54 60.4 (7.6)

Figure S72.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 9.3e-05

Table S79.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T2 T3 T4
ALL 188 284 9
subtype1 65 80 0
subtype2 45 47 4
subtype3 21 37 1
subtype4 27 94 4
subtype5 30 26 0

Figure S73.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.00025 (Fisher's exact test), Q value = 0.00082

Table S80.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 339 76
subtype1 110 18
subtype2 70 8
subtype3 40 12
subtype4 78 35
subtype5 41 3

Figure S74.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.782 (Fisher's exact test), Q value = 0.83

Table S81.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 15 469
subtype1 4 143
subtype2 2 95
subtype3 3 56
subtype4 5 120
subtype5 1 55

Figure S75.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 9e-05 (Fisher's exact test), Q value = 0.00035

Table S82.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 310 140 5 15
subtype1 108 31 0 6
subtype2 57 35 1 2
subtype3 41 14 0 2
subtype4 64 54 2 3
subtype5 40 6 2 2

Figure S76.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 3.2e-05 (Kruskal-Wallis (anova)), Q value = 0.00014

Table S83.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 397 0.4 (1.4)
subtype1 121 0.2 (0.8)
subtype2 76 0.2 (0.8)
subtype3 50 0.3 (0.7)
subtype4 106 1.0 (2.3)
subtype5 44 0.1 (0.4)

Figure S77.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_COMBINED'

P value = 7.95e-12 (Kruskal-Wallis (anova)), Q value = 2.2e-10

Table S84.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

nPatients Mean (Std.Dev)
ALL 484 7.6 (1.0)
subtype1 147 7.4 (0.9)
subtype2 97 7.3 (1.1)
subtype3 59 7.6 (1.0)
subtype4 125 8.1 (1.0)
subtype5 56 7.2 (0.7)

Figure S78.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_PRIMARY'

P value = 1.07e-08 (Kruskal-Wallis (anova)), Q value = 1.3e-07

Table S85.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

nPatients Mean (Std.Dev)
ALL 484 3.7 (0.7)
subtype1 147 3.6 (0.6)
subtype2 97 3.6 (0.7)
subtype3 59 3.7 (0.7)
subtype4 125 4.0 (0.7)
subtype5 56 3.5 (0.5)

Figure S79.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.000515 (Kruskal-Wallis (anova)), Q value = 0.0015

Table S86.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

nPatients Mean (Std.Dev)
ALL 484 3.9 (0.7)
subtype1 147 3.8 (0.7)
subtype2 97 3.7 (0.7)
subtype3 59 3.9 (0.7)
subtype4 125 4.1 (0.7)
subtype5 56 3.7 (0.6)

Figure S80.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE'

P value = 7.15e-11 (Kruskal-Wallis (anova)), Q value = 1.6e-09

Table S87.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'GLEASON_SCORE'

nPatients Mean (Std.Dev)
ALL 484 7.6 (1.0)
subtype1 147 7.4 (0.9)
subtype2 97 7.3 (1.1)
subtype3 59 7.6 (1.0)
subtype4 125 8.1 (1.0)
subtype5 56 7.2 (0.7)

Figure S81.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'GLEASON_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'PSA_RESULT_PREOP'

P value = 0.294 (Kruskal-Wallis (anova)), Q value = 0.38

Table S88.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

nPatients Mean (Std.Dev)
ALL 481 10.9 (11.7)
subtype1 145 10.0 (12.3)
subtype2 97 12.8 (15.3)
subtype3 59 10.1 (8.5)
subtype4 124 11.8 (11.0)
subtype5 56 8.3 (5.7)

Figure S82.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

'MIRSEQ CHIERARCHICAL' versus 'PSA_VALUE'

P value = 0.684 (Kruskal-Wallis (anova)), Q value = 0.76

Table S89.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 430 1.1 (4.1)
subtype1 136 0.9 (3.9)
subtype2 93 0.7 (3.5)
subtype3 53 1.4 (5.8)
subtype4 103 1.5 (4.4)
subtype5 45 0.9 (2.7)

Figure S83.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'PSA_VALUE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.708 (Fisher's exact test), Q value = 0.78

Table S90.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 146
subtype1 1 1 51
subtype2 0 3 30
subtype3 0 0 18
subtype4 0 0 9
subtype5 1 3 38

Figure S84.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S91.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 62 103 58 88
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.721 (logrank test), Q value = 0.78

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

nPatients nDeath Duration Range (Median), Month
ALL 311 6 0.8 - 165.2 (25.4)
subtype1 62 1 1.0 - 97.4 (22.0)
subtype2 103 3 0.8 - 165.2 (28.2)
subtype3 58 0 1.6 - 88.2 (28.4)
subtype4 88 2 1.1 - 101.8 (25.8)

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00962 (Kruskal-Wallis (anova)), Q value = 0.02

Table S93.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 304 60.9 (7.0)
subtype1 59 59.7 (7.4)
subtype2 102 62.7 (6.1)
subtype3 58 61.3 (6.8)
subtype4 85 59.3 (7.4)

Figure S86.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00767 (Fisher's exact test), Q value = 0.017

Table S94.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T2 T3 T4
ALL 119 184 5
subtype1 32 29 0
subtype2 26 74 3
subtype3 22 34 1
subtype4 39 47 1

Figure S87.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.296 (Fisher's exact test), Q value = 0.38

Table S95.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 222 47
subtype1 43 10
subtype2 72 18
subtype3 40 11
subtype4 67 8

Figure S88.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.151 (Fisher's exact test), Q value = 0.22

Table S96.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 8 303
subtype1 2 60
subtype2 5 98
subtype3 1 57
subtype4 0 88

Figure S89.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 8e-04 (Fisher's exact test), Q value = 0.0022

Table S97.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 194 93 2 11
subtype1 48 10 0 3
subtype2 52 45 0 5
subtype3 33 21 1 1
subtype4 61 17 1 2

Figure S90.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.344 (Kruskal-Wallis (anova)), Q value = 0.43

Table S98.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 254 0.5 (1.6)
subtype1 50 0.2 (0.4)
subtype2 85 0.5 (1.3)
subtype3 48 0.8 (2.4)
subtype4 71 0.4 (1.7)

Figure S91.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 3.49e-06 (Kruskal-Wallis (anova)), Q value = 3e-05

Table S99.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

nPatients Mean (Std.Dev)
ALL 311 7.6 (1.1)
subtype1 62 7.3 (0.9)
subtype2 103 8.0 (1.1)
subtype3 58 7.8 (1.3)
subtype4 88 7.3 (0.8)

Figure S92.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 1.42e-05 (Kruskal-Wallis (anova)), Q value = 7.9e-05

Table S100.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

nPatients Mean (Std.Dev)
ALL 311 3.7 (0.7)
subtype1 62 3.5 (0.6)
subtype2 103 4.0 (0.7)
subtype3 58 3.9 (0.8)
subtype4 88 3.5 (0.6)

Figure S93.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.0121 (Kruskal-Wallis (anova)), Q value = 0.025

Table S101.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

nPatients Mean (Std.Dev)
ALL 311 3.9 (0.7)
subtype1 62 3.8 (0.7)
subtype2 103 4.1 (0.7)
subtype3 58 3.9 (0.7)
subtype4 88 3.8 (0.6)

Figure S94.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE'

P value = 9.56e-07 (Kruskal-Wallis (anova)), Q value = 8.9e-06

Table S102.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

nPatients Mean (Std.Dev)
ALL 311 7.6 (1.0)
subtype1 62 7.3 (1.0)
subtype2 103 8.0 (1.0)
subtype3 58 7.9 (1.2)
subtype4 88 7.3 (0.8)

Figure S95.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

'MIRseq Mature CNMF subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.295 (Kruskal-Wallis (anova)), Q value = 0.38

Table S103.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

nPatients Mean (Std.Dev)
ALL 310 10.4 (11.0)
subtype1 62 11.0 (11.5)
subtype2 102 10.0 (9.8)
subtype3 58 13.3 (16.4)
subtype4 88 8.6 (6.2)

Figure S96.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

'MIRseq Mature CNMF subtypes' versus 'PSA_VALUE'

P value = 0.0734 (Kruskal-Wallis (anova)), Q value = 0.12

Table S104.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 276 0.9 (3.3)
subtype1 59 1.2 (3.5)
subtype2 90 0.4 (1.4)
subtype3 52 1.9 (6.1)
subtype4 75 0.5 (1.5)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.805 (Fisher's exact test), Q value = 0.83

Table S105.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 91
subtype1 0 0 27
subtype2 0 0 3
subtype3 0 0 7
subtype4 1 3 54

Figure S98.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S106.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 68 40 35 83 25 60
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.942 (logrank test), Q value = 0.95

Table S107.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 311 6 0.8 - 165.2 (25.4)
subtype1 68 1 1.2 - 97.4 (24.8)
subtype2 40 1 3.9 - 93.7 (26.2)
subtype3 35 1 2.0 - 111.4 (22.8)
subtype4 83 2 0.8 - 165.2 (27.2)
subtype5 25 0 1.0 - 68.3 (28.9)
subtype6 60 1 1.1 - 88.3 (26.0)

Figure S99.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0334 (Kruskal-Wallis (anova)), Q value = 0.06

Table S108.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 304 60.9 (7.0)
subtype1 65 59.7 (6.7)
subtype2 39 59.2 (7.0)
subtype3 35 61.7 (6.7)
subtype4 83 63.0 (6.6)
subtype5 25 60.3 (7.6)
subtype6 57 60.0 (7.2)

Figure S100.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 9.3e-05

Table S109.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T2 T3 T4
ALL 119 184 5
subtype1 35 31 1
subtype2 23 16 0
subtype3 6 29 0
subtype4 19 61 3
subtype5 8 15 1
subtype6 28 32 0

Figure S101.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 9.3e-05

Table S110.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 222 47
subtype1 45 8
subtype2 31 0
subtype3 18 16
subtype4 61 15
subtype5 19 5
subtype6 48 3

Figure S102.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.627 (Fisher's exact test), Q value = 0.71

Table S111.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA OTHER SUBTYPE PROSTATE ADENOCARCINOMA ACINAR TYPE
ALL 8 303
subtype1 3 65
subtype2 1 39
subtype3 1 34
subtype4 3 80
subtype5 0 25
subtype6 0 60

Figure S103.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.00043 (Fisher's exact test), Q value = 0.0013

Table S112.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 194 93 2 11
subtype1 52 10 0 4
subtype2 27 11 1 0
subtype3 16 17 0 2
subtype4 42 38 0 2
subtype5 17 7 0 1
subtype6 40 10 1 2

Figure S104.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 4.35e-07 (Kruskal-Wallis (anova)), Q value = 4.4e-06

Table S113.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 254 0.5 (1.6)
subtype1 45 0.2 (0.7)
subtype2 30 0.0 (0.0)
subtype3 31 2.0 (3.6)
subtype4 73 0.5 (1.4)
subtype5 24 0.2 (0.4)
subtype6 51 0.1 (0.3)

Figure S105.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

P value = 1.43e-10 (Kruskal-Wallis (anova)), Q value = 2.3e-09

Table S114.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

nPatients Mean (Std.Dev)
ALL 311 7.6 (1.1)
subtype1 68 7.5 (1.0)
subtype2 40 7.0 (1.2)
subtype3 35 8.3 (1.0)
subtype4 83 8.1 (1.1)
subtype5 25 7.3 (0.8)
subtype6 60 7.3 (0.8)

Figure S106.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GLEASON_SCORE_COMBINED'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

P value = 7.2e-09 (Kruskal-Wallis (anova)), Q value = 1e-07

Table S115.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

nPatients Mean (Std.Dev)
ALL 311 3.7 (0.7)
subtype1 68 3.7 (0.6)
subtype2 40 3.4 (0.7)
subtype3 35 4.1 (0.8)
subtype4 83 4.0 (0.7)
subtype5 25 3.4 (0.5)
subtype6 60 3.5 (0.5)

Figure S107.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'GLEASON_SCORE_PRIMARY'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

P value = 0.000405 (Kruskal-Wallis (anova)), Q value = 0.0013

Table S116.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

nPatients Mean (Std.Dev)
ALL 311 3.9 (0.7)
subtype1 68 3.8 (0.7)
subtype2 40 3.6 (0.7)
subtype3 35 4.1 (0.7)
subtype4 83 4.1 (0.7)
subtype5 25 3.9 (0.6)
subtype6 60 3.8 (0.6)

Figure S108.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'GLEASON_SCORE_SECONDARY'

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 1.27e-10 (Kruskal-Wallis (anova)), Q value = 2.3e-09

Table S117.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

nPatients Mean (Std.Dev)
ALL 311 7.6 (1.0)
subtype1 68 7.5 (1.0)
subtype2 40 7.1 (1.1)
subtype3 35 8.4 (0.8)
subtype4 83 8.1 (1.0)
subtype5 25 7.2 (0.8)
subtype6 60 7.3 (0.8)

Figure S109.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'GLEASON_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'PSA_RESULT_PREOP'

P value = 0.134 (Kruskal-Wallis (anova)), Q value = 0.2

Table S118.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

nPatients Mean (Std.Dev)
ALL 310 10.4 (11.0)
subtype1 68 8.1 (6.0)
subtype2 40 11.7 (14.8)
subtype3 35 13.4 (15.0)
subtype4 82 11.6 (13.5)
subtype5 25 11.8 (7.0)
subtype6 60 8.2 (5.3)

Figure S110.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'PSA_RESULT_PREOP'

'MIRseq Mature cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.363 (Kruskal-Wallis (anova)), Q value = 0.44

Table S119.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 276 0.9 (3.3)
subtype1 62 0.2 (0.5)
subtype2 39 0.4 (1.5)
subtype3 31 2.5 (6.2)
subtype4 71 1.3 (4.2)
subtype5 25 1.2 (3.8)
subtype6 48 0.3 (0.8)

Figure S111.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'PSA_VALUE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.804 (Fisher's exact test), Q value = 0.83

Table S120.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 91
subtype1 0 0 5
subtype2 0 0 11
subtype3 0 0 3
subtype4 0 0 8
subtype5 0 0 25
subtype6 1 3 39

Figure S112.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/PRAD-TP/15115159/PRAD-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PRAD-TP/15087329/PRAD-TP.merged_data.txt

  • Number of patients = 488

  • Number of clustering approaches = 8

  • 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

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)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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