Correlation between aggregated molecular cancer subtypes and selected clinical features
Prostate Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1MP52QP
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1MP52QP
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 11 clinical features across 498 patients, 55 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 4 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',  'RADIATION_THERAPY',  'RESIDUAL_TUMOR',  'NUMBER_OF_LYMPH_NODES',  'GLEASON_SCORE', and 'PSA_VALUE'.

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'NUMBER_OF_LYMPH_NODES', and 'GLEASON_SCORE'.

  • CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'RESIDUAL_TUMOR',  'NUMBER_OF_LYMPH_NODES', and 'GLEASON_SCORE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'RESIDUAL_TUMOR', and 'GLEASON_SCORE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'RADIATION_THERAPY',  'RESIDUAL_TUMOR',  'NUMBER_OF_LYMPH_NODES', and 'GLEASON_SCORE'.

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

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'RESIDUAL_TUMOR',  'NUMBER_OF_LYMPH_NODES', and 'GLEASON_SCORE'.

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

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE',  'RESIDUAL_TUMOR', and 'GLEASON_SCORE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'RESIDUAL_TUMOR',  'NUMBER_OF_LYMPH_NODES', and 'GLEASON_SCORE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 55 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.778
(0.831) 		0.64
(0.711) 		0.615
(0.69) 		0.674
(0.742) 		0.585
(0.671) 		0.905
(0.916) 		0.83
(0.869) 		0.973
(0.973) 		0.557
(0.645) 		0.865
(0.889)
YEARS TO BIRTH Kruskal-Wallis (anova) 3.06e-06
(3.2e-05) 		0.00013
(0.00057) 		0.0106
(0.0277) 		0.00234
(0.00782) 		0.00103
(0.00367) 		0.0113
(0.0289) 		0.438
(0.541) 		0.0228
(0.0544) 		0.0581
(0.114) 		0.196
(0.295)
PATHOLOGY T STAGE Fisher's exact test 1e-05
(7.86e-05) 		1e-05
(7.86e-05) 		4e-05
(0.00021) 		0.00405
(0.012) 		5e-05
(0.000239) 		0.00212
(0.00729) 		0.00025
(0.00106) 		3e-05
(0.000174) 		0.00444
(0.0129) 		0.00322
(0.0101)
PATHOLOGY N STAGE Fisher's exact test 0.0001
(0.000458) 		0.0649
(0.121) 		0.00371
(0.0113) 		0.0906
(0.155) 		0.025
(0.0552) 		0.0494
(0.0987) 		0.00087
(0.00319) 		0.00056
(0.00212) 		0.289
(0.383) 		0.0355
(0.0737)
RADIATION THERAPY Fisher's exact test 0.00692
(0.019) 		0.481
(0.588) 		0.128
(0.204) 		0.163
(0.252) 		0.0365
(0.0743) 		0.0648
(0.121) 		0.299
(0.392) 		0.0868
(0.153) 		0.367
(0.47) 		0.492
(0.594)
HISTOLOGICAL TYPE Fisher's exact test 0.527
(0.623) 		0.36
(0.465) 		0.0795
(0.146) 		0.0877
(0.153) 		0.247
(0.34) 		0.244
(0.34) 		0.196
(0.295) 		0.803
(0.85) 		0.0925
(0.155) 		0.271
(0.364)
RESIDUAL TUMOR Fisher's exact test 3e-05
(0.000174) 		0.142
(0.223) 		5e-05
(0.000239) 		0.0181
(0.0444) 		0.00674
(0.019) 		0.0152
(0.0379) 		1e-05
(7.86e-05) 		3e-05
(0.000174) 		3e-05
(0.000174) 		0.00037
(0.00151)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 2.36e-05
(0.000173) 		0.0251
(0.0552) 		0.00262
(0.00849) 		0.0931
(0.155) 		0.0233
(0.0544) 		0.0285
(0.0603) 		0.000391
(0.00154) 		3.22e-05
(0.000177) 		0.198
(0.295) 		0.0102
(0.0275)
GLEASON SCORE Kruskal-Wallis (anova) 1.24e-18
(1.37e-16) 		1.07e-06
(1.46e-05) 		6.36e-08
(1.17e-06) 		3.2e-06
(3.2e-05) 		2.81e-09
(1.03e-07) 		2.37e-06
(2.9e-05) 		4.39e-09
(1.21e-07) 		5.41e-12
(2.97e-10) 		5.77e-07
(9.07e-06) 		3.39e-08
(7.46e-07)
PSA VALUE Kruskal-Wallis (anova) 0.0276
(0.0595) 		0.113
(0.183) 		0.594
(0.674) 		0.0631
(0.121) 		0.436
(0.541) 		0.54
(0.632) 		0.0831
(0.15) 		0.751
(0.809) 		0.222
(0.317) 		0.395
(0.5)
RACE Fisher's exact test 0.262
(0.356) 		0.514
(0.614) 		0.0953
(0.156) 		0.246
(0.34) 		0.213
(0.308) 		0.0237
(0.0544) 		0.208
(0.306) 		0.707
(0.77) 		0.86
(0.889) 		0.908
(0.916)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 108 91 98 195
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.778 (logrank test), Q value = 0.83

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

nPatients nDeath Duration Range (Median), Month
ALL 492 9 0.8 - 165.2 (30.6)
subtype1 108 1 1.0 - 140.2 (32.8)
subtype2 91 1 3.5 - 93.7 (24.0)
subtype3 98 2 1.0 - 102.9 (29.7)
subtype4 195 5 0.8 - 165.2 (33.0)

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 = 3.06e-06 (Kruskal-Wallis (anova)), Q value = 3.2e-05

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

nPatients Mean (Std.Dev)
ALL 481 61.0 (6.8)
subtype1 105 59.4 (6.8)
subtype2 89 59.1 (7.0)
subtype3 97 60.6 (7.4)
subtype4 190 62.9 (5.8)

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 = 7.9e-05

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

nPatients T2 T3 T4
ALL 187 288 10
subtype1 47 58 3
subtype2 55 32 1
subtype3 46 51 0
subtype4 39 147 6

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-04 (Fisher's exact test), Q value = 0.00046

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

nPatients 0 1
ALL 343 77
subtype1 78 13
subtype2 64 4
subtype3 73 11
subtype4 128 49

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 'RADIATION_THERAPY'

P value = 0.00692 (Fisher's exact test), Q value = 0.019

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

nPatients NO YES
ALL 390 59
subtype1 91 11
subtype2 82 4
subtype3 80 11
subtype4 137 33

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.527 (Fisher's exact test), Q value = 0.62

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

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 478 14
subtype1 103 5
subtype2 90 1
subtype3 96 2
subtype4 189 6

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 3e-05 (Fisher's exact test), Q value = 0.00017

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

nPatients R0 R1 R2 RX
ALL 313 144 5 15
subtype1 81 19 3 3
subtype2 68 16 1 2
subtype3 64 26 0 4
subtype4 100 83 1 6

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 2.36e-05 (Kruskal-Wallis (anova)), Q value = 0.00017

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

nPatients Mean (Std.Dev)
ALL 402 0.4 (1.4)
subtype1 85 0.4 (1.7)
subtype2 65 0.1 (0.2)
subtype3 84 0.2 (0.5)
subtype4 168 0.7 (1.6)

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE'

P value = 1.24e-18 (Kruskal-Wallis (anova)), Q value = 1.4e-16

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

nPatients Mean (Std.Dev)
ALL 492 7.6 (1.0)
subtype1 108 7.4 (1.0)
subtype2 91 7.1 (0.8)
subtype3 98 7.3 (0.8)
subtype4 195 8.1 (1.0)

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

'Copy Number Ratio CNMF subtypes' versus 'PSA_VALUE'

P value = 0.0276 (Kruskal-Wallis (anova)), Q value = 0.059

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

nPatients Mean (Std.Dev)
ALL 435 1.8 (15.9)
subtype1 96 0.5 (2.1)
subtype2 85 0.3 (1.5)
subtype3 89 0.6 (2.3)
subtype4 165 3.8 (25.7)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.262 (Fisher's exact test), Q value = 0.36

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 146
subtype1 2 1 36
subtype2 0 0 29
subtype3 0 3 33
subtype4 0 3 48

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 161 140 126 71
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.64 (logrank test), Q value = 0.71

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

nPatients nDeath Duration Range (Median), Month
ALL 498 10 0.8 - 165.2 (30.4)
subtype1 161 1 1.0 - 102.9 (27.9)
subtype2 140 4 1.0 - 122.2 (35.9)
subtype3 126 4 1.0 - 140.2 (30.4)
subtype4 71 1 0.8 - 165.2 (28.8)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00013 (Kruskal-Wallis (anova)), Q value = 0.00057

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

nPatients Mean (Std.Dev)
ALL 487 61.0 (6.8)
subtype1 157 59.8 (7.0)
subtype2 137 61.8 (7.0)
subtype3 125 60.2 (6.4)
subtype4 68 63.8 (5.9)

Figure S13.  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-05 (Fisher's exact test), Q value = 7.9e-05

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

nPatients T2 T3 T4
ALL 188 293 10
subtype1 79 77 1
subtype2 57 81 1
subtype3 43 81 2
subtype4 9 54 6

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0649 (Fisher's exact test), Q value = 0.12

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 346 79
subtype1 115 19
subtype2 101 19
subtype3 84 22
subtype4 46 19

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.481 (Fisher's exact test), Q value = 0.59

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 395 59
subtype1 131 21
subtype2 116 12
subtype3 97 16
subtype4 51 10

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.36 (Fisher's exact test), Q value = 0.47

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 483 15
subtype1 157 4
subtype2 138 2
subtype3 120 6
subtype4 68 3

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

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.142 (Fisher's exact test), Q value = 0.22

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

nPatients R0 R1 R2 RX
ALL 316 147 5 15
subtype1 113 38 1 4
subtype2 84 46 1 5
subtype3 84 33 2 4
subtype4 35 30 1 2

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0251 (Kruskal-Wallis (anova)), Q value = 0.055

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 407 0.4 (1.4)
subtype1 127 0.2 (0.8)
subtype2 115 0.3 (1.2)
subtype3 101 0.7 (2.0)
subtype4 64 0.7 (1.3)

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

'METHLYATION CNMF' versus 'GLEASON_SCORE'

P value = 1.07e-06 (Kruskal-Wallis (anova)), Q value = 1.5e-05

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

nPatients Mean (Std.Dev)
ALL 498 7.6 (1.0)
subtype1 161 7.4 (0.9)
subtype2 140 7.6 (1.0)
subtype3 126 7.6 (1.0)
subtype4 71 8.2 (1.0)

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

'METHLYATION CNMF' versus 'PSA_VALUE'

P value = 0.113 (Kruskal-Wallis (anova)), Q value = 0.18

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 441 1.7 (15.8)
subtype1 147 0.7 (3.2)
subtype2 121 0.7 (2.5)
subtype3 115 1.5 (5.7)
subtype4 58 6.9 (42.5)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.514 (Fisher's exact test), Q value = 0.61

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 147
subtype1 1 1 52
subtype2 0 2 35
subtype3 0 3 42
subtype4 1 1 18

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3 4 5 6
Number of samples 106 51 45 53 58 39
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.615 (logrank test), Q value = 0.69

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

nPatients nDeath Duration Range (Median), Month
ALL 352 7 0.9 - 165.2 (32.4)
subtype1 106 1 0.9 - 109.2 (32.5)
subtype2 51 1 1.6 - 101.8 (36.1)
subtype3 45 0 1.0 - 140.2 (29.0)
subtype4 53 4 0.9 - 165.2 (27.2)
subtype5 58 1 1.0 - 80.5 (29.0)
subtype6 39 0 2.0 - 113.1 (35.1)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0106 (Kruskal-Wallis (anova)), Q value = 0.028

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

nPatients Mean (Std.Dev)
ALL 349 61.4 (6.6)
subtype1 105 60.7 (6.7)
subtype2 50 61.0 (7.4)
subtype3 44 63.8 (5.4)
subtype4 53 59.6 (6.7)
subtype5 58 62.2 (6.5)
subtype6 39 62.9 (5.7)

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00021

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

nPatients T2 T3 T4
ALL 110 227 10
subtype1 46 56 2
subtype2 11 40 0
subtype3 19 23 1
subtype4 18 34 1
subtype5 13 43 1
subtype6 3 31 5

Figure S25.  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 = 0.00371 (Fisher's exact test), Q value = 0.011

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 246 63
subtype1 80 9
subtype2 30 13
subtype3 36 8
subtype4 40 7
subtype5 38 12
subtype6 22 14

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.128 (Fisher's exact test), Q value = 0.2

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

nPatients NO YES
ALL 283 41
subtype1 94 7
subtype2 39 9
subtype3 37 3
subtype4 41 7
subtype5 40 10
subtype6 32 5

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0795 (Fisher's exact test), Q value = 0.15

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

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 341 11
subtype1 105 1
subtype2 49 2
subtype3 43 2
subtype4 49 4
subtype5 58 0
subtype6 37 2

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

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 5e-05 (Fisher's exact test), Q value = 0.00024

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

nPatients R0 R1 R2 RX
ALL 221 108 3 10
subtype1 81 20 0 0
subtype2 21 21 2 2
subtype3 35 9 0 1
subtype4 34 16 1 2
subtype5 29 26 0 3
subtype6 21 16 0 2

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

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00262 (Kruskal-Wallis (anova)), Q value = 0.0085

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

nPatients Mean (Std.Dev)
ALL 300 0.5 (1.5)
subtype1 88 0.2 (0.7)
subtype2 41 0.8 (2.1)
subtype3 44 0.3 (0.8)
subtype4 45 0.6 (2.4)
subtype5 47 0.5 (1.1)
subtype6 35 1.1 (2.0)

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

'RPPA CNMF subtypes' versus 'GLEASON_SCORE'

P value = 6.36e-08 (Kruskal-Wallis (anova)), Q value = 1.2e-06

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

nPatients Mean (Std.Dev)
ALL 352 7.6 (1.0)
subtype1 106 7.3 (0.9)
subtype2 51 7.8 (1.1)
subtype3 45 7.4 (0.9)
subtype4 53 7.6 (0.9)
subtype5 58 7.9 (1.1)
subtype6 39 8.4 (0.9)

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

'RPPA CNMF subtypes' versus 'PSA_VALUE'

P value = 0.594 (Kruskal-Wallis (anova)), Q value = 0.67

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 317 1.2 (4.6)
subtype1 101 0.6 (2.5)
subtype2 41 0.9 (2.6)
subtype3 45 1.2 (4.9)
subtype4 46 2.0 (6.4)
subtype5 49 1.3 (3.6)
subtype6 35 2.5 (7.7)

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0953 (Fisher's exact test), Q value = 0.16

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 121
subtype1 1 1 41
subtype2 1 1 13
subtype3 0 0 25
subtype4 0 1 17
subtype5 0 3 11
subtype6 0 0 14

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 75 114 93 70
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.674 (logrank test), Q value = 0.74

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

nPatients nDeath Duration Range (Median), Month
ALL 352 7 0.9 - 165.2 (32.4)
subtype1 75 1 0.9 - 109.2 (38.0)
subtype2 114 3 1.0 - 140.2 (28.7)
subtype3 93 1 1.6 - 165.2 (33.1)
subtype4 70 2 0.9 - 113.1 (31.6)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00234 (Kruskal-Wallis (anova)), Q value = 0.0078

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

nPatients Mean (Std.Dev)
ALL 349 61.4 (6.6)
subtype1 75 60.6 (7.2)
subtype2 112 62.8 (6.4)
subtype3 92 59.7 (6.5)
subtype4 70 62.5 (5.8)

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00405 (Fisher's exact test), Q value = 0.012

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

nPatients T2 T3 T4
ALL 110 227 10
subtype1 34 38 1
subtype2 38 73 1
subtype3 24 66 3
subtype4 14 50 5

Figure S36.  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 = 0.0906 (Fisher's exact test), Q value = 0.16

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 246 63
subtype1 55 6
subtype2 79 26
subtype3 62 15
subtype4 50 16

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.163 (Fisher's exact test), Q value = 0.25

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

nPatients NO YES
ALL 283 41
subtype1 70 5
subtype2 85 16
subtype3 76 9
subtype4 52 11

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0877 (Fisher's exact test), Q value = 0.15

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

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 341 11
subtype1 73 2
subtype2 109 5
subtype3 93 0
subtype4 66 4

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

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0181 (Fisher's exact test), Q value = 0.044

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

nPatients R0 R1 R2 RX
ALL 221 108 3 10
subtype1 56 15 0 0
subtype2 75 32 2 4
subtype3 56 29 1 3
subtype4 34 32 0 3

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

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0931 (Kruskal-Wallis (anova)), Q value = 0.16

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

nPatients Mean (Std.Dev)
ALL 300 0.5 (1.5)
subtype1 60 0.2 (0.8)
subtype2 103 0.5 (1.2)
subtype3 75 0.5 (2.0)
subtype4 62 0.8 (1.9)

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

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 3.2e-06 (Kruskal-Wallis (anova)), Q value = 3.2e-05

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

nPatients Mean (Std.Dev)
ALL 352 7.6 (1.0)
subtype1 75 7.3 (0.9)
subtype2 114 7.6 (1.0)
subtype3 93 7.6 (0.9)
subtype4 70 8.2 (1.0)

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

'RPPA cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.0631 (Kruskal-Wallis (anova)), Q value = 0.12

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 317 1.2 (4.6)
subtype1 72 0.4 (2.3)
subtype2 104 1.6 (5.6)
subtype3 81 0.6 (1.7)
subtype4 60 2.5 (6.6)

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.246 (Fisher's exact test), Q value = 0.34

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 121
subtype1 1 1 27
subtype2 0 1 47
subtype3 1 1 27
subtype4 0 3 20

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 96 142 151 108
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.585 (logrank test), Q value = 0.67

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

nPatients nDeath Duration Range (Median), Month
ALL 497 10 0.8 - 165.2 (30.5)
subtype1 96 0 0.9 - 109.2 (30.8)
subtype2 142 4 1.0 - 165.2 (31.6)
subtype3 151 4 0.8 - 140.2 (28.7)
subtype4 108 2 1.0 - 102.9 (29.2)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00103 (Kruskal-Wallis (anova)), Q value = 0.0037

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

nPatients Mean (Std.Dev)
ALL 486 61.0 (6.8)
subtype1 94 59.1 (7.1)
subtype2 140 60.5 (6.6)
subtype3 148 62.8 (6.6)
subtype4 104 60.9 (6.5)

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00024

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

nPatients T2 T3 T4
ALL 187 293 10
subtype1 53 39 2
subtype2 40 99 3
subtype3 45 100 4
subtype4 49 55 1

Figure S47.  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 = 0.025 (Fisher's exact test), Q value = 0.055

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 345 79
subtype1 68 5
subtype2 98 28
subtype3 106 27
subtype4 73 19

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0365 (Fisher's exact test), Q value = 0.074

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 394 59
subtype1 86 5
subtype2 109 22
subtype3 117 15
subtype4 82 17

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.247 (Fisher's exact test), Q value = 0.34

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

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 482 15
subtype1 94 2
subtype2 138 4
subtype3 143 8
subtype4 107 1

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

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00674 (Fisher's exact test), Q value = 0.019

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

nPatients R0 R1 R2 RX
ALL 315 147 5 15
subtype1 73 21 1 0
subtype2 93 36 3 6
subtype3 81 60 1 4
subtype4 68 30 0 5

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

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0233 (Kruskal-Wallis (anova)), Q value = 0.054

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

nPatients Mean (Std.Dev)
ALL 406 0.4 (1.4)
subtype1 70 0.1 (0.3)
subtype2 121 0.6 (1.8)
subtype3 127 0.6 (1.6)
subtype4 88 0.3 (0.7)

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE'

P value = 2.81e-09 (Kruskal-Wallis (anova)), Q value = 1e-07

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

nPatients Mean (Std.Dev)
ALL 497 7.6 (1.0)
subtype1 96 7.1 (0.8)
subtype2 142 7.7 (1.0)
subtype3 151 7.9 (1.1)
subtype4 108 7.5 (0.9)

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

'RNAseq CNMF subtypes' versus 'PSA_VALUE'

P value = 0.436 (Kruskal-Wallis (anova)), Q value = 0.54

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 440 1.7 (15.9)
subtype1 88 0.3 (1.1)
subtype2 131 1.4 (5.3)
subtype3 124 3.7 (29.1)
subtype4 97 1.1 (4.0)

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.213 (Fisher's exact test), Q value = 0.31

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 147
subtype1 0 2 43
subtype2 0 2 58
subtype3 0 2 20
subtype4 2 1 26

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 173 208 116
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.905 (logrank test), Q value = 0.92

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

nPatients nDeath Duration Range (Median), Month
ALL 497 10 0.8 - 165.2 (30.5)
subtype1 173 3 1.0 - 122.2 (30.4)
subtype2 208 4 0.8 - 114.4 (30.7)
subtype3 116 3 1.0 - 165.2 (30.6)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0113 (Kruskal-Wallis (anova)), Q value = 0.029

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

nPatients Mean (Std.Dev)
ALL 486 61.0 (6.8)
subtype1 168 60.1 (6.8)
subtype2 204 62.2 (6.8)
subtype3 114 60.3 (6.4)

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00212 (Fisher's exact test), Q value = 0.0073

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

nPatients T2 T3 T4
ALL 187 293 10
subtype1 84 84 2
subtype2 70 128 6
subtype3 33 81 2

Figure S58.  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 = 0.0494 (Fisher's exact test), Q value = 0.099

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 345 79
subtype1 127 18
subtype2 139 37
subtype3 79 24

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0648 (Fisher's exact test), Q value = 0.12

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

nPatients NO YES
ALL 394 59
subtype1 144 17
subtype2 166 21
subtype3 84 21

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.244 (Fisher's exact test), Q value = 0.34

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 482 15
subtype1 170 3
subtype2 202 6
subtype3 110 6

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

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0152 (Fisher's exact test), Q value = 0.038

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

nPatients R0 R1 R2 RX
ALL 315 147 5 15
subtype1 122 38 0 6
subtype2 125 72 3 3
subtype3 68 37 2 6

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

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0285 (Kruskal-Wallis (anova)), Q value = 0.06

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

nPatients Mean (Std.Dev)
ALL 406 0.4 (1.4)
subtype1 137 0.2 (0.5)
subtype2 171 0.5 (1.4)
subtype3 98 0.7 (1.9)

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 2.37e-06 (Kruskal-Wallis (anova)), Q value = 2.9e-05

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

nPatients Mean (Std.Dev)
ALL 497 7.6 (1.0)
subtype1 173 7.3 (0.9)
subtype2 208 7.8 (1.1)
subtype3 116 7.7 (1.0)

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

'RNAseq cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.54 (Kruskal-Wallis (anova)), Q value = 0.63

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 440 1.7 (15.9)
subtype1 156 0.8 (3.2)
subtype2 180 2.6 (24.2)
subtype3 104 1.7 (5.9)

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0237 (Fisher's exact test), Q value = 0.054

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: '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 S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #7: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 213 71 124 86
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.83 (logrank test), Q value = 0.87

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

nPatients nDeath Duration Range (Median), Month
ALL 494 10 0.8 - 165.2 (30.4)
subtype1 213 6 0.8 - 165.2 (28.7)
subtype2 71 1 1.6 - 115.1 (36.8)
subtype3 124 2 1.0 - 83.9 (27.3)
subtype4 86 1 1.0 - 119.4 (32.4)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.438 (Kruskal-Wallis (anova)), Q value = 0.54

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

nPatients Mean (Std.Dev)
ALL 483 61.0 (6.8)
subtype1 208 61.6 (6.5)
subtype2 71 61.0 (6.9)
subtype3 121 60.2 (7.3)
subtype4 83 60.6 (6.7)

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00025 (Fisher's exact test), Q value = 0.0011

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

nPatients T2 T3 T4
ALL 187 291 9
subtype1 58 148 4
subtype2 35 32 3
subtype3 50 70 1
subtype4 44 41 1

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

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00087 (Fisher's exact test), Q value = 0.0032

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 344 78
subtype1 137 50
subtype2 49 8
subtype3 94 15
subtype4 64 5

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.299 (Fisher's exact test), Q value = 0.39

Table S78.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 392 59
subtype1 160 29
subtype2 60 7
subtype3 98 17
subtype4 74 6

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.196 (Fisher's exact test), Q value = 0.29

Table S79.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 479 15
subtype1 202 11
subtype2 70 1
subtype3 122 2
subtype4 85 1

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 7.9e-05

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

nPatients R0 R1 R2 RX
ALL 313 146 5 15
subtype1 114 83 1 10
subtype2 39 28 2 0
subtype3 92 25 0 3
subtype4 68 10 2 2

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

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.000391 (Kruskal-Wallis (anova)), Q value = 0.0015

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 404 0.4 (1.4)
subtype1 178 0.8 (1.9)
subtype2 56 0.3 (1.0)
subtype3 101 0.2 (0.4)
subtype4 69 0.1 (0.4)

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

'MIRSEQ CNMF' versus 'GLEASON_SCORE'

P value = 4.39e-09 (Kruskal-Wallis (anova)), Q value = 1.2e-07

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

nPatients Mean (Std.Dev)
ALL 494 7.6 (1.0)
subtype1 213 7.9 (1.0)
subtype2 71 7.4 (1.2)
subtype3 124 7.4 (0.8)
subtype4 86 7.2 (0.8)

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

'MIRSEQ CNMF' versus 'PSA_VALUE'

P value = 0.0831 (Kruskal-Wallis (anova)), Q value = 0.15

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 439 1.8 (15.9)
subtype1 185 3.2 (24.2)
subtype2 68 0.5 (1.7)
subtype3 114 1.0 (3.8)
subtype4 72 0.6 (1.8)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.208 (Fisher's exact test), Q value = 0.31

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 7 146
subtype1 0 0 23
subtype2 0 2 17
subtype3 1 0 45
subtype4 1 5 61

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

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels 1 2 3 4 5
Number of samples 148 98 59 133 56
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.973 (logrank test), Q value = 0.97

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

nPatients nDeath Duration Range (Median), Month
ALL 494 10 0.8 - 165.2 (30.4)
subtype1 148 2 1.0 - 141.2 (28.7)
subtype2 98 2 3.7 - 115.1 (38.2)
subtype3 59 2 1.0 - 140.2 (24.5)
subtype4 133 3 0.8 - 165.2 (29.8)
subtype5 56 1 1.0 - 119.4 (31.6)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.0228 (Kruskal-Wallis (anova)), Q value = 0.054

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

nPatients Mean (Std.Dev)
ALL 483 61.0 (6.8)
subtype1 145 60.5 (6.6)
subtype2 95 60.7 (6.7)
subtype3 59 59.6 (6.4)
subtype4 130 62.7 (6.7)
subtype5 54 60.4 (7.6)

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00017

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

nPatients T2 T3 T4
ALL 187 291 9
subtype1 64 81 0
subtype2 45 48 4
subtype3 21 37 1
subtype4 27 99 4
subtype5 30 26 0

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.00056 (Fisher's exact test), Q value = 0.0021

Table S89.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 344 78
subtype1 111 18
subtype2 70 9
subtype3 40 12
subtype4 82 36
subtype5 41 3

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0868 (Fisher's exact test), Q value = 0.15

Table S90.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 392 59
subtype1 119 14
subtype2 83 7
subtype3 43 10
subtype4 101 23
subtype5 46 5

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.803 (Fisher's exact test), Q value = 0.85

Table S91.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 479 15
subtype1 144 4
subtype2 96 2
subtype3 56 3
subtype4 128 5
subtype5 55 1

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

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 3e-05 (Fisher's exact test), Q value = 0.00017

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

nPatients R0 R1 R2 RX
ALL 313 146 5 15
subtype1 109 31 0 6
subtype2 57 36 1 2
subtype3 41 14 0 2
subtype4 65 59 2 3
subtype5 41 6 2 2

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 3.22e-05 (Kruskal-Wallis (anova)), Q value = 0.00018

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 404 0.4 (1.4)
subtype1 122 0.2 (0.8)
subtype2 77 0.2 (0.8)
subtype3 50 0.3 (0.7)
subtype4 111 1.0 (2.3)
subtype5 44 0.1 (0.4)

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

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE'

P value = 5.41e-12 (Kruskal-Wallis (anova)), Q value = 3e-10

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

nPatients Mean (Std.Dev)
ALL 494 7.6 (1.0)
subtype1 148 7.4 (0.9)
subtype2 98 7.3 (1.1)
subtype3 59 7.6 (1.0)
subtype4 133 8.2 (1.0)
subtype5 56 7.2 (0.7)

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

'MIRSEQ CHIERARCHICAL' versus 'PSA_VALUE'

P value = 0.751 (Kruskal-Wallis (anova)), Q value = 0.81

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 439 1.8 (15.9)
subtype1 137 0.9 (3.9)
subtype2 94 0.7 (3.5)
subtype3 53 1.4 (5.8)
subtype4 110 4.2 (30.9)
subtype5 45 0.9 (2.7)

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.707 (Fisher's exact test), Q value = 0.77

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: '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 S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 65 93 78 84
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.557 (logrank test), Q value = 0.64

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

nPatients nDeath Duration Range (Median), Month
ALL 320 7 0.8 - 165.2 (28.7)
subtype1 65 1 1.0 - 102.9 (23.9)
subtype2 93 4 0.8 - 165.2 (28.2)
subtype3 78 0 0.9 - 109.2 (28.7)
subtype4 84 2 3.8 - 94.4 (31.5)

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0581 (Kruskal-Wallis (anova)), Q value = 0.11

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

nPatients Mean (Std.Dev)
ALL 313 61.0 (7.0)
subtype1 62 59.7 (7.5)
subtype2 92 62.4 (6.3)
subtype3 77 61.8 (6.8)
subtype4 82 59.8 (7.2)

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00444 (Fisher's exact test), Q value = 0.013

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

nPatients T2 T3 T4
ALL 118 191 5
subtype1 35 28 0
subtype2 23 67 2
subtype3 26 49 1
subtype4 34 47 2

Figure S91.  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 = 0.289 (Fisher's exact test), Q value = 0.38

Table S101.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 227 49
subtype1 47 8
subtype2 65 16
subtype3 51 16
subtype4 64 9

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.367 (Fisher's exact test), Q value = 0.47

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

nPatients NO YES
ALL 252 44
subtype1 52 6
subtype2 70 13
subtype3 58 15
subtype4 72 10

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0925 (Fisher's exact test), Q value = 0.16

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

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 312 8
subtype1 63 2
subtype2 88 5
subtype3 77 1
subtype4 84 0

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

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 3e-05 (Fisher's exact test), Q value = 0.00017

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

nPatients R0 R1 R2 RX
ALL 196 99 2 11
subtype1 52 9 0 3
subtype2 47 41 0 4
subtype3 38 32 2 2
subtype4 59 17 0 2

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

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.198 (Kruskal-Wallis (anova)), Q value = 0.29

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

nPatients Mean (Std.Dev)
ALL 261 0.5 (1.6)
subtype1 51 0.2 (0.4)
subtype2 76 0.5 (1.2)
subtype3 63 0.8 (2.5)
subtype4 71 0.4 (1.3)

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

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE'

P value = 5.77e-07 (Kruskal-Wallis (anova)), Q value = 9.1e-06

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

nPatients Mean (Std.Dev)
ALL 320 7.7 (1.0)
subtype1 65 7.2 (0.9)
subtype2 93 8.0 (1.0)
subtype3 78 8.0 (1.1)
subtype4 84 7.4 (0.9)

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

'MIRseq Mature CNMF subtypes' versus 'PSA_VALUE'

P value = 0.222 (Kruskal-Wallis (anova)), Q value = 0.32

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 285 0.8 (3.3)
subtype1 62 1.1 (3.4)
subtype2 80 0.2 (0.6)
subtype3 70 1.7 (5.5)
subtype4 73 0.6 (1.6)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.86 (Fisher's exact test), Q value = 0.89

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 91
subtype1 0 0 27
subtype2 0 0 3
subtype3 0 0 13
subtype4 1 3 48

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

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 75 88 69 23 65
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.865 (logrank test), Q value = 0.89

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

nPatients nDeath Duration Range (Median), Month
ALL 320 7 0.8 - 165.2 (28.7)
subtype1 75 1 0.8 - 165.2 (25.1)
subtype2 88 3 1.8 - 122.2 (29.7)
subtype3 69 2 0.9 - 115.1 (28.7)
subtype4 23 0 1.0 - 68.3 (44.0)
subtype5 65 1 1.6 - 88.3 (30.8)

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

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.196 (Kruskal-Wallis (anova)), Q value = 0.29

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

nPatients Mean (Std.Dev)
ALL 313 61.0 (7.0)
subtype1 71 60.4 (6.7)
subtype2 88 62.4 (6.7)
subtype3 67 61.5 (6.9)
subtype4 23 60.4 (7.8)
subtype5 64 59.5 (7.3)

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00322 (Fisher's exact test), Q value = 0.01

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

nPatients T2 T3 T4
ALL 118 191 5
subtype1 36 37 1
subtype2 20 64 3
subtype3 24 43 0
subtype4 6 14 1
subtype5 32 33 0

Figure S102.  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 = 0.0355 (Fisher's exact test), Q value = 0.074

Table S113.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 227 49
subtype1 52 8
subtype2 59 21
subtype3 46 11
subtype4 17 5
subtype5 53 4

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.492 (Fisher's exact test), Q value = 0.59

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

nPatients NO YES
ALL 252 44
subtype1 55 11
subtype2 67 15
subtype3 55 10
subtype4 18 3
subtype5 57 5

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.271 (Fisher's exact test), Q value = 0.36

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

nPatients PROSTATE ADENOCARCINOMA ACINAR TYPE PROSTATE ADENOCARCINOMA, OTHER SUBTYPE
ALL 312 8
subtype1 73 2
subtype2 83 5
subtype3 68 1
subtype4 23 0
subtype5 65 0

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

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00037 (Fisher's exact test), Q value = 0.0015

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

nPatients R0 R1 R2 RX
ALL 196 99 2 11
subtype1 55 16 0 3
subtype2 43 39 0 4
subtype3 35 29 2 1
subtype4 16 6 0 1
subtype5 47 9 0 2

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

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0102 (Kruskal-Wallis (anova)), Q value = 0.027

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

nPatients Mean (Std.Dev)
ALL 261 0.5 (1.6)
subtype1 54 0.1 (0.4)
subtype2 74 0.9 (2.1)
subtype3 54 0.7 (2.3)
subtype4 22 0.2 (0.4)
subtype5 57 0.1 (0.3)

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

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE'

P value = 3.39e-08 (Kruskal-Wallis (anova)), Q value = 7.5e-07

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

nPatients Mean (Std.Dev)
ALL 320 7.7 (1.0)
subtype1 75 7.5 (1.0)
subtype2 88 8.2 (1.0)
subtype3 69 7.7 (1.2)
subtype4 23 7.2 (0.7)
subtype5 65 7.2 (0.7)

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

'MIRseq Mature cHierClus subtypes' versus 'PSA_VALUE'

P value = 0.395 (Kruskal-Wallis (anova)), Q value = 0.5

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'PSA_VALUE'

nPatients Mean (Std.Dev)
ALL 285 0.8 (3.3)
subtype1 67 0.4 (1.4)
subtype2 77 0.8 (2.5)
subtype3 63 1.7 (5.6)
subtype4 23 1.3 (4.0)
subtype5 55 0.3 (0.8)

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.908 (Fisher's exact test), Q value = 0.92

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 91
subtype1 0 0 6
subtype2 0 0 6
subtype3 0 0 12
subtype4 0 0 23
subtype5 1 3 44

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

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/PRAD-TP/22553796/PRAD-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PRAD-TP/22506989/PRAD-TP.merged_data.txt

  • Number of patients = 498

  • Number of clustering approaches = 10

  • Number of selected clinical features = 11

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

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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