Correlation between aggregated molecular cancer subtypes and selected clinical features
Prostate Adenocarcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C128064Q
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C128064Q
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 14 clinical features across 185 patients, 18 significant findings detected with P value < 0.05 and Q value < 0.25.

- 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'GLEASON_SCORE_COMBINED', 'GLEASON_SCORE_PRIMARY', and 'GLEASON_SCORE'.

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', 'GLEASON_SCORE_PRIMARY', and 'PSA_RESULT_PREOP'.

- CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'GLEASON_SCORE_PRIMARY', 'GLEASON_SCORE', and 'DAYS_TO_PREOP_PSA'.

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'GLEASON_SCORE_PRIMARY', 'GLEASON_SCORE', and 'DAYS_TO_PREOP_PSA'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GLEASON_SCORE_PRIMARY'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GLEASON_SCORE_PRIMARY'.

- 5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PSA_VALUE'.

- 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PSA_VALUE'.

- 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.

- 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 18 significant findings detected.

Clinical Features | Statistical Tests | Copy Number Ratio CNMF subtypes | METHLYATION CNMF | RPPA CNMF subtypes | RPPA cHierClus subtypes | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL | MIRseq Mature CNMF subtypes | MIRseq Mature cHierClus subtypes
Time to Death | logrank test | 100 (1.00) | 100 (1.00) | 100 (1.00) | 100 (1.00) | 100 (1.00) | 100 (1.00) | 100 (1.00) | 100 (1.00) | 100 (1.00) | 100 (1.00)
AGE | ANOVA | 0.0339 (1.00) | 0.000685 (0.0869) | 0.0163 (1.00) | 0.0225 (1.00) | 0.746 (1.00) | 0.0273 (1.00) | 0.861 (1.00) | 0.821 (1.00) | 0.347 (1.00) | 0.327 (1.00)
PATHOLOGY T STAGE | Chi-square test | 7.79e-05 (0.0107) | 0.0104 (1.00) | 0.0197 (1.00) | 0.0961 (1.00) | 0.558 (1.00) | 0.0835 (1.00) | 0.098 (1.00) | 0.0185 (1.00) | 0.0666 (1.00) | 0.0579 (1.00)
PATHOLOGY N STAGE | Fisher's exact test | 0.00159 (0.197) | 0.0242 (1.00) | 0.0347 (1.00) | 0.0535 (1.00) | 0.162 (1.00) | 0.0998 (1.00) | 0.138 (1.00) | 0.0439 (1.00) | 0.566 (1.00) | 0.249 (1.00)
COMPLETENESS OF RESECTION | Chi-square test | 0.224 (1.00) | 0.134 (1.00) | 0.208 (1.00) | 0.0366 (1.00) | 0.681 (1.00) | 0.703 (1.00) | 0.397 (1.00) | 0.96 (1.00) | 0.614 (1.00) | 0.535 (1.00)
NUMBER OF LYMPH NODES | ANOVA | 0.00422 (0.502) | 0.044 (1.00) | 0.0431 (1.00) | 0.0417 (1.00) | 0.278 (1.00) | 0.135 (1.00) | 0.114 (1.00) | 0.0282 (1.00) | 0.67 (1.00) | 0.0867 (1.00)
GLEASON SCORE COMBINED | ANOVA | 7.53e-05 (0.0104) | 0.00451 (0.532) | 0.00291 (0.355) | 0.00475 (0.556) | 0.0236 (1.00) | 0.195 (1.00) | 0.226 (1.00) | 0.0716 (1.00) | 0.152 (1.00) | 0.622 (1.00)
GLEASON SCORE PRIMARY | ANOVA | 2.3e-05 (0.0032) | 0.000516 (0.0666) | 0.000154 (0.0208) | 0.000279 (0.0371) | 0.000484 (0.0629) | 0.000568 (0.0727) | 0.0799 (1.00) | 0.148 (1.00) | 0.238 (1.00) | 0.475 (1.00)
GLEASON SCORE SECONDARY | ANOVA | 0.0743 (1.00) | 0.719 (1.00) | 0.799 (1.00) | 0.418 (1.00) | 0.878 (1.00) | 0.724 (1.00) | 0.116 (1.00) | 0.238 (1.00) | 0.581 (1.00) | 0.737 (1.00)
GLEASON SCORE | ANOVA | 6.82e-06 (0.000955) | 0.00392 (0.47) | 0.000466 (0.061) | 0.000393 (0.0518) | 0.0468 (1.00) | 0.151 (1.00) | 0.118 (1.00) | 0.136 (1.00) | 0.0787 (1.00) | 0.365 (1.00)
PSA RESULT PREOP | ANOVA | 0.0109 (1.00) | 0.000199 (0.0267) | 0.0239 (1.00) | 0.131 (1.00) | 0.106 (1.00) | 0.0179 (1.00) | 0.0775 (1.00) | 0.317 (1.00) | 0.0907 (1.00) | 0.0293 (1.00)
DAYS TO PREOP PSA | ANOVA | 0.479 (1.00) | 0.603 (1.00) | 0.000766 (0.0965) | 0.000107 (0.0146) | 0.84 (1.00) | 0.266 (1.00) | 0.131 (1.00) | 0.0184 (1.00) | 0.0174 (1.00) | 0.023 (1.00)
PSA VALUE | ANOVA | 0.144 (1.00) | 0.0479 (1.00) | 0.13 (1.00) | 0.296 (1.00) | 0.0643 (1.00) | 0.00623 (0.722) | 0.00124 (0.154) | 0.00172 (0.211) | 0.0086 (0.989) | 0.00349 (0.423)
DAYS TO PSA | ANOVA | 0.361 (1.00) | 0.386 (1.00) | 0.231 (1.00) | 0.0204 (1.00) | 0.433 (1.00) | 0.603 (1.00) | 0.22 (1.00) | 0.099 (1.00) | 0.0295 (1.00) | 0.0892 (1.00)

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

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 31 | 98 | 55

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

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

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 184 | 1 | 0.3 - 66.1 (17.8)
subtype1 | 31 | 0 | 0.3 - 61.1 (9.4)
subtype2 | 98 | 0 | 1.0 - 66.0 (17.4)
subtype3 | 55 | 1 | 0.8 - 66.1 (25.1)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0339 (ANOVA), Q value = 1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'GLEASON_SCORE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PSA_RESULT_PREOP'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PSA_VALUE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 52 | 63 | 70

'METHLYATION CNMF' versus 'Time to Death'

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

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

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

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

'METHLYATION CNMF' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'METHLYATION CNMF' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'METHLYATION CNMF' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'METHLYATION CNMF' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'METHLYATION CNMF' versus 'GLEASON_SCORE'

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

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

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

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

'METHLYATION CNMF' versus 'PSA_RESULT_PREOP'

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

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

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

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

'METHLYATION CNMF' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'METHLYATION CNMF' versus 'PSA_VALUE'

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

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

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

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

'METHLYATION CNMF' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 41 | 50 | 52

'RPPA CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RPPA CNMF subtypes' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'RPPA CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'RPPA CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'RPPA CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'RPPA CNMF subtypes' versus 'GLEASON_SCORE'

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

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

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

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

'RPPA CNMF subtypes' versus 'PSA_RESULT_PREOP'

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

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

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

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

'RPPA CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'RPPA CNMF subtypes' versus 'PSA_VALUE'

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

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

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

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

'RPPA CNMF subtypes' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 50 | 53 | 40

'RPPA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RPPA cHierClus subtypes' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'RPPA cHierClus subtypes' versus 'GLEASON_SCORE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PSA_RESULT_PREOP'

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

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

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

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

'RPPA cHierClus subtypes' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PSA_VALUE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

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

'RNAseq CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq CNMF subtypes' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'RNAseq CNMF subtypes' versus 'GLEASON_SCORE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'PSA_RESULT_PREOP'

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

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

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

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

'RNAseq CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'RNAseq CNMF subtypes' versus 'PSA_VALUE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

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

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

'RNAseq cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'GLEASON_SCORE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'PSA_RESULT_PREOP'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'PSA_VALUE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #7: 'MIRSEQ CNMF'

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

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

'MIRSEQ CNMF' versus 'Time to Death'

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

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

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

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

'MIRSEQ CNMF' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'MIRSEQ CNMF' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'MIRSEQ CNMF' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'MIRSEQ CNMF' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'MIRSEQ CNMF' versus 'GLEASON_SCORE'

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

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

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

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

'MIRSEQ CNMF' versus 'PSA_RESULT_PREOP'

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

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

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

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

'MIRSEQ CNMF' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'MIRSEQ CNMF' versus 'PSA_VALUE'

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

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

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

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

'MIRSEQ CNMF' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 68 | 96 | 15

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GLEASON_SCORE'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'PSA_RESULT_PREOP'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'PSA_VALUE'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 82 | 79 | 18

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

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

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

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

Figure S113. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'GLEASON_SCORE'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'PSA_RESULT_PREOP'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'PSA_VALUE'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'DAYS_TO_PSA'

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

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

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

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

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 80 | 81 | 18

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

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

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

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

Figure S127. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_COMBINED'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_PRIMARY'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE_SECONDARY'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'GLEASON_SCORE'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'PSA_RESULT_PREOP'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'DAYS_TO_PREOP_PSA'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'PSA_VALUE'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'DAYS_TO_PSA'

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

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

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

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

Methods & Data
Input

- Cluster data file = PRAD-TP.mergedcluster.txt

- Clinical data file = PRAD-TP.merged_data.txt

- Number of patients = 185

- Number of clustering approaches = 10

- Number of selected clinical features = 14

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

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

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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