Correlation between aggregated molecular cancer subtypes and selected clinical features
Uveal Melanoma (Primary solid tumor)
13 July 2018 | None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
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 7 clinical features across 80 patients, 13 significant findings detected with P value < 0.05 and Q value < 0.25.

- CNMF clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP' and 'PATHOLOGY_T_STAGE'.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP' and 'RADIATION_THERAPY'.

- 5 subtypes identified in current cancer cohort by 'LINCRNA CNMF'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- 4 subtypes identified in current cancer cohort by 'LINCRNA CHIERARCHICAL'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- CNMF clustering analysis on array-based miR expression data identified 5 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- Consensus hierarchical clustering analysis on array-based miR expression data identified 5 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP' and 'YEARS_TO_BIRTH'.

- 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- 6 subtypes identified in current cancer cohort by 'Copy Number Threshold CNMF subtypes'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- CNMF clustering analysis on methylation data identified 4 subtypes that correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- 3 subtypes identified in current cancer cohort by 'METHYLATION CHIERARCHICAL'. These subtypes correlate to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 13 significant findings detected.

Clinical Features | DAYS TO DEATH OR LAST FUP | YEARS TO BIRTH | PATHOLOGIC STAGE | PATHOLOGY T STAGE | PATHOLOGY M STAGE | GENDER | RADIATION THERAPY
Statistical Tests | logrank test | Kruskal-Wallis (anova) | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
mRNA CNMF subtypes | 0.000583 (0.00453) | 0.394 (0.541) | 0.0529 (0.248) | 0.027 (0.172) | 0.287 (0.472) | 0.783 (0.879) | 0.386 (0.541)
mRNA cHierClus subtypes | 8.91e-07 (3.12e-05) | 0.291 (0.472) | 0.15 (0.438) | 0.309 (0.48) | 0.13 (0.403) | 0.791 (0.879) | 0.0497 (0.248)
LINCRNA CNMF | 0.0428 (0.248) | 0.06 (0.262) | 0.158 (0.443) | 0.271 (0.472) | 0.929 (0.971) | 0.966 (0.994) | 0.27 (0.472)
LINCRNA CHIERARCHICAL | 4.57e-05 (0.000496) | 0.223 (0.472) | 0.287 (0.472) | 0.244 (0.472) | 0.25 (0.472) | 0.78 (0.879) | 0.183 (0.462)
miR CNMF subtypes | 1.79e-06 (4.17e-05) | 0.285 (0.472) | 0.231 (0.472) | 0.292 (0.472) | 0.053 (0.248) | 0.444 (0.556) | 0.889 (0.943)
miR cHierClus subtypes | 5.2e-06 (7.28e-05) | 0.00464 (0.0325) | 0.332 (0.484) | 0.292 (0.472) | 0.461 (0.556) | 0.327 (0.484) | 0.439 (0.556)
Copy Number Ratio CNMF subtypes | 2.39e-06 (4.19e-05) | 0.0867 (0.32) | 0.201 (0.472) | 0.43 (0.556) | 0.0658 (0.271) | 0.83 (0.908) | 0.429 (0.556)
Copy Number Threshold CNMF subtypes | 0.000237 (0.00207) | 0.0745 (0.29) | 0.682 (0.796) | 0.394 (0.541) | 0.112 (0.374) | 0.327 (0.484) | 0.297 (0.472)
Methylation CNMF subtypes | 6.68e-08 (4.67e-06) | 0.0928 (0.325) | 0.448 (0.556) | 0.642 (0.762) | 0.185 (0.462) | 0.246 (0.472) | 0.879 (0.943)
METHYLATION CHIERARCHICAL | 4.96e-05 (0.000496) | 0.133 (0.403) | 0.293 (0.472) | 0.456 (0.556) | 0.173 (0.462) | 1 (1.00) | 1 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 11 | 11 | 29 | 21 | 8

'mRNA CNMF subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.000583 (logrank test), Q value = 0.0045

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 10 | 5 | 1.3 - 49.7 (18.2)
subtype2 | 11 | 4 | 0.4 - 61.2 (31.1)
subtype3 | 29 | 1 | 0.2 - 85.5 (27.6)
subtype4 | 21 | 7 | 1.4 - 82.2 (21.0)
subtype5 | 8 | 5 | 0.1 - 45.3 (23.8)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.394 (Kruskal-Wallis (anova)), Q value = 0.54

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 10 | 67.4 (15.6)
subtype2 | 11 | 59.8 (14.1)
subtype3 | 29 | 57.8 (15.0)
subtype4 | 21 | 64.7 (12.8)
subtype5 | 8 | 62.1 (8.7)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0529 (Fisher's exact test), Q value = 0.25

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 1 | 2 | 3 | 2 | 1 | 1
subtype2 | 0 | 3 | 4 | 3 | 0 | 0
subtype3 | 9 | 12 | 6 | 2 | 0 | 0
subtype4 | 0 | 7 | 9 | 3 | 0 | 2
subtype5 | 1 | 3 | 3 | 0 | 0 | 1

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.027 (Kruskal-Wallis (anova)), Q value = 0.17

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 1 | 3 | 6
subtype2 | 2 | 5 | 4
subtype3 | 9 | 14 | 6
subtype4 | 0 | 7 | 14
subtype5 | 1 | 3 | 4

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.287 (Fisher's exact test), Q value = 0.47

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 8 | 1
subtype2 | 8 | 0
subtype3 | 17 | 0
subtype4 | 14 | 2
subtype5 | 4 | 1

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.783 (Fisher's exact test), Q value = 0.88

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 6 | 4
subtype2 | 4 | 7
subtype3 | 12 | 17
subtype4 | 8 | 13
subtype5 | 4 | 4

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.386 (Fisher's exact test), Q value = 0.54

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 8 | 1
subtype2 | 10 | 1
subtype3 | 28 | 1
subtype4 | 21 | 0
subtype5 | 8 | 0

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 15 | 15 | 27 | 23

'mRNA cHierClus subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 8.91e-07 (logrank test), Q value = 3.1e-05

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 15 | 9 | 0.1 - 45.3 (18.9)
subtype2 | 15 | 2 | 0.4 - 61.2 (27.6)
subtype3 | 27 | 1 | 0.2 - 85.5 (38.7)
subtype4 | 22 | 10 | 1.3 - 49.7 (19.8)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.291 (Kruskal-Wallis (anova)), Q value = 0.47

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 15 | 63.2 (12.9)
subtype2 | 15 | 60.9 (14.0)
subtype3 | 27 | 57.4 (14.7)
subtype4 | 22 | 65.9 (13.3)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.15 (Fisher's exact test), Q value = 0.44

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 1 | 6 | 4 | 1 | 1 | 2
subtype2 | 1 | 5 | 4 | 4 | 0 | 0
subtype3 | 8 | 10 | 7 | 2 | 0 | 0
subtype4 | 1 | 6 | 10 | 3 | 0 | 2

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.309 (Kruskal-Wallis (anova)), Q value = 0.48

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 2 | 5 | 8
subtype2 | 2 | 7 | 6
subtype3 | 8 | 11 | 8
subtype4 | 1 | 9 | 12

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.13 (Fisher's exact test), Q value = 0.4

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 9 | 2
subtype2 | 10 | 0
subtype3 | 18 | 0
subtype4 | 14 | 2

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.791 (Fisher's exact test), Q value = 0.88

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 8 | 7
subtype2 | 6 | 9
subtype3 | 12 | 15
subtype4 | 8 | 14

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0497 (Fisher's exact test), Q value = 0.25

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 13 | 1
subtype2 | 13 | 2
subtype3 | 27 | 0
subtype4 | 22 | 0

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #3: 'LINCRNA CNMF'

Table S17. Description of clustering approach #3: 'LINCRNA CNMF'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 18 | 7 | 28 | 11 | 16

'LINCRNA CNMF' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.0428 (logrank test), Q value = 0.25

Table S18. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 18 | 10 | 0.1 - 61.2 (24.7)
subtype2 | 7 | 1 | 0.2 - 27.0 (15.1)
subtype3 | 28 | 7 | 1.4 - 82.2 (24.3)
subtype4 | 10 | 3 | 1.3 - 85.5 (39.7)
subtype5 | 16 | 1 | 0.6 - 52.0 (34.4)

Figure S15. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'LINCRNA CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.06 (Kruskal-Wallis (anova)), Q value = 0.26

Table S19. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 18 | 62.9 (11.4)
subtype2 | 7 | 61.3 (15.0)
subtype3 | 28 | 64.2 (12.9)
subtype4 | 10 | 67.0 (14.3)
subtype5 | 16 | 52.0 (15.0)

Figure S16. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'LINCRNA CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.158 (Fisher's exact test), Q value = 0.44

Table S20. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 1 | 5 | 7 | 2 | 1 | 1
subtype2 | 2 | 3 | 0 | 2 | 0 | 0
subtype3 | 1 | 9 | 12 | 4 | 0 | 2
subtype4 | 2 | 3 | 2 | 2 | 0 | 1
subtype5 | 5 | 7 | 4 | 0 | 0 | 0

Figure S17. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'LINCRNA CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.271 (Kruskal-Wallis (anova)), Q value = 0.47

Table S21. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 2 | 7 | 9
subtype2 | 2 | 3 | 2
subtype3 | 2 | 10 | 16
subtype4 | 2 | 4 | 4
subtype5 | 5 | 8 | 3

Figure S18. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'LINCRNA CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.929 (Fisher's exact test), Q value = 0.97

Table S22. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 12 | 1
subtype2 | 5 | 0
subtype3 | 20 | 2
subtype4 | 6 | 1
subtype5 | 8 | 0

Figure S19. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'LINCRNA CNMF' versus 'GENDER'

P value = 0.966 (Fisher's exact test), Q value = 0.99

Table S23. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 9 | 9
subtype2 | 3 | 4
subtype3 | 12 | 16
subtype4 | 4 | 6
subtype5 | 6 | 10

Figure S20. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #6: 'GENDER'

'LINCRNA CNMF' versus 'RADIATION_THERAPY'

P value = 0.27 (Fisher's exact test), Q value = 0.47

Table S24. Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 15 | 2
subtype2 | 7 | 0
subtype3 | 28 | 0
subtype4 | 10 | 0
subtype5 | 15 | 1

Figure S21. Get High-res Image Clustering Approach #3: 'LINCRNA CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #4: 'LINCRNA CHIERARCHICAL'

Table S25. Description of clustering approach #4: 'LINCRNA CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 21 | 24 | 20 | 15

'LINCRNA CHIERARCHICAL' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 4.57e-05 (logrank test), Q value = 5e-04

Table S26. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 20 | 11 | 0.1 - 61.2 (23.7)
subtype2 | 24 | 1 | 0.2 - 82.2 (24.3)
subtype3 | 20 | 9 | 1.4 - 45.9 (19.8)
subtype4 | 15 | 1 | 20.8 - 85.5 (39.8)

Figure S22. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'LINCRNA CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.223 (Kruskal-Wallis (anova)), Q value = 0.47

Table S27. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 20 | 64.8 (12.5)
subtype2 | 24 | 59.6 (13.4)
subtype3 | 20 | 65.2 (13.2)
subtype4 | 15 | 55.4 (16.4)

Figure S23. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'LINCRNA CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.287 (Fisher's exact test), Q value = 0.47

Table S28. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 1 | 6 | 6 | 3 | 1 | 2
subtype2 | 5 | 8 | 6 | 5 | 0 | 0
subtype3 | 1 | 6 | 9 | 2 | 0 | 2
subtype4 | 4 | 7 | 4 | 0 | 0 | 0

Figure S24. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'LINCRNA CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.244 (Kruskal-Wallis (anova)), Q value = 0.47

Table S29. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 2 | 8 | 10
subtype2 | 6 | 8 | 10
subtype3 | 1 | 8 | 11
subtype4 | 4 | 8 | 3

Figure S25. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'LINCRNA CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.25 (Fisher's exact test), Q value = 0.47

Table S30. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 12 | 2
subtype2 | 19 | 0
subtype3 | 12 | 2
subtype4 | 8 | 0

Figure S26. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'LINCRNA CHIERARCHICAL' versus 'GENDER'

P value = 0.78 (Fisher's exact test), Q value = 0.88

Table S31. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 9 | 11
subtype2 | 12 | 12
subtype3 | 7 | 13
subtype4 | 6 | 9

Figure S27. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

'LINCRNA CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.183 (Fisher's exact test), Q value = 0.46

Table S32. Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 17 | 2
subtype2 | 24 | 0
subtype3 | 20 | 0
subtype4 | 14 | 1

Figure S28. Get High-res Image Clustering Approach #4: 'LINCRNA CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #5: 'miR CNMF subtypes'

Table S33. Description of clustering approach #5: 'miR CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 13 | 21 | 18 | 24 | 4

'miR CNMF subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 1.79e-06 (logrank test), Q value = 4.2e-05

Table S34. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 13 | 6 | 0.4 - 61.2 (28.7)
subtype2 | 21 | 12 | 0.1 - 45.3 (18.9)
subtype3 | 17 | 2 | 0.2 - 85.5 (26.2)
subtype4 | 24 | 0 | 0.2 - 82.2 (32.8)
subtype5 | 4 | 2 | 1.3 - 27.0 (14.1)

Figure S29. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'miR CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.285 (Kruskal-Wallis (anova)), Q value = 0.47

Table S35. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 13 | 60.4 (15.9)
subtype2 | 21 | 65.2 (11.6)
subtype3 | 17 | 61.2 (12.6)
subtype4 | 24 | 57.4 (15.9)
subtype5 | 4 | 72.2 (3.1)

Figure S30. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.231 (Fisher's exact test), Q value = 0.47

Table S36. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 0 | 3 | 5 | 2 | 1 | 1
subtype2 | 1 | 8 | 7 | 3 | 0 | 2
subtype3 | 2 | 8 | 6 | 1 | 0 | 0
subtype4 | 7 | 7 | 7 | 3 | 0 | 0
subtype5 | 1 | 1 | 0 | 1 | 0 | 1

Figure S31. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'miR CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.292 (Kruskal-Wallis (anova)), Q value = 0.47

Table S37. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 0 | 7 | 6
subtype2 | 2 | 7 | 12
subtype3 | 3 | 9 | 5
subtype4 | 7 | 8 | 9
subtype5 | 1 | 1 | 2

Figure S32. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'miR CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.053 (Fisher's exact test), Q value = 0.25

Table S38. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 9 | 1
subtype2 | 12 | 2
subtype3 | 13 | 0
subtype4 | 16 | 0
subtype5 | 1 | 1

Figure S33. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'miR CNMF subtypes' versus 'GENDER'

P value = 0.444 (Fisher's exact test), Q value = 0.56

Table S39. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 6 | 7
subtype2 | 6 | 15
subtype3 | 10 | 7
subtype4 | 10 | 14
subtype5 | 2 | 2

Figure S34. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'miR CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.889 (Fisher's exact test), Q value = 0.94

Table S40. Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 11 | 1
subtype2 | 20 | 1
subtype3 | 17 | 0
subtype4 | 23 | 1
subtype5 | 4 | 0

Figure S35. Get High-res Image Clustering Approach #5: 'miR CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #6: 'miR cHierClus subtypes'

Table S41. Description of clustering approach #6: 'miR cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 21 | 20 | 8 | 17 | 14

'miR cHierClus subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 5.2e-06 (logrank test), Q value = 7.3e-05

Table S42. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 20 | 12 | 0.1 - 49.7 (13.4)
subtype2 | 20 | 7 | 2.1 - 61.2 (32.0)
subtype3 | 8 | 1 | 0.2 - 85.5 (23.2)
subtype4 | 17 | 1 | 0.2 - 82.2 (35.0)
subtype5 | 14 | 1 | 0.4 - 44.3 (27.0)

Figure S36. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'miR cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00464 (Kruskal-Wallis (anova)), Q value = 0.032

Table S43. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 20 | 68.6 (13.4)
subtype2 | 20 | 61.0 (9.9)
subtype3 | 8 | 59.8 (14.5)
subtype4 | 17 | 64.1 (15.8)
subtype5 | 14 | 50.2 (11.1)

Figure S37. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.332 (Fisher's exact test), Q value = 0.48

Table S44. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 1 | 8 | 6 | 1 | 1 | 3
subtype2 | 1 | 6 | 7 | 4 | 0 | 1
subtype3 | 0 | 5 | 2 | 1 | 0 | 0
subtype4 | 5 | 5 | 4 | 3 | 0 | 0
subtype5 | 4 | 3 | 6 | 1 | 0 | 0

Figure S38. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'miR cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.292 (Kruskal-Wallis (anova)), Q value = 0.47

Table S45. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 1 | 10 | 9
subtype2 | 3 | 7 | 10
subtype3 | 0 | 5 | 3
subtype4 | 5 | 7 | 5
subtype5 | 4 | 3 | 7

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

'miR cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.461 (Fisher's exact test), Q value = 0.56

Table S46. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 14 | 3
subtype2 | 10 | 1
subtype3 | 6 | 0
subtype4 | 11 | 0
subtype5 | 10 | 0

Figure S40. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.327 (Fisher's exact test), Q value = 0.48

Table S47. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 11 | 9
subtype2 | 5 | 15
subtype3 | 3 | 5
subtype4 | 9 | 8
subtype5 | 6 | 8

Figure S41. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'miR cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.439 (Fisher's exact test), Q value = 0.56

Table S48. Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 19 | 0
subtype2 | 18 | 2
subtype3 | 8 | 0
subtype4 | 17 | 0
subtype5 | 13 | 1

Figure S42. Get High-res Image Clustering Approach #6: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #7: 'Copy Number Ratio CNMF subtypes'

Table S49. Description of clustering approach #7: 'Copy Number Ratio CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 18 | 21 | 37 | 2 | 2

'Copy Number Ratio CNMF subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 2.39e-06 (logrank test), Q value = 4.2e-05

Table S50. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 75 | 22 | 0.1 - 85.5 (25.0)
subtype1 | 17 | 9 | 1.6 - 49.7 (19.6)
subtype2 | 21 | 11 | 0.1 - 45.3 (21.0)
subtype3 | 36 | 2 | 0.2 - 85.5 (29.7)

Figure S43. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0867 (Kruskal-Wallis (anova)), Q value = 0.32

Table S51. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 75 | 61.6 (14.1)
subtype1 | 17 | 64.3 (14.0)
subtype2 | 21 | 66.3 (12.0)
subtype3 | 37 | 57.6 (14.5)

Figure S44. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.201 (Fisher's exact test), Q value = 0.47

Table S52. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 22 | 9 | 1 | 4
subtype1 | 1 | 5 | 7 | 2 | 0 | 2
subtype2 | 1 | 8 | 5 | 3 | 1 | 2
subtype3 | 9 | 14 | 10 | 4 | 0 | 0

Figure S45. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.43 (Kruskal-Wallis (anova)), Q value = 0.56

Table S53. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 30 | 32
subtype1 | 2 | 6 | 9
subtype2 | 2 | 8 | 11
subtype3 | 9 | 16 | 12

Figure S46. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0658 (Fisher's exact test), Q value = 0.27

Table S54. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 50 | 4
subtype1 | 12 | 2
subtype2 | 12 | 2
subtype3 | 26 | 0

Figure S47. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.83 (Fisher's exact test), Q value = 0.91

Table S55. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 41
subtype1 | 9 | 8
subtype2 | 9 | 12
subtype3 | 16 | 21

Figure S48. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.429 (Fisher's exact test), Q value = 0.56

Table S56. Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 71 | 3
subtype1 | 16 | 0
subtype2 | 19 | 2
subtype3 | 36 | 1

Figure S49. Get High-res Image Clustering Approach #7: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #8: 'Copy Number Threshold CNMF subtypes'

Table S57. Description of clustering approach #8: 'Copy Number Threshold CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
--- | --- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 16 | 7 | 22 | 12 | 2 | 14 | 5 | 2

'Copy Number Threshold CNMF subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.000237 (logrank test), Q value = 0.0021

Table S58. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 75 | 21 | 0.1 - 85.5 (25.0)
subtype1 | 15 | 7 | 1.6 - 49.7 (19.6)
subtype2 | 7 | 3 | 1.4 - 32.3 (13.6)
subtype3 | 21 | 2 | 0.2 - 85.5 (26.1)
subtype4 | 12 | 0 | 0.6 - 82.2 (36.4)
subtype6 | 14 | 6 | 0.1 - 61.2 (36.9)
subtype7 | 5 | 3 | 2.2 - 36.6 (24.0)

Figure S50. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'Copy Number Threshold CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0745 (Kruskal-Wallis (anova)), Q value = 0.29

Table S59. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 75 | 61.7 (14.0)
subtype1 | 15 | 64.3 (16.3)
subtype2 | 7 | 73.4 (11.3)
subtype3 | 22 | 61.2 (14.4)
subtype4 | 12 | 55.2 (11.3)
subtype6 | 14 | 59.1 (11.8)
subtype7 | 5 | 63.4 (13.9)

Figure S51. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Threshold CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.682 (Fisher's exact test), Q value = 0.8

Table S60. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 10 | 26 | 23 | 10 | 1 | 4
subtype1 | 1 | 5 | 5 | 2 | 0 | 2
subtype2 | 0 | 2 | 2 | 1 | 1 | 1
subtype3 | 5 | 8 | 6 | 3 | 0 | 0
subtype4 | 3 | 4 | 3 | 2 | 0 | 0
subtype6 | 0 | 6 | 5 | 2 | 0 | 0
subtype7 | 1 | 1 | 2 | 0 | 0 | 1

Figure S52. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Threshold CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.394 (Kruskal-Wallis (anova)), Q value = 0.54

Table S61. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 12 | 31 | 32
subtype1 | 2 | 6 | 7
subtype2 | 1 | 1 | 5
subtype3 | 5 | 10 | 7
subtype4 | 3 | 4 | 5
subtype6 | 0 | 9 | 5
subtype7 | 1 | 1 | 3

Figure S53. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Threshold CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.112 (Fisher's exact test), Q value = 0.37

Table S62. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 48 | 4
subtype1 | 11 | 2
subtype2 | 5 | 1
subtype3 | 16 | 0
subtype4 | 8 | 0
subtype6 | 6 | 0
subtype7 | 2 | 1

Figure S54. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'Copy Number Threshold CNMF subtypes' versus 'GENDER'

P value = 0.327 (Fisher's exact test), Q value = 0.48

Table S63. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 32 | 43
subtype1 | 6 | 9
subtype2 | 1 | 6
subtype3 | 11 | 11
subtype4 | 4 | 8
subtype6 | 6 | 8
subtype7 | 4 | 1

Figure S55. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'Copy Number Threshold CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.297 (Fisher's exact test), Q value = 0.47

Table S64. Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 72 | 2
subtype1 | 14 | 0
subtype2 | 6 | 1
subtype3 | 22 | 0
subtype4 | 12 | 0
subtype6 | 13 | 1
subtype7 | 5 | 0

Figure S56. Get High-res Image Clustering Approach #8: 'Copy Number Threshold CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #9: 'Methylation CNMF subtypes'

Table S65. Description of clustering approach #9: 'Methylation CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 28 | 13 | 16 | 23

'Methylation CNMF subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 6.68e-08 (logrank test), Q value = 4.7e-06

Table S66. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 28 | 17 | 0.1 - 45.3 (19.3)
subtype2 | 13 | 1 | 0.2 - 85.5 (27.5)
subtype3 | 15 | 2 | 1.3 - 82.2 (31.8)
subtype4 | 23 | 2 | 0.2 - 46.8 (27.6)

Figure S57. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'Methylation CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0928 (Kruskal-Wallis (anova)), Q value = 0.32

Table S67. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 28 | 66.9 (12.1)
subtype2 | 13 | 60.8 (12.9)
subtype3 | 15 | 60.0 (13.2)
subtype4 | 23 | 56.4 (15.8)

Figure S58. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Methylation CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.448 (Fisher's exact test), Q value = 0.56

Table S68. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 1 | 10 | 8 | 3 | 1 | 4
subtype2 | 2 | 4 | 5 | 2 | 0 | 0
subtype3 | 2 | 4 | 7 | 2 | 0 | 0
subtype4 | 6 | 9 | 5 | 3 | 0 | 0

Figure S59. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Methylation CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.642 (Kruskal-Wallis (anova)), Q value = 0.76

Table S69. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 2 | 11 | 15
subtype2 | 2 | 6 | 5
subtype3 | 3 | 6 | 6
subtype4 | 6 | 9 | 8

Figure S60. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Methylation CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.185 (Fisher's exact test), Q value = 0.46

Table S70. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 18 | 4
subtype2 | 6 | 0
subtype3 | 11 | 0
subtype4 | 16 | 0

Figure S61. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'Methylation CNMF subtypes' versus 'GENDER'

P value = 0.246 (Fisher's exact test), Q value = 0.47

Table S71. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 11 | 17
subtype2 | 5 | 8
subtype3 | 10 | 5
subtype4 | 8 | 15

Figure S62. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'Methylation CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.879 (Fisher's exact test), Q value = 0.94

Table S72. Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 25 | 2
subtype2 | 13 | 0
subtype3 | 15 | 0
subtype4 | 22 | 1

Figure S63. Get High-res Image Clustering Approach #9: 'Methylation CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

Clustering Approach #10: 'METHYLATION CHIERARCHICAL'

Table S73. Description of clustering approach #10: 'METHYLATION CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 40 | 15 | 25

'METHYLATION CHIERARCHICAL' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 4.96e-05 (logrank test), Q value = 5e-04

Table S74. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 79 | 22 | 0.1 - 85.5 (26.1)
subtype1 | 39 | 19 | 0.1 - 61.2 (19.9)
subtype2 | 15 | 2 | 0.2 - 85.5 (31.8)
subtype3 | 25 | 1 | 0.2 - 46.8 (27.5)

Figure S64. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'METHYLATION CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.133 (Kruskal-Wallis (anova)), Q value = 0.4

Table S75. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 79 | 61.5 (14.0)
subtype1 | 39 | 64.9 (12.8)
subtype2 | 15 | 60.9 (13.0)
subtype3 | 25 | 56.7 (15.4)

Figure S65. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHYLATION CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.293 (Fisher's exact test), Q value = 0.47

Table S76. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients | STAGE IIA | STAGE IIB | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV
--- | --- | --- | --- | --- | --- | ---
ALL | 11 | 27 | 25 | 10 | 1 | 4
subtype1 | 2 | 13 | 14 | 4 | 1 | 4
subtype2 | 2 | 5 | 5 | 3 | 0 | 0
subtype3 | 7 | 9 | 6 | 3 | 0 | 0

Figure S66. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHYLATION CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.456 (Kruskal-Wallis (anova)), Q value = 0.56

Table S77. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

| T2 | T3 | T4
--- | --- | --- | ---
ALL | 13 | 32 | 34
subtype1 | 4 | 16 | 19
subtype2 | 2 | 7 | 6
subtype3 | 7 | 9 | 9

Figure S67. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHYLATION CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.173 (Fisher's exact test), Q value = 0.46

Table S78. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients | CLASS0 | CLASS1
--- | --- | ---
ALL | 51 | 4
subtype1 | 24 | 4
subtype2 | 11 | 0
subtype3 | 16 | 0

Figure S68. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'METHYLATION CHIERARCHICAL' versus 'GENDER'

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

Table S79. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 34 | 45
subtype1 | 17 | 22
subtype2 | 6 | 9
subtype3 | 11 | 14

Figure S69. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

'METHYLATION CHIERARCHICAL' versus 'RADIATION_THERAPY'

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

Table S80. Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 3
subtype1 | 36 | 2
subtype2 | 15 | 0
subtype3 | 24 | 1

Figure S70. Get High-res Image Clustering Approach #10: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

Methods & Data
Input

- Cluster data file = /cromwell_root/fc-f5144117-2d5a-42c2-8998-5b38e52db5d9/9d897bdb-7a9a-423a-ad17-06f28ae98803/aggregate_clusters_workflow/1d8bac82-5a1d-4cd2-aeb1-8ddd0e1e7a20/call-aggregate_clusters/TCGA-UVM-TP.mergedcluster.txt

- Clinical data file = /cromwell_root/fc-2289d790-de74-4808-9b0a-cefafc34d859/0d7c7dcf-18e0-4b2d-afc0-a0b2ee1e45ff/preprocess_clinical_workflow/70152ac6-f707-4277-8d60-8770b1b366c6/call-preprocess_clinical/TCGA-UVM-TP.clin.merged.picked.txt

- Number of patients = 80

- Number of clustering approaches = 10

- Number of selected clinical features = 7

- 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.

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] 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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