Bladder Urothelial Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 4 clinical features across 102 patients, one significant finding detected with P value < 0.05.

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

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

- CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death'.

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 8 different clustering approaches and 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, one significant finding detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA
CN CNMF | 0.679 | 0.969 | 0.313 | 0.409
METHLYATION CNMF | 0.131 | 0.759 | 0.457 | 0.279
RPPA CNMF subtypes | 0.0407 | 0.473 | 0.633 | 0.465
RPPA cHierClus subtypes | 0.093 | 0.164 | 0.196 | 0.342
RNAseq CNMF subtypes | 0.191 | 0.285 | 0.25 | 0.477
RNAseq cHierClus subtypes | 0.0732 | 0.996 | 0.341 | 0.909
MIRseq CNMF subtypes | 0.243 | 0.364 | 0.302 | 0.504
MIRseq cHierClus subtypes | 0.63 | 0.195 | 0.795 | 0.795

Clustering Approach #1: 'CN CNMF'

Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 27 | 41 | 20

'CN CNMF' versus 'Time to Death'

P value = 0.679 (logrank test)

Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 84 | 26 | 0.4 - 118.9 (7.3)
subtype1 | 25 | 7 | 2.1 - 75.3 (6.7)
subtype2 | 40 | 13 | 0.5 - 37.8 (7.2)
subtype3 | 19 | 6 | 0.4 - 118.9 (7.8)

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

'CN CNMF' versus 'AGE'

P value = 0.969 (ANOVA)

Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 87 | 67.2 (11.0)
subtype1 | 26 | 67.3 (11.3)
subtype2 | 41 | 66.9 (11.9)
subtype3 | 20 | 67.7 (9.3)

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

'CN CNMF' versus 'GENDER'

P value = 0.313 (Fisher's exact test)

Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 27 | 61
subtype1 | 7 | 20
subtype2 | 11 | 30
subtype3 | 9 | 11

Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.409 (ANOVA)

Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 23 | 77.8 (18.6)
subtype1 | 9 | 84.4 (8.8)
subtype2 | 9 | 73.3 (24.0)
subtype3 | 5 | 74.0 (20.7)

Figure S4. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S6. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 32 | 36 | 34

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.131 (logrank test)

Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 98 | 30 | 0.4 - 118.9 (7.2)
subtype1 | 31 | 7 | 0.8 - 118.9 (7.6)
subtype2 | 35 | 14 | 0.4 - 75.3 (7.8)
subtype3 | 32 | 9 | 0.7 - 37.8 (6.7)

Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.759 (ANOVA)

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 101 | 67.1 (10.6)
subtype1 | 31 | 67.0 (9.7)
subtype2 | 36 | 68.1 (9.8)
subtype3 | 34 | 66.2 (12.2)

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.457 (Fisher's exact test)

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 29 | 73
subtype1 | 10 | 22
subtype2 | 12 | 24
subtype3 | 7 | 27

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.279 (ANOVA)

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 79.2 (17.9)
subtype1 | 8 | 87.5 (4.6)
subtype2 | 4 | 72.5 (23.6)
subtype3 | 14 | 76.4 (20.2)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S11. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 12 | 10 | 14 | 17

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0407 (logrank test)

Table S12. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 52 | 20 | 0.4 - 118.9 (8.3)
subtype1 | 12 | 4 | 5.7 - 61.9 (10.6)
subtype2 | 10 | 5 | 1.8 - 100.5 (5.5)
subtype3 | 13 | 2 | 0.5 - 19.5 (6.6)
subtype4 | 17 | 9 | 0.4 - 118.9 (14.9)

Figure S9. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.473 (ANOVA)

Table S13. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 52 | 67.3 (9.8)
subtype1 | 12 | 63.9 (9.2)
subtype2 | 10 | 69.5 (9.3)
subtype3 | 13 | 66.5 (10.2)
subtype4 | 17 | 69.1 (10.3)

Figure S10. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.633 (Fisher's exact test)

Table S14. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 21 | 32
subtype1 | 6 | 6
subtype2 | 5 | 5
subtype3 | 5 | 9
subtype4 | 5 | 12

Figure S11. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.465 (ANOVA)

Table S15. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 80.0 (16.7)
subtype1 | 5 | 88.0 (4.5)
subtype2 | 1 | 70.0 (NA)
subtype3 | 1 | 60.0 (NA)
subtype4 | 4 | 77.5 (25.0)

Figure S12. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S16. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 14 | 17 | 22

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.093 (logrank test)

Table S17. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 52 | 20 | 0.4 - 118.9 (8.3)
subtype1 | 14 | 5 | 1.9 - 61.9 (8.3)
subtype2 | 17 | 10 | 0.4 - 118.9 (8.2)
subtype3 | 21 | 5 | 0.5 - 100.5 (7.7)

Figure S13. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.164 (ANOVA)

Table S18. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 52 | 67.3 (9.8)
subtype1 | 14 | 63.5 (9.6)
subtype2 | 17 | 70.2 (9.0)
subtype3 | 21 | 67.5 (10.2)

Figure S14. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.196 (Fisher's exact test)

Table S19. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 21 | 32
subtype1 | 8 | 6
subtype2 | 7 | 10
subtype3 | 6 | 16

Figure S15. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.342 (ANOVA)

Table S20. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 80.0 (16.7)
subtype1 | 4 | 90.0 (0.0)
subtype2 | 4 | 72.5 (23.6)
subtype3 | 3 | 76.7 (15.3)

Figure S16. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 35 | 17 | 25 | 10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.191 (logrank test)

Table S22. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 83 | 25 | 0.4 - 118.9 (7.2)
subtype1 | 32 | 9 | 0.5 - 100.5 (7.1)
subtype2 | 17 | 8 | 4.1 - 118.9 (6.7)
subtype3 | 25 | 6 | 0.4 - 75.3 (8.2)
subtype4 | 9 | 2 | 1.8 - 11.1 (5.1)

Figure S17. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.285 (ANOVA)

Table S23. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 86 | 67.0 (11.1)
subtype1 | 34 | 67.6 (11.2)
subtype2 | 17 | 63.2 (9.8)
subtype3 | 25 | 66.9 (12.1)
subtype4 | 10 | 71.6 (9.9)

Figure S18. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.25 (Fisher's exact test)

Table S24. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 27 | 60
subtype1 | 8 | 27
subtype2 | 4 | 13
subtype3 | 10 | 15
subtype4 | 5 | 5

Figure S19. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.477 (ANOVA)

Table S25. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 24 | 78.3 (18.3)
subtype1 | 10 | 77.0 (19.5)
subtype2 | 7 | 85.7 (7.9)
subtype3 | 6 | 73.3 (25.8)
subtype4 | 1 | 70.0 (NA)

Figure S20. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S26. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 25 | 42 | 20

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0732 (logrank test)

Table S27. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 83 | 25 | 0.4 - 118.9 (7.2)
subtype1 | 25 | 6 | 0.4 - 75.3 (8.3)
subtype2 | 39 | 10 | 0.5 - 100.5 (6.7)
subtype3 | 19 | 9 | 1.8 - 118.9 (5.9)

Figure S21. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.996 (ANOVA)

Table S28. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 86 | 67.0 (11.1)
subtype1 | 25 | 67.0 (12.0)
subtype2 | 41 | 67.1 (10.9)
subtype3 | 20 | 66.8 (11.0)

Figure S22. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.341 (Fisher's exact test)

Table S29. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 27 | 60
subtype1 | 10 | 15
subtype2 | 10 | 32
subtype3 | 7 | 13

Figure S23. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.909 (ANOVA)

Table S30. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 24 | 78.3 (18.3)
subtype1 | 7 | 75.7 (24.4)
subtype2 | 12 | 79.2 (18.3)
subtype3 | 5 | 80.0 (10.0)

Figure S24. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 22 | 52 | 26

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.243 (logrank test)

Table S32. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 30 | 0.4 - 118.9 (7.3)
subtype1 | 19 | 3 | 0.8 - 118.9 (6.2)
subtype2 | 51 | 20 | 0.4 - 61.9 (7.3)
subtype3 | 26 | 7 | 0.5 - 100.5 (8.5)

Figure S25. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.364 (ANOVA)

Table S33. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 99 | 67.4 (10.5)
subtype1 | 21 | 69.5 (9.8)
subtype2 | 52 | 66.0 (10.9)
subtype3 | 26 | 68.4 (10.3)

Figure S26. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.302 (Fisher's exact test)

Table S34. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 29 | 71
subtype1 | 4 | 18
subtype2 | 15 | 37
subtype3 | 10 | 16

Figure S27. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.504 (ANOVA)

Table S35. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 25 | 78.8 (18.1)
subtype1 | 5 | 86.0 (8.9)
subtype2 | 16 | 75.6 (21.3)
subtype3 | 4 | 82.5 (9.6)

Figure S28. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S36. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 21 | 67 | 12

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.63 (logrank test)

Table S37. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 30 | 0.4 - 118.9 (7.3)
subtype1 | 18 | 4 | 1.5 - 118.9 (6.1)
subtype2 | 66 | 22 | 0.4 - 100.5 (7.8)
subtype3 | 12 | 4 | 0.8 - 46.8 (5.6)

Figure S29. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.195 (ANOVA)

Table S38. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 99 | 67.4 (10.5)
subtype1 | 20 | 70.8 (9.9)
subtype2 | 67 | 66.1 (11.0)
subtype3 | 12 | 68.5 (7.6)

Figure S30. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.795 (Fisher's exact test)

Table S39. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 29 | 71
subtype1 | 5 | 16
subtype2 | 21 | 46
subtype3 | 3 | 9

Figure S31. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.795 (ANOVA)

Table S40. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 25 | 78.8 (18.1)
subtype1 | 3 | 83.3 (11.5)
subtype2 | 19 | 77.4 (20.0)
subtype3 | 3 | 83.3 (11.5)

Figure S32. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data
Input

- Cluster data file = BLCA.mergedcluster.txt

- Clinical data file = BLCA.clin.merged.picked.txt

- Number of patients = 102

- Number of clustering approaches = 8

- Number of selected clinical features = 4

- 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

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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- 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] 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)
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