Bladder Urothelial Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 7 clinical features across 117 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

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

- 4 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 do not correlate to any clinical features.

- 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 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | NUMBERPACKYEARSSMOKED | STOPPEDSMOKINGYEAR | TOBACCOSMOKINGHISTORYINDICATOR
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | ANOVA | ANOVA | ANOVA
CN CNMF | 0.877 (1.00) | 0.493 (1.00) | 0.859 (1.00) | 0.614 (1.00) | 0.803 (1.00) | 0.755 (1.00) | 0.92 (1.00)
METHLYATION CNMF | 0.484 (1.00) | 0.0426 (1.00) | 0.032 (1.00) | 0.863 (1.00) | 0.763 (1.00) | 0.24 (1.00) | 0.537 (1.00)
RPPA CNMF subtypes | 0.245 (1.00) | 0.473 (1.00) | 0.633 (1.00) | 0.465 (1.00) | 0.657 (1.00) | 0.955 (1.00) | 0.258 (1.00)
RPPA cHierClus subtypes | 0.25 (1.00) | 0.164 (1.00) | 0.196 (1.00) | 0.342 (1.00) | 0.677 (1.00) | 0.56 (1.00) | 0.412 (1.00)
RNAseq CNMF subtypes | 0.641 (1.00) | 0.269 (1.00) | 0.19 (1.00) | 0.412 (1.00) | 0.664 (1.00) | 0.643 (1.00) | 0.0533 (1.00)
RNAseq cHierClus subtypes | 0.209 (1.00) | 0.958 (1.00) | 0.283 (1.00) | 0.923 (1.00) | 0.688 (1.00) | 0.744 (1.00) | 0.46 (1.00)
MIRseq CNMF subtypes | 0.478 (1.00) | 0.389 (1.00) | 0.25 (1.00) | 0.559 (1.00) | 0.816 (1.00) | 0.993 (1.00) | 0.463 (1.00)
MIRseq cHierClus subtypes | 0.878 (1.00) | 0.47 (1.00) | 0.801 (1.00) | 0.715 (1.00) | 0.15 (1.00) | 0.627 (1.00) | 0.385 (1.00)

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 | 26 | 55 | 33

'CN CNMF' versus 'Time to Death'

P value = 0.877 (logrank test), Q value = 1

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 108 | 29 | 0.4 - 118.9 (6.8)
subtype1 | 25 | 6 | 0.8 - 75.3 (5.7)
subtype2 | 51 | 16 | 0.5 - 46.8 (7.2)
subtype3 | 32 | 7 | 0.4 - 118.9 (6.3)

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

'CN CNMF' versus 'AGE'

P value = 0.493 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 113 | 67.2 (11.1)
subtype1 | 25 | 69.6 (11.2)
subtype2 | 55 | 66.7 (12.2)
subtype3 | 33 | 66.4 (8.8)

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

'CN CNMF' versus 'GENDER'

P value = 0.859 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 31 | 83
subtype1 | 8 | 18
subtype2 | 14 | 41
subtype3 | 9 | 24

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

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

P value = 0.614 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 35 | 77.7 (16.6)
subtype1 | 9 | 82.2 (13.9)
subtype2 | 17 | 75.3 (18.7)
subtype3 | 9 | 77.8 (15.6)

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

'CN CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.803 (ANOVA), Q value = 1

Table S6. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 70 | 34.6 (21.7)
subtype1 | 17 | 34.5 (30.2)
subtype2 | 29 | 32.8 (19.3)
subtype3 | 24 | 36.8 (18.0)

Figure S5. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'CN CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.755 (ANOVA), Q value = 1

Table S7. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 57 | 1991.5 (17.6)
subtype1 | 14 | 1989.6 (19.6)
subtype2 | 26 | 1990.7 (18.5)
subtype3 | 17 | 1994.1 (15.2)

Figure S6. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'CN CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.92 (ANOVA), Q value = 1

Table S8. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 108 | 2.6 (1.1)
subtype1 | 25 | 2.6 (1.1)
subtype2 | 52 | 2.6 (1.2)
subtype3 | 31 | 2.7 (1.1)

Figure S7. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #2: 'METHLYATION CNMF'

Table S9. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 29 | 37 | 32 | 19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.484 (logrank test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 111 | 30 | 0.4 - 118.9 (6.7)
subtype1 | 28 | 5 | 0.8 - 118.9 (6.3)
subtype2 | 36 | 13 | 0.4 - 75.3 (7.2)
subtype3 | 28 | 5 | 0.7 - 37.8 (6.4)
subtype4 | 19 | 7 | 0.5 - 46.8 (8.7)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0426 (ANOVA), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 116 | 67.3 (11.0)
subtype1 | 28 | 67.1 (10.5)
subtype2 | 37 | 68.9 (10.2)
subtype3 | 32 | 63.1 (12.3)
subtype4 | 19 | 71.4 (9.2)

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.032 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 32 | 85
subtype1 | 10 | 19
subtype2 | 14 | 23
subtype3 | 3 | 29
subtype4 | 5 | 14

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.863 (ANOVA), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 36 | 78.1 (16.5)
subtype1 | 7 | 82.9 (15.0)
subtype2 | 8 | 77.5 (16.7)
subtype3 | 16 | 76.2 (18.9)
subtype4 | 5 | 78.0 (13.0)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.763 (ANOVA), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 71 | 35.1 (22.1)
subtype1 | 18 | 31.4 (24.5)
subtype2 | 26 | 34.2 (20.6)
subtype3 | 21 | 38.2 (21.7)
subtype4 | 6 | 39.5 (26.1)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.24 (ANOVA), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 58 | 1991.8 (17.6)
subtype1 | 11 | 1996.0 (15.5)
subtype2 | 20 | 1987.7 (17.5)
subtype3 | 17 | 1997.1 (16.3)
subtype4 | 10 | 1986.2 (20.7)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.537 (ANOVA), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 111 | 2.6 (1.1)
subtype1 | 27 | 2.5 (1.2)
subtype2 | 34 | 2.6 (1.0)
subtype3 | 32 | 2.9 (1.3)
subtype4 | 18 | 2.5 (1.1)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S17. 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 = 0.245 (logrank test), Q value = 1

Table S18. 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 (10.9)
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 S15. 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), Q value = 1

Table S19. 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 S16. 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), Q value = 1

Table S20. 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 S17. 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), Q value = 1

Table S21. 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 S18. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.657 (ANOVA), Q value = 1

Table S22. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 27 | 32.1 (17.5)
subtype1 | 9 | 27.6 (12.4)
subtype2 | 5 | 28.8 (18.4)
subtype3 | 6 | 35.2 (20.4)
subtype4 | 7 | 37.9 (21.6)

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

'RPPA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.955 (ANOVA), Q value = 1

Table S23. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 1990.1 (16.1)
subtype1 | 6 | 1989.7 (18.5)
subtype2 | 2 | 1979.0 (11.3)
subtype3 | 5 | 1992.8 (13.9)
subtype4 | 8 | 1991.5 (17.9)

Figure S20. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.258 (ANOVA), Q value = 1

Table S24. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 48 | 2.3 (1.1)
subtype1 | 12 | 2.6 (0.9)
subtype2 | 8 | 1.9 (0.8)
subtype3 | 14 | 2.1 (1.1)
subtype4 | 14 | 2.6 (1.2)

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S25. 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 = 0.25 (logrank test), Q value = 1

Table S26. 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 S22. 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), Q value = 1

Table S27. 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 S23. 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), Q value = 1

Table S28. 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 S24. 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), Q value = 1

Table S29. 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 S25. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.677 (ANOVA), Q value = 1

Table S30. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 27 | 32.1 (17.5)
subtype1 | 9 | 30.4 (15.6)
subtype2 | 8 | 29.1 (18.5)
subtype3 | 10 | 36.1 (19.4)

Figure S26. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.56 (ANOVA), Q value = 1

Table S31. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 1990.1 (16.1)
subtype1 | 4 | 1997.8 (13.0)
subtype2 | 9 | 1987.0 (16.8)
subtype3 | 8 | 1989.8 (17.2)

Figure S27. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.412 (ANOVA), Q value = 1

Table S32. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 48 | 2.3 (1.1)
subtype1 | 13 | 2.3 (0.9)
subtype2 | 14 | 2.6 (1.1)
subtype3 | 21 | 2.1 (1.2)

Figure S28. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S33. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 37 | 18 | 25 | 10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.641 (logrank test), Q value = 1

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 84 | 25 | 0.4 - 118.9 (7.2)
subtype1 | 32 | 9 | 0.5 - 100.5 (7.1)
subtype2 | 18 | 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 S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.269 (ANOVA), Q value = 1

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 89 | 66.4 (11.6)
subtype1 | 36 | 66.5 (12.2)
subtype2 | 18 | 62.7 (9.7)
subtype3 | 25 | 66.9 (12.1)
subtype4 | 10 | 71.6 (9.9)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.19 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 27 | 63
subtype1 | 8 | 29
subtype2 | 4 | 14
subtype3 | 10 | 15
subtype4 | 5 | 5

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

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

P value = 0.412 (ANOVA), Q value = 1

Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 77.7 (17.7)
subtype1 | 12 | 75.8 (17.8)
subtype2 | 7 | 85.7 (7.9)
subtype3 | 6 | 73.3 (25.8)
subtype4 | 1 | 70.0 (NA)

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.664 (ANOVA), Q value = 1

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 51 | 34.6 (22.7)
subtype1 | 15 | 40.8 (30.9)
subtype2 | 15 | 31.6 (19.5)
subtype3 | 16 | 33.2 (19.5)
subtype4 | 5 | 30.0 (12.2)

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

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.643 (ANOVA), Q value = 1

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 42 | 1990.6 (18.5)
subtype1 | 14 | 1988.9 (20.8)
subtype2 | 9 | 1997.4 (17.3)
subtype3 | 14 | 1987.6 (17.6)
subtype4 | 5 | 1991.8 (18.9)

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

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0533 (ANOVA), Q value = 1

Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 84 | 2.5 (1.1)
subtype1 | 34 | 2.2 (1.2)
subtype2 | 16 | 3.0 (0.8)
subtype3 | 24 | 2.7 (1.0)
subtype4 | 10 | 2.1 (1.1)

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S41. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 25 | 44 | 21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.209 (logrank test), Q value = 1

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 84 | 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 | 20 | 9 | 1.8 - 118.9 (5.9)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.958 (ANOVA), Q value = 1

Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 89 | 66.4 (11.6)
subtype1 | 25 | 67.0 (12.0)
subtype2 | 43 | 66.2 (11.8)
subtype3 | 21 | 66.2 (11.1)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.283 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 27 | 63
subtype1 | 10 | 15
subtype2 | 10 | 34
subtype3 | 7 | 14

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

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

P value = 0.923 (ANOVA), Q value = 1

Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 77.7 (17.7)
subtype1 | 7 | 75.7 (24.4)
subtype2 | 14 | 77.9 (17.2)
subtype3 | 5 | 80.0 (10.0)

Figure S39. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.688 (ANOVA), Q value = 1

Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 51 | 34.6 (22.7)
subtype1 | 16 | 33.5 (19.5)
subtype2 | 20 | 38.0 (30.3)
subtype3 | 15 | 31.4 (13.0)

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

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.744 (ANOVA), Q value = 1

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 42 | 1990.6 (18.5)
subtype1 | 14 | 1987.6 (17.6)
subtype2 | 18 | 1991.6 (20.0)
subtype3 | 10 | 1993.2 (18.4)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.46 (ANOVA), Q value = 1

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 84 | 2.5 (1.1)
subtype1 | 24 | 2.7 (1.0)
subtype2 | 41 | 2.3 (1.2)
subtype3 | 19 | 2.6 (1.0)

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

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 24 | 57 | 29

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.478 (logrank test), Q value = 1

Table S50. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 104 | 30 | 0.4 - 118.9 (7.0)
subtype1 | 20 | 3 | 0.8 - 118.9 (6.4)
subtype2 | 55 | 20 | 0.4 - 61.9 (7.0)
subtype3 | 29 | 7 | 0.5 - 100.5 (8.1)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.389 (ANOVA), Q value = 1

Table S51. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 109 | 67.3 (11.0)
subtype1 | 23 | 68.4 (12.1)
subtype2 | 57 | 65.9 (10.9)
subtype3 | 29 | 69.1 (10.2)

Figure S44. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.25 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 31 | 79
subtype1 | 4 | 20
subtype2 | 16 | 41
subtype3 | 11 | 18

Figure S45. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.559 (ANOVA), Q value = 1

Table S53. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 31 | 76.8 (17.4)
subtype1 | 6 | 83.3 (10.3)
subtype2 | 20 | 74.5 (19.9)
subtype3 | 5 | 78.0 (13.0)

Figure S46. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.816 (ANOVA), Q value = 1

Table S54. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 65 | 35.6 (22.5)
subtype1 | 14 | 32.1 (27.0)
subtype2 | 35 | 36.3 (19.3)
subtype3 | 16 | 36.9 (26.1)

Figure S47. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'MIRseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.993 (ANOVA), Q value = 1

Table S55. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 54 | 1991.4 (17.8)
subtype1 | 13 | 1990.9 (23.0)
subtype2 | 24 | 1991.7 (16.3)
subtype3 | 17 | 1991.4 (16.5)

Figure S48. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'MIRseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.463 (ANOVA), Q value = 1

Table S56. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 104 | 2.6 (1.1)
subtype1 | 23 | 2.6 (1.3)
subtype2 | 54 | 2.5 (1.0)
subtype3 | 27 | 2.8 (1.2)

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S57. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 23 | 75 | 12

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.878 (logrank test), Q value = 1

Table S58. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 104 | 30 | 0.4 - 118.9 (7.0)
subtype1 | 19 | 4 | 1.5 - 118.9 (6.7)
subtype2 | 73 | 22 | 0.4 - 100.5 (7.3)
subtype3 | 12 | 4 | 0.8 - 46.8 (5.6)

Figure S50. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.47 (ANOVA), Q value = 1

Table S59. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 109 | 67.3 (11.0)
subtype1 | 22 | 69.5 (12.4)
subtype2 | 75 | 66.4 (11.0)
subtype3 | 12 | 68.5 (7.6)

Figure S51. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.801 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 31 | 79
subtype1 | 5 | 18
subtype2 | 23 | 52
subtype3 | 3 | 9

Figure S52. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.715 (ANOVA), Q value = 1

Table S61. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 31 | 76.8 (17.4)
subtype1 | 4 | 80.0 (11.5)
subtype2 | 24 | 75.4 (18.9)
subtype3 | 3 | 83.3 (11.5)

Figure S53. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.15 (ANOVA), Q value = 1

Table S62. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 65 | 35.6 (22.5)
subtype1 | 13 | 28.5 (24.2)
subtype2 | 43 | 35.2 (19.8)
subtype3 | 9 | 47.4 (29.8)

Figure S54. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'MIRseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.627 (ANOVA), Q value = 1

Table S63. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 54 | 1991.4 (17.8)
subtype1 | 12 | 1987.2 (22.3)
subtype2 | 33 | 1992.2 (17.2)
subtype3 | 9 | 1994.2 (13.8)

Figure S55. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'MIRseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.385 (ANOVA), Q value = 1

Table S64. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 104 | 2.6 (1.1)
subtype1 | 22 | 2.5 (1.3)
subtype2 | 70 | 2.6 (1.1)
subtype3 | 12 | 3.0 (1.0)

Figure S56. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Methods & Data
Input

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

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

- Number of patients = 117

- Number of clustering approaches = 8

- 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

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

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

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)
[6] 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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