Kidney Renal Papillary Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
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 8 clinical features across 101 patients, 9 significant findings detected with P value < 0.05.

- CNMF clustering analysis on array-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.

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

- 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 correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'TUMOR.STAGE'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGY.T'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 9 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | PATHOLOGY T | PATHOLOGY N | PATHOLOGICSPREAD(M) | TUMOR STAGE
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | Chi-square test | Chi-square test | Chi-square test | Chi-square test
mRNA CNMF subtypes | 100 | 0.182 | 0.585 |  | 0.0623 |  | 1 | 0.292
mRNA cHierClus subtypes | 100 | 0.948 | 1 |  | 0.216 |  | 1 |
CN CNMF | 0.156 | 0.875 | 0.488 | 0.311 | 0.415 | 0.451 | 0.51 | 0.297
METHLYATION CNMF | 0.15 | 0.0523 | 0.226 | 0.144 | 6.37e-08 | 0.142 | 0.0567 | 7.93e-06
RNAseq CNMF subtypes | 0.325 | 0.434 | 0.0644 |  | 0.00023 | 0.934 | 0.108 | 0.000784
RNAseq cHierClus subtypes | 0.0431 | 0.246 | 0.0625 | 0.481 | 0.37 | 0.0693 | 0.0402 | 0.00334
MIRseq CNMF subtypes | 0.582 | 0.574 | 0.178 | 0.295 | 0.137 | 0.267 | 0.266 | 0.0296
MIRseq cHierClus subtypes | 0.693 | 0.925 | 0.0974 | 0.647 | 0.0183 | 0.262 | 0.239 | 0.0732

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 7 | 9

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 16 | 2 | 0.5 - 58.5 (7.8)
subtype1 | 7 | 1 | 0.5 - 53.8 (5.9)
subtype2 | 9 | 1 | 1.1 - 58.5 (10.8)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.182 (t-test)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 16 | 57.9 (11.5)
subtype1 | 7 | 53.6 (10.3)
subtype2 | 9 | 61.3 (11.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.585 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 4 | 12
subtype1 | 1 | 6
subtype2 | 3 | 6

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0623 (Chi-square test)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 7 | 7 | 2
subtype1 | 1 | 4 | 2
subtype2 | 6 | 3 | 0

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1 (Fisher's exact test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 9 | 1
subtype1 | 4 | 1
subtype2 | 5 | 0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.292 (Chi-square test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 5 | 2 | 1 | 1
subtype1 | 1 | 1 | 1 | 1
subtype2 | 4 | 1 | 0 | 0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 4 | 7 | 5

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 16 | 2 | 0.5 - 58.5 (7.8)
subtype1 | 4 | 1 | 10.8 - 58.5 (40.4)
subtype2 | 7 | 1 | 0.5 - 25.1 (4.4)
subtype3 | 5 | 0 | 0.7 - 53.8 (4.1)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.948 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 16 | 57.9 (11.5)
subtype1 | 4 | 57.0 (5.0)
subtype2 | 7 | 57.4 (13.0)
subtype3 | 5 | 59.4 (14.8)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 4 | 12
subtype1 | 1 | 3
subtype2 | 2 | 5
subtype3 | 1 | 4

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 7 | 7 | 2
subtype1 | 3 | 1 | 0
subtype2 | 1 | 4 | 2
subtype3 | 3 | 2 | 0

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1 (Fisher's exact test)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 9 | 1
subtype1 | 2 | 0
subtype2 | 4 | 1
subtype3 | 3 | 0

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #3: 'CN CNMF'

Table S14. Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 27 | 54 | 20

'CN CNMF' versus 'Time to Death'

P value = 0.156 (logrank test)

Table S15. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 94 | 14 | 0.0 - 182.7 (13.9)
subtype1 | 24 | 3 | 0.0 - 182.7 (10.4)
subtype2 | 50 | 7 | 0.2 - 129.9 (23.7)
subtype3 | 20 | 4 | 0.1 - 79.8 (11.1)

Figure S12. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.875 (ANOVA)

Table S16. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 98 | 59.7 (12.4)
subtype1 | 24 | 58.7 (12.4)
subtype2 | 54 | 60.2 (12.7)
subtype3 | 20 | 59.4 (12.4)

Figure S13. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.488 (Fisher's exact test)

Table S17. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 32 | 69
subtype1 | 6 | 21
subtype2 | 19 | 35
subtype3 | 7 | 13

Figure S14. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.311 (ANOVA)

Table S18. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 87.6 (23.9)
subtype1 | 4 | 97.5 (5.0)
subtype2 | 12 | 90.0 (16.5)
subtype3 | 5 | 74.0 (41.6)

Figure S15. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.415 (Chi-square test)

Table S19. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 56 | 13 | 32
subtype1 | 15 | 5 | 7
subtype2 | 33 | 5 | 16
subtype3 | 8 | 3 | 9

Figure S16. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.451 (Chi-square test)

Table S20. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 11 | 4
subtype1 | 2 | 4 | 1
subtype2 | 10 | 4 | 1
subtype3 | 8 | 3 | 2

Figure S17. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.51 (Chi-square test)

Table S21. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 33
subtype1 | 11 | 1 | 11
subtype2 | 32 | 2 | 17
subtype3 | 11 | 2 | 5

Figure S18. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.297 (Chi-square test)

Table S22. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 51 | 7 | 23 | 9
subtype1 | 14 | 2 | 3 | 3
subtype2 | 29 | 4 | 15 | 2
subtype3 | 8 | 1 | 5 | 4

Figure S19. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S23. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 21 | 26 | 38

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.15 (logrank test)

Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 78 | 12 | 0.0 - 182.7 (15.7)
subtype1 | 19 | 4 | 0.0 - 80.8 (26.0)
subtype2 | 25 | 6 | 0.2 - 182.7 (12.0)
subtype3 | 34 | 2 | 0.0 - 129.9 (14.8)

Figure S20. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0523 (ANOVA)

Table S25. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 82 | 60.0 (12.7)
subtype1 | 20 | 65.8 (10.0)
subtype2 | 25 | 59.4 (16.3)
subtype3 | 37 | 57.3 (10.2)

Figure S21. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.226 (Fisher's exact test)

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 28 | 57
subtype1 | 6 | 15
subtype2 | 12 | 14
subtype3 | 10 | 28

Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.144 (ANOVA)

Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 20 | 92.0 (13.2)
subtype1 | 6 | 81.7 (20.4)
subtype2 | 1 | 100.0 (NA)
subtype3 | 13 | 96.2 (5.1)

Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 6.37e-08 (Chi-square test)

Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 49 | 6 | 30
subtype1 | 15 | 0 | 6
subtype2 | 4 | 1 | 21
subtype3 | 30 | 5 | 3

Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.142 (Chi-square test)

Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 9 | 4
subtype1 | 3 | 2 | 1
subtype2 | 9 | 7 | 3
subtype3 | 8 | 0 | 0

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0567 (Chi-square test)

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 45 | 4 | 33
subtype1 | 13 | 1 | 6
subtype2 | 16 | 3 | 7
subtype3 | 16 | 0 | 20

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 7.93e-06 (Chi-square test)

Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 46 | 5 | 22 | 8
subtype1 | 14 | 1 | 3 | 2
subtype2 | 4 | 1 | 15 | 6
subtype3 | 28 | 3 | 4 | 0

Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S32. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 22 | 18 | 12 | 11

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.325 (logrank test)

Table S33. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 13 | 0.5 - 123.6 (15.5)
subtype1 | 22 | 7 | 0.9 - 93.3 (16.2)
subtype2 | 18 | 2 | 0.5 - 63.7 (12.6)
subtype3 | 12 | 2 | 7.0 - 123.6 (29.1)
subtype4 | 11 | 2 | 3.8 - 80.8 (15.5)

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA)

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 63 | 60.3 (12.5)
subtype1 | 22 | 59.2 (14.2)
subtype2 | 18 | 58.7 (11.7)
subtype3 | 12 | 65.8 (11.2)
subtype4 | 11 | 59.3 (11.6)

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0644 (Fisher's exact test)

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 20 | 43
subtype1 | 11 | 11
subtype2 | 6 | 12
subtype3 | 1 | 11
subtype4 | 2 | 9

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00023 (Chi-square test)

Table S36. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 32 | 8 | 23
subtype1 | 6 | 1 | 15
subtype2 | 10 | 5 | 3
subtype3 | 5 | 2 | 5
subtype4 | 11 | 0 | 0

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.934 (Chi-square test)

Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 14 | 9 | 3
subtype1 | 7 | 6 | 2
subtype2 | 2 | 1 | 0
subtype3 | 4 | 2 | 1
subtype4 | 1 | 0 | 0

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.108 (Chi-square test)

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 42 | 5 | 9
subtype1 | 14 | 5 | 3
subtype2 | 10 | 0 | 2
subtype3 | 11 | 0 | 1
subtype4 | 7 | 0 | 3

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.000784 (Chi-square test)

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 29 | 3 | 16 | 7
subtype1 | 5 | 1 | 8 | 7
subtype2 | 9 | 0 | 3 | 0
subtype3 | 5 | 2 | 5 | 0
subtype4 | 10 | 0 | 0 | 0

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S40. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 16 | 25 | 22

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0431 (logrank test)

Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 13 | 0.5 - 123.6 (15.5)
subtype1 | 16 | 6 | 2.8 - 80.8 (10.9)
subtype2 | 25 | 3 | 0.5 - 123.6 (13.6)
subtype3 | 22 | 4 | 6.4 - 93.3 (25.2)

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.246 (ANOVA)

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 63 | 60.3 (12.5)
subtype1 | 16 | 59.1 (13.4)
subtype2 | 25 | 58.0 (11.3)
subtype3 | 22 | 63.9 (13.1)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0625 (Fisher's exact test)

Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 20 | 43
subtype1 | 9 | 7
subtype2 | 6 | 19
subtype3 | 5 | 17

Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.481 (ANOVA)

Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 8 | 83.8 (34.2)
subtype1 | 3 | 93.3 (5.8)
subtype2 | 5 | 78.0 (43.8)

Figure S38. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.37 (Chi-square test)

Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 32 | 8 | 23
subtype1 | 7 | 2 | 7
subtype2 | 14 | 5 | 6
subtype3 | 11 | 1 | 10

Figure S39. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0693 (Chi-square test)

Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 14 | 9 | 3
subtype1 | 1 | 5 | 2
subtype2 | 4 | 2 | 0
subtype3 | 9 | 2 | 1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0402 (Chi-square test)

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 42 | 5 | 9
subtype1 | 8 | 4 | 3
subtype2 | 15 | 1 | 4
subtype3 | 19 | 0 | 2

Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00334 (Chi-square test)

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 29 | 3 | 16 | 7
subtype1 | 6 | 1 | 1 | 6
subtype2 | 13 | 1 | 5 | 1
subtype3 | 10 | 1 | 10 | 0

Figure S42. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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 | 34 | 36 | 31

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.582 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 94 | 14 | 0.0 - 182.7 (13.9)
subtype1 | 33 | 4 | 0.0 - 96.9 (25.1)
subtype2 | 34 | 7 | 0.2 - 182.7 (17.4)
subtype3 | 27 | 3 | 0.0 - 123.6 (10.8)

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 = 0.574 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 98 | 59.7 (12.4)
subtype1 | 34 | 60.3 (12.5)
subtype2 | 36 | 58.0 (14.4)
subtype3 | 28 | 61.1 (9.5)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.178 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 32 | 69
subtype1 | 7 | 27
subtype2 | 15 | 21
subtype3 | 10 | 21

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 = 0.295 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 87.6 (23.9)
subtype1 | 7 | 94.3 (5.3)
subtype2 | 10 | 79.0 (32.8)
subtype3 | 4 | 97.5 (5.0)

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.137 (Chi-square test)

Table S54. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 56 | 13 | 32
subtype1 | 18 | 3 | 13
subtype2 | 19 | 3 | 14
subtype3 | 19 | 7 | 5

Figure S47. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.267 (Chi-square test)

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 11 | 4
subtype1 | 9 | 3 | 1
subtype2 | 6 | 7 | 3
subtype3 | 5 | 1 | 0

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

'MIRseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.266 (Chi-square test)

Table S56. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 33
subtype1 | 22 | 0 | 10
subtype2 | 19 | 4 | 13
subtype3 | 13 | 1 | 10

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

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0296 (Chi-square test)

Table S57. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 51 | 7 | 23 | 9
subtype1 | 17 | 3 | 12 | 0
subtype2 | 18 | 3 | 6 | 8
subtype3 | 16 | 1 | 5 | 1

Figure S50. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S58. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 21 | 40 | 40

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.693 (logrank test)

Table S59. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 94 | 14 | 0.0 - 182.7 (13.9)
subtype1 | 19 | 2 | 0.0 - 123.6 (5.9)
subtype2 | 36 | 7 | 0.2 - 182.7 (17.8)
subtype3 | 39 | 5 | 0.0 - 96.9 (19.2)

Figure S51. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.925 (ANOVA)

Table S60. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 98 | 59.7 (12.4)
subtype1 | 19 | 60.7 (9.8)
subtype2 | 39 | 59.3 (13.8)
subtype3 | 40 | 59.5 (12.4)

Figure S52. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0974 (Fisher's exact test)

Table S61. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 32 | 69
subtype1 | 7 | 14
subtype2 | 17 | 23
subtype3 | 8 | 32

Figure S53. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.647 (ANOVA)

Table S62. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 87.6 (23.9)
subtype1 | 3 | 96.7 (5.8)
subtype2 | 11 | 89.1 (17.0)
subtype3 | 7 | 81.4 (36.3)

Figure S54. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0183 (Chi-square test)

Table S63. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 56 | 13 | 32
subtype1 | 14 | 6 | 1
subtype2 | 21 | 3 | 16
subtype3 | 21 | 4 | 15

Figure S55. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.262 (Chi-square test)

Table S64. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 11 | 4
subtype1 | 2 | 0 | 0
subtype2 | 7 | 7 | 3
subtype3 | 11 | 4 | 1

Figure S56. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.239 (Chi-square test)

Table S65. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 33
subtype1 | 10 | 0 | 7
subtype2 | 19 | 4 | 16
subtype3 | 25 | 1 | 10

Figure S57. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0732 (Chi-square test)

Table S66. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 51 | 7 | 23 | 9
subtype1 | 11 | 1 | 2 | 0
subtype2 | 20 | 3 | 8 | 8
subtype3 | 20 | 3 | 13 | 1

Figure S58. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Methods & Data
Input

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

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

- Number of patients = 101

- Number of clustering approaches = 8

- Number of selected clinical features = 8

- 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' function in R

Fisher's exact test

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

Chi-square test

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

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
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