Kidney Renal Clear Cell 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 7 different clustering approaches and 8 clinical features across 500 patients, 26 significant findings detected with P value < 0.05.

- CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T'.

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

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGY.T', 'PATHOLOGY.N', and 'NEOADJUVANT.THERAPY'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 26 significant findings detected.

Clinical Features | Statistical Tests | mRNA CNMF subtypes | mRNA cHierClus subtypes | METHLYATION CNMF | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRseq CNMF subtypes | MIRseq cHierClus subtypes
Time to Death | logrank test | 0.0868 | 0.189 | 0.000454 | 1.14e-07 | 2.35e-09 | 3.83e-06 | 0.000319
AGE | ANOVA | 0.795 | 0.607 | 0.256 | 0.099 | 0.0589 | 0.0827 | 0.108
GENDER | Fisher's exact test | 0.634 | 0.82 | 0.00307 | 2.33e-05 | 0.00775 | 0.0076 | 0.0901
KARNOFSKY PERFORMANCE SCORE | ANOVA |  |  |  | 0.709 | 0.338 | 0.201 | 0.926
PATHOLOGY T | Chi-square test | 0.00911 | 0.00779 | 0.000248 | 4.98e-06 | 3.6e-12 | 0.000289 | 0.000543
PATHOLOGY N | Fisher's exact test | 0.0704 | 0.124 | 0.0104 | 0.0102 | 0.00115 | 0.0158 | 0.0333
PATHOLOGICSPREAD(M) | Fisher's exact test | 0.12 | 0.0988 | 0.000576 | 0.000382 | 1.75e-06 | 0.00365 | 0.844
NEOADJUVANT THERAPY | Fisher's exact test | 0.194 | 0.208 | 1 | 0.37 | 0.186 | 0.54 | 0.00445

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 34 | 24 | 14

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0868 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 71 | 13 | 0.5 - 101.1 (32.6)
subtype1 | 33 | 4 | 0.5 - 101.1 (31.0)
subtype2 | 24 | 8 | 0.5 - 93.3 (36.7)
subtype3 | 14 | 1 | 1.3 - 84.4 (25.0)

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 = 0.795 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 71 | 60.5 (12.4)
subtype1 | 33 | 60.2 (13.8)
subtype2 | 24 | 59.9 (11.1)
subtype3 | 14 | 62.6 (11.3)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 43 | 29
subtype1 | 19 | 15
subtype2 | 14 | 10
subtype3 | 10 | 4

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 = 0.00911 (Chi-square test)

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

nPatients | T1 | T2 | T3
--- | --- | --- | ---
ALL | 41 | 14 | 17
subtype1 | 23 | 4 | 7
subtype2 | 10 | 4 | 10
subtype3 | 8 | 6 | 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0704 (Fisher's exact test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 35 | 3
subtype1 | 18 | 0
subtype2 | 10 | 3
subtype3 | 7 | 0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.12 (Fisher's exact test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 67 | 5
subtype1 | 33 | 1
subtype2 | 20 | 4
subtype3 | 14 | 0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.194 (Fisher's exact test)

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 71 | 1
subtype1 | 34 | 0
subtype2 | 24 | 0
subtype3 | 13 | 1

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 15 | 23 | 34

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.189 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 71 | 13 | 0.5 - 101.1 (32.6)
subtype1 | 15 | 2 | 1.3 - 84.4 (24.2)
subtype2 | 23 | 7 | 0.5 - 93.3 (36.8)
subtype3 | 33 | 4 | 0.5 - 101.1 (31.0)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.607 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 71 | 60.5 (12.4)
subtype1 | 15 | 63.2 (11.2)
subtype2 | 23 | 59.1 (10.7)
subtype3 | 33 | 60.4 (14.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.82 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 43 | 29
subtype1 | 10 | 5
subtype2 | 14 | 9
subtype3 | 19 | 15

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.00779 (Chi-square test)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3
--- | --- | --- | ---
ALL | 41 | 14 | 17
subtype1 | 9 | 6 | 0
subtype2 | 9 | 4 | 10
subtype3 | 23 | 4 | 7

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.124 (Fisher's exact test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 35 | 3
subtype1 | 7 | 0
subtype2 | 11 | 3
subtype3 | 17 | 0

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0988 (Fisher's exact test)

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 67 | 5
subtype1 | 15 | 0
subtype2 | 19 | 4
subtype3 | 33 | 1

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.208 (Fisher's exact test)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 71 | 1
subtype1 | 14 | 1
subtype2 | 23 | 0
subtype3 | 34 | 0

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S17. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 77 | 91 | 51

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000454 (logrank test)

Table S18. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 218 | 65 | 0.1 - 111.0 (39.7)
subtype1 | 77 | 32 | 0.5 - 90.3 (36.3)
subtype2 | 90 | 16 | 1.3 - 111.0 (45.3)
subtype3 | 51 | 17 | 0.1 - 101.1 (42.4)

Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.256 (ANOVA)

Table S19. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 218 | 59.5 (12.5)
subtype1 | 76 | 61.3 (12.0)
subtype2 | 91 | 59.0 (13.4)
subtype3 | 51 | 57.7 (11.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00307 (Fisher's exact test)

Table S20. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 142 | 77
subtype1 | 57 | 20
subtype2 | 47 | 44
subtype3 | 38 | 13

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.000248 (Chi-square test)

Table S21. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 117 | 29 | 70 | 3
subtype1 | 30 | 8 | 36 | 3
subtype2 | 63 | 12 | 16 | 0
subtype3 | 24 | 9 | 18 | 0

Figure S18. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0104 (Fisher's exact test)

Table S22. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 106 | 9
subtype1 | 35 | 7
subtype2 | 41 | 0
subtype3 | 30 | 2

Figure S19. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000576 (Fisher's exact test)

Table S23. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 193 | 26
subtype1 | 59 | 18
subtype2 | 87 | 4
subtype3 | 47 | 4

Figure S20. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S24. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 217 | 2
subtype1 | 76 | 1
subtype2 | 90 | 1
subtype3 | 51 | 0

Figure S21. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 200 | 102 | 167

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.14e-07 (logrank test)

Table S26. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 467 | 155 | 0.1 - 111.0 (34.3)
subtype1 | 199 | 45 | 0.1 - 111.0 (37.5)
subtype2 | 101 | 31 | 0.1 - 93.3 (35.2)
subtype3 | 167 | 79 | 0.1 - 90.3 (29.1)

Figure S22. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.099 (ANOVA)

Table S27. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 468 | 60.6 (12.2)
subtype1 | 199 | 61.8 (12.4)
subtype2 | 102 | 58.7 (12.3)
subtype3 | 167 | 60.3 (11.8)

Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 2.33e-05 (Fisher's exact test)

Table S28. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 307 | 162
subtype1 | 109 | 91
subtype2 | 69 | 33
subtype3 | 129 | 38

Figure S24. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.709 (ANOVA)

Table S29. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 31 | 91.0 (18.7)
subtype1 | 14 | 92.1 (8.9)
subtype2 | 7 | 94.3 (7.9)
subtype3 | 10 | 87.0 (31.3)

Figure S25. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 4.98e-06 (Chi-square test)

Table S30. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 228 | 59 | 171 | 11
subtype1 | 117 | 24 | 57 | 2
subtype2 | 59 | 11 | 29 | 3
subtype3 | 52 | 24 | 85 | 6

Figure S26. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0102 (Fisher's exact test)

Table S31. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 223 | 17
subtype1 | 96 | 2
subtype2 | 48 | 3
subtype3 | 79 | 12

Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000382 (Fisher's exact test)

Table S32. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 392 | 77
subtype1 | 178 | 22
subtype2 | 90 | 12
subtype3 | 124 | 43

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.37 (Fisher's exact test)

Table S33. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 464 | 5
subtype1 | 199 | 1
subtype2 | 100 | 2
subtype3 | 165 | 2

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S34. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 70 | 187 | 212

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.35e-09 (logrank test)

Table S35. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 467 | 155 | 0.1 - 111.0 (34.3)
subtype1 | 69 | 13 | 0.2 - 92.0 (34.3)
subtype2 | 187 | 92 | 0.1 - 93.3 (29.1)
subtype3 | 211 | 50 | 0.1 - 111.0 (37.5)

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0589 (ANOVA)

Table S36. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 468 | 60.6 (12.2)
subtype1 | 70 | 57.4 (12.9)
subtype2 | 187 | 61.2 (11.6)
subtype3 | 211 | 61.1 (12.3)

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00775 (Fisher's exact test)

Table S37. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 307 | 162
subtype1 | 48 | 22
subtype2 | 136 | 51
subtype3 | 123 | 89

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.338 (ANOVA)

Table S38. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 31 | 91.0 (18.7)
subtype1 | 11 | 95.5 (9.3)
subtype2 | 9 | 83.3 (32.4)
subtype3 | 11 | 92.7 (6.5)

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 3.6e-12 (Chi-square test)

Table S39. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 228 | 59 | 171 | 11
subtype1 | 54 | 6 | 9 | 1
subtype2 | 53 | 26 | 100 | 8
subtype3 | 121 | 27 | 62 | 2

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00115 (Fisher's exact test)

Table S40. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 223 | 17
subtype1 | 28 | 2
subtype2 | 92 | 14
subtype3 | 103 | 1

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 1.75e-06 (Fisher's exact test)

Table S41. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 392 | 77
subtype1 | 67 | 3
subtype2 | 137 | 50
subtype3 | 188 | 24

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.186 (Fisher's exact test)

Table S42. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 464 | 5
subtype1 | 68 | 2
subtype2 | 185 | 2
subtype3 | 211 | 1

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S43. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 195 | 102 | 166

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 3.83e-06 (logrank test)

Table S44. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 461 | 149 | 0.1 - 111.0 (33.5)
subtype1 | 195 | 44 | 0.1 - 111.0 (34.6)
subtype2 | 102 | 30 | 0.1 - 109.9 (37.2)
subtype3 | 164 | 75 | 0.2 - 93.3 (29.5)

Figure S38. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0827 (ANOVA)

Table S45. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 463 | 60.7 (12.2)
subtype1 | 195 | 62.1 (12.2)
subtype2 | 102 | 58.9 (12.0)
subtype3 | 166 | 60.3 (12.1)

Figure S39. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0076 (Fisher's exact test)

Table S46. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 304 | 159
subtype1 | 113 | 82
subtype2 | 69 | 33
subtype3 | 122 | 44

Figure S40. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.201 (ANOVA)

Table S47. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 35 | 88.0 (23.2)
subtype1 | 15 | 92.0 (8.6)
subtype2 | 8 | 95.0 (7.6)
subtype3 | 12 | 78.3 (37.1)

Figure S41. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.000289 (Chi-square test)

Table S48. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 225 | 59 | 169 | 10
subtype1 | 106 | 26 | 61 | 2
subtype2 | 61 | 6 | 31 | 4
subtype3 | 58 | 27 | 77 | 4

Figure S42. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0158 (Fisher's exact test)

Table S49. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 213 | 16
subtype1 | 91 | 2
subtype2 | 47 | 3
subtype3 | 75 | 11

Figure S43. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.00365 (Fisher's exact test)

Table S50. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 391 | 72
subtype1 | 170 | 25
subtype2 | 93 | 9
subtype3 | 128 | 38

Figure S44. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.54 (Fisher's exact test)

Table S51. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 458 | 5
subtype1 | 194 | 1
subtype2 | 101 | 1
subtype3 | 163 | 3

Figure S45. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S52. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 46 | 154 | 263

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000319 (logrank test)

Table S53. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 461 | 149 | 0.1 - 111.0 (33.5)
subtype1 | 45 | 15 | 0.6 - 93.3 (36.4)
subtype2 | 154 | 67 | 0.1 - 109.9 (30.9)
subtype3 | 262 | 67 | 0.1 - 111.0 (34.5)

Figure S46. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.108 (ANOVA)

Table S54. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 463 | 60.7 (12.2)
subtype1 | 46 | 57.5 (11.8)
subtype2 | 154 | 60.4 (12.8)
subtype3 | 263 | 61.5 (11.8)

Figure S47. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0901 (Fisher's exact test)

Table S55. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 304 | 159
subtype1 | 31 | 15
subtype2 | 111 | 43
subtype3 | 162 | 101

Figure S48. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.926 (ANOVA)

Table S56. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 35 | 88.0 (23.2)
subtype1 | 1 | 90.0 (NA)
subtype2 | 13 | 88.5 (27.3)
subtype3 | 21 | 87.6 (21.7)

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.000543 (Chi-square test)

Table S57. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 225 | 59 | 169 | 10
subtype1 | 21 | 11 | 13 | 1
subtype2 | 60 | 15 | 72 | 7
subtype3 | 144 | 33 | 84 | 2

Figure S50. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0333 (Fisher's exact test)

Table S58. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 213 | 16
subtype1 | 21 | 3
subtype2 | 71 | 9
subtype3 | 121 | 4

Figure S51. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.844 (Fisher's exact test)

Table S59. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 391 | 72
subtype1 | 39 | 7
subtype2 | 128 | 26
subtype3 | 224 | 39

Figure S52. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00445 (Fisher's exact test)

Table S60. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 458 | 5
subtype1 | 43 | 3
subtype2 | 154 | 0
subtype3 | 261 | 2

Figure S53. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = KIRC.mergedcluster.txt

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

- Number of patients = 500

- Number of clustering approaches = 7

- 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

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

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

Download Results

This is an experimental feature. Location of data archives could not be determined.

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
Meta

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