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 499 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', 'AGE', 'GENDER', 'PATHOLOGY.T', 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 4 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', and 'PATHOLOGICSPREAD(M)'.

- Consensus hierarchical 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)'.

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 | 1.16e-05 | 2.59e-07 | 1.03e-09 | 3.31e-07 | 1.62e-08
AGE | ANOVA | 0.795 | 0.607 | 0.00437 | 0.323 | 0.335 | 0.0787 | 0.892
GENDER | Fisher's exact test | 0.634 | 0.82 | 0.00516 | 1.17e-05 | 0.00361 | 0.0142 | 0.000362
KARNOFSKY PERFORMANCE SCORE | ANOVA |  |  | 0.595 | 0.709 | 0.342 | 0.18 | 0.827
PATHOLOGY T | Chi-square test | 0.00911 | 0.00779 | 2.71e-09 | 1.37e-05 | 1.66e-10 | 0.00171 | 3.55e-06
PATHOLOGY N | Fisher's exact test | 0.0704 | 0.124 | 0.0842 | 0.0106 | 0.00494 | 0.0763 | 0.0297
PATHOLOGICSPREAD(M) | Fisher's exact test | 0.12 | 0.0988 | 0.000181 | 0.000475 | 4.85e-05 | 0.00738 | 0.000451
NEOADJUVANT THERAPY | Fisher's exact test | 0.194 | 0.208 | 0.386 | 1 | 0.654 | 0.52 | 0.133

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 | 79 | 117 | 87

'METHLYATION CNMF' versus 'Time to Death'

P value = 1.16e-05 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 281 | 95 | 0.1 - 109.9 (28.5)
subtype1 | 78 | 38 | 0.2 - 84.7 (29.7)
subtype2 | 116 | 21 | 0.1 - 109.6 (31.4)
subtype3 | 87 | 36 | 0.1 - 109.9 (22.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00437 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 283 | 61.5 (12.0)
subtype1 | 79 | 64.7 (10.3)
subtype2 | 117 | 59.0 (12.8)
subtype3 | 87 | 61.9 (11.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00516 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 187 | 96
subtype1 | 62 | 17
subtype2 | 66 | 51
subtype3 | 59 | 28

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

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

P value = 0.595 (ANOVA)

Table S21. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 28 | 92.5 (8.0)
subtype1 | 6 | 91.7 (7.5)
subtype2 | 14 | 91.4 (8.6)
subtype3 | 8 | 95.0 (7.6)

Figure S18. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 2.71e-09 (Chi-square test)

Table S22. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 132 | 36 | 107 | 8
subtype1 | 18 | 13 | 46 | 2
subtype2 | 76 | 18 | 23 | 0
subtype3 | 38 | 5 | 38 | 6

Figure S19. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0842 (Fisher's exact test)

Table S23. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 126 | 9
subtype1 | 33 | 2
subtype2 | 52 | 1
subtype3 | 41 | 6

Figure S20. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000181 (Fisher's exact test)

Table S24. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 232 | 51
subtype1 | 53 | 26
subtype2 | 106 | 11
subtype3 | 73 | 14

Figure S21. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.386 (Fisher's exact test)

Table S25. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 279 | 4
subtype1 | 77 | 2
subtype2 | 115 | 2
subtype3 | 87 | 0

Figure S22. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S26. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 198 | 100 | 171

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 2.59e-07 (logrank test)

Table S27. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 467 | 154 | 0.1 - 111.0 (34.3)
subtype1 | 198 | 45 | 0.1 - 111.0 (37.9)
subtype2 | 99 | 29 | 0.1 - 93.3 (31.3)
subtype3 | 170 | 80 | 0.1 - 90.3 (29.8)

Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.323 (ANOVA)

Table S28. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 468 | 60.6 (12.2)
subtype1 | 197 | 61.3 (12.2)
subtype2 | 100 | 59.1 (12.5)
subtype3 | 171 | 60.7 (12.0)

Figure S24. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1.17e-05 (Fisher's exact test)

Table S29. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 307 | 162
subtype1 | 106 | 92
subtype2 | 70 | 30
subtype3 | 131 | 40

Figure S25. 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 S30. 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 S26. 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 = 1.37e-05 (Chi-square test)

Table S31. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 229 | 58 | 171 | 11
subtype1 | 114 | 23 | 59 | 2
subtype2 | 60 | 10 | 27 | 3
subtype3 | 55 | 25 | 85 | 6

Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0106 (Fisher's exact test)

Table S32. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 223 | 17
subtype1 | 95 | 2
subtype2 | 49 | 3
subtype3 | 79 | 12

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000475 (Fisher's exact test)

Table S33. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 393 | 76
subtype1 | 175 | 23
subtype2 | 90 | 10
subtype3 | 128 | 43

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S34. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 464 | 5
subtype1 | 196 | 2
subtype2 | 99 | 1
subtype3 | 169 | 2

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S35. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 52 | 221 | 196

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1.03e-09 (logrank test)

Table S36. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 467 | 154 | 0.1 - 111.0 (34.3)
subtype1 | 51 | 10 | 0.2 - 92.0 (24.2)
subtype2 | 220 | 49 | 0.1 - 111.0 (38.5)
subtype3 | 196 | 95 | 0.1 - 93.3 (28.9)

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.335 (ANOVA)

Table S37. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 468 | 60.6 (12.2)
subtype1 | 52 | 58.5 (12.9)
subtype2 | 220 | 60.5 (12.4)
subtype3 | 196 | 61.3 (11.7)

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00361 (Fisher's exact test)

Table S38. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 307 | 162
subtype1 | 35 | 17
subtype2 | 128 | 93
subtype3 | 144 | 52

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.342 (ANOVA)

Table S39. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 31 | 91.0 (18.7)
subtype1 | 9 | 95.6 (10.1)
subtype2 | 13 | 93.1 (6.3)
subtype3 | 9 | 83.3 (32.4)

Figure S34. 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 = 1.66e-10 (Chi-square test)

Table S40. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 229 | 58 | 171 | 11
subtype1 | 40 | 5 | 6 | 1
subtype2 | 128 | 28 | 64 | 1
subtype3 | 61 | 25 | 101 | 9

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00494 (Fisher's exact test)

Table S41. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 223 | 17
subtype1 | 26 | 1
subtype2 | 103 | 2
subtype3 | 94 | 14

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 4.85e-05 (Fisher's exact test)

Table S42. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 393 | 76
subtype1 | 49 | 3
subtype2 | 197 | 24
subtype3 | 147 | 49

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.654 (Fisher's exact test)

Table S43. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 464 | 5
subtype1 | 51 | 1
subtype2 | 219 | 2
subtype3 | 194 | 2

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S44. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 172 | 84 | 64 | 143

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 3.31e-07 (logrank test)

Table S45. 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 | 172 | 37 | 0.1 - 111.0 (36.6)
subtype2 | 84 | 22 | 0.1 - 109.9 (37.1)
subtype3 | 64 | 22 | 0.1 - 91.4 (31.4)
subtype4 | 141 | 68 | 0.2 - 93.3 (29.1)

Figure S39. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0787 (ANOVA)

Table S46. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 463 | 60.7 (12.2)
subtype1 | 172 | 62.3 (12.3)
subtype2 | 84 | 58.1 (11.5)
subtype3 | 64 | 60.7 (12.7)
subtype4 | 143 | 60.4 (11.9)

Figure S40. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0142 (Fisher's exact test)

Table S47. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 304 | 159
subtype1 | 98 | 74
subtype2 | 55 | 29
subtype3 | 46 | 18
subtype4 | 105 | 38

Figure S41. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.18 (ANOVA)

Table S48. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 35 | 88.0 (23.2)
subtype1 | 14 | 92.1 (8.9)
subtype2 | 6 | 95.0 (8.4)
subtype3 | 6 | 93.3 (5.2)
subtype4 | 9 | 73.3 (42.1)

Figure S42. 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 = 0.00171 (Chi-square test)

Table S49. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 225 | 59 | 169 | 10
subtype1 | 95 | 22 | 53 | 2
subtype2 | 50 | 5 | 25 | 4
subtype3 | 31 | 7 | 26 | 0
subtype4 | 49 | 25 | 65 | 4

Figure S43. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0763 (Fisher's exact test)

Table S50. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 213 | 16
subtype1 | 77 | 2
subtype2 | 41 | 2
subtype3 | 26 | 2
subtype4 | 69 | 10

Figure S44. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.00738 (Fisher's exact test)

Table S51. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 391 | 72
subtype1 | 154 | 18
subtype2 | 75 | 9
subtype3 | 53 | 11
subtype4 | 109 | 34

Figure S45. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.52 (Fisher's exact test)

Table S52. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 458 | 5
subtype1 | 171 | 1
subtype2 | 83 | 1
subtype3 | 64 | 0
subtype4 | 140 | 3

Figure S46. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S53. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 99 | 123 | 241

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 1.62e-08 (logrank test)

Table S54. 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 | 99 | 15 | 0.4 - 109.6 (48.4)
subtype2 | 123 | 57 | 0.1 - 109.9 (28.8)
subtype3 | 239 | 77 | 0.1 - 111.0 (31.1)

Figure S47. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.892 (ANOVA)

Table S55. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 463 | 60.7 (12.2)
subtype1 | 99 | 60.3 (12.2)
subtype2 | 123 | 61.1 (12.8)
subtype3 | 241 | 60.8 (11.9)

Figure S48. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.000362 (Fisher's exact test)

Table S56. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 304 | 159
subtype1 | 48 | 51
subtype2 | 87 | 36
subtype3 | 169 | 72

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.827 (ANOVA)

Table S57. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 35 | 88.0 (23.2)
subtype1 | 5 | 94.0 (5.5)
subtype2 | 11 | 86.4 (29.4)
subtype3 | 19 | 87.4 (22.8)

Figure S50. 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 = 3.55e-06 (Chi-square test)

Table S58. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 225 | 59 | 169 | 10
subtype1 | 68 | 10 | 21 | 0
subtype2 | 47 | 11 | 58 | 7
subtype3 | 110 | 38 | 90 | 3

Figure S51. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0297 (Fisher's exact test)

Table S59. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 213 | 16
subtype1 | 42 | 0
subtype2 | 54 | 8
subtype3 | 117 | 8

Figure S52. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000451 (Fisher's exact test)

Table S60. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 391 | 72
subtype1 | 95 | 4
subtype2 | 99 | 24
subtype3 | 197 | 44

Figure S53. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.133 (Fisher's exact test)

Table S61. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 458 | 5
subtype1 | 99 | 0
subtype2 | 123 | 0
subtype3 | 236 | 5

Figure S54. 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 = 499

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