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 9 clinical features across 499 patients, 33 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' and 'TUMOR.STAGE'.

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

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

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

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

- CNMF clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 9 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 33 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 | 0.0837 | 0.00986 | 0.00446 | 0.072 | 0.0257
PATHOLOGICSPREAD(M) | Fisher's exact test | 0.12 | 0.0988 | 0.000181 | 0.000475 | 4.85e-05 | 0.00738 | 0.000451
TUMOR STAGE | Chi-square test | 0.0134 | 0.0102 | 3.34e-08 | 2.6e-06 | 7.47e-11 | 0.00357 | 1.61e-05
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 'TUMOR.STAGE'

P value = 0.0134 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 40 | 15 | 12 | 5
subtype1 | 23 | 5 | 5 | 1
subtype2 | 9 | 4 | 7 | 4
subtype3 | 8 | 6 | 0 | 0

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.194 (Fisher's exact test)

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10. 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 S11. 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 S9. 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 S12. 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 S10. 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 S13. 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 S11. 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 S14. 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 S12. 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 S15. 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 S13. 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 S16. 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 S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0102 (Chi-square test)

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 40 | 15 | 12 | 5
subtype1 | 9 | 6 | 0 | 0
subtype2 | 8 | 4 | 7 | 4
subtype3 | 23 | 5 | 5 | 1

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.208 (Fisher's exact test)

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S19. 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 S20. 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 S17. 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 S21. 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 S18. 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 S22. 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 S19. 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 S23. 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 S20. 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 S24. 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 S21. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0837 (Fisher's exact test)

Table S25. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 127 | 9
subtype1 | 33 | 2
subtype2 | 53 | 1
subtype3 | 41 | 6

Figure S22. 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 S26. 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 S23. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 3.34e-08 (Chi-square test)

Table S27. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 130 | 24 | 73 | 56
subtype1 | 18 | 8 | 25 | 28
subtype2 | 76 | 12 | 18 | 11
subtype3 | 36 | 4 | 30 | 17

Figure S24. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.386 (Fisher's exact test)

Table S28. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S25. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S29. 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 S30. 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 S26. 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 S31. 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 S27. 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 S32. 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 S28. 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 S33. 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 S29. 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 S34. 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 S30. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00986 (Fisher's exact test)

Table S35. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 224 | 17
subtype1 | 96 | 2
subtype2 | 49 | 3
subtype3 | 79 | 12

Figure S31. 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 S36. 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 S32. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 2.6e-06 (Chi-square test)

Table S37. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 225 | 47 | 117 | 80
subtype1 | 114 | 19 | 41 | 24
subtype2 | 59 | 9 | 20 | 12
subtype3 | 52 | 19 | 56 | 44

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S38. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S39. 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 S40. 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 S35. 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 S41. 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 S36. 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 S42. 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 S37. 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 S43. 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 S38. 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 S44. 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 S39. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00446 (Fisher's exact test)

Table S45. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 224 | 17
subtype1 | 26 | 1
subtype2 | 104 | 2
subtype3 | 94 | 14

Figure S40. 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 S46. 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 S41. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 7.47e-11 (Chi-square test)

Table S47. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 225 | 47 | 117 | 80
subtype1 | 40 | 5 | 4 | 3
subtype2 | 127 | 22 | 47 | 25
subtype3 | 58 | 20 | 66 | 52

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.654 (Fisher's exact test)

Table S48. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S49. 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 S50. 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 S44. 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 S51. 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 S45. 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 S52. 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 S46. 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 S53. 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 S47. 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 S54. 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 S48. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.072 (Fisher's exact test)

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

nPatients | 0 | 1
--- | --- | ---
ALL | 214 | 16
subtype1 | 78 | 2
subtype2 | 41 | 2
subtype3 | 26 | 2
subtype4 | 69 | 10

Figure S49. 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 S56. 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 S50. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00357 (Chi-square test)

Table S57. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 221 | 47 | 119 | 76
subtype1 | 95 | 17 | 40 | 20
subtype2 | 49 | 6 | 19 | 10
subtype3 | 29 | 5 | 20 | 10
subtype4 | 48 | 19 | 40 | 36

Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.52 (Fisher's exact test)

Table S58. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S59. 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 S60. 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 S53. 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 S61. 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 S54. 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 S62. 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 S55. 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 S63. 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 S56. 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 S64. 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 S57. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0257 (Fisher's exact test)

Table S65. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | 0 | 1
--- | --- | ---
ALL | 214 | 16
subtype1 | 43 | 0
subtype2 | 54 | 8
subtype3 | 117 | 8

Figure S58. 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 S66. 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 S59. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 1.61e-05 (Chi-square test)

Table S67. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 221 | 47 | 119 | 76
subtype1 | 68 | 9 | 18 | 4
subtype2 | 46 | 9 | 42 | 26
subtype3 | 107 | 29 | 59 | 46

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.133 (Fisher's exact test)

Table S68. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: '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 = 9

- 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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle