Kidney Renal Papillary 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 7 clinical features across 95 patients, 11 significant findings detected with P value < 0.05.

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

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

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

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

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

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

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 7 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 11 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | PATHOLOGY T | PATHOLOGY N | PATHOLOGICSPREAD(M)
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | Chi-square test | Fisher's exact test | Fisher's exact test
mRNA CNMF subtypes | 0.666 | 0.182 | 0.585 |  | 0.0623 |  | 1
mRNA cHierClus subtypes | 0.0699 | 0.948 | 1 |  | 0.216 |  | 1
METHLYATION CNMF | 0.0814 | 0.00131 | 0.379 | 0.241 | 4.2e-07 | 0.222 | 0.0368
RNAseq CNMF subtypes | 0.0626 | 0.434 | 0.0644 |  | 0.00023 | 0.934 | 0.108
RNAseq cHierClus subtypes | 0.0122 | 0.246 | 0.0625 | 0.481 | 0.37 | 0.0693 | 0.0402
MIRseq CNMF subtypes | 0.00206 | 0.257 | 0.918 |  | 0.0389 | 0.0601 | 0.0161
MIRseq cHierClus subtypes | 0.0135 | 0.789 | 0.597 |  | 0.0252 | 0.119 | 0.143

Clustering Approach #1: 'mRNA CNMF subtypes'

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

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

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.666 (logrank test)

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

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

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.182 (t-test)

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.585 (Fisher's exact test)

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0623 (Chi-square test)

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

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

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

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

P value = 1 (Fisher's exact test)

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

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

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0699 (logrank test)

Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

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

Figure S6. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.948 (ANOVA)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

P value = 1 (Fisher's exact test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #3: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 36 | 19 | 24

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0814 (logrank test)

Table S14. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 72 | 12 | 0.0 - 182.7 (20.9)
subtype1 | 32 | 2 | 0.0 - 129.9 (15.1)
subtype2 | 17 | 4 | 0.7 - 80.8 (26.3)
subtype3 | 23 | 6 | 0.9 - 182.7 (20.1)

Figure S11. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00131 (ANOVA)

Table S15. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 76 | 60.5 (12.0)
subtype1 | 35 | 55.9 (9.4)
subtype2 | 18 | 67.9 (8.7)
subtype3 | 23 | 61.8 (14.5)

Figure S12. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.379 (Fisher's exact test)

Table S16. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 55 | 24
subtype1 | 28 | 8
subtype2 | 12 | 7
subtype3 | 15 | 9

Figure S13. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.241 (ANOVA)

Table S17. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 92.1 (13.6)
subtype1 | 14 | 95.7 (5.1)
subtype2 | 4 | 77.5 (25.0)
subtype3 | 1 | 100.0 (NA)

Figure S14. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 4.2e-07 (Chi-square test)

Table S18. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 47 | 3 | 29
subtype1 | 30 | 2 | 4
subtype2 | 14 | 0 | 5
subtype3 | 3 | 1 | 20

Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.222 (Chi-square test)

Table S19. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 19 | 8 | 4
subtype1 | 7 | 0 | 0
subtype2 | 3 | 2 | 1
subtype3 | 9 | 6 | 3

Figure S16. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0368 (Chi-square test)

Table S20. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 45 | 4 | 27
subtype1 | 16 | 0 | 17
subtype2 | 12 | 1 | 6
subtype3 | 17 | 3 | 4

Figure S17. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

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

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0626 (logrank test)

Table S22. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

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

Figure S18. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA)

Table S23. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

Figure S19. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0644 (Fisher's exact test)

Table S24. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00023 (Chi-square test)

Table S25. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S21. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.934 (Chi-square test)

Table S26. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

Figure S22. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.108 (Chi-square test)

Table S27. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S28. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

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

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0122 (logrank test)

Table S29. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

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

Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.246 (ANOVA)

Table S30. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0625 (Fisher's exact test)

Table S31. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.481 (ANOVA)

Table S32. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Figure S27. 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 = 0.37 (Chi-square test)

Table S33. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0693 (Chi-square test)

Table S34. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0402 (Chi-square test)

Table S35. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S36. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 14 | 15 | 14

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00206 (logrank test)

Table S37. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 43 | 8 | 0.5 - 86.7 (11.6)
subtype1 | 14 | 2 | 0.9 - 86.7 (25.6)
subtype2 | 15 | 1 | 0.5 - 58.5 (5.8)
subtype3 | 14 | 5 | 2.8 - 40.8 (11.2)

Figure S31. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.257 (ANOVA)

Table S38. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 43 | 60.0 (12.3)
subtype1 | 14 | 64.3 (10.0)
subtype2 | 15 | 59.0 (10.5)
subtype3 | 14 | 56.8 (15.4)

Figure S32. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.918 (Fisher's exact test)

Table S39. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 30 | 13
subtype1 | 10 | 4
subtype2 | 11 | 4
subtype3 | 9 | 5

Figure S33. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0389 (Chi-square test)

Table S40. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 19 | 7 | 17
subtype1 | 4 | 1 | 9
subtype2 | 8 | 5 | 2
subtype3 | 7 | 1 | 6

Figure S34. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0601 (Fisher's exact test)

Table S41. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1
--- | --- | ---
ALL | 7 | 9
subtype1 | 6 | 3
subtype2 | 0 | 0
subtype3 | 1 | 6

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

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

P value = 0.0161 (Fisher's exact test)

Table S42. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 30 | 5
subtype1 | 13 | 0
subtype2 | 8 | 0
subtype3 | 9 | 5

Figure S36. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S43. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 13 | 15 | 15

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0135 (logrank test)

Table S44. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 43 | 8 | 0.5 - 86.7 (11.6)
subtype1 | 13 | 4 | 2.8 - 40.8 (11.6)
subtype2 | 15 | 3 | 0.9 - 86.7 (21.6)
subtype3 | 15 | 1 | 0.5 - 63.7 (5.9)

Figure S37. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.789 (ANOVA)

Table S45. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 43 | 60.0 (12.3)
subtype1 | 13 | 58.0 (15.3)
subtype2 | 15 | 60.9 (10.7)
subtype3 | 15 | 60.8 (11.5)

Figure S38. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.597 (Fisher's exact test)

Table S46. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 30 | 13
subtype1 | 8 | 5
subtype2 | 12 | 3
subtype3 | 10 | 5

Figure S39. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0252 (Chi-square test)

Table S47. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 19 | 7 | 17
subtype1 | 7 | 1 | 5
subtype2 | 4 | 1 | 10
subtype3 | 8 | 5 | 2

Figure S40. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.119 (Fisher's exact test)

Table S48. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

Figure S41. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.143 (Fisher's exact test)

Table S49. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 30 | 5
subtype1 | 9 | 4
subtype2 | 13 | 1
subtype3 | 8 | 0

Figure S42. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Methods & Data
Input

- Cluster data file = KIRP.mergedcluster.txt

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

- Number of patients = 95

- Number of clustering approaches = 7

- Number of selected clinical features = 7

- Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

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

Fisher's exact test

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

Chi-square test

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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
Copyright © 2011 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle