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 8 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', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

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

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

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

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 11 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 | 0.666 | 0.0699 | 0.0501 | 0.0626 | 0.0122 | 0.282 | 0.373
AGE | ANOVA | 0.182 | 0.948 | 0.00254 | 0.434 | 0.246 | 0.716 | 0.908
GENDER | Fisher's exact test | 0.585 | 1 | 0.306 | 0.0644 | 0.0625 | 0.175 | 0.0887
KARNOFSKY PERFORMANCE SCORE | ANOVA |  |  | 0.241 |  | 0.481 | 0.282 | 0.665
PATHOLOGY T | Chi-square test | 0.0623 | 0.216 | 9.15e-08 | 0.00023 | 0.37 | 0.12 | 0.0193
PATHOLOGY N | Chi-square test |  |  | 0.145 | 0.934 | 0.0693 | 0.247 | 0.242
PATHOLOGICSPREAD(M) | Chi-square test | 1 | 1 | 0.0476 | 0.108 | 0.0402 | 0.3 | 0.309
TUMOR STAGE | Chi-square test | 0.292 |  | 3.75e-06 | 0.000784 | 0.00334 | 0.026 | 0.0681

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 | 4 | 12
subtype1 | 1 | 6
subtype2 | 3 | 6

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0623 (Chi-square test)

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

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

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

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

P value = 1 (Fisher's exact test)

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

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

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.292 (Chi-square test)

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

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

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0699 (logrank test)

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

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

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.948 (ANOVA)

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test)

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

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

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

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

P value = 1 (Fisher's exact test)

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

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

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

Clustering Approach #3: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 37 | 19 | 23

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0501 (logrank test)

Table S15. 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 | 33 | 2 | 0.0 - 129.9 (15.5)
subtype2 | 17 | 4 | 0.7 - 80.8 (26.3)
subtype3 | 22 | 6 | 0.9 - 182.7 (19.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00254 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 76 | 60.5 (12.0)
subtype1 | 36 | 56.4 (9.8)
subtype2 | 18 | 67.9 (8.7)
subtype3 | 22 | 61.2 (14.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.306 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 24 | 55
subtype1 | 8 | 29
subtype2 | 7 | 12
subtype3 | 9 | 14

Figure S14. 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 S18. 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 S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 9.15e-08 (Chi-square test)

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

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

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.145 (Chi-square test)

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

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

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0476 (Chi-square test)

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

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

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 3.75e-06 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 44 | 3 | 21 | 8
subtype1 | 28 | 2 | 4 | 0
subtype2 | 13 | 1 | 3 | 2
subtype3 | 3 | 0 | 14 | 6

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

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S23. 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 S24. 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 S20. 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 S25. 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 S21. 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 S26. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S22. 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 S27. 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 S23. 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 S28. 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 S24. 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 S29. 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 S25. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.000784 (Chi-square test)

Table S30. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

Figure S26. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S31. 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 S32. 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 S27. 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 S33. 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 S28. 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 S34. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S29. 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 S35. 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 S30. 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 S36. 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 S31. 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 S37. 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 S32. 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 S38. 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 S33. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00334 (Chi-square test)

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

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

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

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S40. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 33 | 33 | 29

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.282 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 88 | 14 | 0.0 - 182.7 (15.7)
subtype1 | 32 | 4 | 0.0 - 96.9 (25.6)
subtype2 | 31 | 7 | 0.7 - 182.7 (20.1)
subtype3 | 25 | 3 | 0.1 - 123.6 (12.7)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.716 (ANOVA)

Table S42. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 92 | 60.1 (11.9)
subtype1 | 33 | 60.3 (12.7)
subtype2 | 33 | 58.8 (12.7)
subtype3 | 26 | 61.3 (9.8)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.175 (Fisher's exact test)

Table S43. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 28 | 67
subtype1 | 6 | 27
subtype2 | 13 | 20
subtype3 | 9 | 20

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

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

P value = 0.282 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 20 | 87.5 (24.5)
subtype1 | 7 | 94.3 (5.3)
subtype2 | 9 | 77.8 (34.6)
subtype3 | 4 | 97.5 (5.0)

Figure S38. 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 = 0.12 (Chi-square test)

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

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 54 | 10 | 31
subtype1 | 18 | 2 | 13
subtype2 | 18 | 2 | 13
subtype3 | 18 | 6 | 5

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.247 (Chi-square test)

Table S46. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 19 | 10 | 4
subtype1 | 9 | 3 | 1
subtype2 | 5 | 6 | 3
subtype3 | 5 | 1 | 0

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

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

P value = 0.3 (Chi-square test)

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

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 27
subtype1 | 22 | 0 | 9
subtype2 | 19 | 4 | 10
subtype3 | 13 | 1 | 8

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

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.026 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 49 | 5 | 22 | 9
subtype1 | 17 | 2 | 12 | 0
subtype2 | 17 | 2 | 5 | 8
subtype3 | 15 | 1 | 5 | 1

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

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S49. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 19 | 37 | 39

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.373 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 88 | 14 | 0.0 - 182.7 (15.7)
subtype1 | 17 | 2 | 0.1 - 123.6 (12.7)
subtype2 | 33 | 7 | 0.6 - 182.7 (22.9)
subtype3 | 38 | 5 | 0.0 - 96.9 (20.4)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.908 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 92 | 60.1 (11.9)
subtype1 | 17 | 61.0 (10.3)
subtype2 | 36 | 60.2 (12.1)
subtype3 | 39 | 59.5 (12.6)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0887 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 28 | 67
subtype1 | 6 | 13
subtype2 | 15 | 22
subtype3 | 7 | 32

Figure S45. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.665 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 20 | 87.5 (24.5)
subtype1 | 3 | 96.7 (5.8)
subtype2 | 10 | 89.0 (17.9)
subtype3 | 7 | 81.4 (36.3)

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0193 (Chi-square test)

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

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 54 | 10 | 31
subtype1 | 13 | 5 | 1
subtype2 | 20 | 2 | 15
subtype3 | 21 | 3 | 15

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.242 (Chi-square test)

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 19 | 10 | 4
subtype1 | 2 | 0 | 0
subtype2 | 6 | 6 | 3
subtype3 | 11 | 4 | 1

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

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

P value = 0.309 (Chi-square test)

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

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 27
subtype1 | 10 | 0 | 5
subtype2 | 19 | 4 | 13
subtype3 | 25 | 1 | 9

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

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0681 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 49 | 5 | 22 | 9
subtype1 | 10 | 1 | 2 | 0
subtype2 | 19 | 2 | 7 | 8
subtype3 | 20 | 2 | 13 | 1

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

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 = 8

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Student's t-test analysis

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

Fisher's exact test

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

Chi-square test

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

ANOVA analysis

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

Download Results

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

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle