Correlate_Clinical_vs_Molecular_Signatures
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1V12305
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C1V12305
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 8 different clustering approaches and 8 clinical features across 103 patients, 9 significant findings detected with P value < 0.05 and Q value < 0.25.

- 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.

- 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 3 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 'GENDER', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

- 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.

- 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.T'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 9 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | PATHOLOGY T | PATHOLOGY N | PATHOLOGICSPREAD(M) | TUMOR STAGE
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | Chi-square test | Chi-square test | Chi-square test | Chi-square test
mRNA CNMF subtypes | 100 (1.00) | 0.182 (1.00) | 0.585 (1.00) |  | 0.0623 (1.00) |  | 1 (1.00) | 0.292 (1.00)
mRNA cHierClus subtypes | 100 (1.00) | 0.948 (1.00) | 1 (1.00) |  | 0.216 (1.00) |  | 1 (1.00) |
Copy Number Ratio CNMF subtypes | 0.12 (1.00) | 0.575 (1.00) | 0.707 (1.00) | 0.421 (1.00) | 0.0662 (1.00) | 0.551 (1.00) | 0.152 (1.00) | 0.0318 (1.00)
METHLYATION CNMF | 0.15 (1.00) | 0.0523 (1.00) | 0.226 (1.00) | 0.144 (1.00) | 6.37e-08 (3.69e-06) | 0.142 (1.00) | 0.0567 (1.00) | 7.93e-06 (0.000452)
RNAseq CNMF subtypes | 0.0277 (1.00) | 0.00648 (0.318) | 0.0242 (1.00) |  | 0.000102 (0.00552) | 0.636 (1.00) | 0.0115 (0.521) | 9.52e-05 (0.00524)
RNAseq cHierClus subtypes | 0.0127 (0.557) | 0.454 (1.00) | 0.000595 (0.031) | 0.578 (1.00) | 0.000293 (0.0155) | 0.0583 (1.00) | 0.00397 (0.198) | 2.81e-05 (0.00157)
MIRSEQ CNMF | 0.99 (1.00) | 0.118 (1.00) | 0.00719 (0.345) | 0.488 (1.00) | 0.0113 (0.521) | 0.185 (1.00) | 0.192 (1.00) | 0.0283 (1.00)
MIRSEQ CHIERARCHICAL | 0.344 (1.00) | 0.496 (1.00) | 0.0367 (1.00) | 0.324 (1.00) | 0.00108 (0.0553) | 0.288 (1.00) | 0.343 (1.00) | 0.00899 (0.422)

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 = 100 (logrank test), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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 'TUMOR.STAGE'

P value = 0.292 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'

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

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'

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 = 100 (logrank test), Q value = 1

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), Q value = 1

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), Q value = 1

Table S10. 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 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), Q value = 1

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 'TUMOR.STAGE'

P value = NA (Chi-square test), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 5 | 2 | 1 | 1
subtype1 | 2 | 0 | 0 | 0
subtype2 | 1 | 2 | 1 | 1
subtype3 | 2 | 0 | 0 | 0

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S13. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 21 | 16 | 25 | 41

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.12 (logrank test), Q value = 1

Table S14. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
subtype1 | 20 | 2 | 0.0 - 182.7 (9.5)
subtype2 | 15 | 1 | 0.0 - 50.5 (13.6)
subtype3 | 25 | 6 | 0.1 - 80.8 (11.1)
subtype4 | 36 | 5 | 0.2 - 129.9 (24.9)

Figure S10. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.575 (ANOVA), Q value = 1

Table S15. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 100 | 59.7 (12.4)
subtype1 | 20 | 60.1 (11.2)
subtype2 | 15 | 55.5 (9.1)
subtype3 | 25 | 60.1 (14.1)
subtype4 | 40 | 60.8 (13.1)

Figure S11. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.707 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 33 | 70
subtype1 | 6 | 15
subtype2 | 4 | 12
subtype3 | 7 | 18
subtype4 | 16 | 25

Figure S12. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.421 (ANOVA), Q value = 1

Table S17. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 87.6 (23.9)
subtype1 | 4 | 97.5 (5.0)
subtype2 | 5 | 96.0 (5.5)
subtype3 | 5 | 74.0 (41.6)
subtype4 | 7 | 85.7 (20.7)

Figure S13. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0662 (Chi-square test), Q value = 1

Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 58 | 13 | 32
subtype1 | 13 | 4 | 4
subtype2 | 10 | 4 | 2
subtype3 | 10 | 2 | 13
subtype4 | 25 | 3 | 13

Figure S14. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.551 (Chi-square test), Q value = 1

Table S19. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 11 | 4
subtype1 | 2 | 1 | 0
subtype2 | 3 | 0 | 0
subtype3 | 7 | 7 | 3
subtype4 | 8 | 3 | 1

Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.152 (Chi-square test), Q value = 1

Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 35
subtype1 | 11 | 0 | 6
subtype2 | 7 | 0 | 7
subtype3 | 12 | 4 | 8
subtype4 | 24 | 1 | 14

Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0318 (Chi-square test), Q value = 1

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 53 | 7 | 23 | 9
subtype1 | 12 | 2 | 2 | 1
subtype2 | 9 | 1 | 2 | 0
subtype3 | 10 | 1 | 6 | 7
subtype4 | 22 | 3 | 13 | 1

Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S22. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 21 | 26 | 38

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.15 (logrank test), Q value = 1

Table S23. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 78 | 12 | 0.0 - 182.7 (15.7)
subtype1 | 19 | 4 | 0.0 - 80.8 (26.0)
subtype2 | 25 | 6 | 0.2 - 182.7 (12.0)
subtype3 | 34 | 2 | 0.0 - 129.9 (14.8)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0523 (ANOVA), Q value = 1

Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 82 | 60.0 (12.7)
subtype1 | 20 | 65.8 (10.0)
subtype2 | 25 | 59.4 (16.3)
subtype3 | 37 | 57.3 (10.2)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.226 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 28 | 57
subtype1 | 6 | 15
subtype2 | 12 | 14
subtype3 | 10 | 28

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

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

P value = 0.144 (ANOVA), Q value = 1

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 20 | 92.0 (13.2)
subtype1 | 6 | 81.7 (20.4)
subtype2 | 1 | 100.0 (NA)
subtype3 | 13 | 96.2 (5.1)

Figure S21. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 6.37e-08 (Chi-square test), Q value = 3.7e-06

Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 49 | 6 | 30
subtype1 | 15 | 0 | 6
subtype2 | 4 | 1 | 21
subtype3 | 30 | 5 | 3

Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.142 (Chi-square test), Q value = 1

Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 9 | 4
subtype1 | 3 | 2 | 1
subtype2 | 9 | 7 | 3
subtype3 | 8 | 0 | 0

Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0567 (Chi-square test), Q value = 1

Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 45 | 4 | 33
subtype1 | 13 | 1 | 6
subtype2 | 16 | 3 | 7
subtype3 | 16 | 0 | 20

Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 7.93e-06 (Chi-square test), Q value = 0.00045

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 46 | 5 | 22 | 8
subtype1 | 14 | 1 | 3 | 2
subtype2 | 4 | 1 | 15 | 6
subtype3 | 28 | 3 | 4 | 0

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 26 | 23 | 25

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0277 (logrank test), Q value = 1

Table S32. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 71 | 13 | 0.5 - 182.7 (15.5)
subtype1 | 24 | 3 | 0.5 - 54.9 (13.1)
subtype2 | 22 | 8 | 0.9 - 93.3 (12.2)
subtype3 | 25 | 2 | 6.4 - 182.7 (26.0)

Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00648 (ANOVA), Q value = 0.32

Table S33. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 71 | 59.7 (13.2)
subtype1 | 24 | 56.5 (12.1)
subtype2 | 22 | 55.8 (14.6)
subtype3 | 25 | 66.3 (10.5)

Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0242 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 22 | 52
subtype1 | 5 | 21
subtype2 | 12 | 11
subtype3 | 5 | 20

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.000102 (Chi-square test), Q value = 0.0055

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 38 | 9 | 27
subtype1 | 20 | 5 | 1
subtype2 | 5 | 2 | 16
subtype3 | 13 | 2 | 10

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.636 (Chi-square test), Q value = 1

Table S36. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 15 | 11 | 3
subtype1 | 2 | 0 | 0
subtype2 | 6 | 7 | 2
subtype3 | 7 | 4 | 1

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0115 (Chi-square test), Q value = 0.52

Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 46 | 5 | 16
subtype1 | 14 | 0 | 7
subtype2 | 12 | 5 | 5
subtype3 | 20 | 0 | 4

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 9.52e-05 (Chi-square test), Q value = 0.0052

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 35 | 3 | 20 | 8
subtype1 | 19 | 0 | 2 | 0
subtype2 | 4 | 1 | 9 | 7
subtype3 | 12 | 2 | 9 | 1

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S39. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 18 | 36 | 20

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0127 (logrank test), Q value = 0.56

Table S40. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 71 | 13 | 0.5 - 182.7 (15.5)
subtype1 | 17 | 6 | 2.8 - 80.8 (10.8)
subtype2 | 34 | 3 | 0.5 - 182.7 (13.8)
subtype3 | 20 | 4 | 0.9 - 123.6 (25.2)

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.454 (ANOVA), Q value = 1

Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 71 | 59.7 (13.2)
subtype1 | 17 | 57.3 (15.0)
subtype2 | 34 | 59.3 (11.7)
subtype3 | 20 | 62.6 (14.0)

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000595 (Fisher's exact test), Q value = 0.031

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 22 | 52
subtype1 | 11 | 7
subtype2 | 4 | 32
subtype3 | 7 | 13

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.578 (ANOVA), Q value = 1

Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 86.4 (29.1)
subtype1 | 3 | 93.3 (5.8)
subtype2 | 7 | 95.7 (5.3)
subtype3 | 1 | 0.0 (NA)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.000293 (Chi-square test), Q value = 0.016

Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 38 | 9 | 27
subtype1 | 7 | 2 | 9
subtype2 | 26 | 6 | 4
subtype3 | 5 | 1 | 14

Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0583 (Chi-square test), Q value = 1

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 15 | 11 | 3
subtype1 | 1 | 6 | 2
subtype2 | 4 | 1 | 0
subtype3 | 10 | 4 | 1

Figure S38. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.00397 (Chi-square test), Q value = 0.2

Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 46 | 5 | 16
subtype1 | 9 | 4 | 4
subtype2 | 19 | 0 | 11
subtype3 | 18 | 1 | 1

Figure S39. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 2.81e-05 (Chi-square test), Q value = 0.0016

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 35 | 3 | 20 | 8
subtype1 | 6 | 1 | 3 | 6
subtype2 | 24 | 1 | 4 | 1
subtype3 | 5 | 1 | 13 | 1

Figure S40. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S48. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 24 | 34 | 29 | 16

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.99 (logrank test), Q value = 1

Table S49. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
subtype1 | 24 | 4 | 0.0 - 63.7 (18.1)
subtype2 | 32 | 5 | 0.2 - 182.7 (17.4)
subtype3 | 24 | 3 | 0.0 - 123.6 (13.0)
subtype4 | 16 | 2 | 0.5 - 86.7 (11.3)

Figure S41. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.118 (ANOVA), Q value = 1

Table S50. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 100 | 59.7 (12.4)
subtype1 | 24 | 62.1 (12.6)
subtype2 | 34 | 56.2 (13.7)
subtype3 | 26 | 63.1 (10.1)
subtype4 | 16 | 58.0 (11.5)

Figure S42. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00719 (Fisher's exact test), Q value = 0.35

Table S51. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 33 | 70
subtype1 | 3 | 21
subtype2 | 16 | 18
subtype3 | 6 | 23
subtype4 | 8 | 8

Figure S43. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.488 (ANOVA), Q value = 1

Table S52. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 87.6 (23.9)
subtype1 | 5 | 94.0 (5.5)
subtype2 | 11 | 80.9 (31.8)
subtype3 | 4 | 95.0 (5.8)
subtype4 | 1 | 100.0 (NA)

Figure S44. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.0113 (Chi-square test), Q value = 0.52

Table S53. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 58 | 13 | 32
subtype1 | 13 | 2 | 9
subtype2 | 19 | 2 | 13
subtype3 | 22 | 4 | 3
subtype4 | 4 | 5 | 7

Figure S45. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.185 (Chi-square test), Q value = 1

Table S54. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 11 | 4
subtype1 | 7 | 2 | 1
subtype2 | 5 | 6 | 3
subtype3 | 5 | 0 | 0
subtype4 | 3 | 3 | 0

Figure S46. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.192 (Chi-square test), Q value = 1

Table S55. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 35
subtype1 | 14 | 0 | 8
subtype2 | 17 | 3 | 14
subtype3 | 14 | 0 | 11
subtype4 | 9 | 2 | 2

Figure S47. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.0283 (Chi-square test), Q value = 1

Table S56. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 53 | 7 | 23 | 9
subtype1 | 12 | 2 | 8 | 0
subtype2 | 18 | 3 | 6 | 7
subtype3 | 20 | 1 | 4 | 0
subtype4 | 3 | 1 | 5 | 2

Figure S48. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S57. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 24 | 40 | 11 | 28

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.344 (logrank test), Q value = 1

Table S58. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 96 | 14 | 0.0 - 182.7 (13.7)
subtype1 | 24 | 5 | 0.9 - 86.7 (18.1)
subtype2 | 36 | 7 | 0.2 - 182.7 (17.8)
subtype3 | 10 | 0 | 3.8 - 96.9 (22.6)
subtype4 | 26 | 2 | 0.0 - 123.6 (6.6)

Figure S49. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.496 (ANOVA), Q value = 1

Table S59. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 100 | 59.7 (12.4)
subtype1 | 24 | 63.0 (13.2)
subtype2 | 39 | 58.7 (13.8)
subtype3 | 10 | 57.1 (12.0)
subtype4 | 27 | 59.1 (9.6)

Figure S50. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0367 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 33 | 70
subtype1 | 4 | 20
subtype2 | 18 | 22
subtype3 | 1 | 10
subtype4 | 10 | 18

Figure S51. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.324 (ANOVA), Q value = 1

Table S61. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 87.6 (23.9)
subtype1 | 4 | 70.0 (46.9)
subtype2 | 11 | 89.1 (17.0)
subtype3 | 2 | 100.0 (0.0)
subtype4 | 4 | 95.0 (5.8)

Figure S52. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.00108 (Chi-square test), Q value = 0.055

Table S62. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3+T4
--- | --- | --- | ---
ALL | 58 | 13 | 32
subtype1 | 9 | 2 | 13
subtype2 | 22 | 3 | 15
subtype3 | 10 | 0 | 1
subtype4 | 17 | 8 | 3

Figure S53. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.288 (Chi-square test), Q value = 1

Table S63. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 20 | 11 | 4
subtype1 | 8 | 4 | 1
subtype2 | 7 | 7 | 3
subtype3 | 1 | 0 | 0
subtype4 | 4 | 0 | 0

Figure S54. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.343 (Chi-square test), Q value = 1

Table S64. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | MX
--- | --- | --- | ---
ALL | 54 | 5 | 35
subtype1 | 17 | 1 | 6
subtype2 | 19 | 4 | 17
subtype3 | 7 | 0 | 3
subtype4 | 11 | 0 | 9

Figure S55. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.00899 (Chi-square test), Q value = 0.42

Table S65. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 53 | 7 | 23 | 9
subtype1 | 9 | 2 | 12 | 1
subtype2 | 21 | 3 | 7 | 8
subtype3 | 9 | 0 | 1 | 0
subtype4 | 14 | 2 | 3 | 0

Figure S56. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

Methods & Data
Input

- Cluster data file = KIRP-TP.mergedcluster.txt

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

- Number of patients = 103

- Number of clustering approaches = 8

- 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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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)
[8] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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