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 8 different clustering approaches and 8 clinical features across 95 patients, 17 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 'CN CNMF'. These subtypes correlate to 'Time to Death'.

  • 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 'Time to Death',  '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',  'PATHOLOGY.T', and 'TUMOR.STAGE'.

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 from statistical tests. Thresholded by P value < 0.05, 17 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 Fisher's exact test Fisher's exact test Chi-square test
mRNA CNMF subtypes 0.666 0.182 0.585 0.0623 1 0.292
mRNA cHierClus subtypes 0.0699 0.948 1 0.216 1
CN CNMF 0.0417 0.731 0.252 0.253 0.354 0.687 0.666 0.181
METHLYATION CNMF 0.0814 0.00131 0.379 0.241 4.2e-07 0.222 0.0368 8.48e-06
RNAseq CNMF subtypes 0.0626 0.434 0.0644 0.00023 0.934 0.108 0.000784
RNAseq cHierClus subtypes 0.0122 0.246 0.0625 0.481 0.37 0.0693 0.0402 0.00334
MIRseq CNMF subtypes 0.00206 0.257 0.918 0.0389 0.0601 0.0161 0.00425
MIRseq cHierClus subtypes 0.0135 0.789 0.597 0.0252 0.119 0.143 0.0222
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: 'CN CNMF'

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

Cluster Labels 1 2 3
Number of samples 24 52 19
'CN CNMF' versus 'Time to Death'

P value = 0.0417 (logrank test)

Table S15.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 88 14 0.0 - 182.7 (15.7)
subtype1 21 3 0.0 - 182.7 (13.6)
subtype2 48 7 0.6 - 129.9 (24.9)
subtype3 19 4 0.1 - 79.8 (11.1)

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

'CN CNMF' versus 'AGE'

P value = 0.731 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 92 60.1 (11.9)
subtype1 21 60.2 (11.3)
subtype2 52 60.7 (12.4)
subtype3 19 58.2 (11.3)

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

'CN CNMF' versus 'GENDER'

P value = 0.252 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 28 67
subtype1 4 20
subtype2 17 35
subtype3 7 12

Figure S14.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.253 (ANOVA)

Table S18.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 20 87.5 (24.5)
subtype1 4 97.5 (5.0)
subtype2 12 90.0 (16.5)
subtype3 4 70.0 (46.9)

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

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.354 (Chi-square test)

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

nPatients T1 T2 T3+T4
ALL 54 10 31
subtype1 15 3 6
subtype2 32 4 16
subtype3 7 3 9

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

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.687 (Chi-square test)

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

nPatients N0 N1 N2
ALL 19 10 4
subtype1 2 3 1
subtype2 9 4 1
subtype3 8 3 2

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

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.666 (Chi-square test)

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

nPatients M0 M1 MX
ALL 54 5 27
subtype1 11 1 8
subtype2 32 2 15
subtype3 11 2 4

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

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.181 (Chi-square test)

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

nPatients I II III IV
ALL 49 5 22 9
subtype1 14 1 2 3
subtype2 28 3 15 2
subtype3 7 1 5 4

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

Clustering Approach #4: 'METHLYATION CNMF'

Table S23.  Get Full Table Description of clustering approach #4: '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 S24.  Clustering Approach #4: '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 S20.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00131 (ANOVA)

Table S25.  Clustering Approach #4: '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 S21.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.379 (Fisher's exact test)

Table S26.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

Figure S22.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.241 (ANOVA)

Table S27.  Clustering Approach #4: '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 S23.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

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

Table S28.  Clustering Approach #4: '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 S24.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.222 (Chi-square test)

Table S29.  Clustering Approach #4: '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 S25.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0368 (Chi-square test)

Table S30.  Clustering Approach #4: '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 S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 8.48e-06 (Chi-square test)

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S32.  Get Full Table Description of clustering approach #5: '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 S33.  Clustering Approach #5: '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 S28.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA)

Table S34.  Clustering Approach #5: '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 S29.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0644 (Fisher's exact test)

Table S35.  Clustering Approach #5: '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 S30.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00023 (Chi-square test)

Table S36.  Clustering Approach #5: '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 S31.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.934 (Chi-square test)

Table S37.  Clustering Approach #5: '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 S32.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.108 (Chi-square test)

Table S38.  Clustering Approach #5: '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 S33.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.000784 (Chi-square test)

Table S39.  Clustering Approach #5: '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 S34.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S40.  Get Full Table Description of clustering approach #6: '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 S41.  Clustering Approach #6: '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 S35.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.246 (ANOVA)

Table S42.  Clustering Approach #6: '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 S36.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0625 (Fisher's exact test)

Table S43.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

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

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

P value = 0.481 (ANOVA)

Table S44.  Clustering Approach #6: '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 S38.  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 = 0.37 (Chi-square test)

Table S45.  Clustering Approach #6: '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 S39.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0693 (Chi-square test)

Table S46.  Clustering Approach #6: '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 S40.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0402 (Chi-square test)

Table S47.  Clustering Approach #6: '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 S41.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00334 (Chi-square test)

Table S48.  Clustering Approach #6: '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 S42.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S49.  Get Full Table Description of clustering approach #7: '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 S50.  Clustering Approach #7: '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 S43.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.257 (ANOVA)

Table S51.  Clustering Approach #7: '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 S44.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.918 (Fisher's exact test)

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

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

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0389 (Chi-square test)

Table S53.  Clustering Approach #7: '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 S46.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0601 (Fisher's exact test)

Table S54.  Clustering Approach #7: '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 S47.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0161 (Fisher's exact test)

Table S55.  Clustering Approach #7: '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 S48.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00425 (Chi-square test)

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

nPatients I II III IV
ALL 16 2 12 5
subtype1 3 1 9 0
subtype2 6 0 2 0
subtype3 7 1 1 5

Figure S49.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S57.  Get Full Table Description of clustering approach #8: '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 S58.  Clustering Approach #8: '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 S50.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.789 (ANOVA)

Table S59.  Clustering Approach #8: '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 S51.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.597 (Fisher's exact test)

Table S60.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S52.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0252 (Chi-square test)

Table S61.  Clustering Approach #8: '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 S53.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.119 (Fisher's exact test)

Table S62.  Clustering Approach #8: '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 S54.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.143 (Fisher's exact test)

Table S63.  Clustering Approach #8: '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 S55.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0222 (Chi-square test)

Table S64.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 16 2 12 5
subtype1 7 1 1 4
subtype2 3 1 9 1
subtype3 6 0 2 0

Figure S56.  Get High-res Image Clustering Approach #8: '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 = 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

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.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[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)
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