Kidney Renal Clear Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 7 different clustering approaches and 8 clinical features across 500 patients, 26 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T'.

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

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

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

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

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

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

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, 26 significant findings detected.

Clinical
Features 		Statistical
Tests 		mRNA
CNMF
subtypes 		mRNA
cHierClus
subtypes 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRseq
CNMF
subtypes 		MIRseq
cHierClus
subtypes
Time to Death logrank test 0.0868 0.189 0.000454 1.14e-07 2.35e-09 3.83e-06 0.000319
AGE ANOVA 0.795 0.607 0.256 0.099 0.0589 0.0827 0.108
GENDER Fisher's exact test 0.634 0.82 0.00307 2.33e-05 0.00775 0.0076 0.0901
KARNOFSKY PERFORMANCE SCORE ANOVA 0.709 0.338 0.201 0.926
PATHOLOGY T Chi-square test 0.00911 0.00779 0.000248 4.98e-06 3.6e-12 0.000289 0.000543
PATHOLOGY N Fisher's exact test 0.0704 0.124 0.0104 0.0102 0.00115 0.0158 0.0333
PATHOLOGICSPREAD(M) Fisher's exact test 0.12 0.0988 0.000576 0.000382 1.75e-06 0.00365 0.844
NEOADJUVANT THERAPY Fisher's exact test 0.194 0.208 1 0.37 0.186 0.54 0.00445
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 34 24 14
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0868 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 71 13 0.5 - 101.1 (32.6)
subtype1 33 4 0.5 - 101.1 (31.0)
subtype2 24 8 0.5 - 93.3 (36.7)
subtype3 14 1 1.3 - 84.4 (25.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.795 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 33 60.2 (13.8)
subtype2 24 59.9 (11.1)
subtype3 14 62.6 (11.3)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 43 29
subtype1 19 15
subtype2 14 10
subtype3 10 4

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00911 (Chi-square test)

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

nPatients T1 T2 T3
ALL 41 14 17
subtype1 23 4 7
subtype2 10 4 10
subtype3 8 6 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0704 (Fisher's exact test)

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 35 3
subtype1 18 0
subtype2 10 3
subtype3 7 0

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.12 (Fisher's exact test)

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 67 5
subtype1 33 1
subtype2 20 4
subtype3 14 0

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.194 (Fisher's exact test)

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 15 23 34
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.189 (logrank test)

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 71 13 0.5 - 101.1 (32.6)
subtype1 15 2 1.3 - 84.4 (24.2)
subtype2 23 7 0.5 - 93.3 (36.8)
subtype3 33 4 0.5 - 101.1 (31.0)

Figure S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.607 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 15 63.2 (11.2)
subtype2 23 59.1 (10.7)
subtype3 33 60.4 (14.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.82 (Fisher's exact test)

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 43 29
subtype1 10 5
subtype2 14 9
subtype3 19 15

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.00779 (Chi-square test)

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3
ALL 41 14 17
subtype1 9 6 0
subtype2 9 4 10
subtype3 23 4 7

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.124 (Fisher's exact test)

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 35 3
subtype1 7 0
subtype2 11 3
subtype3 17 0

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0988 (Fisher's exact test)

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 67 5
subtype1 15 0
subtype2 19 4
subtype3 33 1

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.208 (Fisher's exact test)

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

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

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S17.  Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 77 91 51
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000454 (logrank test)

Table S18.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 218 65 0.1 - 111.0 (39.7)
subtype1 77 32 0.5 - 90.3 (36.3)
subtype2 90 16 1.3 - 111.0 (45.3)
subtype3 51 17 0.1 - 101.1 (42.4)

Figure S15.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.256 (ANOVA)

Table S19.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 218 59.5 (12.5)
subtype1 76 61.3 (12.0)
subtype2 91 59.0 (13.4)
subtype3 51 57.7 (11.5)

Figure S16.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00307 (Fisher's exact test)

Table S20.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 142 77
subtype1 57 20
subtype2 47 44
subtype3 38 13

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.000248 (Chi-square test)

Table S21.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 117 29 70 3
subtype1 30 8 36 3
subtype2 63 12 16 0
subtype3 24 9 18 0

Figure S18.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0104 (Fisher's exact test)

Table S22.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 106 9
subtype1 35 7
subtype2 41 0
subtype3 30 2

Figure S19.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000576 (Fisher's exact test)

Table S23.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 193 26
subtype1 59 18
subtype2 87 4
subtype3 47 4

Figure S20.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S24.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 217 2
subtype1 76 1
subtype2 90 1
subtype3 51 0

Figure S21.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S25.  Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 200 102 167
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.14e-07 (logrank test)

Table S26.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 467 155 0.1 - 111.0 (34.3)
subtype1 199 45 0.1 - 111.0 (37.5)
subtype2 101 31 0.1 - 93.3 (35.2)
subtype3 167 79 0.1 - 90.3 (29.1)

Figure S22.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.099 (ANOVA)

Table S27.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 468 60.6 (12.2)
subtype1 199 61.8 (12.4)
subtype2 102 58.7 (12.3)
subtype3 167 60.3 (11.8)

Figure S23.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 2.33e-05 (Fisher's exact test)

Table S28.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 307 162
subtype1 109 91
subtype2 69 33
subtype3 129 38

Figure S24.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.709 (ANOVA)

Table S29.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 31 91.0 (18.7)
subtype1 14 92.1 (8.9)
subtype2 7 94.3 (7.9)
subtype3 10 87.0 (31.3)

Figure S25.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 4.98e-06 (Chi-square test)

Table S30.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 228 59 171 11
subtype1 117 24 57 2
subtype2 59 11 29 3
subtype3 52 24 85 6

Figure S26.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0102 (Fisher's exact test)

Table S31.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 223 17
subtype1 96 2
subtype2 48 3
subtype3 79 12

Figure S27.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000382 (Fisher's exact test)

Table S32.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 392 77
subtype1 178 22
subtype2 90 12
subtype3 124 43

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.37 (Fisher's exact test)

Table S33.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 464 5
subtype1 199 1
subtype2 100 2
subtype3 165 2

Figure S29.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S34.  Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 70 187 212
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.35e-09 (logrank test)

Table S35.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 467 155 0.1 - 111.0 (34.3)
subtype1 69 13 0.2 - 92.0 (34.3)
subtype2 187 92 0.1 - 93.3 (29.1)
subtype3 211 50 0.1 - 111.0 (37.5)

Figure S30.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0589 (ANOVA)

Table S36.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 468 60.6 (12.2)
subtype1 70 57.4 (12.9)
subtype2 187 61.2 (11.6)
subtype3 211 61.1 (12.3)

Figure S31.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00775 (Fisher's exact test)

Table S37.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 307 162
subtype1 48 22
subtype2 136 51
subtype3 123 89

Figure S32.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.338 (ANOVA)

Table S38.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 31 91.0 (18.7)
subtype1 11 95.5 (9.3)
subtype2 9 83.3 (32.4)
subtype3 11 92.7 (6.5)

Figure S33.  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 = 3.6e-12 (Chi-square test)

Table S39.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 228 59 171 11
subtype1 54 6 9 1
subtype2 53 26 100 8
subtype3 121 27 62 2

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00115 (Fisher's exact test)

Table S40.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 223 17
subtype1 28 2
subtype2 92 14
subtype3 103 1

Figure S35.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 1.75e-06 (Fisher's exact test)

Table S41.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 392 77
subtype1 67 3
subtype2 137 50
subtype3 188 24

Figure S36.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.186 (Fisher's exact test)

Table S42.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 464 5
subtype1 68 2
subtype2 185 2
subtype3 211 1

Figure S37.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S43.  Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 195 102 166
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 3.83e-06 (logrank test)

Table S44.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 461 149 0.1 - 111.0 (33.5)
subtype1 195 44 0.1 - 111.0 (34.6)
subtype2 102 30 0.1 - 109.9 (37.2)
subtype3 164 75 0.2 - 93.3 (29.5)

Figure S38.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0827 (ANOVA)

Table S45.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 463 60.7 (12.2)
subtype1 195 62.1 (12.2)
subtype2 102 58.9 (12.0)
subtype3 166 60.3 (12.1)

Figure S39.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0076 (Fisher's exact test)

Table S46.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 304 159
subtype1 113 82
subtype2 69 33
subtype3 122 44

Figure S40.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.201 (ANOVA)

Table S47.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 88.0 (23.2)
subtype1 15 92.0 (8.6)
subtype2 8 95.0 (7.6)
subtype3 12 78.3 (37.1)

Figure S41.  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 = 0.000289 (Chi-square test)

Table S48.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 225 59 169 10
subtype1 106 26 61 2
subtype2 61 6 31 4
subtype3 58 27 77 4

Figure S42.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0158 (Fisher's exact test)

Table S49.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 213 16
subtype1 91 2
subtype2 47 3
subtype3 75 11

Figure S43.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.00365 (Fisher's exact test)

Table S50.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 391 72
subtype1 170 25
subtype2 93 9
subtype3 128 38

Figure S44.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.54 (Fisher's exact test)

Table S51.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 458 5
subtype1 194 1
subtype2 101 1
subtype3 163 3

Figure S45.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S52.  Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 46 154 263
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000319 (logrank test)

Table S53.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 461 149 0.1 - 111.0 (33.5)
subtype1 45 15 0.6 - 93.3 (36.4)
subtype2 154 67 0.1 - 109.9 (30.9)
subtype3 262 67 0.1 - 111.0 (34.5)

Figure S46.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.108 (ANOVA)

Table S54.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 463 60.7 (12.2)
subtype1 46 57.5 (11.8)
subtype2 154 60.4 (12.8)
subtype3 263 61.5 (11.8)

Figure S47.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0901 (Fisher's exact test)

Table S55.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 304 159
subtype1 31 15
subtype2 111 43
subtype3 162 101

Figure S48.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.926 (ANOVA)

Table S56.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 88.0 (23.2)
subtype1 1 90.0 (NA)
subtype2 13 88.5 (27.3)
subtype3 21 87.6 (21.7)

Figure S49.  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 = 0.000543 (Chi-square test)

Table S57.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 225 59 169 10
subtype1 21 11 13 1
subtype2 60 15 72 7
subtype3 144 33 84 2

Figure S50.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0333 (Fisher's exact test)

Table S58.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 213 16
subtype1 21 3
subtype2 71 9
subtype3 121 4

Figure S51.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.844 (Fisher's exact test)

Table S59.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 391 72
subtype1 39 7
subtype2 128 26
subtype3 224 39

Figure S52.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00445 (Fisher's exact test)

Table S60.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 458 5
subtype1 43 3
subtype2 154 0
subtype3 261 2

Figure S53.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = KIRC.mergedcluster.txt

  • Clinical data file = KIRC.clin.merged.picked.txt

  • Number of patients = 500

  • 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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

Download Results

This is an experimental feature. Location of data archives could not be determined.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
Meta

  • Maintainer = TCGA GDAC Team

Copyright © 2011 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle