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 499 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',  'AGE',  'GENDER',  'PATHOLOGY.T', 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 4 subtypes that correlate to 'Time to Death',  'GENDER',  'PATHOLOGY.T', and 'PATHOLOGICSPREAD(M)'.

  • Consensus hierarchical 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)'.

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 1.16e-05 2.59e-07 1.03e-09 3.31e-07 1.62e-08
AGE ANOVA 0.795 0.607 0.00437 0.323 0.335 0.0787 0.892
GENDER Fisher's exact test 0.634 0.82 0.00516 1.17e-05 0.00361 0.0142 0.000362
KARNOFSKY PERFORMANCE SCORE ANOVA 0.595 0.709 0.342 0.18 0.827
PATHOLOGY T Chi-square test 0.00911 0.00779 2.71e-09 1.37e-05 1.66e-10 0.00171 3.55e-06
PATHOLOGY N Fisher's exact test 0.0704 0.124 0.0842 0.0106 0.00494 0.0763 0.0297
PATHOLOGICSPREAD(M) Fisher's exact test 0.12 0.0988 0.000181 0.000475 4.85e-05 0.00738 0.000451
NEOADJUVANT THERAPY Fisher's exact test 0.194 0.208 0.386 1 0.654 0.52 0.133
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 79 117 87
'METHLYATION CNMF' versus 'Time to Death'

P value = 1.16e-05 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 281 95 0.1 - 109.9 (28.5)
subtype1 78 38 0.2 - 84.7 (29.7)
subtype2 116 21 0.1 - 109.6 (31.4)
subtype3 87 36 0.1 - 109.9 (22.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00437 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 283 61.5 (12.0)
subtype1 79 64.7 (10.3)
subtype2 117 59.0 (12.8)
subtype3 87 61.9 (11.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00516 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 187 96
subtype1 62 17
subtype2 66 51
subtype3 59 28

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

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

P value = 0.595 (ANOVA)

Table S21.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 28 92.5 (8.0)
subtype1 6 91.7 (7.5)
subtype2 14 91.4 (8.6)
subtype3 8 95.0 (7.6)

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 2.71e-09 (Chi-square test)

Table S22.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 132 36 107 8
subtype1 18 13 46 2
subtype2 76 18 23 0
subtype3 38 5 38 6

Figure S19.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0842 (Fisher's exact test)

Table S23.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 126 9
subtype1 33 2
subtype2 52 1
subtype3 41 6

Figure S20.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000181 (Fisher's exact test)

Table S24.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 232 51
subtype1 53 26
subtype2 106 11
subtype3 73 14

Figure S21.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.386 (Fisher's exact test)

Table S25.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 279 4
subtype1 77 2
subtype2 115 2
subtype3 87 0

Figure S22.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S26.  Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 198 100 171
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 2.59e-07 (logrank test)

Table S27.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 467 154 0.1 - 111.0 (34.3)
subtype1 198 45 0.1 - 111.0 (37.9)
subtype2 99 29 0.1 - 93.3 (31.3)
subtype3 170 80 0.1 - 90.3 (29.8)

Figure S23.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.323 (ANOVA)

Table S28.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 468 60.6 (12.2)
subtype1 197 61.3 (12.2)
subtype2 100 59.1 (12.5)
subtype3 171 60.7 (12.0)

Figure S24.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1.17e-05 (Fisher's exact test)

Table S29.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 307 162
subtype1 106 92
subtype2 70 30
subtype3 131 40

Figure S25.  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 S30.  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 S26.  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 = 1.37e-05 (Chi-square test)

Table S31.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 229 58 171 11
subtype1 114 23 59 2
subtype2 60 10 27 3
subtype3 55 25 85 6

Figure S27.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0106 (Fisher's exact test)

Table S32.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 223 17
subtype1 95 2
subtype2 49 3
subtype3 79 12

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000475 (Fisher's exact test)

Table S33.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 393 76
subtype1 175 23
subtype2 90 10
subtype3 128 43

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S34.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 464 5
subtype1 196 2
subtype2 99 1
subtype3 169 2

Figure S30.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S35.  Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 52 221 196
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1.03e-09 (logrank test)

Table S36.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 467 154 0.1 - 111.0 (34.3)
subtype1 51 10 0.2 - 92.0 (24.2)
subtype2 220 49 0.1 - 111.0 (38.5)
subtype3 196 95 0.1 - 93.3 (28.9)

Figure S31.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.335 (ANOVA)

Table S37.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 468 60.6 (12.2)
subtype1 52 58.5 (12.9)
subtype2 220 60.5 (12.4)
subtype3 196 61.3 (11.7)

Figure S32.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00361 (Fisher's exact test)

Table S38.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 307 162
subtype1 35 17
subtype2 128 93
subtype3 144 52

Figure S33.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.342 (ANOVA)

Table S39.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 31 91.0 (18.7)
subtype1 9 95.6 (10.1)
subtype2 13 93.1 (6.3)
subtype3 9 83.3 (32.4)

Figure S34.  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 = 1.66e-10 (Chi-square test)

Table S40.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 229 58 171 11
subtype1 40 5 6 1
subtype2 128 28 64 1
subtype3 61 25 101 9

Figure S35.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00494 (Fisher's exact test)

Table S41.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 223 17
subtype1 26 1
subtype2 103 2
subtype3 94 14

Figure S36.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 4.85e-05 (Fisher's exact test)

Table S42.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 393 76
subtype1 49 3
subtype2 197 24
subtype3 147 49

Figure S37.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.654 (Fisher's exact test)

Table S43.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 464 5
subtype1 51 1
subtype2 219 2
subtype3 194 2

Figure S38.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S44.  Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 172 84 64 143
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 3.31e-07 (logrank test)

Table S45.  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 172 37 0.1 - 111.0 (36.6)
subtype2 84 22 0.1 - 109.9 (37.1)
subtype3 64 22 0.1 - 91.4 (31.4)
subtype4 141 68 0.2 - 93.3 (29.1)

Figure S39.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0787 (ANOVA)

Table S46.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 463 60.7 (12.2)
subtype1 172 62.3 (12.3)
subtype2 84 58.1 (11.5)
subtype3 64 60.7 (12.7)
subtype4 143 60.4 (11.9)

Figure S40.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0142 (Fisher's exact test)

Table S47.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 304 159
subtype1 98 74
subtype2 55 29
subtype3 46 18
subtype4 105 38

Figure S41.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.18 (ANOVA)

Table S48.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 88.0 (23.2)
subtype1 14 92.1 (8.9)
subtype2 6 95.0 (8.4)
subtype3 6 93.3 (5.2)
subtype4 9 73.3 (42.1)

Figure S42.  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 = 0.00171 (Chi-square test)

Table S49.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 225 59 169 10
subtype1 95 22 53 2
subtype2 50 5 25 4
subtype3 31 7 26 0
subtype4 49 25 65 4

Figure S43.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0763 (Fisher's exact test)

Table S50.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 213 16
subtype1 77 2
subtype2 41 2
subtype3 26 2
subtype4 69 10

Figure S44.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.00738 (Fisher's exact test)

Table S51.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 391 72
subtype1 154 18
subtype2 75 9
subtype3 53 11
subtype4 109 34

Figure S45.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.52 (Fisher's exact test)

Table S52.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 458 5
subtype1 171 1
subtype2 83 1
subtype3 64 0
subtype4 140 3

Figure S46.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S53.  Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 99 123 241
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 1.62e-08 (logrank test)

Table S54.  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 99 15 0.4 - 109.6 (48.4)
subtype2 123 57 0.1 - 109.9 (28.8)
subtype3 239 77 0.1 - 111.0 (31.1)

Figure S47.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.892 (ANOVA)

Table S55.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 463 60.7 (12.2)
subtype1 99 60.3 (12.2)
subtype2 123 61.1 (12.8)
subtype3 241 60.8 (11.9)

Figure S48.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.000362 (Fisher's exact test)

Table S56.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 304 159
subtype1 48 51
subtype2 87 36
subtype3 169 72

Figure S49.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.827 (ANOVA)

Table S57.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 88.0 (23.2)
subtype1 5 94.0 (5.5)
subtype2 11 86.4 (29.4)
subtype3 19 87.4 (22.8)

Figure S50.  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 = 3.55e-06 (Chi-square test)

Table S58.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 225 59 169 10
subtype1 68 10 21 0
subtype2 47 11 58 7
subtype3 110 38 90 3

Figure S51.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0297 (Fisher's exact test)

Table S59.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 213 16
subtype1 42 0
subtype2 54 8
subtype3 117 8

Figure S52.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000451 (Fisher's exact test)

Table S60.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 391 72
subtype1 95 4
subtype2 99 24
subtype3 197 44

Figure S53.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.133 (Fisher's exact test)

Table S61.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 458 5
subtype1 99 0
subtype2 123 0
subtype3 236 5

Figure S54.  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 = 499

  • 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. 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] 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)
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