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 10 different clustering approaches and 9 clinical features across 502 patients, 47 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' and 'TUMOR.STAGE'.

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

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

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death',  'PATHOLOGY.T',  'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death',  'KARNOFSKY.PERFORMANCE.SCORE',  'PATHOLOGY.T',  'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

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

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 9 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 47 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE 		NEOADJUVANT
THERAPY
Statistical Tests logrank test ANOVA Fisher's exact test ANOVA Chi-square test Fisher's exact test Fisher's exact test Chi-square test Fisher's exact test
mRNA CNMF subtypes 0.0868 0.795 0.634 0.00911 0.0704 0.12 0.0134 0.194
mRNA cHierClus subtypes 0.189 0.607 0.82 0.00779 0.124 0.0988 0.0102 0.208
CN CNMF 1.14e-05 0.0703 0.0383 0.888 0.00254 0.0122 0.000525 0.00253 0.735
METHLYATION CNMF 9.14e-08 0.0456 0.0262 0.647 8.27e-11 0.0718 9.62e-05 2.15e-10 0.782
RPPA CNMF subtypes 5.91e-08 0.127 0.22 0.552 4.3e-06 0.149 5.29e-06 9.71e-08 0.332
RPPA cHierClus subtypes 1.06e-07 0.0772 0.336 0.0291 1.02e-07 0.252 0.000361 1.59e-07 0.1
RNAseq CNMF subtypes 2.59e-07 0.323 1.17e-05 0.709 1.37e-05 0.00986 0.000475 2.6e-06 1
RNAseq cHierClus subtypes 1.03e-09 0.335 0.00361 0.342 1.66e-10 0.00446 4.85e-05 7.47e-11 0.654
MIRseq CNMF subtypes 1.29e-06 0.0537 0.00265 0.149 0.00028 0.00693 0.000305 8.11e-05 0.53
MIRseq cHierClus subtypes 4.57e-06 0.0521 0.182 0.849 0.00064 0.00892 0.282 0.0143 0.123
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 29 43
subtype1 15 19
subtype2 10 14
subtype3 4 10

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 'TUMOR.STAGE'

P value = 0.0134 (Chi-square test)

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

nPatients I II III IV
ALL 40 15 12 5
subtype1 23 5 5 1
subtype2 9 4 7 4
subtype3 8 6 0 0

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.194 (Fisher's exact test)

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10.  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 S11.  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 S9.  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 S12.  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 S10.  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 S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S11.  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 S14.  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 S12.  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 S15.  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 S13.  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 S16.  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 S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0102 (Chi-square test)

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 40 15 12 5
subtype1 9 6 0 0
subtype2 8 4 7 4
subtype3 23 5 5 1

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.208 (Fisher's exact test)

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S19.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3
Number of samples 129 206 155
'CN CNMF' versus 'Time to Death'

P value = 1.14e-05 (logrank test)

Table S20.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 487 157 0.1 - 111.0 (34.6)
subtype1 129 53 0.3 - 97.5 (37.1)
subtype2 204 44 0.1 - 111.0 (37.3)
subtype3 154 60 0.1 - 109.9 (27.0)

Figure S17.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.0703 (ANOVA)

Table S21.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 489 60.6 (12.2)
subtype1 128 62.8 (11.8)
subtype2 206 59.8 (12.6)
subtype3 155 59.9 (11.8)

Figure S18.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.0383 (Fisher's exact test)

Table S22.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 171 319
subtype1 37 92
subtype2 85 121
subtype3 49 106

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

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

P value = 0.888 (ANOVA)

Table S23.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 13 88.5 (27.0)
subtype2 10 91.0 (9.9)
subtype3 13 86.2 (26.9)

Figure S20.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.00254 (Chi-square test)

Table S24.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 239 64 176 11
subtype1 54 16 57 2
subtype2 118 28 59 1
subtype3 67 20 60 8

Figure S21.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.0122 (Fisher's exact test)

Table S25.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 227 18
subtype1 51 5
subtype2 98 2
subtype3 78 11

Figure S22.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000525 (Fisher's exact test)

Table S26.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 414 76
subtype1 98 31
subtype2 188 18
subtype3 128 27

Figure S23.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.00253 (Chi-square test)

Table S27.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 235 52 123 80
subtype1 52 13 32 32
subtype2 118 22 47 19
subtype3 65 17 44 29

Figure S24.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.735 (Fisher's exact test)

Table S28.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 5 485
subtype1 2 127
subtype2 2 204
subtype3 1 154

Figure S25.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S29.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 79 84 60 60
'METHLYATION CNMF' versus 'Time to Death'

P value = 9.14e-08 (logrank test)

Table S30.  Clustering Approach #4: '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 41 0.2 - 84.7 (27.7)
subtype2 84 18 0.1 - 109.6 (34.9)
subtype3 59 9 0.3 - 91.4 (29.9)
subtype4 60 27 0.1 - 109.9 (19.1)

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0456 (ANOVA)

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 283 61.5 (12.0)
subtype1 79 64.2 (10.7)
subtype2 84 60.8 (11.7)
subtype3 60 58.6 (13.7)
subtype4 60 61.7 (11.7)

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0262 (Fisher's exact test)

Table S32.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 96 187
subtype1 19 60
subtype2 32 52
subtype3 28 32
subtype4 17 43

Figure S28.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.647 (ANOVA)

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 28 92.5 (8.0)
subtype1 5 90.0 (7.1)
subtype2 10 91.0 (9.9)
subtype3 9 94.4 (5.3)
subtype4 4 95.0 (10.0)

Figure S29.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 8.27e-11 (Chi-square test)

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 132 36 107 8
subtype1 14 14 48 3
subtype2 52 14 18 0
subtype3 44 4 11 1
subtype4 22 4 30 4

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0718 (Fisher's exact test)

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 127 9
subtype1 35 3
subtype2 38 1
subtype3 27 0
subtype4 27 5

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 9.62e-05 (Fisher's exact test)

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 232 51
subtype1 53 26
subtype2 74 10
subtype3 57 3
subtype4 48 12

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 2.15e-10 (Chi-square test)

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 130 24 73 56
subtype1 14 9 28 28
subtype2 52 9 13 10
subtype3 44 3 10 3
subtype4 20 3 22 15

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.782 (Fisher's exact test)

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 4 279
subtype1 2 77
subtype2 1 83
subtype3 1 59
subtype4 0 60

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S39.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 160 150 144
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 5.91e-08 (logrank test)

Table S40.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 452 151 0.1 - 111.0 (34.3)
subtype1 158 75 0.2 - 97.5 (30.6)
subtype2 150 29 0.3 - 111.0 (38.3)
subtype3 144 47 0.1 - 96.8 (30.2)

Figure S35.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.127 (ANOVA)

Table S41.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 160 61.9 (12.1)
subtype2 149 60.2 (12.7)
subtype3 144 59.0 (12.0)

Figure S36.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.22 (Fisher's exact test)

Table S42.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 45 115
subtype2 55 95
subtype3 51 93

Figure S37.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.552 (ANOVA)

Table S43.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 9 91.1 (7.8)
subtype2 15 94.7 (8.3)
subtype3 10 94.0 (7.0)

Figure S38.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 4.3e-06 (Chi-square test)

Table S44.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 51 24 77 8
subtype2 93 13 43 1
subtype3 80 17 45 2

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.149 (Fisher's exact test)

Table S45.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 208 16
subtype1 77 10
subtype2 60 2
subtype3 71 4

Figure S40.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 5.29e-06 (Fisher's exact test)

Table S46.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 380 74
subtype1 116 44
subtype2 139 11
subtype3 125 19

Figure S41.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 9.71e-08 (Chi-square test)

Table S47.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 219 43 114 78
subtype1 49 18 46 47
subtype2 92 10 37 11
subtype3 78 15 31 20

Figure S42.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.332 (Fisher's exact test)

Table S48.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 4 450
subtype1 3 157
subtype2 1 149
subtype3 0 144

Figure S43.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S49.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 132 215 107
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 1.06e-07 (logrank test)

Table S50.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 452 151 0.1 - 111.0 (34.3)
subtype1 132 43 0.2 - 111.0 (36.6)
subtype2 215 53 0.1 - 96.8 (37.0)
subtype3 105 55 0.1 - 91.4 (21.2)

Figure S44.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0772 (ANOVA)

Table S51.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 131 61.9 (12.0)
subtype2 215 59.1 (12.9)
subtype3 107 61.4 (11.3)

Figure S45.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.336 (Fisher's exact test)

Table S52.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 39 93
subtype2 79 136
subtype3 33 74

Figure S46.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0291 (ANOVA)

Table S53.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 10 89.0 (8.8)
subtype2 18 96.7 (4.9)
subtype3 6 91.7 (9.8)

Figure S47.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 1.02e-07 (Chi-square test)

Table S54.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 55 19 57 1
subtype2 135 20 58 2
subtype3 34 15 50 8

Figure S48.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.252 (Fisher's exact test)

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

nPatients 0 1
ALL 208 16
subtype1 66 4
subtype2 82 4
subtype3 60 8

Figure S49.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000361 (Fisher's exact test)

Table S56.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 380 74
subtype1 113 19
subtype2 191 24
subtype3 76 31

Figure S50.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 1.59e-07 (Chi-square test)

Table S57.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 219 43 114 78
subtype1 53 15 43 21
subtype2 133 17 42 23
subtype3 33 11 29 34

Figure S51.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.1 (Fisher's exact test)

Table S58.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 4 450
subtype1 2 130
subtype2 0 215
subtype3 2 105

Figure S52.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S59.  Get Full Table Description of clustering approach #7: '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 S60.  Clustering Approach #7: '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 S53.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.323 (ANOVA)

Table S61.  Clustering Approach #7: '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 S54.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

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

Table S62.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S55.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.709 (ANOVA)

Table S63.  Clustering Approach #7: '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 S56.  Get High-res Image Clustering Approach #7: '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 S64.  Clustering Approach #7: '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 S57.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00986 (Fisher's exact test)

Table S65.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 224 17
subtype1 96 2
subtype2 49 3
subtype3 79 12

Figure S58.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000475 (Fisher's exact test)

Table S66.  Clustering Approach #7: '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 S59.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 2.6e-06 (Chi-square test)

Table S67.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 225 47 117 80
subtype1 114 19 41 24
subtype2 59 9 20 12
subtype3 52 19 56 44

Figure S60.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S68.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S61.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S69.  Get Full Table Description of clustering approach #8: '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 S70.  Clustering Approach #8: '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 S62.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.335 (ANOVA)

Table S71.  Clustering Approach #8: '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 S63.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00361 (Fisher's exact test)

Table S72.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S64.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.342 (ANOVA)

Table S73.  Clustering Approach #8: '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 S65.  Get High-res Image Clustering Approach #8: '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 S74.  Clustering Approach #8: '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 S66.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00446 (Fisher's exact test)

Table S75.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 224 17
subtype1 26 1
subtype2 104 2
subtype3 94 14

Figure S67.  Get High-res Image Clustering Approach #8: '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 S76.  Clustering Approach #8: '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 S68.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 7.47e-11 (Chi-square test)

Table S77.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 225 47 117 80
subtype1 40 5 4 3
subtype2 127 22 47 25
subtype3 58 20 66 52

Figure S69.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.654 (Fisher's exact test)

Table S78.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

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

Figure S70.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S79.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 209 106 165
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 1.29e-06 (logrank test)

Table S80.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 478 155 0.1 - 111.0 (35.2)
subtype1 209 47 0.1 - 111.0 (36.2)
subtype2 106 30 0.1 - 109.9 (35.8)
subtype3 163 78 0.2 - 93.3 (31.1)

Figure S71.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0537 (ANOVA)

Table S81.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 480 60.6 (12.2)
subtype1 209 62.0 (12.2)
subtype2 106 58.7 (12.7)
subtype3 165 59.9 (11.7)

Figure S72.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.00265 (Fisher's exact test)

Table S82.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 163 317
subtype1 88 121
subtype2 33 73
subtype3 42 123

Figure S73.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.149 (ANOVA)

Table S83.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 16 91.9 (8.3)
subtype2 9 95.6 (7.3)
subtype3 11 77.3 (38.8)

Figure S74.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00028 (Chi-square test)

Table S84.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 233 62 174 11
subtype1 115 26 66 2
subtype2 62 8 32 4
subtype3 56 28 76 5

Figure S75.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00693 (Fisher's exact test)

Table S85.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 222 17
subtype1 95 2
subtype2 51 3
subtype3 76 12

Figure S76.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000305 (Fisher's exact test)

Table S86.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 405 75
subtype1 184 25
subtype2 97 9
subtype3 124 41

Figure S77.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 8.11e-05 (Chi-square test)

Table S87.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 229 50 122 79
subtype1 114 20 49 26
subtype2 60 8 28 10
subtype3 55 22 45 43

Figure S78.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.53 (Fisher's exact test)

Table S88.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 5 475
subtype1 1 208
subtype2 1 105
subtype3 3 162

Figure S79.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S89.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 51 281 148
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 4.57e-06 (logrank test)

Table S90.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 478 155 0.1 - 111.0 (35.2)
subtype1 50 16 0.5 - 93.3 (39.6)
subtype2 280 70 0.1 - 111.0 (36.0)
subtype3 148 69 0.1 - 109.9 (29.5)

Figure S80.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0521 (ANOVA)

Table S91.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 480 60.6 (12.2)
subtype1 51 56.7 (12.1)
subtype2 281 61.1 (11.9)
subtype3 148 61.0 (12.6)

Figure S81.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.182 (Fisher's exact test)

Table S92.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 163 317
subtype1 15 36
subtype2 105 176
subtype3 43 105

Figure S82.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.849 (ANOVA)

Table S93.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 4 92.5 (9.6)
subtype2 19 86.3 (22.4)
subtype3 13 90.0 (27.4)

Figure S83.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.00064 (Chi-square test)

Table S94.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 233 62 174 11
subtype1 22 12 15 2
subtype2 153 35 91 2
subtype3 58 15 68 7

Figure S84.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00892 (Fisher's exact test)

Table S95.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 222 17
subtype1 26 5
subtype2 128 4
subtype3 68 8

Figure S85.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.282 (Fisher's exact test)

Table S96.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 405 75
subtype1 45 6
subtype2 241 40
subtype3 119 29

Figure S86.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0143 (Chi-square test)

Table S97.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 229 50 122 79
subtype1 22 10 13 6
subtype2 150 27 62 42
subtype3 57 13 47 31

Figure S87.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.123 (Fisher's exact test)

Table S98.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 5 475
subtype1 2 49
subtype2 2 279
subtype3 1 147

Figure S88.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = KIRC.mergedcluster.txt

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

  • Number of patients = 502

  • Number of clustering approaches = 10

  • Number of selected clinical features = 9

  • 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

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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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