Correlation between aggregated molecular cancer subtypes and selected clinical features
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1VT1RH9
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 12 different clustering approaches and 14 clinical features across 894 patients, 93 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGY_T_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'RACE'.

  • 5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_M_STAGE',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 9 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER',  'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.

  • 7 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 93 significant findings detected.

Clinical
Features
Statistical
Tests
mRNA
CNMF
subtypes
mRNA
cHierClus
subtypes
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RPPA
CNMF
subtypes
RPPA
cHierClus
subtypes
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.211
(0.31)
0.0913
(0.153)
7.35e-07
(1.24e-05)
1.11e-16
(1.87e-14)
1.39e-09
(3.33e-08)
5.59e-12
(1.88e-10)
7.77e-16
(4.35e-14)
2.22e-16
(1.87e-14)
6.25e-07
(1.17e-05)
1.07e-14
(4.48e-13)
3.15e-09
(6.61e-08)
8.3e-10
(2.32e-08)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.317
(0.413)
0.214
(0.31)
0.0393
(0.0717)
1.07e-05
(3.26e-05)
0.0532
(0.0951)
0.00264
(0.00577)
0.000578
(0.00135)
3.46e-06
(3.11e-05)
0.135
(0.206)
1.31e-05
(3.94e-05)
0.00195
(0.00438)
0.0129
(0.026)
PATHOLOGIC STAGE Fisher's exact test 0.0236
(0.0451)
0.0639
(0.113)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
PATHOLOGY T STAGE Fisher's exact test 0.0073
(0.0155)
0.0234
(0.0451)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
PATHOLOGY N STAGE Fisher's exact test 0.113
(0.176)
0.322
(0.416)
0.0135
(0.0269)
1e-05
(3.11e-05)
0.201
(0.302)
0.00042
(0.00101)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
0.00011
(0.000276)
1e-05
(3.11e-05)
6e-05
(0.00016)
PATHOLOGY M STAGE Fisher's exact test 0.221
(0.317)
0.245
(0.34)
9e-05
(0.000229)
1e-05
(3.11e-05)
2e-05
(5.6e-05)
1e-05
(3.11e-05)
0.00315
(0.00678)
2e-05
(5.6e-05)
0.0794
(0.135)
2e-05
(5.6e-05)
0.00899
(0.0188)
0.0136
(0.027)
GENDER Fisher's exact test 0.578
(0.66)
0.00019
(0.000469)
0.012
(0.0246)
4e-05
(0.000108)
0.405
(0.497)
0.0233
(0.0451)
8e-05
(0.00021)
1e-05
(3.11e-05)
0.0736
(0.126)
3e-05
(8.26e-05)
0.00232
(0.00513)
0.00024
(0.000584)
RADIATION THERAPY Fisher's exact test 0.237
(0.332)
0.306
(0.401)
0.376
(0.472)
0.735
(0.807)
0.393
(0.486)
0.429
(0.515)
0.167
(0.253)
0.655
(0.729)
0.294
(0.389)
0.231
(0.326)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.208
(0.309)
0.0976
(0.159)
0.414
(0.504)
0.0332
(0.0619)
0.335
(0.426)
0.00151
(0.00343)
0.249
(0.34)
0.0691
(0.12)
0.0398
(0.0719)
0.108
(0.17)
HISTOLOGICAL TYPE Fisher's exact test 1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
9e-05
(0.000229)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.1
(0.16)
0.908
(0.96)
0.545
(0.628)
0.968
(1.00)
0.806
(0.862)
0.533
(0.618)
0.898
(0.955)
0.787
(0.847)
0.604
(0.681)
0.438
(0.522)
YEAR OF TOBACCO SMOKING ONSET Kruskal-Wallis (anova) 0.0244
(0.0461)
0.477
(0.561)
0.745
(0.812)
0.453
(0.535)
0.529
(0.617)
0.367
(0.463)
0.214
(0.31)
0.099
(0.16)
0.273
(0.37)
0.422
(0.51)
RACE Fisher's exact test 2e-05
(5.6e-05)
1e-05
(3.11e-05)
0.00058
(0.00135)
0.0917
(0.153)
0.0377
(0.0696)
0.00907
(0.0188)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
1e-05
(3.11e-05)
0.00062
(0.00143)
0.766
(0.831)
0.122
(0.188)
ETHNICITY Fisher's exact test 0.106
(0.169)
0.0662
(0.116)
0.283
(0.381)
0.681
(0.752)
0.595
(0.676)
0.249
(0.34)
0.381
(0.474)
0.225
(0.321)
0.288
(0.384)
0.327
(0.419)
0.65
(0.728)
0.0937
(0.154)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 32 25 14 17
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.211 (logrank test), Q value = 0.31

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

nPatients nDeath Duration Range (Median), Month
ALL 88 19 0.5 - 117.8 (38.0)
subtype1 32 5 1.4 - 115.0 (38.7)
subtype2 25 8 0.5 - 114.4 (43.2)
subtype3 14 1 12.1 - 117.8 (40.4)
subtype4 17 5 0.5 - 76.6 (29.0)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.317 (Kruskal-Wallis (anova)), Q value = 0.41

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

nPatients Mean (Std.Dev)
ALL 87 60.1 (12.2)
subtype1 31 61.2 (13.6)
subtype2 25 58.5 (11.2)
subtype3 14 64.2 (11.4)
subtype4 17 56.9 (11.2)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0236 (Fisher's exact test), Q value = 0.045

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 45 16 15 6
subtype1 22 3 6 1
subtype2 8 5 8 4
subtype3 9 5 0 0
subtype4 6 3 1 1

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0073 (Fisher's exact test), Q value = 0.016

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 48 21 19
subtype1 22 3 7
subtype2 9 6 10
subtype3 9 5 0
subtype4 8 7 2

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.113 (Fisher's exact test), Q value = 0.18

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1
ALL 37 4
subtype1 17 0
subtype2 12 3
subtype3 5 0
subtype4 3 1

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.221 (Fisher's exact test), Q value = 0.32

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 77 6
subtype1 31 1
subtype2 21 4
subtype3 14 0
subtype4 11 1

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.578 (Fisher's exact test), Q value = 0.66

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 33 55
subtype1 15 17
subtype2 9 16
subtype3 4 10
subtype4 5 12

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 72 16
subtype1 32 0
subtype2 24 1
subtype3 13 1
subtype4 3 14

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RACE'

P value = 2e-05 (Fisher's exact test), Q value = 5.6e-05

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 71
subtype1 0 0 30
subtype2 0 1 22
subtype3 0 2 12
subtype4 1 9 7

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.106 (Fisher's exact test), Q value = 0.17

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 58
subtype1 4 19
subtype2 3 11
subtype3 0 12
subtype4 0 16

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 21 24 18 13 12
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0913 (logrank test), Q value = 0.15

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 88 19 0.5 - 117.8 (38.0)
subtype1 21 2 1.4 - 115.0 (35.9)
subtype2 24 8 0.5 - 114.4 (46.1)
subtype3 18 6 0.5 - 76.6 (28.6)
subtype4 13 3 11.1 - 106.2 (48.4)
subtype5 12 0 12.1 - 117.8 (43.1)

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.214 (Kruskal-Wallis (anova)), Q value = 0.31

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 87 60.1 (12.2)
subtype1 20 57.6 (13.7)
subtype2 24 59.2 (10.8)
subtype3 18 57.0 (10.9)
subtype4 13 64.1 (13.5)
subtype5 12 66.2 (10.8)

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

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0639 (Fisher's exact test), Q value = 0.11

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 45 16 15 6
subtype1 16 2 3 0
subtype2 8 4 8 4
subtype3 7 3 1 1
subtype4 7 2 3 1
subtype5 7 5 0 0

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0234 (Fisher's exact test), Q value = 0.045

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 48 21 19
subtype1 16 2 3
subtype2 9 5 10
subtype3 9 7 2
subtype4 7 2 4
subtype5 7 5 0

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.322 (Fisher's exact test), Q value = 0.42

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1
ALL 37 4
subtype1 9 0
subtype2 11 3
subtype3 4 1
subtype4 9 0
subtype5 4 0

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.245 (Fisher's exact test), Q value = 0.34

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 77 6
subtype1 21 0
subtype2 20 4
subtype3 12 1
subtype4 12 1
subtype5 12 0

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00019 (Fisher's exact test), Q value = 0.00047

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 33 55
subtype1 3 18
subtype2 9 15
subtype3 5 13
subtype4 12 1
subtype5 4 8

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 72 16
subtype1 21 0
subtype2 23 1
subtype3 4 14
subtype4 13 0
subtype5 11 1

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 71
subtype1 0 0 19
subtype2 0 1 21
subtype3 1 10 7
subtype4 0 0 13
subtype5 0 1 11

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0662 (Fisher's exact test), Q value = 0.12

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 58
subtype1 2 12
subtype2 2 11
subtype3 0 16
subtype4 3 7
subtype5 0 12

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

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

Table S23.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 328 200 354
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 7.35e-07 (logrank test), Q value = 1.2e-05

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 876 228 0.1 - 194.8 (35.9)
subtype1 324 58 0.1 - 194.8 (29.2)
subtype2 198 38 0.1 - 149.2 (37.4)
subtype3 354 132 0.1 - 133.7 (38.4)

Figure S21.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0393 (Kruskal-Wallis (anova)), Q value = 0.072

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 876 60.1 (12.5)
subtype1 324 59.8 (12.9)
subtype2 200 58.6 (12.8)
subtype3 352 61.3 (11.9)

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S26.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 454 103 190 104
subtype1 182 43 52 24
subtype2 127 21 37 14
subtype3 145 39 101 66

Figure S23.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S27.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 480 127 258 15
subtype1 200 53 66 7
subtype2 130 24 45 1
subtype3 150 50 147 7

Figure S24.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0135 (Fisher's exact test), Q value = 0.027

Table S28.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 323 44 6
subtype1 92 19 4
subtype2 74 3 0
subtype3 157 22 2

Figure S25.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 9e-05 (Fisher's exact test), Q value = 0.00023

Table S29.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 546 89
subtype1 136 13
subtype2 155 13
subtype3 255 63

Figure S26.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.012 (Fisher's exact test), Q value = 0.025

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 292 590
subtype1 93 235
subtype2 82 118
subtype3 117 237

Figure S27.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.237 (Fisher's exact test), Q value = 0.33

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 408 3
subtype1 234 1
subtype2 80 0
subtype3 94 2

Figure S28.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.208 (Kruskal-Wallis (anova)), Q value = 0.31

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 143 87.7 (22.8)
subtype1 72 91.7 (13.7)
subtype2 29 92.4 (11.5)
subtype3 42 77.6 (35.0)

Figure S29.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 66 528 288
subtype1 52 40 236
subtype2 10 164 26
subtype3 4 324 26

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.1 (Kruskal-Wallis (anova)), Q value = 0.16

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 106 30.8 (24.9)
subtype1 71 31.1 (21.5)
subtype2 14 42.8 (44.4)
subtype3 21 21.7 (12.6)

Figure S31.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0244 (Kruskal-Wallis (anova)), Q value = 0.046

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 74 1973.7 (16.0)
subtype1 49 1970.6 (15.2)
subtype2 11 1976.5 (18.2)
subtype3 14 1982.6 (14.0)

Figure S32.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00058 (Fisher's exact test), Q value = 0.0014

Table S36.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 16 121 719
subtype1 2 8 59 244
subtype2 0 4 32 163
subtype3 0 4 30 312

Figure S33.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.283 (Fisher's exact test), Q value = 0.38

Table S37.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 42 622
subtype1 14 254
subtype2 7 138
subtype3 21 230

Figure S34.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S38.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 104 123 172 71 190
'METHLYATION CNMF' versus 'Time to Death'

P value = 1.11e-16 (logrank test), Q value = 1.9e-14

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 654 154 0.1 - 194.8 (30.6)
subtype1 103 16 0.2 - 153.7 (54.9)
subtype2 120 46 0.4 - 131.1 (22.2)
subtype3 171 39 0.1 - 149.2 (37.2)
subtype4 71 37 0.6 - 133.7 (21.6)
subtype5 189 16 0.1 - 194.8 (27.1)

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 1.07e-05 (Kruskal-Wallis (anova)), Q value = 3.3e-05

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 655 60.5 (12.6)
subtype1 103 53.7 (14.6)
subtype2 120 61.4 (11.7)
subtype3 172 61.5 (12.6)
subtype4 71 63.1 (9.7)
subtype5 189 61.8 (11.8)

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 344 74 138 79
subtype1 46 28 20 8
subtype2 47 8 45 21
subtype3 108 16 27 19
subtype4 7 8 28 26
subtype5 136 14 18 5

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 367 92 187 12
subtype1 47 28 27 2
subtype2 51 9 56 7
subtype3 110 20 41 1
subtype4 9 16 45 1
subtype5 150 19 18 1

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 221 34 6
subtype1 51 5 3
subtype2 46 13 0
subtype3 69 0 0
subtype4 24 12 2
subtype5 31 4 1

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 353 63
subtype1 57 5
subtype2 76 17
subtype3 135 18
subtype4 34 21
subtype5 51 2

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 4e-05 (Fisher's exact test), Q value = 0.00011

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 214 446
subtype1 46 58
subtype2 38 85
subtype3 71 101
subtype4 18 53
subtype5 41 149

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.306 (Fisher's exact test), Q value = 0.4

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 383 3
subtype1 86 0
subtype2 47 0
subtype3 71 1
subtype4 29 1
subtype5 150 1

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0976 (Kruskal-Wallis (anova)), Q value = 0.16

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 134 89.9 (17.7)
subtype1 22 91.4 (15.8)
subtype2 13 91.5 (9.0)
subtype3 25 91.6 (9.9)
subtype4 14 67.9 (40.2)
subtype5 60 93.5 (8.4)

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 66 319 275
subtype1 64 20 20
subtype2 2 82 39
subtype3 0 168 4
subtype4 0 46 25
subtype5 0 3 187

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.908 (Kruskal-Wallis (anova)), Q value = 0.96

Table S49.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 102 31.3 (25.2)
subtype1 15 26.2 (20.0)
subtype2 17 37.8 (41.4)
subtype3 8 28.0 (19.4)
subtype4 9 29.9 (14.5)
subtype5 53 31.3 (22.2)

Figure S45.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.477 (Kruskal-Wallis (anova)), Q value = 0.56

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 72 1973.0 (16.0)
subtype1 12 1975.9 (14.4)
subtype2 12 1979.3 (19.1)
subtype3 4 1978.0 (26.7)
subtype4 5 1970.4 (5.0)
subtype5 39 1970.0 (15.1)

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0917 (Fisher's exact test), Q value = 0.15

Table S51.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 9 107 522
subtype1 0 4 20 77
subtype2 0 2 14 101
subtype3 0 0 24 148
subtype4 0 1 12 55
subtype5 2 2 37 141

Figure S47.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.681 (Fisher's exact test), Q value = 0.75

Table S52.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 26 519
subtype1 5 64
subtype2 3 100
subtype3 6 133
subtype4 2 59
subtype5 10 163

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S53.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 185 185 189 197
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 1.39e-09 (logrank test), Q value = 3.3e-08

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 754 206 0.1 - 194.8 (36.9)
subtype1 185 72 0.1 - 153.7 (35.2)
subtype2 185 36 0.2 - 194.8 (37.4)
subtype3 187 22 0.9 - 137.1 (37.0)
subtype4 197 76 0.1 - 130.7 (36.5)

Figure S49.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0532 (Kruskal-Wallis (anova)), Q value = 0.095

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 751 60.0 (12.6)
subtype1 185 58.3 (12.1)
subtype2 183 59.4 (13.4)
subtype3 188 61.6 (12.7)
subtype4 195 60.8 (11.9)

Figure S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S56.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 382 91 172 101
subtype1 75 22 50 37
subtype2 102 23 41 16
subtype3 128 20 33 6
subtype4 77 26 48 42

Figure S51.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S57.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 396 109 236 15
subtype1 78 25 76 6
subtype2 105 27 51 2
subtype3 131 20 38 0
subtype4 82 37 71 7

Figure S52.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.201 (Fisher's exact test), Q value = 0.3

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 300 39 5
subtype1 84 17 3
subtype2 66 5 1
subtype3 67 5 0
subtype4 83 12 1

Figure S53.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 5.6e-05

Table S59.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 487 86
subtype1 116 33
subtype2 121 12
subtype3 122 5
subtype4 128 36

Figure S54.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.405 (Fisher's exact test), Q value = 0.5

Table S60.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 243 513
subtype1 64 121
subtype2 63 122
subtype3 62 127
subtype4 54 143

Figure S55.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.376 (Fisher's exact test), Q value = 0.47

Table S61.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 331 3
subtype1 76 1
subtype2 89 2
subtype3 99 0
subtype4 67 0

Figure S56.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.414 (Kruskal-Wallis (anova)), Q value = 0.5

Table S62.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 118 89.8 (19.8)
subtype1 29 86.9 (26.2)
subtype2 34 91.2 (15.7)
subtype3 27 95.2 (6.4)
subtype4 28 86.1 (24.2)

Figure S57.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 9e-05 (Fisher's exact test), Q value = 0.00023

Table S63.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 63 478 215
subtype1 17 122 46
subtype2 17 112 56
subtype3 21 96 72
subtype4 8 148 41

Figure S58.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.545 (Kruskal-Wallis (anova)), Q value = 0.63

Table S64.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 84 29.8 (24.4)
subtype1 18 28.7 (16.3)
subtype2 21 25.9 (20.0)
subtype3 24 31.6 (35.5)
subtype4 21 32.6 (19.4)

Figure S59.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.745 (Kruskal-Wallis (anova)), Q value = 0.81

Table S65.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 60 1971.7 (15.2)
subtype1 14 1969.1 (12.2)
subtype2 15 1973.5 (17.2)
subtype3 16 1970.1 (14.8)
subtype4 15 1974.0 (17.1)

Figure S60.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0377 (Fisher's exact test), Q value = 0.07

Table S66.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 16 93 625
subtype1 0 9 24 146
subtype2 0 2 23 153
subtype3 1 2 30 153
subtype4 0 3 16 173

Figure S61.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.595 (Fisher's exact test), Q value = 0.68

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 32 523
subtype1 7 125
subtype2 12 136
subtype3 6 125
subtype4 7 137

Figure S62.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S68.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 200 65 116 149 60 71 95
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 5.59e-12 (logrank test), Q value = 1.9e-10

Table S69.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 754 206 0.1 - 194.8 (36.9)
subtype1 200 47 0.1 - 194.8 (36.3)
subtype2 64 3 1.1 - 137.1 (38.0)
subtype3 116 56 0.1 - 122.6 (33.1)
subtype4 148 29 0.1 - 129.9 (47.1)
subtype5 60 23 1.9 - 118.8 (36.4)
subtype6 71 8 0.6 - 152.0 (23.7)
subtype7 95 40 0.2 - 133.7 (30.6)

Figure S63.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00264 (Kruskal-Wallis (anova)), Q value = 0.0058

Table S70.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 751 60.0 (12.6)
subtype1 198 60.0 (11.9)
subtype2 65 62.4 (13.2)
subtype3 116 59.7 (12.6)
subtype4 147 61.0 (12.6)
subtype5 59 64.4 (12.3)
subtype6 71 58.1 (12.1)
subtype7 95 56.1 (13.2)

Figure S64.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S71.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 382 91 172 101
subtype1 101 28 41 27
subtype2 38 9 15 1
subtype3 32 17 35 29
subtype4 101 17 24 7
subtype5 20 7 22 11
subtype6 44 8 14 4
subtype7 46 5 21 22

Figure S65.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 396 109 236 15
subtype1 103 37 59 1
subtype2 40 9 16 0
subtype3 34 19 52 11
subtype4 102 18 28 1
subtype5 24 9 27 0
subtype6 45 9 17 0
subtype7 48 8 37 2

Figure S66.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00042 (Fisher's exact test), Q value = 0.001

Table S73.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 300 39 5
subtype1 87 4 0
subtype2 19 3 0
subtype3 60 12 0
subtype4 46 1 1
subtype5 28 4 1
subtype6 25 2 0
subtype7 35 13 3

Figure S67.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S74.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 487 86
subtype1 125 24
subtype2 28 0
subtype3 71 25
subtype4 121 5
subtype5 41 9
subtype6 41 4
subtype7 60 19

Figure S68.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0233 (Fisher's exact test), Q value = 0.045

Table S75.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 243 513
subtype1 59 141
subtype2 20 45
subtype3 27 89
subtype4 63 86
subtype5 15 45
subtype6 23 48
subtype7 36 59

Figure S69.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.735 (Fisher's exact test), Q value = 0.81

Table S76.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 331 3
subtype1 104 2
subtype2 48 0
subtype3 45 0
subtype4 43 0
subtype5 18 0
subtype6 38 0
subtype7 35 1

Figure S70.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0332 (Kruskal-Wallis (anova)), Q value = 0.062

Table S77.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 118 89.8 (19.8)
subtype1 33 88.5 (21.1)
subtype2 14 96.4 (6.3)
subtype3 16 96.2 (7.2)
subtype4 20 93.5 (7.5)
subtype5 7 74.3 (33.1)
subtype6 13 93.1 (7.5)
subtype7 15 79.3 (34.7)

Figure S71.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S78.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 63 478 215
subtype1 19 122 59
subtype2 18 1 46
subtype3 19 73 24
subtype4 4 120 25
subtype5 1 49 10
subtype6 1 44 26
subtype7 1 69 25

Figure S72.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.968 (Kruskal-Wallis (anova)), Q value = 1

Table S79.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 84 29.8 (24.4)
subtype1 24 26.5 (21.1)
subtype2 14 38.2 (45.4)
subtype3 11 29.2 (14.4)
subtype4 9 29.7 (22.1)
subtype5 9 30.9 (17.8)
subtype6 8 25.6 (11.5)
subtype7 9 29.1 (14.6)

Figure S73.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.453 (Kruskal-Wallis (anova)), Q value = 0.54

Table S80.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 60 1971.7 (15.2)
subtype1 18 1967.4 (15.9)
subtype2 11 1968.2 (14.0)
subtype3 5 1969.4 (9.2)
subtype4 4 1987.0 (15.2)
subtype5 7 1972.4 (13.9)
subtype6 6 1975.8 (17.4)
subtype7 9 1975.6 (16.2)

Figure S74.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.00907 (Fisher's exact test), Q value = 0.019

Table S81.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 16 93 625
subtype1 0 1 18 175
subtype2 1 0 14 48
subtype3 0 5 12 96
subtype4 0 1 20 124
subtype5 0 3 5 51
subtype6 0 1 12 57
subtype7 0 5 12 74

Figure S75.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.249 (Fisher's exact test), Q value = 0.34

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 32 523
subtype1 10 151
subtype2 5 49
subtype3 2 75
subtype4 8 90
subtype5 3 39
subtype6 0 50
subtype7 4 69

Figure S76.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S83.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 125 200 288 276
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 7.77e-16 (logrank test), Q value = 4.4e-14

Table S84.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 883 228 0.1 - 194.8 (35.9)
subtype1 123 22 0.2 - 153.7 (50.0)
subtype2 200 96 0.1 - 133.7 (31.2)
subtype3 287 68 0.1 - 149.2 (45.5)
subtype4 273 42 0.1 - 194.8 (25.6)

Figure S77.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000578 (Kruskal-Wallis (anova)), Q value = 0.0014

Table S85.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 883 60.2 (12.5)
subtype1 124 55.4 (14.5)
subtype2 200 60.3 (11.9)
subtype3 287 60.7 (12.4)
subtype4 272 61.8 (11.6)

Figure S78.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S86.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 460 103 189 105
subtype1 59 33 19 12
subtype2 61 21 70 47
subtype3 169 29 57 31
subtype4 171 20 43 15

Figure S79.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S87.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 487 127 258 15
subtype1 60 34 29 2
subtype2 65 26 99 10
subtype3 170 35 81 2
subtype4 192 32 49 1

Figure S80.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S88.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 330 43 6
subtype1 61 6 3
subtype2 89 18 1
subtype3 126 1 0
subtype4 54 18 2

Figure S81.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00315 (Fisher's exact test), Q value = 0.0068

Table S89.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 551 90
subtype1 75 9
subtype2 146 42
subtype3 239 29
subtype4 91 10

Figure S82.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 8e-05 (Fisher's exact test), Q value = 0.00021

Table S90.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 291 598
subtype1 48 77
subtype2 52 148
subtype3 120 168
subtype4 71 205

Figure S83.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.393 (Fisher's exact test), Q value = 0.49

Table S91.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 415 3
subtype1 88 0
subtype2 39 0
subtype3 87 2
subtype4 201 1

Figure S84.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.335 (Kruskal-Wallis (anova)), Q value = 0.43

Table S92.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 143 88.4 (21.5)
subtype1 21 94.3 (11.2)
subtype2 21 77.6 (35.3)
subtype3 28 91.8 (9.4)
subtype4 73 88.5 (21.3)

Figure S85.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S93.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 66 533 290
subtype1 64 42 19
subtype2 2 187 11
subtype3 0 286 2
subtype4 0 18 258

Figure S86.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.806 (Kruskal-Wallis (anova)), Q value = 0.86

Table S94.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 107 30.6 (24.8)
subtype1 13 27.4 (21.2)
subtype2 8 29.1 (17.1)
subtype3 10 25.9 (19.4)
subtype4 76 32.0 (26.8)

Figure S87.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.529 (Kruskal-Wallis (anova)), Q value = 0.62

Table S95.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 75 1973.6 (15.9)
subtype1 10 1974.9 (15.4)
subtype2 6 1980.0 (14.7)
subtype3 6 1978.5 (21.9)
subtype4 53 1972.0 (15.6)

Figure S88.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S96.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 16 121 726
subtype1 0 4 20 98
subtype2 0 4 13 180
subtype3 0 2 27 254
subtype4 2 6 61 194

Figure S89.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.381 (Fisher's exact test), Q value = 0.47

Table S97.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 42 629
subtype1 5 82
subtype2 7 128
subtype3 18 186
subtype4 12 233

Figure S90.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S98.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7 8 9
Number of samples 90 62 151 84 122 38 111 59 172
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.22e-16 (logrank test), Q value = 1.9e-14

Table S99.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 883 228 0.1 - 194.8 (35.9)
subtype1 89 12 2.5 - 153.7 (54.7)
subtype2 61 24 0.2 - 105.4 (19.3)
subtype3 151 34 0.1 - 131.2 (44.9)
subtype4 84 18 0.2 - 194.8 (20.4)
subtype5 122 66 0.4 - 133.7 (34.9)
subtype6 37 13 0.1 - 130.7 (39.0)
subtype7 110 30 0.1 - 149.2 (49.2)
subtype8 59 14 1.4 - 107.5 (31.7)
subtype9 170 17 0.1 - 129.9 (26.4)

Figure S91.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.46e-06 (Kruskal-Wallis (anova)), Q value = 3.1e-05

Table S100.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 883 60.2 (12.5)
subtype1 90 54.0 (14.2)
subtype2 61 59.7 (13.3)
subtype3 150 59.8 (12.3)
subtype4 83 63.2 (12.5)
subtype5 122 62.7 (11.0)
subtype6 37 57.8 (11.7)
subtype7 111 62.9 (12.0)
subtype8 59 56.4 (13.7)
subtype9 170 60.8 (11.3)

Figure S92.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S101.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 460 103 189 105
subtype1 38 30 15 5
subtype2 29 3 14 15
subtype3 84 15 29 23
subtype4 36 5 30 7
subtype5 31 14 45 31
subtype6 8 6 14 10
subtype7 72 9 22 8
subtype8 38 6 10 3
subtype9 124 15 10 3

Figure S93.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S102.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 487 127 258 15
subtype1 39 30 20 1
subtype2 29 5 22 6
subtype3 85 21 43 2
subtype4 42 9 32 1
subtype5 33 16 69 4
subtype6 10 8 19 1
subtype7 72 10 29 0
subtype8 40 6 13 0
subtype9 137 22 11 0

Figure S94.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S103.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 330 43 6
subtype1 51 2 2
subtype2 11 16 2
subtype3 54 1 0
subtype4 25 12 1
subtype5 60 7 0
subtype6 17 4 0
subtype7 60 0 0
subtype8 23 1 0
subtype9 29 0 1

Figure S95.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 5.6e-05

Table S104.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 551 90
subtype1 55 3
subtype2 30 9
subtype3 125 21
subtype4 35 5
subtype5 86 31
subtype6 23 9
subtype7 93 8
subtype8 52 3
subtype9 52 1

Figure S96.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S105.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 291 598
subtype1 34 56
subtype2 26 36
subtype3 9 142
subtype4 33 51
subtype5 31 91
subtype6 17 21
subtype7 108 3
subtype8 3 56
subtype9 30 142

Figure S97.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.429 (Fisher's exact test), Q value = 0.52

Table S106.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 415 3
subtype1 77 0
subtype2 29 0
subtype3 45 2
subtype4 54 0
subtype5 19 0
subtype6 12 0
subtype7 36 0
subtype8 11 0
subtype9 132 1

Figure S98.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00151 (Kruskal-Wallis (anova)), Q value = 0.0034

Table S107.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 143 88.4 (21.5)
subtype1 15 97.3 (4.6)
subtype2 11 74.5 (31.4)
subtype3 15 94.0 (8.3)
subtype4 17 77.6 (33.5)
subtype5 13 70.0 (40.6)
subtype6 2 90.0 (14.1)
subtype7 13 89.2 (10.4)
subtype8 5 100.0 (0.0)
subtype9 52 93.8 (8.0)

Figure S99.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S108.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 66 533 290
subtype1 64 19 7
subtype2 2 23 37
subtype3 0 151 0
subtype4 0 11 73
subtype5 0 122 0
subtype6 0 34 4
subtype7 0 111 0
subtype8 0 58 1
subtype9 0 4 168

Figure S100.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.533 (Kruskal-Wallis (anova)), Q value = 0.62

Table S109.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 107 30.6 (24.8)
subtype1 12 27.2 (22.1)
subtype2 5 31.4 (12.6)
subtype3 4 40.2 (22.8)
subtype4 23 38.2 (40.2)
subtype5 5 32.0 (13.0)
subtype6 6 23.7 (19.6)
subtype7 3 13.0 (6.1)
subtype8 2 23.5 (13.4)
subtype9 47 29.1 (18.6)

Figure S101.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.367 (Kruskal-Wallis (anova)), Q value = 0.46

Table S110.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 75 1973.6 (15.9)
subtype1 9 1976.0 (15.9)
subtype2 5 1974.4 (12.6)
subtype3 3 1971.0 (27.8)
subtype4 18 1974.1 (15.8)
subtype5 3 1975.7 (17.6)
subtype6 4 1989.8 (13.9)
subtype8 2 1983.5 (21.9)
subtype9 31 1969.7 (15.1)

Figure S102.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S111.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 16 121 726
subtype1 0 2 10 76
subtype2 0 3 14 42
subtype3 0 1 11 135
subtype4 0 3 24 55
subtype5 0 2 7 113
subtype6 0 0 5 32
subtype7 0 1 12 97
subtype8 0 2 4 52
subtype9 2 2 34 124

Figure S103.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.225 (Fisher's exact test), Q value = 0.32

Table S112.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 42 629
subtype1 4 53
subtype2 3 48
subtype3 7 102
subtype4 5 66
subtype5 4 81
subtype6 1 30
subtype7 11 67
subtype8 2 32
subtype9 5 150

Figure S104.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S113.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 346 197 330
'MIRSEQ CNMF' versus 'Time to Death'

P value = 6.25e-07 (logrank test), Q value = 1.2e-05

Table S114.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 867 225 0.1 - 194.8 (35.3)
subtype1 342 50 0.1 - 194.8 (29.4)
subtype2 196 68 0.1 - 131.1 (33.5)
subtype3 329 107 0.2 - 149.2 (41.8)

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.135 (Kruskal-Wallis (anova)), Q value = 0.21

Table S115.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 868 60.2 (12.5)
subtype1 342 59.6 (13.0)
subtype2 197 59.1 (12.0)
subtype3 329 61.4 (12.1)

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S116.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 447 101 189 104
subtype1 191 50 59 18
subtype2 93 15 52 34
subtype3 163 36 78 52

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S117.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 474 125 257 15
subtype1 212 62 66 4
subtype2 96 16 76 9
subtype3 166 47 115 2

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S118.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 318 44 6
subtype1 94 24 2
subtype2 79 14 4
subtype3 145 6 0

Figure S109.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0794 (Fisher's exact test), Q value = 0.13

Table S119.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 535 89
subtype1 135 13
subtype2 134 26
subtype3 266 50

Figure S110.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0736 (Fisher's exact test), Q value = 0.13

Table S120.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 285 588
subtype1 98 248
subtype2 73 124
subtype3 114 216

Figure S111.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.167 (Fisher's exact test), Q value = 0.25

Table S121.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 410 3
subtype1 266 1
subtype2 61 0
subtype3 83 2

Figure S112.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.249 (Kruskal-Wallis (anova)), Q value = 0.34

Table S122.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 144 87.8 (22.7)
subtype1 82 90.9 (17.7)
subtype2 27 79.3 (32.6)
subtype3 35 87.1 (23.0)

Figure S113.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S123.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 66 516 291
subtype1 59 29 258
subtype2 6 160 31
subtype3 1 327 2

Figure S114.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.898 (Kruskal-Wallis (anova)), Q value = 0.95

Table S124.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 108 30.4 (24.8)
subtype1 82 31.3 (26.9)
subtype2 17 27.4 (14.9)
subtype3 9 27.6 (20.8)

Figure S115.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.214 (Kruskal-Wallis (anova)), Q value = 0.31

Table S125.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 76 1973.4 (15.9)
subtype1 57 1971.6 (15.1)
subtype2 14 1977.9 (14.5)
subtype3 5 1982.2 (25.0)

Figure S116.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S126.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 16 121 710
subtype1 2 9 66 254
subtype2 0 3 35 156
subtype3 0 4 20 300

Figure S117.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.288 (Fisher's exact test), Q value = 0.38

Table S127.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 40 620
subtype1 14 271
subtype2 7 132
subtype3 19 217

Figure S118.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S128.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 77 350 234 212
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 1.07e-14 (logrank test), Q value = 4.5e-13

Table S129.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 867 225 0.1 - 194.8 (35.3)
subtype1 76 11 2.5 - 153.7 (67.0)
subtype2 349 137 0.1 - 133.7 (30.6)
subtype3 233 59 0.1 - 149.2 (45.5)
subtype4 209 18 0.1 - 194.8 (25.9)

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.31e-05 (Kruskal-Wallis (anova)), Q value = 3.9e-05

Table S130.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 868 60.2 (12.5)
subtype1 77 53.0 (14.0)
subtype2 347 60.3 (12.5)
subtype3 234 60.9 (12.1)
subtype4 210 61.8 (11.6)

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S131.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 447 101 189 104
subtype1 28 29 15 4
subtype2 139 32 103 70
subtype3 138 23 48 25
subtype4 142 17 23 5

Figure S121.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S132.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 474 125 257 15
subtype1 29 29 19 0
subtype2 145 41 151 13
subtype3 140 29 64 1
subtype4 160 26 23 1

Figure S122.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.00011 (Fisher's exact test), Q value = 0.00028

Table S133.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 318 44 6
subtype1 48 2 2
subtype2 134 32 4
subtype3 100 3 0
subtype4 36 7 0

Figure S123.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 5.6e-05

Table S134.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 535 89
subtype1 50 2
subtype2 227 61
subtype3 196 24
subtype4 62 2

Figure S124.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 3e-05 (Fisher's exact test), Q value = 8.3e-05

Table S135.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 285 588
subtype1 28 49
subtype2 112 238
subtype3 99 135
subtype4 46 166

Figure S125.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.655 (Fisher's exact test), Q value = 0.73

Table S136.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 410 3
subtype1 67 0
subtype2 108 2
subtype3 70 0
subtype4 165 1

Figure S126.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0691 (Kruskal-Wallis (anova)), Q value = 0.12

Table S137.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 144 87.8 (22.7)
subtype1 10 98.0 (4.2)
subtype2 53 81.1 (30.7)
subtype3 22 87.7 (21.4)
subtype4 59 92.0 (13.5)

Figure S127.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S138.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 66 516 291
subtype1 60 15 2
subtype2 6 262 82
subtype3 0 234 0
subtype4 0 5 207

Figure S128.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.787 (Kruskal-Wallis (anova)), Q value = 0.85

Table S139.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 108 30.4 (24.8)
subtype1 10 25.6 (23.0)
subtype2 32 34.0 (32.5)
subtype3 6 31.7 (22.4)
subtype4 60 29.1 (20.6)

Figure S129.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.099 (Kruskal-Wallis (anova)), Q value = 0.16

Table S140.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 76 1973.4 (15.9)
subtype1 7 1975.0 (16.2)
subtype2 22 1980.3 (16.3)
subtype3 3 1971.0 (27.8)
subtype4 44 1970.0 (14.1)

Figure S130.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.00062 (Fisher's exact test), Q value = 0.0014

Table S141.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 16 121 710
subtype1 0 2 6 67
subtype2 0 9 47 288
subtype3 0 2 22 206
subtype4 2 3 46 149

Figure S131.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.327 (Fisher's exact test), Q value = 0.42

Table S142.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 40 620
subtype1 4 41
subtype2 17 245
subtype3 12 152
subtype4 7 182

Figure S132.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S143.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 158 69 65 124
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 3.15e-09 (logrank test), Q value = 6.6e-08

Table S144.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 411 89 0.1 - 194.8 (30.2)
subtype1 156 14 0.1 - 194.8 (26.0)
subtype2 68 10 1.2 - 153.7 (41.8)
subtype3 64 27 0.2 - 129.9 (19.3)
subtype4 123 38 0.1 - 149.2 (36.1)

Figure S133.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00195 (Kruskal-Wallis (anova)), Q value = 0.0044

Table S145.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 412 59.8 (12.6)
subtype1 156 61.5 (11.6)
subtype2 67 54.0 (14.2)
subtype3 65 61.2 (13.0)
subtype4 124 60.1 (11.9)

Figure S134.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S146.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 221 47 81 46
subtype1 107 10 20 4
subtype2 27 17 18 6
subtype3 25 4 19 15
subtype4 62 16 24 21

Figure S135.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S147.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 238 56 111 9
subtype1 119 16 20 1
subtype2 27 17 24 1
subtype3 27 5 27 6
subtype4 65 18 40 1

Figure S136.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S148.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 126 31 6
subtype1 27 7 0
subtype2 33 5 2
subtype3 15 15 4
subtype4 51 4 0

Figure S137.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00899 (Fisher's exact test), Q value = 0.019

Table S149.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 198 33
subtype1 48 1
subtype2 34 4
subtype3 28 8
subtype4 88 20

Figure S138.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.00232 (Fisher's exact test), Q value = 0.0051

Table S150.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 133 283
subtype1 35 123
subtype2 31 38
subtype3 26 39
subtype4 41 83

Figure S139.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.294 (Fisher's exact test), Q value = 0.39

Table S151.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 254 1
subtype1 119 0
subtype2 61 0
subtype3 31 0
subtype4 43 1

Figure S140.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0398 (Kruskal-Wallis (anova)), Q value = 0.072

Table S152.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 94 89.9 (18.6)
subtype1 47 94.5 (7.2)
subtype2 13 90.8 (21.8)
subtype3 16 73.8 (33.6)
subtype4 18 91.7 (11.0)

Figure S141.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S153.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 48 144 224
subtype1 0 1 157
subtype2 46 5 18
subtype3 2 16 47
subtype4 0 122 2

Figure S142.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.604 (Kruskal-Wallis (anova)), Q value = 0.68

Table S154.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 79 31.6 (27.2)
subtype1 45 30.6 (21.3)
subtype2 15 35.4 (46.0)
subtype3 13 33.9 (20.8)
subtype4 6 24.7 (21.2)

Figure S143.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.273 (Kruskal-Wallis (anova)), Q value = 0.37

Table S155.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 59 1972.3 (17.0)
subtype1 33 1968.5 (15.8)
subtype2 13 1977.5 (18.0)
subtype3 9 1975.4 (14.6)
subtype4 4 1979.0 (26.2)

Figure S144.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.766 (Fisher's exact test), Q value = 0.83

Table S156.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 10 71 318
subtype1 2 2 25 122
subtype2 0 2 11 52
subtype3 0 3 14 47
subtype4 0 3 21 97

Figure S145.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.65 (Fisher's exact test), Q value = 0.73

Table S157.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 20 308
subtype1 7 134
subtype2 4 36
subtype3 3 49
subtype4 6 89

Figure S146.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S158.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 92 84 50 20 46 78 46
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 8.3e-10 (logrank test), Q value = 2.3e-08

Table S159.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 411 89 0.1 - 194.8 (30.2)
subtype1 91 7 0.5 - 123.6 (26.4)
subtype2 84 33 0.2 - 96.7 (19.7)
subtype3 49 3 0.1 - 194.8 (25.6)
subtype4 20 2 11.7 - 100.9 (30.2)
subtype5 45 17 0.5 - 131.1 (35.2)
subtype6 77 20 0.1 - 149.2 (36.1)
subtype7 45 7 2.5 - 153.7 (66.5)

Figure S147.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0129 (Kruskal-Wallis (anova)), Q value = 0.026

Table S160.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 412 59.8 (12.6)
subtype1 91 61.5 (10.5)
subtype2 82 61.0 (13.3)
subtype3 50 60.3 (13.3)
subtype4 19 55.1 (12.6)
subtype5 46 59.3 (11.3)
subtype6 78 61.4 (12.1)
subtype7 46 53.1 (14.6)

Figure S148.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S161.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 221 47 81 46
subtype1 59 9 11 2
subtype2 30 8 28 15
subtype3 43 0 2 1
subtype4 14 0 4 1
subtype5 24 2 5 13
subtype6 38 11 19 10
subtype7 13 17 12 4

Figure S149.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S162.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 238 56 111 9
subtype1 68 12 12 0
subtype2 34 11 37 2
subtype3 44 1 2 1
subtype4 14 0 6 0
subtype5 25 2 13 6
subtype6 40 13 25 0
subtype7 13 17 16 0

Figure S150.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.00016

Table S163.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 126 31 6
subtype1 17 4 0
subtype2 21 20 4
subtype3 7 1 0
subtype4 4 1 0
subtype5 20 1 0
subtype6 28 2 0
subtype7 29 2 2

Figure S151.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0136 (Fisher's exact test), Q value = 0.027

Table S164.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 198 33
subtype1 26 0
subtype2 38 12
subtype3 14 0
subtype4 6 1
subtype5 29 9
subtype6 57 9
subtype7 28 2

Figure S152.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00024 (Fisher's exact test), Q value = 0.00058

Table S165.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 133 283
subtype1 18 74
subtype2 32 52
subtype3 10 40
subtype4 12 8
subtype5 10 36
subtype6 31 47
subtype7 20 26

Figure S153.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.231 (Fisher's exact test), Q value = 0.33

Table S166.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 254 1
subtype1 70 0
subtype2 44 0
subtype3 38 0
subtype4 14 0
subtype5 15 0
subtype6 29 1
subtype7 44 0

Figure S154.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.108 (Kruskal-Wallis (anova)), Q value = 0.17

Table S167.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 94 89.9 (18.6)
subtype1 32 93.8 (7.9)
subtype2 18 76.7 (32.5)
subtype3 14 95.0 (6.5)
subtype4 8 86.2 (27.2)
subtype5 3 100.0 (0.0)
subtype6 14 90.0 (11.8)
subtype7 5 98.0 (4.5)

Figure S155.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.1e-05

Table S168.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients KIDNEY CHROMOPHOBE KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 48 144 224
subtype1 0 1 91
subtype2 0 18 66
subtype3 0 0 50
subtype4 4 2 14
subtype5 2 43 1
subtype6 0 77 1
subtype7 42 3 1

Figure S156.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.438 (Kruskal-Wallis (anova)), Q value = 0.52

Table S169.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 79 31.6 (27.2)
subtype1 21 35.4 (24.6)
subtype2 16 30.3 (15.3)
subtype3 21 29.1 (20.8)
subtype4 6 51.5 (66.7)
subtype5 3 14.7 (5.0)
subtype6 3 28.0 (32.0)
subtype7 9 24.6 (24.2)

Figure S157.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.422 (Kruskal-Wallis (anova)), Q value = 0.51

Table S170.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 59 1972.3 (17.0)
subtype1 16 1968.5 (13.5)
subtype2 12 1975.0 (16.7)
subtype3 14 1967.5 (17.0)
subtype4 7 1980.0 (19.3)
subtype5 2 1984.5 (20.5)
subtype6 2 1973.5 (38.9)
subtype7 6 1974.7 (17.7)

Figure S158.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.122 (Fisher's exact test), Q value = 0.19

Table S171.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 10 71 318
subtype1 2 1 17 69
subtype2 0 4 17 58
subtype3 0 1 6 40
subtype4 0 0 8 12
subtype5 0 1 8 37
subtype6 0 2 13 61
subtype7 0 1 2 41

Figure S159.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0937 (Fisher's exact test), Q value = 0.15

Table S172.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 20 308
subtype1 1 81
subtype2 4 64
subtype3 4 42
subtype4 1 16
subtype5 2 29
subtype6 4 58
subtype7 4 18

Figure S160.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KIPAN-TP/22570984/KIPAN-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIPAN-TP/22506733/KIPAN-TP.merged_data.txt

  • Number of patients = 894

  • Number of clustering approaches = 12

  • Number of selected clinical features = 14

  • 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

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

Q value calculation

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

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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