Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1ZG6QRD
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1ZG6QRD
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 11 clinical features across 139 patients, 20 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'PATHOLOGY.M.STAGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'PATHOLOGY.M.STAGE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'GENDER'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE' and 'PATHOLOGY.T.STAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.T.STAGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 20 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.00393
(0.212) 		0.0181
(0.871) 		0.617
(1.00) 		0.00291
(0.16) 		0.342
(1.00) 		0.0568
(1.00) 		0.211
(1.00) 		6.4e-05
(0.00429)
AGE ANOVA 0.157
(1.00) 		0.164
(1.00) 		0.71
(1.00) 		0.011
(0.538) 		0.806
(1.00) 		0.575
(1.00) 		0.535
(1.00) 		0.53
(1.00)
NEOPLASM DISEASESTAGE Chi-square test 2.28e-05
(0.00158) 		2.42e-07
(1.72e-05) 		7.74e-05
(0.00503) 		1.79e-06
(0.000125) 		0.00735
(0.375) 		0.000292
(0.0184) 		0.00609
(0.316) 		0.00451
(0.239)
PATHOLOGY T STAGE Chi-square test 0.000561
(0.0342) 		1.77e-08
(1.28e-06) 		5.57e-05
(0.00378) 		0.00181
(0.107) 		0.0511
(1.00) 		6.87e-05
(0.00453) 		0.0333
(1.00) 		0.00271
(0.152)
PATHOLOGY N STAGE Chi-square test 0.297
(1.00) 		0.0473
(1.00) 		0.219
(1.00) 		0.0449
(1.00) 		0.196
(1.00) 		0.166
(1.00) 		0.196
(1.00) 		0.159
(1.00)
PATHOLOGY M STAGE Chi-square test 0.00261
(0.149) 		0.000423
(0.0262) 		0.0423
(1.00) 		0.00117
(0.07) 		0.238
(1.00) 		0.0663
(1.00) 		0.153
(1.00) 		0.0273
(1.00)
GENDER Fisher's exact test 0.0321
(1.00) 		0.21
(1.00) 		0.000127
(0.00811) 		0.00192
(0.111) 		0.149
(1.00) 		0.165
(1.00) 		0.396
(1.00) 		0.129
(1.00)
KARNOFSKY PERFORMANCE SCORE ANOVA 0.362
(1.00) 		0.072
(1.00) 		0.0456
(1.00) 		0.225
(1.00) 		0.227
(1.00) 		0.247
(1.00) 		0.258
(1.00) 		0.288
(1.00)
HISTOLOGICAL TYPE Fisher's exact test 0.885
(1.00) 		1
(1.00) 		0.207
(1.00) 		0.304
(1.00) 		0.0287
(1.00) 		0.489
(1.00) 		0.00859
(0.429) 		0.549
(1.00)
NUMBERPACKYEARSSMOKED ANOVA
YEAROFTOBACCOSMOKINGONSET ANOVA
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 27 57 27 28
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.00393 (logrank test), Q value = 0.21

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

nPatients nDeath Duration Range (Median), Month
ALL 127 15 0.0 - 194.8 (14.6)
subtype1 23 2 0.1 - 194.8 (26.4)
subtype2 53 6 0.0 - 129.9 (20.1)
subtype3 25 1 0.0 - 50.5 (9.2)
subtype4 26 6 0.1 - 79.8 (8.4)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.157 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 133 59.9 (12.4)
subtype1 25 60.0 (10.8)
subtype2 56 62.0 (12.9)
subtype3 25 55.2 (10.7)
subtype4 27 59.8 (13.8)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.28e-05 (Chi-square test), Q value = 0.0016

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 77 11 31 10
subtype1 19 2 2 1
subtype2 34 6 15 1
subtype3 18 1 4 0
subtype4 6 2 10 8

Figure S3.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.000561 (Chi-square test), Q value = 0.034

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 82 17 40
subtype1 20 3 4
subtype2 37 5 15
subtype3 19 4 4
subtype4 6 5 17

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.297 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 26 12 4
subtype1 3 1 0
subtype2 11 4 1
subtype3 5 0 0
subtype4 7 7 3

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00261 (Chi-square test), Q value = 0.15

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 58 6 62
subtype1 9 0 13
subtype2 32 1 23
subtype3 9 0 14
subtype4 8 5 12

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0321 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 42 97
subtype1 4 23
subtype2 20 37
subtype3 5 22
subtype4 13 15

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.362 (ANOVA), Q value = 1

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 88.8 (19.2)
subtype1 8 95.0 (7.6)
subtype2 9 87.8 (18.6)
subtype3 8 93.8 (7.4)
subtype4 9 80.0 (30.4)

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.885 (Fisher's exact test), Q value = 1

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 133
subtype1 1 26
subtype2 2 55
subtype3 2 25
subtype4 1 27

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S11.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 35 35 53
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0181 (logrank test), Q value = 0.87

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 111 13 0.0 - 194.8 (13.6)
subtype1 32 4 0.0 - 80.8 (22.0)
subtype2 33 8 0.2 - 194.8 (11.1)
subtype3 46 1 0.0 - 129.9 (13.3)

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.164 (ANOVA), Q value = 1

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 117 60.2 (12.6)
subtype1 33 63.1 (12.2)
subtype2 34 60.7 (15.5)
subtype3 50 57.8 (10.2)

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.42e-07 (Chi-square test), Q value = 1.7e-05

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 72 8 30 9
subtype1 26 2 4 2
subtype2 7 2 19 7
subtype3 39 4 7 0

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 1.77e-08 (Chi-square test), Q value = 1.3e-06

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 75 10 38
subtype1 27 1 7
subtype2 7 3 25
subtype3 41 6 6

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0473 (Chi-square test), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 24 11 4
subtype1 5 1 1
subtype2 10 10 3
subtype3 9 0 0

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.000423 (Chi-square test), Q value = 0.026

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 48 5 62
subtype1 19 1 12
subtype2 17 4 13
subtype3 12 0 37

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.21 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 38 85
subtype1 9 26
subtype2 15 20
subtype3 14 39

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.072 (ANOVA), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 33 91.5 (11.2)
subtype1 8 83.8 (18.5)
subtype2 4 92.5 (5.0)
subtype3 21 94.3 (6.8)

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 117
subtype1 2 33
subtype2 1 34
subtype3 3 50

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S21.  Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 47 35 26 20
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.617 (logrank test), Q value = 1

Table S22.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 116 15 0.0 - 194.8 (17.1)
subtype1 44 8 0.2 - 194.8 (12.3)
subtype2 30 2 0.1 - 53.8 (13.7)
subtype3 24 2 0.0 - 96.9 (16.9)
subtype4 18 3 3.8 - 129.9 (35.9)

Figure S19.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.71 (ANOVA), Q value = 1

Table S23.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 122 60.0 (12.8)
subtype1 46 61.3 (14.2)
subtype2 33 58.5 (11.6)
subtype3 24 61.0 (11.2)
subtype4 19 58.3 (13.5)

Figure S20.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 7.74e-05 (Chi-square test), Q value = 0.005

Table S24.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 71 8 31 10
subtype1 14 4 19 8
subtype2 24 1 5 0
subtype3 16 3 7 0
subtype4 17 0 0 2

Figure S21.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 5.57e-05 (Chi-square test), Q value = 0.0038

Table S25.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 75 14 39
subtype1 16 5 26
subtype2 25 6 4
subtype3 16 3 7
subtype4 18 0 2

Figure S22.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.219 (Chi-square test), Q value = 1

Table S26.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 24 12 4
subtype1 11 10 3
subtype2 3 0 0
subtype3 8 1 0
subtype4 2 1 1

Figure S23.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0423 (Chi-square test), Q value = 1

Table S27.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 56 6 54
subtype1 25 5 14
subtype2 9 0 19
subtype3 13 0 12
subtype4 9 1 9

Figure S24.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.000127 (Fisher's exact test), Q value = 0.0081

Table S28.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 38 90
subtype1 25 22
subtype2 7 28
subtype3 2 24
subtype4 4 16

Figure S25.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0456 (ANOVA), Q value = 1

Table S29.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 29 88.3 (20.5)
subtype1 6 68.3 (39.7)
subtype2 11 96.4 (5.0)
subtype3 7 91.4 (9.0)
subtype4 5 90.0 (0.0)

Figure S26.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.207 (Fisher's exact test), Q value = 1

Table S30.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 122
subtype1 1 46
subtype2 3 32
subtype3 0 26
subtype4 2 18

Figure S27.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S31.  Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 25 54 49
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00291 (logrank test), Q value = 0.16

Table S32.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 116 15 0.0 - 194.8 (17.1)
subtype1 23 6 0.2 - 80.8 (10.1)
subtype2 50 6 0.0 - 123.6 (20.9)
subtype3 43 3 0.0 - 194.8 (19.1)

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.011 (ANOVA), Q value = 0.54

Table S33.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 122 60.0 (12.8)
subtype1 24 58.5 (14.4)
subtype2 51 64.0 (12.0)
subtype3 47 56.4 (11.8)

Figure S29.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.79e-06 (Chi-square test), Q value = 0.00013

Table S34.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 71 8 31 10
subtype1 9 4 4 7
subtype2 26 3 22 2
subtype3 36 1 5 1

Figure S30.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00181 (Chi-square test), Q value = 0.11

Table S35.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 75 14 39
subtype1 10 4 11
subtype2 27 4 23
subtype3 38 6 5

Figure S31.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0449 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 24 12 4
subtype1 3 6 3
subtype2 17 5 1
subtype3 4 1 0

Figure S32.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00117 (Chi-square test), Q value = 0.07

Table S37.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 56 6 54
subtype1 11 4 9
subtype2 31 2 17
subtype3 14 0 28

Figure S33.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00192 (Fisher's exact test), Q value = 0.11

Table S38.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 38 90
subtype1 15 10
subtype2 12 42
subtype3 11 38

Figure S34.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.225 (ANOVA), Q value = 1

Table S39.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 29 88.3 (20.5)
subtype1 4 80.0 (27.1)
subtype2 10 82.0 (29.7)
subtype3 15 94.7 (5.2)

Figure S35.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.304 (Fisher's exact test), Q value = 1

Table S40.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 122
subtype1 2 23
subtype2 1 53
subtype3 3 46

Figure S36.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S41.  Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 50 50 39
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.342 (logrank test), Q value = 1

Table S42.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 127 15 0.0 - 194.8 (14.6)
subtype1 46 4 0.0 - 123.6 (23.4)
subtype2 48 8 0.1 - 194.8 (13.4)
subtype3 33 3 0.0 - 58.5 (10.1)

Figure S37.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.806 (ANOVA), Q value = 1

Table S43.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 133 59.9 (12.4)
subtype1 48 59.9 (11.7)
subtype2 50 59.2 (14.4)
subtype3 35 61.0 (10.4)

Figure S38.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00735 (Chi-square test), Q value = 0.37

Table S44.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 77 11 31 10
subtype1 22 5 18 1
subtype2 29 4 8 8
subtype3 26 2 5 1

Figure S39.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0511 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 82 17 40
subtype1 23 8 19
subtype2 30 4 16
subtype3 29 5 5

Figure S40.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.196 (Chi-square test), Q value = 1

Table S46.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 26 12 4
subtype1 13 3 1
subtype2 8 8 3
subtype3 5 1 0

Figure S41.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.238 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 58 6 62
subtype1 23 1 20
subtype2 24 4 21
subtype3 11 1 21

Figure S42.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.149 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 42 97
subtype1 11 39
subtype2 20 30
subtype3 11 28

Figure S43.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.227 (ANOVA), Q value = 1

Table S49.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 88.8 (19.2)
subtype1 11 91.8 (7.5)
subtype2 15 82.7 (27.1)
subtype3 8 96.2 (5.2)

Figure S44.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0287 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 133
subtype1 0 50
subtype2 5 45
subtype3 1 38

Figure S45.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S51.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 17 66 56
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0568 (logrank test), Q value = 1

Table S52.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 127 15 0.0 - 194.8 (14.6)
subtype1 17 4 0.9 - 86.7 (14.6)
subtype2 58 3 0.0 - 123.6 (17.1)
subtype3 52 8 0.1 - 194.8 (12.7)

Figure S46.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.575 (ANOVA), Q value = 1

Table S53.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 133 59.9 (12.4)
subtype1 17 62.8 (14.3)
subtype2 61 59.3 (10.6)
subtype3 55 59.7 (13.8)

Figure S47.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000292 (Chi-square test), Q value = 0.018

Table S54.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 77 11 31 10
subtype1 4 1 11 1
subtype2 41 6 9 1
subtype3 32 4 11 8

Figure S48.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 6.87e-05 (Chi-square test), Q value = 0.0045

Table S55.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 82 17 40
subtype1 4 1 12
subtype2 45 12 9
subtype3 33 4 19

Figure S49.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.166 (Chi-square test), Q value = 1

Table S56.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 26 12 4
subtype1 7 3 1
subtype2 10 1 0
subtype3 9 8 3

Figure S50.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.0663 (Chi-square test), Q value = 1

Table S57.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 58 6 62
subtype1 12 1 3
subtype2 24 1 31
subtype3 22 4 28

Figure S51.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.165 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 42 97
subtype1 4 13
subtype2 16 50
subtype3 22 34

Figure S52.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.247 (ANOVA), Q value = 1

Table S59.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 88.8 (19.2)
subtype1 2 45.0 (63.6)
subtype2 15 94.0 (7.4)
subtype3 17 89.4 (13.9)

Figure S53.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.489 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 133
subtype1 0 17
subtype2 2 64
subtype3 4 52

Figure S54.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S61.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 50 54 35
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.211 (logrank test), Q value = 1

Table S62.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 127 15 0.0 - 194.8 (14.6)
subtype1 47 4 0.0 - 123.6 (18.2)
subtype2 52 10 0.1 - 194.8 (19.7)
subtype3 28 1 0.1 - 58.5 (9.6)

Figure S55.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.535 (ANOVA), Q value = 1

Table S63.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 133 59.9 (12.4)
subtype1 49 61.4 (11.4)
subtype2 54 58.6 (14.2)
subtype3 30 59.8 (10.7)

Figure S56.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00609 (Chi-square test), Q value = 0.32

Table S64.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 77 11 31 10
subtype1 24 4 18 0
subtype2 32 4 8 9
subtype3 21 3 5 1

Figure S57.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0333 (Chi-square test), Q value = 1

Table S65.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 82 17 40
subtype1 26 5 19
subtype2 33 4 17
subtype3 23 8 4

Figure S58.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.196 (Chi-square test), Q value = 1

Table S66.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 26 12 4
subtype1 13 3 1
subtype2 8 8 3
subtype3 5 1 0

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.153 (Chi-square test), Q value = 1

Table S67.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 58 6 62
subtype1 23 0 22
subtype2 25 5 23
subtype3 10 1 17

Figure S60.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.396 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 42 97
subtype1 13 37
subtype2 20 34
subtype3 9 26

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.258 (ANOVA), Q value = 1

Table S69.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 88.8 (19.2)
subtype1 11 93.6 (6.7)
subtype2 15 82.7 (27.1)
subtype3 8 93.8 (7.4)

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00859 (Fisher's exact test), Q value = 0.43

Table S70.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 133
subtype1 0 50
subtype2 6 48
subtype3 0 35

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S71.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 13 71 55
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 6.4e-05 (logrank test), Q value = 0.0043

Table S72.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 127 15 0.0 - 194.8 (14.6)
subtype1 13 5 0.9 - 43.2 (14.6)
subtype2 63 2 0.0 - 123.6 (15.1)
subtype3 51 8 0.1 - 194.8 (12.5)

Figure S64.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.53 (ANOVA), Q value = 1

Table S73.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 133 59.9 (12.4)
subtype1 13 63.1 (13.5)
subtype2 66 60.1 (10.5)
subtype3 54 58.8 (14.4)

Figure S65.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00451 (Chi-square test), Q value = 0.24

Table S74.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 77 11 31 10
subtype1 3 1 8 1
subtype2 42 6 13 1
subtype3 32 4 10 8

Figure S66.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00271 (Chi-square test), Q value = 0.15

Table S75.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 82 17 40
subtype1 3 1 9
subtype2 46 12 13
subtype3 33 4 18

Figure S67.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.159 (Chi-square test), Q value = 1

Table S76.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 26 12 4
subtype1 5 3 1
subtype2 11 1 0
subtype3 10 8 3

Figure S68.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0273 (Chi-square test), Q value = 1

Table S77.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 58 6 62
subtype1 10 1 1
subtype2 26 1 33
subtype3 22 4 28

Figure S69.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.129 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 42 97
subtype1 3 10
subtype2 17 54
subtype3 22 33

Figure S70.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.288 (ANOVA), Q value = 1

Table S79.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 88.8 (19.2)
subtype1 2 45.0 (63.6)
subtype2 16 93.8 (7.2)
subtype3 16 89.4 (14.4)

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.549 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients KIDNEY CLEAR CELL RENAL CARCINOMA KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL 6 133
subtype1 0 13
subtype2 2 69
subtype3 4 51

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

Methods & Data
Input

  • Cluster data file = KIRP-TP.mergedcluster.txt

  • Clinical data file = KIRP-TP.merged_data.txt

  • Number of patients = 139

  • Number of clustering approaches = 8

  • Number of selected clinical features = 11

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

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

Chi-square test

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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] 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)
[7] 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)
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