Correlation between molecular cancer subtypes and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C19884ZV
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Kidney Renal Clear Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19884ZV
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 8 clinical features across 502 patients, 37 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

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

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

  • CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death',  'PATHOLOGY.T',  'PATHOLOGY.N',  'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

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

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

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

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'PATHOLOGY.T', and 'TUMOR.STAGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 37 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE
Statistical Tests logrank test ANOVA Fisher's exact test ANOVA Chi-square test Fisher's exact test Fisher's exact test Chi-square test
mRNA CNMF subtypes 0.231
(1.00) 		0.795
(1.00) 		0.634
(1.00) 		0.00911
(0.355) 		0.0704
(1.00) 		0.12
(1.00) 		0.0134
(0.442)
mRNA cHierClus subtypes 0.422
(1.00) 		0.607
(1.00) 		0.82
(1.00) 		0.00779
(0.32) 		0.124
(1.00) 		0.0988
(1.00) 		0.0102
(0.368)
Copy Number Ratio CNMF subtypes 0.000165
(0.00889) 		0.0782
(1.00) 		0.177
(1.00) 		0.889
(1.00) 		0.00196
(0.0884) 		0.0272
(0.87) 		0.000486
(0.0253) 		0.00154
(0.0709)
METHLYATION CNMF 6.32e-06
(0.000385) 		0.00858
(0.343) 		0.00326
(0.14) 		0.134
(1.00) 		2.64e-10
(2.01e-08) 		0.147
(1.00) 		6.41e-05
(0.00372) 		3.89e-09
(2.88e-07)
RPPA CNMF subtypes 1.79e-09
(1.34e-07) 		0.129
(1.00) 		0.0891
(1.00) 		0.322
(1.00) 		9.52e-07
(6.38e-05) 		0.00106
(0.0511) 		3.89e-07
(2.72e-05) 		8e-09
(5.84e-07)
RPPA cHierClus subtypes 8.92e-08
(6.33e-06) 		0.00927
(0.355) 		0.578
(1.00) 		0.0463
(1.00) 		4.03e-07
(2.78e-05) 		0.253
(1.00) 		9.01e-05
(0.00513) 		6.23e-07
(4.24e-05)
RNAseq CNMF subtypes 1.63e-06
(0.000106) 		0.09
(1.00) 		0.000154
(0.00847) 		0.66
(1.00) 		4.85e-06
(0.000301) 		0.0117
(0.398) 		0.000645
(0.0323) 		1.27e-06
(8.38e-05)
RNAseq cHierClus subtypes 3.34e-08
(2.4e-06) 		0.358
(1.00) 		0.00326
(0.14) 		0.357
(1.00) 		7.56e-12
(5.82e-10) 		0.000966
(0.0474) 		2e-06
(0.000128) 		2.63e-12
(2.05e-10)
MIRSEQ CNMF 2.31e-06
(0.000146) 		0.0424
(1.00) 		0.000437
(0.0232) 		0.172
(1.00) 		0.000549
(0.028) 		0.00922
(0.355) 		5.25e-05
(0.0031) 		2.55e-05
(0.00153)
MIRSEQ CHIERARCHICAL 0.00211
(0.0928) 		0.298
(1.00) 		0.124
(1.00) 		0.943
(1.00) 		0.000115
(0.00642) 		0.011
(0.385) 		0.351
(1.00) 		0.00108
(0.0511)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

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

P value = 0.231 (logrank test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.795 (ANOVA), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00911 (Chi-square test), Q value = 0.36

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0704 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.12 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0134 (Chi-square test), Q value = 0.44

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

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

P value = 0.422 (logrank test), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.607 (ANOVA), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.82 (Fisher's exact test), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.00779 (Chi-square test), Q value = 0.32

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.124 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.0988 (Fisher's exact test), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0102 (Chi-square test), Q value = 0.37

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

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

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

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

Table S17.  Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 132 197 164
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.000165 (logrank test), Q value = 0.0089

Table S18.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 490 158 0.1 - 111.0 (35.2)
subtype1 132 50 0.2 - 97.5 (36.6)
subtype2 195 43 0.1 - 111.0 (37.5)
subtype3 163 65 0.1 - 109.9 (28.5)

Figure S15.  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 'AGE'

P value = 0.0782 (ANOVA), Q value = 1

Table S19.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 492 60.6 (12.2)
subtype1 131 62.5 (11.8)
subtype2 197 59.5 (12.7)
subtype3 164 60.3 (11.7)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.177 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 171 322
subtype1 42 90
subtype2 78 119
subtype3 51 113

Figure S17.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.889 (ANOVA), Q value = 1

Table S21.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 14 88.6 (26.0)
subtype2 9 91.1 (10.5)
subtype3 13 86.2 (26.9)

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

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

P value = 0.00196 (Chi-square test), Q value = 0.088

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 242 64 176 11
subtype1 54 19 57 2
subtype2 116 25 55 1
subtype3 72 20 64 8

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

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

P value = 0.0272 (Fisher's exact test), Q value = 0.87

Table S23.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 228 18
subtype1 55 5
subtype2 91 2
subtype3 82 11

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000486 (Fisher's exact test), Q value = 0.025

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 417 76
subtype1 102 30
subtype2 181 16
subtype3 134 30

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00154 (Chi-square test), Q value = 0.071

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 238 52 123 80
subtype1 52 16 33 31
subtype2 116 19 45 17
subtype3 70 17 45 32

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 97 120 66
'METHLYATION CNMF' versus 'Time to Death'

P value = 6.32e-06 (logrank test), Q value = 0.00039

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

nPatients nDeath Duration Range (Median), Month
ALL 281 95 0.1 - 109.9 (28.5)
subtype1 96 49 0.2 - 84.7 (28.6)
subtype2 119 21 0.1 - 109.6 (31.5)
subtype3 66 25 0.1 - 109.9 (21.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00858 (ANOVA), Q value = 0.34

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

nPatients Mean (Std.Dev)
ALL 283 61.5 (12.0)
subtype1 97 63.6 (10.2)
subtype2 120 59.0 (12.9)
subtype3 66 63.0 (11.9)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00326 (Fisher's exact test), Q value = 0.14

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

nPatients FEMALE MALE
ALL 96 187
subtype1 21 76
subtype2 52 68
subtype3 23 43

Figure S25.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.134 (ANOVA), Q value = 1

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 28 92.5 (8.0)
subtype1 6 88.3 (7.5)
subtype2 17 92.4 (8.3)
subtype3 5 98.0 (4.5)

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 2.64e-10 (Chi-square test), Q value = 2e-08

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

nPatients T1 T2 T3 T4
ALL 132 36 107 8
subtype1 20 14 59 4
subtype2 78 18 24 0
subtype3 34 4 24 4

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.147 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 127 9
subtype1 43 5
subtype2 54 1
subtype3 30 3

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 6.41e-05 (Fisher's exact test), Q value = 0.0037

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

nPatients M0 M1
ALL 232 51
subtype1 66 31
subtype2 109 11
subtype3 57 9

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 3.89e-09 (Chi-square test), Q value = 2.9e-07

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 130 24 73 56
subtype1 20 9 35 33
subtype2 78 12 19 11
subtype3 32 3 19 12

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S35.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 101 90 86 76 44 57
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 1.79e-09 (logrank test), Q value = 1.3e-07

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

nPatients nDeath Duration Range (Median), Month
ALL 452 151 0.1 - 111.0 (34.3)
subtype1 101 26 0.2 - 111.0 (43.7)
subtype2 90 35 0.1 - 90.4 (29.4)
subtype3 85 22 0.2 - 93.0 (35.3)
subtype4 75 23 0.1 - 96.8 (27.9)
subtype5 44 8 0.2 - 83.8 (36.5)
subtype6 57 37 0.6 - 84.0 (19.7)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.129 (ANOVA), Q value = 1

Table S37.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 100 61.8 (11.1)
subtype2 90 58.0 (12.2)
subtype3 86 62.3 (12.6)
subtype4 76 60.0 (11.8)
subtype5 44 58.3 (15.7)
subtype6 57 61.2 (11.4)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0891 (Chi-square test), Q value = 1

Table S38.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 43 58
subtype2 29 61
subtype3 28 58
subtype4 18 58
subtype5 18 26
subtype6 15 42

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

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

P value = 0.322 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 7 92.9 (7.6)
subtype2 8 93.8 (5.2)
subtype3 10 90.0 (9.4)
subtype4 2 100.0 (0.0)
subtype5 4 100.0 (0.0)
subtype6 3 93.3 (11.5)

Figure S34.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 9.52e-07 (Chi-square test), Q value = 6.4e-05

Table S40.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 54 15 31 1
subtype2 43 10 32 5
subtype3 41 12 33 0
subtype4 45 7 23 1
subtype5 33 2 9 0
subtype6 8 8 37 4

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00106 (Chi-square test), Q value = 0.051

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

nPatients 0 1
ALL 208 16
subtype1 48 1
subtype2 46 2
subtype3 43 2
subtype4 35 3
subtype5 13 0
subtype6 23 8

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

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

P value = 3.89e-07 (Chi-square test), Q value = 2.7e-05

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

nPatients M0 M1
ALL 380 74
subtype1 87 14
subtype2 71 19
subtype3 76 10
subtype4 68 8
subtype5 44 0
subtype6 34 23

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

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 8e-09 (Chi-square test), Q value = 5.8e-07

Table S43.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 219 43 114 78
subtype1 54 10 22 15
subtype2 42 10 18 20
subtype3 39 11 26 10
subtype4 44 7 16 9
subtype5 33 2 9 0
subtype6 7 3 23 24

Figure S38.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S44.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 189 153 112
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 8.92e-08 (logrank test), Q value = 6.3e-06

Table S45.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 452 151 0.1 - 111.0 (34.3)
subtype1 189 42 0.1 - 96.8 (37.0)
subtype2 153 50 0.2 - 111.0 (36.8)
subtype3 110 59 0.1 - 91.4 (21.5)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00927 (ANOVA), Q value = 0.36

Table S46.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 189 58.4 (12.7)
subtype2 152 62.3 (12.3)
subtype3 112 61.3 (11.1)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.578 (Fisher's exact test), Q value = 1

Table S47.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 68 121
subtype2 47 106
subtype3 36 76

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

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

P value = 0.0463 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 17 96.5 (4.9)
subtype2 10 89.0 (8.8)
subtype3 7 92.9 (9.5)

Figure S42.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 4.03e-07 (Chi-square test), Q value = 2.8e-05

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

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 120 19 48 2
subtype2 67 20 65 1
subtype3 37 15 52 8

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.253 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 208 16
subtype1 72 4
subtype2 76 4
subtype3 60 8

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

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

P value = 9.01e-05 (Fisher's exact test), Q value = 0.0051

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

nPatients M0 M1
ALL 380 74
subtype1 170 19
subtype2 131 22
subtype3 79 33

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

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 6.23e-07 (Chi-square test), Q value = 4.2e-05

Table S52.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 219 43 114 78
subtype1 118 16 36 19
subtype2 65 16 48 24
subtype3 36 11 30 35

Figure S46.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S53.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 199 180 101
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.63e-06 (logrank test), Q value = 0.00011

Table S54.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 478 155 0.1 - 111.0 (34.3)
subtype1 199 44 0.1 - 111.0 (37.0)
subtype2 179 83 0.1 - 90.3 (30.6)
subtype3 100 28 0.1 - 93.3 (35.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.09 (ANOVA), Q value = 1

Table S55.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 479 60.6 (12.2)
subtype1 198 61.7 (12.2)
subtype2 180 60.6 (11.8)
subtype3 101 58.4 (12.7)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.000154 (Fisher's exact test), Q value = 0.0085

Table S56.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 167 313
subtype1 90 109
subtype2 45 135
subtype3 32 69

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

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

P value = 0.66 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 34 90.9 (17.8)
subtype1 14 91.4 (8.6)
subtype2 12 87.5 (28.3)
subtype3 8 95.0 (7.6)

Figure S50.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 4.85e-06 (Chi-square test), Q value = 3e-04

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

nPatients T1 T2 T3 T4
ALL 238 60 171 11
subtype1 118 22 57 2
subtype2 59 27 88 6
subtype3 61 11 26 3

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0117 (Fisher's exact test), Q value = 0.4

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

nPatients 0 1
ALL 228 17
subtype1 96 2
subtype2 85 12
subtype3 47 3

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

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

P value = 0.000645 (Fisher's exact test), Q value = 0.032

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

nPatients M0 M1
ALL 403 77
subtype1 176 23
subtype2 136 44
subtype3 91 10

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 1.27e-06 (Chi-square test), Q value = 8.4e-05

Table S61.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 234 48 117 81
subtype1 118 18 39 24
subtype2 56 21 58 45
subtype3 60 9 20 12

Figure S54.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S62.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 73 215 192
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.34e-08 (logrank test), Q value = 2.4e-06

Table S63.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 478 155 0.1 - 111.0 (34.3)
subtype1 72 14 0.2 - 92.0 (27.8)
subtype2 214 48 0.1 - 111.0 (37.2)
subtype3 192 93 0.1 - 93.3 (30.5)

Figure S55.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.358 (ANOVA), Q value = 1

Table S64.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 479 60.6 (12.2)
subtype1 73 58.7 (13.2)
subtype2 214 61.0 (12.4)
subtype3 192 60.8 (11.5)

Figure S56.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00326 (Fisher's exact test), Q value = 0.14

Table S65.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 167 313
subtype1 23 50
subtype2 92 123
subtype3 52 140

Figure S57.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.357 (ANOVA), Q value = 1

Table S66.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 90.9 (17.8)
subtype1 10 95.0 (9.7)
subtype2 13 93.1 (6.3)
subtype3 11 84.5 (29.1)

Figure S58.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 7.56e-12 (Chi-square test), Q value = 5.8e-10

Table S67.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 238 60 171 11
subtype1 55 6 11 1
subtype2 126 27 61 1
subtype3 57 27 99 9

Figure S59.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.000966 (Fisher's exact test), Q value = 0.047

Table S68.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 228 17
subtype1 32 2
subtype2 104 1
subtype3 92 14

Figure S60.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 2e-06 (Fisher's exact test), Q value = 0.00013

Table S69.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 403 77
subtype1 70 3
subtype2 191 24
subtype3 142 50

Figure S61.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 2.63e-12 (Chi-square test), Q value = 2.1e-10

Table S70.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 234 48 117 81
subtype1 54 6 10 3
subtype2 126 21 43 25
subtype3 54 21 64 53

Figure S62.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S71.  Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 117 192 172
'MIRSEQ CNMF' versus 'Time to Death'

P value = 2.31e-06 (logrank test), Q value = 0.00015

Table S72.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 479 156 0.1 - 111.0 (35.2)
subtype1 117 32 0.1 - 109.9 (37.0)
subtype2 192 43 0.1 - 111.0 (35.8)
subtype3 170 81 0.2 - 93.3 (30.6)

Figure S63.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0424 (ANOVA), Q value = 1

Table S73.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 481 60.6 (12.2)
subtype1 117 58.6 (12.3)
subtype2 192 62.1 (12.2)
subtype3 172 60.2 (11.9)

Figure S64.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.000437 (Fisher's exact test), Q value = 0.023

Table S74.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 163 318
subtype1 37 80
subtype2 84 108
subtype3 42 130

Figure S65.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.172 (ANOVA), Q value = 1

Table S75.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 9 95.6 (7.3)
subtype2 15 92.0 (8.6)
subtype3 12 78.3 (37.1)

Figure S66.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.000549 (Chi-square test), Q value = 0.028

Table S76.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 233 62 175 11
subtype1 68 11 34 4
subtype2 106 23 61 2
subtype3 59 28 80 5

Figure S67.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.00922 (Fisher's exact test), Q value = 0.36

Table S77.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 222 18
subtype1 56 3
subtype2 86 2
subtype3 80 13

Figure S68.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 5.25e-05 (Fisher's exact test), Q value = 0.0031

Table S78.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 405 76
subtype1 108 9
subtype2 169 23
subtype3 128 44

Figure S69.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 2.55e-05 (Chi-square test), Q value = 0.0015

Table S79.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 229 50 122 80
subtype1 66 11 30 10
subtype2 106 17 45 24
subtype3 57 22 47 46

Figure S70.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S80.  Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 37 162 282
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00211 (logrank test), Q value = 0.093

Table S81.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 479 156 0.1 - 111.0 (35.2)
subtype1 36 13 0.5 - 85.2 (29.0)
subtype2 162 70 0.1 - 109.9 (35.4)
subtype3 281 73 0.1 - 111.0 (35.2)

Figure S71.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.298 (ANOVA), Q value = 1

Table S82.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 481 60.6 (12.2)
subtype1 37 57.8 (11.0)
subtype2 162 60.3 (12.1)
subtype3 282 61.1 (12.4)

Figure S72.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.124 (Fisher's exact test), Q value = 1

Table S83.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 163 318
subtype1 13 24
subtype2 45 117
subtype3 105 177

Figure S73.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.943 (ANOVA), Q value = 1

Table S84.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 2 95.0 (7.1)
subtype2 12 87.5 (28.3)
subtype3 22 88.2 (21.3)

Figure S74.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.000115 (Chi-square test), Q value = 0.0064

Table S85.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 233 62 175 11
subtype1 16 10 10 1
subtype2 62 17 75 8
subtype3 155 35 90 2

Figure S75.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.011 (Fisher's exact test), Q value = 0.38

Table S86.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients 0 1
ALL 222 18
subtype1 19 3
subtype2 76 11
subtype3 127 4

Figure S76.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.351 (Fisher's exact test), Q value = 1

Table S87.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 405 76
subtype1 30 7
subtype2 132 30
subtype3 243 39

Figure S77.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.00108 (Chi-square test), Q value = 0.051

Table S88.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 229 50 122 80
subtype1 16 9 5 7
subtype2 61 15 54 32
subtype3 152 26 63 41

Figure S78.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

Methods & Data
Input

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

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

  • Number of patients = 502

  • Number of clustering approaches = 10

  • Number of selected clinical features = 8

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle