Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C12805Z2
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12805Z2
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 9 clinical features across 503 patients, 40 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',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

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

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

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

  • CNMF 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'.

  • 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.N.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

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

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 40 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER KARNOFSKY
PERFORMANCE
SCORE 		NUMBERPACKYEARSSMOKED
Statistical Tests logrank test ANOVA Chi-square test Chi-square test Fisher's exact test Fisher's exact test Fisher's exact test ANOVA ANOVA
mRNA CNMF subtypes 0.131
(1.00) 		0.795
(1.00) 		0.00527
(0.285) 		0.00911
(0.446) 		0.0704
(1.00) 		0.12
(1.00) 		0.634
(1.00)
mRNA cHierClus subtypes 0.414
(1.00) 		0.651
(1.00) 		0.00728
(0.364) 		0.0125
(0.551) 		0.124
(1.00) 		0.104
(1.00) 		0.705
(1.00)
Copy Number Ratio CNMF subtypes 0.000483
(0.0324) 		0.015
(0.647) 		0.000386
(0.0263) 		0.000867
(0.0555) 		0.0252
(1.00) 		0.000976
(0.0609) 		0.00244
(0.137) 		0.29
(1.00)
METHLYATION CNMF 2.21e-06
(0.000177) 		0.0063
(0.328) 		1.47e-11
(1.34e-09) 		5.4e-12
(5.03e-10) 		0.169
(1.00) 		0.000767
(0.0499) 		0.000985
(0.0609) 		0.595
(1.00)
RPPA CNMF subtypes 2.81e-10
(2.53e-08) 		0.129
(1.00) 		1.2e-08
(1.06e-06) 		9.52e-07
(7.81e-05) 		0.00106
(0.0628) 		6.21e-07
(5.16e-05) 		0.0611
(1.00) 		0.322
(1.00)
RPPA cHierClus subtypes 1.21e-07
(1.05e-05) 		0.00927
(0.446) 		2e-06
(0.000162) 		4.03e-07
(3.46e-05) 		0.253
(1.00) 		0.000111
(0.00814) 		0.578
(1.00) 		0.0463
(1.00)
RNAseq CNMF subtypes 4.81e-07
(4.04e-05) 		0.09
(1.00) 		4.02e-07
(3.46e-05) 		4.85e-06
(0.000378) 		0.0117
(0.536) 		0.000985
(0.0609) 		6.19e-05
(0.00458) 		0.66
(1.00)
RNAseq cHierClus subtypes 4.98e-09
(4.43e-07) 		0.358
(1.00) 		1e-12
(9.45e-11) 		7.56e-12
(6.96e-10) 		0.000966
(0.0609) 		7.15e-06
(0.000551) 		0.00158
(0.09) 		0.357
(1.00)
MIRSEQ CNMF 4.65e-06
(0.000367) 		0.0777
(1.00) 		4.59e-05
(0.00349) 		0.000178
(0.0128) 		0.00288
(0.159) 		0.000224
(0.0154) 		0.0117
(0.536) 		0.152
(1.00)
MIRSEQ CHIERARCHICAL 0.000509
(0.0336) 		0.143
(1.00) 		0.00123
(0.0715) 		4.63e-05
(0.00349) 		0.006
(0.318) 		0.369
(1.00) 		0.0404
(1.00) 		0.943
(1.00)
MIRseq Mature CNMF subtypes 0.0002
(0.0142) 		0.425
(1.00) 		0.0168
(0.705) 		0.0107
(0.502) 		0.134
(1.00) 		0.0192
(0.787) 		0.00681
(0.347) 		0.47
(1.00)
MIRseq Mature cHierClus subtypes 0.000203
(0.0142) 		0.0369
(1.00) 		0.233
(1.00) 		0.143
(1.00) 		0.0494
(1.00) 		0.161
(1.00) 		0.0992
(1.00) 		0.464
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  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 = 0.131 (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 14 0.5 - 101.1 (34.3)
subtype1 33 4 0.6 - 101.1 (31.0)
subtype2 24 9 0.5 - 93.3 (38.1)
subtype3 14 1 1.3 - 84.4 (30.7)

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 'NEOPLASM.DISEASESTAGE'

P value = 0.00527 (Chi-square test), Q value = 0.28

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 40 13 14 5
subtype1 23 4 6 1
subtype2 9 3 8 4
subtype3 8 6 0 0

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

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

P value = 0.00911 (Chi-square test), Q value = 0.45

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

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 #4: 'PATHOLOGY.T.STAGE'

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

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

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

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 #5: 'PATHOLOGY.N.STAGE'

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

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

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

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 #6: 'PATHOLOGY.M.STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

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

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 15 33 24
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.414 (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 14 0.5 - 101.1 (34.3)
subtype1 15 2 1.3 - 84.4 (27.2)
subtype2 32 4 0.6 - 101.1 (30.5)
subtype3 24 8 0.5 - 93.3 (38.1)

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 = 0.651 (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 32 59.9 (14.0)
subtype3 24 59.7 (10.9)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00728 (Chi-square test), Q value = 0.36

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 40 13 14 5
subtype1 9 6 0 0
subtype2 22 4 6 1
subtype3 9 3 8 4

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

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

P value = 0.0125 (Chi-square test), Q value = 0.55

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3
ALL 41 14 17
subtype1 9 6 0
subtype2 22 4 7
subtype3 10 4 10

Figure S11.  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 = 0.124 (Fisher's exact test), Q value = 1

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

Figure S12.  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 = 0.104 (Fisher's exact test), Q value = 1

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 67 5
subtype1 15 0
subtype2 32 1
subtype3 20 4

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.705 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 29 43
subtype1 5 10
subtype2 15 18
subtype3 9 15

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

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

Cluster Labels 1 2 3
Number of samples 161 211 122
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.000483 (logrank test), Q value = 0.032

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

nPatients nDeath Duration Range (Median), Month
ALL 493 163 0.1 - 120.6 (36.5)
subtype1 161 71 0.1 - 109.9 (31.9)
subtype2 210 50 0.1 - 120.6 (37.2)
subtype3 122 42 0.1 - 112.8 (36.4)

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 = 0.015 (ANOVA), Q value = 0.65

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

nPatients Mean (Std.Dev)
ALL 493 60.6 (12.2)
subtype1 160 62.8 (11.7)
subtype2 211 59.4 (12.5)
subtype3 122 59.6 (11.9)

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 'NEOPLASM.DISEASESTAGE'

P value = 0.000386 (Chi-square test), Q value = 0.026

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 237 54 125 78
subtype1 57 17 49 38
subtype2 122 23 47 19
subtype3 58 14 29 21

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

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

P value = 0.000867 (Chi-square test), Q value = 0.055

Table S21.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 242 65 176 11
subtype1 61 21 75 4
subtype2 122 27 61 1
subtype3 59 17 40 6

Figure S18.  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 = 0.0252 (Fisher's exact test), Q value = 1

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 228 18
subtype1 72 11
subtype2 101 3
subtype3 55 4

Figure S19.  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 = 0.000976 (Fisher's exact test), Q value = 0.061

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

nPatients M0 M1
ALL 417 77
subtype1 123 38
subtype2 191 20
subtype3 103 19

Figure S20.  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 = 0.00244 (Fisher's exact test), Q value = 0.14

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 171 323
subtype1 39 122
subtype2 86 125
subtype3 46 76

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

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

P value = 0.29 (ANOVA), Q value = 1

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 15 81.3 (33.6)
subtype2 12 91.7 (9.4)
subtype3 9 95.6 (7.3)

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 97 114 74
'METHLYATION CNMF' versus 'Time to Death'

P value = 2.21e-06 (logrank test), Q value = 0.00018

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

nPatients nDeath Duration Range (Median), Month
ALL 285 95 0.1 - 120.6 (29.1)
subtype1 97 49 0.1 - 90.1 (28.8)
subtype2 114 18 0.2 - 120.6 (35.5)
subtype3 74 28 0.1 - 109.9 (23.8)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0063 (ANOVA), Q value = 0.33

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

nPatients Mean (Std.Dev)
ALL 285 61.5 (11.9)
subtype1 97 63.9 (10.5)
subtype2 114 58.9 (12.8)
subtype3 74 62.5 (11.7)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.47e-11 (Chi-square test), Q value = 1.3e-09

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 130 28 73 54
subtype1 19 9 39 30
subtype2 76 16 12 10
subtype3 35 3 22 14

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

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

P value = 5.4e-12 (Chi-square test), Q value = 5e-10

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 133 37 107 8
subtype1 20 14 60 3
subtype2 76 19 19 0
subtype3 37 4 28 5

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.169 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 127 9
subtype1 41 5
subtype2 51 1
subtype3 35 3

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.000767 (Fisher's exact test), Q value = 0.05

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

nPatients M0 M1
ALL 233 52
subtype1 68 29
subtype2 103 11
subtype3 62 12

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.000985 (Fisher's exact test), Q value = 0.061

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 96 189
subtype1 20 77
subtype2 51 63
subtype3 25 49

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

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

P value = 0.595 (ANOVA), Q value = 1

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S35.  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 = 2.81e-10 (logrank test), Q value = 2.5e-08

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

nPatients nDeath Duration Range (Median), Month
ALL 454 156 0.1 - 120.6 (36.1)
subtype1 101 27 0.2 - 120.6 (45.1)
subtype2 90 35 0.1 - 90.4 (31.0)
subtype3 86 23 0.2 - 112.8 (35.8)
subtype4 76 24 0.1 - 99.8 (37.1)
subtype5 44 8 0.3 - 83.8 (36.7)
subtype6 57 39 0.6 - 102.4 (20.2)

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 'NEOPLASM.DISEASESTAGE'

P value = 1.2e-08 (Chi-square test), Q value = 1.1e-06

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 115 76
subtype1 54 11 22 14
subtype2 42 9 19 20
subtype3 39 12 24 11
subtype4 44 7 17 8
subtype5 33 2 9 0
subtype6 7 3 24 23

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

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

P value = 9.52e-07 (Chi-square test), Q value = 7.8e-05

Table S39.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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 S34.  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 = 0.00106 (Chi-square test), Q value = 0.063

Table S40.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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 S35.  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 = 6.21e-07 (Chi-square test), Q value = 5.2e-05

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

nPatients M0 M1
ALL 379 75
subtype1 87 14
subtype2 71 19
subtype3 75 11
subtype4 68 8
subtype5 44 0
subtype6 34 23

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0611 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 44 57
subtype2 29 61
subtype3 27 59
subtype4 18 58
subtype5 18 26
subtype6 15 42

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

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

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

Table S43.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: '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 S38.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S44.  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 = 1.21e-07 (logrank test), Q value = 1.1e-05

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

nPatients nDeath Duration Range (Median), Month
ALL 454 156 0.1 - 120.6 (36.1)
subtype1 189 43 0.1 - 101.1 (37.1)
subtype2 153 51 0.2 - 120.6 (37.4)
subtype3 112 62 0.1 - 112.8 (26.1)

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 = 0.45

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 'NEOPLASM.DISEASESTAGE'

P value = 2e-06 (Chi-square test), Q value = 0.00016

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 115 76
subtype1 118 16 36 19
subtype2 65 17 47 24
subtype3 36 11 32 33

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

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

P value = 4.03e-07 (Chi-square test), Q value = 3.5e-05

Table S48.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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 S42.  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 = 0.253 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

Figure S43.  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 = 0.000111 (Fisher's exact test), Q value = 0.0081

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

nPatients M0 M1
ALL 379 75
subtype1 170 19
subtype2 130 23
subtype3 79 33

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

'RPPA cHierClus subtypes' versus 'GENDER'

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

Table S51.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

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

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

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

Table S52.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: '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 S46.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S53.  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 = 4.81e-07 (logrank test), Q value = 4e-05

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

nPatients nDeath Duration Range (Median), Month
ALL 480 160 0.1 - 120.6 (36.1)
subtype1 199 44 0.1 - 120.6 (38.7)
subtype2 180 86 0.1 - 94.0 (30.9)
subtype3 101 30 0.1 - 112.8 (36.5)

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 'NEOPLASM.DISEASESTAGE'

P value = 4.02e-07 (Chi-square test), Q value = 3.5e-05

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 233 49 119 79
subtype1 118 19 38 24
subtype2 55 20 61 44
subtype3 60 10 20 11

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

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

P value = 4.85e-06 (Chi-square test), Q value = 0.00038

Table S57.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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 S50.  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 = 0.0117 (Fisher's exact test), Q value = 0.54

Table S58.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

Figure S51.  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 = 0.000985 (Fisher's exact test), Q value = 0.061

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

nPatients M0 M1
ALL 402 78
subtype1 175 24
subtype2 136 44
subtype3 91 10

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 6.19e-05 (Fisher's exact test), Q value = 0.0046

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

nPatients FEMALE MALE
ALL 167 313
subtype1 91 108
subtype2 44 136
subtype3 32 69

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

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

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

Table S61.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: '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 S54.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S62.  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 = 4.98e-09 (logrank test), Q value = 4.4e-07

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

nPatients nDeath Duration Range (Median), Month
ALL 480 160 0.1 - 120.6 (36.1)
subtype1 73 15 0.3 - 112.8 (35.6)
subtype2 215 48 0.1 - 120.6 (38.7)
subtype3 192 97 0.1 - 102.4 (30.6)

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 'NEOPLASM.DISEASESTAGE'

P value = 1e-12 (Chi-square test), Q value = 9.4e-11

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 233 49 119 79
subtype1 54 6 9 4
subtype2 126 22 44 23
subtype3 53 21 66 52

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

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

P value = 7.56e-12 (Chi-square test), Q value = 7e-10

Table S66.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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 S58.  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 = 0.000966 (Fisher's exact test), Q value = 0.061

Table S67.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

Figure S59.  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 = 7.15e-06 (Fisher's exact test), Q value = 0.00055

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

nPatients M0 M1
ALL 402 78
subtype1 69 4
subtype2 191 24
subtype3 142 50

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00158 (Fisher's exact test), Q value = 0.09

Table S69.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 167 313
subtype1 23 50
subtype2 93 122
subtype3 51 141

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

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

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

Table S70.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: '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 S62.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3
Number of samples 114 211 157
'MIRSEQ CNMF' versus 'Time to Death'

P value = 4.65e-06 (logrank test), Q value = 0.00037

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

nPatients nDeath Duration Range (Median), Month
ALL 482 161 0.1 - 120.6 (36.4)
subtype1 114 34 0.1 - 112.8 (37.2)
subtype2 211 50 0.1 - 120.6 (37.0)
subtype3 157 77 0.2 - 94.0 (31.1)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0777 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 482 60.6 (12.2)
subtype1 114 59.0 (12.0)
subtype2 211 61.9 (12.4)
subtype3 157 59.9 (11.7)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 4.59e-05 (Chi-square test), Q value = 0.0035

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 228 52 124 78
subtype1 63 11 30 10
subtype2 116 21 46 28
subtype3 49 20 48 40

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

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

P value = 0.000178 (Chi-square test), Q value = 0.013

Table S75.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 233 63 175 11
subtype1 65 11 34 4
subtype2 117 27 65 2
subtype3 51 25 76 5

Figure S66.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.00288 (Fisher's exact test), Q value = 0.16

Table S76.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 222 18
subtype1 54 3
subtype2 94 2
subtype3 74 13

Figure S67.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.000224 (Fisher's exact test), Q value = 0.015

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

nPatients M0 M1
ALL 405 77
subtype1 105 9
subtype2 183 28
subtype3 117 40

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0117 (Fisher's exact test), Q value = 0.54

Table S78.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 163 319
subtype1 36 78
subtype2 86 125
subtype3 41 116

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

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

P value = 0.152 (ANOVA), Q value = 1

Table S79.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 7 95.7 (7.9)
subtype2 18 92.2 (8.1)
subtype3 11 77.3 (38.8)

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

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels 1 2 3
Number of samples 160 37 285
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.000509 (logrank test), Q value = 0.034

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

nPatients nDeath Duration Range (Median), Month
ALL 482 161 0.1 - 120.6 (36.4)
subtype1 160 72 0.1 - 109.9 (36.5)
subtype2 37 14 0.5 - 93.0 (25.7)
subtype3 285 75 0.1 - 120.6 (37.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.143 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 482 60.6 (12.2)
subtype1 160 60.4 (12.4)
subtype2 37 57.0 (11.0)
subtype3 285 61.2 (12.1)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00123 (Chi-square test), Q value = 0.071

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 228 52 124 78
subtype1 58 15 56 31
subtype2 17 8 5 7
subtype3 153 29 63 40

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

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

P value = 4.63e-05 (Chi-square test), Q value = 0.0035

Table S84.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 233 63 175 11
subtype1 60 17 76 7
subtype2 17 10 8 2
subtype3 156 36 91 2

Figure S74.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.006 (Fisher's exact test), Q value = 0.32

Table S85.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 222 18
subtype1 75 10
subtype2 19 4
subtype3 128 4

Figure S75.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.369 (Fisher's exact test), Q value = 1

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

nPatients M0 M1
ALL 405 77
subtype1 130 30
subtype2 30 7
subtype3 245 40

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0404 (Fisher's exact test), Q value = 1

Table S87.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 163 319
subtype1 42 118
subtype2 13 24
subtype3 108 177

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

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

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

Table S88.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S89.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 50 97 71
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 2e-04 (logrank test), Q value = 0.014

Table S90.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 218 67 0.2 - 120.6 (38.5)
subtype1 50 16 3.9 - 112.8 (34.1)
subtype2 97 17 0.2 - 120.6 (45.1)
subtype3 71 34 0.2 - 93.0 (37.2)

Figure S79.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.425 (ANOVA), Q value = 1

Table S91.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 218 59.4 (12.7)
subtype1 50 58.2 (12.7)
subtype2 97 60.6 (12.2)
subtype3 71 58.5 (13.4)

Figure S80.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0168 (Chi-square test), Q value = 0.71

Table S92.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 105 24 47 42
subtype1 31 2 8 9
subtype2 50 13 22 12
subtype3 24 9 17 21

Figure S81.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0107 (Chi-square test), Q value = 0.5

Table S93.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 108 27 77 6
subtype1 32 2 13 3
subtype2 50 16 30 1
subtype3 26 9 34 2

Figure S82.  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 = 0.134 (Fisher's exact test), Q value = 1

Table S94.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 96 8
subtype1 18 2
subtype2 44 1
subtype3 34 5

Figure S83.  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 = 0.0192 (Fisher's exact test), Q value = 0.79

Table S95.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 179 39
subtype1 42 8
subtype2 86 11
subtype3 51 20

Figure S84.  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 = 0.00681 (Fisher's exact test), Q value = 0.35

Table S96.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 65 153
subtype1 15 35
subtype2 38 59
subtype3 12 59

Figure S85.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.47 (ANOVA), Q value = 1

Table S97.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 30 93.3 (8.0)
subtype1 7 95.7 (7.9)
subtype2 14 91.4 (8.6)
subtype3 9 94.4 (7.3)

Figure S86.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S98.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 106 19 93
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.000203 (logrank test), Q value = 0.014

Table S99.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 218 67 0.2 - 120.6 (38.5)
subtype1 106 21 0.2 - 120.6 (45.7)
subtype2 19 5 6.7 - 97.4 (41.9)
subtype3 93 41 0.2 - 112.8 (31.3)

Figure S87.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0369 (ANOVA), Q value = 1

Table S100.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 218 59.4 (12.7)
subtype1 106 61.0 (11.7)
subtype2 19 53.1 (12.6)
subtype3 93 58.8 (13.5)

Figure S88.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.233 (Chi-square test), Q value = 1

Table S101.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 105 24 47 42
subtype1 50 16 24 16
subtype2 12 1 4 2
subtype3 43 7 19 24

Figure S89.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.143 (Chi-square test), Q value = 1

Table S102.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 108 27 77 6
subtype1 51 19 35 1
subtype2 12 1 5 1
subtype3 45 7 37 4

Figure S90.  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 = 0.0494 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 96 8
subtype1 52 1
subtype2 7 1
subtype3 37 6

Figure S91.  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 = 0.161 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 179 39
subtype1 91 15
subtype2 17 2
subtype3 71 22

Figure S92.  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 = 0.0992 (Fisher's exact test), Q value = 1

Table S105.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 65 153
subtype1 39 67
subtype2 4 15
subtype3 22 71

Figure S93.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.464 (ANOVA), Q value = 1

Table S106.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 30 93.3 (8.0)
subtype1 14 92.1 (8.9)
subtype3 16 94.4 (7.3)

Figure S94.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data
Input

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

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

  • Number of patients = 503

  • Number of clustering approaches = 12

  • Number of selected clinical features = 9

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

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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