Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1T72G5Q
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 11 clinical features across 515 patients, 33 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 7 subtypes that correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'GENDER', and 'RACE'.

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

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

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE',  'PATHOLOGY.M.STAGE',  'GENDER', and 'RACE'.

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

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

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 33 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 RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.257
(1.00)
0.873
(1.00)
0.00598
(0.448)
0.00828
(0.584)
0.0697
(1.00)
0.119
(1.00)
0.633
(1.00)
0.00762
(0.556)
0.312
(1.00)
mRNA cHierClus subtypes 2.96e-06
(0.000353)
0.168
(1.00)
0.00062
(0.0577)
0.00465
(0.386)
0.0994
(1.00)
0.0113
(0.756)
0.0001
(0.0097)
5e-05
(0.005)
0.475
(1.00)
Copy Number Ratio CNMF subtypes 0.197
(1.00)
0.0322
(1.00)
1e-05
(0.00118)
1e-05
(0.00118)
0.0242
(1.00)
1e-05
(0.00118)
0.00794
(0.572)
0.234
(1.00)
0.0226
(1.00)
0.833
(1.00)
METHLYATION CNMF 0.229
(1.00)
0.00822
(0.584)
1e-05
(0.00118)
1e-05
(0.00118)
0.248
(1.00)
0.00018
(0.0173)
1e-05
(0.00118)
0.142
(1.00)
0.858
(1.00)
0.0524
(1.00)
0.901
(1.00)
RPPA CNMF subtypes 0.00865
(0.597)
0.159
(1.00)
1e-05
(0.00118)
1e-05
(0.00118)
0.00558
(0.433)
1e-05
(0.00118)
0.0644
(1.00)
0.251
(1.00)
0.139
(1.00)
0.494
(1.00)
RPPA cHierClus subtypes 6.99e-05
(0.00685)
0.0361
(1.00)
1e-05
(0.00118)
1e-05
(0.00118)
0.0674
(1.00)
1e-05
(0.00118)
0.116
(1.00)
0.116
(1.00)
0.462
(1.00)
0.212
(1.00)
RNAseq CNMF subtypes 0.00477
(0.39)
0.181
(1.00)
1e-05
(0.00118)
1e-05
(0.00118)
0.0108
(0.738)
0.00137
(0.123)
5e-05
(0.005)
0.558
(1.00)
0.031
(1.00)
0.0813
(1.00)
RNAseq cHierClus subtypes 0.00476
(0.39)
0.159
(1.00)
1e-05
(0.00118)
1e-05
(0.00118)
0.00241
(0.21)
3e-05
(0.00303)
1e-05
(0.00118)
0.417
(1.00)
0.00147
(0.129)
0.514
(1.00)
MIRSEQ CNMF 0.00358
(0.308)
0.0125
(0.825)
0.00022
(0.0209)
0.00061
(0.0573)
0.0067
(0.496)
0.00142
(0.126)
0.00482
(0.39)
0.432
(1.00)
0.547
(1.00)
0.252
(1.00)
MIRSEQ CHIERARCHICAL 0.158
(1.00)
0.827
(1.00)
0.00555
(0.433)
0.00545
(0.431)
0.0471
(1.00)
0.0309
(1.00)
0.0782
(1.00)
0.535
(1.00)
0.772
(1.00)
0.432
(1.00)
MIRseq Mature CNMF subtypes 0.0044
(0.37)
0.147
(1.00)
0.00076
(0.0692)
0.00062
(0.0577)
0.229
(1.00)
0.00574
(0.436)
0.0037
(0.314)
0.35
(1.00)
0.721
(1.00)
0.825
(1.00)
MIRseq Mature cHierClus subtypes 0.436
(1.00)
0.491
(1.00)
0.208
(1.00)
0.291
(1.00)
0.0877
(1.00)
0.239
(1.00)
0.184
(1.00)
0.046
(1.00)
0.699
(1.00)
0.675
(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 = 0.257 (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), Year
ALL 61 3 16.0 - 3074.0 (1137.0)
subtype1 30 0 43.0 - 3074.0 (1106.0)
subtype2 17 2 16.0 - 2839.0 (1314.0)
subtype3 14 1 319.0 - 2566.0 (1180.0)

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 = 0.873 (Kruskal-Wallis (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 = 0.00598 (Fisher's exact test), Q value = 0.45

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 = 0.00828 (Fisher's exact test), Q value = 0.58

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 = 0.0697 (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 = 0.119 (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 = 0.633 (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'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.00762 (Fisher's exact test), Q value = 0.56

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 5 62
subtype1 0 0 32
subtype2 0 2 20
subtype3 1 3 10

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.312 (Fisher's exact test), Q value = 1

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 42
subtype1 5 19
subtype2 2 11
subtype3 0 12

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 12 12 4 11 13 9 11
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 2.96e-06 (logrank test), Q value = 0.00035

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 61 3 16.0 - 3074.0 (1137.0)
subtype1 11 0 374.0 - 1436.0 (952.0)
subtype2 10 0 16.0 - 2746.0 (941.5)
subtype3 4 2 51.0 - 1143.0 (527.0)
subtype4 6 1 1491.0 - 2839.0 (1961.0)
subtype5 10 0 523.0 - 3074.0 (1496.5)
subtype6 9 0 43.0 - 1820.0 (873.0)
subtype7 11 0 369.0 - 2566.0 (1238.0)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.168 (Kruskal-Wallis (anova)), Q value = 1

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 11 63.3 (13.7)
subtype2 12 62.2 (10.8)
subtype3 4 57.2 (10.9)
subtype4 11 56.2 (10.9)
subtype5 13 64.1 (13.5)
subtype6 9 50.7 (10.7)
subtype7 11 65.4 (10.9)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00062 (Fisher's exact test), Q value = 0.058

Table S14.  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 8 2 2 0
subtype2 8 2 2 0
subtype3 2 2 0 0
subtype4 0 1 6 4
subtype5 7 2 3 1
subtype6 8 0 1 0
subtype7 7 4 0 0

Figure S12.  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 = 0.00465 (Fisher's exact test), Q value = 0.39

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3
ALL 41 14 17
subtype1 8 2 2
subtype2 8 2 2
subtype3 2 2 0
subtype4 1 2 8
subtype5 7 2 4
subtype6 8 0 1
subtype7 7 4 0

Figure S13.  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 = 0.0994 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 35 3
subtype1 6 0
subtype2 5 0
subtype3 3 0
subtype4 5 3
subtype5 9 0
subtype6 3 0
subtype7 4 0

Figure S14.  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 = 0.0113 (Fisher's exact test), Q value = 0.76

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 67 5
subtype1 12 0
subtype2 12 0
subtype3 4 0
subtype4 7 4
subtype5 12 1
subtype6 9 0
subtype7 11 0

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 1e-04 (Fisher's exact test), Q value = 0.0097

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 29 43
subtype1 0 12
subtype2 3 9
subtype3 1 3
subtype4 6 5
subtype5 12 1
subtype6 3 6
subtype7 4 7

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 5e-05 (Fisher's exact test), Q value = 0.005

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 5 62
subtype1 0 0 11
subtype2 0 1 10
subtype3 1 3 0
subtype4 0 0 10
subtype5 0 0 13
subtype6 0 0 8
subtype7 0 1 10

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.475 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 42
subtype1 1 7
subtype2 0 4
subtype3 0 2
subtype4 2 6
subtype5 3 7
subtype6 1 5
subtype7 0 11

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

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

Table S21.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 202 145 158
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.197 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 364 26 3.0 - 3668.0 (1299.0)
subtype1 162 7 3.0 - 3668.0 (1152.5)
subtype2 105 9 16.0 - 3431.0 (1385.0)
subtype3 97 10 7.0 - 3343.0 (1404.0)

Figure S19.  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 = 0.0322 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 504 60.5 (12.1)
subtype1 202 59.4 (12.7)
subtype2 145 60.3 (11.9)
subtype3 157 62.3 (11.5)

Figure S20.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 244 56 126 79
subtype1 119 26 44 13
subtype2 75 13 32 25
subtype3 50 17 50 41

Figure S21.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

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

nPatients T1 T2 T3 T4
ALL 250 67 177 11
subtype1 119 30 53 0
subtype2 76 16 46 7
subtype3 55 21 78 4

Figure S22.  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 = 0.0242 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 230 18
subtype1 93 3
subtype2 69 4
subtype3 68 11

Figure S23.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

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

nPatients M0 M1 MX
ALL 417 78 9
subtype1 180 14 7
subtype2 121 23 1
subtype3 116 41 1

Figure S24.  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 = 0.00794 (Fisher's exact test), Q value = 0.57

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

nPatients FEMALE MALE
ALL 178 327
subtype1 76 126
subtype2 61 84
subtype3 41 117

Figure S25.  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 = 0.234 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 37 88.4 (22.7)
subtype1 13 91.5 (9.0)
subtype2 10 96.0 (7.0)
subtype3 14 80.0 (34.4)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0226 (Fisher's exact test), Q value = 1

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 33 457
subtype1 1 12 186
subtype2 4 16 124
subtype3 3 5 147

Figure S27.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.833 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 330
subtype1 9 132
subtype2 6 96
subtype3 9 102

Figure S28.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S32.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 118 72 107
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.229 (logrank test), Q value = 1

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 211 13 3.0 - 3668.0 (1092.0)
subtype1 103 4 3.0 - 3668.0 (1130.0)
subtype2 49 3 15.0 - 3343.0 (932.0)
subtype3 59 6 7.0 - 2741.0 (1280.0)

Figure S29.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00822 (Kruskal-Wallis (anova)), Q value = 0.58

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 297 61.4 (11.8)
subtype1 118 59.1 (12.7)
subtype2 72 61.8 (11.6)
subtype3 107 63.8 (10.6)

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 138 30 74 55
subtype1 79 17 13 9
subtype2 38 2 20 12
subtype3 21 11 41 34

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 142 39 108 8
subtype1 79 20 19 0
subtype2 40 3 25 4
subtype3 23 16 64 4

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.248 (Fisher's exact test), Q value = 1

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 129 9
subtype1 51 1
subtype2 30 3
subtype3 48 5

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.00018 (Fisher's exact test), Q value = 0.017

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 233 53 9
subtype1 102 10 5
subtype2 58 10 3
subtype3 73 33 1

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 104 193
subtype1 56 62
subtype2 27 45
subtype3 21 86

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 1

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 29 92.4 (7.9)
subtype1 15 91.3 (8.3)
subtype2 7 97.1 (4.9)
subtype3 7 90.0 (8.2)

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.858 (Kruskal-Wallis (anova)), Q value = 1

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 9 24.7 (15.7)
subtype1 3 28.3 (18.6)
subtype2 4 24.2 (18.2)
subtype3 2 20.0 (14.1)

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0524 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 27 266
subtype1 0 9 109
subtype2 0 12 60
subtype3 1 6 97

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.901 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 240
subtype1 3 90
subtype2 1 59
subtype3 4 91

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S44.  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 = 0.00865 (logrank test), Q value = 0.6

Table S45.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 321 25 7.0 - 3668.0 (1367.0)
subtype1 77 3 7.0 - 3668.0 (1729.0)
subtype2 59 5 15.0 - 3222.0 (1385.0)
subtype3 68 6 7.0 - 3431.0 (1074.5)
subtype4 57 5 25.0 - 3037.0 (1133.0)
subtype5 36 0 29.0 - 3146.0 (1228.0)
subtype6 24 6 18.0 - 3117.0 (1408.5)

Figure S40.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.159 (Kruskal-Wallis (anova)), Q value = 1

Table S46.  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 S41.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S47.  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 S42.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S48.  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 S43.  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 = 0.00558 (Fisher's exact test), Q value = 0.43

Table S49.  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 S44.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S50.  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 S45.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0644 (Fisher's exact test), Q value = 1

Table S51.  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 S46.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.251 (Kruskal-Wallis (anova)), Q value = 1

Table S52.  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 S47.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.139 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 20 420
subtype1 0 5 94
subtype2 3 6 81
subtype3 1 1 80
subtype4 1 6 69
subtype5 1 2 41
subtype6 2 0 55

Figure S48.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.494 (Fisher's exact test), Q value = 1

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 19 295
subtype1 6 65
subtype2 2 63
subtype3 5 60
subtype4 1 40
subtype5 3 24
subtype6 2 43

Figure S49.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S55.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 95 203 156
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 6.99e-05 (logrank test), Q value = 0.0069

Table S56.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 321 25 7.0 - 3668.0 (1367.0)
subtype1 72 3 16.0 - 3668.0 (1451.5)
subtype2 168 7 11.0 - 3377.0 (1405.5)
subtype3 81 15 7.0 - 3431.0 (1126.0)

Figure S50.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0361 (Kruskal-Wallis (anova)), Q value = 1

Table S57.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 94 61.0 (11.7)
subtype2 203 58.8 (12.8)
subtype3 156 62.2 (11.7)

Figure S51.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S58.  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 39 13 31 12
subtype2 132 16 39 16
subtype3 48 15 45 48

Figure S52.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S59.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 40 14 40 1
subtype2 134 19 49 1
subtype3 50 21 76 9

Figure S53.  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 = 0.0674 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 208 16
subtype1 48 1
subtype2 80 4
subtype3 80 11

Figure S54.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S61.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 379 75
subtype1 84 11
subtype2 186 17
subtype3 109 47

Figure S55.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.116 (Fisher's exact test), Q value = 1

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 24 71
subtype2 76 127
subtype3 51 105

Figure S56.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.116 (Kruskal-Wallis (anova)), Q value = 1

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 6 90.0 (11.0)
subtype2 17 96.5 (4.9)
subtype3 11 90.9 (8.3)

Figure S57.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.462 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 20 420
subtype1 0 2 90
subtype2 4 10 187
subtype3 4 8 143

Figure S58.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.212 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 19 295
subtype1 7 65
subtype2 8 118
subtype3 4 112

Figure S59.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S66.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 212 109 186
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00477 (logrank test), Q value = 0.39

Table S67.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 366 25 7.0 - 3668.0 (1311.0)
subtype1 170 4 7.0 - 3668.0 (1337.0)
subtype2 84 8 13.0 - 3431.0 (1311.0)
subtype3 112 13 7.0 - 3343.0 (1342.0)

Figure S60.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.181 (Kruskal-Wallis (anova)), Q value = 1

Table S68.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 506 60.6 (12.2)
subtype1 211 61.4 (12.1)
subtype2 109 58.7 (12.9)
subtype3 186 60.8 (11.8)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S69.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 246 56 125 80
subtype1 125 23 41 23
subtype2 63 12 22 12
subtype3 58 21 62 45

Figure S62.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S70.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 252 67 177 11
subtype1 125 26 59 2
subtype2 64 13 29 3
subtype3 63 28 89 6

Figure S63.  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 = 0.0108 (Fisher's exact test), Q value = 0.74

Table S71.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 236 17
subtype1 101 2
subtype2 51 3
subtype3 84 12

Figure S64.  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 = 0.00137 (Fisher's exact test), Q value = 0.12

Table S72.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 421 79 7
subtype1 186 23 3
subtype2 97 11 1
subtype3 138 45 3

Figure S65.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 5e-05 (Fisher's exact test), Q value = 0.005

Table S73.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 174 333
subtype1 95 117
subtype2 36 73
subtype3 43 143

Figure S66.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.558 (Kruskal-Wallis (anova)), Q value = 1

Table S74.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 90.9 (17.6)
subtype1 14 91.4 (8.6)
subtype2 8 95.0 (7.6)
subtype3 13 87.7 (27.1)

Figure S67.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.031 (Fisher's exact test), Q value = 1

Table S75.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 30 462
subtype1 2 8 198
subtype2 4 12 92
subtype3 2 10 172

Figure S68.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0813 (Fisher's exact test), Q value = 1

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 332
subtype1 15 131
subtype2 2 76
subtype3 7 125

Figure S69.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S77.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 118 173 126 29 32 29
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00476 (logrank test), Q value = 0.39

Table S78.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 366 25 7.0 - 3668.0 (1311.0)
subtype1 89 6 13.0 - 2963.0 (970.0)
subtype2 141 3 7.0 - 3668.0 (1446.0)
subtype3 65 10 7.0 - 3343.0 (1413.0)
subtype4 27 4 16.0 - 3431.0 (1130.0)
subtype5 19 0 53.0 - 3117.0 (1785.0)
subtype6 25 2 110.0 - 2566.0 (1371.0)

Figure S70.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.159 (Kruskal-Wallis (anova)), Q value = 1

Table S79.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 506 60.6 (12.2)
subtype1 118 59.0 (12.4)
subtype2 172 61.8 (12.3)
subtype3 126 61.6 (11.2)
subtype4 29 56.6 (14.6)
subtype5 32 58.6 (12.4)
subtype6 29 61.9 (11.4)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S80.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 246 56 125 80
subtype1 61 12 25 20
subtype2 106 19 36 12
subtype3 33 12 45 36
subtype4 21 3 2 3
subtype5 4 5 15 8
subtype6 21 5 2 1

Figure S72.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S81.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 252 67 177 11
subtype1 64 16 36 2
subtype2 106 21 46 0
subtype3 34 15 70 7
subtype4 21 3 5 0
subtype5 6 7 18 1
subtype6 21 5 2 1

Figure S73.  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 = 0.00241 (Fisher's exact test), Q value = 0.21

Table S82.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 236 17
subtype1 47 1
subtype2 82 1
subtype3 62 10
subtype4 15 1
subtype5 16 4
subtype6 14 0

Figure S74.  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 = 3e-05 (Fisher's exact test), Q value = 0.003

Table S83.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 421 79 7
subtype1 98 19 1
subtype2 157 13 3
subtype3 90 35 1
subtype4 26 3 0
subtype5 22 8 2
subtype6 28 1 0

Figure S75.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S84.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 174 333
subtype1 19 99
subtype2 91 82
subtype3 30 96
subtype4 11 18
subtype5 15 17
subtype6 8 21

Figure S76.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.417 (Kruskal-Wallis (anova)), Q value = 1

Table S85.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 90.9 (17.6)
subtype1 5 96.0 (5.5)
subtype2 12 92.5 (6.2)
subtype3 8 81.2 (33.6)
subtype4 6 95.0 (12.2)
subtype5 1 80.0 (NA)
subtype6 3 96.7 (5.8)

Figure S77.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.00147 (Fisher's exact test), Q value = 0.13

Table S86.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 30 462
subtype1 3 6 105
subtype2 1 6 164
subtype3 2 4 120
subtype4 2 5 22
subtype5 0 3 28
subtype6 0 6 23

Figure S78.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.514 (Fisher's exact test), Q value = 1

Table S87.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 332
subtype1 5 73
subtype2 12 101
subtype3 5 85
subtype4 1 25
subtype5 1 23
subtype6 0 25

Figure S79.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S88.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 119 203 170
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00358 (logrank test), Q value = 0.31

Table S89.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 352 25 7.0 - 3668.0 (1321.5)
subtype1 90 4 13.0 - 3431.0 (1459.0)
subtype2 161 7 7.0 - 3668.0 (1371.0)
subtype3 101 14 7.0 - 2859.0 (1132.0)

Figure S80.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0125 (Kruskal-Wallis (anova)), Q value = 0.83

Table S90.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 492 60.6 (12.1)
subtype1 119 58.0 (12.0)
subtype2 203 62.3 (12.2)
subtype3 170 60.3 (11.8)

Figure S81.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00022 (Fisher's exact test), Q value = 0.021

Table S91.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 234 54 125 79
subtype1 69 11 29 10
subtype2 107 23 45 28
subtype3 58 20 51 41

Figure S82.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00061 (Fisher's exact test), Q value = 0.057

Table S92.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 240 65 176 11
subtype1 71 11 33 4
subtype2 108 29 64 2
subtype3 61 25 79 5

Figure S83.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0067 (Fisher's exact test), Q value = 0.5

Table S93.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 224 18
subtype1 55 3
subtype2 90 2
subtype3 79 13

Figure S84.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.00142 (Fisher's exact test), Q value = 0.13

Table S94.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 405 78 8
subtype1 106 9 3
subtype2 173 28 2
subtype3 126 41 3

Figure S85.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00482 (Fisher's exact test), Q value = 0.39

Table S95.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 169 323
subtype1 40 79
subtype2 85 118
subtype3 44 126

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.432 (Kruskal-Wallis (anova)), Q value = 1

Table S96.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.547 (Fisher's exact test), Q value = 1

Table S97.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 32 445
subtype1 1 11 106
subtype2 3 10 186
subtype3 4 11 153

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.252 (Fisher's exact test), Q value = 1

Table S98.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 22 324
subtype1 2 75
subtype2 12 131
subtype3 8 118

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S99.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 156 233 103
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.158 (logrank test), Q value = 1

Table S100.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 352 25 7.0 - 3668.0 (1321.5)
subtype1 94 8 15.0 - 3431.0 (1454.5)
subtype2 185 9 11.0 - 3668.0 (1355.0)
subtype3 73 8 7.0 - 2859.0 (1133.0)

Figure S90.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.827 (Kruskal-Wallis (anova)), Q value = 1

Table S101.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 492 60.6 (12.1)
subtype1 156 60.5 (12.2)
subtype2 233 60.8 (12.1)
subtype3 103 59.9 (12.0)

Figure S91.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00555 (Fisher's exact test), Q value = 0.43

Table S102.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 234 54 125 79
subtype1 65 14 49 28
subtype2 130 25 51 27
subtype3 39 15 25 24

Figure S92.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00545 (Fisher's exact test), Q value = 0.43

Table S103.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 240 65 176 11
subtype1 67 17 65 7
subtype2 132 30 69 2
subtype3 41 18 42 2

Figure S93.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0471 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 224 18
subtype1 71 9
subtype2 102 3
subtype3 51 6

Figure S94.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0309 (Fisher's exact test), Q value = 1

Table S105.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 405 78 8
subtype1 125 27 4
subtype2 203 27 2
subtype3 77 24 2

Figure S95.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0782 (Fisher's exact test), Q value = 1

Table S106.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 169 323
subtype1 47 109
subtype2 92 141
subtype3 30 73

Figure S96.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.535 (Kruskal-Wallis (anova)), Q value = 1

Table S107.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 14 88.6 (26.3)
subtype2 14 87.9 (25.8)
subtype3 8 88.8 (11.3)

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.772 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 32 445
subtype1 3 13 139
subtype2 4 12 213
subtype3 1 7 93

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.432 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 22 324
subtype1 5 107
subtype2 13 142
subtype3 4 75

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S110.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 52 91 87
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0044 (logrank test), Q value = 0.37

Table S111.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 170 10 7.0 - 3668.0 (1391.0)
subtype1 38 0 110.0 - 3431.0 (1294.5)
subtype2 78 3 7.0 - 3668.0 (1439.5)
subtype3 54 7 7.0 - 2828.0 (1049.5)

Figure S100.  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 = 0.147 (Kruskal-Wallis (anova)), Q value = 1

Table S112.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 230 59.4 (12.6)
subtype1 52 57.9 (12.0)
subtype2 91 61.4 (12.2)
subtype3 87 58.3 (13.1)

Figure S101.  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 = 0.00076 (Fisher's exact test), Q value = 0.069

Table S113.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 112 27 48 43
subtype1 36 3 6 7
subtype2 46 14 21 10
subtype3 30 10 21 26

Figure S102.  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 = 0.00062 (Fisher's exact test), Q value = 0.058

Table S114.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 116 30 78 6
subtype1 37 3 9 3
subtype2 46 16 28 1
subtype3 33 11 41 2

Figure S103.  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 = 0.229 (Fisher's exact test), Q value = 1

Table S115.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 98 8
subtype1 17 2
subtype2 40 1
subtype3 41 5

Figure S104.  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 = 0.00574 (Fisher's exact test), Q value = 0.44

Table S116.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 181 40 8
subtype1 43 6 2
subtype2 80 9 2
subtype3 58 25 4

Figure S105.  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 = 0.0037 (Fisher's exact test), Q value = 0.31

Table S117.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 71 159
subtype1 18 34
subtype2 37 54
subtype3 16 71

Figure S106.  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 = 0.35 (Kruskal-Wallis (anova)), Q value = 1

Table S118.  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 9 95.6 (7.3)
subtype2 12 90.8 (9.0)
subtype3 9 94.4 (7.3)

Figure S107.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.721 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 22 199
subtype1 2 6 43
subtype2 2 7 81
subtype3 1 9 75

Figure S108.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.825 (Fisher's exact test), Q value = 1

Table S120.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 167
subtype1 2 33
subtype2 3 70
subtype3 4 64

Figure S109.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S121.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 90 96 44
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.436 (logrank test), Q value = 1

Table S122.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 170 10 7.0 - 3668.0 (1391.0)
subtype1 58 4 7.0 - 3431.0 (1100.5)
subtype2 78 3 11.0 - 3668.0 (1431.5)
subtype3 34 3 7.0 - 2799.0 (1435.5)

Figure S110.  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 = 0.491 (Kruskal-Wallis (anova)), Q value = 1

Table S123.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 230 59.4 (12.6)
subtype1 90 58.2 (13.1)
subtype2 96 60.6 (12.9)
subtype3 44 59.5 (10.3)

Figure S111.  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 = 0.208 (Fisher's exact test), Q value = 1

Table S124.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 112 27 48 43
subtype1 43 10 17 20
subtype2 52 10 23 11
subtype3 17 7 8 12

Figure S112.  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 = 0.291 (Fisher's exact test), Q value = 1

Table S125.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 116 30 78 6
subtype1 44 10 32 4
subtype2 55 11 29 1
subtype3 17 9 17 1

Figure S113.  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 = 0.0877 (Fisher's exact test), Q value = 1

Table S126.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 98 8
subtype1 36 6
subtype2 36 2
subtype3 26 0

Figure S114.  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 = 0.239 (Fisher's exact test), Q value = 1

Table S127.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 181 40 8
subtype1 67 18 4
subtype2 82 11 3
subtype3 32 11 1

Figure S115.  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 = 0.184 (Fisher's exact test), Q value = 1

Table S128.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 71 159
subtype1 24 66
subtype2 36 60
subtype3 11 33

Figure S116.  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 = 0.046 (Kruskal-Wallis (anova)), Q value = 1

Table S129.  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 16 94.4 (7.3)
subtype2 11 95.5 (5.2)
subtype3 3 80.0 (10.0)

Figure S117.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.699 (Fisher's exact test), Q value = 1

Table S130.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 22 199
subtype1 3 10 76
subtype2 2 7 86
subtype3 0 5 37

Figure S118.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.675 (Fisher's exact test), Q value = 1

Table S131.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 167
subtype1 3 64
subtype2 3 69
subtype3 3 34

Figure S119.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = KIRC-TP.mergedcluster.txt

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

  • Number of patients = 515

  • Number of clustering approaches = 12

  • Number of selected clinical features = 11

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

Download Results

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

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)