Correlation between aggregated molecular cancer subtypes and selected clinical features
Bladder Urothelial 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/C1R49PG7
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 11 clinical features across 218 patients, 10 significant findings detected with P value < 0.05 and Q value < 0.25.

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

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

  • CNMF clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'RACE'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.

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

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 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, 10 significant findings detected.

Clinical
Features
Statistical
Tests
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RPPA
CNMF
subtypes
RPPA
cHierClus
subtypes
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.991
(1.00)
0.878
(1.00)
0.0979
(1.00)
0.365
(1.00)
0.422
(1.00)
0.0783
(1.00)
0.295
(1.00)
0.72
(1.00)
0.357
(1.00)
0.806
(1.00)
AGE Kruskal-Wallis (anova) 0.339
(1.00)
0.0274
(1.00)
0.457
(1.00)
0.322
(1.00)
0.0505
(1.00)
0.00482
(0.457)
0.0285
(1.00)
0.0742
(1.00)
0.00462
(0.443)
0.00771
(0.709)
NEOPLASM DISEASESTAGE Fisher's exact test 0.115
(1.00)
0.00295
(0.295)
0.124
(1.00)
0.0583
(1.00)
0.0107
(0.934)
1e-05
(0.0011)
0.0008
(0.0848)
0.00029
(0.031)
0.00084
(0.0874)
0.0173
(1.00)
PATHOLOGY T STAGE Fisher's exact test 0.816
(1.00)
0.2
(1.00)
0.687
(1.00)
0.357
(1.00)
0.0103
(0.911)
0.0008
(0.0848)
0.0248
(1.00)
0.00215
(0.217)
0.00514
(0.483)
0.027
(1.00)
PATHOLOGY N STAGE Fisher's exact test 0.0448
(1.00)
0.0646
(1.00)
0.427
(1.00)
0.616
(1.00)
0.598
(1.00)
0.00115
(0.117)
0.0112
(0.961)
0.00383
(0.372)
0.128
(1.00)
0.336
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.798
(1.00)
0.0344
(1.00)
0.0306
(1.00)
0.171
(1.00)
0.0184
(1.00)
0.0528
(1.00)
0.53
(1.00)
0.202
(1.00)
0.137
(1.00)
0.533
(1.00)
GENDER Fisher's exact test 0.956
(1.00)
0.00907
(0.816)
0.516
(1.00)
0.171
(1.00)
0.102
(1.00)
0.00841
(0.765)
0.00349
(0.342)
0.0676
(1.00)
0.0601
(1.00)
0.13
(1.00)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.0228
(1.00)
0.0278
(1.00)
0.369
(1.00)
0.393
(1.00)
0.602
(1.00)
0.691
(1.00)
0.623
(1.00)
0.293
(1.00)
0.9
(1.00)
0.552
(1.00)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.234
(1.00)
0.332
(1.00)
0.187
(1.00)
0.156
(1.00)
0.149
(1.00)
0.237
(1.00)
0.184
(1.00)
0.783
(1.00)
0.424
(1.00)
0.814
(1.00)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.112
(1.00)
0.0348
(1.00)
0.0993
(1.00)
0.242
(1.00)
0.585
(1.00)
0.00346
(0.342)
0.00988
(0.879)
0.0196
(1.00)
0.0178
(1.00)
0.304
(1.00)
RACE Fisher's exact test 0.869
(1.00)
0.0292
(1.00)
0.0658
(1.00)
0.00614
(0.571)
4e-05
(0.00432)
2e-05
(0.00218)
0.232
(1.00)
0.0226
(1.00)
0.0144
(1.00)
0.00084
(0.0874)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 54 77 83
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.991 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 208 57 0.1 - 140.8 (8.6)
subtype1 54 14 0.4 - 130.9 (7.7)
subtype2 75 19 0.1 - 140.8 (7.8)
subtype3 79 24 0.1 - 88.9 (11.3)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.339 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 213 67.6 (10.7)
subtype1 53 65.9 (9.9)
subtype2 77 67.9 (11.1)
subtype3 83 68.4 (10.9)

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

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

P value = 0.115 (Fisher's exact test), Q value = 1

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

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 68 72 67
subtype1 0 1 16 12 23
subtype2 0 1 29 29 18
subtype3 1 0 23 31 26

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

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

P value = 0.816 (Fisher's exact test), Q value = 1

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

nPatients T0+T1+T2 T3 T4
ALL 62 101 32
subtype1 15 25 7
subtype2 26 34 11
subtype3 21 42 14

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

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

P value = 0.0448 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 130 22 38 7
subtype1 21 6 15 3
subtype2 57 7 9 2
subtype3 52 9 14 2

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

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

P value = 0.798 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 106 5 102
subtype1 26 2 25
subtype2 36 1 40
subtype3 44 2 37

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.956 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 50 164
subtype1 12 42
subtype2 19 58
subtype3 19 64

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

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

P value = 0.0228 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 60 79.7 (15.3)
subtype1 18 86.1 (12.4)
subtype2 18 76.7 (11.9)
subtype3 24 77.1 (18.3)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.234 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 125 37.8 (28.1)
subtype1 32 33.1 (28.1)
subtype2 43 40.9 (31.6)
subtype3 50 38.2 (24.8)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.112 (Kruskal-Wallis (anova)), Q value = 1

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 163 1.5 (3.2)
subtype1 41 1.6 (2.8)
subtype2 60 1.3 (3.2)
subtype3 62 1.7 (3.5)

Figure S10.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.869 (Fisher's exact test), Q value = 1

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 11 170
subtype1 4 3 43
subtype2 8 5 62
subtype3 10 3 65

Figure S11.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S13.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 77 48 49 40
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.878 (logrank test), Q value = 1

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 208 58 0.1 - 140.8 (8.2)
subtype1 76 23 0.4 - 140.8 (9.3)
subtype2 48 13 0.5 - 130.9 (9.8)
subtype3 46 12 0.1 - 50.3 (5.8)
subtype4 38 10 0.1 - 76.6 (7.0)

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0274 (Kruskal-Wallis (anova)), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 213 67.6 (10.6)
subtype1 77 70.2 (10.0)
subtype2 47 68.2 (9.4)
subtype3 49 64.4 (12.1)
subtype4 40 66.0 (10.3)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00295 (Fisher's exact test), Q value = 0.29

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 70 69 68
subtype1 0 0 18 38 20
subtype2 1 2 14 11 19
subtype3 0 0 21 13 14
subtype4 0 0 17 7 15

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.2 (Fisher's exact test), Q value = 1

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 63 98 33
subtype1 16 40 16
subtype2 14 22 5
subtype3 20 22 5
subtype4 13 14 7

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0646 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 129 22 39 7
subtype1 54 8 9 4
subtype2 22 6 12 1
subtype3 33 7 6 1
subtype4 20 1 12 1

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0344 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 108 5 100
subtype1 31 1 45
subtype2 26 2 19
subtype3 25 0 24
subtype4 26 2 12

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00907 (Fisher's exact test), Q value = 0.82

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 51 163
subtype1 26 51
subtype2 14 34
subtype3 6 43
subtype4 5 35

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0278 (Kruskal-Wallis (anova)), Q value = 1

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 57 79.3 (15.6)
subtype1 13 83.1 (14.9)
subtype2 12 86.7 (12.3)
subtype3 20 75.5 (17.9)
subtype4 12 74.2 (12.4)

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 1

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 123 38.9 (28.1)
subtype1 46 43.6 (31.6)
subtype2 26 37.8 (28.6)
subtype3 31 38.9 (26.9)
subtype4 20 29.4 (18.1)

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0348 (Kruskal-Wallis (anova)), Q value = 1

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 160 1.6 (3.2)
subtype1 66 0.9 (2.0)
subtype2 34 1.8 (3.1)
subtype3 37 1.7 (4.2)
subtype4 23 2.9 (4.1)

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0292 (Fisher's exact test), Q value = 1

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 12 167
subtype1 2 6 64
subtype2 4 1 39
subtype3 8 3 37
subtype4 8 2 27

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S25.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 37 29 36 5 20
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0979 (logrank test), Q value = 1

Table S26.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 122 47 0.1 - 140.8 (9.3)
subtype1 33 6 0.1 - 130.9 (7.8)
subtype2 28 11 1.8 - 140.8 (7.8)
subtype3 36 17 0.4 - 123.8 (12.5)
subtype4 5 3 3.1 - 11.9 (5.7)
subtype5 20 10 2.1 - 61.9 (10.1)

Figure S23.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.457 (Kruskal-Wallis (anova)), Q value = 1

Table S27.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 126 67.2 (10.5)
subtype1 36 65.3 (10.7)
subtype2 29 69.4 (9.5)
subtype3 36 68.7 (10.6)
subtype4 5 64.4 (11.4)
subtype5 20 65.0 (11.0)

Figure S24.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 1 36 42 44
subtype1 1 12 9 12
subtype2 0 5 8 16
subtype3 0 8 16 11
subtype4 0 1 3 1
subtype5 0 10 6 4

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

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

P value = 0.687 (Fisher's exact test), Q value = 1

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 27 63 19
subtype1 10 15 4
subtype2 6 15 8
subtype3 8 21 5
subtype4 0 3 1
subtype5 3 9 1

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.427 (Fisher's exact test), Q value = 1

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 71 13 24 6
subtype1 20 1 9 2
subtype2 13 4 9 2
subtype3 23 5 4 2
subtype4 4 1 0 0
subtype5 11 2 2 0

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0306 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 73 5 48
subtype1 25 2 9
subtype2 10 2 17
subtype3 25 0 11
subtype4 4 0 1
subtype5 9 1 10

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.516 (Fisher's exact test), Q value = 1

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 33 94
subtype1 7 30
subtype2 10 19
subtype3 8 28
subtype4 2 3
subtype5 6 14

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.369 (Kruskal-Wallis (anova)), Q value = 1

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 79.1 (15.8)
subtype1 12 75.0 (17.8)
subtype2 2 80.0 (14.1)
subtype3 10 77.0 (20.0)
subtype4 1 90.0 (NA)
subtype5 10 85.0 (7.1)

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.187 (Kruskal-Wallis (anova)), Q value = 1

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 79 36.8 (23.8)
subtype1 24 34.7 (28.9)
subtype2 18 44.4 (22.5)
subtype3 22 36.8 (20.3)
subtype4 2 35.5 (6.4)
subtype5 13 30.1 (22.2)

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0993 (Kruskal-Wallis (anova)), Q value = 1

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 90 1.8 (3.8)
subtype1 22 1.8 (3.1)
subtype2 26 3.2 (5.4)
subtype3 27 0.9 (2.4)
subtype4 4 0.2 (0.5)
subtype5 11 1.4 (3.6)

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0658 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 9 100
subtype1 7 1 27
subtype2 0 2 24
subtype3 1 3 29
subtype4 0 0 5
subtype5 0 3 15

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S37.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 43 38 27 19
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.365 (logrank test), Q value = 1

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 122 47 0.1 - 140.8 (9.3)
subtype1 39 10 0.1 - 112.4 (10.6)
subtype2 37 14 1.8 - 140.8 (8.3)
subtype3 27 15 0.4 - 130.9 (14.9)
subtype4 19 8 2.1 - 61.9 (8.3)

Figure S34.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.322 (Kruskal-Wallis (anova)), Q value = 1

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 126 67.2 (10.5)
subtype1 42 65.4 (10.8)
subtype2 38 68.7 (10.4)
subtype3 27 69.0 (10.9)
subtype4 19 65.3 (9.0)

Figure S35.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0583 (Fisher's exact test), Q value = 1

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 1 36 42 44
subtype1 1 15 12 12
subtype2 0 7 11 20
subtype3 0 5 14 7
subtype4 0 9 5 5

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.357 (Fisher's exact test), Q value = 1

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 27 63 19
subtype1 14 16 6
subtype2 7 22 8
subtype3 4 18 4
subtype4 2 7 1

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.616 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 71 13 24 6
subtype1 24 4 8 1
subtype2 19 6 10 2
subtype3 19 3 4 1
subtype4 9 0 2 2

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.171 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 73 5 48
subtype1 30 1 11
subtype2 18 2 18
subtype3 16 0 11
subtype4 9 2 8

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.171 (Fisher's exact test), Q value = 1

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 33 94
subtype1 7 36
subtype2 14 24
subtype3 6 21
subtype4 6 13

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.393 (Kruskal-Wallis (anova)), Q value = 1

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 35 79.1 (15.8)
subtype1 12 75.8 (17.8)
subtype2 5 74.0 (20.7)
subtype3 8 78.8 (17.3)
subtype4 10 86.0 (7.0)

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.156 (Kruskal-Wallis (anova)), Q value = 1

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 79 36.8 (23.8)
subtype1 27 34.8 (26.9)
subtype2 24 43.2 (24.2)
subtype3 16 35.9 (18.1)
subtype4 12 29.5 (21.8)

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.242 (Kruskal-Wallis (anova)), Q value = 1

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 90 1.8 (3.8)
subtype1 27 1.6 (2.9)
subtype2 33 2.6 (4.9)
subtype3 21 0.5 (0.9)
subtype4 9 2.9 (5.2)

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.00614 (Fisher's exact test), Q value = 0.57

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 9 100
subtype1 7 1 32
subtype2 0 1 32
subtype3 1 4 20
subtype4 0 3 16

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S49.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 91 60 65
'RNAseq CNMF subtypes' versus 'Time to Death'

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

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 59 0.1 - 140.8 (8.3)
subtype1 86 17 0.1 - 112.4 (6.3)
subtype2 59 21 1.2 - 140.8 (11.0)
subtype3 65 21 0.4 - 97.5 (9.3)

Figure S45.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0505 (Kruskal-Wallis (anova)), Q value = 1

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 215 67.7 (10.7)
subtype1 90 65.6 (11.3)
subtype2 60 69.8 (8.8)
subtype3 65 68.5 (11.0)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0107 (Fisher's exact test), Q value = 0.93

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 69 73 67
subtype1 1 2 39 23 23
subtype2 0 0 11 24 25
subtype3 0 0 19 26 19

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0103 (Fisher's exact test), Q value = 0.91

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 62 101 33
subtype1 35 33 9
subtype2 12 38 10
subtype3 15 30 14

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.598 (Fisher's exact test), Q value = 1

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 132 21 39 7
subtype1 55 6 16 2
subtype2 36 7 15 2
subtype3 41 8 8 3

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0184 (Fisher's exact test), Q value = 1

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 109 5 101
subtype1 55 2 33
subtype2 23 3 34
subtype3 31 0 34

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.102 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 51 165
subtype1 16 75
subtype2 14 46
subtype3 21 44

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

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

P value = 0.602 (Kruskal-Wallis (anova)), Q value = 1

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 61 79.8 (15.2)
subtype1 35 78.6 (15.2)
subtype2 8 83.8 (13.0)
subtype3 18 80.6 (16.6)

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

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.149 (Kruskal-Wallis (anova)), Q value = 1

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 125 38.3 (28.2)
subtype1 51 35.4 (28.8)
subtype2 38 39.3 (33.1)
subtype3 36 41.4 (21.3)

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.585 (Kruskal-Wallis (anova)), Q value = 1

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 162 1.5 (3.2)
subtype1 55 1.5 (2.7)
subtype2 55 1.8 (3.4)
subtype3 52 1.3 (3.6)

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 4e-05 (Fisher's exact test), Q value = 0.0043

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 12 169
subtype1 20 2 65
subtype2 1 3 52
subtype3 1 7 52

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S61.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 71 38 55 52
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0783 (logrank test), Q value = 1

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 59 0.1 - 140.8 (8.3)
subtype1 66 11 0.1 - 130.9 (6.3)
subtype2 37 18 1.2 - 140.8 (8.2)
subtype3 55 18 0.5 - 112.4 (12.8)
subtype4 52 12 0.4 - 97.5 (9.1)

Figure S56.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00482 (Kruskal-Wallis (anova)), Q value = 0.46

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 215 67.7 (10.7)
subtype1 70 64.1 (10.9)
subtype2 38 69.4 (9.8)
subtype3 55 70.7 (9.1)
subtype4 52 68.0 (11.5)

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 69 73 67
subtype1 1 2 37 17 12
subtype2 0 0 9 16 13
subtype3 0 0 9 15 30
subtype4 0 0 14 25 12

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 8e-04 (Fisher's exact test), Q value = 0.085

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 62 101 33
subtype1 33 21 5
subtype2 7 20 8
subtype3 10 33 10
subtype4 12 27 10

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.00115 (Fisher's exact test), Q value = 0.12

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 132 21 39 7
subtype1 46 3 8 2
subtype2 23 4 6 3
subtype3 26 8 21 0
subtype4 37 6 4 2

Figure S60.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0528 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 109 5 101
subtype1 44 2 24
subtype2 13 1 24
subtype3 27 2 26
subtype4 25 0 27

Figure S61.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00841 (Fisher's exact test), Q value = 0.77

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 51 165
subtype1 8 63
subtype2 9 29
subtype3 15 40
subtype4 19 33

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.691 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 61 79.8 (15.2)
subtype1 31 78.4 (14.9)
subtype2 9 84.4 (11.3)
subtype3 8 78.8 (16.4)
subtype4 13 80.8 (18.5)

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.237 (Kruskal-Wallis (anova)), Q value = 1

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 125 38.3 (28.2)
subtype1 43 32.7 (25.7)
subtype2 27 37.8 (25.0)
subtype3 29 45.0 (38.7)
subtype4 26 40.4 (20.2)

Figure S64.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00346 (Kruskal-Wallis (anova)), Q value = 0.34

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 162 1.5 (3.2)
subtype1 39 1.1 (2.6)
subtype2 32 1.7 (4.3)
subtype3 48 2.5 (3.6)
subtype4 43 0.8 (2.1)

Figure S65.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 2e-05 (Fisher's exact test), Q value = 0.0022

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 12 169
subtype1 19 2 48
subtype2 1 3 31
subtype3 1 1 47
subtype4 1 6 43

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S73.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 57 93 66
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.295 (logrank test), Q value = 1

Table S74.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 59 0.1 - 140.8 (8.3)
subtype1 54 14 0.1 - 130.9 (6.4)
subtype2 90 21 0.1 - 97.5 (9.8)
subtype3 66 24 0.4 - 140.8 (10.8)

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0285 (Kruskal-Wallis (anova)), Q value = 1

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 215 67.6 (10.7)
subtype1 56 68.7 (11.1)
subtype2 93 65.4 (11.1)
subtype3 66 69.6 (9.2)

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 8e-04 (Fisher's exact test), Q value = 0.085

Table S76.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 69 72 68
subtype1 0 1 12 18 25
subtype2 1 1 43 30 17
subtype3 0 0 14 24 26

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0248 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 62 102 32
subtype1 15 27 11
subtype2 35 35 10
subtype3 12 40 11

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0112 (Fisher's exact test), Q value = 0.96

Table S78.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 131 22 39 7
subtype1 29 10 12 3
subtype2 63 7 8 3
subtype3 39 5 19 1

Figure S71.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.53 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 109 5 101
subtype1 27 1 28
subtype2 53 2 38
subtype3 29 2 35

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00349 (Fisher's exact test), Q value = 0.34

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 50 166
subtype1 8 49
subtype2 17 76
subtype3 25 41

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.623 (Kruskal-Wallis (anova)), Q value = 1

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 61 79.8 (15.2)
subtype1 10 84.0 (8.4)
subtype2 43 79.8 (15.7)
subtype3 8 75.0 (19.3)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.184 (Kruskal-Wallis (anova)), Q value = 1

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 125 38.2 (28.3)
subtype1 31 37.5 (30.6)
subtype2 58 33.0 (19.9)
subtype3 36 47.2 (35.4)

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00988 (Kruskal-Wallis (anova)), Q value = 0.88

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 163 1.5 (3.2)
subtype1 47 2.1 (4.1)
subtype2 58 0.7 (1.7)
subtype3 58 1.9 (3.5)

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.232 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 12 169
subtype1 6 2 44
subtype2 13 4 71
subtype3 3 6 54

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S85.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 39 23 26 26 36 29 37
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.72 (logrank test), Q value = 1

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 59 0.1 - 140.8 (8.3)
subtype1 36 10 0.1 - 130.9 (5.8)
subtype2 23 7 1.1 - 87.3 (6.9)
subtype3 26 7 0.1 - 88.9 (6.6)
subtype4 26 13 1.7 - 50.3 (15.2)
subtype5 36 10 0.5 - 140.8 (11.5)
subtype6 29 7 0.4 - 97.5 (10.6)
subtype7 34 5 0.1 - 76.6 (7.1)

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0742 (Kruskal-Wallis (anova)), Q value = 1

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 215 67.6 (10.7)
subtype1 38 68.2 (11.5)
subtype2 23 70.5 (11.1)
subtype3 26 66.5 (10.8)
subtype4 26 67.6 (10.0)
subtype5 36 69.8 (9.5)
subtype6 29 68.1 (12.1)
subtype7 37 63.3 (9.2)

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00029 (Fisher's exact test), Q value = 0.031

Table S88.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 69 72 68
subtype1 0 1 9 11 17
subtype2 0 0 4 9 10
subtype3 1 0 9 13 3
subtype4 0 0 7 10 9
subtype5 0 0 7 10 18
subtype6 0 0 9 12 7
subtype7 0 1 24 7 4

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00215 (Fisher's exact test), Q value = 0.22

Table S89.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 62 102 32
subtype1 10 18 7
subtype2 6 13 4
subtype3 7 15 1
subtype4 5 10 8
subtype5 7 22 6
subtype6 7 18 2
subtype7 20 6 4

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.00383 (Fisher's exact test), Q value = 0.37

Table S90.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 131 22 39 7
subtype1 18 7 9 2
subtype2 14 4 4 1
subtype3 19 0 2 1
subtype4 15 2 7 0
subtype5 18 3 14 0
subtype6 21 4 2 2
subtype7 26 2 1 1

Figure S82.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.202 (Fisher's exact test), Q value = 1

Table S91.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 109 5 101
subtype1 21 1 16
subtype2 7 0 16
subtype3 12 0 14
subtype4 12 2 12
subtype5 17 1 18
subtype6 15 0 14
subtype7 25 1 11

Figure S83.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0676 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 50 166
subtype1 6 33
subtype2 3 20
subtype3 6 20
subtype4 8 18
subtype5 15 21
subtype6 7 22
subtype7 5 32

Figure S84.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.293 (Kruskal-Wallis (anova)), Q value = 1

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 61 79.8 (15.2)
subtype1 8 83.8 (9.2)
subtype2 4 85.0 (5.8)
subtype3 14 84.3 (12.8)
subtype4 7 84.3 (9.8)
subtype5 4 80.0 (18.3)
subtype6 9 76.7 (21.8)
subtype7 15 72.0 (17.0)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.783 (Kruskal-Wallis (anova)), Q value = 1

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 125 38.2 (28.3)
subtype1 21 40.0 (30.6)
subtype2 13 29.7 (17.6)
subtype3 18 33.3 (27.5)
subtype4 13 43.5 (27.8)
subtype5 18 47.9 (42.9)
subtype6 19 39.8 (26.0)
subtype7 23 33.3 (17.7)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0196 (Kruskal-Wallis (anova)), Q value = 1

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 163 1.5 (3.2)
subtype1 29 2.8 (5.0)
subtype2 23 1.1 (1.8)
subtype3 17 0.7 (2.0)
subtype4 20 1.1 (1.9)
subtype5 33 2.3 (3.9)
subtype6 23 1.1 (2.8)
subtype7 18 0.4 (1.2)

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

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

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 12 169
subtype1 6 1 27
subtype2 0 1 21
subtype3 4 0 19
subtype4 0 2 21
subtype5 1 2 33
subtype6 2 3 23
subtype7 9 3 25

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S97.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 42 90 84
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.357 (logrank test), Q value = 1

Table S98.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 59 0.1 - 140.8 (8.3)
subtype1 39 10 0.1 - 130.9 (6.7)
subtype2 90 31 0.4 - 140.8 (9.9)
subtype3 81 18 0.1 - 88.9 (7.8)

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.00462 (Kruskal-Wallis (anova)), Q value = 0.44

Table S99.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 215 67.6 (10.7)
subtype1 41 69.8 (11.4)
subtype2 90 69.1 (10.2)
subtype3 84 64.8 (10.3)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.00084 (Fisher's exact test), Q value = 0.087

Table S100.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 69 72 68
subtype1 0 1 8 13 19
subtype2 0 0 21 34 33
subtype3 1 1 40 25 16

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00514 (Fisher's exact test), Q value = 0.48

Table S101.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 62 102 32
subtype1 11 19 10
subtype2 18 54 14
subtype3 33 29 8

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.128 (Fisher's exact test), Q value = 1

Table S102.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 131 22 39 7
subtype1 20 7 11 2
subtype2 56 10 19 3
subtype3 55 5 9 2

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.137 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 109 5 101
subtype1 21 1 20
subtype2 39 1 50
subtype3 49 3 31

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0601 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 50 166
subtype1 6 36
subtype2 28 62
subtype3 16 68

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.9 (Kruskal-Wallis (anova)), Q value = 1

Table S105.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 61 79.8 (15.2)
subtype1 7 82.9 (9.5)
subtype2 17 79.4 (17.8)
subtype3 37 79.5 (15.1)

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.424 (Kruskal-Wallis (anova)), Q value = 1

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 125 38.2 (28.3)
subtype1 23 41.0 (33.4)
subtype2 47 42.7 (32.2)
subtype3 55 33.2 (21.2)

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0178 (Kruskal-Wallis (anova)), Q value = 1

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 163 1.5 (3.2)
subtype1 34 2.7 (4.7)
subtype2 79 1.5 (3.1)
subtype3 50 0.7 (1.6)

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.0144 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 12 169
subtype1 5 1 31
subtype2 3 8 77
subtype3 14 3 61

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S109.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 39 111 66
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.806 (logrank test), Q value = 1

Table S110.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 59 0.1 - 140.8 (8.3)
subtype1 36 10 0.1 - 130.9 (6.6)
subtype2 111 33 0.4 - 140.8 (8.9)
subtype3 63 16 0.1 - 123.8 (7.8)

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.00771 (Kruskal-Wallis (anova)), Q value = 0.71

Table S111.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 215 67.6 (10.7)
subtype1 38 68.3 (11.2)
subtype2 111 69.2 (10.3)
subtype3 66 64.4 (10.4)

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0173 (Fisher's exact test), Q value = 1

Table S112.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE 0A STAGE I STAGE II STAGE III STAGE IV
ALL 1 2 69 72 68
subtype1 0 1 9 13 15
subtype2 0 0 29 41 39
subtype3 1 1 31 18 14

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.027 (Fisher's exact test), Q value = 1

Table S113.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 62 102 32
subtype1 10 17 8
subtype2 25 63 16
subtype3 27 22 8

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.336 (Fisher's exact test), Q value = 1

Table S114.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 131 22 39 7
subtype1 20 7 7 2
subtype2 68 11 24 3
subtype3 43 4 8 2

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.533 (Fisher's exact test), Q value = 1

Table S115.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 109 5 101
subtype1 21 1 16
subtype2 51 2 58
subtype3 37 2 27

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.13 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 50 166
subtype1 6 33
subtype2 32 79
subtype3 12 54

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.552 (Kruskal-Wallis (anova)), Q value = 1

Table S117.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 61 79.8 (15.2)
subtype1 9 84.4 (8.8)
subtype2 21 80.5 (16.3)
subtype3 31 78.1 (16.0)

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.814 (Kruskal-Wallis (anova)), Q value = 1

Table S118.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 125 38.2 (28.3)
subtype1 21 37.9 (31.7)
subtype2 61 40.3 (30.6)
subtype3 43 35.3 (22.9)

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.304 (Kruskal-Wallis (anova)), Q value = 1

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 163 1.5 (3.2)
subtype1 29 2.2 (4.7)
subtype2 95 1.6 (3.1)
subtype3 39 0.9 (1.8)

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.00084 (Fisher's exact test), Q value = 0.087

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 22 12 169
subtype1 6 1 27
subtype2 3 9 95
subtype3 13 2 47

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

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

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

  • Number of patients = 218

  • Number of clustering approaches = 10

  • 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)