Correlation between aggregated molecular cancer subtypes and selected clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
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/C1BK1B5S
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 12 clinical features across 281 patients, 19 significant findings detected with P value < 0.05 and Q value < 0.25.

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

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'RACE'.

  • 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 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGY.N.STAGE'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE' and 'NEOPLASM.DISEASESTAGE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE', and 'RACE'.

  • 6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 19 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 0.825
(1.00)
0.326
(1.00)
0.0889
(1.00)
0.506
(1.00)
0.389
(1.00)
0.0353
(1.00)
0.594
(1.00)
0.643
(1.00)
0.236
(1.00)
0.211
(1.00)
AGE Kruskal-Wallis (anova) 0.266
(1.00)
0.0179
(1.00)
0.457
(1.00)
0.322
(1.00)
0.0339
(1.00)
0.000201
(0.0231)
0.0226
(1.00)
0.000213
(0.0241)
0.00102
(0.108)
0.0053
(0.514)
NEOPLASM DISEASESTAGE Fisher's exact test 0.226
(1.00)
0.0162
(1.00)
0.119
(1.00)
0.0574
(1.00)
0.00129
(0.134)
1e-05
(0.0012)
0.00343
(0.34)
0.00193
(0.197)
0.00051
(0.0551)
0.00387
(0.379)
PATHOLOGY T STAGE Fisher's exact test 0.698
(1.00)
0.204
(1.00)
0.686
(1.00)
0.356
(1.00)
0.00026
(0.0289)
2e-05
(0.00234)
0.00804
(0.716)
0.00565
(0.542)
0.00053
(0.0567)
0.00123
(0.129)
PATHOLOGY N STAGE Fisher's exact test 0.0127
(1.00)
0.203
(1.00)
0.426
(1.00)
0.618
(1.00)
0.0842
(1.00)
0.00021
(0.0239)
0.0018
(0.185)
0.0746
(1.00)
0.00014
(0.0162)
0.0133
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.551
(1.00)
0.125
(1.00)
0.0297
(1.00)
0.17
(1.00)
0.0285
(1.00)
0.00961
(0.846)
0.0633
(1.00)
0.0475
(1.00)
0.007
(0.644)
0.00741
(0.667)
GENDER Fisher's exact test 0.0314
(1.00)
0.396
(1.00)
0.518
(1.00)
0.17
(1.00)
0.234
(1.00)
0.187
(1.00)
0.00604
(0.574)
0.0178
(1.00)
0.0477
(1.00)
0.198
(1.00)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.543
(1.00)
0.00321
(0.321)
0.369
(1.00)
0.393
(1.00)
0.639
(1.00)
0.263
(1.00)
0.53
(1.00)
0.0189
(1.00)
0.0179
(1.00)
0.00644
(0.605)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.0729
(1.00)
0.258
(1.00)
0.187
(1.00)
0.156
(1.00)
0.384
(1.00)
0.0569
(1.00)
0.869
(1.00)
0.934
(1.00)
0.444
(1.00)
0.881
(1.00)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.157
(1.00)
0.0302
(1.00)
0.0993
(1.00)
0.242
(1.00)
0.287
(1.00)
0.00044
(0.0479)
0.00704
(0.644)
0.256
(1.00)
0.0174
(1.00)
0.211
(1.00)
RACE Fisher's exact test 0.206
(1.00)
0.00023
(0.0258)
0.0667
(1.00)
0.00662
(0.616)
1e-05
(0.0012)
1e-05
(0.0012)
0.639
(1.00)
0.19
(1.00)
0.0004
(0.044)
0.00266
(0.269)
ETHNICITY Fisher's exact test 0.183
(1.00)
0.681
(1.00)
0.704
(1.00)
0.22
(1.00)
0.815
(1.00)
0.144
(1.00)
0.173
(1.00)
0.766
(1.00)
1
(1.00)
1
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 60 64 113 41
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.825 (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 270 88 0.1 - 140.8 (9.2)
subtype1 59 21 1.9 - 103.5 (10.6)
subtype2 63 18 0.4 - 130.9 (7.8)
subtype3 107 35 0.1 - 140.8 (10.8)
subtype4 41 14 1.7 - 123.8 (6.9)

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 = 0.266 (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 277 67.8 (10.9)
subtype1 59 68.8 (11.3)
subtype2 64 68.5 (10.3)
subtype3 113 66.3 (11.5)
subtype4 41 69.7 (8.9)

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 = 0.226 (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 86 93 92
subtype1 0 0 20 18 21
subtype2 0 1 16 18 28
subtype3 1 1 38 45 26
subtype4 0 0 12 12 17

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 = 0.698 (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 82 136 39
subtype1 19 29 5
subtype2 17 34 9
subtype3 36 50 19
subtype4 10 23 6

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 = 0.0127 (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 166 28 55 7
subtype1 31 9 9 1
subtype2 33 8 18 2
subtype3 82 9 14 2
subtype4 20 2 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 = 0.551 (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 129 7 141
subtype1 27 1 32
subtype2 30 1 32
subtype3 58 3 52
subtype4 14 2 25

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 = 0.0314 (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 77 201
subtype1 25 35
subtype2 13 51
subtype3 26 87
subtype4 13 28

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 = 0.543 (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 99 80.9 (14.9)
subtype1 26 80.0 (15.7)
subtype2 25 82.4 (17.1)
subtype3 35 79.7 (14.4)
subtype4 13 83.1 (10.3)

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 = 0.0729 (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 159 38.3 (27.2)
subtype1 36 30.1 (21.7)
subtype2 39 45.7 (30.7)
subtype3 63 37.6 (28.6)
subtype4 21 40.9 (21.5)

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 = 0.157 (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 219 2.2 (7.6)
subtype1 46 1.6 (3.9)
subtype2 56 3.0 (12.9)
subtype3 80 1.4 (4.1)
subtype4 37 3.2 (6.2)

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 = 0.206 (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 31 13 215
subtype1 2 4 46
subtype2 6 2 51
subtype3 19 6 83
subtype4 4 1 35

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.183 (Fisher's exact test), Q value = 1

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 258
subtype1 3 50
subtype2 1 59
subtype3 1 109
subtype4 0 40

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S14.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 112 97 51
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.326 (logrank test), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 252 77 0.1 - 140.8 (8.9)
subtype1 111 34 0.4 - 140.8 (10.0)
subtype2 95 26 0.1 - 112.4 (7.8)
subtype3 46 17 0.1 - 103.5 (7.9)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0179 (Kruskal-Wallis (anova)), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 259 67.6 (10.8)
subtype1 112 69.6 (10.0)
subtype2 96 67.3 (10.3)
subtype3 51 63.8 (12.7)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0162 (Fisher's exact test), Q value = 1

Table S17.  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 84 85 84
subtype1 0 0 28 49 34
subtype2 1 2 35 26 31
subtype3 0 0 21 10 19

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.204 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 78 123 37
subtype1 27 62 16
subtype2 33 42 12
subtype3 18 19 9

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.203 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 156 26 50 7
subtype1 74 14 16 4
subtype2 56 10 19 2
subtype3 26 2 15 1

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.125 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 128 7 124
subtype1 48 3 61
subtype2 48 2 46
subtype3 32 2 17

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

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

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 66 194
subtype1 33 79
subtype2 23 74
subtype3 10 41

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.00321 (Kruskal-Wallis (anova)), Q value = 0.32

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 80 79.6 (15.6)
subtype1 24 85.0 (11.4)
subtype2 34 81.5 (14.8)
subtype3 22 70.9 (17.7)

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.258 (Kruskal-Wallis (anova)), Q value = 1

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 149 37.9 (27.4)
subtype1 67 40.7 (28.8)
subtype2 57 38.1 (28.2)
subtype3 25 30.1 (20.2)

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.0302 (Kruskal-Wallis (anova)), Q value = 1

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 199 2.1 (7.7)
subtype1 96 1.2 (2.5)
subtype2 73 2.9 (11.7)
subtype3 30 3.3 (6.5)

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00023 (Fisher's exact test), Q value = 0.026

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 30 13 198
subtype1 4 8 93
subtype2 13 5 70
subtype3 13 0 35

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.681 (Fisher's exact test), Q value = 1

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 239
subtype1 1 103
subtype2 2 88
subtype3 1 48

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27.  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 = 0.0889 (logrank test), Q value = 1

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 122 48 0.1 - 140.8 (9.0)
subtype1 33 6 0.1 - 130.9 (7.8)
subtype2 28 12 1.8 - 140.8 (7.1)
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 S25.  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 S29.  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 S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.119 (Fisher's exact test), Q value = 1

Table S30.  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 S27.  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 = 0.686 (Fisher's exact test), Q value = 1

Table S31.  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 S28.  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 = 0.426 (Fisher's exact test), Q value = 1

Table S32.  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 S29.  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 = 0.0297 (Fisher's exact test), Q value = 1

Table S33.  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 S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.518 (Fisher's exact test), Q value = 1

Table S34.  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 S31.  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 S35.  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 S32.  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 S36.  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 S33.  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 S37.  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 S34.  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 = 0.0667 (Fisher's exact test), Q value = 1

Table S38.  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 S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.704 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 112
subtype1 0 33
subtype2 1 25
subtype3 1 31
subtype4 0 5
subtype5 0 18

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40.  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 = 0.506 (logrank test), Q value = 1

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 122 48 0.1 - 140.8 (9.0)
subtype1 39 11 0.1 - 112.4 (8.9)
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 S37.  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 S42.  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 S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0574 (Fisher's exact test), Q value = 1

Table S43.  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 S39.  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 = 0.356 (Fisher's exact test), Q value = 1

Table S44.  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 S40.  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 = 0.618 (Fisher's exact test), Q value = 1

Table S45.  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 S41.  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 = 0.17 (Fisher's exact test), Q value = 1

Table S46.  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 S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.17 (Fisher's exact test), Q value = 1

Table S47.  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 S43.  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 S48.  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 S44.  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 S49.  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 S45.  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 S50.  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 S46.  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 = 0.00662 (Fisher's exact test), Q value = 0.62

Table S51.  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 S47.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.22 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 112
subtype1 0 38
subtype2 2 31
subtype3 0 24
subtype4 0 19

Figure S48.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S53.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 71 110 75
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.389 (logrank test), Q value = 1

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 248 77 0.1 - 140.8 (8.6)
subtype1 70 27 1.2 - 140.8 (10.8)
subtype2 103 24 0.1 - 112.4 (6.7)
subtype3 75 26 0.4 - 103.5 (9.0)

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0339 (Kruskal-Wallis (anova)), Q value = 1

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 255 67.6 (10.9)
subtype1 71 70.0 (9.0)
subtype2 109 65.6 (11.6)
subtype3 75 68.2 (11.0)

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00129 (Fisher's exact test), Q value = 0.13

Table S56.  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 82 84 83
subtype1 0 0 12 28 31
subtype2 1 2 48 27 29
subtype3 0 0 22 29 23

Figure S51.  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 = 0.00026 (Fisher's exact test), Q value = 0.029

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 76 121 37
subtype1 11 47 12
subtype2 46 39 11
subtype3 19 35 14

Figure S52.  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 = 0.0842 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 153 25 50 7
subtype1 40 8 20 2
subtype2 66 6 22 2
subtype3 47 11 8 3

Figure S53.  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 = 0.0285 (Fisher's exact test), Q value = 1

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 127 7 121
subtype1 26 4 41
subtype2 64 2 43
subtype3 37 1 37

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.234 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 64 192
subtype1 19 52
subtype2 22 88
subtype3 23 52

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.639 (Kruskal-Wallis (anova)), Q value = 1

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 78 79.4 (15.7)
subtype1 14 83.6 (10.8)
subtype2 43 78.1 (16.1)
subtype3 21 79.0 (17.9)

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.384 (Kruskal-Wallis (anova)), Q value = 1

Table S62.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 147 38.1 (27.5)
subtype1 45 38.3 (31.3)
subtype2 60 36.7 (28.3)
subtype3 42 40.0 (22.1)

Figure S57.  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 = 0.287 (Kruskal-Wallis (anova)), Q value = 1

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 195 2.2 (7.8)
subtype1 65 2.0 (3.5)
subtype2 70 3.2 (12.2)
subtype3 60 1.2 (3.3)

Figure S58.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S64.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 29 13 195
subtype1 1 3 63
subtype2 26 3 75
subtype3 2 7 57

Figure S59.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.815 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 235
subtype1 2 65
subtype2 1 104
subtype3 1 66

Figure S60.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 71 81 61 43
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0353 (logrank test), Q value = 1

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 248 77 0.1 - 140.8 (8.6)
subtype1 70 24 0.5 - 130.9 (10.6)
subtype2 74 13 0.1 - 84.0 (6.6)
subtype3 61 18 0.4 - 103.5 (9.0)
subtype4 43 22 0.7 - 140.8 (8.2)

Figure S61.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000201 (Kruskal-Wallis (anova)), Q value = 0.023

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 255 67.6 (10.9)
subtype1 71 70.9 (9.1)
subtype2 80 63.3 (11.2)
subtype3 61 67.7 (11.4)
subtype4 43 70.0 (9.8)

Figure S62.  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 = 0.0012

Table S69.  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 82 84 83
subtype1 0 0 13 18 39
subtype2 1 2 44 21 11
subtype3 0 0 17 27 16
subtype4 0 0 8 18 17

Figure S63.  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 = 2e-05 (Fisher's exact test), Q value = 0.0023

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 76 121 37
subtype1 13 42 13
subtype2 41 23 5
subtype3 15 32 10
subtype4 7 24 9

Figure S64.  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 = 0.00021 (Fisher's exact test), Q value = 0.024

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 153 25 50 7
subtype1 31 10 27 2
subtype2 55 2 9 1
subtype3 41 8 6 2
subtype4 26 5 8 2

Figure S65.  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 = 0.00961 (Fisher's exact test), Q value = 0.85

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

nPatients M0 M1 MX
ALL 127 7 121
subtype1 34 2 35
subtype2 50 2 28
subtype3 30 0 31
subtype4 13 3 27

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.187 (Fisher's exact test), Q value = 1

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 64 192
subtype1 19 52
subtype2 14 67
subtype3 20 41
subtype4 11 32

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.263 (Kruskal-Wallis (anova)), Q value = 1

Table S74.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 78 79.4 (15.7)
subtype1 16 82.5 (12.4)
subtype2 37 75.9 (17.1)
subtype3 18 81.1 (16.8)
subtype4 7 85.7 (9.8)

Figure S68.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0569 (Kruskal-Wallis (anova)), Q value = 1

Table S75.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 147 38.1 (27.5)
subtype1 37 46.7 (35.5)
subtype2 48 33.0 (24.8)
subtype3 34 41.4 (21.8)
subtype4 28 31.6 (23.7)

Figure S69.  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 = 0.00044 (Kruskal-Wallis (anova)), Q value = 0.048

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 195 2.2 (7.8)
subtype1 63 2.4 (3.3)
subtype2 45 3.6 (15.1)
subtype3 50 0.9 (2.1)
subtype4 37 1.9 (4.3)

Figure S70.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S77.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 29 13 195
subtype1 2 2 61
subtype2 24 2 50
subtype3 2 6 48
subtype4 1 3 36

Figure S71.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.144 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 235
subtype1 0 64
subtype2 2 77
subtype3 0 56
subtype4 2 38

Figure S72.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 81 72 126
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.594 (logrank test), Q value = 1

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 271 89 0.1 - 140.8 (9.0)
subtype1 81 31 0.4 - 140.8 (11.9)
subtype2 69 21 0.1 - 130.9 (6.9)
subtype3 121 37 0.1 - 112.4 (10.0)

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0226 (Kruskal-Wallis (anova)), Q value = 1

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 278 67.8 (10.9)
subtype1 81 69.8 (9.7)
subtype2 71 68.9 (11.4)
subtype3 126 65.9 (11.0)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00343 (Fisher's exact test), Q value = 0.34

Table S82.  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 87 93 92
subtype1 0 0 17 28 34
subtype2 0 1 16 25 29
subtype3 1 1 54 40 29

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00804 (Fisher's exact test), Q value = 0.72

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 82 136 39
subtype1 15 51 12
subtype2 19 36 13
subtype3 48 49 14

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0018 (Fisher's exact test), Q value = 0.19

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 167 27 56 7
subtype1 45 6 27 0
subtype2 38 10 16 3
subtype3 84 11 13 4

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0633 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 131 7 140
subtype1 30 2 49
subtype2 31 1 39
subtype3 70 4 52

Figure S78.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00604 (Fisher's exact test), Q value = 0.57

Table S86.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 77 202
subtype1 32 49
subtype2 12 60
subtype3 33 93

Figure S79.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.53 (Kruskal-Wallis (anova)), Q value = 1

Table S87.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 100 81.0 (14.9)
subtype1 22 83.2 (13.6)
subtype2 20 81.5 (16.0)
subtype3 58 80.0 (15.1)

Figure S80.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.869 (Kruskal-Wallis (anova)), Q value = 1

Table S88.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 158 38.8 (27.3)
subtype1 45 42.3 (32.8)
subtype2 38 39.5 (29.6)
subtype3 75 36.4 (22.2)

Figure S81.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.00704 (Kruskal-Wallis (anova)), Q value = 0.64

Table S89.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 218 2.2 (7.6)
subtype1 71 2.3 (4.6)
subtype2 60 3.5 (12.8)
subtype3 87 1.1 (3.9)

Figure S82.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.639 (Fisher's exact test), Q value = 1

Table S90.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 31 14 213
subtype1 6 4 70
subtype2 9 3 52
subtype3 16 7 91

Figure S83.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.173 (Fisher's exact test), Q value = 1

Table S91.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 257
subtype1 1 79
subtype2 3 64
subtype3 1 114

Figure S84.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 58 72 61 45 43
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.643 (logrank test), Q value = 1

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 271 89 0.1 - 140.8 (9.0)
subtype1 58 22 0.4 - 97.5 (12.0)
subtype2 70 25 0.1 - 130.9 (6.8)
subtype3 55 15 0.1 - 103.5 (10.5)
subtype4 45 14 0.1 - 98.3 (9.3)
subtype5 43 13 0.5 - 140.8 (10.8)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000213 (Kruskal-Wallis (anova)), Q value = 0.024

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 278 67.8 (10.9)
subtype1 58 67.5 (11.4)
subtype2 71 71.0 (10.1)
subtype3 61 63.1 (10.5)
subtype4 45 67.0 (10.5)
subtype5 43 70.7 (9.9)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00193 (Fisher's exact test), Q value = 0.2

Table S95.  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 87 93 92
subtype1 0 0 14 24 19
subtype2 0 1 15 25 30
subtype3 0 1 33 13 13
subtype4 1 0 16 17 11
subtype5 0 0 9 14 19

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00565 (Fisher's exact test), Q value = 0.54

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 82 136 39
subtype1 13 35 7
subtype2 18 36 14
subtype3 30 16 7
subtype4 12 22 5
subtype5 9 27 6

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0746 (Fisher's exact test), Q value = 1

Table S97.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 167 27 56 7
subtype1 37 9 7 2
subtype2 39 9 18 3
subtype3 40 4 8 1
subtype4 27 3 6 1
subtype5 24 2 17 0

Figure S89.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0475 (Fisher's exact test), Q value = 1

Table S98.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 131 7 140
subtype1 29 0 29
subtype2 30 1 40
subtype3 38 1 22
subtype4 19 3 23
subtype5 15 2 26

Figure S90.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0178 (Fisher's exact test), Q value = 1

Table S99.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 77 202
subtype1 20 38
subtype2 12 60
subtype3 15 46
subtype4 11 34
subtype5 19 24

Figure S91.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0189 (Kruskal-Wallis (anova)), Q value = 1

Table S100.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 100 81.0 (14.9)
subtype1 20 81.5 (16.3)
subtype2 20 85.0 (10.0)
subtype3 30 74.0 (17.7)
subtype4 20 84.5 (11.0)
subtype5 10 86.0 (11.7)

Figure S92.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.934 (Kruskal-Wallis (anova)), Q value = 1

Table S101.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 158 38.8 (27.3)
subtype1 33 40.9 (26.6)
subtype2 40 36.3 (25.8)
subtype3 35 36.7 (22.2)
subtype4 26 37.8 (26.2)
subtype5 24 44.3 (38.0)

Figure S93.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.256 (Kruskal-Wallis (anova)), Q value = 1

Table S102.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 218 2.2 (7.6)
subtype1 48 1.0 (2.2)
subtype2 63 3.4 (12.5)
subtype3 37 2.4 (7.1)
subtype4 30 1.0 (2.0)
subtype5 40 2.2 (3.9)

Figure S94.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.19 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 31 14 213
subtype1 3 4 46
subtype2 8 3 56
subtype3 13 3 40
subtype4 5 2 32
subtype5 2 2 39

Figure S95.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.766 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 257
subtype1 0 53
subtype2 1 66
subtype3 2 56
subtype4 1 41
subtype5 1 41

Figure S96.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 67 31 89 83
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.236 (logrank test), Q value = 1

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 263 88 0.1 - 140.8 (9.3)
subtype1 67 22 0.4 - 97.5 (9.0)
subtype2 30 13 0.8 - 130.9 (7.0)
subtype3 83 19 0.1 - 103.5 (8.0)
subtype4 83 34 0.5 - 140.8 (10.8)

Figure S97.  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 = 0.00102 (Kruskal-Wallis (anova)), Q value = 0.11

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 269 68.0 (10.8)
subtype1 67 67.8 (11.6)
subtype2 30 72.4 (7.8)
subtype3 89 64.6 (11.4)
subtype4 83 70.4 (9.3)

Figure S98.  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 = 0.00051 (Fisher's exact test), Q value = 0.055

Table S108.  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 81 90 92
subtype1 0 0 20 24 22
subtype2 0 1 3 11 15
subtype3 1 1 41 26 19
subtype4 0 0 17 29 36

Figure S99.  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 = 0.00053 (Fisher's exact test), Q value = 0.057

Table S109.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 79 133 39
subtype1 19 35 8
subtype2 6 16 9
subtype3 39 29 10
subtype4 15 53 12

Figure S100.  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 = 0.00014 (Fisher's exact test), Q value = 0.016

Table S110.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 161 27 56 7
subtype1 42 11 7 3
subtype2 13 4 11 2
subtype3 59 7 9 2
subtype4 47 5 29 0

Figure S101.  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 = 0.007 (Fisher's exact test), Q value = 0.64

Table S111.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 126 7 136
subtype1 24 1 42
subtype2 12 0 19
subtype3 55 3 30
subtype4 35 3 45

Figure S102.  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 = 0.0477 (Fisher's exact test), Q value = 1

Table S112.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 77 193
subtype1 20 47
subtype2 5 26
subtype3 20 69
subtype4 32 51

Figure S103.  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 = 0.0179 (Kruskal-Wallis (anova)), Q value = 1

Table S113.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 96 80.9 (15.1)
subtype1 24 83.8 (14.7)
subtype2 8 83.8 (14.1)
subtype3 41 75.9 (17.0)
subtype4 23 86.1 (8.9)

Figure S104.  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 = 0.444 (Kruskal-Wallis (anova)), Q value = 1

Table S114.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 153 39.4 (27.3)
subtype1 39 42.4 (23.8)
subtype2 20 41.8 (26.8)
subtype3 52 35.8 (25.3)
subtype4 42 40.1 (32.8)

Figure S105.  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 = 0.0174 (Kruskal-Wallis (anova)), Q value = 1

Table S115.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 215 2.2 (7.7)
subtype1 56 0.9 (2.0)
subtype2 29 2.8 (4.8)
subtype3 54 3.0 (13.8)
subtype4 76 2.4 (4.6)

Figure S106.  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 = 4e-04 (Fisher's exact test), Q value = 0.044

Table S116.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 28 13 208
subtype1 2 6 54
subtype2 1 1 27
subtype3 20 2 57
subtype4 5 4 70

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

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

Table S117.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 248
subtype1 1 62
subtype2 0 28
subtype3 2 80
subtype4 2 78

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 54 32 62 46 20 56
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

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

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 263 88 0.1 - 140.8 (9.3)
subtype1 54 20 0.5 - 140.8 (12.2)
subtype2 31 12 0.1 - 130.9 (6.4)
subtype3 59 13 0.1 - 103.5 (9.0)
subtype4 46 22 0.8 - 98.3 (9.9)
subtype5 17 2 0.1 - 76.6 (3.8)
subtype6 56 19 0.4 - 97.5 (8.6)

Figure S109.  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 = 0.0053 (Kruskal-Wallis (anova)), Q value = 0.51

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 269 68.0 (10.8)
subtype1 54 69.7 (9.4)
subtype2 31 71.7 (9.8)
subtype3 62 64.5 (10.9)
subtype4 46 70.5 (8.9)
subtype5 20 63.3 (10.8)
subtype6 56 68.0 (12.4)

Figure S110.  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 = 0.00387 (Fisher's exact test), Q value = 0.38

Table S121.  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 81 90 92
subtype1 0 0 12 19 22
subtype2 0 1 5 12 13
subtype3 1 1 27 15 18
subtype4 0 0 10 18 18
subtype5 0 0 13 5 1
subtype6 0 0 14 21 20

Figure S111.  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 = 0.00123 (Fisher's exact test), Q value = 0.13

Table S122.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1+T2 T3 T4
ALL 79 133 39
subtype1 10 34 7
subtype2 8 16 8
subtype3 24 21 5
subtype4 9 28 8
subtype5 14 4 2
subtype6 14 30 9

Figure S112.  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 = 0.0133 (Fisher's exact test), Q value = 1

Table S123.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 161 27 56 7
subtype1 31 3 19 0
subtype2 16 3 10 2
subtype3 34 5 9 2
subtype4 27 6 11 0
subtype5 17 0 1 0
subtype6 36 10 6 3

Figure S113.  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 = 0.00741 (Fisher's exact test), Q value = 0.67

Table S124.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 126 7 136
subtype1 21 2 31
subtype2 15 0 17
subtype3 31 4 26
subtype4 21 1 24
subtype5 17 0 3
subtype6 21 0 35

Figure S114.  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 = 0.198 (Fisher's exact test), Q value = 1

Table S125.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 77 193
subtype1 22 32
subtype2 6 26
subtype3 14 48
subtype4 15 31
subtype5 4 16
subtype6 16 40

Figure S115.  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 = 0.00644 (Kruskal-Wallis (anova)), Q value = 0.61

Table S126.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 96 80.9 (15.1)
subtype1 16 86.2 (9.6)
subtype2 8 82.5 (13.9)
subtype3 30 80.3 (16.7)
subtype4 10 86.0 (7.0)
subtype5 11 66.4 (16.3)
subtype6 21 82.4 (15.1)

Figure S116.  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 = 0.881 (Kruskal-Wallis (anova)), Q value = 1

Table S127.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 153 39.4 (27.3)
subtype1 26 43.8 (37.1)
subtype2 17 37.7 (22.9)
subtype3 37 38.9 (31.6)
subtype4 27 41.9 (21.3)
subtype5 12 36.4 (19.1)
subtype6 34 36.6 (23.0)

Figure S117.  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 = 0.211 (Kruskal-Wallis (anova)), Q value = 1

Table S128.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 215 2.2 (7.7)
subtype1 49 2.0 (3.6)
subtype2 27 3.0 (5.1)
subtype3 39 3.3 (15.5)
subtype4 41 1.4 (2.0)
subtype5 10 3.2 (10.1)
subtype6 49 1.6 (4.7)

Figure S118.  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 = 0.00266 (Fisher's exact test), Q value = 0.27

Table S129.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 28 13 208
subtype1 3 2 48
subtype2 3 1 25
subtype3 10 2 43
subtype4 1 4 34
subtype5 8 1 10
subtype6 3 3 48

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 248
subtype1 1 51
subtype2 0 28
subtype3 2 55
subtype4 1 41
subtype5 0 20
subtype6 1 53

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

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

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

  • Number of patients = 281

  • Number of clustering approaches = 10

  • Number of selected clinical features = 12

  • 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)