Correlation between aggregated molecular cancer subtypes and selected clinical features
Stomach and Esophageal carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1W37VSG
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 628 patients, 60 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 correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'GENDER',  'RADIATION_THERAPY', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'RACE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'PATHOLOGY_M_STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY_T_STAGE' and 'RACE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER', and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'NUMBER_PACK_YEARS_SMOKED',  'RACE', and 'ETHNICITY'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'RADIATION_THERAPY',  'KARNOFSKY_PERFORMANCE_SCORE',  'NUMBER_PACK_YEARS_SMOKED', and 'RACE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'RADIATION_THERAPY',  'NUMBER_PACK_YEARS_SMOKED', and 'RACE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'RADIATION_THERAPY', and 'RACE'.

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, 60 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 0.402
(0.492)
0.351
(0.444)
0.00384
(0.0115)
0.0642
(0.122)
0.524
(0.605)
0.0305
(0.0679)
0.59
(0.656)
0.269
(0.354)
0.146
(0.216)
0.235
(0.321)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.0473
(0.0962)
9.75e-05
(0.000468)
0.115
(0.189)
0.594
(0.656)
9.5e-07
(2.28e-05)
1.71e-08
(7.28e-07)
1.82e-08
(7.28e-07)
1.55e-09
(1.87e-07)
1.24e-05
(6.47e-05)
6.51e-08
(1.95e-06)
PATHOLOGIC STAGE Fisher's exact test 0.00019
(0.000814)
0.00019
(0.000814)
0.00576
(0.0157)
0.336
(0.428)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
6e-05
(3e-04)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
PATHOLOGY T STAGE Fisher's exact test 0.0604
(0.117)
0.00108
(0.00393)
0.00134
(0.00459)
0.0127
(0.0317)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
0.00057
(0.00221)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
PATHOLOGY N STAGE Fisher's exact test 0.23
(0.317)
0.17
(0.246)
0.413
(0.501)
0.418
(0.502)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
0.00343
(0.0106)
1e-05
(5.45e-05)
0.0489
(0.0978)
1e-05
(5.45e-05)
PATHOLOGY M STAGE Fisher's exact test 0.75
(0.79)
0.136
(0.209)
0.00525
(0.0147)
0.253
(0.337)
0.882
(0.882)
0.569
(0.644)
0.773
(0.806)
0.357
(0.446)
0.783
(0.81)
0.81
(0.83)
GENDER Fisher's exact test 0.00038
(0.00152)
0.183
(0.256)
0.439
(0.522)
0.54
(0.617)
0.00082
(0.00307)
0.0113
(0.0294)
0.00171
(0.00555)
0.0139
(0.0335)
0.0234
(0.0541)
0.0115
(0.0294)
RADIATION THERAPY Fisher's exact test 0.0411
(0.0851)
0.183
(0.256)
0.142
(0.213)
0.0563
(0.111)
0.107
(0.179)
0.117
(0.189)
0.286
(0.373)
0.00147
(0.0049)
0.00911
(0.0243)
0.0136
(0.0332)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.525
(0.605)
0.0918
(0.162)
0.391
(0.483)
0.737
(0.786)
0.0842
(0.151)
0.102
(0.173)
0.139
(0.212)
0.0384
(0.0823)
0.135
(0.209)
0.0955
(0.166)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.0712
(0.131)
0.0653
(0.122)
0.178
(0.255)
0.873
(0.881)
0.601
(0.656)
0.305
(0.393)
0.00463
(0.0132)
0.0406
(0.0851)
0.0203
(0.0477)
0.133
(0.209)
RACE Fisher's exact test 0.00311
(0.00982)
0.00129
(0.00455)
0.244
(0.329)
0.0298
(0.0675)
0.00036
(0.00149)
0.00457
(0.0132)
1e-05
(5.45e-05)
1e-05
(5.45e-05)
0.00011
(0.000508)
1e-05
(5.45e-05)
ETHNICITY Fisher's exact test 0.465
(0.548)
0.862
(0.877)
0.0807
(0.147)
0.66
(0.714)
0.161
(0.235)
0.102
(0.173)
0.0327
(0.0713)
0.74
(0.786)
0.601
(0.656)
0.13
(0.208)
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 151 98 182 194
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.402 (logrank test), Q value = 0.49

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

nPatients nDeath Duration Range (Median), Month
ALL 599 245 0.0 - 122.3 (13.9)
subtype1 144 65 0.4 - 122.3 (13.3)
subtype2 97 45 0.7 - 115.7 (14.0)
subtype3 177 63 0.1 - 79.1 (13.1)
subtype4 181 72 0.0 - 122.1 (16.9)

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 'YEARS_TO_BIRTH'

P value = 0.0473 (Kruskal-Wallis (anova)), Q value = 0.096

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

nPatients Mean (Std.Dev)
ALL 616 64.8 (11.2)
subtype1 148 65.9 (10.5)
subtype2 96 65.7 (10.1)
subtype3 181 63.0 (11.4)
subtype4 191 65.2 (12.0)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00019 (Fisher's exact test), Q value = 0.00081

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 10 21 47 33 87 87 28 95 72 46 49 4
subtype1 5 2 12 8 13 24 3 27 19 6 14 0
subtype2 1 6 4 8 12 10 10 14 12 8 4 3
subtype3 1 3 12 7 42 24 13 22 16 15 13 1
subtype4 3 10 19 10 20 29 2 32 25 17 18 0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0604 (Fisher's exact test), Q value = 0.12

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 55 136 283 124
subtype1 15 33 67 26
subtype2 13 17 50 15
subtype3 11 46 87 29
subtype4 16 40 79 54

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 = 0.23 (Fisher's exact test), Q value = 0.32

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

nPatients N0 N1 N2 N3
ALL 207 187 98 96
subtype1 41 44 29 22
subtype2 28 34 18 13
subtype3 71 57 20 25
subtype4 67 52 31 36

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 = 0.75 (Fisher's exact test), Q value = 0.79

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

nPatients 0 1
ALL 524 39
subtype1 124 8
subtype2 83 4
subtype3 149 13
subtype4 168 14

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 = 0.00038 (Fisher's exact test), Q value = 0.0015

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

nPatients FEMALE MALE
ALL 185 440
subtype1 37 114
subtype2 31 67
subtype3 39 143
subtype4 78 116

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 'RADIATION_THERAPY'

P value = 0.0411 (Fisher's exact test), Q value = 0.085

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

nPatients NO YES
ALL 445 119
subtype1 111 23
subtype2 73 20
subtype3 118 47
subtype4 143 29

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.525 (Kruskal-Wallis (anova)), Q value = 0.61

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

nPatients Mean (Std.Dev)
ALL 68 73.8 (16.2)
subtype1 4 82.5 (15.0)
subtype2 7 75.7 (16.2)
subtype3 54 72.6 (16.6)
subtype4 3 80.0 (10.0)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0712 (Kruskal-Wallis (anova)), Q value = 0.13

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 98 34.5 (21.5)
subtype1 22 41.0 (23.8)
subtype2 19 41.7 (24.4)
subtype3 50 29.0 (18.4)
subtype4 7 33.4 (19.8)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00311 (Fisher's exact test), Q value = 0.0098

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 134 17 1 392
subtype1 23 4 1 102
subtype2 20 1 0 62
subtype3 57 8 0 96
subtype4 34 4 0 132

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 = 0.465 (Fisher's exact test), Q value = 0.55

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 404
subtype1 3 91
subtype2 0 59
subtype3 5 118
subtype4 3 136

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 242 162 176
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.351 (logrank test), Q value = 0.44

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

nPatients nDeath Duration Range (Median), Month
ALL 569 227 0.1 - 122.3 (14.0)
subtype1 237 88 0.1 - 122.1 (14.7)
subtype2 160 64 0.1 - 72.2 (13.2)
subtype3 172 75 0.3 - 122.3 (15.1)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 9.75e-05 (Kruskal-Wallis (anova)), Q value = 0.00047

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

nPatients Mean (Std.Dev)
ALL 571 64.3 (11.2)
subtype1 237 66.2 (10.8)
subtype2 160 61.7 (10.9)
subtype3 174 64.1 (11.3)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00019 (Fisher's exact test), Q value = 0.00081

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 10 20 41 30 88 86 28 89 71 45 38 4
subtype1 8 10 17 13 20 42 11 35 37 19 14 0
subtype2 2 5 10 7 39 23 15 20 13 10 8 3
subtype3 0 5 14 10 29 21 2 34 21 16 16 1

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00108 (Fisher's exact test), Q value = 0.0039

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 53 121 274 115
subtype1 33 40 110 49
subtype2 12 44 80 21
subtype3 8 37 84 45

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 = 0.17 (Fisher's exact test), Q value = 0.25

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 201 170 92 91
subtype1 77 69 43 41
subtype2 65 53 20 17
subtype3 59 48 29 33

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 = 0.136 (Fisher's exact test), Q value = 0.21

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 489 32
subtype1 204 9
subtype2 138 8
subtype3 147 15

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.183 (Fisher's exact test), Q value = 0.26

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

nPatients FEMALE MALE
ALL 163 417
subtype1 66 176
subtype2 39 123
subtype3 58 118

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.183 (Fisher's exact test), Q value = 0.26

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 426 118
subtype1 179 45
subtype2 111 41
subtype3 136 32

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0918 (Kruskal-Wallis (anova)), Q value = 0.16

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

nPatients Mean (Std.Dev)
ALL 68 73.8 (16.2)
subtype1 6 85.0 (12.2)
subtype2 52 71.9 (16.7)
subtype3 10 77.0 (13.4)

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0653 (Kruskal-Wallis (anova)), Q value = 0.12

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 98 34.5 (21.5)
subtype1 31 42.2 (24.5)
subtype2 54 29.9 (18.2)
subtype3 13 35.4 (23.0)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.00129 (Fisher's exact test), Q value = 0.0046

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 135 18 1 367
subtype1 43 6 1 164
subtype2 55 8 0 84
subtype3 37 4 0 119

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.862 (Fisher's exact test), Q value = 0.88

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 381
subtype1 4 146
subtype2 4 106
subtype3 3 129

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
Number of samples 145 137 137 64
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00384 (logrank test), Q value = 0.012

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

nPatients nDeath Duration Range (Median), Month
ALL 457 183 0.1 - 122.3 (13.5)
subtype1 131 54 0.1 - 77.3 (15.9)
subtype2 132 43 0.1 - 122.3 (14.5)
subtype3 133 63 0.3 - 52.6 (12.6)
subtype4 61 23 0.3 - 79.1 (15.8)

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.115 (Kruskal-Wallis (anova)), Q value = 0.19

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 475 64.9 (11.2)
subtype1 139 65.9 (12.0)
subtype2 137 65.3 (10.6)
subtype3 137 62.9 (11.1)
subtype4 62 65.9 (10.2)

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00576 (Fisher's exact test), Q value = 0.016

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 14 36 30 74 67 19 79 58 34 36 1
subtype1 3 6 13 8 19 20 5 25 18 12 6 0
subtype2 3 2 15 8 27 19 4 21 18 8 6 0
subtype3 0 1 3 11 20 23 10 17 18 11 14 1
subtype4 2 5 5 3 8 5 0 16 4 3 10 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00134 (Fisher's exact test), Q value = 0.0046

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 32 111 225 103
subtype1 13 40 60 29
subtype2 6 40 66 23
subtype3 5 16 75 37
subtype4 8 15 24 14

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 = 0.413 (Fisher's exact test), Q value = 0.5

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 171 145 74 74
subtype1 46 43 28 20
subtype2 58 36 20 19
subtype3 43 46 16 27
subtype4 24 20 10 8

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 = 0.00525 (Fisher's exact test), Q value = 0.015

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 427 25
subtype1 130 4
subtype2 128 3
subtype3 121 10
subtype4 48 8

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 = 0.439 (Fisher's exact test), Q value = 0.52

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 139 344
subtype1 36 109
subtype2 46 91
subtype3 38 99
subtype4 19 45

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.142 (Fisher's exact test), Q value = 0.21

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 349 89
subtype1 97 29
subtype2 96 28
subtype3 113 18
subtype4 43 14

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.391 (Kruskal-Wallis (anova)), Q value = 0.48

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 48 69.2 (16.2)
subtype1 14 65.7 (19.5)
subtype2 12 69.2 (17.8)
subtype3 15 68.0 (13.2)
subtype4 7 78.6 (10.7)

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.178 (Kruskal-Wallis (anova)), Q value = 0.25

Table S37.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 66 36.4 (20.6)
subtype1 19 33.3 (16.1)
subtype2 17 30.6 (18.3)
subtype3 22 46.4 (25.6)
subtype4 8 28.4 (7.7)

Figure S34.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.244 (Fisher's exact test), Q value = 0.33

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 109 7 1 305
subtype1 35 3 0 82
subtype2 29 2 1 89
subtype3 36 0 0 93
subtype4 9 2 0 41

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0807 (Fisher's exact test), Q value = 0.15

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 323
subtype1 1 92
subtype2 1 91
subtype3 0 105
subtype4 2 35

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
Number of samples 273 111 99
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0642 (logrank test), Q value = 0.12

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

nPatients nDeath Duration Range (Median), Month
ALL 457 183 0.1 - 122.3 (13.5)
subtype1 254 103 0.1 - 77.3 (13.8)
subtype2 107 37 0.5 - 122.3 (15.5)
subtype3 96 43 0.3 - 52.6 (12.8)

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.594 (Kruskal-Wallis (anova)), Q value = 0.66

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 475 64.9 (11.2)
subtype1 265 65.2 (11.4)
subtype2 111 65.0 (11.2)
subtype3 99 63.9 (10.6)

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.336 (Fisher's exact test), Q value = 0.43

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 14 36 30 74 67 19 79 58 34 36 1
subtype1 5 10 20 15 42 33 13 49 31 20 20 1
subtype2 3 2 14 9 17 18 2 16 13 6 4 0
subtype3 0 2 2 6 15 16 4 14 14 8 12 0

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0127 (Fisher's exact test), Q value = 0.032

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

nPatients T0+T1 T2 T3 T4
ALL 32 111 225 103
subtype1 22 63 128 55
subtype2 6 36 45 20
subtype3 4 12 52 28

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 = 0.418 (Fisher's exact test), Q value = 0.5

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 171 145 74 74
subtype1 96 89 42 37
subtype2 44 25 18 17
subtype3 31 31 14 20

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 = 0.253 (Fisher's exact test), Q value = 0.34

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 427 25
subtype1 237 14
subtype2 102 3
subtype3 88 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 = 0.54 (Fisher's exact test), Q value = 0.62

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 139 344
subtype1 75 198
subtype2 31 80
subtype3 33 66

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0563 (Fisher's exact test), Q value = 0.11

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 349 89
subtype1 183 58
subtype2 83 19
subtype3 83 12

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.737 (Kruskal-Wallis (anova)), Q value = 0.79

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 48 69.2 (16.2)
subtype1 33 68.8 (16.0)
subtype2 7 72.9 (19.8)
subtype3 8 67.5 (15.8)

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.873 (Kruskal-Wallis (anova)), Q value = 0.88

Table S50.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 66 36.4 (20.6)
subtype1 39 35.2 (19.6)
subtype2 15 36.1 (22.1)
subtype3 12 40.5 (23.4)

Figure S46.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.0298 (Fisher's exact test), Q value = 0.068

Table S51.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 109 7 1 305
subtype1 71 4 0 157
subtype2 17 3 1 76
subtype3 21 0 0 72

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.66 (Fisher's exact test), Q value = 0.71

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 323
subtype1 3 173
subtype2 1 71
subtype3 0 79

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 170 275 154
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.524 (logrank test), Q value = 0.61

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

nPatients nDeath Duration Range (Median), Month
ALL 573 235 0.0 - 122.3 (14.0)
subtype1 168 64 0.1 - 105.1 (12.8)
subtype2 261 106 0.0 - 122.3 (15.9)
subtype3 144 65 0.1 - 116.4 (14.1)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 9.5e-07 (Kruskal-Wallis (anova)), Q value = 2.3e-05

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

nPatients Mean (Std.Dev)
ALL 590 64.7 (11.2)
subtype1 170 61.9 (11.7)
subtype2 267 67.2 (10.8)
subtype3 153 63.4 (10.2)

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

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 10 21 45 30 84 87 29 88 66 44 46 4
subtype1 2 8 13 5 45 24 15 18 10 3 9 2
subtype2 8 13 24 15 20 36 12 46 31 23 25 2
subtype3 0 0 8 10 19 27 2 24 25 18 12 0

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 54 130 269 120
subtype1 19 51 77 13
subtype2 34 54 115 59
subtype3 1 25 77 48

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 = 1e-05 (Fisher's exact test), Q value = 5.5e-05

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

nPatients N0 N1 N2 N3
ALL 199 181 92 90
subtype1 77 57 17 7
subtype2 74 86 51 48
subtype3 48 38 24 35

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 = 0.882 (Fisher's exact test), Q value = 0.88

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

nPatients 0 1
ALL 502 36
subtype1 138 9
subtype2 226 18
subtype3 138 9

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 = 0.00082 (Fisher's exact test), Q value = 0.0031

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

nPatients FEMALE MALE
ALL 173 426
subtype1 33 137
subtype2 81 194
subtype3 59 95

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.107 (Fisher's exact test), Q value = 0.18

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

nPatients NO YES
ALL 426 115
subtype1 118 39
subtype2 187 54
subtype3 121 22

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0842 (Kruskal-Wallis (anova)), Q value = 0.15

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

nPatients Mean (Std.Dev)
ALL 67 73.6 (16.2)
subtype1 61 72.5 (16.2)
subtype2 5 84.0 (13.4)
subtype3 1 90.0 (NA)

Figure S57.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.601 (Kruskal-Wallis (anova)), Q value = 0.66

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 97 34.7 (21.5)
subtype1 74 33.2 (19.8)
subtype2 17 39.5 (26.5)
subtype3 6 39.8 (27.2)

Figure S58.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.00036 (Fisher's exact test), Q value = 0.0015

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 133 17 1 373
subtype1 53 7 0 89
subtype2 50 10 1 167
subtype3 30 0 0 117

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.161 (Fisher's exact test), Q value = 0.23

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 390
subtype1 5 105
subtype2 4 157
subtype3 1 128

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 5
Number of samples 132 116 99 121 131
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0305 (logrank test), Q value = 0.068

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

nPatients nDeath Duration Range (Median), Month
ALL 573 235 0.0 - 122.3 (14.0)
subtype1 123 61 0.6 - 74.5 (13.0)
subtype2 112 41 0.0 - 122.3 (18.2)
subtype3 90 30 1.0 - 66.8 (15.8)
subtype4 121 42 0.1 - 68.0 (12.8)
subtype5 127 61 0.1 - 116.4 (14.1)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.71e-08 (Kruskal-Wallis (anova)), Q value = 7.3e-07

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

nPatients Mean (Std.Dev)
ALL 590 64.7 (11.2)
subtype1 129 66.8 (10.5)
subtype2 113 65.9 (10.4)
subtype3 98 68.9 (9.6)
subtype4 121 60.4 (11.2)
subtype5 129 62.4 (11.9)

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 10 21 45 30 84 87 29 88 66 44 46 4
subtype1 0 2 12 7 14 18 9 22 12 10 11 1
subtype2 6 5 6 9 6 18 4 16 14 10 11 2
subtype3 2 7 13 3 12 16 3 13 11 8 6 0
subtype4 2 6 6 2 40 18 11 11 8 2 5 1
subtype5 0 1 8 9 12 17 2 26 21 14 13 0

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 54 130 269 120
subtype1 4 31 67 23
subtype2 22 23 42 25
subtype3 11 22 38 24
subtype4 16 35 59 4
subtype5 1 19 63 44

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 = 1e-05 (Fisher's exact test), Q value = 5.5e-05

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

nPatients N0 N1 N2 N3
ALL 199 181 92 90
subtype1 35 46 18 22
subtype2 30 33 29 19
subtype3 42 19 19 14
subtype4 60 39 10 4
subtype5 32 44 16 31

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 = 0.569 (Fisher's exact test), Q value = 0.64

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

nPatients 0 1
ALL 502 36
subtype1 106 8
subtype2 96 9
subtype3 89 3
subtype4 97 6
subtype5 114 10

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 = 0.0113 (Fisher's exact test), Q value = 0.029

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

nPatients FEMALE MALE
ALL 173 426
subtype1 37 95
subtype2 35 81
subtype3 37 62
subtype4 21 100
subtype5 43 88

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.117 (Fisher's exact test), Q value = 0.19

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

nPatients NO YES
ALL 426 115
subtype1 94 22
subtype2 76 26
subtype3 69 16
subtype4 82 32
subtype5 105 19

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

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.102 (Kruskal-Wallis (anova)), Q value = 0.17

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

nPatients Mean (Std.Dev)
ALL 67 73.6 (16.2)
subtype1 4 75.0 (17.3)
subtype2 3 90.0 (0.0)
subtype3 2 75.0 (21.2)
subtype4 57 72.3 (16.3)
subtype5 1 90.0 (NA)

Figure S69.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.305 (Kruskal-Wallis (anova)), Q value = 0.39

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 97 34.7 (21.5)
subtype1 17 36.9 (28.1)
subtype2 6 38.0 (21.2)
subtype3 6 29.1 (22.7)
subtype4 65 33.2 (19.3)
subtype5 3 59.3 (19.0)

Figure S70.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.00457 (Fisher's exact test), Q value = 0.013

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 133 17 1 373
subtype1 21 5 0 85
subtype2 19 4 0 69
subtype3 19 3 1 61
subtype4 43 5 0 65
subtype5 31 0 0 93

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.102 (Fisher's exact test), Q value = 0.17

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 390
subtype1 5 75
subtype2 0 64
subtype3 1 64
subtype4 3 80
subtype5 1 107

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 167 281 172
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.59 (logrank test), Q value = 0.66

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

nPatients nDeath Duration Range (Median), Month
ALL 594 244 0.0 - 122.3 (13.9)
subtype1 165 67 0.1 - 68.0 (12.8)
subtype2 262 110 0.0 - 122.3 (16.1)
subtype3 167 67 0.1 - 116.4 (13.2)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 1.82e-08 (Kruskal-Wallis (anova)), Q value = 7.3e-07

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

nPatients Mean (Std.Dev)
ALL 611 64.8 (11.2)
subtype1 166 60.6 (10.7)
subtype2 273 67.2 (10.4)
subtype3 172 64.9 (11.8)

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

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 3e-04

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 10 21 46 33 87 86 29 94 70 46 48 4
subtype1 1 5 10 4 42 24 14 18 15 6 11 3
subtype2 8 13 25 17 21 36 11 48 33 24 21 1
subtype3 1 3 11 12 24 26 4 28 22 16 16 0

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00057 (Fisher's exact test), Q value = 0.0022

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 55 135 280 123
subtype1 13 44 82 19
subtype2 34 61 117 56
subtype3 8 30 81 48

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 = 0.00343 (Fisher's exact test), Q value = 0.011

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 205 186 97 95
subtype1 67 57 15 15
subtype2 83 81 54 46
subtype3 55 48 28 34

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 = 0.773 (Fisher's exact test), Q value = 0.81

Table S85.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 519 39
subtype1 134 12
subtype2 235 16
subtype3 150 11

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00171 (Fisher's exact test), Q value = 0.0055

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

nPatients FEMALE MALE
ALL 182 438
subtype1 32 135
subtype2 89 192
subtype3 61 111

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.286 (Fisher's exact test), Q value = 0.37

Table S87.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 443 116
subtype1 117 38
subtype2 192 50
subtype3 134 28

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.139 (Kruskal-Wallis (anova)), Q value = 0.21

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

nPatients Mean (Std.Dev)
ALL 67 74.0 (16.2)
subtype1 55 74.2 (15.7)
subtype2 5 84.0 (13.4)
subtype3 7 65.7 (19.9)

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

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00463 (Kruskal-Wallis (anova)), Q value = 0.013

Table S89.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 97 34.7 (21.5)
subtype1 59 29.0 (16.5)
subtype2 20 36.7 (21.9)
subtype3 18 51.1 (27.5)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 133 18 387
subtype1 60 7 82
subtype2 37 9 184
subtype3 36 2 121

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0327 (Fisher's exact test), Q value = 0.071

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 404
subtype1 7 106
subtype2 3 162
subtype3 1 136

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 128 144 165 94 89
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.269 (logrank test), Q value = 0.35

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

nPatients nDeath Duration Range (Median), Month
ALL 594 244 0.0 - 122.3 (13.9)
subtype1 121 57 0.2 - 79.1 (13.2)
subtype2 136 56 0.4 - 122.1 (14.4)
subtype3 155 59 0.0 - 122.3 (16.3)
subtype4 94 29 0.1 - 68.0 (12.8)
subtype5 88 43 0.1 - 116.4 (13.9)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.55e-09 (Kruskal-Wallis (anova)), Q value = 1.9e-07

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

nPatients Mean (Std.Dev)
ALL 611 64.8 (11.2)
subtype1 125 66.6 (10.0)
subtype2 141 67.5 (10.8)
subtype3 162 65.6 (11.2)
subtype4 94 58.7 (10.4)
subtype5 89 62.9 (12.0)

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 10 21 46 33 87 86 29 94 70 46 48 4
subtype1 3 1 9 11 7 19 5 25 11 12 14 3
subtype2 6 9 17 6 10 19 11 15 16 9 8 0
subtype3 1 8 9 10 21 24 3 28 22 12 14 0
subtype4 0 3 4 1 39 15 10 9 5 2 3 1
subtype5 0 0 7 5 10 9 0 17 16 11 9 0

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 55 135 280 123
subtype1 7 30 57 29
subtype2 26 26 55 24
subtype3 14 36 78 33
subtype4 8 31 48 5
subtype5 0 12 42 32

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 = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S97.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 205 186 97 95
subtype1 29 43 25 22
subtype2 50 40 21 17
subtype3 47 52 35 27
subtype4 53 29 6 3
subtype5 26 22 10 26

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 = 0.357 (Fisher's exact test), Q value = 0.45

Table S98.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 519 39
subtype1 101 12
subtype2 114 5
subtype3 145 12
subtype4 82 4
subtype5 77 6

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0139 (Fisher's exact test), Q value = 0.033

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

nPatients FEMALE MALE
ALL 182 438
subtype1 37 91
subtype2 40 104
subtype3 54 111
subtype4 16 78
subtype5 35 54

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.00147 (Fisher's exact test), Q value = 0.0049

Table S100.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 443 116
subtype1 93 21
subtype2 99 28
subtype3 118 25
subtype4 56 32
subtype5 77 10

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

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0384 (Kruskal-Wallis (anova)), Q value = 0.082

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

nPatients Mean (Std.Dev)
ALL 67 74.0 (16.2)
subtype2 6 85.0 (12.2)
subtype3 1 90.0 (NA)
subtype4 59 72.4 (16.2)
subtype5 1 90.0 (NA)

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0406 (Kruskal-Wallis (anova)), Q value = 0.085

Table S102.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 97 34.7 (21.5)
subtype1 9 46.3 (17.3)
subtype2 21 32.1 (21.9)
subtype3 12 45.6 (27.9)
subtype4 54 30.5 (18.6)
subtype5 1 80.0 (NA)

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 133 18 387
subtype1 22 4 77
subtype2 25 5 91
subtype3 19 2 116
subtype4 45 5 41
subtype5 22 2 62

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.74 (Fisher's exact test), Q value = 0.79

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 404
subtype1 3 77
subtype2 2 78
subtype3 2 106
subtype4 3 65
subtype5 1 78

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 5
Number of samples 124 156 121 47 74
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.146 (logrank test), Q value = 0.22

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

nPatients nDeath Duration Range (Median), Month
ALL 512 213 0.1 - 122.3 (13.7)
subtype1 122 55 0.1 - 63.6 (12.7)
subtype2 155 61 0.4 - 122.3 (18.2)
subtype3 120 48 0.1 - 83.2 (12.9)
subtype4 45 20 0.3 - 68.0 (13.8)
subtype5 70 29 0.3 - 105.1 (14.6)

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 'YEARS_TO_BIRTH'

P value = 1.24e-05 (Kruskal-Wallis (anova)), Q value = 6.5e-05

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

nPatients Mean (Std.Dev)
ALL 514 64.3 (11.3)
subtype1 123 60.0 (11.0)
subtype2 152 66.6 (10.9)
subtype3 121 64.3 (11.4)
subtype4 46 64.2 (10.3)
subtype5 72 66.8 (11.1)

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 9 14 35 30 81 77 27 81 61 39 35 4
subtype1 0 3 7 1 34 14 14 17 11 5 6 3
subtype2 8 7 11 7 15 26 5 20 24 12 8 1
subtype3 0 3 5 7 18 22 4 21 15 15 8 0
subtype4 0 0 3 1 7 6 4 8 7 3 4 0
subtype5 1 1 9 14 7 9 0 15 4 4 9 0

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S109.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 46 114 248 98
subtype1 10 31 65 11
subtype2 28 24 72 26
subtype3 6 21 62 32
subtype4 0 9 21 14
subtype5 2 29 28 15

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 = 0.0489 (Fisher's exact test), Q value = 0.098

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

nPatients N0 N1 N2 N3
ALL 176 156 86 79
subtype1 45 47 13 10
subtype2 50 41 36 21
subtype3 39 34 19 25
subtype4 13 13 6 11
subtype5 29 21 12 12

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 = 0.783 (Fisher's exact test), Q value = 0.81

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

nPatients 0 1
ALL 438 31
subtype1 100 8
subtype2 127 6
subtype3 106 8
subtype4 38 3
subtype5 67 6

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 = 0.0234 (Fisher's exact test), Q value = 0.054

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

nPatients FEMALE MALE
ALL 143 379
subtype1 22 102
subtype2 43 113
subtype3 41 80
subtype4 11 36
subtype5 26 48

Figure S103.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00911 (Fisher's exact test), Q value = 0.024

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

nPatients NO YES
ALL 386 104
subtype1 84 31
subtype2 109 37
subtype3 99 20
subtype4 35 11
subtype5 59 5

Figure S104.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.135 (Kruskal-Wallis (anova)), Q value = 0.21

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

nPatients Mean (Std.Dev)
ALL 65 74.2 (16.4)
subtype1 45 72.7 (16.2)
subtype2 5 84.0 (13.4)
subtype3 9 70.0 (19.4)
subtype4 6 83.3 (12.1)

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0203 (Kruskal-Wallis (anova)), Q value = 0.048

Table S115.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 94 35.1 (21.6)
subtype1 51 27.9 (15.8)
subtype2 20 40.7 (20.3)
subtype3 18 46.1 (28.3)
subtype4 5 45.6 (29.9)

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.00011 (Fisher's exact test), Q value = 0.00051

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 111 16 338
subtype1 44 5 59
subtype2 23 8 109
subtype3 28 2 89
subtype4 10 0 36
subtype5 6 1 45

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 = 0.601 (Fisher's exact test), Q value = 0.66

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 334
subtype1 4 75
subtype2 3 79
subtype3 3 97
subtype4 1 31
subtype5 0 52

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
Number of samples 52 125 144 109 92
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.235 (logrank test), Q value = 0.32

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

nPatients nDeath Duration Range (Median), Month
ALL 512 213 0.1 - 122.3 (13.7)
subtype1 50 24 0.6 - 63.6 (12.5)
subtype2 123 57 0.4 - 122.3 (16.5)
subtype3 141 52 0.3 - 115.7 (15.7)
subtype4 106 50 0.1 - 83.2 (13.5)
subtype5 92 30 0.1 - 68.0 (12.7)

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 'YEARS_TO_BIRTH'

P value = 6.51e-08 (Kruskal-Wallis (anova)), Q value = 2e-06

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

nPatients Mean (Std.Dev)
ALL 514 64.3 (11.3)
subtype1 51 64.5 (11.0)
subtype2 122 66.1 (10.7)
subtype3 141 66.9 (11.3)
subtype4 108 63.5 (11.2)
subtype5 92 58.7 (10.4)

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S121.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 9 14 35 30 81 77 27 81 61 39 35 4
subtype1 0 0 6 1 4 7 5 11 7 3 3 0
subtype2 8 6 12 5 12 20 7 12 14 9 6 3
subtype3 1 5 7 13 13 21 4 27 20 13 12 0
subtype4 0 0 7 10 12 15 2 22 15 12 11 0
subtype5 0 3 3 1 40 14 9 9 5 2 3 1

Figure S111.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

Table S122.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 46 114 248 98
subtype1 3 12 23 11
subtype2 24 24 48 19
subtype3 10 27 75 31
subtype4 1 20 56 32
subtype5 8 31 46 5

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 = 1e-05 (Fisher's exact test), Q value = 5.5e-05

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

nPatients N0 N1 N2 N3
ALL 176 156 86 79
subtype1 10 19 10 8
subtype2 43 38 22 11
subtype3 42 39 27 32
subtype4 29 32 21 25
subtype5 52 28 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 = 0.81 (Fisher's exact test), Q value = 0.83

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

nPatients 0 1
ALL 438 31
subtype1 41 2
subtype2 94 8
subtype3 127 8
subtype4 95 9
subtype5 81 4

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 = 0.0115 (Fisher's exact test), Q value = 0.029

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

nPatients FEMALE MALE
ALL 143 379
subtype1 12 40
subtype2 35 90
subtype3 39 105
subtype4 42 67
subtype5 15 77

Figure S115.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0136 (Fisher's exact test), Q value = 0.033

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

nPatients NO YES
ALL 386 104
subtype1 42 6
subtype2 90 25
subtype3 111 25
subtype4 87 18
subtype5 56 30

Figure S116.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0955 (Kruskal-Wallis (anova)), Q value = 0.17

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

nPatients Mean (Std.Dev)
ALL 65 74.2 (16.4)
subtype1 1 90.0 (NA)
subtype2 5 84.0 (13.4)
subtype3 1 90.0 (NA)
subtype5 58 72.8 (16.4)

Figure S117.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.133 (Kruskal-Wallis (anova)), Q value = 0.21

Table S128.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 94 35.1 (21.6)
subtype1 5 38.6 (19.9)
subtype2 22 36.5 (19.9)
subtype3 14 48.6 (29.8)
subtype4 1 40.0 (NA)
subtype5 52 30.4 (18.9)

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5.5e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 111 16 338
subtype1 8 2 32
subtype2 18 5 81
subtype3 14 5 105
subtype4 27 0 79
subtype5 44 4 41

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 = 0.13 (Fisher's exact test), Q value = 0.21

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 334
subtype1 3 26
subtype2 2 56
subtype3 2 91
subtype4 1 98
subtype5 3 63

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

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/STES-TP/22555833/STES-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/STES-TP/22507048/STES-TP.merged_data.txt

  • Number of patients = 628

  • 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)