Correlation between aggregated molecular cancer subtypes and selected clinical features
Lung Squamous Cell 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/C17H1J1V
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 15 clinical features across 504 patients, 19 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGIC_STAGE' and 'PATHOLOGY_T_STAGE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE' and 'PATHOLOGY_T_STAGE'.

  • 6 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL_TYPE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'GENDER'.

  • Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 15 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
mRNA
CNMF
subtypes
mRNA
cHierClus
subtypes
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 0.772
(0.933)
0.812
(0.933)
0.0484
(0.292)
0.588
(0.876)
0.12
(0.388)
0.628
(0.887)
0.109
(0.388)
0.078
(0.319)
0.527
(0.804)
0.798
(0.933)
0.162
(0.471)
0.899
(0.959)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.927
(0.976)
0.645
(0.887)
0.000212
(0.00763)
0.0316
(0.263)
0.000164
(0.00763)
0.357
(0.66)
0.0101
(0.13)
0.000564
(0.0169)
7.77e-05
(0.00763)
0.00641
(0.0961)
0.0709
(0.31)
0.225
(0.562)
PATHOLOGIC STAGE Fisher's exact test 0.00073
(0.0188)
0.00384
(0.0691)
0.736
(0.931)
0.866
(0.939)
0.00852
(0.118)
0.65
(0.887)
0.249
(0.57)
0.066
(0.31)
0.121
(0.388)
0.733
(0.931)
0.225
(0.562)
0.447
(0.745)
PATHOLOGY T STAGE Fisher's exact test 0.00088
(0.0198)
9e-05
(0.00763)
0.249
(0.57)
0.666
(0.89)
0.00597
(0.0961)
0.828
(0.933)
0.52
(0.804)
0.104
(0.388)
0.0995
(0.381)
0.514
(0.804)
0.0503
(0.292)
0.115
(0.388)
PATHOLOGY N STAGE Fisher's exact test 0.851
(0.935)
0.361
(0.66)
0.0316
(0.263)
0.833
(0.933)
0.268
(0.57)
0.871
(0.939)
0.273
(0.57)
0.902
(0.959)
0.695
(0.907)
0.815
(0.933)
0.506
(0.804)
0.471
(0.77)
PATHOLOGY M STAGE Fisher's exact test 0.112
(0.388)
0.072
(0.31)
0.117
(0.388)
0.0387
(0.288)
0.268
(0.57)
0.489
(0.792)
0.0951
(0.372)
0.0722
(0.31)
0.835
(0.933)
0.355
(0.66)
0.596
(0.879)
0.502
(0.804)
GENDER Fisher's exact test 0.276
(0.57)
0.0572
(0.304)
0.0439
(0.288)
0.401
(0.697)
0.00018
(0.00763)
0.0723
(0.31)
0.668
(0.89)
0.0379
(0.288)
0.988
(1.00)
0.821
(0.933)
0.161
(0.471)
0.855
(0.935)
RADIATION THERAPY Fisher's exact test 1
(1.00)
1
(1.00)
0.37
(0.666)
0.139
(0.435)
0.429
(0.723)
0.789
(0.933)
0.984
(1.00)
0.14
(0.435)
0.338
(0.645)
0.857
(0.935)
0.526
(0.804)
0.327
(0.633)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.977
(1.00)
0.238
(0.57)
0.193
(0.511)
0.565
(0.855)
0.189
(0.507)
0.149
(0.455)
0.643
(0.887)
0.247
(0.57)
0.0321
(0.263)
0.0849
(0.339)
0.395
(0.697)
0.05
(0.292)
HISTOLOGICAL TYPE Fisher's exact test 0.324
(0.633)
0.321
(0.633)
0.16
(0.471)
0.0229
(0.242)
0.0574
(0.304)
0.778
(0.933)
0.316
(0.632)
0.0404
(0.288)
0.303
(0.614)
0.0693
(0.31)
0.0448
(0.288)
0.107
(0.388)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.265
(0.57)
0.999
(1.00)
0.275
(0.57)
0.745
(0.931)
0.589
(0.876)
0.301
(0.614)
0.266
(0.57)
0.782
(0.933)
0.812
(0.933)
0.678
(0.898)
0.733
(0.931)
0.47
(0.77)
YEAR OF TOBACCO SMOKING ONSET Kruskal-Wallis (anova) 0.906
(0.959)
0.516
(0.804)
0.0109
(0.131)
0.768
(0.933)
0.246
(0.57)
0.389
(0.694)
0.168
(0.479)
0.00108
(0.0215)
0.43
(0.723)
0.631
(0.887)
0.0163
(0.183)
0.0248
(0.248)
RESIDUAL TUMOR Fisher's exact test 0.363
(0.66)
0.819
(0.933)
0.688
(0.904)
0.268
(0.57)
0.625
(0.887)
0.176
(0.485)
0.744
(0.931)
0.627
(0.887)
0.34
(0.645)
0.218
(0.561)
0.993
(1.00)
0.726
(0.931)
RACE Fisher's exact test 0.994
(1.00)
0.974
(1.00)
0.075
(0.314)
0.216
(0.561)
0.833
(0.933)
0.17
(0.479)
0.821
(0.933)
0.647
(0.887)
0.403
(0.697)
0.0262
(0.248)
0.428
(0.723)
0.0447
(0.288)
ETHNICITY Fisher's exact test 0.245
(0.57)
0.0682
(0.31)
0.619
(0.887)
0.763
(0.933)
0.0522
(0.293)
0.178
(0.485)
0.113
(0.388)
0.665
(0.89)
0.615
(0.887)
0.857
(0.935)
0.259
(0.57)
0.0711
(0.31)
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 41 50 34 29
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.772 (logrank test), Q value = 0.93

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

nPatients nDeath Duration Range (Median), Month
ALL 152 72 0.4 - 173.8 (23.0)
subtype1 41 17 0.4 - 122.4 (23.0)
subtype2 49 24 0.4 - 129.0 (32.0)
subtype3 34 18 0.4 - 173.8 (14.0)
subtype4 28 13 0.4 - 133.7 (18.0)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.927 (Kruskal-Wallis (anova)), Q value = 0.98

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

nPatients Mean (Std.Dev)
ALL 152 66.5 (8.6)
subtype1 41 66.2 (7.6)
subtype2 49 66.8 (8.2)
subtype3 34 66.6 (9.9)
subtype4 28 66.4 (9.3)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00073 (Fisher's exact test), Q value = 0.019

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

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 23 60 7 27 19 13 4
subtype1 2 21 0 8 7 1 2
subtype2 2 23 4 9 5 7 0
subtype3 8 12 2 4 4 3 0
subtype4 11 4 1 6 3 2 2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00088 (Fisher's exact test), Q value = 0.02

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

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 4 30 6 1
subtype2 4 39 1 6
subtype3 11 19 2 2
subtype4 11 12 3 3

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.851 (Fisher's exact test), Q value = 0.93

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

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 26 10 4 1
subtype2 27 17 4 2
subtype3 23 6 3 2
subtype4 20 7 2 0

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.112 (Fisher's exact test), Q value = 0.39

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

nPatients 0 1
ALL 146 4
subtype1 37 2
subtype2 48 0
subtype3 34 0
subtype4 27 2

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.276 (Fisher's exact test), Q value = 0.57

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

nPatients FEMALE MALE
ALL 44 110
subtype1 9 32
subtype2 12 38
subtype3 11 23
subtype4 12 17

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 101 13
subtype1 27 4
subtype2 34 4
subtype3 20 3
subtype4 20 2

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

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.977 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 46 40.0 (41.8)
subtype1 8 38.8 (42.9)
subtype2 13 36.9 (42.3)
subtype3 11 40.9 (47.4)
subtype4 14 42.9 (40.8)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.324 (Fisher's exact test), Q value = 0.63

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 5 1 148
subtype1 0 0 41
subtype2 3 0 47
subtype3 1 0 33
subtype4 1 1 27

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.265 (Kruskal-Wallis (anova)), Q value = 0.57

Table S12.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 133 54.8 (36.5)
subtype1 37 59.8 (42.4)
subtype2 45 53.1 (25.4)
subtype3 28 46.9 (35.2)
subtype4 23 59.8 (45.6)

Figure S11.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'mRNA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.906 (Kruskal-Wallis (anova)), Q value = 0.96

Table S13.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 97 1958.0 (10.6)
subtype1 28 1957.1 (8.8)
subtype2 25 1958.5 (11.0)
subtype3 23 1957.5 (10.9)
subtype4 21 1959.2 (12.4)

Figure S12.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.363 (Fisher's exact test), Q value = 0.66

Table S14.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 139 3 2 5
subtype1 37 0 1 1
subtype2 45 1 1 1
subtype3 28 2 0 3
subtype4 29 0 0 0

Figure S13.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.994 (Fisher's exact test), Q value = 1

Table S15.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 7 93
subtype1 1 2 24
subtype2 1 3 27
subtype3 1 1 24
subtype4 0 1 18

Figure S14.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.245 (Fisher's exact test), Q value = 0.57

Table S16.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 89
subtype1 3 23
subtype2 1 25
subtype3 0 23
subtype4 0 18

Figure S15.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S17.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 47 56 51
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.812 (logrank test), Q value = 0.93

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 152 72 0.4 - 173.8 (23.0)
subtype1 46 23 0.4 - 122.4 (21.5)
subtype2 55 26 0.4 - 129.0 (29.9)
subtype3 51 23 0.4 - 173.8 (21.1)

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.645 (Kruskal-Wallis (anova)), Q value = 0.89

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

nPatients Mean (Std.Dev)
ALL 152 66.5 (8.6)
subtype1 46 66.0 (8.7)
subtype2 55 66.2 (8.0)
subtype3 51 67.4 (9.1)

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

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00384 (Fisher's exact test), Q value = 0.069

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 23 60 7 27 19 13 4
subtype1 4 20 2 9 7 2 3
subtype2 2 25 4 10 7 8 0
subtype3 17 15 1 8 5 3 1

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 9e-05 (Fisher's exact test), Q value = 0.0076

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 6 32 7 2
subtype2 4 44 1 7
subtype3 20 24 4 3

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.361 (Fisher's exact test), Q value = 0.66

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 28 14 4 1
subtype2 30 18 5 3
subtype3 38 8 4 1

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.072 (Fisher's exact test), Q value = 0.31

Table S23.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 146 4
subtype1 42 3
subtype2 54 0
subtype3 50 1

Figure S21.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0572 (Fisher's exact test), Q value = 0.3

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 44 110
subtype1 10 37
subtype2 13 43
subtype3 21 30

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

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

Table S25.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 101 13
subtype1 34 4
subtype2 36 5
subtype3 31 4

Figure S23.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.238 (Kruskal-Wallis (anova)), Q value = 0.57

Table S26.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 46 40.0 (41.8)
subtype1 15 30.7 (40.1)
subtype2 15 35.3 (40.5)
subtype3 16 53.1 (43.9)

Figure S24.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.321 (Fisher's exact test), Q value = 0.63

Table S27.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 5 1 148
subtype1 0 1 46
subtype2 3 0 53
subtype3 2 0 49

Figure S25.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.999 (Kruskal-Wallis (anova)), Q value = 1

Table S28.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 133 54.8 (36.5)
subtype1 41 57.5 (41.9)
subtype2 51 51.1 (24.6)
subtype3 41 56.8 (43.0)

Figure S26.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'mRNA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.516 (Kruskal-Wallis (anova)), Q value = 0.8

Table S29.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 97 1958.0 (10.6)
subtype1 32 1956.4 (8.9)
subtype2 29 1959.5 (10.7)
subtype3 36 1958.3 (11.9)

Figure S27.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.819 (Fisher's exact test), Q value = 0.93

Table S30.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 139 3 2 5
subtype1 42 0 1 2
subtype2 51 1 1 1
subtype3 46 2 0 2

Figure S28.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.974 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 7 93
subtype1 1 2 27
subtype2 1 3 31
subtype3 1 2 35

Figure S29.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0682 (Fisher's exact test), Q value = 0.31

Table S32.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 89
subtype1 3 25
subtype2 1 29
subtype3 0 35

Figure S30.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

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

Table S33.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 43 115 29 87 169 58
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0484 (logrank test), Q value = 0.29

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 494 214 0.0 - 173.8 (21.8)
subtype1 42 17 0.0 - 119.5 (23.4)
subtype2 114 58 0.1 - 173.8 (19.6)
subtype3 29 19 0.8 - 123.2 (20.0)
subtype4 86 30 0.2 - 104.1 (18.2)
subtype5 166 70 0.2 - 154.3 (29.4)
subtype6 57 20 0.1 - 156.7 (22.4)

Figure S31.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000212 (Kruskal-Wallis (anova)), Q value = 0.0076

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 491 67.3 (8.6)
subtype1 42 65.3 (7.9)
subtype2 113 68.2 (9.8)
subtype3 29 71.8 (9.3)
subtype4 86 67.0 (7.6)
subtype5 165 65.8 (8.0)
subtype6 56 69.5 (8.1)

Figure S32.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.736 (Fisher's exact test), Q value = 0.93

Table S36.  Clustering Approach #3: '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 IV
ALL 2 89 152 2 65 95 3 63 19 7
subtype1 0 6 13 0 2 10 1 9 1 1
subtype2 1 25 37 0 12 24 1 12 1 0
subtype3 0 6 9 0 4 3 0 4 1 2
subtype4 0 15 28 1 10 16 0 11 5 1
subtype5 0 26 48 1 29 33 1 19 9 2
subtype6 1 11 17 0 8 9 0 8 2 1

Figure S33.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.249 (Fisher's exact test), Q value = 0.57

Table S37.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 112 294 71 24
subtype1 7 24 11 1
subtype2 30 61 20 4
subtype3 6 16 4 3
subtype4 19 51 14 3
subtype5 37 103 20 9
subtype6 13 39 2 4

Figure S34.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0316 (Fisher's exact test), Q value = 0.26

Table S38.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 317 133 40 5
subtype1 26 8 7 0
subtype2 79 28 5 0
subtype3 22 4 3 0
subtype4 59 18 7 3
subtype5 93 61 12 2
subtype6 38 14 6 0

Figure S35.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.117 (Fisher's exact test), Q value = 0.39

Table S39.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 411 7
subtype1 34 1
subtype2 92 0
subtype3 24 2
subtype4 68 1
subtype5 145 2
subtype6 48 1

Figure S36.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0439 (Fisher's exact test), Q value = 0.29

Table S40.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 130 371
subtype1 11 32
subtype2 43 72
subtype3 4 25
subtype4 22 65
subtype5 38 131
subtype6 12 46

Figure S37.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.37 (Fisher's exact test), Q value = 0.67

Table S41.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 384 53
subtype1 29 8
subtype2 94 8
subtype3 23 3
subtype4 64 8
subtype5 131 21
subtype6 43 5

Figure S38.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.51

Table S42.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 166 60.3 (41.1)
subtype1 15 60.7 (45.1)
subtype2 31 57.4 (43.0)
subtype3 11 35.5 (42.0)
subtype4 29 67.9 (37.9)
subtype5 62 64.4 (39.8)
subtype6 18 53.9 (41.5)

Figure S39.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.16 (Fisher's exact test), Q value = 0.47

Table S43.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 15 6 1 479
subtype1 1 0 0 42
subtype2 0 4 0 111
subtype3 1 0 0 28
subtype4 2 1 1 83
subtype5 8 1 0 160
subtype6 3 0 0 55

Figure S40.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.275 (Kruskal-Wallis (anova)), Q value = 0.57

Table S44.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 425 52.9 (31.2)
subtype1 39 58.4 (39.3)
subtype2 93 49.9 (32.5)
subtype3 23 45.8 (25.8)
subtype4 76 55.8 (31.0)
subtype5 141 54.0 (31.4)
subtype6 53 49.9 (23.1)

Figure S41.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0109 (Kruskal-Wallis (anova)), Q value = 0.13

Table S45.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 320 1960.4 (11.6)
subtype1 31 1960.6 (9.3)
subtype2 74 1958.1 (12.8)
subtype3 15 1954.7 (12.0)
subtype4 59 1962.9 (11.1)
subtype5 99 1962.3 (11.2)
subtype6 42 1958.6 (10.9)

Figure S42.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.688 (Fisher's exact test), Q value = 0.9

Table S46.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 399 12 4 23
subtype1 36 1 1 1
subtype2 88 3 0 8
subtype3 24 1 0 1
subtype4 66 0 2 4
subtype5 139 5 1 8
subtype6 46 2 0 1

Figure S43.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.075 (Fisher's exact test), Q value = 0.31

Table S47.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 31 349
subtype1 0 5 29
subtype2 6 4 84
subtype3 1 3 19
subtype4 0 8 57
subtype5 2 8 121
subtype6 0 3 39

Figure S44.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.619 (Fisher's exact test), Q value = 0.89

Table S48.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 317
subtype1 1 23
subtype2 3 83
subtype3 1 17
subtype4 1 57
subtype5 2 103
subtype6 0 34

Figure S45.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S49.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 90 96 58 126
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.588 (logrank test), Q value = 0.88

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 363 155 0.0 - 173.8 (22.1)
subtype1 87 35 0.0 - 156.7 (20.0)
subtype2 93 39 0.2 - 154.3 (28.5)
subtype3 58 25 0.1 - 111.0 (24.7)
subtype4 125 56 0.1 - 173.8 (20.2)

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0316 (Kruskal-Wallis (anova)), Q value = 0.26

Table S51.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 360 67.6 (8.7)
subtype1 87 67.8 (8.5)
subtype2 92 65.7 (8.6)
subtype3 58 67.2 (8.2)
subtype4 123 68.9 (9.1)

Figure S47.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.866 (Fisher's exact test), Q value = 0.94

Table S52.  Clustering Approach #4: '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 IV
ALL 3 71 98 3 60 72 3 47 6 4
subtype1 1 16 24 0 16 17 0 11 2 3
subtype2 0 17 29 1 16 17 1 11 2 0
subtype3 2 13 14 1 10 10 1 6 0 1
subtype4 0 25 31 1 18 28 1 19 2 0

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.666 (Fisher's exact test), Q value = 0.89

Table S53.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 90 207 60 13
subtype1 19 56 10 5
subtype2 24 55 14 3
subtype3 17 30 10 1
subtype4 30 66 26 4

Figure S49.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.833 (Fisher's exact test), Q value = 0.93

Table S54.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 236 99 29
subtype1 59 24 6
subtype2 59 27 9
subtype3 38 17 2
subtype4 80 31 12

Figure S50.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0387 (Fisher's exact test), Q value = 0.29

Table S55.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 287 4
subtype1 70 3
subtype2 77 0
subtype3 43 1
subtype4 97 0

Figure S51.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.401 (Fisher's exact test), Q value = 0.7

Table S56.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 96 274
subtype1 18 72
subtype2 24 72
subtype3 16 42
subtype4 38 88

Figure S52.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.139 (Fisher's exact test), Q value = 0.43

Table S57.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 302 41
subtype1 74 7
subtype2 73 17
subtype3 49 4
subtype4 106 13

Figure S53.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.565 (Kruskal-Wallis (anova)), Q value = 0.85

Table S58.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 126 66.0 (39.1)
subtype1 31 61.3 (40.8)
subtype2 36 64.2 (40.3)
subtype3 22 69.5 (38.8)
subtype4 37 69.5 (37.7)

Figure S54.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0229 (Fisher's exact test), Q value = 0.24

Table S59.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 12 5 1 352
subtype1 4 1 1 84
subtype2 3 1 0 92
subtype3 5 0 0 53
subtype4 0 3 0 123

Figure S55.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.745 (Kruskal-Wallis (anova)), Q value = 0.93

Table S60.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 313 52.2 (29.4)
subtype1 78 54.3 (28.4)
subtype2 80 51.7 (31.2)
subtype3 51 51.4 (25.5)
subtype4 104 51.4 (30.8)

Figure S56.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.768 (Kruskal-Wallis (anova)), Q value = 0.93

Table S61.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 237 1961.2 (11.7)
subtype1 60 1960.8 (11.3)
subtype2 64 1962.3 (12.0)
subtype3 34 1961.9 (11.1)
subtype4 79 1960.3 (12.0)

Figure S57.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.268 (Fisher's exact test), Q value = 0.57

Table S62.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 279 9 2 18
subtype1 65 2 1 4
subtype2 75 6 0 5
subtype3 46 0 1 2
subtype4 93 1 0 7

Figure S58.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'RACE'

P value = 0.216 (Fisher's exact test), Q value = 0.56

Table S63.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 7 24 274
subtype1 0 7 64
subtype2 1 4 71
subtype3 0 4 50
subtype4 6 9 89

Figure S59.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.763 (Fisher's exact test), Q value = 0.93

Table S64.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 239
subtype1 1 56
subtype2 2 53
subtype3 0 42
subtype4 3 88

Figure S60.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S65.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 105 110 86 27
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.12 (logrank test), Q value = 0.39

Table S66.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 323 134 0.0 - 173.8 (23.0)
subtype1 105 42 0.2 - 154.3 (25.1)
subtype2 106 44 0.1 - 133.2 (21.7)
subtype3 86 34 0.5 - 173.8 (28.6)
subtype4 26 14 0.0 - 75.1 (19.4)

Figure S61.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000164 (Kruskal-Wallis (anova)), Q value = 0.0076

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 320 67.0 (9.0)
subtype1 103 68.4 (8.7)
subtype2 107 66.5 (8.2)
subtype3 85 64.5 (10.0)
subtype4 25 72.6 (6.0)

Figure S62.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00852 (Fisher's exact test), Q value = 0.12

Table S68.  Clustering Approach #5: '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 IV
ALL 2 52 101 3 45 62 2 42 14 3
subtype1 0 27 33 1 12 19 1 8 3 1
subtype2 0 11 30 1 17 26 0 18 5 1
subtype3 1 7 29 1 13 17 0 12 5 0
subtype4 1 7 9 0 3 0 1 4 1 1

Figure S63.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00597 (Fisher's exact test), Q value = 0.096

Table S69.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 70 193 49 16
subtype1 33 56 12 4
subtype2 15 68 22 5
subtype3 12 59 10 5
subtype4 10 10 5 2

Figure S64.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.268 (Fisher's exact test), Q value = 0.57

Table S70.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 205 89 26 4
subtype1 72 27 4 0
subtype2 63 33 11 2
subtype3 50 24 10 2
subtype4 20 5 1 0

Figure S65.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.268 (Fisher's exact test), Q value = 0.57

Table S71.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 281 3
subtype1 89 1
subtype2 99 1
subtype3 76 0
subtype4 17 1

Figure S66.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.00018 (Fisher's exact test), Q value = 0.0076

Table S72.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 80 248
subtype1 39 66
subtype2 20 90
subtype3 11 75
subtype4 10 17

Figure S67.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.429 (Fisher's exact test), Q value = 0.72

Table S73.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 259 37
subtype1 89 10
subtype2 89 11
subtype3 58 13
subtype4 23 3

Figure S68.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.189 (Kruskal-Wallis (anova)), Q value = 0.51

Table S74.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 110 64.1 (40.1)
subtype1 38 68.2 (40.3)
subtype2 34 54.7 (43.8)
subtype3 32 74.7 (29.9)
subtype4 6 35.0 (47.2)

Figure S69.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0574 (Fisher's exact test), Q value = 0.3

Table S75.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 8 4 316
subtype1 1 0 104
subtype2 1 2 107
subtype3 6 2 78
subtype4 0 0 27

Figure S70.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.589 (Kruskal-Wallis (anova)), Q value = 0.88

Table S76.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 278 50.5 (29.9)
subtype1 89 48.6 (28.5)
subtype2 94 49.7 (28.3)
subtype3 73 53.1 (35.3)
subtype4 22 52.9 (23.6)

Figure S71.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.57

Table S77.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 210 1960.9 (11.7)
subtype1 70 1960.9 (12.7)
subtype2 71 1960.9 (10.7)
subtype3 54 1962.4 (11.9)
subtype4 15 1955.3 (11.0)

Figure S72.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.625 (Fisher's exact test), Q value = 0.89

Table S78.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 257 7 4 12
subtype1 82 3 1 2
subtype2 82 4 1 6
subtype3 70 0 2 3
subtype4 23 0 0 1

Figure S73.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.833 (Fisher's exact test), Q value = 0.93

Table S79.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 19 242
subtype1 3 7 80
subtype2 2 8 78
subtype3 1 4 62
subtype4 0 0 22

Figure S74.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0522 (Fisher's exact test), Q value = 0.29

Table S80.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 213
subtype1 0 77
subtype2 4 65
subtype3 0 56
subtype4 0 15

Figure S75.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S81.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 79 68 62 60 31 28
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.628 (logrank test), Q value = 0.89

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 323 134 0.0 - 173.8 (23.0)
subtype1 79 34 0.4 - 154.3 (27.7)
subtype2 68 31 0.0 - 173.8 (23.0)
subtype3 61 22 0.1 - 126.2 (21.9)
subtype4 58 23 0.3 - 129.0 (24.0)
subtype5 29 13 0.2 - 132.4 (16.9)
subtype6 28 11 0.8 - 92.7 (15.6)

Figure S76.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.357 (Kruskal-Wallis (anova)), Q value = 0.66

Table S83.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 320 67.0 (9.0)
subtype1 77 65.1 (10.3)
subtype2 68 67.5 (8.2)
subtype3 59 67.3 (7.4)
subtype4 58 67.8 (8.3)
subtype5 30 67.3 (9.7)
subtype6 28 69.0 (9.8)

Figure S77.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.65 (Fisher's exact test), Q value = 0.89

Table S84.  Clustering Approach #6: '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 IV
ALL 2 52 101 3 45 62 2 42 14 3
subtype1 0 15 21 2 11 16 1 11 1 0
subtype2 2 12 21 0 12 7 1 7 4 2
subtype3 0 5 18 0 9 15 0 9 5 1
subtype4 0 9 22 0 6 10 0 8 4 0
subtype5 0 5 11 1 5 5 0 4 0 0
subtype6 0 6 8 0 2 9 0 3 0 0

Figure S78.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.828 (Fisher's exact test), Q value = 0.93

Table S85.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 70 193 49 16
subtype1 19 46 10 4
subtype2 19 36 9 4
subtype3 7 38 13 4
subtype4 11 38 8 3
subtype5 7 18 5 1
subtype6 7 17 4 0

Figure S79.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.871 (Fisher's exact test), Q value = 0.94

Table S86.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 205 89 26 4
subtype1 45 28 6 0
subtype2 46 14 5 1
subtype3 37 18 4 2
subtype4 38 14 7 1
subtype5 21 7 2 0
subtype6 18 8 2 0

Figure S80.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.489 (Fisher's exact test), Q value = 0.79

Table S87.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 281 3
subtype1 66 0
subtype2 54 2
subtype3 57 1
subtype4 54 0
subtype5 24 0
subtype6 26 0

Figure S81.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0723 (Fisher's exact test), Q value = 0.31

Table S88.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 80 248
subtype1 22 57
subtype2 13 55
subtype3 11 51
subtype4 15 45
subtype5 6 25
subtype6 13 15

Figure S82.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.789 (Fisher's exact test), Q value = 0.93

Table S89.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 259 37
subtype1 60 9
subtype2 53 8
subtype3 50 4
subtype4 46 9
subtype5 27 3
subtype6 23 4

Figure S83.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.149 (Kruskal-Wallis (anova)), Q value = 0.45

Table S90.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 110 64.1 (40.1)
subtype1 26 70.8 (37.5)
subtype2 25 72.8 (34.1)
subtype3 17 52.9 (42.1)
subtype4 19 51.1 (40.7)
subtype5 10 58.0 (50.1)
subtype6 13 72.3 (42.3)

Figure S84.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.778 (Fisher's exact test), Q value = 0.93

Table S91.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 8 4 316
subtype1 2 1 76
subtype2 4 0 64
subtype3 1 2 59
subtype4 1 1 58
subtype5 0 0 31
subtype6 0 0 28

Figure S85.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.301 (Kruskal-Wallis (anova)), Q value = 0.61

Table S92.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 278 50.5 (29.9)
subtype1 64 56.0 (40.9)
subtype2 55 45.6 (24.7)
subtype3 54 52.9 (27.8)
subtype4 53 52.6 (23.3)
subtype5 26 42.9 (19.6)
subtype6 26 45.6 (32.1)

Figure S86.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.389 (Kruskal-Wallis (anova)), Q value = 0.69

Table S93.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 210 1960.9 (11.7)
subtype1 56 1963.2 (12.1)
subtype2 35 1959.9 (11.6)
subtype3 38 1961.7 (10.1)
subtype4 39 1957.9 (11.3)
subtype5 20 1959.8 (10.6)
subtype6 22 1961.1 (14.8)

Figure S87.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.176 (Fisher's exact test), Q value = 0.48

Table S94.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 257 7 4 12
subtype1 63 0 0 4
subtype2 53 1 0 1
subtype3 47 2 1 3
subtype4 53 0 2 2
subtype5 20 2 1 1
subtype6 21 2 0 1

Figure S88.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.17 (Fisher's exact test), Q value = 0.48

Table S95.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 19 242
subtype1 2 7 59
subtype2 1 3 55
subtype3 1 6 41
subtype4 1 0 41
subtype5 0 0 26
subtype6 1 3 20

Figure S89.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.178 (Fisher's exact test), Q value = 0.48

Table S96.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 213
subtype1 3 54
subtype2 0 49
subtype3 0 38
subtype4 0 32
subtype5 1 17
subtype6 0 23

Figure S90.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S97.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 106 151 160 84
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.109 (logrank test), Q value = 0.39

Table S98.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 494 212 0.0 - 173.8 (21.8)
subtype1 104 42 0.1 - 156.7 (19.0)
subtype2 148 60 0.2 - 154.3 (30.0)
subtype3 158 68 0.1 - 173.8 (20.7)
subtype4 84 42 0.0 - 133.2 (20.7)

Figure S91.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0101 (Kruskal-Wallis (anova)), Q value = 0.13

Table S99.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 491 67.2 (8.6)
subtype1 104 67.4 (9.1)
subtype2 148 65.5 (8.2)
subtype3 156 67.9 (8.5)
subtype4 83 68.7 (8.4)

Figure S92.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.249 (Fisher's exact test), Q value = 0.57

Table S100.  Clustering Approach #7: '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 IV
ALL 3 90 151 3 65 94 3 63 18 7
subtype1 1 19 36 1 9 20 0 16 3 1
subtype2 1 18 52 1 24 30 1 15 7 0
subtype3 1 30 38 1 25 33 2 21 5 3
subtype4 0 23 25 0 7 11 0 11 3 3

Figure S93.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.52 (Fisher's exact test), Q value = 0.8

Table S101.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 114 293 71 23
subtype1 23 64 15 4
subtype2 28 97 17 9
subtype3 37 89 27 7
subtype4 26 43 12 3

Figure S94.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.273 (Fisher's exact test), Q value = 0.57

Table S102.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 319 131 40 5
subtype1 69 25 10 1
subtype2 91 47 11 1
subtype3 103 46 10 1
subtype4 56 13 9 2

Figure S95.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0951 (Fisher's exact test), Q value = 0.37

Table S103.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 411 7
subtype1 84 1
subtype2 127 0
subtype3 129 3
subtype4 71 3

Figure S96.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.668 (Fisher's exact test), Q value = 0.89

Table S104.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 130 371
subtype1 26 80
subtype2 35 116
subtype3 44 116
subtype4 25 59

Figure S97.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.984 (Fisher's exact test), Q value = 1

Table S105.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 384 53
subtype1 79 11
subtype2 119 17
subtype3 118 17
subtype4 68 8

Figure S98.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.643 (Kruskal-Wallis (anova)), Q value = 0.89

Table S106.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 166 60.3 (41.1)
subtype1 27 54.4 (44.0)
subtype2 56 64.8 (39.6)
subtype3 50 60.8 (39.8)
subtype4 33 56.7 (43.7)

Figure S99.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.316 (Fisher's exact test), Q value = 0.63

Table S107.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 15 6 1 479
subtype1 2 2 0 102
subtype2 6 1 0 144
subtype3 7 1 1 151
subtype4 0 2 0 82

Figure S100.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.266 (Kruskal-Wallis (anova)), Q value = 0.57

Table S108.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 424 52.7 (31.0)
subtype1 90 48.7 (32.6)
subtype2 128 53.1 (27.4)
subtype3 134 53.6 (32.1)
subtype4 72 55.1 (33.3)

Figure S101.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.168 (Kruskal-Wallis (anova)), Q value = 0.48

Table S109.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 318 1960.6 (11.4)
subtype1 69 1960.4 (12.6)
subtype2 89 1962.0 (11.1)
subtype3 105 1960.6 (10.4)
subtype4 55 1958.6 (12.4)

Figure S102.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.744 (Fisher's exact test), Q value = 0.93

Table S110.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 398 12 4 23
subtype1 82 2 2 3
subtype2 126 6 0 8
subtype3 123 3 1 7
subtype4 67 1 1 5

Figure S103.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.821 (Fisher's exact test), Q value = 0.93

Table S111.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 30 349
subtype1 2 7 69
subtype2 1 9 105
subtype3 3 10 115
subtype4 3 4 60

Figure S104.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.113 (Fisher's exact test), Q value = 0.39

Table S112.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 316
subtype1 4 62
subtype2 1 92
subtype3 1 109
subtype4 2 53

Figure S105.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S113.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 179 191 131
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.078 (logrank test), Q value = 0.32

Table S114.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 494 212 0.0 - 173.8 (21.8)
subtype1 178 71 0.0 - 156.7 (20.1)
subtype2 187 76 0.2 - 154.3 (27.2)
subtype3 129 65 0.2 - 173.8 (20.2)

Figure S106.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000564 (Kruskal-Wallis (anova)), Q value = 0.017

Table S115.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 491 67.2 (8.6)
subtype1 176 69.0 (8.1)
subtype2 186 65.5 (8.7)
subtype3 129 67.1 (8.6)

Figure S107.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.066 (Fisher's exact test), Q value = 0.31

Table S116.  Clustering Approach #8: '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 IV
ALL 3 90 151 3 65 94 3 63 18 7
subtype1 1 30 60 1 20 31 0 28 4 4
subtype2 1 26 62 2 28 39 1 20 10 0
subtype3 1 34 29 0 17 24 2 15 4 3

Figure S108.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.104 (Fisher's exact test), Q value = 0.39

Table S117.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 114 293 71 23
subtype1 35 105 32 7
subtype2 38 121 22 10
subtype3 41 67 17 6

Figure S109.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.902 (Fisher's exact test), Q value = 0.96

Table S118.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 319 131 40 5
subtype1 118 43 14 1
subtype2 116 55 16 3
subtype3 85 33 10 1

Figure S110.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0722 (Fisher's exact test), Q value = 0.31

Table S119.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 411 7
subtype1 145 4
subtype2 160 0
subtype3 106 3

Figure S111.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0379 (Fisher's exact test), Q value = 0.29

Table S120.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 130 371
subtype1 56 123
subtype2 38 153
subtype3 36 95

Figure S112.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.14 (Fisher's exact test), Q value = 0.43

Table S121.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 384 53
subtype1 141 17
subtype2 143 27
subtype3 100 9

Figure S113.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.247 (Kruskal-Wallis (anova)), Q value = 0.57

Table S122.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 166 60.3 (41.1)
subtype1 45 53.3 (42.7)
subtype2 73 65.5 (39.4)
subtype3 48 59.0 (41.8)

Figure S114.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0404 (Fisher's exact test), Q value = 0.29

Table S123.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 15 6 1 479
subtype1 1 3 0 175
subtype2 7 1 0 183
subtype3 7 2 1 121

Figure S115.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.782 (Kruskal-Wallis (anova)), Q value = 0.93

Table S124.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 424 52.7 (31.0)
subtype1 152 54.1 (33.3)
subtype2 160 52.2 (27.4)
subtype3 112 51.5 (32.9)

Figure S116.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.00108 (Kruskal-Wallis (anova)), Q value = 0.022

Table S125.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 318 1960.6 (11.4)
subtype1 118 1957.6 (10.4)
subtype2 113 1963.3 (12.2)
subtype3 87 1961.2 (11.0)

Figure S117.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.627 (Fisher's exact test), Q value = 0.89

Table S126.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 398 12 4 23
subtype1 142 2 1 9
subtype2 153 7 3 9
subtype3 103 3 0 5

Figure S118.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.647 (Fisher's exact test), Q value = 0.89

Table S127.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 30 349
subtype1 3 11 116
subtype2 2 13 133
subtype3 4 6 100

Figure S119.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.665 (Fisher's exact test), Q value = 0.89

Table S128.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 316
subtype1 4 104
subtype2 2 119
subtype3 2 93

Figure S120.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S129.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 185 88 125 80
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.527 (logrank test), Q value = 0.8

Table S130.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 471 199 0.0 - 173.8 (22.0)
subtype1 182 78 0.1 - 173.8 (23.1)
subtype2 86 36 0.1 - 132.4 (20.3)
subtype3 124 56 0.0 - 156.7 (21.0)
subtype4 79 29 0.2 - 154.3 (26.8)

Figure S121.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 7.77e-05 (Kruskal-Wallis (anova)), Q value = 0.0076

Table S131.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 469 67.4 (8.6)
subtype1 181 65.7 (8.6)
subtype2 87 69.7 (7.3)
subtype3 122 69.0 (9.3)
subtype4 79 66.1 (8.1)

Figure S122.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.121 (Fisher's exact test), Q value = 0.39

Table S132.  Clustering Approach #9: '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 IV
ALL 3 83 144 3 62 93 3 61 16 6
subtype1 0 20 65 2 19 37 3 27 7 3
subtype2 3 19 20 1 11 18 0 12 3 1
subtype3 0 26 32 0 20 24 0 17 3 2
subtype4 0 18 27 0 12 14 0 5 3 0

Figure S123.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0995 (Fisher's exact test), Q value = 0.38

Table S133.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 106 281 69 22
subtype1 28 120 27 10
subtype2 21 49 15 3
subtype3 33 65 21 6
subtype4 24 47 6 3

Figure S124.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.695 (Fisher's exact test), Q value = 0.91

Table S134.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 301 127 40 4
subtype1 114 47 21 3
subtype2 56 27 5 0
subtype3 78 30 10 1
subtype4 53 23 4 0

Figure S125.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.835 (Fisher's exact test), Q value = 0.93

Table S135.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 390 6
subtype1 160 3
subtype2 61 1
subtype3 102 2
subtype4 67 0

Figure S126.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.988 (Fisher's exact test), Q value = 1

Table S136.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 124 354
subtype1 48 137
subtype2 22 66
subtype3 32 93
subtype4 22 58

Figure S127.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.338 (Fisher's exact test), Q value = 0.65

Table S137.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 371 48
subtype1 123 22
subtype2 76 9
subtype3 106 9
subtype4 66 8

Figure S128.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0321 (Kruskal-Wallis (anova)), Q value = 0.26

Table S138.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 157 61.8 (40.6)
subtype1 58 52.9 (41.8)
subtype2 24 65.0 (36.0)
subtype3 42 63.6 (41.3)
subtype4 33 73.0 (39.3)

Figure S129.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.303 (Fisher's exact test), Q value = 0.61

Table S139.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 13 6 1 458
subtype1 7 3 1 174
subtype2 1 0 0 87
subtype3 1 3 0 121
subtype4 4 0 0 76

Figure S130.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.812 (Kruskal-Wallis (anova)), Q value = 0.93

Table S140.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 406 53.3 (31.6)
subtype1 155 55.2 (34.3)
subtype2 77 51.4 (29.5)
subtype3 102 53.0 (28.7)
subtype4 72 51.8 (32.1)

Figure S131.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.43 (Kruskal-Wallis (anova)), Q value = 0.72

Table S141.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 305 1960.5 (11.6)
subtype1 117 1960.8 (10.7)
subtype2 57 1961.1 (11.2)
subtype3 78 1959.0 (12.4)
subtype4 53 1961.7 (12.6)

Figure S132.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.34 (Fisher's exact test), Q value = 0.65

Table S142.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 379 11 3 21
subtype1 154 3 2 6
subtype2 62 3 1 6
subtype3 99 1 0 6
subtype4 64 4 0 3

Figure S133.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.403 (Fisher's exact test), Q value = 0.7

Table S143.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 30 333
subtype1 3 8 125
subtype2 0 8 61
subtype3 5 8 91
subtype4 1 6 56

Figure S134.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.615 (Fisher's exact test), Q value = 0.89

Table S144.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 302
subtype1 3 105
subtype2 0 61
subtype3 2 83
subtype4 2 53

Figure S135.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S145.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 89 118 201 70
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.798 (logrank test), Q value = 0.93

Table S146.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 471 199 0.0 - 173.8 (22.0)
subtype1 87 33 0.2 - 154.3 (25.3)
subtype2 117 52 0.4 - 173.8 (19.3)
subtype3 199 84 0.1 - 140.1 (21.7)
subtype4 68 30 0.0 - 156.7 (22.6)

Figure S136.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00641 (Kruskal-Wallis (anova)), Q value = 0.096

Table S147.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 469 67.4 (8.6)
subtype1 86 65.6 (8.3)
subtype2 116 66.1 (9.4)
subtype3 199 68.3 (8.0)
subtype4 68 69.2 (9.1)

Figure S137.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.733 (Fisher's exact test), Q value = 0.93

Table S148.  Clustering Approach #10: '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 IV
ALL 3 83 144 3 62 93 3 61 16 6
subtype1 0 14 29 0 12 19 1 7 6 0
subtype2 0 17 40 1 17 24 1 12 2 2
subtype3 3 40 56 2 22 35 1 34 6 2
subtype4 0 12 19 0 11 15 0 8 2 2

Figure S138.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.514 (Fisher's exact test), Q value = 0.8

Table S149.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 106 281 69 22
subtype1 19 56 8 6
subtype2 22 74 19 3
subtype3 49 111 33 8
subtype4 16 40 9 5

Figure S139.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.815 (Fisher's exact test), Q value = 0.93

Table S150.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 301 127 40 4
subtype1 52 30 6 1
subtype2 79 29 9 1
subtype3 130 50 20 1
subtype4 40 18 5 1

Figure S140.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.355 (Fisher's exact test), Q value = 0.66

Table S151.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 390 6
subtype1 76 0
subtype2 101 2
subtype3 159 2
subtype4 54 2

Figure S141.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.821 (Fisher's exact test), Q value = 0.93

Table S152.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 124 354
subtype1 20 69
subtype2 30 88
subtype3 54 147
subtype4 20 50

Figure S142.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.857 (Fisher's exact test), Q value = 0.93

Table S153.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 371 48
subtype1 69 11
subtype2 83 11
subtype3 159 20
subtype4 60 6

Figure S143.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0849 (Kruskal-Wallis (anova)), Q value = 0.34

Table S154.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 157 61.8 (40.6)
subtype1 31 75.8 (35.3)
subtype2 38 52.9 (45.0)
subtype3 63 59.5 (39.2)
subtype4 25 64.0 (41.0)

Figure S144.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.0693 (Fisher's exact test), Q value = 0.31

Table S155.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 13 6 1 458
subtype1 4 0 0 85
subtype2 6 3 0 109
subtype3 3 1 1 196
subtype4 0 2 0 68

Figure S145.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.678 (Kruskal-Wallis (anova)), Q value = 0.9

Table S156.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 406 53.3 (31.6)
subtype1 80 56.3 (31.6)
subtype2 94 53.5 (36.1)
subtype3 176 52.1 (28.4)
subtype4 56 52.9 (33.6)

Figure S146.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.631 (Kruskal-Wallis (anova)), Q value = 0.89

Table S157.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 305 1960.5 (11.6)
subtype1 60 1962.8 (13.7)
subtype2 76 1960.0 (11.6)
subtype3 132 1959.8 (10.4)
subtype4 37 1960.8 (12.0)

Figure S147.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.218 (Fisher's exact test), Q value = 0.56

Table S158.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 379 11 3 21
subtype1 72 4 1 4
subtype2 97 2 0 5
subtype3 159 4 1 5
subtype4 51 1 1 7

Figure S148.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0262 (Fisher's exact test), Q value = 0.25

Table S159.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 30 333
subtype1 0 7 60
subtype2 3 2 83
subtype3 2 17 141
subtype4 4 4 49

Figure S149.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.857 (Fisher's exact test), Q value = 0.93

Table S160.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 302
subtype1 1 58
subtype2 1 67
subtype3 3 129
subtype4 2 48

Figure S150.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S161.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 61 116 67 93
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.162 (logrank test), Q value = 0.47

Table S162.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 333 134 0.0 - 156.7 (21.2)
subtype1 61 26 0.1 - 156.7 (21.3)
subtype2 115 54 0.2 - 140.1 (19.6)
subtype3 66 24 0.0 - 126.2 (23.6)
subtype4 91 30 0.1 - 150.2 (21.7)

Figure S151.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0709 (Kruskal-Wallis (anova)), Q value = 0.31

Table S163.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 331 67.5 (8.6)
subtype1 60 68.4 (9.6)
subtype2 114 67.5 (8.3)
subtype3 65 69.1 (8.1)
subtype4 92 65.8 (8.7)

Figure S152.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.225 (Fisher's exact test), Q value = 0.56

Table S164.  Clustering Approach #11: '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 IV
ALL 3 64 90 3 55 67 3 40 6 3
subtype1 0 10 14 1 8 15 0 9 3 1
subtype2 1 17 37 1 19 22 1 14 0 1
subtype3 0 23 18 0 9 9 0 6 1 1
subtype4 2 14 21 1 19 21 2 11 2 0

Figure S153.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0503 (Fisher's exact test), Q value = 0.29

Table S165.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 80 189 57 11
subtype1 12 32 13 4
subtype2 25 70 18 3
subtype3 27 28 10 2
subtype4 16 59 16 2

Figure S154.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.506 (Fisher's exact test), Q value = 0.8

Table S166.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 216 89 26
subtype1 36 19 5
subtype2 76 30 9
subtype3 48 12 3
subtype4 56 28 9

Figure S155.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.596 (Fisher's exact test), Q value = 0.88

Table S167.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 256 3
subtype1 43 1
subtype2 88 1
subtype3 51 1
subtype4 74 0

Figure S156.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.161 (Fisher's exact test), Q value = 0.47

Table S168.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 85 252
subtype1 20 41
subtype2 28 88
subtype3 20 47
subtype4 17 76

Figure S157.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.526 (Fisher's exact test), Q value = 0.8

Table S169.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 278 36
subtype1 52 7
subtype2 90 14
subtype3 60 4
subtype4 76 11

Figure S158.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.395 (Kruskal-Wallis (anova)), Q value = 0.7

Table S170.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 115 68.9 (37.8)
subtype1 16 65.6 (41.3)
subtype2 53 68.3 (37.2)
subtype3 17 78.2 (35.4)
subtype4 29 66.2 (39.4)

Figure S159.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0448 (Fisher's exact test), Q value = 0.29

Table S171.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 10 5 1 321
subtype1 0 1 0 60
subtype2 3 2 1 110
subtype3 0 2 0 65
subtype4 7 0 0 86

Figure S160.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.733 (Kruskal-Wallis (anova)), Q value = 0.93

Table S172.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 283 51.9 (28.6)
subtype1 52 51.0 (25.1)
subtype2 99 48.8 (25.2)
subtype3 53 56.4 (36.6)
subtype4 79 53.3 (28.6)

Figure S161.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0163 (Kruskal-Wallis (anova)), Q value = 0.18

Table S173.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 215 1961.9 (11.8)
subtype1 38 1957.0 (13.3)
subtype2 79 1963.0 (11.2)
subtype3 39 1961.8 (12.6)
subtype4 59 1963.6 (10.3)

Figure S162.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.993 (Fisher's exact test), Q value = 1

Table S174.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 250 9 2 18
subtype1 41 2 0 4
subtype2 88 3 1 7
subtype3 51 1 0 3
subtype4 70 3 1 4

Figure S163.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.428 (Fisher's exact test), Q value = 0.72

Table S175.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 23 251
subtype1 1 6 40
subtype2 1 10 98
subtype3 3 3 50
subtype4 1 4 63

Figure S164.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.259 (Fisher's exact test), Q value = 0.57

Table S176.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 224
subtype1 2 37
subtype2 1 86
subtype3 1 47
subtype4 0 54

Figure S165.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S177.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 37 190 110
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.899 (logrank test), Q value = 0.96

Table S178.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 333 134 0.0 - 156.7 (21.2)
subtype1 37 14 0.1 - 104.1 (17.7)
subtype2 188 77 0.1 - 150.2 (20.7)
subtype3 108 43 0.0 - 156.7 (22.2)

Figure S166.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.225 (Kruskal-Wallis (anova)), Q value = 0.56

Table S179.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 331 67.5 (8.6)
subtype1 36 69.1 (7.4)
subtype2 188 67.1 (8.6)
subtype3 107 67.6 (9.1)

Figure S167.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.447 (Fisher's exact test), Q value = 0.75

Table S180.  Clustering Approach #12: '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 IV
ALL 3 64 90 3 55 67 3 40 6 3
subtype1 0 7 8 1 3 7 0 9 2 0
subtype2 3 35 50 1 35 38 2 22 2 1
subtype3 0 22 32 1 17 22 1 9 2 2

Figure S168.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.115 (Fisher's exact test), Q value = 0.39

Table S181.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 80 189 57 11
subtype1 7 16 12 2
subtype2 44 114 28 4
subtype3 29 59 17 5

Figure S169.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.471 (Fisher's exact test), Q value = 0.77

Table S182.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 216 89 26
subtype1 24 8 5
subtype2 121 51 16
subtype3 71 30 5

Figure S170.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.502 (Fisher's exact test), Q value = 0.8

Table S183.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 256 3
subtype1 29 0
subtype2 147 1
subtype3 80 2

Figure S171.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.855 (Fisher's exact test), Q value = 0.93

Table S184.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 85 252
subtype1 10 27
subtype2 46 144
subtype3 29 81

Figure S172.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.327 (Fisher's exact test), Q value = 0.63

Table S185.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 278 36
subtype1 30 4
subtype2 152 24
subtype3 96 8

Figure S173.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.05 (Kruskal-Wallis (anova)), Q value = 0.29

Table S186.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 115 68.9 (37.8)
subtype1 8 51.2 (44.2)
subtype2 68 65.7 (38.6)
subtype3 39 77.9 (33.7)

Figure S174.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.107 (Fisher's exact test), Q value = 0.39

Table S187.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 10 5 1 321
subtype1 2 1 0 34
subtype2 8 2 1 179
subtype3 0 2 0 108

Figure S175.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.47 (Kruskal-Wallis (anova)), Q value = 0.77

Table S188.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 283 51.9 (28.6)
subtype1 31 55.3 (27.5)
subtype2 163 50.7 (26.6)
subtype3 89 52.7 (32.4)

Figure S176.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0248 (Kruskal-Wallis (anova)), Q value = 0.25

Table S189.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 215 1961.9 (11.8)
subtype1 20 1955.3 (8.8)
subtype2 126 1962.6 (11.2)
subtype3 69 1962.5 (13.1)

Figure S177.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.726 (Fisher's exact test), Q value = 0.93

Table S190.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 250 9 2 18
subtype1 27 1 0 1
subtype2 141 7 2 10
subtype3 82 1 0 7

Figure S178.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0447 (Fisher's exact test), Q value = 0.29

Table S191.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 23 251
subtype1 0 6 18
subtype2 3 9 148
subtype3 3 8 85

Figure S179.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0711 (Fisher's exact test), Q value = 0.31

Table S192.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 224
subtype1 0 19
subtype2 0 124
subtype3 4 81

Figure S180.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LUSC-TP/22552638/LUSC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LUSC-TP/22506957/LUSC-TP.merged_data.txt

  • Number of patients = 504

  • Number of clustering approaches = 12

  • Number of selected clinical features = 15

  • 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)