Correlation between aggregated molecular cancer subtypes and selected clinical features
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 518 patients, 54 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 'NEOPLASM_DISEASESTAGE'.

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

  • 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'GENDER', and 'NUMBER_PACK_YEARS_SMOKED'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'GENDER',  'NUMBER_PACK_YEARS_SMOKED', and 'ETHNICITY'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'HISTOLOGICAL_TYPE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'GENDER',  'HISTOLOGICAL_TYPE',  'NUMBER_PACK_YEARS_SMOKED', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'NUMBER_PACK_YEARS_SMOKED',  'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'ETHNICITY'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'HISTOLOGICAL_TYPE', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'GENDER',  'HISTOLOGICAL_TYPE',  'NUMBER_PACK_YEARS_SMOKED', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION', and 'NUMBER_PACK_YEARS_SMOKED'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE', and '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, 54 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 0.75
(0.871) 		0.925
(0.991) 		0.0192
(0.0825) 		0.139
(0.324) 		0.459
(0.663) 		0.329
(0.565) 		0.00333
(0.024) 		2.5e-07
(4.51e-05) 		0.178
(0.381) 		0.817
(0.919) 		0.267
(0.494) 		0.0545
(0.179)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.65
(0.812) 		0.513
(0.684) 		0.00156
(0.0134) 		0.206
(0.422) 		0.0437
(0.154) 		0.242
(0.465) 		0.0479
(0.16) 		0.000674
(0.00933) 		0.0102
(0.0508) 		0.00155
(0.0134) 		0.657
(0.816) 		2.92e-06
(0.000263)
NEOPLASM DISEASESTAGE Fisher's exact test 0.0466
(0.158) 		0.0359
(0.135) 		0.101
(0.27) 		0.00023
(0.00414) 		0.67
(0.816) 		0.121
(0.294) 		0.00368
(0.0255) 		0.00119
(0.0126) 		0.00131
(0.0126) 		0.59
(0.758) 		0.476
(0.675) 		0.398
(0.634)
PATHOLOGY T STAGE Fisher's exact test 0.512
(0.684) 		0.275
(0.494) 		0.114
(0.285) 		0.00112
(0.0126) 		0.877
(0.961) 		0.0734
(0.228) 		0.00175
(0.0143) 		0.00093
(0.0112) 		0.0172
(0.0794) 		0.135
(0.319) 		0.015
(0.0731) 		0.00931
(0.0493)
PATHOLOGY N STAGE Fisher's exact test 0.484
(0.68) 		0.456
(0.663) 		0.25
(0.474) 		0.0617
(0.198) 		0.881
(0.961) 		0.309
(0.54) 		0.0279
(0.117) 		0.00293
(0.022) 		0.276
(0.494) 		0.716
(0.842) 		0.191
(0.395) 		0.0695
(0.22)
PATHOLOGY M STAGE Fisher's exact test 0.504
(0.684) 		0.491
(0.684) 		0.18
(0.381) 		0.26
(0.487) 		0.277
(0.494) 		0.394
(0.634) 		0.447
(0.663) 		0.513
(0.684) 		0.55
(0.722) 		0.846
(0.946) 		0.434
(0.663) 		0.582
(0.753)
GENDER Fisher's exact test 0.275
(0.494) 		0.671
(0.816) 		0.019
(0.0825) 		4e-05
(0.0012) 		0.0988
(0.27) 		0.00628
(0.0377) 		1e-05
(0.00036) 		1e-05
(0.00036) 		0.476
(0.675) 		0.0046
(0.0296) 		0.96
(1.00) 		0.00045
(0.00736)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.095
(0.267) 		0.862
(0.957) 		0.154
(0.355) 		0.12
(0.294) 		0.228
(0.447) 		0.0947
(0.267) 		0.685
(0.822) 		0.715
(0.842) 		0.109
(0.276) 		0.668
(0.816)
HISTOLOGICAL TYPE Fisher's exact test 0.435
(0.663) 		0.396
(0.634) 		0.394
(0.634) 		0.127
(0.305) 		0.00801
(0.0465) 		0.0192
(0.0825) 		1e-05
(0.00036) 		0.00018
(0.0036) 		0.00075
(0.00964) 		0.00133
(0.0126) 		5e-05
(0.00129) 		7e-05
(0.00157)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 1
(1.00) 		1
(1.00) 		0.213
(0.431) 		0.159
(0.357) 		0.291
(0.514) 		0.776
(0.89) 		0.925
(0.991) 		0.18
(0.381) 		1
(1.00) 		0.782
(0.891) 		0.0325
(0.127) 		0.0923
(0.267)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.46
(0.663) 		0.362
(0.604) 		0.0303
(0.124) 		0.00402
(0.0268) 		0.109
(0.276) 		0.031
(0.124) 		0.0931
(0.267) 		0.0416
(0.15) 		0.0797
(0.239) 		0.0407
(0.15) 		0.00889
(0.049) 		0.224
(0.442)
YEAR OF TOBACCO SMOKING ONSET Kruskal-Wallis (anova) 0.715
(0.842) 		0.676
(0.817) 		0.173
(0.381) 		0.888
(0.963) 		0.416
(0.656) 		0.00899
(0.049) 		0.355
(0.597) 		0.000498
(0.00747) 		0.017
(0.0794) 		0.00525
(0.0326) 		0.506
(0.684) 		0.00232
(0.0182)
COMPLETENESS OF RESECTION Fisher's exact test 0.533
(0.705) 		0.456
(0.663) 		0.877
(0.961) 		0.8
(0.906) 		0.454
(0.663) 		0.747
(0.871) 		0.648
(0.812) 		0.243
(0.465) 		0.451
(0.663) 		0.954
(1.00) 		0.158
(0.357) 		0.0999
(0.27)
RACE Fisher's exact test 0.0787
(0.239) 		0.00972
(0.05) 		0.562
(0.733) 		0.184
(0.386) 		0.334
(0.567) 		0.174
(0.381) 		0.972
(1.00) 		0.6
(0.767) 		0.441
(0.663) 		0.316
(0.546) 		0.95
(1.00) 		0.103
(0.27)
ETHNICITY Fisher's exact test 1
(1.00) 		1
(1.00) 		0.221
(0.442) 		0.0445
(0.154) 		1
(1.00) 		0.632
(0.802) 		0.497
(0.684) 		0.0351
(0.135) 		0.366
(0.605) 		0.757
(0.874) 		0.102
(0.27) 		0.454
(0.663)
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 5 9 12 6
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.75 (logrank test), Q value = 0.87

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

nPatients nDeath Duration Range (Median), Month
ALL 31 6 0.5 - 56.8 (25.0)
subtype1 4 0 6.0 - 48.6 (24.5)
subtype2 9 2 4.0 - 56.8 (38.2)
subtype3 12 2 0.5 - 44.9 (16.4)
subtype4 6 2 20.1 - 45.2 (30.9)

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 = 0.65 (Kruskal-Wallis (anova)), Q value = 0.81

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

nPatients Mean (Std.Dev)
ALL 30 65.7 (10.8)
subtype1 4 58.5 (15.5)
subtype2 9 65.0 (9.1)
subtype3 12 67.1 (11.1)
subtype4 5 69.4 (9.0)

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

'mRNA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0466 (Fisher's exact test), Q value = 0.16

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

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IV
ALL 12 11 1 3 3 2
subtype1 3 0 0 1 0 1
subtype2 4 4 0 0 1 0
subtype3 3 7 0 1 0 1
subtype4 2 0 1 1 2 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.512 (Fisher's exact test), Q value = 0.68

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

nPatients T1 T2 T3
ALL 12 19 1
subtype1 3 2 0
subtype2 4 4 1
subtype3 4 8 0
subtype4 1 5 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.484 (Fisher's exact test), Q value = 0.68

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

nPatients N0 N1 N2+N3
ALL 23 4 4
subtype1 3 1 1
subtype2 8 1 0
subtype3 9 1 1
subtype4 3 1 2

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 = 0.504 (Fisher's exact test), Q value = 0.68

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

nPatients 0 1
ALL 30 2
subtype1 4 1
subtype2 9 0
subtype3 11 1
subtype4 6 0

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.275 (Fisher's exact test), Q value = 0.49

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

nPatients FEMALE MALE
ALL 18 14
subtype1 3 2
subtype2 3 6
subtype3 9 3
subtype4 3 3

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.435 (Fisher's exact test), Q value = 0.66

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG CLEAR CELL ADENOCARCINOMA
ALL 1 30 1
subtype1 0 4 1
subtype2 0 9 0
subtype3 1 11 0
subtype4 0 6 0

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 1 31
subtype1 0 5
subtype2 0 9
subtype3 1 11
subtype4 0 6

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.46 (Kruskal-Wallis (anova)), Q value = 0.66

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 20 41.1 (15.0)
subtype1 2 29.0 (12.7)
subtype2 9 47.0 (15.5)
subtype3 6 37.0 (13.1)
subtype4 3 40.0 (17.3)

Figure S10.  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 = 0.715 (Kruskal-Wallis (anova)), Q value = 0.84

Table S12.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 19 1968.5 (11.4)
subtype1 2 1977.0 (18.4)
subtype2 6 1971.2 (14.2)
subtype3 6 1965.8 (8.2)
subtype4 5 1965.2 (9.8)

Figure S11.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'mRNA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.533 (Fisher's exact test), Q value = 0.71

Table S13.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R2 RX
ALL 26 1 2
subtype1 3 0 1
subtype2 7 1 0
subtype3 10 0 1
subtype4 6 0 0

Figure S12.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.0787 (Fisher's exact test), Q value = 0.24

Table S14.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 1 26
subtype1 1 0 3
subtype2 0 0 7
subtype3 0 0 12
subtype4 1 1 4

Figure S13.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

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

Table S15.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 1 28
subtype1 0 4
subtype2 0 7
subtype3 1 11
subtype4 0 6

Figure S14.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S16.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 14 11 7
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.925 (logrank test), Q value = 0.99

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 31 6 0.5 - 56.8 (25.0)
subtype1 13 2 0.5 - 47.0 (18.8)
subtype2 11 2 4.0 - 56.8 (34.1)
subtype3 7 2 20.1 - 48.6 (38.7)

Figure S15.  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 = 0.513 (Kruskal-Wallis (anova)), Q value = 0.68

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

nPatients Mean (Std.Dev)
ALL 30 65.7 (10.8)
subtype1 14 66.7 (10.5)
subtype2 11 62.7 (12.0)
subtype3 5 69.4 (9.0)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0359 (Fisher's exact test), Q value = 0.13

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IV
ALL 12 11 1 3 3 2
subtype1 3 8 0 1 0 2
subtype2 6 3 0 1 1 0
subtype3 3 0 1 1 2 0

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.275 (Fisher's exact test), Q value = 0.49

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 12 19 1
subtype1 4 10 0
subtype2 6 4 1
subtype3 2 5 0

Figure S18.  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 = 0.456 (Fisher's exact test), Q value = 0.66

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 23 4 4
subtype1 10 1 2
subtype2 9 2 0
subtype3 4 1 2

Figure S19.  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 = 0.491 (Fisher's exact test), Q value = 0.68

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 30 2
subtype1 12 2
subtype2 11 0
subtype3 7 0

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.671 (Fisher's exact test), Q value = 0.82

Table S23.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 18 14
subtype1 9 5
subtype2 5 6
subtype3 4 3

Figure S21.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.396 (Fisher's exact test), Q value = 0.63

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG CLEAR CELL ADENOCARCINOMA
ALL 1 30 1
subtype1 1 13 0
subtype2 0 11 0
subtype3 0 6 1

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

Table S25.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 1 31
subtype1 1 13
subtype2 0 11
subtype3 0 7

Figure S23.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.362 (Kruskal-Wallis (anova)), Q value = 0.6

Table S26.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 20 41.1 (15.0)
subtype1 8 35.4 (12.2)
subtype2 9 46.7 (16.1)
subtype3 3 40.0 (17.3)

Figure S24.  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 = 0.676 (Kruskal-Wallis (anova)), Q value = 0.82

Table S27.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 19 1968.5 (11.4)
subtype1 7 1969.9 (13.0)
subtype2 6 1970.5 (12.9)
subtype3 6 1965.0 (8.8)

Figure S25.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'mRNA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.456 (Fisher's exact test), Q value = 0.66

Table S28.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R2 RX
ALL 26 1 2
subtype1 10 0 2
subtype2 10 1 0
subtype3 6 0 0

Figure S26.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.00972 (Fisher's exact test), Q value = 0.05

Table S29.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 1 26
subtype1 0 0 13
subtype2 0 0 9
subtype3 2 1 4

Figure S27.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

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

Table S30.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 1 28
subtype1 1 12
subtype2 0 9
subtype3 0 7

Figure S28.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

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

Table S31.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 69 228 78 140
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0192 (logrank test), Q value = 0.082

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 491 154 0.0 - 224.0 (18.6)
subtype1 65 18 0.1 - 88.1 (15.7)
subtype2 217 56 0.1 - 224.0 (18.2)
subtype3 75 31 0.0 - 86.1 (16.4)
subtype4 134 49 0.1 - 214.6 (19.4)

Figure S29.  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 = 0.00156 (Kruskal-Wallis (anova)), Q value = 0.013

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 484 65.2 (10.0)
subtype1 65 64.4 (11.0)
subtype2 213 67.2 (8.9)
subtype3 74 64.9 (10.8)
subtype4 132 62.7 (10.2)

Figure S30.  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 'NEOPLASM_DISEASESTAGE'

P value = 0.101 (Fisher's exact test), Q value = 0.27

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 135 140 1 51 73 72 11 26
subtype1 0 21 17 0 4 7 13 2 5
subtype2 2 75 63 1 20 28 27 3 8
subtype3 1 15 18 0 7 17 11 2 7
subtype4 2 24 42 0 20 21 21 4 6

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.114 (Fisher's exact test), Q value = 0.29

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 170 278 46 19
subtype1 24 36 5 4
subtype2 90 112 16 9
subtype3 19 45 11 2
subtype4 37 85 14 4

Figure S32.  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 = 0.25 (Fisher's exact test), Q value = 0.47

Table S36.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 332 96 76
subtype1 48 8 12
subtype2 154 40 27
subtype3 49 16 12
subtype4 81 32 25

Figure S33.  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 = 0.18 (Fisher's exact test), Q value = 0.38

Table S37.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 348 25
subtype1 51 5
subtype2 156 7
subtype3 51 7
subtype4 90 6

Figure S34.  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 = 0.019 (Fisher's exact test), Q value = 0.082

Table S38.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 275 240
subtype1 28 41
subtype2 128 100
subtype3 35 43
subtype4 84 56

Figure S35.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.095 (Kruskal-Wallis (anova)), Q value = 0.27

Table S39.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 85.1 (20.8)
subtype1 13 86.2 (27.2)
subtype2 34 91.2 (6.9)
subtype3 16 83.1 (25.0)
subtype4 35 79.7 (24.1)

Figure S36.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.394 (Fisher's exact test), Q value = 0.63

Table S40.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 19 107 319 5 19 2 3 2 23 1 5 10
subtype1 6 14 41 0 1 0 0 0 7 0 0 0
subtype2 7 50 132 3 11 1 1 2 10 1 3 7
subtype3 2 14 54 0 1 1 0 0 2 0 1 3
subtype4 4 29 92 2 6 0 2 0 4 0 1 0

Figure S37.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.213 (Fisher's exact test), Q value = 0.43

Table S41.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 21 494
subtype1 5 64
subtype2 6 222
subtype3 5 73
subtype4 5 135

Figure S38.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0303 (Kruskal-Wallis (anova)), Q value = 0.12

Table S42.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 355 41.6 (27.3)
subtype1 54 40.7 (28.1)
subtype2 152 38.2 (26.3)
subtype3 55 41.9 (25.5)
subtype4 94 47.5 (28.8)

Figure S39.  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 = 0.173 (Kruskal-Wallis (anova)), Q value = 0.38

Table S43.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 276 1965.0 (12.6)
subtype1 43 1965.9 (13.6)
subtype2 119 1963.5 (12.1)
subtype3 41 1968.3 (12.6)
subtype4 73 1965.1 (12.5)

Figure S40.  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 'COMPLETENESS_OF_RESECTION'

P value = 0.877 (Fisher's exact test), Q value = 0.96

Table S44.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 343 12 4 26
subtype1 43 2 0 5
subtype2 146 3 2 12
subtype3 55 2 1 3
subtype4 99 5 1 6

Figure S41.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.562 (Fisher's exact test), Q value = 0.73

Table S45.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 7 51 389
subtype1 0 2 5 53
subtype2 0 2 20 180
subtype3 0 1 10 55
subtype4 1 2 16 101

Figure S42.  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 = 0.221 (Fisher's exact test), Q value = 0.44

Table S46.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 382
subtype1 1 51
subtype2 2 177
subtype3 3 55
subtype4 1 99

Figure S43.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S47.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 145 148 164
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.139 (logrank test), Q value = 0.32

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 434 133 0.1 - 224.0 (17.6)
subtype1 137 33 0.1 - 224.0 (18.7)
subtype2 141 53 0.1 - 214.6 (17.6)
subtype3 156 47 0.1 - 163.1 (16.1)

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.206 (Kruskal-Wallis (anova)), Q value = 0.42

Table S49.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 428 65.0 (10.2)
subtype1 134 66.0 (9.7)
subtype2 140 63.9 (10.4)
subtype3 154 65.2 (10.3)

Figure S45.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00023 (Fisher's exact test), Q value = 0.0041

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 120 125 1 48 63 64 9 21
subtype1 1 57 34 1 16 17 12 1 5
subtype2 4 36 36 0 17 15 26 4 10
subtype3 0 27 55 0 15 31 26 4 6

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00112 (Fisher's exact test), Q value = 0.013

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

nPatients T1 T2 T3 T4
ALL 154 243 41 16
subtype1 68 62 10 3
subtype2 48 83 11 5
subtype3 38 98 20 8

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0617 (Fisher's exact test), Q value = 0.2

Table S52.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 298 83 66
subtype1 101 25 12
subtype2 92 24 30
subtype3 105 34 24

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.26 (Fisher's exact test), Q value = 0.49

Table S53.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 294 19
subtype1 84 4
subtype2 98 10
subtype3 112 5

Figure S49.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 4e-05 (Fisher's exact test), Q value = 0.0012

Table S54.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 243 214
subtype1 97 48
subtype2 59 89
subtype3 87 77

Figure S50.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.862 (Kruskal-Wallis (anova)), Q value = 0.96

Table S55.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 92 85.0 (19.9)
subtype1 25 86.4 (19.6)
subtype2 37 83.2 (24.0)
subtype3 30 86.0 (14.0)

Figure S51.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.127 (Fisher's exact test), Q value = 0.3

Table S56.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 19 92 279 5 19 1 2 2 22 1 5 10
subtype1 5 23 88 4 8 0 1 2 7 1 0 6
subtype2 8 29 92 0 3 1 0 0 9 0 3 3
subtype3 6 40 99 1 8 0 1 0 6 0 2 1

Figure S52.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.159 (Fisher's exact test), Q value = 0.36

Table S57.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 18 439
subtype1 6 139
subtype2 9 139
subtype3 3 161

Figure S53.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00402 (Kruskal-Wallis (anova)), Q value = 0.027

Table S58.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 313 40.7 (27.3)
subtype1 91 37.4 (24.6)
subtype2 110 47.4 (29.6)
subtype3 112 36.8 (25.9)

Figure S54.  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 = 0.888 (Kruskal-Wallis (anova)), Q value = 0.96

Table S59.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 251 1965.3 (12.5)
subtype1 72 1964.7 (11.4)
subtype2 88 1965.8 (12.9)
subtype3 91 1965.2 (13.0)

Figure S55.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.8 (Fisher's exact test), Q value = 0.91

Table S60.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 293 10 1 23
subtype1 87 2 1 9
subtype2 94 4 0 6
subtype3 112 4 0 8

Figure S56.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'METHLYATION CNMF' versus 'RACE'

P value = 0.184 (Fisher's exact test), Q value = 0.39

Table S61.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 49 352
subtype1 0 18 114
subtype2 2 19 114
subtype3 4 12 124

Figure S57.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.0445 (Fisher's exact test), Q value = 0.15

Table S62.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 341
subtype1 2 118
subtype2 5 108
subtype3 0 115

Figure S58.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S63.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 44 73 64
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.459 (logrank test), Q value = 0.66

Table S64.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 168 63 0.1 - 224.0 (20.1)
subtype1 43 15 0.5 - 163.1 (19.3)
subtype2 65 22 0.1 - 70.5 (17.6)
subtype3 60 26 0.1 - 224.0 (20.6)

Figure S59.  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 = 0.0437 (Kruskal-Wallis (anova)), Q value = 0.15

Table S65.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 164 65.5 (9.6)
subtype1 43 67.0 (8.6)
subtype2 63 63.3 (10.1)
subtype3 58 66.7 (9.5)

Figure S60.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.67 (Fisher's exact test), Q value = 0.82

Table S66.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 1 41 52 15 25 33 7 7
subtype1 0 12 9 6 7 6 1 3
subtype2 1 17 20 5 12 14 2 2
subtype3 0 12 23 4 6 13 4 2

Figure S61.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.877 (Fisher's exact test), Q value = 0.96

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 48 108 13 12
subtype1 12 27 2 3
subtype2 20 43 7 3
subtype3 16 38 4 6

Figure S62.  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 = 0.881 (Fisher's exact test), Q value = 0.96

Table S68.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 106 34 35
subtype1 27 9 7
subtype2 40 15 14
subtype3 39 10 14

Figure S63.  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 = 0.277 (Fisher's exact test), Q value = 0.49

Table S69.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 137 6
subtype1 30 3
subtype2 57 2
subtype3 50 1

Figure S64.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0988 (Fisher's exact test), Q value = 0.27

Table S70.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 105 76
subtype1 20 24
subtype2 48 25
subtype3 37 27

Figure S65.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.154 (Kruskal-Wallis (anova)), Q value = 0.36

Table S71.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 14 60.0 (40.6)
subtype1 6 83.3 (8.2)
subtype2 5 30.0 (42.4)
subtype3 3 63.3 (55.1)

Figure S66.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00801 (Fisher's exact test), Q value = 0.047

Table S72.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 5 43 111 3 8 2 1 5 3
subtype1 3 15 17 0 5 1 0 2 1
subtype2 2 14 51 0 2 1 1 1 1
subtype3 0 14 43 3 1 0 0 2 1

Figure S67.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.291 (Fisher's exact test), Q value = 0.51

Table S73.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 13 168
subtype1 3 41
subtype2 3 70
subtype3 7 57

Figure S68.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.109 (Kruskal-Wallis (anova)), Q value = 0.28

Table S74.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 127 41.8 (27.1)
subtype1 38 35.3 (23.7)
subtype2 38 44.1 (32.0)
subtype3 51 44.9 (25.3)

Figure S69.  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 = 0.416 (Kruskal-Wallis (anova)), Q value = 0.66

Table S75.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 95 1961.8 (12.8)
subtype1 29 1959.6 (12.4)
subtype2 29 1964.5 (13.1)
subtype3 37 1961.3 (12.8)

Figure S70.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.454 (Fisher's exact test), Q value = 0.66

Table S76.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 119 8 1 5
subtype1 30 3 0 1
subtype2 47 3 1 4
subtype3 42 2 0 0

Figure S71.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.334 (Fisher's exact test), Q value = 0.57

Table S77.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 141
subtype1 0 3 36
subtype2 2 1 52
subtype3 0 2 53

Figure S72.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

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

Table S78.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 106
subtype1 0 28
subtype2 1 42
subtype3 1 36

Figure S73.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S79.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 40 33 45 31 32
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.329 (logrank test), Q value = 0.56

Table S80.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 168 63 0.1 - 224.0 (20.1)
subtype1 38 13 0.1 - 163.1 (18.5)
subtype2 31 11 0.1 - 224.0 (22.7)
subtype3 41 18 0.8 - 78.7 (23.2)
subtype4 28 14 0.8 - 64.9 (16.1)
subtype5 30 7 0.6 - 120.8 (15.1)

Figure S74.  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 = 0.242 (Kruskal-Wallis (anova)), Q value = 0.46

Table S81.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 164 65.5 (9.6)
subtype1 38 64.7 (9.0)
subtype2 30 64.7 (9.5)
subtype3 38 64.1 (10.4)
subtype4 28 65.9 (9.8)
subtype5 30 68.7 (9.1)

Figure S75.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.121 (Fisher's exact test), Q value = 0.29

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 1 41 52 15 25 33 7 7
subtype1 1 16 12 3 3 3 0 2
subtype2 0 7 12 1 3 5 2 3
subtype3 0 5 14 4 9 9 3 1
subtype4 0 4 8 2 6 10 1 0
subtype5 0 9 6 5 4 6 1 1

Figure S76.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0734 (Fisher's exact test), Q value = 0.23

Table S83.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 48 108 13 12
subtype1 17 22 1 0
subtype2 6 21 2 4
subtype3 9 29 3 4
subtype4 6 18 6 1
subtype5 10 18 1 3

Figure S77.  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 = 0.309 (Fisher's exact test), Q value = 0.54

Table S84.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 106 34 35
subtype1 27 7 3
subtype2 23 4 6
subtype3 22 11 11
subtype4 15 6 9
subtype5 19 6 6

Figure S78.  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 = 0.394 (Fisher's exact test), Q value = 0.63

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

nPatients 0 1
ALL 137 6
subtype1 31 2
subtype2 28 3
subtype3 32 1
subtype4 26 0
subtype5 20 0

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.00628 (Fisher's exact test), Q value = 0.038

Table S86.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 105 76
subtype1 30 10
subtype2 11 22
subtype3 27 18
subtype4 16 15
subtype5 21 11

Figure S80.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.12 (Kruskal-Wallis (anova)), Q value = 0.29

Table S87.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 14 60.0 (40.6)
subtype1 3 50.0 (45.8)
subtype2 2 80.0 (0.0)
subtype3 2 0.0 (0.0)
subtype4 1 0.0 (NA)
subtype5 6 88.3 (9.8)

Figure S81.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0192 (Fisher's exact test), Q value = 0.082

Table S88.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 5 43 111 3 8 2 1 5 3
subtype1 1 7 29 0 1 1 0 1 0
subtype2 0 17 13 1 1 0 0 1 0
subtype3 1 8 33 0 2 0 0 0 1
subtype4 1 4 20 2 1 0 1 1 1
subtype5 2 7 16 0 3 1 0 2 1

Figure S82.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.776 (Fisher's exact test), Q value = 0.89

Table S89.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 13 168
subtype1 2 38
subtype2 2 31
subtype3 3 42
subtype4 4 27
subtype5 2 30

Figure S83.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.031 (Kruskal-Wallis (anova)), Q value = 0.12

Table S90.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 127 41.8 (27.1)
subtype1 17 50.4 (28.5)
subtype2 29 45.9 (32.3)
subtype3 26 41.1 (21.8)
subtype4 27 46.7 (30.8)
subtype5 28 28.4 (15.7)

Figure S84.  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 = 0.00899 (Kruskal-Wallis (anova)), Q value = 0.049

Table S91.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 95 1961.8 (12.8)
subtype1 16 1964.8 (13.5)
subtype2 22 1952.8 (11.9)
subtype3 16 1963.2 (10.7)
subtype4 19 1963.6 (12.6)
subtype5 22 1966.0 (11.2)

Figure S85.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.747 (Fisher's exact test), Q value = 0.87

Table S92.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 119 8 1 5
subtype1 30 1 1 3
subtype2 28 1 0 1
subtype3 22 2 0 1
subtype4 19 1 0 0
subtype5 20 3 0 0

Figure S86.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.174 (Fisher's exact test), Q value = 0.38

Table S93.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 141
subtype1 1 1 30
subtype2 0 3 22
subtype3 1 0 38
subtype4 0 0 25
subtype5 0 2 26

Figure S87.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.632 (Fisher's exact test), Q value = 0.8

Table S94.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 106
subtype1 0 27
subtype2 0 11
subtype3 1 28
subtype4 1 16
subtype5 0 24

Figure S88.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S95.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 167 197 150
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00333 (logrank test), Q value = 0.024

Table S96.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 490 153 0.0 - 224.0 (18.1)
subtype1 157 34 0.1 - 224.0 (18.8)
subtype2 189 75 0.0 - 211.8 (17.6)
subtype3 144 44 0.1 - 214.6 (18.3)

Figure S89.  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 = 0.0479 (Kruskal-Wallis (anova)), Q value = 0.16

Table S97.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 483 65.3 (10.0)
subtype1 157 66.9 (9.3)
subtype2 184 64.2 (10.6)
subtype3 142 64.9 (9.6)

Figure S90.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00368 (Fisher's exact test), Q value = 0.025

Table S98.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 133 140 1 51 72 73 11 27
subtype1 2 63 47 0 14 15 19 1 5
subtype2 3 33 54 1 21 35 29 7 14
subtype3 0 37 39 0 16 22 25 3 8

Figure S91.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00175 (Fisher's exact test), Q value = 0.014

Table S99.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 168 277 47 19
subtype1 77 72 13 4
subtype2 49 117 19 10
subtype3 42 88 15 5

Figure S92.  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 = 0.0279 (Fisher's exact test), Q value = 0.12

Table S100.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 330 96 76
subtype1 119 25 17
subtype2 113 47 33
subtype3 98 24 26

Figure S93.  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 = 0.447 (Fisher's exact test), Q value = 0.66

Table S101.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 345 25
subtype1 109 5
subtype2 129 12
subtype3 107 8

Figure S94.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.00036

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

nPatients FEMALE MALE
ALL 276 238
subtype1 112 55
subtype2 113 84
subtype3 51 99

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.228 (Kruskal-Wallis (anova)), Q value = 0.45

Table S103.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 99 84.6 (21.2)
subtype1 30 84.0 (24.4)
subtype2 40 82.0 (22.6)
subtype3 29 89.0 (14.5)

Figure S96.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00036

Table S104.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 19 107 318 5 19 2 3 2 23 1 5 10
subtype1 7 35 90 5 14 0 1 1 8 1 1 4
subtype2 3 34 146 0 3 2 2 0 4 0 3 0
subtype3 9 38 82 0 2 0 0 1 11 0 1 6

Figure S97.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.925 (Fisher's exact test), Q value = 0.99

Table S105.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 21 493
subtype1 6 161
subtype2 8 189
subtype3 7 143

Figure S98.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0931 (Kruskal-Wallis (anova)), Q value = 0.27

Table S106.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 351 41.6 (27.3)
subtype1 97 37.8 (25.7)
subtype2 132 44.9 (27.7)
subtype3 122 41.0 (27.9)

Figure S99.  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 = 0.355 (Kruskal-Wallis (anova)), Q value = 0.6

Table S107.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 273 1965.0 (12.5)
subtype1 80 1964.1 (10.3)
subtype2 98 1966.3 (13.9)
subtype3 95 1964.3 (12.7)

Figure S100.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.648 (Fisher's exact test), Q value = 0.81

Table S108.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 342 12 4 25
subtype1 112 3 1 11
subtype2 127 6 3 8
subtype3 103 3 0 6

Figure S101.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.972 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 51 387
subtype1 0 3 18 131
subtype2 1 2 19 148
subtype3 0 3 14 108

Figure S102.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.497 (Fisher's exact test), Q value = 0.68

Table S110.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 381
subtype1 1 136
subtype2 4 147
subtype3 2 98

Figure S103.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S111.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 79 112 97 48 52 49 77
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.5e-07 (logrank test), Q value = 4.5e-05

Table S112.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 490 153 0.0 - 224.0 (18.1)
subtype1 75 20 0.1 - 163.1 (17.9)
subtype2 107 27 0.1 - 97.7 (18.1)
subtype3 93 35 0.1 - 224.0 (19.3)
subtype4 45 13 0.4 - 104.2 (20.8)
subtype5 49 12 0.1 - 88.0 (15.8)
subtype6 48 29 0.0 - 55.9 (14.1)
subtype7 73 17 0.1 - 164.1 (20.6)

Figure S104.  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 = 0.000674 (Kruskal-Wallis (anova)), Q value = 0.0093

Table S113.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 483 65.3 (10.0)
subtype1 75 68.3 (9.2)
subtype2 103 63.5 (10.8)
subtype3 90 65.5 (9.7)
subtype4 46 67.7 (7.7)
subtype5 49 60.7 (9.6)
subtype6 47 66.0 (9.9)
subtype7 73 65.5 (10.2)

Figure S105.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00119 (Fisher's exact test), Q value = 0.013

Table S114.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 133 140 1 51 72 73 11 27
subtype1 0 30 22 0 8 6 9 1 3
subtype2 1 16 35 0 9 24 18 3 6
subtype3 3 26 27 1 14 7 11 2 6
subtype4 0 17 15 0 4 5 3 2 2
subtype5 0 9 16 0 7 5 12 1 2
subtype6 0 7 7 0 2 13 12 2 6
subtype7 1 28 18 0 7 12 8 0 2

Figure S106.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00093 (Fisher's exact test), Q value = 0.011

Table S115.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 168 277 47 19
subtype1 36 36 4 3
subtype2 24 69 13 5
subtype3 39 51 3 3
subtype4 17 24 4 3
subtype5 11 35 5 1
subtype6 8 29 8 3
subtype7 33 33 10 1

Figure S107.  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 = 0.00293 (Fisher's exact test), Q value = 0.022

Table S116.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 330 96 76
subtype1 57 12 9
subtype2 66 25 19
subtype3 60 23 12
subtype4 37 8 2
subtype5 36 3 12
subtype6 22 12 14
subtype7 52 13 8

Figure S108.  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 = 0.513 (Fisher's exact test), Q value = 0.68

Table S117.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 345 25
subtype1 52 3
subtype2 79 4
subtype3 55 6
subtype4 38 2
subtype5 37 2
subtype6 37 6
subtype7 47 2

Figure S109.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.00036

Table S118.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 276 238
subtype1 63 16
subtype2 59 53
subtype3 52 45
subtype4 16 32
subtype5 23 29
subtype6 19 30
subtype7 44 33

Figure S110.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0947 (Kruskal-Wallis (anova)), Q value = 0.27

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

nPatients Mean (Std.Dev)
ALL 99 84.6 (21.2)
subtype1 12 94.2 (6.7)
subtype2 20 81.5 (24.6)
subtype3 24 83.8 (20.4)
subtype4 7 94.3 (7.9)
subtype5 13 83.1 (17.5)
subtype6 6 93.3 (10.3)
subtype7 17 77.1 (30.0)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00018 (Fisher's exact test), Q value = 0.0036

Table S120.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 19 107 318 5 19 2 3 2 23 1 5 10
subtype1 5 16 45 1 6 0 1 0 5 0 0 0
subtype2 2 19 80 0 3 1 2 0 4 0 1 0
subtype3 3 15 71 1 2 1 0 0 2 0 2 0
subtype4 2 19 18 0 1 0 0 0 7 0 0 1
subtype5 4 10 36 0 1 0 0 0 0 0 0 1
subtype6 1 12 31 0 0 0 0 0 2 0 1 2
subtype7 2 16 37 3 6 0 0 2 3 1 1 6

Figure S112.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.18 (Fisher's exact test), Q value = 0.38

Table S121.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 21 493
subtype1 2 77
subtype2 4 108
subtype3 2 95
subtype4 0 48
subtype5 4 48
subtype6 4 45
subtype7 5 72

Figure S113.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0416 (Kruskal-Wallis (anova)), Q value = 0.15

Table S122.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 351 41.6 (27.3)
subtype1 46 35.4 (25.7)
subtype2 78 41.1 (25.7)
subtype3 64 49.9 (31.5)
subtype4 37 40.4 (30.5)
subtype5 42 37.3 (26.3)
subtype6 39 45.9 (24.1)
subtype7 45 38.2 (24.2)

Figure S114.  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 = 0.000498 (Kruskal-Wallis (anova)), Q value = 0.0075

Table S123.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 273 1965.0 (12.5)
subtype1 37 1963.0 (10.3)
subtype2 59 1967.5 (14.2)
subtype3 50 1963.5 (12.3)
subtype4 29 1956.9 (11.4)
subtype5 34 1970.3 (9.7)
subtype6 29 1965.1 (13.3)
subtype7 35 1966.2 (11.2)

Figure S115.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.243 (Fisher's exact test), Q value = 0.46

Table S124.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 342 12 4 25
subtype1 54 4 0 2
subtype2 77 1 2 3
subtype3 58 3 2 9
subtype4 28 0 0 3
subtype5 37 1 0 2
subtype6 35 3 0 2
subtype7 53 0 0 4

Figure S116.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.6 (Fisher's exact test), Q value = 0.77

Table S125.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 51 387
subtype1 0 2 6 62
subtype2 1 3 9 84
subtype3 0 1 14 70
subtype4 0 0 5 37
subtype5 0 0 5 38
subtype6 0 2 2 38
subtype7 0 0 10 58

Figure S117.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0351 (Fisher's exact test), Q value = 0.13

Table S126.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 381
subtype1 1 60
subtype2 0 85
subtype3 4 71
subtype4 0 34
subtype5 0 36
subtype6 2 31
subtype7 0 64

Figure S118.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S127.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 190 227 95
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.178 (logrank test), Q value = 0.38

Table S128.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 488 152 0.0 - 224.0 (18.4)
subtype1 180 48 0.1 - 164.1 (18.4)
subtype2 219 70 0.0 - 214.6 (19.0)
subtype3 89 34 0.1 - 224.0 (16.4)

Figure S119.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0102 (Kruskal-Wallis (anova)), Q value = 0.051

Table S129.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 482 65.3 (10.0)
subtype1 180 67.0 (9.4)
subtype2 217 64.0 (10.3)
subtype3 85 65.1 (9.7)

Figure S120.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00131 (Fisher's exact test), Q value = 0.013

Table S130.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 134 140 1 51 71 73 11 25
subtype1 1 72 40 0 19 25 22 1 9
subtype2 4 46 70 0 22 34 30 8 13
subtype3 0 16 30 1 10 12 21 2 3

Figure S121.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0172 (Fisher's exact test), Q value = 0.079

Table S131.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 169 274 47 19
subtype1 83 85 16 6
subtype2 60 133 23 9
subtype3 26 56 8 4

Figure S122.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.276 (Fisher's exact test), Q value = 0.49

Table S132.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 329 95 76
subtype1 124 39 21
subtype2 149 38 36
subtype3 56 18 19

Figure S123.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.55 (Fisher's exact test), Q value = 0.72

Table S133.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 345 23
subtype1 130 8
subtype2 156 13
subtype3 59 2

Figure S124.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.476 (Fisher's exact test), Q value = 0.68

Table S134.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 273 239
subtype1 108 82
subtype2 116 111
subtype3 49 46

Figure S125.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.685 (Kruskal-Wallis (anova)), Q value = 0.82

Table S135.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 97 84.3 (21.3)
subtype1 31 83.9 (23.8)
subtype2 54 85.0 (20.9)
subtype3 12 82.5 (17.1)

Figure S126.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.00075 (Fisher's exact test), Q value = 0.0096

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

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 19 106 318 5 19 2 2 2 23 1 5 10
subtype1 10 45 99 4 12 1 1 2 8 1 2 5
subtype2 8 53 147 0 4 1 0 0 9 0 3 2
subtype3 1 8 72 1 3 0 1 0 6 0 0 3

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

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

Table S137.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 20 492
subtype1 7 183
subtype2 9 218
subtype3 4 91

Figure S128.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0797 (Kruskal-Wallis (anova)), Q value = 0.24

Table S138.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 351 41.8 (27.4)
subtype1 125 38.2 (25.8)
subtype2 168 43.2 (27.9)
subtype3 58 45.3 (28.8)

Figure S129.  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 = 0.017 (Kruskal-Wallis (anova)), Q value = 0.079

Table S139.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 273 1964.8 (12.5)
subtype1 104 1962.3 (11.9)
subtype2 129 1967.1 (12.7)
subtype3 40 1963.5 (12.1)

Figure S130.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.451 (Fisher's exact test), Q value = 0.66

Table S140.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 339 12 4 26
subtype1 122 2 2 10
subtype2 162 7 2 9
subtype3 55 3 0 7

Figure S131.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.441 (Fisher's exact test), Q value = 0.66

Table S141.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 51 386
subtype1 1 18 152
subtype2 5 25 158
subtype3 2 8 76

Figure S132.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.366 (Fisher's exact test), Q value = 0.6

Table S142.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 379
subtype1 1 144
subtype2 5 161
subtype3 1 74

Figure S133.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S143.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 72 198 142 100
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.817 (logrank test), Q value = 0.92

Table S144.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 488 152 0.0 - 224.0 (18.4)
subtype1 69 20 0.1 - 107.2 (20.6)
subtype2 191 58 0.0 - 224.0 (15.7)
subtype3 133 41 0.1 - 164.1 (17.5)
subtype4 95 33 0.1 - 214.6 (20.2)

Figure S134.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00155 (Kruskal-Wallis (anova)), Q value = 0.013

Table S145.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 482 65.3 (10.0)
subtype1 68 68.1 (7.8)
subtype2 188 63.7 (10.0)
subtype3 133 66.9 (9.8)
subtype4 93 64.1 (10.8)

Figure S135.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.59 (Fisher's exact test), Q value = 0.76

Table S146.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 134 140 1 51 71 73 11 25
subtype1 0 29 16 0 3 10 10 1 3
subtype2 3 39 61 0 20 30 30 6 9
subtype3 1 42 36 1 16 19 16 2 8
subtype4 1 24 27 0 12 12 17 2 5

Figure S136.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.135 (Fisher's exact test), Q value = 0.32

Table S147.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 169 274 47 19
subtype1 31 33 4 4
subtype2 50 120 19 8
subtype3 55 67 14 4
subtype4 33 54 10 3

Figure S137.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.716 (Fisher's exact test), Q value = 0.84

Table S148.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 329 95 76
subtype1 48 14 7
subtype2 128 36 32
subtype3 89 29 18
subtype4 64 16 19

Figure S138.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.846 (Fisher's exact test), Q value = 0.95

Table S149.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 345 23
subtype1 50 2
subtype2 144 9
subtype3 89 7
subtype4 62 5

Figure S139.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0046 (Fisher's exact test), Q value = 0.03

Table S150.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 273 239
subtype1 35 37
subtype2 113 85
subtype3 86 56
subtype4 39 61

Figure S140.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.715 (Kruskal-Wallis (anova)), Q value = 0.84

Table S151.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 97 84.3 (21.3)
subtype1 8 90.0 (7.6)
subtype2 46 85.0 (22.3)
subtype3 21 80.5 (27.8)
subtype4 22 84.5 (14.7)

Figure S141.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.00133 (Fisher's exact test), Q value = 0.013

Table S152.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 19 106 318 5 19 2 2 2 23 1 5 10
subtype1 6 18 39 0 4 1 0 0 4 0 0 0
subtype2 4 40 135 2 4 0 2 0 10 0 1 0
subtype3 6 30 73 3 10 0 0 2 7 1 2 8
subtype4 3 18 71 0 1 1 0 0 2 0 2 2

Figure S142.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.782 (Fisher's exact test), Q value = 0.89

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

nPatients NO YES
ALL 20 492
subtype1 4 68
subtype2 8 190
subtype3 4 138
subtype4 4 96

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0407 (Kruskal-Wallis (anova)), Q value = 0.15

Table S154.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 351 41.8 (27.4)
subtype1 49 43.3 (29.9)
subtype2 136 41.4 (25.3)
subtype3 92 36.2 (24.3)
subtype4 74 48.4 (31.5)

Figure S144.  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 = 0.00525 (Kruskal-Wallis (anova)), Q value = 0.033

Table S155.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 273 1964.8 (12.5)
subtype1 47 1959.6 (12.7)
subtype2 102 1966.3 (12.0)
subtype3 71 1964.1 (11.7)
subtype4 53 1967.2 (13.1)

Figure S145.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.954 (Fisher's exact test), Q value = 1

Table S156.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 339 12 4 26
subtype1 41 1 0 4
subtype2 140 6 1 8
subtype3 90 3 2 9
subtype4 68 2 1 5

Figure S146.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.316 (Fisher's exact test), Q value = 0.55

Table S157.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 51 386
subtype1 1 6 58
subtype2 4 18 150
subtype3 2 11 114
subtype4 1 16 64

Figure S147.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.757 (Fisher's exact test), Q value = 0.87

Table S158.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 379
subtype1 0 52
subtype2 3 149
subtype3 2 108
subtype4 2 70

Figure S148.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S159.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 146 197 93
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.267 (logrank test), Q value = 0.49

Table S160.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 414 127 0.1 - 224.0 (17.6)
subtype1 137 47 0.1 - 163.1 (17.6)
subtype2 190 52 0.1 - 214.6 (17.9)
subtype3 87 28 0.1 - 224.0 (15.8)

Figure S149.  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 = 0.657 (Kruskal-Wallis (anova)), Q value = 0.82

Table S161.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 408 65.1 (10.0)
subtype1 137 65.4 (10.7)
subtype2 188 64.7 (9.7)
subtype3 83 65.4 (9.9)

Figure S150.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.476 (Fisher's exact test), Q value = 0.68

Table S162.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 113 114 1 48 61 64 8 21
subtype1 1 36 33 0 18 19 24 4 10
subtype2 4 58 52 1 23 26 22 3 8
subtype3 0 19 29 0 7 16 18 1 3

Figure S151.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.015 (Fisher's exact test), Q value = 0.073

Table S163.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 147 231 40 15
subtype1 48 73 14 11
subtype2 76 100 18 2
subtype3 23 58 8 2

Figure S152.  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 = 0.191 (Fisher's exact test), Q value = 0.4

Table S164.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 280 81 65
subtype1 86 34 21
subtype2 138 31 26
subtype3 56 16 18

Figure S153.  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 = 0.434 (Fisher's exact test), Q value = 0.66

Table S165.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 279 19
subtype1 97 9
subtype2 123 8
subtype3 59 2

Figure S154.  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 = 0.96 (Fisher's exact test), Q value = 1

Table S166.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 233 203
subtype1 77 69
subtype2 105 92
subtype3 51 42

Figure S155.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.109 (Kruskal-Wallis (anova)), Q value = 0.28

Table S167.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 90 85.0 (20.1)
subtype1 17 75.3 (30.2)
subtype2 56 87.9 (16.8)
subtype3 17 85.3 (15.0)

Figure S156.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 5e-05 (Fisher's exact test), Q value = 0.0013

Table S168.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 17 86 266 5 19 1 2 2 22 1 5 10
subtype1 4 38 75 3 11 1 2 1 7 0 0 4
subtype2 13 39 117 0 7 0 0 0 11 1 5 4
subtype3 0 9 74 2 1 0 0 1 4 0 0 2

Figure S157.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0325 (Fisher's exact test), Q value = 0.13

Table S169.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 18 418
subtype1 9 137
subtype2 3 194
subtype3 6 87

Figure S158.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00889 (Kruskal-Wallis (anova)), Q value = 0.049

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

nPatients Mean (Std.Dev)
ALL 296 41.3 (27.5)
subtype1 104 36.2 (26.9)
subtype2 139 43.8 (28.0)
subtype3 53 44.9 (26.3)

Figure S159.  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 = 0.506 (Kruskal-Wallis (anova)), Q value = 0.68

Table S171.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 235 1965.0 (12.3)
subtype1 77 1964.6 (13.2)
subtype2 121 1966.0 (11.8)
subtype3 37 1962.8 (11.9)

Figure S160.  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 'COMPLETENESS_OF_RESECTION'

P value = 0.158 (Fisher's exact test), Q value = 0.36

Table S172.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 279 9 1 22
subtype1 83 5 1 4
subtype2 139 2 0 15
subtype3 57 2 0 3

Figure S161.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.95 (Fisher's exact test), Q value = 1

Table S173.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 49 332
subtype1 1 15 110
subtype2 3 24 148
subtype3 1 10 74

Figure S162.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.102 (Fisher's exact test), Q value = 0.27

Table S174.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 324
subtype1 4 91
subtype2 2 159
subtype3 0 74

Figure S163.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S175.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 103 247 86
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0545 (logrank test), Q value = 0.18

Table S176.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 414 127 0.1 - 224.0 (17.6)
subtype1 99 20 0.1 - 164.1 (19.0)
subtype2 233 77 0.1 - 214.6 (15.8)
subtype3 82 30 0.1 - 224.0 (17.4)

Figure S164.  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 = 2.92e-06 (Kruskal-Wallis (anova)), Q value = 0.00026

Table S177.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 408 65.1 (10.0)
subtype1 98 68.3 (8.1)
subtype2 229 62.9 (10.4)
subtype3 81 67.2 (9.7)

Figure S165.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.398 (Fisher's exact test), Q value = 0.63

Table S178.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IV
ALL 5 113 114 1 48 61 64 8 21
subtype1 1 35 22 1 10 16 9 2 6
subtype2 4 54 68 0 30 37 39 4 11
subtype3 0 24 24 0 8 8 16 2 4

Figure S166.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00931 (Fisher's exact test), Q value = 0.049

Table S179.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 147 231 40 15
subtype1 43 42 13 4
subtype2 74 143 24 5
subtype3 30 46 3 6

Figure S167.  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 = 0.0695 (Fisher's exact test), Q value = 0.22

Table S180.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 280 81 65
subtype1 75 15 8
subtype2 155 46 43
subtype3 50 20 14

Figure S168.  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 = 0.582 (Fisher's exact test), Q value = 0.75

Table S181.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 279 19
subtype1 66 6
subtype2 160 11
subtype3 53 2

Figure S169.  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 = 0.00045 (Fisher's exact test), Q value = 0.0074

Table S182.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 233 203
subtype1 52 51
subtype2 119 128
subtype3 62 24

Figure S170.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.668 (Kruskal-Wallis (anova)), Q value = 0.82

Table S183.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 90 85.0 (20.1)
subtype1 17 90.6 (6.6)
subtype2 59 84.4 (20.0)
subtype3 14 80.7 (29.2)

Figure S171.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 7e-05 (Fisher's exact test), Q value = 0.0016

Table S184.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SIGNET RING ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) CARCINOMA
ALL 17 86 266 5 19 1 2 2 22 1 5 10
subtype1 5 27 43 3 6 0 0 2 8 1 1 7
subtype2 9 44 168 0 8 1 0 0 10 0 4 3
subtype3 3 15 55 2 5 0 2 0 4 0 0 0

Figure S172.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0923 (Fisher's exact test), Q value = 0.27

Table S185.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 18 418
subtype1 1 102
subtype2 11 236
subtype3 6 80

Figure S173.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.224 (Kruskal-Wallis (anova)), Q value = 0.44

Table S186.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 296 41.3 (27.5)
subtype1 67 39.6 (29.0)
subtype2 176 43.1 (27.8)
subtype3 53 37.6 (24.5)

Figure S174.  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 = 0.00232 (Kruskal-Wallis (anova)), Q value = 0.018

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

nPatients Mean (Std.Dev)
ALL 235 1965.0 (12.3)
subtype1 53 1961.2 (10.6)
subtype2 138 1967.3 (12.1)
subtype3 44 1962.6 (13.5)

Figure S175.  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 'COMPLETENESS_OF_RESECTION'

P value = 0.0999 (Fisher's exact test), Q value = 0.27

Table S188.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 279 9 1 22
subtype1 65 0 0 9
subtype2 164 5 1 10
subtype3 50 4 0 3

Figure S176.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.103 (Fisher's exact test), Q value = 0.27

Table S189.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 49 332
subtype1 2 9 81
subtype2 1 34 181
subtype3 2 6 70

Figure S177.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.454 (Fisher's exact test), Q value = 0.66

Table S190.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 324
subtype1 0 82
subtype2 5 182
subtype3 1 60

Figure S178.  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/LUAD-TP/15111023/LUAD-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LUAD-TP/15084450/LUAD-TP.merged_data.txt

  • Number of patients = 518

  • 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.

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)
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