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 14 clinical features across 422 patients, 4 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 'PATHOLOGY.T.STAGE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T.STAGE'.

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

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

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

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGY.M.STAGE' and 'KARNOFSKY.PERFORMANCE.SCORE'.

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

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 4 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 0.145
(1.00) 		0.293
(1.00) 		0.755
(1.00) 		0.75
(1.00) 		0.0176
(1.00) 		0.0416
(1.00) 		0.306
(1.00) 		0.412
(1.00) 		0.00899
(1.00) 		0.0947
(1.00) 		0.791
(1.00) 		0.0433
(1.00)
AGE Kruskal-Wallis (anova) 0.924
(1.00) 		0.645
(1.00) 		0.0586
(1.00) 		0.0324
(1.00) 		0.0128
(1.00) 		0.288
(1.00) 		0.0218
(1.00) 		0.00523
(0.843) 		0.00346
(0.56) 		0.0533
(1.00) 		0.0527
(1.00) 		0.0762
(1.00)
NEOPLASM DISEASESTAGE Fisher's exact test 0.0118
(1.00) 		0.0226
(1.00) 		0.653
(1.00) 		0.765
(1.00) 		0.398
(1.00) 		0.784
(1.00) 		0.0832
(1.00) 		0.0714
(1.00) 		0.00216
(0.354) 		0.334
(1.00) 		0.386
(1.00) 		0.113
(1.00)
PATHOLOGY T STAGE Fisher's exact test 0.00049
(0.0808) 		0.0001
(0.0167) 		0.543
(1.00) 		0.635
(1.00) 		0.146
(1.00) 		0.5
(1.00) 		0.767
(1.00) 		0.136
(1.00) 		0.0738
(1.00) 		0.7
(1.00) 		0.255
(1.00) 		0.117
(1.00)
PATHOLOGY N STAGE Fisher's exact test 0.906
(1.00) 		0.359
(1.00) 		0.301
(1.00) 		0.816
(1.00) 		0.31
(1.00) 		0.0659
(1.00) 		0.48
(1.00) 		0.956
(1.00) 		0.468
(1.00) 		0.891
(1.00) 		0.502
(1.00) 		0.297
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.107
(1.00) 		0.0707
(1.00) 		0.64
(1.00) 		0.362
(1.00) 		0.61
(1.00) 		0.911
(1.00) 		0.413
(1.00) 		0.432
(1.00) 		0.00044
(0.073) 		0.28
(1.00) 		1
(1.00) 		0.463
(1.00)
GENDER Fisher's exact test 0.334
(1.00) 		0.0577
(1.00) 		0.00658
(1.00) 		0.594
(1.00) 		0.264
(1.00) 		0.666
(1.00) 		0.549
(1.00) 		0.0321
(1.00) 		0.174
(1.00) 		0.536
(1.00) 		0.376
(1.00) 		0.722
(1.00)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.166
(1.00) 		0.0261
(1.00) 		0.466
(1.00) 		0.0445
(1.00) 		0.373
(1.00) 		0.927
(1.00) 		0.907
(1.00) 		0.451
(1.00) 		7.6e-05
(0.0128) 		0.0856
(1.00) 		0.979
(1.00) 		0.108
(1.00)
HISTOLOGICAL TYPE Fisher's exact test 0.293
(1.00) 		0.323
(1.00) 		0.0549
(1.00) 		0.248
(1.00) 		0.0839
(1.00) 		0.0658
(1.00) 		0.507
(1.00) 		0.102
(1.00) 		0.164
(1.00) 		0.0348
(1.00) 		0.274
(1.00) 		0.0252
(1.00)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.551
(1.00) 		0.533
(1.00) 		0.467
(1.00) 		0.638
(1.00) 		0.85
(1.00) 		0.895
(1.00) 		0.437
(1.00) 		0.83
(1.00) 		0.712
(1.00) 		0.203
(1.00) 		0.182
(1.00) 		0.696
(1.00)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.485
(1.00) 		0.999
(1.00) 		0.0609
(1.00) 		0.413
(1.00) 		0.0791
(1.00) 		0.604
(1.00) 		0.585
(1.00) 		0.741
(1.00) 		0.82
(1.00) 		0.599
(1.00) 		0.354
(1.00) 		0.116
(1.00)
COMPLETENESS OF RESECTION Fisher's exact test 0.561
(1.00) 		0.817
(1.00) 		0.0213
(1.00) 		0.302
(1.00) 		0.0156
(1.00) 		0.0489
(1.00) 		0.646
(1.00) 		0.716
(1.00) 		0.42
(1.00) 		0.314
(1.00) 		0.407
(1.00) 		0.556
(1.00)
RACE Fisher's exact test 0.985
(1.00) 		0.975
(1.00) 		0.326
(1.00) 		0.0711
(1.00) 		0.197
(1.00) 		0.833
(1.00) 		0.618
(1.00) 		0.563
(1.00) 		0.129
(1.00) 		0.0443
(1.00) 		0.227
(1.00) 		0.00321
(0.523)
ETHNICITY Fisher's exact test 0.269
(1.00) 		0.069
(1.00) 		0.221
(1.00) 		0.578
(1.00) 		0.383
(1.00) 		0.84
(1.00) 		0.173
(1.00) 		0.748
(1.00) 		0.948
(1.00) 		0.861
(1.00) 		0.807
(1.00) 		0.204
(1.00)
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 42 52 32 28
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.145 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 147 63 12.0 - 5287.0 (762.0)
subtype1 41 17 13.0 - 3724.0 (717.0)
subtype2 49 18 12.0 - 3016.0 (1058.0)
subtype3 30 17 12.0 - 5287.0 (819.0)
subtype4 27 11 12.0 - 1966.0 (358.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.924 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 152 66.5 (8.6)
subtype1 41 66.4 (7.7)
subtype2 51 66.5 (8.2)
subtype3 32 67.2 (9.8)
subtype4 28 66.0 (9.4)

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0118 (Fisher's exact test), Q value = 1

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 IIIB STAGE IV
ALL 20 61 7 27 19 15 4
subtype1 2 21 0 9 6 2 2
subtype2 2 24 4 9 6 7 0
subtype3 8 11 2 4 4 2 0
subtype4 8 5 1 5 3 4 2

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 = 0.00049 (Fisher's exact test), Q value = 0.081

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

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 5 30 6 1
subtype2 4 41 1 6
subtype3 11 18 2 1
subtype4 10 11 3 4

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 = 0.906 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 26 11 3 2
subtype2 28 17 5 2
subtype3 22 6 3 1
subtype4 20 6 2 0

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

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

P value = 0.107 (Fisher's exact test), Q value = 1

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

nPatients M0 M1
ALL 146 4
subtype1 38 2
subtype2 50 0
subtype3 32 0
subtype4 26 2

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.334 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 44 110
subtype1 9 33
subtype2 13 39
subtype3 11 21
subtype4 11 17

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

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

P value = 0.166 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 26 24.2 (38.5)
subtype1 4 0.0 (0.0)
subtype2 4 0.0 (0.0)
subtype3 9 31.1 (46.8)
subtype4 9 38.9 (39.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.293 (Fisher's exact test), Q value = 1

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

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 5 1 148
subtype1 0 0 42
subtype2 3 0 49
subtype3 1 0 31
subtype4 1 1 26

Figure S9.  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 = 0.551 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 2 152
subtype1 1 41
subtype2 0 52
subtype3 1 31
subtype4 0 28

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

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.485 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 133 54.8 (36.5)
subtype1 38 60.2 (41.4)
subtype2 47 52.0 (25.5)
subtype3 27 48.6 (36.2)
subtype4 21 59.6 (47.7)

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

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.561 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 139 3 2 5
subtype1 38 0 1 1
subtype2 47 1 1 1
subtype3 27 1 0 3
subtype4 27 1 0 0

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.985 (Fisher's exact test), Q value = 1

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 7 91
subtype1 1 2 24
subtype2 1 3 27
subtype3 1 1 21
subtype4 0 1 19

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.269 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 88
subtype1 3 23
subtype2 1 26
subtype3 0 20
subtype4 0 19

Figure S14.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: '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 47 56 51
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.293 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 147 63 12.0 - 5287.0 (762.0)
subtype1 46 21 13.0 - 3724.0 (708.0)
subtype2 53 20 12.0 - 3016.0 (913.0)
subtype3 48 22 12.0 - 5287.0 (663.0)

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.645 (Kruskal-Wallis (anova)), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

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 IIIB STAGE IV
ALL 20 61 7 27 19 15 4
subtype1 4 20 2 9 7 2 3
subtype2 2 25 4 10 6 9 0
subtype3 14 16 1 8 6 4 1

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 = 1e-04 (Fisher's exact test), Q value = 0.017

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

Figure 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 = 0.359 (Fisher's exact test), Q value = 1

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

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

Figure 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 = 0.0707 (Fisher's exact test), Q value = 1

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

nPatients M0 M1
ALL 146 4
subtype1 42 3
subtype2 54 0
subtype3 50 1

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 = 0.0577 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.0261 (Kruskal-Wallis (anova)), Q value = 1

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 26 24.2 (38.5)
subtype1 8 0.0 (0.0)
subtype2 5 10.0 (22.4)
subtype3 13 44.6 (44.6)

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.323 (Fisher's exact test), Q value = 1

Table S25.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

Figure S23.  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 = 0.533 (Fisher's exact test), Q value = 1

Table S26.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 152
subtype1 1 46
subtype2 0 56
subtype3 1 50

Figure S24.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S27.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

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

Figure S25.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.817 (Fisher's exact test), Q value = 1

Table S28.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

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

Figure S26.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.975 (Fisher's exact test), Q value = 1

Table S29.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 7 91
subtype1 1 2 27
subtype2 1 3 30
subtype3 1 2 34

Figure S27.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.069 (Fisher's exact test), Q value = 1

Table S30.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 88
subtype1 3 25
subtype2 1 29
subtype3 0 34

Figure S28.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: '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
Number of samples 158 138 124
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.755 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 388 123 1.0 - 5287.0 (641.5)
subtype1 146 55 3.0 - 5287.0 (710.0)
subtype2 129 37 3.0 - 3636.0 (734.0)
subtype3 113 31 1.0 - 3724.0 (430.0)

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 'AGE'

P value = 0.0586 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 412 67.2 (8.7)
subtype1 155 67.4 (9.6)
subtype2 135 65.9 (8.3)
subtype3 122 68.4 (7.6)

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.653 (Fisher's exact test), Q value = 1

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 1 72 137 1 52 71 57 20 6
subtype1 0 29 57 0 20 26 17 6 1
subtype2 0 18 46 1 19 24 19 9 2
subtype3 1 25 34 0 13 21 21 5 3

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 = 0.543 (Fisher's exact test), Q value = 1

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 98 254 48 20
subtype1 36 94 22 6
subtype2 28 88 16 6
subtype3 34 72 10 8

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 = 0.301 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 261 113 35 5
subtype1 107 35 10 2
subtype2 77 46 12 2
subtype3 77 32 13 1

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 = 0.64 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 M1A MX
ALL 358 5 1 50
subtype1 133 1 0 23
subtype2 118 2 0 15
subtype3 107 2 1 12

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 = 0.00658 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 107 313
subtype1 54 104
subtype2 26 112
subtype3 27 97

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 = 0.466 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 93 52.6 (41.9)
subtype1 28 55.7 (42.1)
subtype2 31 54.2 (43.0)
subtype3 34 48.5 (41.7)

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 = 0.0549 (Fisher's exact test), Q value = 1

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

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 12 6 1 401
subtype1 1 4 1 152
subtype2 8 1 0 129
subtype3 3 1 0 120

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 = 0.467 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 13 407
subtype1 5 153
subtype2 6 132
subtype3 2 122

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 'NUMBERPACKYEARSSMOKED'

P value = 0.0609 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 356 53.0 (31.7)
subtype1 132 49.8 (31.9)
subtype2 119 53.8 (31.8)
subtype3 105 56.1 (31.3)

Figure S39.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0213 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 342 8 4 18
subtype1 122 3 2 11
subtype2 113 3 2 7
subtype3 107 2 0 0

Figure S40.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.326 (Fisher's exact test), Q value = 1

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 17 298
subtype1 6 4 114
subtype2 2 6 97
subtype3 1 7 87

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.221 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 267
subtype1 6 106
subtype2 1 81
subtype3 1 80

Figure S42.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S46.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 118 91 79
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.75 (logrank test), Q value = 1

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 260 71 1.0 - 5287.0 (492.5)
subtype1 104 31 3.0 - 5287.0 (430.0)
subtype2 82 21 4.0 - 2754.0 (548.0)
subtype3 74 19 1.0 - 1970.0 (492.5)

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0324 (Kruskal-Wallis (anova)), Q value = 1

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 280 67.6 (8.8)
subtype1 115 68.9 (9.6)
subtype2 89 66.0 (8.6)
subtype3 76 67.4 (7.5)

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.765 (Fisher's exact test), Q value = 1

Table S49.  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 2 57 82 1 47 48 41 5 3
subtype1 1 25 30 0 19 23 15 2 3
subtype2 1 15 33 1 15 12 12 2 0
subtype3 0 17 19 0 13 13 14 1 0

Figure S45.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.635 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 76 166 37 9
subtype1 30 66 17 5
subtype2 26 49 14 2
subtype3 20 51 6 2

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

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

Table S51.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 179 79 24
subtype1 72 32 9
subtype2 60 24 6
subtype3 47 23 9

Figure S47.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.362 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 233 2 1 50
subtype1 90 2 1 25
subtype2 76 0 0 15
subtype3 67 0 0 10

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.594 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 73 215
subtype1 33 85
subtype2 23 68
subtype3 17 62

Figure S49.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0445 (Kruskal-Wallis (anova)), Q value = 1

Table S54.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 70 60.9 (39.4)
subtype1 19 75.8 (32.0)
subtype2 28 54.6 (43.3)
subtype3 23 56.1 (38.3)

Figure S50.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.248 (Fisher's exact test), Q value = 1

Table S55.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 9 5 1 273
subtype1 1 3 0 114
subtype2 5 1 0 85
subtype3 3 1 1 74

Figure S51.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.638 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 277
subtype1 5 113
subtype2 2 89
subtype3 4 75

Figure S52.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.413 (Kruskal-Wallis (anova)), Q value = 1

Table S57.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 244 52.2 (29.6)
subtype1 98 50.1 (29.2)
subtype2 76 51.3 (25.6)
subtype3 70 56.1 (34.1)

Figure S53.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.302 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 222 5 2 13
subtype1 88 1 0 8
subtype2 73 3 1 4
subtype3 61 1 1 1

Figure S54.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0711 (Fisher's exact test), Q value = 1

Table S59.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 7 10 224
subtype1 6 5 88
subtype2 1 1 77
subtype3 0 4 59

Figure S55.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.578 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 189
subtype1 4 81
subtype2 1 59
subtype3 1 49

Figure S56.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S61.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 60 73 62
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0176 (logrank test), Q value = 1

Table S62.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 181 70 1.0 - 5287.0 (833.0)
subtype1 57 17 1.0 - 5287.0 (941.0)
subtype2 69 30 12.0 - 3636.0 (911.0)
subtype3 55 23 3.0 - 3644.0 (700.0)

Figure S57.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0128 (Kruskal-Wallis (anova)), Q value = 1

Table S63.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 187 67.4 (9.5)
subtype1 59 65.5 (10.5)
subtype2 69 66.6 (8.5)
subtype3 59 70.3 (9.1)

Figure S58.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.398 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #5: 'RPPA 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
ALL 1 28 71 1 23 33 22 14
subtype1 0 10 17 0 7 11 7 8
subtype2 0 7 28 0 10 15 8 4
subtype3 1 11 26 1 6 7 7 2

Figure S59.  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 = 0.146 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 45 119 20 11
subtype1 15 35 4 6
subtype2 11 51 9 2
subtype3 19 33 7 3

Figure S60.  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 = 0.31 (Fisher's exact test), Q value = 1

Table S66.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 124 49 16 4
subtype1 34 17 7 2
subtype2 44 21 5 2
subtype3 46 11 4 0

Figure S61.  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 = 0.61 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 MX
ALL 176 16
subtype1 54 6
subtype2 67 4
subtype3 55 6

Figure S62.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.264 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 146
subtype1 12 48
subtype2 17 56
subtype3 20 42

Figure S63.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.373 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 30.3 (39.3)
subtype1 8 43.8 (35.0)
subtype2 14 28.6 (40.0)
subtype3 12 23.3 (42.3)

Figure S64.  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 = 0.0839 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 3 2 190
subtype1 2 2 56
subtype2 0 0 73
subtype3 1 0 61

Figure S65.  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 = 0.85 (Fisher's exact test), Q value = 1

Table S71.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 185
subtype1 4 56
subtype2 3 70
subtype3 3 59

Figure S66.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0791 (Kruskal-Wallis (anova)), Q value = 1

Table S72.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 165 51.7 (32.3)
subtype1 51 60.6 (39.7)
subtype2 62 49.3 (30.0)
subtype3 52 45.9 (25.1)

Figure S67.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0156 (Fisher's exact test), Q value = 1

Table S73.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 149 4 4 9
subtype1 43 0 2 5
subtype2 55 4 2 4
subtype3 51 0 0 0

Figure S68.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.197 (Fisher's exact test), Q value = 1

Table S74.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 9 147
subtype1 2 1 46
subtype2 0 6 53
subtype3 1 2 48

Figure S69.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.383 (Fisher's exact test), Q value = 1

Table S75.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 120
subtype1 1 37
subtype2 3 45
subtype3 0 38

Figure S70.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S76.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 54 41 52 48
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0416 (logrank test), Q value = 1

Table S77.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 181 70 1.0 - 5287.0 (833.0)
subtype1 53 19 1.0 - 5287.0 (1213.0)
subtype2 36 19 10.0 - 1967.0 (869.0)
subtype3 50 20 3.0 - 3636.0 (708.0)
subtype4 42 12 6.0 - 3016.0 (790.5)

Figure S71.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.288 (Kruskal-Wallis (anova)), Q value = 1

Table S78.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 187 67.4 (9.5)
subtype1 52 65.8 (10.9)
subtype2 40 68.2 (9.5)
subtype3 49 66.8 (8.3)
subtype4 46 69.2 (9.0)

Figure S72.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.784 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #6: 'RPPA 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
ALL 1 28 71 1 23 33 22 14
subtype1 0 9 16 0 7 9 8 5
subtype2 0 7 17 1 6 5 3 1
subtype3 0 4 19 0 7 12 5 4
subtype4 1 8 19 0 3 7 6 4

Figure S73.  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 = 0.5 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 45 119 20 11
subtype1 13 32 4 5
subtype2 9 26 5 1
subtype3 7 36 6 3
subtype4 16 25 5 2

Figure S74.  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 = 0.0659 (Fisher's exact test), Q value = 1

Table S81.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 124 49 16 4
subtype1 32 14 8 0
subtype2 31 7 3 0
subtype3 29 19 1 2
subtype4 32 9 4 2

Figure S75.  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 = 0.911 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 MX
ALL 176 16
subtype1 49 5
subtype2 36 4
subtype3 47 3
subtype4 44 4

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.666 (Fisher's exact test), Q value = 1

Table S83.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 146
subtype1 14 40
subtype2 9 32
subtype3 11 41
subtype4 15 33

Figure S77.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.927 (Kruskal-Wallis (anova)), Q value = 1

Table S84.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 30.3 (39.3)
subtype1 9 25.6 (35.4)
subtype2 13 30.8 (42.7)
subtype3 8 28.8 (39.8)
subtype4 4 42.5 (49.2)

Figure S78.  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 = 0.0658 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 3 2 190
subtype1 3 1 50
subtype2 0 1 40
subtype3 0 0 52
subtype4 0 0 48

Figure S79.  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 = 0.895 (Fisher's exact test), Q value = 1

Table S86.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 185
subtype1 3 51
subtype2 1 40
subtype3 3 49
subtype4 3 45

Figure S80.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.604 (Kruskal-Wallis (anova)), Q value = 1

Table S87.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 165 51.7 (32.3)
subtype1 47 61.0 (46.0)
subtype2 34 48.5 (24.3)
subtype3 44 47.2 (23.8)
subtype4 40 48.4 (25.1)

Figure S81.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0489 (Fisher's exact test), Q value = 1

Table S88.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 149 4 4 9
subtype1 46 0 0 4
subtype2 29 0 0 3
subtype3 37 4 2 1
subtype4 37 0 2 1

Figure S82.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.833 (Fisher's exact test), Q value = 1

Table S89.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 9 147
subtype1 2 2 43
subtype2 1 2 32
subtype3 0 2 39
subtype4 0 3 33

Figure S83.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.84 (Fisher's exact test), Q value = 1

Table S90.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 120
subtype1 2 34
subtype2 1 26
subtype3 1 30
subtype4 0 30

Figure S84.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S91.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 96 128 124 71
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.306 (logrank test), Q value = 1

Table S92.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 386 120 1.0 - 5287.0 (640.0)
subtype1 91 29 1.0 - 3724.0 (717.0)
subtype2 117 32 4.0 - 3016.0 (862.0)
subtype3 117 36 1.0 - 5287.0 (407.0)
subtype4 61 23 13.0 - 2589.0 (684.0)

Figure S85.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0218 (Kruskal-Wallis (anova)), Q value = 1

Table S93.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 411 67.1 (8.6)
subtype1 94 67.1 (9.3)
subtype2 126 65.4 (8.1)
subtype3 121 68.3 (8.2)
subtype4 70 68.3 (8.7)

Figure S86.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0832 (Fisher's exact test), Q value = 1

Table S94.  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 2 73 136 1 52 70 57 19 6
subtype1 1 17 35 0 8 17 14 2 1
subtype2 1 14 51 1 16 23 14 8 0
subtype3 0 23 30 0 21 23 18 6 2
subtype4 0 19 20 0 7 7 11 3 3

Figure S87.  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 = 0.767 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 100 252 48 19
subtype1 21 61 11 3
subtype2 24 83 14 7
subtype3 33 70 15 6
subtype4 22 38 8 3

Figure S88.  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 = 0.48 (Fisher's exact test), Q value = 1

Table S96.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 262 111 35 5
subtype1 60 24 9 1
subtype2 80 37 9 1
subtype3 77 38 8 1
subtype4 45 12 9 2

Figure S89.  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 = 0.413 (Fisher's exact test), Q value = 1

Table S97.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 357 5 1 50
subtype1 80 1 0 13
subtype2 114 0 0 12
subtype3 105 2 0 16
subtype4 58 2 1 9

Figure S90.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.549 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 107 312
subtype1 23 73
subtype2 28 100
subtype3 35 89
subtype4 21 50

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

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

P value = 0.907 (Kruskal-Wallis (anova)), Q value = 1

Table S99.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 93 52.6 (41.9)
subtype1 16 58.1 (40.9)
subtype2 28 56.1 (41.7)
subtype3 29 51.4 (42.0)
subtype4 20 45.0 (45.0)

Figure S92.  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 = 0.507 (Fisher's exact test), Q value = 1

Table S100.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 12 6 1 400
subtype1 2 2 0 92
subtype2 5 1 0 122
subtype3 5 1 1 117
subtype4 0 2 0 69

Figure S93.  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 = 0.437 (Fisher's exact test), Q value = 1

Table S101.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 13 406
subtype1 2 94
subtype2 2 126
subtype3 6 118
subtype4 3 68

Figure S94.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S102.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 355 52.8 (31.5)
subtype1 84 50.0 (32.3)
subtype2 109 52.1 (27.7)
subtype3 102 54.7 (33.1)
subtype4 60 54.7 (34.4)

Figure S95.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.646 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 341 8 4 18
subtype1 75 2 2 3
subtype2 111 4 0 5
subtype3 99 2 1 5
subtype4 56 0 1 5

Figure S96.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.618 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 16 297
subtype1 2 5 62
subtype2 1 5 91
subtype3 3 3 94
subtype4 3 3 50

Figure S97.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

Table S105.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 265
subtype1 4 57
subtype2 1 77
subtype3 1 87
subtype4 2 44

Figure S98.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S106.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 156 157 106
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.412 (logrank test), Q value = 1

Table S107.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 386 120 1.0 - 5287.0 (640.0)
subtype1 145 48 1.0 - 3724.0 (734.0)
subtype2 143 38 4.0 - 3016.0 (822.0)
subtype3 98 34 1.0 - 5287.0 (361.0)

Figure S99.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00523 (Kruskal-Wallis (anova)), Q value = 0.84

Table S108.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 411 67.1 (8.6)
subtype1 153 68.7 (8.4)
subtype2 153 65.5 (8.6)
subtype3 105 67.2 (8.5)

Figure S100.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0714 (Fisher's exact test), Q value = 1

Table S109.  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 2 73 136 1 52 70 57 19 6
subtype1 1 26 56 0 16 26 23 3 4
subtype2 1 21 57 1 19 28 19 10 0
subtype3 0 26 23 0 17 16 15 6 2

Figure S101.  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 = 0.136 (Fisher's exact test), Q value = 1

Table S110.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 100 252 48 19
subtype1 32 96 22 6
subtype2 32 101 17 7
subtype3 36 55 9 6

Figure S102.  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 = 0.956 (Fisher's exact test), Q value = 1

Table S111.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 262 111 35 5
subtype1 101 39 12 1
subtype2 96 44 13 3
subtype3 65 28 10 1

Figure S103.  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 = 0.432 (Fisher's exact test), Q value = 1

Table S112.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 357 5 1 50
subtype1 128 3 1 21
subtype2 137 0 0 17
subtype3 92 2 0 12

Figure S104.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0321 (Fisher's exact test), Q value = 1

Table S113.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 107 312
subtype1 50 106
subtype2 30 127
subtype3 27 79

Figure S105.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.451 (Kruskal-Wallis (anova)), Q value = 1

Table S114.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 93 52.6 (41.9)
subtype1 31 47.4 (43.0)
subtype2 35 59.7 (40.4)
subtype3 27 49.3 (42.9)

Figure S106.  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 = 0.102 (Fisher's exact test), Q value = 1

Table S115.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 12 6 1 400
subtype1 1 3 0 152
subtype2 6 1 0 150
subtype3 5 2 1 98

Figure S107.  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 = 0.83 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 13 406
subtype1 4 152
subtype2 6 151
subtype3 3 103

Figure S108.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.741 (Kruskal-Wallis (anova)), Q value = 1

Table S117.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 355 52.8 (31.5)
subtype1 134 54.8 (33.9)
subtype2 132 50.9 (27.1)
subtype3 89 52.5 (33.7)

Figure S109.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.716 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 341 8 4 18
subtype1 125 2 1 9
subtype2 130 4 3 6
subtype3 86 2 0 3

Figure S110.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.563 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 16 297
subtype1 3 8 102
subtype2 2 5 113
subtype3 4 3 82

Figure S111.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.748 (Fisher's exact test), Q value = 1

Table S120.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 265
subtype1 4 95
subtype2 2 94
subtype3 2 76

Figure S112.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S121.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 121 94 99 53 29
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00899 (logrank test), Q value = 1

Table S122.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 363 109 1.0 - 5287.0 (639.0)
subtype1 115 48 1.0 - 5287.0 (699.0)
subtype2 82 14 2.0 - 2167.0 (385.5)
subtype3 88 23 1.0 - 2589.0 (757.5)
subtype4 51 14 6.0 - 2754.0 (555.0)
subtype5 27 10 3.0 - 1967.0 (833.0)

Figure S113.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00346 (Kruskal-Wallis (anova)), Q value = 0.56

Table S123.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 389 67.4 (8.7)
subtype1 120 67.2 (8.2)
subtype2 92 65.3 (8.7)
subtype3 96 68.6 (9.4)
subtype4 52 66.8 (8.4)
subtype5 29 71.6 (6.8)

Figure S114.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00216 (Fisher's exact test), Q value = 0.35

Table S124.  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 2 68 128 1 49 69 55 17 5
subtype1 0 16 48 0 6 19 21 8 3
subtype2 1 10 27 1 20 18 15 1 0
subtype3 1 20 27 0 17 17 12 3 2
subtype4 0 12 21 0 5 7 3 4 0
subtype5 0 10 5 0 1 8 4 1 0

Figure S115.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0738 (Fisher's exact test), Q value = 1

Table S125.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 92 240 46 18
subtype1 22 80 11 8
subtype2 17 64 12 1
subtype3 27 51 16 5
subtype4 15 32 3 3
subtype5 11 13 4 1

Figure S116.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.468 (Fisher's exact test), Q value = 1

Table S126.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 244 107 35 4
subtype1 76 28 13 4
subtype2 51 31 10 0
subtype3 62 27 6 0
subtype4 37 13 3 0
subtype5 18 8 3 0

Figure S117.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.00044 (Fisher's exact test), Q value = 0.073

Table S127.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 336 4 1 49
subtype1 113 3 0 3
subtype2 75 0 0 18
subtype3 78 1 1 19
subtype4 44 0 0 6
subtype5 26 0 0 3

Figure S118.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.174 (Fisher's exact test), Q value = 1

Table S128.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 101 295
subtype1 31 90
subtype2 20 74
subtype3 25 74
subtype4 12 41
subtype5 13 16

Figure S119.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 7.6e-05 (Kruskal-Wallis (anova)), Q value = 0.013

Table S129.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 86 53.1 (41.9)
subtype1 25 15.6 (32.7)
subtype2 26 75.8 (25.5)
subtype3 19 64.2 (38.1)
subtype4 9 62.2 (47.4)
subtype5 7 61.4 (43.0)

Figure S120.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.164 (Fisher's exact test), Q value = 1

Table S130.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 10 6 1 379
subtype1 1 1 0 119
subtype2 4 2 1 87
subtype3 1 3 0 95
subtype4 4 0 0 49
subtype5 0 0 0 29

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

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.712 (Fisher's exact test), Q value = 1

Table S131.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 12 384
subtype1 4 117
subtype2 3 91
subtype3 4 95
subtype4 0 53
subtype5 1 28

Figure S122.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.82 (Kruskal-Wallis (anova)), Q value = 1

Table S132.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 337 53.6 (32.2)
subtype1 109 56.0 (37.8)
subtype2 78 50.1 (26.8)
subtype3 81 54.0 (29.0)
subtype4 46 50.8 (29.8)
subtype5 23 57.7 (36.2)

Figure S123.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.42 (Fisher's exact test), Q value = 1

Table S133.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 322 7 3 16
subtype1 109 2 2 2
subtype2 74 1 0 6
subtype3 75 2 0 5
subtype4 42 2 0 2
subtype5 22 0 1 1

Figure S124.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.129 (Fisher's exact test), Q value = 1

Table S134.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 16 282
subtype1 3 4 73
subtype2 2 0 75
subtype3 4 6 75
subtype4 0 4 39
subtype5 0 2 20

Figure S125.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.948 (Fisher's exact test), Q value = 1

Table S135.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 252
subtype1 3 67
subtype2 1 63
subtype3 2 69
subtype4 1 35
subtype5 0 18

Figure S126.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S136.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 75 105 156 60
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0947 (logrank test), Q value = 1

Table S137.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 363 109 1.0 - 5287.0 (639.0)
subtype1 71 19 6.0 - 3016.0 (700.0)
subtype2 97 34 2.0 - 5287.0 (578.0)
subtype3 145 43 1.0 - 2524.0 (573.0)
subtype4 50 13 17.0 - 2979.0 (758.5)

Figure S127.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0533 (Kruskal-Wallis (anova)), Q value = 1

Table S138.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 389 67.4 (8.7)
subtype1 73 66.0 (8.5)
subtype2 103 66.2 (9.3)
subtype3 154 68.2 (7.9)
subtype4 59 69.1 (9.5)

Figure S128.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.334 (Fisher's exact test), Q value = 1

Table S139.  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 2 68 128 1 49 69 55 17 5
subtype1 0 11 27 0 9 14 7 7 0
subtype2 0 16 40 1 13 18 13 2 2
subtype3 2 32 45 0 15 26 28 5 1
subtype4 0 9 16 0 12 11 7 3 2

Figure S129.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.7 (Fisher's exact test), Q value = 1

Table S140.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 92 240 46 18
subtype1 16 47 7 5
subtype2 21 68 13 3
subtype3 42 90 19 5
subtype4 13 35 7 5

Figure S130.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.891 (Fisher's exact test), Q value = 1

Table S141.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 244 107 35 4
subtype1 44 25 5 1
subtype2 70 25 9 1
subtype3 98 41 16 1
subtype4 32 16 5 1

Figure S131.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.28 (Fisher's exact test), Q value = 1

Table S142.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 336 4 1 49
subtype1 66 0 0 7
subtype2 93 2 0 10
subtype3 130 1 0 21
subtype4 47 1 1 11

Figure S132.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.536 (Fisher's exact test), Q value = 1

Table S143.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 101 295
subtype1 16 59
subtype2 25 80
subtype3 46 110
subtype4 14 46

Figure S133.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0856 (Kruskal-Wallis (anova)), Q value = 1

Table S144.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 86 53.1 (41.9)
subtype1 15 66.7 (42.2)
subtype2 21 39.5 (44.1)
subtype3 36 51.1 (40.0)
subtype4 14 64.3 (39.9)

Figure S134.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.0348 (Fisher's exact test), Q value = 1

Table S145.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 10 6 1 379
subtype1 4 0 0 71
subtype2 5 3 0 97
subtype3 1 1 1 153
subtype4 0 2 0 58

Figure S135.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S146.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 12 384
subtype1 1 74
subtype2 1 104
subtype3 8 148
subtype4 2 58

Figure S136.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.599 (Kruskal-Wallis (anova)), Q value = 1

Table S147.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 337 53.6 (32.2)
subtype1 67 56.6 (32.7)
subtype2 86 53.5 (36.3)
subtype3 137 52.8 (28.5)
subtype4 47 51.7 (34.6)

Figure S137.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.314 (Fisher's exact test), Q value = 1

Table S148.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 322 7 3 16
subtype1 62 3 1 2
subtype2 90 2 0 4
subtype3 127 1 1 5
subtype4 43 1 1 5

Figure S138.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0443 (Fisher's exact test), Q value = 1

Table S149.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 9 16 282
subtype1 0 4 54
subtype2 3 1 72
subtype3 2 10 113
subtype4 4 1 43

Figure S139.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.861 (Fisher's exact test), Q value = 1

Table S150.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 252
subtype1 1 50
subtype2 1 57
subtype3 3 104
subtype4 2 41

Figure S140.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S151.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 65 118 72
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.791 (logrank test), Q value = 1

Table S152.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 230 53 1.0 - 2589.0 (438.5)
subtype1 56 12 3.0 - 1972.0 (629.5)
subtype2 105 25 1.0 - 2167.0 (378.0)
subtype3 69 16 2.0 - 2589.0 (555.0)

Figure S141.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0527 (Kruskal-Wallis (anova)), Q value = 1

Table S153.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 251 67.5 (8.7)
subtype1 63 69.0 (10.2)
subtype2 116 67.7 (8.1)
subtype3 72 65.9 (8.1)

Figure S142.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.386 (Fisher's exact test), Q value = 1

Table S154.  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 2 50 74 1 42 43 34 5 2
subtype1 1 10 21 0 9 14 7 3 0
subtype2 1 27 36 1 20 15 14 0 2
subtype3 0 13 17 0 13 14 13 2 0

Figure S143.  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 = 0.255 (Fisher's exact test), Q value = 1

Table S155.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 66 148 34 7
subtype1 13 38 11 3
subtype2 38 66 11 3
subtype3 15 44 12 1

Figure S144.  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 = 0.502 (Fisher's exact test), Q value = 1

Table S156.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 159 69 21
subtype1 41 17 5
subtype2 78 28 8
subtype3 40 24 8

Figure S145.  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 = 1 (Fisher's exact test), Q value = 1

Table S157.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 202 1 1 49
subtype1 52 0 0 13
subtype2 92 1 1 22
subtype3 58 0 0 14

Figure S146.  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 = 0.376 (Fisher's exact test), Q value = 1

Table S158.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 62 193
subtype1 20 45
subtype2 27 91
subtype3 15 57

Figure S147.  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 = 0.979 (Kruskal-Wallis (anova)), Q value = 1

Table S159.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 64 65.3 (37.2)
subtype1 12 65.0 (36.6)
subtype2 25 70.4 (32.8)
subtype3 27 60.7 (41.8)

Figure S148.  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 = 0.274 (Fisher's exact test), Q value = 1

Table S160.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 5 1 242
subtype1 0 2 0 63
subtype2 3 1 1 113
subtype3 4 2 0 66

Figure S149.  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 = 0.182 (Fisher's exact test), Q value = 1

Table S161.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 246
subtype1 0 65
subtype2 6 112
subtype3 3 69

Figure S150.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.354 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 214 51.7 (28.6)
subtype1 50 56.0 (35.0)
subtype2 101 48.6 (24.7)
subtype3 63 53.3 (28.7)

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

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.407 (Fisher's exact test), Q value = 1

Table S163.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 193 5 2 13
subtype1 44 2 0 6
subtype2 87 1 1 4
subtype3 62 2 1 3

Figure S152.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.227 (Fisher's exact test), Q value = 1

Table S164.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 9 201
subtype1 2 4 46
subtype2 2 5 100
subtype3 2 0 55

Figure S153.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.807 (Fisher's exact test), Q value = 1

Table S165.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 174
subtype1 1 45
subtype2 3 85
subtype3 0 44

Figure S154.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S166.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 26 107 122
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0433 (logrank test), Q value = 1

Table S167.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 230 53 1.0 - 2589.0 (438.5)
subtype1 24 7 16.0 - 1955.0 (427.0)
subtype2 97 24 1.0 - 2167.0 (449.0)
subtype3 109 22 1.0 - 2589.0 (428.0)

Figure S155.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0762 (Kruskal-Wallis (anova)), Q value = 1

Table S168.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 251 67.5 (8.7)
subtype1 26 69.5 (6.7)
subtype2 107 66.2 (8.5)
subtype3 118 68.3 (9.2)

Figure S156.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.113 (Fisher's exact test), Q value = 1

Table S169.  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 2 50 74 1 42 43 34 5 2
subtype1 1 5 6 0 1 5 7 1 0
subtype2 1 18 28 0 20 22 16 1 0
subtype3 0 27 40 1 21 16 11 3 2

Figure S157.  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 = 0.117 (Fisher's exact test), Q value = 1

Table S170.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 66 148 34 7
subtype1 6 12 7 1
subtype2 23 69 14 1
subtype3 37 67 13 5

Figure S158.  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 = 0.297 (Fisher's exact test), Q value = 1

Table S171.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 159 69 21
subtype1 17 5 4
subtype2 62 35 9
subtype3 80 29 8

Figure S159.  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 = 0.463 (Fisher's exact test), Q value = 1

Table S172.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 202 1 1 49
subtype1 21 0 0 5
subtype2 90 0 0 16
subtype3 91 1 1 28

Figure S160.  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 = 0.722 (Fisher's exact test), Q value = 1

Table S173.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 62 193
subtype1 8 18
subtype2 25 82
subtype3 29 93

Figure S161.  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 = 0.108 (Kruskal-Wallis (anova)), Q value = 1

Table S174.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 64 65.3 (37.2)
subtype1 5 44.0 (40.4)
subtype2 39 63.3 (39.3)
subtype3 20 74.5 (30.7)

Figure S162.  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 = 0.0252 (Fisher's exact test), Q value = 1

Table S175.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 5 1 242
subtype1 0 1 0 25
subtype2 7 2 1 97
subtype3 0 2 0 120

Figure S163.  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 = 0.696 (Fisher's exact test), Q value = 1

Table S176.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 246
subtype1 0 26
subtype2 5 102
subtype3 4 118

Figure S164.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.116 (Kruskal-Wallis (anova)), Q value = 1

Table S177.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 214 51.7 (28.6)
subtype1 19 60.3 (25.5)
subtype2 94 50.6 (25.6)
subtype3 101 51.2 (31.6)

Figure S165.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.556 (Fisher's exact test), Q value = 1

Table S178.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 193 5 2 13
subtype1 19 1 0 1
subtype2 87 2 2 4
subtype3 87 2 0 8

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.00321 (Fisher's exact test), Q value = 0.52

Table S179.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 6 9 201
subtype1 0 3 15
subtype2 1 0 94
subtype3 5 6 92

Figure S167.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

Table S180.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 174
subtype1 0 16
subtype2 0 71
subtype3 4 87

Figure S168.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = LUSC-TP.mergedcluster.txt

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

  • Number of patients = 422

  • Number of clustering approaches = 12

  • Number of selected clinical features = 14

  • 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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