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 13 clinical features across 394 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 4 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.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

  • 2 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 13 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 0.384
(1.00) 		0.415
(1.00) 		0.247
(1.00) 		0.158
(1.00) 		0.0101
(1.00) 		0.00999
(1.00) 		0.171
(1.00) 		0.864
(1.00) 		0.992
(1.00) 		0.413
(1.00) 		0.908
(1.00) 		0.875
(1.00)
AGE t-test 0.962
(1.00) 		0.438
(1.00) 		0.033
(1.00) 		0.0972
(1.00) 		0.0153
(1.00) 		0.0759
(1.00) 		0.0394
(1.00) 		0.0437
(1.00) 		0.0751
(1.00) 		0.217
(1.00) 		0.129
(1.00) 		0.648
(1.00)
NEOPLASM DISEASESTAGE Chi-square test 0.0228
(1.00) 		0.00998
(1.00) 		0.588
(1.00) 		0.672
(1.00) 		0.296
(1.00) 		0.454
(1.00) 		0.276
(1.00) 		0.451
(1.00) 		0.366
(1.00) 		0.0578
(1.00) 		0.0159
(1.00) 		0.794
(1.00)
PATHOLOGY T STAGE Chi-square test 0.00124
(0.19) 		9.97e-05
(0.0154) 		0.695
(1.00) 		0.652
(1.00) 		0.185
(1.00) 		0.274
(1.00) 		0.494
(1.00) 		0.527
(1.00) 		0.228
(1.00) 		0.563
(1.00) 		0.00297
(0.452) 		0.277
(1.00)
PATHOLOGY N STAGE Chi-square test 0.877
(1.00) 		0.427
(1.00) 		0.432
(1.00) 		0.771
(1.00) 		0.308
(1.00) 		0.0902
(1.00) 		0.413
(1.00) 		0.333
(1.00) 		0.307
(1.00) 		0.375
(1.00) 		0.172
(1.00) 		0.661
(1.00)
PATHOLOGY M STAGE Chi-square test 0.105
(1.00) 		0.0642
(1.00) 		0.673
(1.00) 		0.316
(1.00) 		0.612
(1.00) 		0.304
(1.00) 		0.64
(1.00) 		0.372
(1.00) 		4.74e-05
(0.0074) 		0.0718
(1.00) 		0.665
(1.00) 		0.0664
(1.00)
GENDER Fisher's exact test 0.331
(1.00) 		0.00305
(0.46) 		0.0445
(1.00) 		0.841
(1.00) 		0.266
(1.00) 		0.874
(1.00) 		0.681
(1.00) 		0.144
(1.00) 		0.888
(1.00) 		0.286
(1.00) 		0.98
(1.00) 		0.433
(1.00)
KARNOFSKY PERFORMANCE SCORE t-test 0.191
(1.00) 		0.108
(1.00) 		0.159
(1.00) 		0.344
(1.00) 		0.527
(1.00) 		0.918
(1.00) 		0.808
(1.00) 		0.649
(1.00) 		0.094
(1.00) 		0.662
(1.00) 		0.76
(1.00) 		0.0888
(1.00)
HISTOLOGICAL TYPE Chi-square test 0.32
(1.00) 		0.264
(1.00) 		0.132
(1.00) 		0.362
(1.00) 		0.134
(1.00) 		0.0593
(1.00) 		0.689
(1.00) 		0.069
(1.00) 		0.0905
(1.00) 		0.474
(1.00) 		0.261
(1.00) 		0.232
(1.00)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.55
(1.00) 		0.183
(1.00) 		1
(1.00) 		0.279
(1.00) 		0.524
(1.00) 		0.583
(1.00) 		0.305
(1.00) 		0.303
(1.00) 		0.665
(1.00) 		0.00107
(0.165) 		0.637
(1.00) 		0.502
(1.00)
NUMBERPACKYEARSSMOKED t-test 0.525
(1.00) 		0.822
(1.00) 		0.268
(1.00) 		0.189
(1.00) 		0.0516
(1.00) 		0.105
(1.00) 		0.894
(1.00) 		0.652
(1.00) 		0.554
(1.00) 		0.793
(1.00) 		0.927
(1.00) 		0.476
(1.00)
YEAROFTOBACCOSMOKINGONSET t-test 0.903
(1.00) 		0.0465
(1.00) 		0.263
(1.00) 		0.596
(1.00) 		0.151
(1.00) 		0.262
(1.00) 		0.276
(1.00) 		0.0771
(1.00) 		0.264
(1.00) 		0.0511
(1.00) 		0.272
(1.00) 		0.114
(1.00)
COMPLETENESS OF RESECTION Chi-square test 0.546
(1.00) 		0.384
(1.00) 		0.238
(1.00) 		0.488
(1.00) 		0.033
(1.00) 		0.0213
(1.00) 		0.746
(1.00) 		0.327
(1.00) 		0.72
(1.00) 		0.882
(1.00) 		0.477
(1.00) 		0.494
(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 = 0.384 (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), Month
ALL 148 63 0.4 - 173.8 (20.7)
subtype1 41 16 0.4 - 122.4 (19.0)
subtype2 50 19 0.4 - 99.2 (29.4)
subtype3 30 17 0.4 - 173.8 (17.5)
subtype4 27 11 0.4 - 114.0 (11.8)

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 = 0.962 (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 = 0.0228 (Chi-square 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 = 0.00124 (Chi-square test), Q value = 0.19

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 = 0.877 (Chi-square 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 = 0.105 (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 = 0.331 (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 = 0.191 (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 = 0.32 (Chi-square 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 = 0.55 (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 = 0.525 (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 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.903 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 97 1958.0 (10.6)
subtype1 28 1957.0 (8.8)
subtype2 26 1958.5 (10.8)
subtype3 23 1957.7 (10.9)
subtype4 20 1959.2 (12.7)

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

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

P value = 0.546 (Chi-square test), Q value = 1

Table S14.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: '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 S13.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S15.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 22 56 23 53
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.415 (logrank test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 148 63 0.4 - 173.8 (20.7)
subtype1 22 7 0.4 - 92.7 (14.3)
subtype2 54 21 0.4 - 99.2 (27.7)
subtype3 22 10 1.0 - 122.4 (19.7)
subtype4 50 25 0.4 - 173.8 (14.5)

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.438 (ANOVA), Q value = 1

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 152 66.5 (8.6)
subtype1 22 64.1 (7.1)
subtype2 55 66.2 (8.0)
subtype3 22 67.1 (9.8)
subtype4 53 67.6 (9.2)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00998 (Chi-square test), Q value = 1

Table S18.  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 9 4 0 2 4 2 1
subtype2 2 25 4 10 6 9 0
subtype3 3 10 1 4 1 2 2
subtype4 6 22 2 11 8 2 1

Figure S16.  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 = 9.97e-05 (Chi-square test), Q value = 0.015

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 12 6 3 1
subtype2 4 44 1 7
subtype3 4 16 1 2
subtype4 10 34 7 2

Figure S17.  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 = 0.427 (Chi-square test), Q value = 1

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 17 2 3 0
subtype2 30 18 5 3
subtype3 13 8 1 1
subtype4 36 12 4 1

Figure S18.  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 = 0.0642 (Fisher's exact test), Q value = 1

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 146 4
subtype1 21 1
subtype2 54 0
subtype3 21 2
subtype4 50 1

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00305 (Fisher's exact test), Q value = 0.46

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 44 110
subtype1 14 8
subtype2 13 43
subtype3 5 18
subtype4 12 41

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.108 (ANOVA), Q value = 1

Table S23.  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 48.8 (42.6)
subtype2 5 10.0 (22.4)
subtype3 5 0.0 (0.0)
subtype4 8 23.8 (44.1)

Figure S21.  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 = 0.264 (Chi-square test), Q value = 1

Table S24.  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 1 0 21
subtype2 3 0 53
subtype3 0 1 22
subtype4 1 0 52

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 = 0.183 (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 2 152
subtype1 1 21
subtype2 0 56
subtype3 0 23
subtype4 1 52

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

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.822 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 133 54.8 (36.5)
subtype1 18 59.3 (49.1)
subtype2 51 51.1 (24.6)
subtype3 20 56.7 (30.2)
subtype4 44 56.5 (44.7)

Figure S24.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0465 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 97 1958.0 (10.6)
subtype1 17 1963.3 (10.5)
subtype2 29 1959.5 (10.7)
subtype3 15 1955.5 (10.3)
subtype4 36 1955.4 (9.9)

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

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

P value = 0.384 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 139 3 2 5
subtype1 21 0 0 0
subtype2 51 1 1 1
subtype3 22 0 1 0
subtype4 45 2 0 4

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

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

Table S29.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 158 129 104
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.247 (logrank test), Q value = 1

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 357 122 0.0 - 173.8 (11.7)
subtype1 141 56 0.0 - 173.8 (12.0)
subtype2 121 36 0.1 - 114.0 (13.6)
subtype3 95 30 0.0 - 122.4 (8.9)

Figure S27.  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 = 0.033 (ANOVA), Q value = 1

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 383 67.2 (8.8)
subtype1 155 67.0 (9.7)
subtype2 126 65.9 (8.2)
subtype3 102 68.9 (7.7)

Figure S28.  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 = 0.588 (Chi-square test), Q value = 1

Table S32.  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 67 131 1 45 65 53 20 5
subtype1 0 30 57 0 19 26 18 5 1
subtype2 0 20 44 1 15 19 17 11 2
subtype3 1 17 30 0 11 20 18 4 2

Figure S29.  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 = 0.695 (Chi-square test), Q value = 1

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 92 239 42 18
subtype1 37 95 21 5
subtype2 30 78 14 7
subtype3 25 66 7 6

Figure S30.  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 = 0.432 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 245 103 33 5
subtype1 109 33 11 2
subtype2 75 40 11 2
subtype3 61 30 11 1

Figure S31.  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 = 0.673 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 333 5 47
subtype1 133 1 23
subtype2 110 2 13
subtype3 90 2 11

Figure S32.  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 = 0.0445 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 98 293
subtype1 50 108
subtype2 25 104
subtype3 23 81

Figure S33.  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 = 0.159 (ANOVA), Q value = 1

Table S37.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 54 28.5 (39.7)
subtype1 21 41.4 (43.9)
subtype2 15 18.7 (34.8)
subtype3 18 21.7 (36.2)

Figure S34.  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 = 0.132 (Chi-square test), Q value = 1

Table S38.  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 5 1 373
subtype1 2 3 1 152
subtype2 8 2 0 119
subtype3 2 0 0 102

Figure S35.  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 = 1 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 12 379
subtype1 5 153
subtype2 4 125
subtype3 3 101

Figure S36.  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 = 0.268 (ANOVA), Q value = 1

Table S40.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 331 52.4 (31.7)
subtype1 133 49.5 (30.6)
subtype2 112 52.4 (31.6)
subtype3 86 56.7 (33.4)

Figure S37.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.263 (ANOVA), Q value = 1

Table S41.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 259 1959.6 (12.0)
subtype1 108 1959.2 (12.5)
subtype2 75 1961.4 (11.3)
subtype3 76 1958.4 (11.9)

Figure S38.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.238 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 317 7 4 18
subtype1 122 2 2 11
subtype2 106 3 1 7
subtype3 89 2 1 0

Figure S39.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S43.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 106 82 72
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.158 (logrank test), Q value = 1

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 230 70 0.0 - 173.8 (9.0)
subtype1 91 31 0.0 - 173.8 (6.9)
subtype2 75 22 0.1 - 141.3 (11.8)
subtype3 64 17 0.0 - 107.0 (10.2)

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0972 (ANOVA), Q value = 1

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 252 67.6 (9.0)
subtype1 103 68.8 (9.8)
subtype2 80 65.9 (8.8)
subtype3 69 67.6 (7.7)

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.672 (Chi-square test), Q value = 1

Table S46.  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 52 76 1 41 42 37 5 2
subtype1 1 24 29 0 15 21 13 1 2
subtype2 1 15 29 1 14 8 12 2 0
subtype3 0 13 18 0 12 13 12 2 0

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.652 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 70 152 31 7
subtype1 28 62 13 3
subtype2 26 42 12 2
subtype3 16 48 6 2

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.771 (Chi-square test), Q value = 1

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 164 69 22
subtype1 66 26 9
subtype2 56 20 6
subtype3 42 23 7

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.316 (Chi-square test), Q value = 1

Table S49.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 209 2 47
subtype1 81 2 23
subtype2 68 0 14
subtype3 60 0 10

Figure S45.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.841 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 64 196
subtype1 28 78
subtype2 20 62
subtype3 16 56

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.344 (ANOVA), Q value = 1

Table S51.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 31 29.4 (40.2)
subtype1 5 42.0 (49.2)
subtype2 13 16.9 (34.5)
subtype3 13 36.9 (41.7)

Figure S47.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.362 (Chi-square test), Q value = 1

Table S52.  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 4 1 246
subtype1 1 2 0 103
subtype2 5 1 0 76
subtype3 3 1 1 67

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.279 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 250
subtype1 5 101
subtype2 1 81
subtype3 4 68

Figure S49.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.189 (ANOVA), Q value = 1

Table S54.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 220 51.3 (29.3)
subtype1 88 49.2 (28.2)
subtype2 69 48.7 (23.9)
subtype3 63 56.9 (35.3)

Figure S50.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.596 (ANOVA), Q value = 1

Table S55.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 177 1960.2 (12.3)
subtype1 71 1959.3 (12.3)
subtype2 53 1961.6 (11.9)
subtype3 53 1960.0 (12.8)

Figure S51.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.488 (Chi-square test), Q value = 1

Table S56.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 198 4 2 13
subtype1 77 1 0 8
subtype2 65 2 1 4
subtype3 56 1 1 1

Figure S52.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S57.  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 = 0.0101 (logrank test), Q value = 1

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 182 75 0.0 - 173.8 (18.4)
subtype1 57 17 0.0 - 173.8 (22.9)
subtype2 69 30 0.2 - 119.5 (16.6)
subtype3 56 28 0.1 - 119.8 (14.8)

Figure S53.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0153 (ANOVA), Q value = 1

Table S59.  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 S54.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.296 (Chi-square test), Q value = 1

Table S60.  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 69 1 23 34 23 14
subtype1 0 10 17 0 7 11 7 8
subtype2 0 7 26 0 11 16 8 4
subtype3 1 11 26 1 5 7 8 2

Figure S55.  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 = 0.185 (Chi-square test), Q value = 1

Table S61.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 44 120 20 11
subtype1 15 35 4 6
subtype2 11 51 9 2
subtype3 18 34 7 3

Figure S56.  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 = 0.308 (Chi-square test), Q value = 1

Table S62.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 124 50 16 4
subtype1 34 17 7 2
subtype2 44 22 5 2
subtype3 46 11 4 0

Figure S57.  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 = 0.612 (Fisher's exact test), Q value = 1

Table S63.  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 S58.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.266 (Fisher's exact test), Q value = 1

Table S64.  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 S59.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.527 (ANOVA), Q value = 1

Table S65.  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 S60.  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 = 0.134 (Chi-square test), Q value = 1

Table S66.  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 S61.  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 = 0.524 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 186
subtype1 4 56
subtype2 2 71
subtype3 3 59

Figure S62.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0516 (ANOVA), Q value = 1

Table S68.  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 S63.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.151 (ANOVA), Q value = 1

Table S69.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 131 1958.5 (12.0)
subtype1 39 1961.3 (11.6)
subtype2 50 1958.2 (11.0)
subtype3 42 1956.1 (13.3)

Figure S64.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.033 (Chi-square test), Q value = 1

Table S70.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: '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 S65.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S71.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 58 48 49 40
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00999 (logrank test), Q value = 1

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 182 75 0.0 - 173.8 (18.4)
subtype1 54 20 0.0 - 173.8 (20.0)
subtype2 44 17 0.2 - 99.2 (23.0)
subtype3 46 18 0.2 - 119.5 (17.3)
subtype4 38 20 0.1 - 92.7 (12.0)

Figure S66.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0759 (ANOVA), Q value = 1

Table S73.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 187 67.4 (9.5)
subtype1 56 65.7 (10.6)
subtype2 46 69.4 (8.9)
subtype3 46 65.8 (7.9)
subtype4 39 69.4 (9.9)

Figure S67.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.454 (Chi-square test), Q value = 1

Table S74.  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 69 1 23 34 23 14
subtype1 0 10 15 0 8 10 9 6
subtype2 1 8 19 0 3 7 6 4
subtype3 0 3 18 0 8 11 4 4
subtype4 0 7 17 1 4 6 4 0

Figure S68.  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 = 0.274 (Chi-square test), Q value = 1

Table S75.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 44 120 20 11
subtype1 14 33 5 6
subtype2 16 25 5 2
subtype3 6 36 6 1
subtype4 8 26 4 2

Figure S69.  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 = 0.0902 (Chi-square test), Q value = 1

Table S76.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 124 50 16 4
subtype1 33 16 9 0
subtype2 32 9 4 2
subtype3 29 17 1 2
subtype4 30 8 2 0

Figure S70.  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 = 0.304 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 MX
ALL 176 16
subtype1 53 5
subtype2 45 3
subtype3 45 2
subtype4 33 6

Figure S71.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.874 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 146
subtype1 15 43
subtype2 14 34
subtype3 11 38
subtype4 9 31

Figure S72.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.918 (ANOVA), Q value = 1

Table S79.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 30.3 (39.3)
subtype1 10 27.0 (33.7)
subtype2 4 42.5 (49.2)
subtype3 7 32.9 (41.1)
subtype4 13 27.7 (43.4)

Figure S73.  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 = 0.0593 (Chi-square test), Q value = 1

Table S80.  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 2 53
subtype2 0 0 48
subtype3 0 0 49
subtype4 0 0 40

Figure S74.  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 = 0.583 (Fisher's exact test), Q value = 1

Table S81.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 186
subtype1 4 54
subtype2 3 45
subtype3 1 48
subtype4 1 39

Figure S75.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.105 (ANOVA), Q value = 1

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 165 51.7 (32.3)
subtype1 50 61.0 (44.8)
subtype2 40 49.0 (25.0)
subtype3 42 47.4 (24.2)
subtype4 33 46.3 (24.0)

Figure S76.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.262 (ANOVA), Q value = 1

Table S83.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 131 1958.5 (12.0)
subtype1 39 1959.2 (10.8)
subtype2 33 1958.5 (13.9)
subtype3 35 1960.5 (11.6)
subtype4 24 1954.3 (11.6)

Figure S77.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0213 (Chi-square test), Q value = 1

Table S84.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 149 4 4 9
subtype1 47 0 0 5
subtype2 38 0 2 0
subtype3 36 4 2 2
subtype4 28 0 0 2

Figure S78.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S85.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 93 113 114 66
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.171 (logrank test), Q value = 1

Table S86.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 352 119 0.0 - 173.8 (11.6)
subtype1 82 27 0.0 - 122.4 (12.6)
subtype2 104 32 0.1 - 141.3 (15.6)
subtype3 107 35 0.0 - 173.8 (8.1)
subtype4 59 25 0.0 - 92.7 (12.2)

Figure S79.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0394 (ANOVA), Q value = 1

Table S87.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 378 67.1 (8.7)
subtype1 91 66.8 (9.3)
subtype2 111 65.4 (8.3)
subtype3 111 68.1 (8.4)
subtype4 65 68.7 (8.9)

Figure S80.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.276 (Chi-square test), Q value = 1

Table S88.  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 68 127 1 46 63 52 19 5
subtype1 1 14 35 0 7 18 13 3 1
subtype2 1 13 44 1 14 19 13 8 0
subtype3 0 22 29 0 18 20 16 6 2
subtype4 0 19 19 0 7 6 10 2 2

Figure S81.  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 = 0.494 (Chi-square test), Q value = 1

Table S89.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 94 233 42 17
subtype1 18 61 11 3
subtype2 23 72 11 7
subtype3 31 64 13 6
subtype4 22 36 7 1

Figure S82.  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 = 0.413 (Chi-square test), Q value = 1

Table S90.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 244 100 32 5
subtype1 57 25 8 1
subtype2 71 32 9 1
subtype3 74 33 6 1
subtype4 42 10 9 2

Figure S83.  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 = 0.64 (Chi-square test), Q value = 1

Table S91.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 328 5 47
subtype1 78 1 12
subtype2 100 0 11
subtype3 96 2 15
subtype4 54 2 9

Figure S84.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

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

Table S92.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 97 289
subtype1 23 70
subtype2 25 88
subtype3 29 85
subtype4 20 46

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

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

P value = 0.808 (ANOVA), Q value = 1

Table S93.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 53 27.4 (39.1)
subtype1 9 35.6 (42.2)
subtype2 13 20.8 (35.2)
subtype3 16 24.4 (38.5)
subtype4 15 31.3 (43.7)

Figure S86.  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 = 0.689 (Chi-square test), Q value = 1

Table S94.  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 11 5 1 369
subtype1 2 2 0 89
subtype2 4 1 0 108
subtype3 5 1 1 107
subtype4 0 1 0 65

Figure S87.  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 = 0.305 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 375
subtype1 2 91
subtype2 1 112
subtype3 5 109
subtype4 3 63

Figure S88.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.894 (ANOVA), Q value = 1

Table S96.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 326 52.4 (31.6)
subtype1 81 52.2 (34.1)
subtype2 96 50.8 (27.4)
subtype3 93 54.3 (33.1)
subtype4 56 52.0 (33.0)

Figure S89.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.276 (ANOVA), Q value = 1

Table S97.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 254 1959.7 (11.8)
subtype1 64 1957.9 (13.2)
subtype2 67 1961.7 (11.6)
subtype3 80 1960.2 (10.2)
subtype4 43 1958.6 (12.3)

Figure S90.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.746 (Chi-square test), Q value = 1

Table S98.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 312 7 4 18
subtype1 74 2 2 3
subtype2 97 3 0 5
subtype3 90 2 1 5
subtype4 51 0 1 5

Figure S91.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S99.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 80 112 194
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.864 (logrank test), Q value = 1

Table S100.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 352 119 0.0 - 173.8 (11.6)
subtype1 76 25 0.0 - 114.0 (9.5)
subtype2 102 33 0.1 - 141.3 (13.6)
subtype3 174 61 0.0 - 173.8 (12.1)

Figure S92.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0437 (ANOVA), Q value = 1

Table S101.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 378 67.1 (8.7)
subtype1 78 68.3 (7.7)
subtype2 109 65.4 (8.6)
subtype3 191 67.6 (9.1)

Figure S93.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.451 (Chi-square test), Q value = 1

Table S102.  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 68 127 1 46 63 52 19 5
subtype1 0 18 18 0 14 10 12 5 2
subtype2 1 16 40 1 13 19 15 7 0
subtype3 1 34 69 0 19 34 25 7 3

Figure S94.  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 = 0.527 (Chi-square test), Q value = 1

Table S103.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 94 233 42 17
subtype1 26 42 7 5
subtype2 25 69 13 5
subtype3 43 122 22 7

Figure S95.  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 = 0.333 (Chi-square test), Q value = 1

Table S104.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 244 100 32 5
subtype1 49 26 5 0
subtype2 70 33 7 2
subtype3 125 41 20 3

Figure S96.  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 = 0.372 (Chi-square test), Q value = 1

Table S105.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 328 5 47
subtype1 68 2 10
subtype2 100 0 10
subtype3 160 3 27

Figure S97.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

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

Table S106.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 97 289
subtype1 27 53
subtype2 25 87
subtype3 45 149

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

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

P value = 0.649 (ANOVA), Q value = 1

Table S107.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 53 27.4 (39.1)
subtype1 14 27.9 (40.0)
subtype2 14 19.3 (34.3)
subtype3 25 31.6 (41.9)

Figure S99.  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 = 0.069 (Chi-square test), Q value = 1

Table S108.  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 11 5 1 369
subtype1 5 0 1 74
subtype2 4 1 0 107
subtype3 2 4 0 188

Figure S100.  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 = 0.303 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 375
subtype1 2 78
subtype2 1 111
subtype3 8 186

Figure S101.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.652 (ANOVA), Q value = 1

Table S110.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 326 52.4 (31.6)
subtype1 69 53.6 (35.0)
subtype2 96 49.8 (28.5)
subtype3 161 53.3 (32.0)

Figure S102.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0771 (ANOVA), Q value = 1

Table S111.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 254 1959.7 (11.8)
subtype1 60 1960.0 (10.4)
subtype2 67 1962.3 (11.8)
subtype3 127 1958.3 (12.2)

Figure S103.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.327 (Chi-square test), Q value = 1

Table S112.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 312 7 4 18
subtype1 63 1 0 3
subtype2 97 3 0 3
subtype3 152 3 4 12

Figure S104.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S113.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 138 89 93 48
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.992 (logrank test), Q value = 1

Table S114.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 335 110 0.0 - 173.8 (11.7)
subtype1 131 52 0.0 - 173.8 (17.9)
subtype2 77 18 0.1 - 96.8 (5.4)
subtype3 82 25 0.0 - 107.0 (8.9)
subtype4 45 15 0.2 - 92.7 (11.7)

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0751 (ANOVA), Q value = 1

Table S115.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 361 67.3 (8.8)
subtype1 137 66.5 (8.4)
subtype2 88 67.5 (8.3)
subtype3 89 69.3 (9.4)
subtype4 47 66.0 (9.2)

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.366 (Chi-square test), Q value = 1

Table S116.  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 63 122 1 43 63 51 17 4
subtype1 0 20 51 1 10 22 22 9 3
subtype2 1 13 27 0 14 17 14 2 0
subtype3 1 20 28 0 14 18 10 1 1
subtype4 0 10 16 0 5 6 5 5 0

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.228 (Chi-square test), Q value = 1

Table S117.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 86 226 40 16
subtype1 27 91 12 8
subtype2 18 56 13 2
subtype3 27 52 12 2
subtype4 14 27 3 4

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.307 (Chi-square test), Q value = 1

Table S118.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 229 97 33 4
subtype1 87 31 16 4
subtype2 54 29 6 0
subtype3 58 23 7 0
subtype4 30 14 4 0

Figure S109.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 4.74e-05 (Chi-square test), Q value = 0.0074

Table S119.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 312 4 46
subtype1 130 3 3
subtype2 68 0 20
subtype3 73 1 19
subtype4 41 0 4

Figure S110.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.888 (Fisher's exact test), Q value = 1

Table S120.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 92 276
subtype1 37 101
subtype2 23 66
subtype3 21 72
subtype4 11 37

Figure S111.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.094 (ANOVA), Q value = 1

Table S121.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 48 27.1 (39.0)
subtype1 25 18.8 (34.9)
subtype2 8 26.2 (39.6)
subtype3 10 54.0 (43.0)
subtype4 5 16.0 (35.8)

Figure S112.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0905 (Chi-square test), Q value = 1

Table S122.  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 5 1 352
subtype1 1 1 0 136
subtype2 4 2 1 82
subtype3 1 2 0 90
subtype4 4 0 0 44

Figure S113.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.665 (Fisher's exact test), Q value = 1

Table S123.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 357
subtype1 4 134
subtype2 3 86
subtype3 4 89
subtype4 0 48

Figure S114.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.554 (ANOVA), Q value = 1

Table S124.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 313 53.0 (32.2)
subtype1 121 56.0 (37.1)
subtype2 73 51.5 (28.5)
subtype3 77 52.3 (29.0)
subtype4 42 48.4 (29.0)

Figure S115.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.264 (ANOVA), Q value = 1

Table S125.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 245 1959.7 (12.0)
subtype1 92 1958.8 (10.2)
subtype2 63 1961.3 (12.0)
subtype3 59 1958.1 (13.3)
subtype4 31 1962.2 (14.1)

Figure S116.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.72 (Chi-square test), Q value = 1

Table S126.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 298 6 3 16
subtype1 124 2 2 3
subtype2 66 2 1 6
subtype3 69 1 0 5
subtype4 39 1 0 2

Figure S117.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S127.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 59 116 193
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.413 (logrank test), Q value = 1

Table S128.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 335 110 0.0 - 173.8 (11.7)
subtype1 56 17 0.2 - 141.3 (10.5)
subtype2 104 31 0.0 - 114.0 (9.8)
subtype3 175 62 0.0 - 173.8 (12.2)

Figure S118.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.217 (ANOVA), Q value = 1

Table S129.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 361 67.3 (8.8)
subtype1 57 66.1 (8.9)
subtype2 115 68.4 (8.3)
subtype3 189 67.1 (9.1)

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0578 (Chi-square test), Q value = 1

Table S130.  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 63 122 1 43 63 51 17 4
subtype1 0 8 21 0 7 9 6 8 0
subtype2 2 25 35 0 11 19 19 3 0
subtype3 0 30 66 1 25 35 26 6 4

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.563 (Chi-square test), Q value = 1

Table S131.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 86 226 40 16
subtype1 13 36 5 5
subtype2 32 67 13 4
subtype3 41 123 22 7

Figure S121.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.375 (Chi-square test), Q value = 1

Table S132.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 229 97 33 4
subtype1 33 21 4 1
subtype2 74 33 9 0
subtype3 122 43 20 3

Figure S122.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0718 (Chi-square test), Q value = 1

Table S133.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 312 4 46
subtype1 52 0 5
subtype2 92 0 21
subtype3 168 4 20

Figure S123.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.286 (Fisher's exact test), Q value = 1

Table S134.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 92 276
subtype1 10 49
subtype2 32 84
subtype3 50 143

Figure S124.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.662 (ANOVA), Q value = 1

Table S135.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 48 27.1 (39.0)
subtype1 4 20.0 (40.0)
subtype2 13 35.4 (41.0)
subtype3 31 24.5 (38.9)

Figure S125.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.474 (Chi-square test), Q value = 1

Table S136.  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 5 1 352
subtype1 3 0 0 56
subtype2 1 1 0 114
subtype3 6 4 1 182

Figure S126.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.00107 (Fisher's exact test), Q value = 0.16

Table S137.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 357
subtype1 1 58
subtype2 9 107
subtype3 1 192

Figure S127.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.793 (ANOVA), Q value = 1

Table S138.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 313 53.0 (32.2)
subtype1 53 53.6 (28.6)
subtype2 99 51.2 (26.8)
subtype3 161 53.9 (36.3)

Figure S128.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0511 (ANOVA), Q value = 1

Table S139.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 245 1959.7 (12.0)
subtype1 39 1963.9 (14.9)
subtype2 80 1958.4 (11.9)
subtype3 126 1959.2 (10.8)

Figure S129.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.882 (Chi-square test), Q value = 1

Table S140.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 298 6 3 16
subtype1 48 2 1 2
subtype2 89 1 1 5
subtype3 161 3 1 9

Figure S130.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S141.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 80 80 72
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.908 (logrank test), Q value = 1

Table S142.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 205 57 0.0 - 107.0 (6.9)
subtype1 70 23 0.0 - 96.8 (10.1)
subtype2 68 17 0.0 - 107.0 (3.9)
subtype3 67 17 0.0 - 71.8 (10.2)

Figure S131.  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 = 0.129 (ANOVA), Q value = 1

Table S143.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 227 67.4 (9.0)
subtype1 79 65.9 (9.3)
subtype2 78 67.6 (9.5)
subtype3 70 68.8 (7.9)

Figure S132.  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 = 0.0159 (Chi-square test), Q value = 1

Table S144.  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 45 69 1 37 38 32 5 1
subtype1 1 7 25 0 13 18 13 2 0
subtype2 1 12 24 1 13 15 11 3 0
subtype3 0 26 20 0 11 5 8 0 1

Figure S133.  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 = 0.00297 (Chi-square test), Q value = 0.45

Table S145.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 60 137 30 5
subtype1 12 55 12 1
subtype2 18 46 12 4
subtype3 30 36 6 0

Figure S134.  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 = 0.172 (Chi-square test), Q value = 1

Table S146.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 147 60 20
subtype1 46 27 7
subtype2 50 22 6
subtype3 51 11 7

Figure S135.  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 = 0.665 (Chi-square test), Q value = 1

Table S147.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 183 1 46
subtype1 64 0 15
subtype2 64 0 16
subtype3 55 1 15

Figure S136.  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 = 0.98 (Fisher's exact test), Q value = 1

Table S148.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 53 179
subtype1 18 62
subtype2 19 61
subtype3 16 56

Figure S137.  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 = 0.76 (ANOVA), Q value = 1

Table S149.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 28 32.5 (41.1)
subtype1 12 31.7 (41.5)
subtype2 9 40.0 (43.6)
subtype3 7 24.3 (41.6)

Figure S138.  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 = 0.261 (Chi-square test), Q value = 1

Table S150.  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 4 1 220
subtype1 5 1 1 73
subtype2 0 2 0 78
subtype3 2 1 0 69

Figure S139.  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 = 0.637 (Fisher's exact test), Q value = 1

Table S151.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 223
subtype1 3 77
subtype2 2 78
subtype3 4 68

Figure S140.  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 = 0.927 (ANOVA), Q value = 1

Table S152.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 194 50.6 (28.1)
subtype1 68 49.6 (25.4)
subtype2 63 51.5 (26.8)
subtype3 63 50.9 (32.2)

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

'MIRseq Mature CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.272 (ANOVA), Q value = 1

Table S153.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 157 1960.9 (12.6)
subtype1 55 1962.8 (12.5)
subtype2 55 1958.9 (13.9)
subtype3 47 1961.2 (11.1)

Figure S142.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.477 (Chi-square test), Q value = 1

Table S154.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 174 4 2 13
subtype1 65 1 0 5
subtype2 57 2 0 5
subtype3 52 1 2 3

Figure S143.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S155.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2
Number of samples 133 99
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.875 (logrank test), Q value = 1

Table S156.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 205 57 0.0 - 107.0 (6.9)
subtype1 117 29 0.0 - 107.0 (6.5)
subtype2 88 28 0.0 - 96.8 (9.9)

Figure S144.  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 = 0.648 (t-test), Q value = 1

Table S157.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 227 67.4 (9.0)
subtype1 129 67.6 (9.4)
subtype2 98 67.1 (8.4)

Figure S145.  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 = 0.794 (Chi-square test), Q value = 1

Table S158.  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 45 69 1 37 38 32 5 1
subtype1 1 30 40 1 21 21 15 3 1
subtype2 1 15 29 0 16 17 17 2 0

Figure S146.  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 = 0.277 (Chi-square test), Q value = 1

Table S159.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 60 137 30 5
subtype1 39 72 18 4
subtype2 21 65 12 1

Figure S147.  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 = 0.661 (Chi-square test), Q value = 1

Table S160.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 147 60 20
subtype1 86 32 10
subtype2 61 28 10

Figure S148.  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 = 0.0664 (Chi-square test), Q value = 1

Table S161.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 183 1 46
subtype1 99 1 33
subtype2 84 0 13

Figure S149.  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 = 0.433 (Fisher's exact test), Q value = 1

Table S162.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 53 179
subtype1 33 100
subtype2 20 79

Figure S150.  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 = 0.0888 (t-test), Q value = 1

Table S163.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 28 32.5 (41.1)
subtype1 11 50.0 (44.9)
subtype2 17 21.2 (35.2)

Figure S151.  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 = 0.232 (Chi-square test), Q value = 1

Table S164.  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 4 1 220
subtype1 2 3 0 128
subtype2 5 1 1 92

Figure S152.  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 = 0.502 (Fisher's exact test), Q value = 1

Table S165.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 223
subtype1 4 129
subtype2 5 94

Figure S153.  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 = 0.476 (t-test), Q value = 1

Table S166.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 194 50.6 (28.1)
subtype1 110 51.9 (29.0)
subtype2 84 49.0 (26.8)

Figure S154.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.114 (t-test), Q value = 1

Table S167.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 157 1960.9 (12.6)
subtype1 89 1959.6 (12.5)
subtype2 68 1962.8 (12.7)

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

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

P value = 0.494 (Chi-square test), Q value = 1

Table S168.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 174 4 2 13
subtype1 95 2 0 7
subtype2 79 2 2 6

Figure S156.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Methods & Data
Input

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

  • Clinical data file = LUSC-TP.clin.merged.picked.txt

  • Number of patients = 394

  • Number of clustering approaches = 12

  • Number of selected clinical features = 13

  • 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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] 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)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[8] 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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