Lung Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 14 clinical features across 327 patients, 2 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 do not correlate to any clinical features.

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

  • 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.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. 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 10 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, 2 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
Time to Death logrank test 0.256
(1.00) 		0.0982
(1.00) 		0.477
(1.00) 		0.259
(1.00) 		0.00318
(0.436) 		0.00426
(0.579) 		0.337
(1.00) 		0.827
(1.00) 		0.86
(1.00) 		0.898
(1.00)
AGE ANOVA 0.546
(1.00) 		0.168
(1.00) 		0.239
(1.00) 		0.153
(1.00) 		0.0181
(1.00) 		0.0866
(1.00) 		0.255
(1.00) 		0.16
(1.00) 		0.638
(1.00) 		0.267
(1.00)
GENDER Fisher's exact test 0.382
(1.00) 		0.00872
(1.00) 		0.0092
(1.00) 		0.555
(1.00) 		0.29
(1.00) 		0.865
(1.00) 		0.279
(1.00) 		0.234
(1.00) 		0.69
(1.00) 		0.289
(1.00)
KARNOFSKY PERFORMANCE SCORE ANOVA 0.191
(1.00) 		0.0891
(1.00) 		0.444
(1.00) 		0.234
(1.00) 		0.527
(1.00) 		0.918
(1.00) 		0.829
(1.00) 		0.796
(1.00) 		0.00524
(0.708) 		0.819
(1.00)
HISTOLOGICAL TYPE Chi-square test 0.355
(1.00) 		0.587
(1.00) 		0.286
(1.00) 		0.321
(1.00) 		0.306
(1.00) 		0.195
(1.00) 		0.484
(1.00) 		0.129
(1.00) 		0.827
(1.00) 		0.681
(1.00)
PATHOLOGY T Chi-square test 0.0029
(0.401) 		0.000811
(0.113) 		0.491
(1.00) 		0.814
(1.00) 		0.177
(1.00) 		0.262
(1.00) 		0.016
(1.00) 		0.36
(1.00) 		0.349
(1.00) 		0.432
(1.00)
PATHOLOGY N Chi-square test 0.818
(1.00) 		0.32
(1.00) 		0.529
(1.00) 		0.525
(1.00) 		0.373
(1.00) 		0.132
(1.00) 		0.264
(1.00) 		0.836
(1.00) 		0.248
(1.00) 		0.297
(1.00)
PATHOLOGICSPREAD(M) Chi-square test 0.146
(1.00) 		0.323
(1.00) 		0.609
(1.00) 		0.471
(1.00) 		0.611
(1.00) 		0.298
(1.00) 		0.491
(1.00) 		0.215
(1.00) 		5.73e-07
(8.02e-05) 		0.3
(1.00)
TUMOR STAGE Chi-square test 0.798
(1.00) 		0.617
(1.00) 		0.841
(1.00) 		0.808
(1.00) 		0.216
(1.00) 		0.217
(1.00) 		0.208
(1.00) 		0.796
(1.00) 		0.0486
(1.00) 		0.789
(1.00)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.445
(1.00) 		0.453
(1.00) 		1
(1.00) 		0.285
(1.00) 		0.736
(1.00) 		0.731
(1.00) 		0.45
(1.00) 		0.813
(1.00) 		0.526
(1.00) 		0.215
(1.00)
NUMBERPACKYEARSSMOKED ANOVA 0.703
(1.00) 		0.74
(1.00) 		0.926
(1.00) 		0.905
(1.00) 		0.0565
(1.00) 		0.116
(1.00) 		0.921
(1.00) 		0.457
(1.00) 		0.178
(1.00) 		0.974
(1.00)
TOBACCOSMOKINGHISTORYINDICATOR Chi-square test 0.217
(1.00) 		0.0089
(1.00) 		0.161
(1.00) 		0.534
(1.00) 		0.079
(1.00) 		0.21
(1.00) 		0.0993
(1.00) 		0.151
(1.00) 		0.11
(1.00) 		0.039
(1.00)
YEAROFTOBACCOSMOKINGONSET ANOVA 0.83
(1.00) 		0.121
(1.00) 		0.334
(1.00) 		0.403
(1.00) 		0.168
(1.00) 		0.244
(1.00) 		0.657
(1.00) 		0.146
(1.00) 		0.387
(1.00) 		0.339
(1.00)
COMPLETENESS OF RESECTION Chi-square test 0.482
(1.00) 		0.752
(1.00) 		0.207
(1.00) 		0.372
(1.00) 		0.037
(1.00) 		0.0263
(1.00) 		0.56
(1.00) 		0.656
(1.00) 		0.149
(1.00) 		0.674
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 43 50 30 31
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.256 (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 62 0.4 - 173.8 (18.2)
subtype1 42 16 0.4 - 122.4 (19.0)
subtype2 48 19 0.4 - 99.2 (24.5)
subtype3 28 16 0.4 - 82.2 (15.9)
subtype4 30 11 0.4 - 173.8 (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 = 0.546 (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 42 66.3 (7.6)
subtype2 49 66.6 (8.4)
subtype3 30 68.2 (8.6)
subtype4 31 65.0 (10.1)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.382 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 44 110
subtype1 9 34
subtype2 13 37
subtype3 11 19
subtype4 11 20

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

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

P value = 0.191 (ANOVA), Q value = 1

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: '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 S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.355 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: '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 43
subtype2 3 0 47
subtype3 1 0 29
subtype4 1 1 29

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0029 (Chi-square test), Q value = 0.4

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 5 31 6 1
subtype2 4 39 1 6
subtype3 11 16 2 1
subtype4 10 14 3 4

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.818 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 26 11 4 2
subtype2 27 17 4 2
subtype3 20 6 3 1
subtype4 23 6 2 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.146 (Fisher's exact test), Q value = 1

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 146 4
subtype1 39 2
subtype2 48 0
subtype3 30 0
subtype4 29 2

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.798 (Chi-square test), Q value = 1

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

nPatients I II III IV
ALL 81 34 34 4
subtype1 23 9 9 2
subtype2 25 13 12 0
subtype3 17 6 6 0
subtype4 16 6 7 2

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

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.445 (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 42
subtype2 0 50
subtype3 1 29
subtype4 0 31

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 = 0.703 (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 39 59.1 (41.5)
subtype2 45 51.5 (24.0)
subtype3 25 50.9 (36.6)
subtype4 24 58.4 (47.1)

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

'mRNA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.217 (Chi-square test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 81 39 26 6
subtype1 25 10 4 2
subtype2 27 10 13 0
subtype3 16 10 3 1
subtype4 13 9 6 3

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

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.83 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 97 1958.0 (10.6)
subtype1 29 1957.1 (8.6)
subtype2 24 1958.6 (11.2)
subtype3 22 1957.1 (10.8)
subtype4 22 1959.5 (12.4)

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

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

P value = 0.482 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 139 3 2 5
subtype1 39 0 1 1
subtype2 45 1 1 1
subtype3 25 1 0 3
subtype4 30 1 0 0

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 28 54 72
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0982 (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), Month
ALL 148 62 0.4 - 173.8 (18.2)
subtype1 27 7 0.4 - 173.8 (15.6)
subtype2 52 21 0.4 - 99.2 (23.6)
subtype3 69 34 0.4 - 122.4 (18.8)

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 = 0.168 (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 28 63.9 (8.4)
subtype2 53 66.5 (7.9)
subtype3 71 67.5 (9.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00872 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 44 110
subtype1 15 13
subtype2 12 42
subtype3 17 55

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

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

P value = 0.0891 (ANOVA), Q value = 1

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: '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 13 14.6 (35.7)

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.587 (Chi-square test), Q value = 1

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: '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 27
subtype2 3 0 51
subtype3 1 1 70

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.000811 (Chi-square test), Q value = 0.11

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 12 11 3 2
subtype2 4 42 1 7
subtype3 14 47 8 3

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.32 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 22 3 3 0
subtype2 29 18 5 2
subtype3 45 19 5 3

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

'mRNA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

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

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 146 4
subtype1 27 1
subtype2 52 0
subtype3 67 3

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.617 (Chi-square test), Q value = 1

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

nPatients I II III IV
ALL 81 34 34 4
subtype1 17 4 6 1
subtype2 26 14 14 0
subtype3 38 16 14 3

Figure S23.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.453 (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 27
subtype2 0 54
subtype3 1 71

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 = 0.74 (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 22 57.5 (45.1)
subtype2 49 51.6 (25.0)
subtype3 62 56.4 (41.0)

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

'mRNA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0089 (Chi-square test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 81 39 26 6
subtype1 17 4 4 3
subtype2 26 12 16 0
subtype3 38 23 6 3

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

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.121 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 97 1958.0 (10.6)
subtype1 19 1961.4 (11.6)
subtype2 28 1959.3 (10.8)
subtype3 50 1956.0 (9.8)

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

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

P value = 0.752 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 139 3 2 5
subtype1 26 1 0 0
subtype2 49 1 1 1
subtype3 64 1 1 4

Figure S28.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

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

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

Cluster Labels 1 2 3
Number of samples 113 108 104
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.477 (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), Month
ALL 308 120 0.0 - 173.8 (13.1)
subtype1 107 50 0.1 - 173.8 (14.1)
subtype2 104 37 0.2 - 114.0 (16.2)
subtype3 97 33 0.0 - 122.4 (11.7)

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 = 0.239 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 317 67.5 (8.6)
subtype1 111 67.3 (9.8)
subtype2 105 66.6 (7.7)
subtype3 101 68.6 (8.1)

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 'GENDER'

P value = 0.0092 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 83 242
subtype1 39 74
subtype2 18 90
subtype3 26 78

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.444 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 54 28.5 (39.7)
subtype1 19 37.4 (44.2)
subtype2 14 20.0 (35.7)
subtype3 21 26.2 (38.1)

Figure S32.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.286 (Chi-square test), Q value = 1

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

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
subtype1 0 0 1 1 111
subtype2 4 1 0 0 103
subtype3 3 0 0 0 101

Figure S33.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.491 (Chi-square test), Q value = 1

Table S37.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 78 196 33 18
subtype1 27 68 14 4
subtype2 22 66 13 7
subtype3 29 62 6 7

Figure S34.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.529 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 201 91 26 5
subtype1 76 24 10 2
subtype2 60 37 8 2
subtype3 65 30 8 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.609 (Chi-square test), Q value = 1

Table S39.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 285 4 30
subtype1 97 1 14
subtype2 96 2 7
subtype3 92 1 9

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.841 (Chi-square test), Q value = 1

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

nPatients I II III IV
ALL 165 90 62 4
subtype1 61 32 17 1
subtype2 51 31 24 2
subtype3 53 27 21 1

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (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 11 314
subtype1 4 109
subtype2 4 104
subtype3 3 101

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 = 0.926 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 274 52.5 (32.6)
subtype1 92 52.1 (33.7)
subtype2 94 51.8 (32.2)
subtype3 88 53.6 (32.3)

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 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.161 (Chi-square test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 158 74 72 14
subtype1 53 30 18 7
subtype2 47 24 32 4
subtype3 58 20 22 3

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

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.334 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 217 1958.7 (11.6)
subtype1 80 1957.2 (12.1)
subtype2 61 1960.0 (11.2)
subtype3 76 1959.2 (11.5)

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

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

P value = 0.207 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 253 6 4 16
subtype1 84 1 2 10
subtype2 88 3 1 5
subtype3 81 2 1 1

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 71 69 53
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.259 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 180 69 0.0 - 173.8 (11.4)
subtype1 66 29 0.1 - 173.8 (9.3)
subtype2 65 24 0.2 - 141.3 (17.8)
subtype3 49 16 0.0 - 107.0 (11.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.153 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 185 68.3 (8.8)
subtype1 68 69.9 (9.4)
subtype2 67 67.0 (7.7)
subtype3 50 67.9 (9.1)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.555 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 49 144
subtype1 21 50
subtype2 15 54
subtype3 13 40

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

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

P value = 0.234 (ANOVA), Q value = 1

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 31 29.4 (40.2)
subtype1 5 42.0 (49.2)
subtype2 14 15.7 (33.4)
subtype3 12 40.0 (42.0)

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.321 (Chi-square test), Q value = 1

Table S51.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 4 1 1 187
subtype1 0 0 0 71
subtype2 2 1 0 66
subtype3 2 0 1 50

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.814 (Chi-square test), Q value = 1

Table S52.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 56 108 22 7
subtype1 19 42 8 2
subtype2 22 35 10 2
subtype3 15 31 4 3

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.525 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2
ALL 119 57 15
subtype1 43 19 8
subtype2 46 20 3
subtype3 30 18 4

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.471 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 160 1 30
subtype1 56 1 14
subtype2 61 0 8
subtype3 43 0 8

Figure S50.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.808 (Chi-square test), Q value = 1

Table S55.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 96 62 31 1
subtype1 35 23 11 1
subtype2 38 21 10 0
subtype3 23 18 10 0

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

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

P value = 0.285 (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 9 184
subtype1 4 67
subtype2 1 68
subtype3 4 49

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.905 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 162 50.9 (30.2)
subtype1 58 49.7 (30.1)
subtype2 57 50.8 (25.2)
subtype3 47 52.4 (35.9)

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.534 (Chi-square test), Q value = 1

Table S58.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 85 41 54 8
subtype1 30 19 16 3
subtype2 28 13 25 3
subtype3 27 9 13 2

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

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.403 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 134 1958.9 (12.0)
subtype1 49 1957.0 (11.3)
subtype2 45 1960.0 (12.4)
subtype3 40 1959.8 (12.5)

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

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

P value = 0.372 (Chi-square test), Q value = 1

Table S60.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 133 3 2 11
subtype1 44 0 0 6
subtype2 56 1 1 3
subtype3 33 2 1 2

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 59 73 62
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00318 (logrank test), Q value = 0.44

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

nPatients nDeath Duration Range (Median), Month
ALL 181 72 0.0 - 173.8 (15.8)
subtype1 56 15 0.0 - 173.8 (23.0)
subtype2 69 30 0.2 - 115.6 (14.1)
subtype3 56 27 0.1 - 119.8 (12.4)

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 = 0.0181 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 186 67.5 (9.5)
subtype1 58 65.6 (10.6)
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 'GENDER'

P value = 0.29 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 49 145
subtype1 12 47
subtype2 17 56
subtype3 20 42

Figure S59.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: '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 #4: '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 #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.306 (Chi-square test), Q value = 1

Table S66.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 3 1 1 189
subtype1 2 1 1 55
subtype2 0 0 0 73
subtype3 1 0 0 61

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.177 (Chi-square test), Q value = 1

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 44 119 20 11
subtype1 15 34 4 6
subtype2 11 51 9 2
subtype3 18 34 7 3

Figure S62.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.373 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 124 50 15 4
subtype1 34 17 6 2
subtype2 44 22 5 2
subtype3 46 11 4 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.611 (Fisher's exact test), Q value = 1

Table S69.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 MX
ALL 175 16
subtype1 53 6
subtype2 67 4
subtype3 55 6

Figure S64.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.216 (Chi-square test), Q value = 1

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

nPatients I II III
ALL 98 57 36
subtype1 27 18 14
subtype2 33 26 12
subtype3 38 13 10

Figure S65.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.736 (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 8 186
subtype1 3 56
subtype2 2 71
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 = 0.0565 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 163 51.7 (32.5)
subtype1 50 60.6 (40.1)
subtype2 61 49.4 (30.2)
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 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.079 (Chi-square test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 91 48 39 10
subtype1 32 11 9 5
subtype2 37 15 14 4
subtype3 22 22 16 1

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

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.168 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 129 1958.4 (12.1)
subtype1 38 1961.2 (11.8)
subtype2 49 1958.2 (11.1)
subtype3 42 1956.1 (13.3)

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

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

P value = 0.037 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 143 4 4 9
subtype1 41 0 2 5
subtype2 54 4 2 4
subtype3 48 0 0 0

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 57 48 49 40
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00426 (logrank test), Q value = 0.58

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

nPatients nDeath Duration Range (Median), Month
ALL 181 72 0.0 - 173.8 (15.8)
subtype1 53 19 0.0 - 173.8 (16.8)
subtype2 44 15 0.2 - 99.2 (23.0)
subtype3 46 18 0.2 - 115.6 (11.9)
subtype4 38 20 0.1 - 82.2 (10.2)

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 = 0.0866 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 186 67.5 (9.5)
subtype1 55 65.8 (10.6)
subtype2 46 69.4 (8.9)
subtype3 46 65.8 (7.9)
subtype4 39 69.4 (9.9)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.865 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 49 145
subtype1 15 42
subtype2 14 34
subtype3 11 38
subtype4 9 31

Figure S73.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

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

Table S80.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: '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 S74.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.195 (Chi-square test), Q value = 1

Table S81.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 3 1 1 189
subtype1 3 1 1 52
subtype2 0 0 0 48
subtype3 0 0 0 49
subtype4 0 0 0 40

Figure S75.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.262 (Chi-square test), Q value = 1

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 44 119 20 11
subtype1 14 32 5 6
subtype2 16 25 5 2
subtype3 6 36 6 1
subtype4 8 26 4 2

Figure S76.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.132 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 124 50 15 4
subtype1 33 16 8 0
subtype2 32 9 4 2
subtype3 29 17 1 2
subtype4 30 8 2 0

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

'RPPA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.298 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 MX
ALL 175 16
subtype1 52 5
subtype2 45 3
subtype3 45 2
subtype4 33 6

Figure S78.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.217 (Chi-square test), Q value = 1

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

nPatients I II III
ALL 98 57 36
subtype1 25 18 14
subtype2 28 10 10
subtype3 21 19 8
subtype4 24 10 4

Figure S79.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.731 (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 8 186
subtype1 3 54
subtype2 3 45
subtype3 1 48
subtype4 1 39

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 = 0.116 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 163 51.7 (32.5)
subtype1 49 61.1 (45.3)
subtype2 40 49.0 (25.0)
subtype3 42 47.4 (24.2)
subtype4 32 46.5 (24.3)

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

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.21 (Chi-square test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 91 48 39 10
subtype1 26 13 10 6
subtype2 22 13 12 1
subtype3 29 8 8 2
subtype4 14 14 9 1

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

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.244 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 129 1958.4 (12.1)
subtype1 38 1959.1 (10.9)
subtype2 33 1958.5 (13.9)
subtype3 35 1960.5 (11.6)
subtype4 23 1954.0 (11.8)

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

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

P value = 0.0263 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 143 4 4 9
subtype1 45 0 0 5
subtype2 37 0 2 0
subtype3 36 4 2 2
subtype4 25 0 0 2

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 60 81 79 38
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.337 (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), Month
ALL 241 100 0.0 - 173.8 (17.0)
subtype1 57 24 0.0 - 122.4 (14.1)
subtype2 76 29 0.2 - 141.3 (25.6)
subtype3 73 31 0.2 - 173.8 (8.8)
subtype4 35 16 0.4 - 82.2 (19.8)

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 = 0.255 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 250 67.6 (8.5)
subtype1 59 66.6 (8.9)
subtype2 79 66.7 (7.9)
subtype3 75 68.7 (8.8)
subtype4 37 69.1 (8.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

Table S94.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 67 191
subtype1 12 48
subtype2 18 63
subtype3 24 55
subtype4 13 25

Figure S87.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.829 (ANOVA), Q value = 1

Table S95.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 51 25.3 (38.4)
subtype1 7 22.9 (39.0)
subtype2 13 20.8 (35.2)
subtype3 17 22.9 (37.7)
subtype4 14 33.6 (44.5)

Figure S88.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.484 (Chi-square test), Q value = 1

Table S96.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 6 1 1 1 249
subtype1 0 0 1 0 59
subtype2 3 1 0 0 77
subtype3 3 0 0 1 75
subtype4 0 0 0 0 38

Figure S89.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.016 (Chi-square test), Q value = 1

Table S97.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 56 163 25 14
subtype1 7 39 11 3
subtype2 13 55 7 6
subtype3 21 48 5 5
subtype4 15 21 2 0

Figure S90.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.264 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 164 68 20 5
subtype1 37 15 6 2
subtype2 50 25 5 1
subtype3 49 25 4 1
subtype4 28 3 5 1

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

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.491 (Chi-square test), Q value = 1

Table S99.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 230 4 18
subtype1 49 2 6
subtype2 75 0 4
subtype3 72 2 5
subtype4 34 0 3

Figure S92.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.208 (Chi-square test), Q value = 1

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

nPatients I II III IV
ALL 135 66 49 4
subtype1 30 13 14 2
subtype2 42 25 14 0
subtype3 37 24 14 2
subtype4 26 4 7 0

Figure S93.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.45 (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 8 250
subtype1 3 57
subtype2 1 80
subtype3 2 77
subtype4 2 36

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 = 0.921 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 212 53.6 (34.2)
subtype1 52 52.0 (37.2)
subtype2 68 53.2 (27.8)
subtype3 60 53.5 (36.5)
subtype4 32 57.3 (38.3)

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

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0993 (Chi-square test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 133 62 45 12
subtype1 30 17 6 3
subtype2 40 14 24 3
subtype3 39 22 11 5
subtype4 24 9 4 1

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

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.657 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 159 1957.7 (10.8)
subtype1 38 1956.7 (11.6)
subtype2 42 1959.0 (11.1)
subtype3 51 1958.2 (10.6)
subtype4 28 1956.2 (9.6)

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

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

P value = 0.56 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 209 5 4 12
subtype1 46 1 2 2
subtype2 71 2 0 3
subtype3 64 2 1 3
subtype4 28 0 1 4

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 116 87 55
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.827 (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), Month
ALL 241 100 0.0 - 173.8 (17.0)
subtype1 106 48 0.0 - 173.8 (19.0)
subtype2 81 33 0.2 - 141.3 (23.0)
subtype3 54 19 0.2 - 114.0 (8.9)

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 = 0.16 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 250 67.6 (8.5)
subtype1 112 67.6 (9.1)
subtype2 84 66.5 (7.9)
subtype3 54 69.4 (8.1)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.234 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 67 191
subtype1 29 87
subtype2 19 68
subtype3 19 36

Figure S101.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.796 (ANOVA), Q value = 1

Table S110.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 51 25.3 (38.4)
subtype1 23 27.4 (41.0)
subtype2 14 19.3 (34.3)
subtype3 14 27.9 (40.0)

Figure S102.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.129 (Chi-square test), Q value = 1

Table S111.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 6 1 1 1 249
subtype1 0 0 1 0 115
subtype2 3 1 0 0 83
subtype3 3 0 0 1 51

Figure S103.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.36 (Chi-square test), Q value = 1

Table S112.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 56 163 25 14
subtype1 24 74 13 5
subtype2 14 59 8 6
subtype3 18 30 4 3

Figure S104.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.836 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 164 68 20 5
subtype1 74 29 10 3
subtype2 54 26 5 2
subtype3 36 13 5 0

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.215 (Chi-square test), Q value = 1

Table S114.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 230 4 18
subtype1 102 3 7
subtype2 81 0 4
subtype3 47 1 7

Figure S106.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.796 (Chi-square test), Q value = 1

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

nPatients I II III IV
ALL 135 66 49 4
subtype1 61 29 21 3
subtype2 46 25 16 0
subtype3 28 12 12 1

Figure S107.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.813 (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 8 250
subtype1 4 112
subtype2 2 85
subtype3 2 53

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 = 0.457 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 212 53.6 (34.2)
subtype1 96 52.5 (33.9)
subtype2 74 51.6 (28.2)
subtype3 42 59.5 (43.7)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.151 (Chi-square test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 133 62 45 12
subtype1 63 28 15 6
subtype2 42 18 24 3
subtype3 28 16 6 3

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

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.146 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 159 1957.7 (10.8)
subtype1 76 1956.0 (10.5)
subtype2 47 1959.2 (11.3)
subtype3 36 1959.4 (10.5)

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

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

P value = 0.656 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 209 5 4 12
subtype1 91 3 2 8
subtype2 77 2 1 2
subtype3 41 0 1 2

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

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3
Number of samples 141 126 33
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.86 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 284 107 0.0 - 173.8 (13.1)
subtype1 134 58 0.1 - 173.8 (16.9)
subtype2 117 38 0.0 - 107.0 (7.0)
subtype3 33 11 0.2 - 73.1 (23.0)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.638 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 293 67.8 (8.6)
subtype1 139 67.5 (8.3)
subtype2 121 67.9 (8.9)
subtype3 33 69.0 (8.8)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.69 (Fisher's exact test), Q value = 1

Table S124.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 78 222
subtype1 40 101
subtype2 30 96
subtype3 8 25

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

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

P value = 0.00524 (ANOVA), Q value = 0.71

Table S125.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 48 27.1 (39.0)
subtype1 29 13.4 (30.7)
subtype2 12 54.2 (39.6)
subtype3 7 37.1 (46.4)

Figure S116.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.827 (Chi-square test), Q value = 1

Table S126.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 5 1 1 1 292
subtype1 2 0 1 0 138
subtype2 2 1 0 1 122
subtype3 1 0 0 0 32

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

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.349 (Chi-square test), Q value = 1

Table S127.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 72 181 31 16
subtype1 29 92 11 9
subtype2 33 70 18 5
subtype3 10 19 2 2

Figure S118.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.248 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 183 85 26 4
subtype1 87 35 15 4
subtype2 74 40 10 0
subtype3 22 10 1 0

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

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 5.73e-07 (Chi-square test), Q value = 8e-05

Table S129.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 261 3 30
subtype1 133 3 1
subtype2 98 0 27
subtype3 30 0 2

Figure S120.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.0486 (Chi-square test), Q value = 1

Table S130.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 153 83 58 3
subtype1 77 29 32 3
subtype2 57 46 21 0
subtype3 19 8 5 0

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

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

P value = 0.526 (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 10 290
subtype1 4 137
subtype2 6 120
subtype3 0 33

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

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.178 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 255 53.2 (33.3)
subtype1 121 56.5 (37.8)
subtype2 104 48.5 (28.0)
subtype3 30 56.1 (30.0)

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

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.11 (Chi-square test), Q value = 1

Table S133.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 146 67 70 11
subtype1 75 35 26 5
subtype2 59 25 30 6
subtype3 12 7 14 0

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

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.387 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 203 1958.7 (11.7)
subtype1 90 1957.4 (10.3)
subtype2 91 1959.6 (12.4)
subtype3 22 1960.0 (13.5)

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

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

P value = 0.149 (Chi-square test), Q value = 1

Table S135.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 232 5 3 14
subtype1 128 2 2 3
subtype2 77 2 1 10
subtype3 27 1 0 1

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

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels 1 2 3
Number of samples 47 160 93
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.898 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 284 107 0.0 - 173.8 (13.1)
subtype1 46 17 0.2 - 141.3 (11.4)
subtype2 147 53 0.0 - 173.8 (12.2)
subtype3 91 37 0.0 - 114.0 (17.7)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.267 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 293 67.8 (8.6)
subtype1 46 68.1 (8.6)
subtype2 156 67.1 (8.7)
subtype3 91 68.9 (8.3)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.289 (Fisher's exact test), Q value = 1

Table S139.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 78 222
subtype1 9 38
subtype2 40 120
subtype3 29 64

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

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

P value = 0.819 (ANOVA), Q value = 1

Table S140.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 48 27.1 (39.0)
subtype1 5 30.0 (41.2)
subtype2 29 29.3 (41.1)
subtype3 14 21.4 (36.1)

Figure S130.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.681 (Chi-square test), Q value = 1

Table S141.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 5 1 1 1 292
subtype1 2 0 0 0 45
subtype2 1 1 1 1 156
subtype3 2 0 0 0 91

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.432 (Chi-square test), Q value = 1

Table S142.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 72 181 31 16
subtype1 8 32 3 4
subtype2 38 99 15 8
subtype3 26 50 13 4

Figure S132.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.297 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 183 85 26 4
subtype1 28 17 1 1
subtype2 97 40 18 3
subtype3 58 28 7 0

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.3 (Chi-square test), Q value = 1

Table S144.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 261 3 30
subtype1 40 0 3
subtype2 136 3 20
subtype3 85 0 7

Figure S134.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.789 (Chi-square test), Q value = 1

Table S145.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 153 83 58 3
subtype1 24 13 8 0
subtype2 79 46 31 3
subtype3 50 24 19 0

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

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

P value = 0.215 (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 10 290
subtype1 2 45
subtype2 3 157
subtype3 5 88

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.974 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 255 53.2 (33.3)
subtype1 41 52.4 (28.3)
subtype2 133 53.7 (36.5)
subtype3 81 52.9 (30.6)

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

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.039 (Chi-square test), Q value = 1

Table S148.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 146 67 70 11
subtype1 23 8 16 0
subtype2 79 43 26 8
subtype3 44 16 28 3

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

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.339 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 203 1958.7 (11.7)
subtype1 29 1961.0 (15.2)
subtype2 108 1959.0 (10.2)
subtype3 66 1957.3 (12.1)

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

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

P value = 0.674 (Chi-square test), Q value = 1

Table S150.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 232 5 3 14
subtype1 36 2 1 1
subtype2 125 2 1 9
subtype3 71 1 1 4

Figure S140.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: '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 = 327

  • Number of clustering approaches = 10

  • 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

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

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

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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] 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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