Lung Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
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, 19 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.T'.

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

  • 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'GENDER' and 'STOPPEDSMOKINGYEAR'.

  • 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 correlate to 'Time to Death',  'AGE', and 'STOPPEDSMOKINGYEAR'.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 8 subtypes that correlate to 'AGE' and 'PATHOLOGY.T'.

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

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'KARNOFSKY.PERFORMANCE.SCORE' and 'PATHOLOGICSPREAD(M)'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'PATHOLOGICSPREAD(M)' and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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 from statistical tests. Thresholded by P value < 0.05, 19 significant findings detected.

Clinical
Features 		Statistical
Tests 		mRNA
CNMF
subtypes 		mRNA
cHierClus
subtypes 		CN
CNMF 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRseq
CNMF
subtypes 		MIRseq
cHierClus
subtypes
Time to Death logrank test 0.256 0.0982 0.482 0.229 0.00318 0.00431 0.271 0.492 0.922 0.632
AGE ANOVA 0.546 0.168 0.247 0.14 0.0181 0.126 0.00813 0.443 0.685 0.668
GENDER Fisher's exact test 0.382 0.00872 0.0173 0.388 0.341 0.709 0.225 0.0447 0.534 0.0715
KARNOFSKY PERFORMANCE SCORE ANOVA 0.191 0.0891 0.278 0.197 0.527 0.918 0.543 0.429 0.00524 0.493
HISTOLOGICAL TYPE Chi-square test 0.355 0.587 0.425 0.255 0.307 0.195 0.897 0.294 0.381 0.516
PATHOLOGY T Chi-square test 0.0029 0.000811 0.735 0.701 0.173 0.243 0.0215 0.364 0.464 0.464
PATHOLOGY N Chi-square test 0.818 0.32 0.423 0.468 0.473 0.159 0.673 0.9 0.2 0.732
PATHOLOGICSPREAD(M) Chi-square test 0.146 0.323 0.411 0.296 0.643 0.204 0.0795 0.655 4.01e-07 0.026
TUMOR STAGE Chi-square test 0.798 0.617 0.713 0.855 0.211 0.233 0.668 0.564 0.0797 0.759
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.445 0.453 0.511 0.692 0.738 0.756 0.399 0.382 0.526 0.0271
NUMBERPACKYEARSSMOKED ANOVA 0.703 0.74 0.84 0.896 0.055 0.114 0.0832 0.926 0.157 0.202
STOPPEDSMOKINGYEAR ANOVA 0.0935 0.0381 0.00289 0.159 0.0017 0.257 0.164 0.564 0.314 0.254
TOBACCOSMOKINGHISTORYINDICATOR Chi-square test 0.217 0.0089 0.192 0.207 0.103 0.121 0.0907 0.0212 0.167 0.198
YEAROFTOBACCOSMOKINGONSET ANOVA 0.83 0.121 0.196 0.541 0.168 0.407 0.233 0.152 0.156 0.573
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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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 'STOPPEDSMOKINGYEAR'

P value = 0.0935 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 111 1996.8 (12.6)
subtype1 34 1996.2 (12.4)
subtype2 34 1998.0 (12.5)
subtype3 23 1991.9 (14.7)
subtype4 20 2001.3 (9.0)

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

'mRNA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.217 (Chi-square test)

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

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.83 (ANOVA)

Table S15.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: '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 S14.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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 'STOPPEDSMOKINGYEAR'

P value = 0.0381 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 111 1996.8 (12.6)
subtype1 19 2002.2 (7.3)
subtype2 35 1998.2 (12.5)
subtype3 57 1994.1 (13.5)

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

'mRNA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0089 (Chi-square test)

Table S29.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: '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 S27.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.121 (ANOVA)

Table S30.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: '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 S28.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #3: 'CN CNMF'

Table S31.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3
Number of samples 116 104 105
'CN CNMF' versus 'Time to Death'

P value = 0.482 (logrank test)

Table S32.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 308 120 0.0 - 173.8 (13.1)
subtype1 109 51 0.0 - 173.8 (15.6)
subtype2 100 34 0.2 - 114.0 (13.6)
subtype3 99 35 0.0 - 122.4 (11.8)

Figure S29.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.247 (ANOVA)

Table S33.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 317 67.5 (8.6)
subtype1 114 67.5 (9.7)
subtype2 101 66.4 (7.8)
subtype3 102 68.5 (8.0)

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

'CN CNMF' versus 'GENDER'

P value = 0.0173 (Fisher's exact test)

Table S34.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 83 242
subtype1 38 78
subtype2 17 87
subtype3 28 77

Figure S31.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.278 (ANOVA)

Table S35.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 54 28.5 (39.7)
subtype1 19 38.9 (45.2)
subtype2 12 15.8 (31.8)
subtype3 23 26.5 (37.9)

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

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.425 (Chi-square test)

Table S36.  Clustering Approach #3: 'CN 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 7 1 1 1 315
subtype1 1 0 1 1 113
subtype2 4 1 0 0 99
subtype3 2 0 0 0 103

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

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.735 (Chi-square test)

Table S37.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 78 196 33 18
subtype1 29 68 14 5
subtype2 21 65 12 6
subtype3 28 63 7 7

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

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.423 (Chi-square test)

Table S38.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 201 91 26 5
subtype1 78 25 10 2
subtype2 56 37 8 2
subtype3 67 29 8 1

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

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.411 (Chi-square test)

Table S39.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 285 4 30
subtype1 101 0 14
subtype2 92 2 7
subtype3 92 2 9

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

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.713 (Chi-square test)

Table S40.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 165 90 62 4
subtype1 63 31 19 0
subtype2 49 31 22 2
subtype3 53 28 21 2

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

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.511 (Fisher's exact test)

Table S41.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 314
subtype1 4 112
subtype2 5 99
subtype3 2 103

Figure S38.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.84 (ANOVA)

Table S42.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 274 52.5 (32.6)
subtype1 95 52.2 (33.5)
subtype2 89 51.2 (33.1)
subtype3 90 54.0 (31.6)

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

'CN CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.00289 (ANOVA)

Table S43.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 214 1997.4 (11.5)
subtype1 79 1993.9 (12.8)
subtype2 62 1999.1 (9.5)
subtype3 73 1999.7 (10.9)

Figure S40.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'CN CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.192 (Chi-square test)

Table S44.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #13: '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 32 20 6
subtype2 45 24 30 4
subtype3 60 18 22 4

Figure S41.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'CN CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.196 (ANOVA)

Table S45.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 217 1958.7 (11.6)
subtype1 81 1957.0 (12.1)
subtype2 57 1960.6 (10.9)
subtype3 79 1958.9 (11.6)

Figure S42.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

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 76 69 48
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.229 (logrank test)

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 71 31 0.1 - 173.8 (9.5)
subtype2 64 24 0.2 - 141.3 (18.3)
subtype3 45 14 0.0 - 107.0 (10.8)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.14 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 185 68.3 (8.8)
subtype1 73 69.5 (9.6)
subtype2 66 66.6 (8.0)
subtype3 46 68.7 (8.4)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.388 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 49 144
subtype1 23 53
subtype2 14 55
subtype3 12 36

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

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

P value = 0.197 (ANOVA)

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 6 48.3 (46.7)
subtype2 14 15.7 (33.4)
subtype3 11 36.4 (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 = 0.255 (Chi-square test)

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 76
subtype2 2 1 0 66
subtype3 2 0 1 45

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.701 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 56 108 22 7
subtype1 20 46 8 2
subtype2 23 34 10 2
subtype3 13 28 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 = 0.468 (Chi-square test)

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

nPatients N0 N1 N2
ALL 119 57 15
subtype1 45 21 9
subtype2 46 20 3
subtype3 28 16 3

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.296 (Chi-square test)

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

nPatients M0 M1 MX
ALL 160 1 30
subtype1 59 1 16
subtype2 62 0 7
subtype3 39 0 7

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.855 (Chi-square test)

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

nPatients I II III IV
ALL 96 62 31 1
subtype1 36 26 12 1
subtype2 38 21 10 0
subtype3 22 15 9 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 = 0.692 (Fisher's exact test)

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

nPatients NO YES
ALL 9 184
subtype1 4 72
subtype2 2 67
subtype3 3 45

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.896 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 162 50.9 (30.2)
subtype1 63 52.3 (35.3)
subtype2 56 49.7 (23.9)
subtype3 43 50.5 (30.0)

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

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.159 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 117 1997.8 (10.2)
subtype1 50 1995.8 (11.9)
subtype2 36 1999.8 (8.5)
subtype3 31 1998.8 (8.7)

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.207 (Chi-square test)

Table S59.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: '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 32 22 16 3
subtype2 28 12 25 4
subtype3 25 7 13 1

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

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.541 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 134 1958.9 (12.0)
subtype1 54 1957.6 (11.6)
subtype2 44 1960.3 (12.4)
subtype3 36 1959.1 (12.2)

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

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 72 63
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00318 (logrank test)

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)

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 = 0.341 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 49 145
subtype1 12 47
subtype2 17 55
subtype3 20 43

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)

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 = 0.307 (Chi-square test)

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 72
subtype3 1 0 0 62

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 = 0.173 (Chi-square test)

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 8 2
subtype3 18 34 8 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 = 0.473 (Chi-square test)

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 21 5 2
subtype3 46 12 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 = 0.643 (Fisher's exact test)

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

nPatients M0 MX
ALL 175 16
subtype1 53 6
subtype2 66 4
subtype3 56 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 = 0.211 (Chi-square test)

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 11
subtype3 38 13 11

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 = 0.738 (Fisher's exact test)

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 70
subtype3 3 60

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 = 0.055 (ANOVA)

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 60 49.6 (30.4)
subtype3 53 45.8 (24.8)

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

'RPPA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.0017 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 130 1996.4 (12.2)
subtype1 39 2000.4 (8.6)
subtype2 49 1997.7 (11.1)
subtype3 42 1991.2 (14.5)

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

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.103 (Chi-square test)

Table S74.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: '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 36 15 14 4
subtype3 23 22 16 1

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

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.168 (ANOVA)

Table S75.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: '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 S70.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

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 47 49 57 41
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00431 (logrank test)

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 44 18 0.2 - 115.6 (14.3)
subtype2 45 15 0.2 - 99.2 (23.0)
subtype3 53 19 0.0 - 173.8 (16.8)
subtype4 39 20 0.1 - 82.2 (8.8)

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 = 0.126 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 186 67.5 (9.5)
subtype1 44 66.0 (8.0)
subtype2 47 69.2 (8.9)
subtype3 55 65.8 (10.6)
subtype4 40 69.3 (9.8)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.709 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 49 145
subtype1 10 37
subtype2 15 34
subtype3 15 42
subtype4 9 32

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)

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 7 32.9 (41.1)
subtype2 4 42.5 (49.2)
subtype3 10 27.0 (33.7)
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)

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 0 0 0 47
subtype2 0 0 0 49
subtype3 3 1 1 52
subtype4 0 0 0 41

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 = 0.243 (Chi-square test)

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 5 35 6 1
subtype2 16 26 5 2
subtype3 14 32 5 6
subtype4 9 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 = 0.159 (Chi-square test)

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 28 16 1 2
subtype2 33 9 4 2
subtype3 33 16 8 0
subtype4 30 9 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 = 0.204 (Fisher's exact test)

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

nPatients M0 MX
ALL 175 16
subtype1 44 1
subtype2 45 4
subtype3 52 5
subtype4 34 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 = 0.233 (Chi-square test)

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

nPatients I II III
ALL 98 57 36
subtype1 20 18 8
subtype2 29 10 10
subtype3 25 18 14
subtype4 24 11 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 = 0.756 (Fisher's exact test)

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

nPatients NO YES
ALL 8 186
subtype1 1 46
subtype2 3 46
subtype3 3 54
subtype4 1 40

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 = 0.114 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 163 51.7 (32.5)
subtype1 40 48.2 (24.4)
subtype2 41 48.6 (24.8)
subtype3 49 61.1 (45.3)
subtype4 33 45.9 (24.2)

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

'RPPA cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.257 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 130 1996.4 (12.2)
subtype1 36 1998.0 (11.3)
subtype2 32 1997.2 (12.0)
subtype3 37 1997.0 (12.5)
subtype4 25 1992.0 (13.0)

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

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.121 (Chi-square test)

Table S89.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: '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 29 8 6 2
subtype2 22 13 13 1
subtype3 26 13 10 6
subtype4 14 14 10 1

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

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.407 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 129 1958.4 (12.1)
subtype1 33 1959.8 (11.5)
subtype2 34 1959.1 (14.0)
subtype3 38 1959.1 (10.9)
subtype4 24 1954.7 (11.9)

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

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 5 6 7 8
Number of samples 22 20 55 40 34 36 4 9
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.271 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 204 85 0.1 - 173.8 (18.5)
subtype1 20 6 0.1 - 115.6 (11.9)
subtype2 17 9 0.8 - 60.9 (13.1)
subtype3 52 20 0.4 - 141.3 (23.7)
subtype4 37 17 0.4 - 114.0 (15.6)
subtype5 32 14 1.0 - 122.4 (23.6)
subtype6 34 13 0.4 - 173.8 (11.3)
subtype7 3 2 1.0 - 27.0 (4.3)
subtype8 9 4 0.6 - 97.9 (32.7)

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 = 0.00813 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 212 67.5 (8.4)
subtype1 22 65.1 (8.7)
subtype2 18 66.8 (7.9)
subtype3 54 66.0 (8.3)
subtype4 38 71.7 (6.9)
subtype5 32 68.8 (9.2)
subtype6 35 64.9 (8.8)
subtype7 4 66.2 (3.4)
subtype8 9 71.0 (6.2)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.225 (Chi-square test)

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

nPatients FEMALE MALE
ALL 59 161
subtype1 5 17
subtype2 4 16
subtype3 10 45
subtype4 17 23
subtype5 8 26
subtype6 10 26
subtype7 1 3
subtype8 4 5

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 = 0.543 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 44 17.7 (33.6)
subtype1 3 6.7 (11.5)
subtype2 4 25.0 (50.0)
subtype3 7 12.9 (22.1)
subtype4 11 16.4 (36.4)
subtype5 7 0.0 (0.0)
subtype6 10 30.0 (40.3)
subtype8 2 45.0 (63.6)

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 = 0.897 (Chi-square test)

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 SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 6 1 1 212
subtype1 0 0 0 22
subtype2 0 0 0 20
subtype3 3 1 0 51
subtype4 2 0 0 38
subtype5 0 0 1 33
subtype6 1 0 0 35
subtype7 0 0 0 4
subtype8 0 0 0 9

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 = 0.0215 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 51 133 23 13
subtype1 2 13 7 0
subtype2 5 12 3 0
subtype3 8 35 6 6
subtype4 11 25 1 3
subtype5 5 26 2 1
subtype6 14 16 4 2
subtype7 2 2 0 0
subtype8 4 4 0 1

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 = 0.673 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 142 54 19 5
subtype1 16 4 2 0
subtype2 14 4 1 1
subtype3 33 17 4 1
subtype4 26 12 1 1
subtype5 23 7 3 1
subtype6 21 8 7 0
subtype7 4 0 0 0
subtype8 5 2 1 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 = 0.0795 (Chi-square test)

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

nPatients M0 M1 MX
ALL 203 4 9
subtype1 16 1 4
subtype2 19 0 0
subtype3 51 0 2
subtype4 40 0 0
subtype5 31 2 1
subtype6 33 1 2
subtype7 4 0 0
subtype8 9 0 0

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 = 0.668 (Chi-square test)

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

nPatients I II III IV
ALL 114 54 46 4
subtype1 11 5 5 1
subtype2 11 6 3 0
subtype3 27 16 12 0
subtype4 21 11 7 0
subtype5 21 6 5 2
subtype6 14 9 11 1
subtype7 4 0 0 0
subtype8 5 1 3 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 = 0.399 (Chi-square test)

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

nPatients NO YES
ALL 6 214
subtype1 0 22
subtype2 2 18
subtype3 0 55
subtype4 2 38
subtype5 1 33
subtype6 1 35
subtype7 0 4
subtype8 0 9

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 = 0.0832 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 181 53.1 (33.7)
subtype1 16 61.1 (47.2)
subtype2 17 40.2 (13.9)
subtype3 46 56.0 (29.3)
subtype4 33 45.7 (24.3)
subtype5 31 48.5 (24.4)
subtype6 28 61.6 (50.8)
subtype7 3 95.7 (61.7)
subtype8 7 50.7 (16.4)

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

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.164 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 155 1997.5 (11.9)
subtype1 16 1993.9 (12.8)
subtype2 13 2001.2 (6.5)
subtype3 34 1997.7 (12.7)
subtype4 31 1995.9 (11.8)
subtype5 26 1995.7 (12.4)
subtype6 25 2003.0 (9.2)
subtype7 3 1997.3 (11.9)
subtype8 7 1992.4 (17.0)

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

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0907 (Chi-square test)

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 117 52 34 11
subtype1 10 7 0 4
subtype2 13 2 4 0
subtype3 27 11 15 2
subtype4 19 14 4 1
subtype5 18 8 6 1
subtype6 23 6 4 3
subtype7 3 1 0 0
subtype8 4 3 1 0

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

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.233 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 130 1957.5 (10.4)
subtype1 13 1955.8 (9.3)
subtype2 10 1962.3 (13.4)
subtype3 29 1957.4 (12.1)
subtype4 27 1955.8 (8.9)
subtype5 21 1954.5 (9.2)
subtype6 22 1961.8 (10.2)
subtype7 3 1953.0 (1.0)
subtype8 5 1958.6 (7.5)

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

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 65 74 81
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.492 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 204 85 0.1 - 173.8 (18.5)
subtype1 60 25 0.4 - 173.8 (8.9)
subtype2 68 28 0.4 - 141.3 (23.1)
subtype3 76 32 0.1 - 122.4 (22.3)

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 = 0.443 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 212 67.5 (8.4)
subtype1 64 67.6 (8.6)
subtype2 71 66.5 (8.2)
subtype3 77 68.3 (8.5)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0447 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 59 161
subtype1 25 40
subtype2 15 59
subtype3 19 62

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 = 0.429 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 44 17.7 (33.6)
subtype1 17 24.1 (37.3)
subtype2 12 7.5 (17.6)
subtype3 15 18.7 (38.7)

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 = 0.294 (Chi-square test)

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 SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 6 1 1 212
subtype1 3 0 0 62
subtype2 3 1 0 70
subtype3 0 0 1 80

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 = 0.364 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 51 133 23 13
subtype1 21 33 6 5
subtype2 13 49 7 5
subtype3 17 51 10 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 = 0.9 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 142 54 19 5
subtype1 41 17 6 1
subtype2 46 21 5 2
subtype3 55 16 8 2

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 = 0.655 (Chi-square test)

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

nPatients M0 M1 MX
ALL 203 4 9
subtype1 61 2 2
subtype2 69 0 3
subtype3 73 2 4

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 = 0.564 (Chi-square test)

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

nPatients I II III IV
ALL 114 54 46 4
subtype1 29 16 16 2
subtype2 38 21 15 0
subtype3 47 17 15 2

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 = 0.382 (Fisher's exact test)

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

nPatients NO YES
ALL 6 214
subtype1 1 64
subtype2 1 73
subtype3 4 77

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 = 0.926 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 181 53.1 (33.7)
subtype1 50 53.4 (37.7)
subtype2 63 51.8 (27.2)
subtype3 68 54.1 (36.5)

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

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.564 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 155 1997.5 (11.9)
subtype1 48 1998.4 (11.3)
subtype2 45 1998.3 (11.7)
subtype3 62 1996.2 (12.6)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0212 (Chi-square test)

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 117 52 34 11
subtype1 35 18 5 5
subtype2 36 15 21 2
subtype3 46 19 8 4

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

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.152 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 130 1957.5 (10.4)
subtype1 42 1959.4 (9.5)
subtype2 37 1958.4 (11.4)
subtype3 51 1955.4 (10.2)

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

Clustering Approach #9: 'MIRseq CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 141 125 33
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.922 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 283 107 0.0 - 173.8 (13.1)
subtype1 135 57 0.1 - 173.8 (17.9)
subtype2 116 38 0.0 - 107.0 (7.0)
subtype3 32 12 0.2 - 99.2 (23.3)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.685 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 292 67.9 (8.5)
subtype1 140 67.5 (8.4)
subtype2 120 68.1 (8.8)
subtype3 32 68.9 (8.2)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.534 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 78 221
subtype1 41 100
subtype2 30 95
subtype3 7 26

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

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

P value = 0.00524 (ANOVA)

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.381 (Chi-square test)

Table S126.  Clustering Approach #9: 'MIRseq 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 5 1 1 1 291
subtype1 1 0 1 0 139
subtype2 2 1 0 1 121
subtype3 2 0 0 0 31

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.464 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 72 181 30 16
subtype1 29 92 12 8
subtype2 33 71 16 5
subtype3 10 18 2 3

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.2 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 183 85 25 4
subtype1 88 34 15 4
subtype2 74 40 9 0
subtype3 21 11 1 0

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

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

P value = 4.01e-07 (Chi-square test)

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

nPatients M0 M1 MX
ALL 260 3 30
subtype1 134 3 1
subtype2 96 0 27
subtype3 30 0 2

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

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0797 (Chi-square test)

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

nPatients I II III IV
ALL 153 83 57 3
subtype1 77 30 31 3
subtype2 57 45 20 0
subtype3 19 8 6 0

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

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

P value = 0.526 (Fisher's exact test)

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

nPatients NO YES
ALL 10 289
subtype1 4 137
subtype2 6 119
subtype3 0 33

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

'MIRseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.157 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 255 53.2 (33.3)
subtype1 124 56.4 (37.4)
subtype2 104 48.4 (28.2)
subtype3 27 57.4 (30.6)

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

'MIRseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.314 (ANOVA)

Table S133.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 196 1997.7 (11.4)
subtype1 100 1998.0 (11.5)
subtype2 78 1998.2 (10.5)
subtype3 18 1993.8 (14.5)

Figure S124.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'MIRseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.167 (Chi-square test)

Table S134.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #13: '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 74 34 28 5
subtype2 60 26 28 6
subtype3 12 7 14 0

Figure S125.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.156 (ANOVA)

Table S135.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 203 1958.7 (11.7)
subtype1 93 1957.1 (10.4)
subtype2 90 1959.8 (12.4)
subtype3 20 1961.3 (13.4)

Figure S126.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S136.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 56 57 86 100
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.632 (logrank test)

Table S137.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 283 107 0.0 - 173.8 (13.1)
subtype1 55 25 0.2 - 114.0 (11.5)
subtype2 55 20 0.2 - 141.3 (12.2)
subtype3 81 26 0.1 - 122.4 (18.1)
subtype4 92 36 0.0 - 173.8 (10.5)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.668 (ANOVA)

Table S138.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 292 67.9 (8.5)
subtype1 55 69.1 (7.1)
subtype2 55 67.9 (8.8)
subtype3 86 67.3 (8.3)
subtype4 96 67.8 (9.4)

Figure S128.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0715 (Fisher's exact test)

Table S139.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 78 221
subtype1 18 38
subtype2 12 45
subtype3 29 57
subtype4 19 81

Figure S129.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.493 (ANOVA)

Table S140.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 48 27.1 (39.0)
subtype1 9 16.7 (33.2)
subtype2 5 16.0 (35.8)
subtype3 16 24.4 (38.5)
subtype4 18 37.8 (43.2)

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

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.516 (Chi-square test)

Table S141.  Clustering Approach #10: 'MIRseq 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 5 1 1 1 291
subtype1 2 0 0 0 54
subtype2 2 0 0 0 55
subtype3 1 0 1 1 83
subtype4 0 1 0 0 99

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.464 (Chi-square test)

Table S142.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 72 181 30 16
subtype1 13 30 11 2
subtype2 14 36 3 4
subtype3 23 52 7 4
subtype4 22 63 9 6

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.732 (Chi-square test)

Table S143.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 183 85 25 4
subtype1 33 18 5 0
subtype2 35 19 2 1
subtype3 57 19 8 2
subtype4 58 29 10 1

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

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

P value = 0.026 (Chi-square test)

Table S144.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 260 3 30
subtype1 48 0 6
subtype2 51 0 3
subtype3 81 1 3
subtype4 80 2 18

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

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.759 (Chi-square test)

Table S145.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 153 83 57 3
subtype1 25 17 13 0
subtype2 32 15 9 0
subtype3 49 20 16 1
subtype4 47 31 19 2

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

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

P value = 0.0271 (Fisher's exact test)

Table S146.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 289
subtype1 5 51
subtype2 3 54
subtype3 1 85
subtype4 1 99

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

'MIRseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.202 (ANOVA)

Table S147.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 255 53.2 (33.3)
subtype1 47 53.3 (27.2)
subtype2 50 55.7 (34.6)
subtype3 72 58.4 (39.9)
subtype4 86 47.4 (29.0)

Figure S137.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.254 (ANOVA)

Table S148.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 196 1997.7 (11.4)
subtype1 35 1998.8 (9.3)
subtype2 37 1998.2 (11.4)
subtype3 59 1999.2 (10.7)
subtype4 65 1995.4 (13.0)

Figure S138.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'MIRseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.198 (Chi-square test)

Table S149.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #13: '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 30 6 16 2
subtype2 29 10 17 1
subtype3 45 20 17 4
subtype4 42 31 20 4

Figure S139.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.573 (ANOVA)

Table S150.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 203 1958.7 (11.7)
subtype1 39 1956.8 (11.0)
subtype2 37 1960.3 (14.8)
subtype3 60 1958.2 (10.6)
subtype4 67 1959.4 (11.1)

Figure S140.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

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

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)
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