Lung Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 11 clinical features across 309 patients, 17 significant findings detected with P value < 0.05.

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

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

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

  • 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' and 'AGE'.

  • 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 'Time to Death',  'AGE', and 'PATHOLOGY.T'.

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

  • 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 11 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 17 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 0.0442 0.0312 0.26 0.112 0.000862 0.000346 0.0031 0.234 0.764 0.182
AGE ANOVA 0.546 0.168 0.156 0.167 0.0454 0.185 0.00813 0.443 0.748 0.667
GENDER Fisher's exact test 0.382 0.00872 0.02 0.509 0.224 0.73 0.225 0.0447 0.652 0.0738
KARNOFSKY PERFORMANCE SCORE ANOVA 0.191 0.0891 0.341 0.239 0.725 0.992 0.543 0.429 0.0223 0.737
HISTOLOGICAL TYPE Chi-square test 0.355 0.587 0.437 0.232 0.292 0.198 0.897 0.294 0.348 0.533
PATHOLOGY T Chi-square test 0.0029 0.000811 0.677 0.438 0.131 0.252 0.0215 0.364 0.464 0.331
PATHOLOGY N Chi-square test 0.818 0.32 0.419 0.37 0.45 0.162 0.673 0.9 0.289 0.689
PATHOLOGICSPREAD(M) Chi-square test 0.146 0.323 0.325 0.22 0.594 0.184 0.0795 0.655 1.29e-06 0.0192
TUMOR STAGE Chi-square test 0.798 0.617 0.76 0.606 0.171 0.139 0.668 0.564 0.0891 0.672
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.445 0.453 0.552 0.626 0.735 0.71 0.399 0.382 0.365 0.0229
NEOADJUVANT THERAPY Fisher's exact test 0.734 0.799 0.649 0.534 0.524 1 0.246 0.958 0.395 1
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 = 0.0442 (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 'NEOADJUVANT.THERAPY'

P value = 0.734 (Fisher's exact test)

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

nPatients NO YES
ALL 13 141
subtype1 4 39
subtype2 5 45
subtype3 3 27
subtype4 1 30

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S13.  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 = 0.0312 (logrank test)

Table S14.  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 S12.  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 S15.  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 S13.  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 S16.  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 S14.  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 S17.  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 S15.  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 S18.  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 S16.  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 S19.  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 S17.  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 S20.  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 S18.  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 S21.  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 S19.  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 S22.  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 S20.  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 S23.  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 S21.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.799 (Fisher's exact test)

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 13 141
subtype1 3 25
subtype2 5 49
subtype3 5 67

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S25.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3
Number of samples 109 100 98
'CN CNMF' versus 'Time to Death'

P value = 0.26 (logrank test)

Table S26.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 290 118 0.0 - 173.8 (14.8)
subtype1 102 49 0.1 - 173.8 (17.3)
subtype2 96 34 0.2 - 114.0 (14.3)
subtype3 92 35 0.0 - 122.4 (12.5)

Figure S23.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.156 (ANOVA)

Table S27.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 299 67.4 (8.7)
subtype1 107 67.4 (9.7)
subtype2 97 66.2 (7.8)
subtype3 95 68.6 (8.2)

Figure S24.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.02 (Fisher's exact test)

Table S28.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 80 227
subtype1 37 72
subtype2 17 83
subtype3 26 72

Figure S25.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.341 (ANOVA)

Table S29.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 52 26.5 (39.1)
subtype1 18 36.7 (45.4)
subtype2 12 15.8 (31.8)
subtype3 22 24.1 (36.9)

Figure S26.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.437 (Chi-square test)

Table S30.  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 1 297
subtype1 1 0 1 1 106
subtype2 4 1 0 0 95
subtype3 2 0 0 0 96

Figure S27.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.677 (Chi-square test)

Table S31.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 70 187 32 18
subtype1 27 63 14 5
subtype2 20 62 12 6
subtype3 23 62 6 7

Figure S28.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.419 (Chi-square test)

Table S32.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 191 84 26 5
subtype1 74 22 10 2
subtype2 55 35 8 2
subtype3 62 27 8 1

Figure S29.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.325 (Chi-square test)

Table S33.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 271 4 26
subtype1 95 0 13
subtype2 89 2 6
subtype3 87 2 7

Figure S30.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.76 (Chi-square test)

Table S34.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 156 81 62 4
subtype1 59 28 19 0
subtype2 48 28 22 2
subtype3 49 25 21 2

Figure S31.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.552 (Fisher's exact test)

Table S35.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 296
subtype1 4 105
subtype2 5 95
subtype3 2 96

Figure S32.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.649 (Fisher's exact test)

Table S36.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 29 278
subtype1 12 97
subtype2 10 90
subtype3 7 91

Figure S33.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S37.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 69 65 41
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.112 (logrank test)

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 162 67 0.0 - 173.8 (13.8)
subtype1 64 29 0.1 - 173.8 (10.9)
subtype2 60 24 0.2 - 141.3 (19.8)
subtype3 38 14 0.0 - 107.0 (14.6)

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.167 (ANOVA)

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 167 68.2 (8.9)
subtype1 66 69.3 (10.0)
subtype2 62 66.5 (8.2)
subtype3 39 69.1 (7.9)

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.509 (Fisher's exact test)

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 46 129
subtype1 21 48
subtype2 14 51
subtype3 11 30

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.239 (ANOVA)

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 29 25.9 (39.1)
subtype1 6 48.3 (46.7)
subtype2 14 15.7 (33.4)
subtype3 9 26.7 (40.3)

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.232 (Chi-square test)

Table S42.  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 1 169
subtype1 0 0 0 69
subtype2 2 1 0 62
subtype3 2 0 1 38

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.438 (Chi-square test)

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 48 99 21 7
subtype1 16 43 8 2
subtype2 22 31 10 2
subtype3 10 25 3 3

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.37 (Chi-square test)

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 109 50 15
subtype1 40 19 9
subtype2 45 17 3
subtype3 24 14 3

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.22 (Chi-square test)

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 146 1 26
subtype1 53 1 15
subtype2 58 0 7
subtype3 35 0 4

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.606 (Chi-square test)

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 87 53 31 1
subtype1 31 24 12 1
subtype2 38 17 10 0
subtype3 18 12 9 0

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.626 (Fisher's exact test)

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 166
subtype1 4 65
subtype2 2 63
subtype3 3 38

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.534 (Fisher's exact test)

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 16 159
subtype1 8 61
subtype2 6 59
subtype3 2 39

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S49.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 57 72 61
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.000862 (logrank test)

Table S50.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 177 72 0.0 - 173.8 (16.6)
subtype1 54 15 0.0 - 173.8 (23.0)
subtype2 69 30 0.2 - 115.6 (14.1)
subtype3 54 27 0.1 - 119.8 (13.9)

Figure S45.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0454 (ANOVA)

Table S51.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 182 67.5 (9.4)
subtype1 56 66.0 (10.4)
subtype2 69 66.6 (8.5)
subtype3 57 70.1 (9.2)

Figure S46.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.224 (Fisher's exact test)

Table S52.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 48 142
subtype1 11 46
subtype2 17 55
subtype3 20 41

Figure S47.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.725 (ANOVA)

Table S53.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 33 28.8 (39.0)
subtype1 7 38.6 (34.4)
subtype2 14 28.6 (40.0)
subtype3 12 23.3 (42.3)

Figure S48.  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 = 0.292 (Chi-square test)

Table S54.  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 1 185
subtype1 2 1 1 53
subtype2 0 0 0 72
subtype3 1 0 0 60

Figure S49.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.131 (Chi-square test)

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 43 117 19 11
subtype1 15 33 3 6
subtype2 11 51 8 2
subtype3 17 33 8 3

Figure S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.45 (Chi-square test)

Table S56.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 121 50 15 4
subtype1 32 17 6 2
subtype2 44 21 5 2
subtype3 45 12 4 0

Figure S51.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.594 (Fisher's exact test)

Table S57.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 MX
ALL 172 15
subtype1 51 6
subtype2 66 4
subtype3 55 5

Figure S52.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.171 (Chi-square test)

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III
ALL 96 55 36
subtype1 26 17 14
subtype2 33 26 11
subtype3 37 12 11

Figure S53.  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 = 0.735 (Fisher's exact test)

Table S59.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 182
subtype1 3 54
subtype2 2 70
subtype3 3 58

Figure S54.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.524 (Fisher's exact test)

Table S60.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 23 167
subtype1 8 49
subtype2 10 62
subtype3 5 56

Figure S55.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S61.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 47 46 56 41
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.000346 (logrank test)

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 177 72 0.0 - 173.8 (16.6)
subtype1 44 18 0.2 - 115.6 (14.3)
subtype2 42 15 0.2 - 99.2 (23.0)
subtype3 52 19 0.0 - 173.8 (17.8)
subtype4 39 20 0.1 - 82.2 (8.8)

Figure S56.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.185 (ANOVA)

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 182 67.5 (9.4)
subtype1 44 66.0 (8.0)
subtype2 44 69.1 (8.9)
subtype3 54 66.1 (10.4)
subtype4 40 69.3 (9.8)

Figure S57.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.73 (Fisher's exact test)

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 48 142
subtype1 10 37
subtype2 14 32
subtype3 15 41
subtype4 9 32

Figure S58.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.992 (ANOVA)

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 33 28.8 (39.0)
subtype1 7 32.9 (41.1)
subtype2 3 30.0 (52.0)
subtype3 10 27.0 (33.7)
subtype4 13 27.7 (43.4)

Figure S59.  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 = 0.198 (Chi-square test)

Table S66.  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 1 185
subtype1 0 0 0 47
subtype2 0 0 0 46
subtype3 3 1 1 51
subtype4 0 0 0 41

Figure S60.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.252 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 43 117 19 11
subtype1 5 35 6 1
subtype2 15 25 4 2
subtype3 14 31 5 6
subtype4 9 26 4 2

Figure S61.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.162 (Chi-square test)

Table S68.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 121 50 15 4
subtype1 28 16 1 2
subtype2 31 9 4 2
subtype3 32 16 8 0
subtype4 30 9 2 0

Figure S62.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.184 (Fisher's exact test)

Table S69.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 MX
ALL 172 15
subtype1 44 1
subtype2 43 3
subtype3 51 5
subtype4 34 6

Figure S63.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.139 (Chi-square test)

Table S70.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III
ALL 96 55 36
subtype1 20 18 8
subtype2 28 8 10
subtype3 24 18 14
subtype4 24 11 4

Figure S64.  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 = 0.71 (Fisher's exact test)

Table S71.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 182
subtype1 1 46
subtype2 3 43
subtype3 3 53
subtype4 1 40

Figure S65.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 23 167
subtype1 6 41
subtype2 5 41
subtype3 7 49
subtype4 5 36

Figure S66.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S73.  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 = 0.0031 (logrank test)

Table S74.  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 S67.  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 S75.  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 S68.  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 S76.  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 S69.  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 S77.  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 S70.  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 S78.  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 S71.  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 S79.  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 S72.  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 S80.  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 S73.  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 S81.  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 S74.  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 S82.  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 S75.  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 S83.  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 S76.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.246 (Chi-square test)

Table S84.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 21 199
subtype1 1 21
subtype2 2 18
subtype3 4 51
subtype4 2 38
subtype5 3 31
subtype6 8 28
subtype7 0 4
subtype8 1 8

Figure S77.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S85.  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 = 0.234 (logrank test)

Table S86.  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 S78.  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 S87.  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 S79.  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 S88.  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 S80.  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 S89.  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 S81.  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 S90.  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 S82.  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 S91.  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 S83.  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 S92.  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 S84.  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 S93.  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 S85.  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 S94.  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 S86.  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 S95.  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 S87.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.958 (Fisher's exact test)

Table S96.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 21 199
subtype1 7 58
subtype2 7 67
subtype3 7 74

Figure S88.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S97.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 141 110 31
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.764 (logrank test)

Table S98.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 266 105 0.0 - 173.8 (15.1)
subtype1 135 57 0.1 - 173.8 (17.9)
subtype2 101 36 0.0 - 107.0 (8.8)
subtype3 30 12 0.2 - 99.2 (23.3)

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.748 (ANOVA)

Table S99.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 275 67.8 (8.6)
subtype1 140 67.5 (8.4)
subtype2 105 68.0 (8.9)
subtype3 30 68.8 (8.4)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.652 (Fisher's exact test)

Table S100.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 75 207
subtype1 41 100
subtype2 27 83
subtype3 7 24

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0223 (ANOVA)

Table S101.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 46 24.8 (38.2)
subtype1 29 13.4 (30.7)
subtype2 10 49.0 (41.8)
subtype3 7 37.1 (46.4)

Figure S92.  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 = 0.348 (Chi-square test)

Table S102.  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 1 274
subtype1 1 0 1 0 139
subtype2 2 1 0 1 106
subtype3 2 0 0 0 29

Figure S93.  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 S103.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 64 173 29 16
subtype1 29 92 12 8
subtype2 25 65 15 5
subtype3 10 16 2 3

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.289 (Chi-square test)

Table S104.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 173 79 25 4
subtype1 88 34 15 4
subtype2 65 35 9 0
subtype3 20 10 1 0

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 1.29e-06 (Chi-square test)

Table S105.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 247 3 26
subtype1 134 3 1
subtype2 85 0 23
subtype3 28 0 2

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0891 (Chi-square test)

Table S106.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 144 75 57 3
subtype1 77 30 31 3
subtype2 48 39 20 0
subtype3 19 6 6 0

Figure S97.  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 = 0.365 (Fisher's exact test)

Table S107.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 272
subtype1 4 137
subtype2 6 104
subtype3 0 31

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.395 (Fisher's exact test)

Table S108.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 28 254
subtype1 14 127
subtype2 13 97
subtype3 1 30

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S109.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 52 54 85 91
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.182 (logrank test)

Table S110.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 266 105 0.0 - 173.8 (15.1)
subtype1 51 24 0.2 - 114.0 (17.7)
subtype2 52 20 0.2 - 141.3 (12.9)
subtype3 80 26 0.1 - 122.4 (18.2)
subtype4 83 35 0.0 - 173.8 (12.0)

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.667 (ANOVA)

Table S111.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 275 67.8 (8.6)
subtype1 51 69.0 (7.3)
subtype2 52 68.2 (8.5)
subtype3 85 67.3 (8.3)
subtype4 87 67.4 (9.6)

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0738 (Fisher's exact test)

Table S112.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 75 207
subtype1 17 35
subtype2 12 42
subtype3 29 56
subtype4 17 74

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.737 (ANOVA)

Table S113.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 46 24.8 (38.2)
subtype1 9 16.7 (33.2)
subtype2 5 16.0 (35.8)
subtype3 16 24.4 (38.5)
subtype4 16 32.5 (43.0)

Figure S103.  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 = 0.533 (Chi-square test)

Table S114.  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 1 274
subtype1 2 0 0 0 50
subtype2 2 0 0 0 52
subtype3 1 0 1 1 82
subtype4 0 1 0 0 90

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.331 (Chi-square test)

Table S115.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 64 173 29 16
subtype1 10 29 11 2
subtype2 14 33 3 4
subtype3 22 52 7 4
subtype4 18 59 8 6

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.689 (Chi-square test)

Table S116.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 173 79 25 4
subtype1 29 18 5 0
subtype2 33 18 2 1
subtype3 56 19 8 2
subtype4 55 24 10 1

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.0192 (Chi-square test)

Table S117.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 247 3 26
subtype1 45 0 5
subtype2 48 0 3
subtype3 81 1 2
subtype4 73 2 16

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.672 (Chi-square test)

Table S118.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 144 75 57 3
subtype1 21 17 13 0
subtype2 31 13 9 0
subtype3 48 20 16 1
subtype4 44 25 19 2

Figure S108.  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 = 0.0229 (Fisher's exact test)

Table S119.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 272
subtype1 5 47
subtype2 3 51
subtype3 1 84
subtype4 1 90

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S120.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 28 254
subtype1 5 47
subtype2 5 49
subtype3 9 76
subtype4 9 82

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = LUSC.mergedcluster.txt

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

  • Number of patients = 309

  • Number of clustering approaches = 10

  • Number of selected clinical features = 11

  • 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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