Lung Adenocarcinoma: 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 292 patients, 16 significant findings detected with P value < 0.05.

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

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

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

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'GENDER'.

  • 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',  'AGE', and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death' and 'GENDER'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death',  'GENDER',  'PATHOLOGY.N', and 'TUMOR.STAGE'.

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

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

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, 16 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 0.337 0.671 0.663 0.0209 0.0207 0.0117 0.00636 2.54e-05 0.0975 0.071
AGE ANOVA 0.477 0.557 0.000101 0.287 0.0204 0.00368 0.0927 0.124 0.206 0.11
GENDER Fisher's exact test 0.272 0.383 0.34 0.024 0.211 0.222 0.000904 0.00868 0.809 0.000771
KARNOFSKY PERFORMANCE SCORE ANOVA 0.376 0.679 0.0806 0.295 0.718 0.644 0.919 0.59
HISTOLOGICAL TYPE Chi-square test 0.3 0.274 0.326 0.274 0.187 0.00941 0.128 0.143 0.206 0.137
PATHOLOGY T Chi-square test 0.489 0.479 0.674 0.84 0.165 0.67 0.588 0.422 0.578 0.841
PATHOLOGY N Chi-square test 0.572 0.651 0.771 0.4 0.54 0.51 0.0827 0.00983 0.721 0.342
PATHOLOGICSPREAD(M) Chi-square test 0.504 1 0.717 0.258 0.913 0.683 0.956 0.496 0.11 0.756
TUMOR STAGE Chi-square test 0.147 0.274 0.321 0.695 0.908 0.982 0.0789 0.0218 0.889 0.246
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 1 1 0.449 0.0788 1 0.415 1 1 0.858 0.74
NEOADJUVANT THERAPY Fisher's exact test 0.921 1 0.956 0.359 0.175 0.633 0.428 0.509 0.194 0.0393
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 5 9 12 6
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.337 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 31 4 0.5 - 56.8 (8.3)
subtype1 4 0 6.0 - 48.6 (9.7)
subtype2 9 1 4.0 - 56.8 (8.3)
subtype3 12 1 0.5 - 37.0 (3.3)
subtype4 6 2 2.0 - 45.2 (30.9)

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 = 0.477 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 30 65.7 (10.8)
subtype1 4 58.5 (15.5)
subtype2 9 65.0 (9.1)
subtype3 12 67.1 (11.1)
subtype4 5 69.4 (9.0)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.272 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 18 14
subtype1 3 2
subtype2 3 6
subtype3 9 3
subtype4 3 3

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.3 (Chi-square test)

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG CLEAR CELL ADENOCARCINOMA
ALL 1 30 1
subtype1 0 4 1
subtype2 0 9 0
subtype3 1 11 0
subtype4 0 6 0

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.489 (Chi-square test)

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3
ALL 12 19 1
subtype1 3 2 0
subtype2 4 4 1
subtype3 4 8 0
subtype4 1 5 0

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.572 (Chi-square test)

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 23 4 4
subtype1 3 1 1
subtype2 8 1 0
subtype3 9 1 1
subtype4 3 1 2

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.504 (Fisher's exact test)

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 30 2
subtype1 4 1
subtype2 9 0
subtype3 11 1
subtype4 6 0

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.147 (Chi-square test)

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 23 4 3 2
subtype1 3 1 0 1
subtype2 8 0 1 0
subtype3 10 1 0 1
subtype4 2 2 2 0

Figure S8.  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 = 1 (Fisher's exact test)

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 31
subtype1 0 5
subtype2 0 9
subtype3 1 11
subtype4 0 6

Figure S9.  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 = 0.921 (Fisher's exact test)

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 6 26
subtype1 1 4
subtype2 1 8
subtype3 3 9
subtype4 1 5

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 7 13 12
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.671 (logrank test)

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 31 4 0.5 - 56.8 (8.3)
subtype1 7 2 2.0 - 48.6 (38.7)
subtype2 13 1 0.5 - 37.0 (3.4)
subtype3 11 1 4.0 - 56.8 (8.1)

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.557 (ANOVA)

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 30 65.7 (10.8)
subtype1 5 69.4 (9.0)
subtype2 13 66.5 (10.9)
subtype3 12 63.3 (11.6)

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.383 (Fisher's exact test)

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 18 14
subtype1 4 3
subtype2 9 4
subtype3 5 7

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.274 (Chi-square test)

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG CLEAR CELL ADENOCARCINOMA
ALL 1 30 1
subtype1 0 6 1
subtype2 1 12 0
subtype3 0 12 0

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.479 (Chi-square test)

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3
ALL 12 19 1
subtype1 2 5 0
subtype2 4 9 0
subtype3 6 5 1

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.651 (Chi-square test)

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 23 4 4
subtype1 4 1 2
subtype2 10 1 1
subtype3 9 2 1

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 1 (Fisher's exact test)

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 30 2
subtype1 7 0
subtype2 12 1
subtype3 11 1

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.274 (Chi-square test)

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 23 4 3 2
subtype1 3 2 2 0
subtype2 11 1 0 1
subtype3 9 1 1 1

Figure S18.  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 = 1 (Fisher's exact test)

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 31
subtype1 0 7
subtype2 1 12
subtype3 0 12

Figure S19.  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 = 1 (Fisher's exact test)

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 6 26
subtype1 1 6
subtype2 3 10
subtype3 2 10

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S23.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3
Number of samples 94 119 74
'CN CNMF' versus 'Time to Death'

P value = 0.663 (logrank test)

Table S24.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 253 81 0.0 - 224.0 (13.2)
subtype1 82 30 0.1 - 224.0 (12.1)
subtype2 106 33 0.0 - 104.2 (13.1)
subtype3 65 18 0.1 - 88.1 (15.0)

Figure S21.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.000101 (ANOVA)

Table S25.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 256 65.4 (9.7)
subtype1 86 62.9 (10.7)
subtype2 103 68.5 (8.1)
subtype3 67 64.0 (9.5)

Figure S22.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.34 (Fisher's exact test)

Table S26.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 155 132
subtype1 55 39
subtype2 65 54
subtype3 35 39

Figure S23.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.376 (ANOVA)

Table S27.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 25 73.6 (34.3)
subtype1 8 60.0 (38.9)
subtype2 10 83.0 (29.8)
subtype3 7 75.7 (34.6)

Figure S24.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.326 (Chi-square test)

Table S28.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 6 65 181 3 10 2 3 2 10 1 4
subtype1 0 20 62 1 5 1 2 0 2 1 0
subtype2 3 29 69 2 4 0 1 2 5 0 4
subtype3 3 16 50 0 1 1 0 0 3 0 0

Figure S25.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.674 (Chi-square test)

Table S29.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 79 169 22 16
subtype1 25 60 5 4
subtype2 34 68 8 8
subtype3 20 41 9 4

Figure S26.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.771 (Chi-square test)

Table S30.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 171 58 52
subtype1 52 20 19
subtype2 74 21 22
subtype3 45 17 11

Figure S27.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.717 (Chi-square test)

Table S31.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 201 16 63
subtype1 61 7 23
subtype2 86 5 26
subtype3 54 4 14

Figure S28.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.321 (Chi-square test)

Table S32.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 148 61 57 16
subtype1 45 20 18 8
subtype2 70 21 22 4
subtype3 33 20 17 4

Figure S29.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.449 (Fisher's exact test)

Table S33.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 19 268
subtype1 6 88
subtype2 6 113
subtype3 7 67

Figure S30.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.956 (Fisher's exact test)

Table S34.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 44 243
subtype1 14 80
subtype2 18 101
subtype3 12 62

Figure S31.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S35.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 52 63 53 50
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0209 (logrank test)

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 190 60 0.0 - 224.0 (11.6)
subtype1 44 12 0.1 - 224.0 (12.2)
subtype2 54 22 0.1 - 88.1 (15.0)
subtype3 46 10 0.0 - 77.9 (5.1)
subtype4 46 16 0.0 - 71.5 (12.4)

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.287 (ANOVA)

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 190 65.3 (9.9)
subtype1 42 67.6 (7.7)
subtype2 56 63.7 (9.5)
subtype3 48 65.4 (10.6)
subtype4 44 65.0 (11.2)

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.024 (Fisher's exact test)

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 116 102
subtype1 35 17
subtype2 26 37
subtype3 25 28
subtype4 30 20

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.679 (ANOVA)

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 18 71.1 (34.3)
subtype1 7 80.0 (35.6)
subtype2 4 62.5 (41.9)
subtype3 5 78.0 (14.8)
subtype4 2 40.0 (56.6)

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.274 (Chi-square test)

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 5 49 133 3 10 1 2 2 9 1 3
subtype1 3 9 28 1 6 0 1 2 1 0 1
subtype2 1 17 37 0 2 0 0 0 3 1 2
subtype3 0 14 33 1 1 0 1 0 3 0 0
subtype4 1 9 35 1 1 1 0 0 2 0 0

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.84 (Chi-square test)

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 60 126 17 13
subtype1 18 25 5 3
subtype2 15 39 4 5
subtype3 13 32 4 4
subtype4 14 30 4 1

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.4 (Chi-square test)

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 127 44 42
subtype1 33 9 9
subtype2 40 11 11
subtype3 29 15 8
subtype4 25 9 14

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.258 (Chi-square test)

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 140 10 62
subtype1 35 0 14
subtype2 42 5 15
subtype3 29 4 18
subtype4 34 1 15

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.695 (Chi-square test)

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 108 47 47 11
subtype1 28 11 11 0
subtype2 32 13 14 4
subtype3 24 12 9 5
subtype4 24 11 13 2

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.0788 (Fisher's exact test)

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 15 203
subtype1 2 50
subtype2 3 60
subtype3 2 51
subtype4 8 42

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.359 (Fisher's exact test)

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 32 186
subtype1 11 41
subtype2 6 57
subtype3 7 46
subtype4 8 42

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S47.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 89 70 70
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0207 (logrank test)

Table S48.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 207 66 0.0 - 224.0 (12.6)
subtype1 74 27 0.0 - 163.1 (12.2)
subtype2 67 17 0.2 - 224.0 (13.7)
subtype3 66 22 0.0 - 83.8 (9.8)

Figure S43.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0204 (ANOVA)

Table S49.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 208 64.8 (9.8)
subtype1 77 64.5 (10.0)
subtype2 67 67.3 (9.1)
subtype3 64 62.6 (9.9)

Figure S44.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.211 (Fisher's exact test)

Table S50.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 126 103
subtype1 55 34
subtype2 37 33
subtype3 34 36

Figure S45.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0806 (ANOVA)

Table S51.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 19 66.3 (36.2)
subtype1 7 44.3 (42.8)
subtype2 5 90.0 (7.1)
subtype3 7 71.4 (31.8)

Figure S46.  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 = 0.187 (Chi-square test)

Table S52.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 6 49 149 3 7 1 2 2 6 1 3
subtype1 2 18 58 1 3 1 1 1 2 1 1
subtype2 3 19 35 2 4 0 1 0 4 0 2
subtype3 1 12 56 0 0 0 0 1 0 0 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.165 (Chi-square test)

Table S53.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 60 138 17 13
subtype1 26 49 9 4
subtype2 20 41 2 7
subtype3 14 48 6 2

Figure S48.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.54 (Chi-square test)

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 133 46 44
subtype1 47 22 16
subtype2 44 13 12
subtype3 42 11 16

Figure S49.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.913 (Chi-square test)

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 160 12 53
subtype1 62 5 19
subtype2 47 4 19
subtype3 51 3 15

Figure S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.908 (Chi-square test)

Table S56.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 116 48 49 13
subtype1 44 20 17 5
subtype2 35 16 14 5
subtype3 37 12 18 3

Figure S51.  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 = 1 (Fisher's exact test)

Table S57.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 15 214
subtype1 6 83
subtype2 4 66
subtype3 5 65

Figure S52.  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 = 0.175 (Fisher's exact test)

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 37 192
subtype1 15 74
subtype2 15 55
subtype3 7 63

Figure S53.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S59.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 22 72 82 53
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0117 (logrank test)

Table S60.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 207 66 0.0 - 224.0 (12.6)
subtype1 18 4 0.0 - 63.7 (7.1)
subtype2 68 22 0.0 - 83.8 (8.9)
subtype3 70 27 0.7 - 163.1 (13.5)
subtype4 51 13 0.1 - 224.0 (15.0)

Figure S54.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00368 (ANOVA)

Table S61.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 208 64.8 (9.8)
subtype1 19 67.9 (7.0)
subtype2 66 62.6 (11.1)
subtype3 73 63.5 (9.4)
subtype4 50 68.4 (8.3)

Figure S55.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.222 (Fisher's exact test)

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 126 103
subtype1 14 8
subtype2 36 36
subtype3 51 31
subtype4 25 28

Figure S56.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.295 (ANOVA)

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 19 66.3 (36.2)
subtype1 3 60.0 (52.0)
subtype2 7 71.4 (31.8)
subtype3 5 44.0 (41.6)
subtype4 4 90.0 (8.2)

Figure S57.  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 = 0.00941 (Chi-square test)

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 6 49 149 3 7 1 2 2 6 1 3
subtype1 1 4 12 0 2 0 2 0 1 0 0
subtype2 2 12 54 0 1 1 0 1 1 0 0
subtype3 1 17 58 0 2 0 0 0 2 1 1
subtype4 2 16 25 3 2 0 0 1 2 0 2

Figure S58.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.67 (Chi-square test)

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 60 138 17 13
subtype1 7 10 2 3
subtype2 18 44 7 3
subtype3 21 53 3 4
subtype4 14 31 5 3

Figure S59.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.51 (Chi-square test)

Table S66.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 133 46 44
subtype1 14 4 4
subtype2 44 10 17
subtype3 41 21 15
subtype4 34 11 8

Figure S60.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.683 (Chi-square test)

Table S67.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 160 12 53
subtype1 13 1 8
subtype2 52 2 17
subtype3 57 6 17
subtype4 38 3 11

Figure S61.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.982 (Chi-square test)

Table S68.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 116 48 49 13
subtype1 11 5 5 1
subtype2 36 14 19 3
subtype3 42 18 14 6
subtype4 27 11 11 3

Figure S62.  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 = 0.415 (Fisher's exact test)

Table S69.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 15 214
subtype1 0 22
subtype2 7 65
subtype3 6 76
subtype4 2 51

Figure S63.  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 = 0.633 (Fisher's exact test)

Table S70.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 37 192
subtype1 5 17
subtype2 9 63
subtype3 14 68
subtype4 9 44

Figure S64.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S71.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 87 87 89
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00636 (logrank test)

Table S72.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 230 78 0.0 - 224.0 (15.0)
subtype1 75 20 0.1 - 224.0 (14.0)
subtype2 76 28 0.0 - 77.9 (12.8)
subtype3 79 30 0.0 - 83.8 (17.5)

Figure S65.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0927 (ANOVA)

Table S73.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 232 65.4 (9.7)
subtype1 76 67.3 (8.8)
subtype2 77 63.9 (10.8)
subtype3 79 65.0 (9.3)

Figure S66.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.000904 (Fisher's exact test)

Table S74.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 144 119
subtype1 58 29
subtype2 51 36
subtype3 35 54

Figure S67.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.718 (ANOVA)

Table S75.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 20 70.5 (37.6)
subtype1 9 70.0 (41.5)
subtype2 8 65.0 (41.4)
subtype3 3 86.7 (5.8)

Figure S68.  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 = 0.128 (Chi-square test)

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 59 170 3 8 2 2 2 10 1 3
subtype1 2 16 53 3 6 0 1 1 4 0 1
subtype2 0 17 64 0 2 1 1 0 2 0 0
subtype3 1 26 53 0 0 1 0 1 4 1 2

Figure S69.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.588 (Chi-square test)

Table S77.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 68 159 21 14
subtype1 29 48 6 3
subtype2 19 55 7 6
subtype3 20 56 8 5

Figure S70.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0827 (Chi-square test)

Table S78.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 152 56 49
subtype1 58 15 10
subtype2 42 23 21
subtype3 52 18 18

Figure S71.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.956 (Chi-square test)

Table S79.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 192 15 49
subtype1 64 5 15
subtype2 63 4 18
subtype3 65 6 16

Figure S72.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0789 (Chi-square test)

Table S80.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 134 56 54 14
subtype1 54 15 10 5
subtype2 35 22 24 4
subtype3 45 19 20 5

Figure S73.  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 = 1 (Fisher's exact test)

Table S81.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 17 246
subtype1 5 82
subtype2 6 81
subtype3 6 83

Figure S74.  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 = 0.428 (Fisher's exact test)

Table S82.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 41 222
subtype1 13 74
subtype2 17 70
subtype3 11 78

Figure S75.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S83.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 43 65 59 96
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.54e-05 (logrank test)

Table S84.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 230 78 0.0 - 224.0 (15.0)
subtype1 38 19 0.0 - 48.5 (15.1)
subtype2 56 22 0.0 - 77.9 (10.4)
subtype3 51 14 0.1 - 83.8 (16.7)
subtype4 85 23 0.1 - 224.0 (17.4)

Figure S76.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.124 (ANOVA)

Table S85.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 232 65.4 (9.7)
subtype1 39 66.6 (10.2)
subtype2 56 63.5 (11.7)
subtype3 52 64.0 (8.7)
subtype4 85 66.9 (8.5)

Figure S77.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00868 (Fisher's exact test)

Table S86.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 144 119
subtype1 19 24
subtype2 38 27
subtype3 24 35
subtype4 63 33

Figure S78.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.644 (ANOVA)

Table S87.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 20 70.5 (37.6)
subtype2 8 62.5 (41.0)
subtype3 4 85.0 (5.8)
subtype4 8 71.2 (44.2)

Figure S79.  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 = 0.143 (Chi-square test)

Table S88.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 3 59 170 3 8 2 2 2 10 1 3
subtype1 0 15 21 2 0 0 0 1 2 0 2
subtype2 0 10 48 0 1 1 1 0 4 0 0
subtype3 1 13 41 0 1 1 0 0 1 1 0
subtype4 2 21 60 1 6 0 1 1 3 0 1

Figure S80.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.422 (Chi-square test)

Table S89.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 68 159 21 14
subtype1 7 26 6 4
subtype2 15 39 7 4
subtype3 16 38 3 2
subtype4 30 56 5 4

Figure S81.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00983 (Chi-square test)

Table S90.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 152 56 49
subtype1 18 14 11
subtype2 31 17 16
subtype3 41 6 11
subtype4 62 19 11

Figure S82.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.496 (Chi-square test)

Table S91.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 192 15 49
subtype1 33 4 6
subtype2 51 3 11
subtype3 39 2 16
subtype4 69 6 16

Figure S83.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0218 (Chi-square test)

Table S92.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 134 56 54 14
subtype1 14 14 11 4
subtype2 27 16 18 3
subtype3 37 8 13 1
subtype4 56 18 12 6

Figure S84.  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 = 1 (Fisher's exact test)

Table S93.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 17 246
subtype1 3 40
subtype2 4 61
subtype3 4 55
subtype4 6 90

Figure S85.  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 = 0.509 (Fisher's exact test)

Table S94.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 41 222
subtype1 7 36
subtype2 13 52
subtype3 6 53
subtype4 15 81

Figure S86.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S95.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 107 119 58
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0975 (logrank test)

Table S96.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 250 79 0.0 - 224.0 (13.4)
subtype1 95 26 0.1 - 163.1 (13.7)
subtype2 102 34 0.0 - 83.8 (11.8)
subtype3 53 19 0.0 - 224.0 (14.9)

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.206 (ANOVA)

Table S97.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 254 65.4 (9.8)
subtype1 99 66.6 (8.8)
subtype2 105 65.2 (10.5)
subtype3 50 63.6 (9.9)

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.809 (Fisher's exact test)

Table S98.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 154 130
subtype1 59 48
subtype2 62 57
subtype3 33 25

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.919 (ANOVA)

Table S99.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 24 72.1 (34.3)
subtype1 10 71.0 (38.4)
subtype2 11 70.9 (35.6)
subtype3 3 80.0 (20.0)

Figure S90.  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 = 0.206 (Chi-square test)

Table S100.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 6 64 181 3 10 2 2 1 10 1 4
subtype1 3 26 60 2 7 0 1 1 6 0 1
subtype2 2 31 78 0 2 2 0 0 2 1 1
subtype3 1 7 43 1 1 0 1 0 2 0 2

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.578 (Chi-square test)

Table S101.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 78 165 23 16
subtype1 32 56 10 8
subtype2 33 71 8 7
subtype3 13 38 5 1

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.721 (Chi-square test)

Table S102.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 168 57 52
subtype1 61 25 18
subtype2 73 23 21
subtype3 34 9 13

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.11 (Chi-square test)

Table S103.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 198 14 65
subtype1 73 6 25
subtype2 91 6 20
subtype3 34 2 20

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.889 (Chi-square test)

Table S104.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 147 59 58 15
subtype1 56 22 19 7
subtype2 65 23 26 5
subtype3 26 14 13 3

Figure S95.  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 = 0.858 (Fisher's exact test)

Table S105.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 18 266
subtype1 8 99
subtype2 7 112
subtype3 3 55

Figure S96.  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 = 0.194 (Fisher's exact test)

Table S106.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 41 243
subtype1 18 89
subtype2 12 107
subtype3 11 47

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S107.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 19 131 134
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.071 (logrank test)

Table S108.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 250 79 0.0 - 224.0 (13.4)
subtype1 16 6 0.1 - 224.0 (10.9)
subtype2 119 32 0.1 - 163.1 (13.7)
subtype3 115 41 0.0 - 83.8 (13.6)

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.11 (ANOVA)

Table S109.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 254 65.4 (9.8)
subtype1 15 67.5 (8.6)
subtype2 121 66.5 (9.2)
subtype3 118 64.1 (10.3)

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.000771 (Fisher's exact test)

Table S110.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 154 130
subtype1 17 2
subtype2 75 56
subtype3 62 72

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.59 (ANOVA)

Table S111.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 24 72.1 (34.3)
subtype1 1 100.0 (NA)
subtype2 14 67.9 (38.5)
subtype3 9 75.6 (28.8)

Figure S101.  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 = 0.137 (Chi-square test)

Table S112.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ACINAR ADENOCARCINOMA LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG MICROPAPILLARY ADENOCARCINOMA LUNG MUCINOUS ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 6 64 181 3 10 2 2 1 10 1 4
subtype1 0 1 15 1 0 0 1 0 1 0 0
subtype2 4 27 78 2 7 1 1 1 7 0 3
subtype3 2 36 88 0 3 1 0 0 2 1 1

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.841 (Chi-square test)

Table S113.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 78 165 23 16
subtype1 6 12 1 0
subtype2 39 71 11 8
subtype3 33 82 11 8

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.342 (Chi-square test)

Table S114.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 168 57 52
subtype1 9 4 6
subtype2 80 28 18
subtype3 79 25 28

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.756 (Chi-square test)

Table S115.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 198 14 65
subtype1 14 1 2
subtype2 88 7 33
subtype3 96 6 30

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.246 (Chi-square test)

Table S116.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 147 59 58 15
subtype1 6 5 6 1
subtype2 74 24 20 9
subtype3 67 30 32 5

Figure S106.  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 = 0.74 (Fisher's exact test)

Table S117.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 18 266
subtype1 0 19
subtype2 9 122
subtype3 9 125

Figure S107.  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 = 0.0393 (Fisher's exact test)

Table S118.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 41 243
subtype1 6 13
subtype2 21 110
subtype3 14 120

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = LUAD.mergedcluster.txt

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

  • Number of patients = 292

  • 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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