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 7 different clustering approaches and 10 clinical features across 283 patients, 10 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.

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

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death',  'GENDER',  'HISTOLOGICAL.TYPE', and 'PATHOLOGY.N'.

  • 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 do not correlate to any clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 10 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 10 significant findings detected.

Clinical
Features 		Statistical
Tests 		mRNA
CNMF
subtypes 		mRNA
cHierClus
subtypes 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRseq
CNMF
subtypes 		MIRseq
cHierClus
subtypes
Time to Death logrank test 0.337 0.671 0.00488 0.00509 2.87e-05 0.657 0.844
AGE ANOVA 0.477 0.557 0.448 0.117 0.197 0.193 0.0506
GENDER Fisher's exact test 0.272 0.383 0.014 2.22e-05 6.45e-06 0.0866 0.466
KARNOFSKY PERFORMANCE SCORE ANOVA 0.679 0.631 0.576 0.43
HISTOLOGICAL TYPE Chi-square test 0.3 0.274 0.191 0.112 0.000331 0.0827 0.0796
PATHOLOGY T Chi-square test 0.489 0.479 0.755 0.224 0.0898 0.682 0.545
PATHOLOGY N Chi-square test 0.572 0.651 0.574 0.0264 0.00175 0.509 0.599
PATHOLOGICSPREAD(M) Chi-square test 0.504 1 0.435 0.421 0.488 0.144 0.0639
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 1 1 0.0255 1 0.661 0.552 0.821
NEOADJUVANT THERAPY Fisher's exact test 0.921 1 0.296 0.576 0.166 1 0.682
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 14 18
subtype1 2 3
subtype2 6 3
subtype3 3 9
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 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test)

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.921 (Fisher's exact test)

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

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

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11.  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 S12.  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 S10.  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 S13.  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 S11.  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 S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S12.  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 S15.  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 S13.  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 S16.  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 S14.  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 S17.  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 S15.  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 S18.  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 S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 1 (Fisher's exact test)

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

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

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S21.  Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 52 60 52 52
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00488 (logrank test)

Table S22.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 188 60 0.0 - 224.0 (11.6)
subtype1 44 10 0.1 - 224.0 (11.6)
subtype2 53 23 0.1 - 88.1 (15.0)
subtype3 45 11 0.0 - 77.9 (5.4)
subtype4 46 16 0.0 - 71.5 (12.8)

Figure S19.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.448 (ANOVA)

Table S23.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 188 65.3 (9.8)
subtype1 42 67.5 (7.8)
subtype2 54 64.7 (9.1)
subtype3 47 64.8 (10.5)
subtype4 45 64.5 (11.5)

Figure S20.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.014 (Fisher's exact test)

Table S24.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 101 115
subtype1 17 35
subtype2 37 23
subtype3 26 26
subtype4 21 31

Figure S21.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.679 (ANOVA)

Table S25.  Clustering Approach #3: '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 S22.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.191 (Chi-square test)

Table S26.  Clustering Approach #3: '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 49 132 3 9 1 2 2 9 1 3
subtype1 3 9 28 1 6 0 1 2 1 0 1
subtype2 1 17 35 0 1 0 0 0 3 1 2
subtype3 0 14 32 1 1 0 1 0 3 0 0
subtype4 1 9 37 1 1 1 0 0 2 0 0

Figure S23.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.755 (Chi-square test)

Table S27.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 60 125 17 12
subtype1 18 26 5 2
subtype2 14 38 4 4
subtype3 13 30 4 5
subtype4 15 31 4 1

Figure S24.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.574 (Chi-square test)

Table S28.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 125 44 42
subtype1 33 9 9
subtype2 38 11 10
subtype3 28 14 9
subtype4 26 10 14

Figure S25.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.435 (Chi-square test)

Table S29.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 139 10 61
subtype1 35 0 14
subtype2 41 4 14
subtype3 29 4 17
subtype4 34 2 16

Figure S26.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.0255 (Fisher's exact test)

Table S30.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 207 9
subtype1 52 0
subtype2 58 2
subtype3 51 1
subtype4 46 6

Figure S27.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.296 (Fisher's exact test)

Table S31.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 185 31
subtype1 41 11
subtype2 55 5
subtype3 45 7
subtype4 44 8

Figure S28.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S32.  Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 88 68 84
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00509 (logrank test)

Table S33.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 214 75 0.0 - 224.0 (15.0)
subtype1 77 19 0.1 - 224.0 (13.9)
subtype2 60 25 0.0 - 77.9 (11.0)
subtype3 77 31 0.0 - 88.1 (18.3)

Figure S29.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.117 (ANOVA)

Table S34.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 214 65.5 (9.7)
subtype1 79 67.1 (8.5)
subtype2 59 63.7 (11.3)
subtype3 76 65.1 (9.5)

Figure S30.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 2.22e-05 (Fisher's exact test)

Table S35.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 110 130
subtype1 26 62
subtype2 30 38
subtype3 54 30

Figure S31.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.631 (ANOVA)

Table S36.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 18 69.4 (39.0)
subtype1 8 71.2 (44.2)
subtype2 7 60.0 (42.0)
subtype3 3 86.7 (5.8)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.112 (Chi-square test)

Table S37.  Clustering Approach #4: '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 52 156 3 8 2 2 2 9 1 2
subtype1 2 17 56 3 6 0 1 1 2 0 0
subtype2 0 11 51 0 2 1 1 0 2 0 0
subtype3 1 24 49 0 0 1 0 1 5 1 2

Figure S33.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.224 (Chi-square test)

Table S38.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 61 144 21 13
subtype1 31 47 6 3
subtype2 14 42 7 5
subtype3 16 55 8 5

Figure S34.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0264 (Chi-square test)

Table S39.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 136 53 46
subtype1 60 15 10
subtype2 30 19 18
subtype3 46 19 18

Figure S35.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.421 (Chi-square test)

Table S40.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 176 12 46
subtype1 64 4 16
subtype2 53 1 14
subtype3 59 7 16

Figure S36.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 1 (Fisher's exact test)

Table S41.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 229 11
subtype1 84 4
subtype2 65 3
subtype3 80 4

Figure S37.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.576 (Fisher's exact test)

Table S42.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 203 37
subtype1 75 13
subtype2 55 13
subtype3 73 11

Figure S38.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S43.  Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 25 12 49 23 56 75
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.87e-05 (logrank test)

Table S44.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 214 75 0.0 - 224.0 (15.0)
subtype1 25 15 0.1 - 45.2 (15.1)
subtype2 10 3 0.0 - 48.5 (16.2)
subtype3 43 12 0.1 - 83.8 (16.4)
subtype4 21 9 0.5 - 104.2 (32.1)
subtype5 48 18 0.0 - 77.9 (10.0)
subtype6 67 18 0.1 - 224.0 (13.7)

Figure S39.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.197 (ANOVA)

Table S45.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 214 65.5 (9.7)
subtype1 24 66.6 (9.3)
subtype2 10 68.1 (10.8)
subtype3 43 62.7 (9.6)
subtype4 22 67.9 (7.1)
subtype5 48 64.2 (11.7)
subtype6 67 66.5 (8.8)

Figure S40.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 6.45e-06 (Chi-square test)

Table S46.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 110 130
subtype1 15 10
subtype2 6 6
subtype3 31 18
subtype4 17 6
subtype5 24 32
subtype6 17 58

Figure S41.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.576 (ANOVA)

Table S47.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 18 69.4 (39.0)
subtype3 4 85.0 (5.8)
subtype4 1 90.0 (NA)
subtype5 6 56.7 (45.0)
subtype6 7 68.6 (47.1)

Figure S42.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000331 (Chi-square test)

Table S48.  Clustering Approach #5: '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 52 156 3 8 2 2 2 9 1 2
subtype1 0 10 11 0 0 0 0 0 2 0 2
subtype2 0 3 6 2 0 0 0 1 0 0 0
subtype3 1 8 36 0 1 1 0 0 1 1 0
subtype4 0 10 11 0 0 0 0 0 2 0 0
subtype5 0 9 41 0 1 1 1 0 3 0 0
subtype6 2 12 51 1 6 0 1 1 1 0 0

Figure S43.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0898 (Chi-square test)

Table S49.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 61 144 21 13
subtype1 2 18 3 2
subtype2 3 5 3 1
subtype3 13 32 3 1
subtype4 4 15 1 3
subtype5 11 34 7 4
subtype6 28 40 4 2

Figure S44.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00175 (Chi-square test)

Table S50.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 136 53 46
subtype1 6 10 9
subtype2 7 4 1
subtype3 32 5 11
subtype4 17 5 1
subtype5 25 15 15
subtype6 49 14 9

Figure S45.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.488 (Chi-square test)

Table S51.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 176 12 46
subtype1 19 2 4
subtype2 9 1 2
subtype3 30 3 14
subtype4 19 2 2
subtype5 47 1 8
subtype6 52 3 16

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.661 (Chi-square test)

Table S52.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 229 11
subtype1 23 2
subtype2 12 0
subtype3 47 2
subtype4 23 0
subtype5 54 2
subtype6 70 5

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.166 (Chi-square test)

Table S53.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 203 37
subtype1 21 4
subtype2 9 3
subtype3 44 5
subtype4 23 0
subtype5 44 12
subtype6 62 13

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S54.  Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 39 29 32
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.657 (logrank test)

Table S55.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 30 0.0 - 85.3 (17.4)
subtype1 31 14 0.0 - 49.0 (25.0)
subtype2 25 7 0.0 - 60.0 (13.2)
subtype3 29 9 0.5 - 85.3 (14.1)

Figure S49.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.193 (ANOVA)

Table S56.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 84 65.3 (9.6)
subtype1 35 64.5 (8.8)
subtype2 21 63.2 (10.7)
subtype3 28 67.9 (9.3)

Figure S50.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0866 (Fisher's exact test)

Table S57.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 45 55
subtype1 16 23
subtype2 18 11
subtype3 11 21

Figure S51.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0827 (Chi-square test)

Table S58.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 20 72 2 1 3 2
subtype1 13 25 0 0 1 0
subtype2 3 23 1 0 0 2
subtype3 4 24 1 1 2 0

Figure S52.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.682 (Chi-square test)

Table S59.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 27 62 8 3
subtype1 10 24 3 2
subtype2 7 18 4 0
subtype3 10 20 1 1

Figure S53.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.509 (Chi-square test)

Table S60.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 58 22 18
subtype1 19 9 10
subtype2 18 7 4
subtype3 21 6 4

Figure S54.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.144 (Chi-square test)

Table S61.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 81 5 13
subtype1 32 2 4
subtype2 22 0 7
subtype3 27 3 2

Figure S55.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.552 (Fisher's exact test)

Table S62.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 96 4
subtype1 37 2
subtype2 29 0
subtype3 30 2

Figure S56.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S63.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 88 12
subtype1 34 5
subtype2 26 3
subtype3 28 4

Figure S57.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S64.  Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 40 31 29
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.844 (logrank test)

Table S65.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 30 0.0 - 85.3 (17.4)
subtype1 36 11 0.0 - 85.3 (8.4)
subtype2 27 10 0.6 - 76.2 (15.2)
subtype3 22 9 0.0 - 47.6 (27.5)

Figure S58.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0506 (ANOVA)

Table S66.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 84 65.3 (9.6)
subtype1 31 62.4 (9.5)
subtype2 28 68.4 (8.7)
subtype3 25 65.5 (9.7)

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.466 (Fisher's exact test)

Table S67.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 45 55
subtype1 20 20
subtype2 11 20
subtype3 14 15

Figure S60.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.43 (ANOVA)

Table S68.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 6 75.0 (37.3)
subtype1 3 90.0 (0.0)
subtype2 3 60.0 (52.9)

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0796 (Chi-square test)

Table S69.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG ADENOCARCINOMA MIXED SUBTYPE LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS LUNG CLEAR CELL ADENOCARCINOMA LUNG PAPILLARY ADENOCARCINOMA MUCINOUS (COLLOID) ADENOCARCINOMA
ALL 20 72 2 1 3 2
subtype1 4 33 1 0 0 2
subtype2 5 22 1 1 2 0
subtype3 11 17 0 0 1 0

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.545 (Chi-square test)

Table S70.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 27 62 8 3
subtype1 9 27 4 0
subtype2 10 19 1 1
subtype3 8 16 3 2

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.599 (Chi-square test)

Table S71.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 58 22 18
subtype1 23 10 7
subtype2 21 5 4
subtype3 14 7 7

Figure S64.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.0639 (Chi-square test)

Table S72.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 81 5 13
subtype1 31 0 9
subtype2 27 3 1
subtype3 23 2 3

Figure S65.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.821 (Fisher's exact test)

Table S73.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 96 4
subtype1 39 1
subtype2 29 2
subtype3 28 1

Figure S66.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.682 (Fisher's exact test)

Table S74.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 88 12
subtype1 34 6
subtype2 27 4
subtype3 27 2

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = LUAD.mergedcluster.txt

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

  • Number of patients = 283

  • Number of clustering approaches = 7

  • Number of selected clinical features = 10

  • 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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