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 11 clinical features across 283 patients, 14 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',  'AGE',  'GENDER',  'HISTOLOGICAL.TYPE', and 'PATHOLOGY.N'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death',  'AGE',  'GENDER',  'HISTOLOGICAL.TYPE',  '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 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 11 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 14 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 0.0195 0.00503 2.19e-06 0.657 0.844
AGE ANOVA 0.477 0.557 0.334 0.0278 0.0276 0.193 0.0506
GENDER Fisher's exact test 0.272 0.383 0.021 0.00668 0.0211 0.0866 0.466
KARNOFSKY PERFORMANCE SCORE ANOVA 0.679 0.631 0.198 0.43
HISTOLOGICAL TYPE Chi-square test 0.3 0.274 0.215 0.0397 0.0295 0.0827 0.0796
PATHOLOGY T Chi-square test 0.489 0.479 0.873 0.587 0.153 0.682 0.545
PATHOLOGY N Chi-square test 0.572 0.651 0.401 0.0321 0.00156 0.509 0.599
PATHOLOGICSPREAD(M) Chi-square test 0.504 1 0.29 0.912 0.365 0.144 0.0639
TUMOR STAGE Chi-square test 0.147 0.274 0.677 0.0624 0.0321 0.0972 0.0556
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 1 1 0.0192 0.863 0.973 0.552 0.821
NEOADJUVANT THERAPY Fisher's exact test 0.921 1 0.246 0.456 0.48 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 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: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 52 62 52 50
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0195 (logrank test)

Table S24.  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 12 0.1 - 224.0 (12.2)
subtype2 53 22 0.1 - 88.1 (15.0)
subtype3 45 10 0.0 - 77.9 (5.4)
subtype4 46 16 0.0 - 71.5 (12.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.334 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 188 65.3 (9.8)
subtype1 42 67.6 (7.7)
subtype2 55 63.9 (9.4)
subtype3 47 65.1 (10.6)
subtype4 44 65.0 (11.2)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.021 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 115 101
subtype1 35 17
subtype2 25 37
subtype3 25 27
subtype4 30 20

Figure S23.  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 S27.  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 S24.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.215 (Chi-square test)

Table S28.  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 37 0 1 0 0 0 3 1 2
subtype3 0 14 32 1 1 0 1 0 3 0 0
subtype4 1 9 35 1 1 1 0 0 2 0 0

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.873 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 60 125 17 12
subtype1 18 25 5 3
subtype2 15 39 4 4
subtype3 13 31 4 4
subtype4 14 30 4 1

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.401 (Chi-square test)

Table S30.  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 39 11 11
subtype3 28 15 8
subtype4 25 9 14

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.29 (Chi-square test)

Table S31.  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 5 15
subtype3 29 4 17
subtype4 34 1 15

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.677 (Chi-square test)

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

nPatients I II III IV
ALL 107 47 46 11
subtype1 28 11 11 0
subtype2 32 13 13 4
subtype3 23 12 9 5
subtype4 24 11 13 2

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

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

P value = 0.0192 (Fisher's exact test)

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

nPatients NO YES
ALL 9 207
subtype1 0 52
subtype2 2 60
subtype3 1 51
subtype4 6 44

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.246 (Fisher's exact test)

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

nPatients NO YES
ALL 31 185
subtype1 11 41
subtype2 5 57
subtype3 7 45
subtype4 8 42

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

Clustering Approach #4: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 86 80 84
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00503 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 221 77 0.0 - 224.0 (15.0)
subtype1 74 21 0.1 - 224.0 (16.2)
subtype2 71 27 0.0 - 77.9 (12.2)
subtype3 76 29 0.0 - 83.8 (17.1)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0278 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 224 65.5 (9.7)
subtype1 76 67.8 (8.5)
subtype2 71 63.9 (11.0)
subtype3 77 64.6 (9.2)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00668 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 135 115
subtype1 55 31
subtype2 46 34
subtype3 34 50

Figure S34.  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 S39.  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 S35.  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 = 0.0397 (Chi-square test)

Table S40.  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 58 160 3 8 2 2 2 9 1 2
subtype1 2 18 51 3 6 0 1 2 3 0 0
subtype2 0 14 60 0 2 1 1 0 2 0 0
subtype3 1 26 49 0 0 1 0 0 4 1 2

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.587 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 63 151 21 14
subtype1 28 47 7 3
subtype2 17 51 7 5
subtype3 18 53 7 6

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0321 (Chi-square test)

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

nPatients N0 N1 N2+N3
ALL 142 54 49
subtype1 59 14 10
subtype2 37 22 20
subtype3 46 18 19

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

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

P value = 0.912 (Chi-square test)

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

nPatients M0 M1 MX
ALL 183 14 46
subtype1 63 5 15
subtype2 60 3 15
subtype3 60 6 16

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0624 (Chi-square test)

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

nPatients I II III IV
ALL 125 53 54 13
subtype1 53 15 10 5
subtype2 32 20 23 3
subtype3 40 18 21 5

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

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

P value = 0.863 (Fisher's exact test)

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

nPatients NO YES
ALL 11 239
subtype1 3 83
subtype2 4 76
subtype3 4 80

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.456 (Fisher's exact test)

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

nPatients NO YES
ALL 38 212
subtype1 13 73
subtype2 15 65
subtype3 10 74

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

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S47.  Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 84 54 68 44
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 2.19e-06 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 221 77 0.0 - 224.0 (15.0)
subtype1 74 19 0.1 - 224.0 (17.5)
subtype2 48 13 0.1 - 83.8 (16.5)
subtype3 60 25 0.0 - 77.9 (12.4)
subtype4 39 20 0.0 - 48.5 (15.0)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0276 (ANOVA)

Table S49.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 224 65.5 (9.7)
subtype1 76 67.2 (8.0)
subtype2 48 62.9 (9.3)
subtype3 59 63.9 (11.8)
subtype4 41 67.5 (9.0)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0211 (Fisher's exact test)

Table S50.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 135 115
subtype1 53 31
subtype2 22 32
subtype3 41 27
subtype4 19 25

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

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

P value = 0.198 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 18 69.4 (39.0)
subtype1 7 81.4 (36.3)
subtype2 4 85.0 (5.8)
subtype3 7 48.6 (46.3)

Figure S46.  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 = 0.0295 (Chi-square test)

Table S52.  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 58 160 3 8 2 2 2 9 1 2
subtype1 2 21 50 1 5 0 1 1 3 0 0
subtype2 1 10 39 0 1 1 0 0 1 1 0
subtype3 0 9 52 0 2 1 1 0 3 0 0
subtype4 0 18 19 2 0 0 0 1 2 0 2

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.153 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 63 151 21 14
subtype1 28 47 4 4
subtype2 14 36 3 1
subtype3 14 42 8 4
subtype4 7 26 6 5

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00156 (Chi-square test)

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

nPatients N0 N1 N2+N3
ALL 142 54 49
subtype1 57 15 9
subtype2 37 5 11
subtype3 30 19 18
subtype4 18 15 11

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

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

P value = 0.365 (Chi-square test)

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

nPatients M0 M1 MX
ALL 183 14 46
subtype1 59 6 15
subtype2 34 3 15
subtype3 55 2 10
subtype4 35 3 6

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

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0321 (Chi-square test)

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

nPatients I II III IV
ALL 125 53 54 13
subtype1 50 14 10 6
subtype2 32 8 12 2
subtype3 28 17 20 2
subtype4 15 14 12 3

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.973 (Fisher's exact test)

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

nPatients NO YES
ALL 11 239
subtype1 3 81
subtype2 3 51
subtype3 3 65
subtype4 2 42

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.48 (Fisher's exact test)

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

nPatients NO YES
ALL 38 212
subtype1 11 73
subtype2 6 48
subtype3 14 54
subtype4 7 37

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

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S59.  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 S60.  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 S54.  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 S61.  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 S55.  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 S62.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S56.  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 S63.  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 S57.  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 S64.  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 S58.  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 S65.  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 S59.  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 S66.  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 S60.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0972 (Chi-square test)

Table S67.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 53 24 19 4
subtype1 18 9 11 1
subtype2 14 10 5 0
subtype3 21 5 3 3

Figure S61.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.552 (Fisher's exact test)

Table S68.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S62.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S69.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

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

Figure S63.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S70.  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 S71.  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 S64.  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 S72.  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 S65.  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 S73.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S66.  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 S74.  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 S67.  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 S75.  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 S68.  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 S76.  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 S69.  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 S77.  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 S70.  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 S78.  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 S71.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0556 (Chi-square test)

Table S79.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 53 24 19 4
subtype1 18 14 8 0
subtype2 21 4 3 3
subtype3 14 6 8 1

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.821 (Fisher's exact test)

Table S80.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.682 (Fisher's exact test)

Table S81.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

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

Figure S74.  Get High-res Image Clustering Approach #7: '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 = 283

  • Number of clustering approaches = 7

  • 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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle