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 282 patients, 12 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 5 subtypes that correlate to 'Time to Death',  'AGE',  'GENDER', and 'PATHOLOGY.N'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that correlate to 'Time to Death',  'AGE',  'GENDER', 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 correlate to 'AGE'.

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, 12 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 1.03e-05 0.00569 0.799 0.647
AGE ANOVA 0.477 0.557 0.448 0.0163 0.0144 0.242 0.0284
GENDER Fisher's exact test 0.272 0.383 0.014 0.0313 0.0014 0.102 0.303
KARNOFSKY PERFORMANCE SCORE ANOVA 0.679 0.862 0.328
HISTOLOGICAL TYPE Chi-square test 0.3 0.274 0.191 0.166 0.192 0.112 0.0594
PATHOLOGY T Chi-square test 0.489 0.479 0.755 0.185 0.354 0.481 0.699
PATHOLOGY N Chi-square test 0.572 0.651 0.574 0.00076 0.00323 0.657 0.872
PATHOLOGICSPREAD(M) Chi-square test 0.504 1 0.435 0.484 0.74 0.166 0.0539
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 1 1 0.0255 0.689 1 0.55 0.558
NEOADJUVANT THERAPY Fisher's exact test 0.921 1 0.296 0.0683 0.71 0.793 0.859
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 4 5
Number of samples 37 41 62 29 41
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.03e-05 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 185 66 0.0 - 224.0 (15.0)
subtype1 33 17 0.0 - 43.2 (12.1)
subtype2 36 10 0.1 - 83.8 (18.7)
subtype3 55 21 0.0 - 56.8 (13.0)
subtype4 25 7 0.1 - 224.0 (15.0)
subtype5 36 11 0.5 - 88.1 (20.2)

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 = 0.0163 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 184 65.9 (9.5)
subtype1 33 68.9 (8.9)
subtype2 36 62.8 (8.8)
subtype3 54 64.6 (10.7)
subtype4 25 65.1 (8.1)
subtype5 36 68.9 (8.2)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0313 (Chi-square test)

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

nPatients FEMALE MALE
ALL 97 113
subtype1 22 15
subtype2 25 16
subtype3 25 37
subtype4 9 20
subtype5 16 25

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 = 0.862 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 14 70.7 (39.3)
subtype2 3 56.7 (49.3)
subtype3 7 62.9 (44.2)
subtype4 2 95.0 (7.1)
subtype5 2 95.0 (7.1)

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 = 0.166 (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 50 128 3 8 2 2 2 9 1 2
subtype1 0 14 16 1 0 1 0 1 2 0 2
subtype2 1 9 28 0 1 0 0 0 1 1 0
subtype3 0 11 44 0 3 1 1 0 2 0 0
subtype4 0 7 16 2 2 0 0 1 1 0 0
subtype5 2 9 24 0 2 0 1 0 3 0 0

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 = 0.185 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 46 132 19 12
subtype1 4 24 5 4
subtype2 11 28 2 0
subtype3 11 39 7 5
subtype4 11 13 3 1
subtype5 9 28 2 2

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 = 0.00076 (Chi-square test)

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

nPatients N0 N1 N2+N3
ALL 116 47 42
subtype1 12 12 13
subtype2 28 4 8
subtype3 27 20 14
subtype4 17 6 3
subtype5 32 5 4

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 = 0.484 (Chi-square test)

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

nPatients M0 M1 MX
ALL 154 10 40
subtype1 31 2 4
subtype2 27 2 10
subtype3 48 1 12
subtype4 17 3 7
subtype5 31 2 7

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 = 0.689 (Chi-square test)

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

nPatients NO YES
ALL 199 11
subtype1 34 3
subtype2 40 1
subtype3 58 4
subtype4 27 2
subtype5 40 1

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 = 0.0683 (Chi-square test)

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

nPatients NO YES
ALL 176 34
subtype1 30 7
subtype2 38 3
subtype3 46 16
subtype4 27 2
subtype5 35 6

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
Number of samples 100 110
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00569 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 185 66 0.0 - 224.0 (15.0)
subtype1 88 36 0.0 - 83.8 (13.5)
subtype2 97 30 0.0 - 224.0 (18.9)

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 = 0.0144 (t-test)

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

nPatients Mean (Std.Dev)
ALL 184 65.9 (9.5)
subtype1 88 64.1 (9.7)
subtype2 96 67.6 (8.9)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0014 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 97 113
subtype1 58 42
subtype2 39 71

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 = 0.328 (t-test)

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

nPatients Mean (Std.Dev)
ALL 14 70.7 (39.3)
subtype1 7 60.0 (42.0)
subtype2 7 81.4 (36.3)

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 = 0.192 (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 50 128 3 8 2 2 2 9 1 2
subtype1 1 24 66 0 1 1 1 0 3 1 2
subtype2 2 26 62 3 7 1 1 2 6 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 = 0.354 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 46 132 19 12
subtype1 17 68 10 5
subtype2 29 64 9 7

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 = 0.00323 (Chi-square test)

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

nPatients N0 N1 N2+N3
ALL 116 47 42
subtype1 48 21 30
subtype2 68 26 12

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 = 0.74 (Chi-square test)

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

nPatients M0 M1 MX
ALL 154 10 40
subtype1 73 4 21
subtype2 81 6 19

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 = 1 (Fisher's exact test)

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

nPatients NO YES
ALL 199 11
subtype1 95 5
subtype2 104 6

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 = 0.71 (Fisher's exact test)

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

nPatients NO YES
ALL 176 34
subtype1 85 15
subtype2 91 19

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 37 29 29
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.799 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 80 27 0.0 - 76.2 (15.5)
subtype1 29 11 0.0 - 49.0 (23.1)
subtype2 25 7 0.0 - 60.0 (13.2)
subtype3 26 9 0.5 - 76.2 (13.6)

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 = 0.242 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 79 65.2 (9.7)
subtype1 33 64.5 (9.4)
subtype2 21 63.2 (10.7)
subtype3 25 67.8 (9.0)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.102 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 43 52
subtype1 14 23
subtype2 18 11
subtype3 11 18

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 = 0.112 (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 19 68 2 1 3 2
subtype1 12 24 0 0 1 0
subtype2 3 23 1 0 0 2
subtype3 4 21 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 = 0.481 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 27 59 6 3
subtype1 11 23 1 2
subtype2 7 18 4 0
subtype3 9 18 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 = 0.657 (Chi-square test)

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

nPatients N0 N1 N2+N3
ALL 55 22 16
subtype1 18 10 8
subtype2 18 7 4
subtype3 19 5 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 = 0.166 (Chi-square test)

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

nPatients M0 M1 MX
ALL 76 5 13
subtype1 30 2 4
subtype2 22 0 7
subtype3 24 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 = 0.55 (Fisher's exact test)

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

nPatients NO YES
ALL 91 4
subtype1 35 2
subtype2 29 0
subtype3 27 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 = 0.793 (Fisher's exact test)

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

nPatients NO YES
ALL 83 12
subtype1 31 6
subtype2 26 3
subtype3 26 3

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 28 28 39
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.647 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 80 27 0.0 - 76.2 (15.5)
subtype1 25 9 0.0 - 60.0 (13.2)
subtype2 22 9 0.0 - 47.0 (24.5)
subtype3 33 9 0.5 - 76.2 (14.1)

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 = 0.0284 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 79 65.2 (9.7)
subtype1 21 60.6 (8.5)
subtype2 25 65.9 (11.2)
subtype3 33 67.6 (8.4)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.303 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 43 52
subtype1 15 13
subtype2 14 14
subtype3 14 25

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

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0594 (Chi-square test)

Table S68.  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 19 68 2 1 3 2
subtype1 2 23 1 0 0 2
subtype2 11 16 0 0 1 0
subtype3 6 29 1 1 2 0

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.699 (Chi-square test)

Table S69.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 27 59 6 3
subtype1 7 18 3 0
subtype2 8 17 1 2
subtype3 12 24 2 1

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.872 (Chi-square test)

Table S70.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2+N3
ALL 55 22 16
subtype1 17 6 5
subtype2 14 7 6
subtype3 24 9 5

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.0539 (Chi-square test)

Table S71.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 76 5 13
subtype1 20 0 8
subtype2 22 2 3
subtype3 34 3 2

Figure S64.  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 = 0.558 (Fisher's exact test)

Table S72.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 91 4
subtype1 28 0
subtype2 26 2
subtype3 37 2

Figure S65.  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 = 0.859 (Fisher's exact test)

Table S73.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 83 12
subtype1 25 3
subtype2 25 3
subtype3 33 6

Figure S66.  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 = 282

  • 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
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