Lung Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 7 different clustering approaches and 11 clinical features across 278 patients, 13 significant findings detected with P value < 0.05.

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

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

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 8 subtypes that correlate to 'Time to Death',  'AGE', and 'PATHOLOGY.T'.

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

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE',  'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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, 13 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 0.0442 0.0312 0.186 0.0031 0.234 0.601 0.503
AGE ANOVA 0.546 0.168 0.213 0.00813 0.443 0.0431 0.897
GENDER Fisher's exact test 0.382 0.00872 0.768 0.225 0.0447 0.654 0.353
KARNOFSKY PERFORMANCE SCORE ANOVA 0.191 0.0891 0.439 0.543 0.429 0.421 0.4
HISTOLOGICAL TYPE Chi-square test 0.355 0.587 0.22 0.897 0.294 0.412 0.401
PATHOLOGY T Chi-square test 0.0029 0.000811 0.314 0.0215 0.364 0.683 0.329
PATHOLOGY N Chi-square test 0.818 0.32 0.37 0.673 0.9 0.0392 0.061
PATHOLOGICSPREAD(M) Chi-square test 0.146 0.323 0.553 0.0795 0.655 0.00673 0.205
TUMOR STAGE Chi-square test 0.798 0.617 0.791 0.668 0.564 0.842 0.336
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.445 0.453 0.581 0.399 0.382 0.656 0.0193
NEOADJUVANT THERAPY Fisher's exact test 0.734 0.799 0.944 0.246 0.958 0.748 0.571
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 43 50 30 31
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0442 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 148 62 0.4 - 173.8 (18.2)
subtype1 42 16 0.4 - 122.4 (19.0)
subtype2 48 19 0.4 - 99.2 (24.5)
subtype3 28 16 0.4 - 82.2 (15.9)
subtype4 30 11 0.4 - 173.8 (11.8)

Figure S1.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.546 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 152 66.5 (8.6)
subtype1 42 66.3 (7.6)
subtype2 49 66.6 (8.4)
subtype3 30 68.2 (8.6)
subtype4 31 65.0 (10.1)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.382 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 44 110
subtype1 9 34
subtype2 13 37
subtype3 11 19
subtype4 11 20

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

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

P value = 0.191 (ANOVA)

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 26 24.2 (38.5)
subtype1 4 0.0 (0.0)
subtype2 4 0.0 (0.0)
subtype3 9 31.1 (46.8)
subtype4 9 38.9 (39.5)

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.355 (Chi-square test)

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 5 1 148
subtype1 0 0 43
subtype2 3 0 47
subtype3 1 0 29
subtype4 1 1 29

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0029 (Chi-square test)

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 5 31 6 1
subtype2 4 39 1 6
subtype3 11 16 2 1
subtype4 10 14 3 4

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.818 (Chi-square test)

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 26 11 4 2
subtype2 27 17 4 2
subtype3 20 6 3 1
subtype4 23 6 2 0

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.146 (Fisher's exact test)

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 146 4
subtype1 39 2
subtype2 48 0
subtype3 30 0
subtype4 29 2

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.798 (Chi-square test)

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 81 34 34 4
subtype1 23 9 9 2
subtype2 25 13 12 0
subtype3 17 6 6 0
subtype4 16 6 7 2

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.445 (Fisher's exact test)

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 152
subtype1 1 42
subtype2 0 50
subtype3 1 29
subtype4 0 31

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.734 (Fisher's exact test)

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S13.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 28 54 72
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0312 (logrank test)

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 148 62 0.4 - 173.8 (18.2)
subtype1 27 7 0.4 - 173.8 (15.6)
subtype2 52 21 0.4 - 99.2 (23.6)
subtype3 69 34 0.4 - 122.4 (18.8)

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.168 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 152 66.5 (8.6)
subtype1 28 63.9 (8.4)
subtype2 53 66.5 (7.9)
subtype3 71 67.5 (9.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00872 (Fisher's exact test)

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 44 110
subtype1 15 13
subtype2 12 42
subtype3 17 55

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0891 (ANOVA)

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 26 24.2 (38.5)
subtype1 8 48.8 (42.6)
subtype2 5 10.0 (22.4)
subtype3 13 14.6 (35.7)

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.587 (Chi-square test)

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 5 1 148
subtype1 1 0 27
subtype2 3 0 51
subtype3 1 1 70

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.000811 (Chi-square test)

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 30 100 12 12
subtype1 12 11 3 2
subtype2 4 42 1 7
subtype3 14 47 8 3

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.32 (Chi-square test)

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 96 40 13 5
subtype1 22 3 3 0
subtype2 29 18 5 2
subtype3 45 19 5 3

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.323 (Fisher's exact test)

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 146 4
subtype1 27 1
subtype2 52 0
subtype3 67 3

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.617 (Chi-square test)

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 81 34 34 4
subtype1 17 4 6 1
subtype2 26 14 14 0
subtype3 38 16 14 3

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.453 (Fisher's exact test)

Table S23.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 152
subtype1 1 27
subtype2 0 54
subtype3 1 71

Figure S21.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.799 (Fisher's exact test)

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

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

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

Clustering Approach #3: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 57 54 33
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.186 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 131 57 0.0 - 173.8 (15.6)
subtype1 52 26 0.1 - 173.8 (13.2)
subtype2 49 19 0.2 - 141.3 (18.8)
subtype3 30 12 0.0 - 107.0 (15.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.213 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 136 68.5 (9.2)
subtype1 54 70.2 (9.8)
subtype2 51 67.2 (8.0)
subtype3 31 67.7 (9.7)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.768 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 40 104
subtype1 17 40
subtype2 13 41
subtype3 10 23

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

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

P value = 0.439 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 28 26.8 (39.5)
subtype1 5 42.0 (49.2)
subtype2 13 16.9 (34.5)
subtype3 10 32.0 (41.6)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.22 (Chi-square test)

Table S30.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 4 1 1 138
subtype1 0 0 0 57
subtype2 2 1 0 51
subtype3 2 0 1 30

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.314 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 40 83 15 6
subtype1 14 35 6 2
subtype2 19 25 8 2
subtype3 7 23 1 2

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.37 (Chi-square test)

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

nPatients N0 N1 N2
ALL 92 40 12
subtype1 34 15 8
subtype2 37 15 2
subtype3 21 10 2

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.553 (Chi-square test)

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

nPatients M0 M1 MX
ALL 119 1 22
subtype1 45 1 11
subtype2 48 0 6
subtype3 26 0 5

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.791 (Chi-square test)

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

nPatients I II III IV
ALL 72 46 22 1
subtype1 25 20 10 1
subtype2 31 16 7 0
subtype3 16 10 5 0

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

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

P value = 0.581 (Fisher's exact test)

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

nPatients NO YES
ALL 8 136
subtype1 3 54
subtype2 2 52
subtype3 3 30

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.944 (Fisher's exact test)

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

nPatients NO YES
ALL 16 128
subtype1 7 50
subtype2 6 48
subtype3 3 30

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

Clustering Approach #4: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4 5 6 7 8
Number of samples 22 20 55 40 34 36 4 9
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0031 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 204 85 0.1 - 173.8 (18.5)
subtype1 20 6 0.1 - 115.6 (11.9)
subtype2 17 9 0.8 - 60.9 (13.1)
subtype3 52 20 0.4 - 141.3 (23.7)
subtype4 37 17 0.4 - 114.0 (15.6)
subtype5 32 14 1.0 - 122.4 (23.6)
subtype6 34 13 0.4 - 173.8 (11.3)
subtype7 3 2 1.0 - 27.0 (4.3)
subtype8 9 4 0.6 - 97.9 (32.7)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00813 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 212 67.5 (8.4)
subtype1 22 65.1 (8.7)
subtype2 18 66.8 (7.9)
subtype3 54 66.0 (8.3)
subtype4 38 71.7 (6.9)
subtype5 32 68.8 (9.2)
subtype6 35 64.9 (8.8)
subtype7 4 66.2 (3.4)
subtype8 9 71.0 (6.2)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.225 (Chi-square test)

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

nPatients FEMALE MALE
ALL 59 161
subtype1 5 17
subtype2 4 16
subtype3 10 45
subtype4 17 23
subtype5 8 26
subtype6 10 26
subtype7 1 3
subtype8 4 5

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

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

P value = 0.543 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 44 17.7 (33.6)
subtype1 3 6.7 (11.5)
subtype2 4 25.0 (50.0)
subtype3 7 12.9 (22.1)
subtype4 11 16.4 (36.4)
subtype5 7 0.0 (0.0)
subtype6 10 30.0 (40.3)
subtype8 2 45.0 (63.6)

Figure S37.  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 = 0.897 (Chi-square test)

Table S42.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 6 1 1 212
subtype1 0 0 0 22
subtype2 0 0 0 20
subtype3 3 1 0 51
subtype4 2 0 0 38
subtype5 0 0 1 33
subtype6 1 0 0 35
subtype7 0 0 0 4
subtype8 0 0 0 9

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0215 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 51 133 23 13
subtype1 2 13 7 0
subtype2 5 12 3 0
subtype3 8 35 6 6
subtype4 11 25 1 3
subtype5 5 26 2 1
subtype6 14 16 4 2
subtype7 2 2 0 0
subtype8 4 4 0 1

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.673 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 142 54 19 5
subtype1 16 4 2 0
subtype2 14 4 1 1
subtype3 33 17 4 1
subtype4 26 12 1 1
subtype5 23 7 3 1
subtype6 21 8 7 0
subtype7 4 0 0 0
subtype8 5 2 1 1

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

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

P value = 0.0795 (Chi-square test)

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

nPatients M0 M1 MX
ALL 203 4 9
subtype1 16 1 4
subtype2 19 0 0
subtype3 51 0 2
subtype4 40 0 0
subtype5 31 2 1
subtype6 33 1 2
subtype7 4 0 0
subtype8 9 0 0

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.668 (Chi-square test)

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

nPatients I II III IV
ALL 114 54 46 4
subtype1 11 5 5 1
subtype2 11 6 3 0
subtype3 27 16 12 0
subtype4 21 11 7 0
subtype5 21 6 5 2
subtype6 14 9 11 1
subtype7 4 0 0 0
subtype8 5 1 3 0

Figure S42.  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 = 0.399 (Chi-square test)

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

nPatients NO YES
ALL 6 214
subtype1 0 22
subtype2 2 18
subtype3 0 55
subtype4 2 38
subtype5 1 33
subtype6 1 35
subtype7 0 4
subtype8 0 9

Figure S43.  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 = 0.246 (Chi-square test)

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

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

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

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S49.  Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 65 74 81
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.234 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 204 85 0.1 - 173.8 (18.5)
subtype1 60 25 0.4 - 173.8 (8.9)
subtype2 68 28 0.4 - 141.3 (23.1)
subtype3 76 32 0.1 - 122.4 (22.3)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.443 (ANOVA)

Table S51.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 212 67.5 (8.4)
subtype1 64 67.6 (8.6)
subtype2 71 66.5 (8.2)
subtype3 77 68.3 (8.5)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0447 (Fisher's exact test)

Table S52.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 59 161
subtype1 25 40
subtype2 15 59
subtype3 19 62

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

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

P value = 0.429 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 44 17.7 (33.6)
subtype1 17 24.1 (37.3)
subtype2 12 7.5 (17.6)
subtype3 15 18.7 (38.7)

Figure S48.  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 = 0.294 (Chi-square test)

Table S54.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 6 1 1 212
subtype1 3 0 0 62
subtype2 3 1 0 70
subtype3 0 0 1 80

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.364 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 51 133 23 13
subtype1 21 33 6 5
subtype2 13 49 7 5
subtype3 17 51 10 3

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.9 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 142 54 19 5
subtype1 41 17 6 1
subtype2 46 21 5 2
subtype3 55 16 8 2

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

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

P value = 0.655 (Chi-square test)

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

nPatients M0 M1 MX
ALL 203 4 9
subtype1 61 2 2
subtype2 69 0 3
subtype3 73 2 4

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

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.564 (Chi-square test)

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

nPatients I II III IV
ALL 114 54 46 4
subtype1 29 16 16 2
subtype2 38 21 15 0
subtype3 47 17 15 2

Figure S53.  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 = 0.382 (Fisher's exact test)

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

nPatients NO YES
ALL 6 214
subtype1 1 64
subtype2 1 73
subtype3 4 77

Figure S54.  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 = 0.958 (Fisher's exact test)

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

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

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

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S61.  Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 84 73 45
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.601 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 187 79 0.1 - 173.8 (18.8)
subtype1 81 34 0.4 - 173.8 (18.3)
subtype2 62 25 0.1 - 114.0 (18.4)
subtype3 44 20 0.4 - 141.3 (20.5)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0431 (ANOVA)

Table S63.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 195 68.3 (8.3)
subtype1 83 66.8 (9.3)
subtype2 68 70.2 (7.1)
subtype3 44 68.2 (7.5)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.654 (Fisher's exact test)

Table S64.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 56 146
subtype1 25 59
subtype2 21 52
subtype3 10 35

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

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

P value = 0.421 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 39 16.2 (32.3)
subtype1 21 18.6 (35.0)
subtype2 12 20.0 (34.9)
subtype3 6 0.0 (0.0)

Figure S59.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.412 (Chi-square test)

Table S66.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 4 1 1 196
subtype1 2 0 1 81
subtype2 0 1 0 72
subtype3 2 0 0 43

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.683 (Chi-square test)

Table S67.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 44 126 20 12
subtype1 18 53 9 4
subtype2 18 44 8 3
subtype3 8 29 3 5

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0392 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 126 53 19 4
subtype1 52 18 13 1
subtype2 51 17 4 1
subtype3 23 18 2 2

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

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

P value = 0.00673 (Chi-square test)

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

nPatients M0 M1 MX
ALL 185 3 10
subtype1 80 2 0
subtype2 63 1 9
subtype3 42 0 1

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

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.842 (Chi-square test)

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

nPatients I II III IV
ALL 104 51 43 3
subtype1 44 18 20 2
subtype2 38 20 13 1
subtype3 22 13 10 0

Figure S64.  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 = 0.656 (Fisher's exact test)

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

nPatients NO YES
ALL 6 196
subtype1 3 81
subtype2 1 72
subtype3 2 43

Figure S65.  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 = 0.748 (Fisher's exact test)

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

nPatients NO YES
ALL 23 179
subtype1 8 76
subtype2 9 64
subtype3 6 39

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

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S73.  Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 97 63 42
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.503 (logrank test)

Table S74.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 187 79 0.1 - 173.8 (18.8)
subtype1 88 35 0.1 - 173.8 (14.3)
subtype2 59 26 0.4 - 122.4 (22.8)
subtype3 40 18 0.4 - 141.3 (20.5)

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.897 (ANOVA)

Table S75.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 195 68.3 (8.3)
subtype1 93 68.1 (8.9)
subtype2 61 68.7 (7.8)
subtype3 41 68.1 (7.5)

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.353 (Fisher's exact test)

Table S76.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 56 146
subtype1 28 69
subtype2 20 43
subtype3 8 34

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.4 (ANOVA)

Table S77.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 39 16.2 (32.3)
subtype1 18 13.3 (29.7)
subtype2 17 22.9 (37.7)
subtype3 4 0.0 (0.0)

Figure S70.  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 = 0.401 (Chi-square test)

Table S78.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 4 1 1 196
subtype1 0 1 1 95
subtype2 2 0 0 61
subtype3 2 0 0 40

Figure S71.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.329 (Chi-square test)

Table S79.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 44 126 20 12
subtype1 17 66 9 5
subtype2 17 36 8 2
subtype3 10 24 3 5

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.061 (Chi-square test)

Table S80.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 126 53 19 4
subtype1 65 24 6 2
subtype2 39 12 11 1
subtype3 22 17 2 1

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.205 (Chi-square test)

Table S81.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 185 3 10
subtype1 86 3 7
subtype2 61 0 1
subtype3 38 0 2

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.336 (Chi-square test)

Table S82.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 104 51 43 3
subtype1 52 26 16 3
subtype2 31 13 18 0
subtype3 21 12 9 0

Figure S75.  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 = 0.0193 (Fisher's exact test)

Table S83.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 6 196
subtype1 0 97
subtype2 4 59
subtype3 2 40

Figure S76.  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 = 0.571 (Fisher's exact test)

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

nPatients NO YES
ALL 23 179
subtype1 13 84
subtype2 5 58
subtype3 5 37

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

Methods & Data
Input

  • Cluster data file = LUSC.mergedcluster.txt

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

  • Number of patients = 278

  • 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