Correlation between aggregated molecular cancer subtypes and selected clinical features
Esophageal Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16M35QR
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 8 different clustering approaches and 9 clinical features across 151 patients, 30 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE' and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'AGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.M.STAGE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 30 significant findings detected.

Clinical
Features
Statistical
Tests
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.0264
(1.00)
0.168
(1.00)
0.472
(1.00)
0.431
(1.00)
0.34
(1.00)
0.00374
(0.168)
0.02
(0.781)
0.0772
(1.00)
AGE Kruskal-Wallis (anova) 0.000396
(0.021)
0.000742
(0.0363)
0.000293
(0.0168)
0.000843
(0.0405)
8.2e-05
(0.00492)
4e-05
(0.00244)
0.00065
(0.0325)
1.13e-05
(0.000724)
NEOPLASM DISEASESTAGE Fisher's exact test 0.0484
(1.00)
0.00029
(0.0168)
0.0294
(1.00)
0.00031
(0.0174)
2e-05
(0.00126)
0.0004
(0.021)
0.0004
(0.021)
3e-05
(0.00186)
PATHOLOGY T STAGE Fisher's exact test 0.011
(0.461)
0.00033
(0.0181)
0.0056
(0.246)
0.0123
(0.504)
0.00015
(0.00885)
0.00133
(0.0612)
0.00116
(0.0545)
0.00038
(0.0205)
PATHOLOGY N STAGE Fisher's exact test 0.158
(1.00)
0.212
(1.00)
0.554
(1.00)
0.0797
(1.00)
0.112
(1.00)
0.112
(1.00)
0.12
(1.00)
0.0126
(0.504)
PATHOLOGY M STAGE Fisher's exact test 0.101
(1.00)
0.465
(1.00)
0.15
(1.00)
0.00565
(0.246)
0.139
(1.00)
0.186
(1.00)
0.113
(1.00)
0.0923
(1.00)
GENDER Fisher's exact test 0.0963
(1.00)
1
(1.00)
0.361
(1.00)
0.234
(1.00)
1
(1.00)
0.638
(1.00)
0.282
(1.00)
0.945
(1.00)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.463
(1.00)
0.187
(1.00)
0.781
(1.00)
0.676
(1.00)
0.115
(1.00)
0.636
(1.00)
0.117
(1.00)
0.134
(1.00)
RACE Fisher's exact test 1e-05
(0.00072)
1e-05
(0.00072)
1e-05
(0.00072)
1e-05
(0.00072)
1e-05
(0.00072)
1e-05
(0.00072)
1e-05
(0.00072)
1e-05
(0.00072)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 59 35 56
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0264 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 142 54 0.0 - 122.1 (7.5)
subtype1 59 28 0.1 - 122.1 (8.9)
subtype2 34 9 0.1 - 83.2 (9.0)
subtype3 49 17 0.0 - 52.6 (0.5)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.000396 (Kruskal-Wallis (anova)), Q value = 0.021

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

nPatients Mean (Std.Dev)
ALL 150 63.2 (12.5)
subtype1 59 64.1 (12.8)
subtype2 35 69.0 (12.0)
subtype3 56 58.7 (10.8)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0484 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 4 6 1 35 26 23 11 8 6 3 2
subtype1 4 2 2 0 5 11 12 3 3 2 2 1
subtype2 4 1 1 0 9 7 2 2 1 3 0 1
subtype3 0 1 3 1 21 8 9 6 4 1 1 0

Figure S3.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.011 (Fisher's exact test), Q value = 0.46

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 26 33 74 4
subtype1 15 7 29 0
subtype2 7 7 16 1
subtype3 4 19 29 3

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.158 (Fisher's exact test), Q value = 1

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 64 54 11 6
subtype1 18 26 5 2
subtype2 17 9 1 3
subtype3 29 19 5 1

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.101 (Fisher's exact test), Q value = 1

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 111 2 3 17
subtype1 36 1 2 10
subtype2 25 0 1 4
subtype3 50 1 0 3

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0963 (Fisher's exact test), Q value = 1

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 21 129
subtype1 6 53
subtype2 9 26
subtype3 6 50

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.463 (Kruskal-Wallis (anova)), Q value = 1

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 81 35.2 (21.3)
subtype1 29 35.5 (22.4)
subtype2 17 40.5 (23.6)
subtype3 35 32.4 (19.2)

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 38 2 93
subtype1 4 0 40
subtype2 3 1 30
subtype3 31 1 23

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S11.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 68 28 55
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.168 (logrank test), Q value = 1

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 143 55 0.0 - 122.1 (7.6)
subtype1 68 29 0.3 - 122.1 (8.9)
subtype2 26 8 0.0 - 83.2 (7.9)
subtype3 49 18 0.1 - 68.0 (2.6)

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000742 (Kruskal-Wallis (anova)), Q value = 0.036

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 151 63.3 (12.4)
subtype1 68 66.1 (12.1)
subtype2 28 65.6 (14.3)
subtype3 55 58.5 (10.4)

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00029 (Fisher's exact test), Q value = 0.017

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 4 6 1 35 26 24 11 8 6 3 2
subtype1 8 2 2 0 4 13 13 2 4 4 2 1
subtype2 0 1 2 0 7 5 3 5 0 1 0 1
subtype3 0 1 2 1 24 8 8 4 4 1 1 0

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.00033 (Fisher's exact test), Q value = 0.018

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 26 33 75 4
subtype1 20 7 31 0
subtype2 2 7 15 2
subtype3 4 19 29 2

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.212 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 64 55 11 6
subtype1 21 29 4 4
subtype2 11 10 2 1
subtype3 32 16 5 1

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.465 (Fisher's exact test), Q value = 1

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 112 2 3 17
subtype1 43 1 2 9
subtype2 21 0 1 4
subtype3 48 1 0 4

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 1 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 21 130
subtype1 9 59
subtype2 4 24
subtype3 8 47

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.187 (Kruskal-Wallis (anova)), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 81 35.2 (21.3)
subtype1 32 40.5 (23.9)
subtype2 16 35.4 (19.8)
subtype3 33 30.1 (18.4)

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 38 2 93
subtype1 1 0 53
subtype2 7 0 19
subtype3 30 2 21

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RACE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S21.  Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 43 8 18 29 10
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.472 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 101 34 0.0 - 122.1 (4.9)
subtype1 43 16 0.3 - 122.1 (7.1)
subtype2 8 2 0.0 - 54.0 (7.5)
subtype3 15 5 0.1 - 44.8 (0.5)
subtype4 26 9 0.1 - 68.0 (0.4)
subtype5 9 2 0.1 - 23.4 (2.6)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.000293 (Kruskal-Wallis (anova)), Q value = 0.017

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

nPatients Mean (Std.Dev)
ALL 108 62.5 (12.5)
subtype1 43 67.8 (12.6)
subtype2 8 65.4 (13.3)
subtype3 18 57.5 (11.1)
subtype4 29 60.1 (11.0)
subtype5 10 53.3 (7.1)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0294 (Fisher's exact test), Q value = 1

Table S24.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 7 4 4 1 30 17 11 8 7 3 2
subtype1 7 2 1 0 3 7 2 2 2 3 1
subtype2 0 1 0 0 2 2 0 1 1 0 0
subtype3 0 1 1 0 7 2 4 0 3 0 0
subtype4 0 0 2 1 13 5 4 2 1 0 1
subtype5 0 0 0 0 5 1 1 3 0 0 0

Figure S21.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0056 (Fisher's exact test), Q value = 0.25

Table S25.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 20 26 49 3
subtype1 15 6 13 0
subtype2 1 3 3 0
subtype3 2 4 12 0
subtype4 2 10 16 1
subtype5 0 3 5 2

Figure S22.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.554 (Fisher's exact test), Q value = 1

Table S26.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 51 34 9 3
subtype1 14 13 4 3
subtype2 3 3 1 0
subtype3 9 6 3 0
subtype4 18 10 1 0
subtype5 7 2 0 0

Figure S23.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.15 (Fisher's exact test), Q value = 1

Table S27.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 77 1 1 15
subtype1 20 0 1 10
subtype2 7 0 0 0
subtype3 16 0 0 1
subtype4 25 1 0 3
subtype5 9 0 0 1

Figure S24.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.361 (Fisher's exact test), Q value = 1

Table S28.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 92
subtype1 9 34
subtype2 0 8
subtype3 3 15
subtype4 2 27
subtype5 2 8

Figure S25.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.781 (Kruskal-Wallis (anova)), Q value = 1

Table S29.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 65 35.7 (19.8)
subtype1 25 35.5 (19.9)
subtype2 7 43.2 (20.7)
subtype3 10 35.7 (25.7)
subtype4 20 33.0 (17.1)
subtype5 3 37.5 (22.5)

Figure S26.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S30.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN WHITE
ALL 36 68
subtype1 0 39
subtype2 1 7
subtype3 12 6
subtype4 18 11
subtype5 5 5

Figure S27.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S31.  Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 36 60 12
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.431 (logrank test), Q value = 1

Table S32.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 101 34 0.0 - 122.1 (4.9)
subtype1 36 14 0.3 - 122.1 (7.8)
subtype2 53 17 0.0 - 68.0 (0.5)
subtype3 12 3 0.4 - 54.0 (4.5)

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000843 (Kruskal-Wallis (anova)), Q value = 0.04

Table S33.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 108 62.5 (12.5)
subtype1 36 67.9 (11.8)
subtype2 60 58.8 (10.9)
subtype3 12 65.0 (15.8)

Figure S29.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00031 (Fisher's exact test), Q value = 0.017

Table S34.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 7 4 4 1 30 17 11 8 7 3 2
subtype1 5 2 1 0 4 7 1 3 1 3 1
subtype2 0 2 3 1 26 9 9 5 4 0 1
subtype3 2 0 0 0 0 1 1 0 2 0 0

Figure S30.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0123 (Fisher's exact test), Q value = 0.5

Table S35.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 20 26 49 3
subtype1 11 6 12 1
subtype2 5 19 34 2
subtype3 4 1 3 0

Figure S31.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0797 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 51 34 9 3
subtype1 12 11 3 3
subtype2 36 20 4 0
subtype3 3 3 2 0

Figure S32.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00565 (Fisher's exact test), Q value = 0.25

Table S37.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 77 1 1 15
subtype1 19 0 1 8
subtype2 54 1 0 4
subtype3 4 0 0 3

Figure S33.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.234 (Fisher's exact test), Q value = 1

Table S38.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 92
subtype1 8 28
subtype2 6 54
subtype3 2 10

Figure S34.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.676 (Kruskal-Wallis (anova)), Q value = 1

Table S39.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 65 35.7 (19.8)
subtype1 18 35.7 (22.4)
subtype2 37 34.6 (19.1)
subtype3 10 39.8 (18.9)

Figure S35.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S40.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN WHITE
ALL 36 68
subtype1 0 33
subtype2 36 24
subtype3 0 11

Figure S36.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S41.  Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 76 63 11
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.34 (logrank test), Q value = 1

Table S42.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 142 55 0.0 - 122.1 (7.6)
subtype1 76 33 0.3 - 122.1 (8.9)
subtype2 56 20 0.0 - 68.0 (2.3)
subtype3 10 2 0.1 - 20.8 (6.6)

Figure S37.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 8.2e-05 (Kruskal-Wallis (anova)), Q value = 0.0049

Table S43.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 150 63.4 (12.4)
subtype1 76 67.0 (12.2)
subtype2 63 58.4 (10.7)
subtype3 11 66.3 (13.8)

Figure S38.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.0013

Table S44.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 4 6 1 34 26 24 11 8 6 3 2
subtype1 8 2 2 0 5 15 13 2 4 5 2 2
subtype2 0 1 3 1 27 9 10 5 4 1 1 0
subtype3 0 1 1 0 2 2 1 4 0 0 0 0

Figure S39.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.00015 (Fisher's exact test), Q value = 0.0088

Table S45.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 26 33 74 4
subtype1 21 10 33 0
subtype2 4 21 35 2
subtype3 1 2 6 2

Figure S40.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.112 (Fisher's exact test), Q value = 1

Table S46.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 63 55 11 6
subtype1 22 31 6 5
subtype2 36 19 5 1
subtype3 5 5 0 0

Figure S41.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.139 (Fisher's exact test), Q value = 1

Table S47.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 111 2 3 17
subtype1 46 1 3 11
subtype2 56 1 0 4
subtype3 9 0 0 2

Figure S42.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 1 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 21 129
subtype1 11 65
subtype2 9 54
subtype3 1 10

Figure S43.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.115 (Kruskal-Wallis (anova)), Q value = 1

Table S49.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 80 35.5 (21.3)
subtype1 34 39.2 (24.1)
subtype2 39 30.0 (15.7)
subtype3 7 48.1 (27.4)

Figure S44.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S50.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 37 2 93
subtype1 1 0 59
subtype2 34 2 25
subtype3 2 0 9

Figure S45.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S51.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 56 24 46 24
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00374 (logrank test), Q value = 0.17

Table S52.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 142 55 0.0 - 122.1 (7.6)
subtype1 56 28 0.3 - 122.1 (8.1)
subtype2 24 6 3.0 - 83.2 (14.1)
subtype3 41 16 0.0 - 33.3 (1.0)
subtype4 21 5 0.1 - 68.0 (4.1)

Figure S46.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 4e-05 (Kruskal-Wallis (anova)), Q value = 0.0024

Table S53.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 150 63.4 (12.4)
subtype1 56 65.2 (12.2)
subtype2 24 71.8 (11.7)
subtype3 46 58.3 (11.9)
subtype4 24 60.5 (9.0)

Figure S47.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 4e-04 (Fisher's exact test), Q value = 0.021

Table S54.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 4 6 1 34 26 24 11 8 6 3 2
subtype1 7 2 2 0 1 10 11 1 3 3 1 2
subtype2 1 0 0 0 4 6 3 3 1 2 1 0
subtype3 0 1 2 1 16 8 8 5 3 1 0 0
subtype4 0 1 2 0 13 2 2 2 1 0 1 0

Figure S48.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.00133 (Fisher's exact test), Q value = 0.061

Table S55.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 26 33 74 4
subtype1 18 8 20 0
subtype2 3 3 15 1
subtype3 2 13 27 3
subtype4 3 9 12 0

Figure S49.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.112 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 63 55 11 6
subtype1 14 25 4 3
subtype2 8 9 2 2
subtype3 25 14 4 1
subtype4 16 7 1 0

Figure S50.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.186 (Fisher's exact test), Q value = 1

Table S57.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 111 2 3 17
subtype1 31 1 2 9
subtype2 18 0 1 3
subtype3 41 0 0 4
subtype4 21 1 0 1

Figure S51.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.638 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 21 129
subtype1 7 49
subtype2 5 19
subtype3 5 41
subtype4 4 20

Figure S52.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.636 (Kruskal-Wallis (anova)), Q value = 1

Table S59.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 80 35.5 (21.3)
subtype1 24 34.7 (18.9)
subtype2 11 46.9 (32.1)
subtype3 28 34.6 (19.8)
subtype4 17 30.7 (17.4)

Figure S53.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S60.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 37 2 93
subtype1 1 0 42
subtype2 0 0 20
subtype3 22 2 21
subtype4 14 0 10

Figure S54.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S61.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 60 47 32 7
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.02 (logrank test), Q value = 0.78

Table S62.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 138 55 0.0 - 122.1 (7.6)
subtype1 60 28 0.3 - 122.1 (8.7)
subtype2 41 17 0.0 - 44.8 (0.8)
subtype3 30 7 0.1 - 83.2 (13.5)
subtype4 7 3 0.8 - 68.0 (12.0)

Figure S55.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.00065 (Kruskal-Wallis (anova)), Q value = 0.033

Table S63.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 146 63.3 (12.5)
subtype1 60 66.3 (12.9)
subtype2 47 57.4 (10.6)
subtype3 32 66.8 (12.4)
subtype4 7 62.0 (9.2)

Figure S56.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 4e-04 (Fisher's exact test), Q value = 0.021

Table S64.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 7 4 5 1 34 25 23 11 8 6 3 2
subtype1 7 2 2 0 2 11 11 2 3 2 2 2
subtype2 0 1 2 1 20 8 7 1 4 1 1 0
subtype3 0 1 1 0 11 6 4 6 1 2 0 0
subtype4 0 0 0 0 1 0 1 2 0 1 0 0

Figure S57.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00116 (Fisher's exact test), Q value = 0.055

Table S65.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 25 32 72 4
subtype1 18 8 24 0
subtype2 4 16 26 0
subtype3 3 7 19 3
subtype4 0 1 3 1

Figure S58.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.12 (Fisher's exact test), Q value = 1

Table S66.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 60 54 11 6
subtype1 16 27 5 2
subtype2 25 15 4 1
subtype3 17 11 1 2
subtype4 2 1 1 1

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.113 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 108 2 3 16
subtype1 34 1 3 10
subtype2 41 1 0 3
subtype3 29 0 0 2
subtype4 4 0 0 1

Figure S60.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.282 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 19 127
subtype1 10 50
subtype2 4 43
subtype3 3 29
subtype4 2 5

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.117 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 78 35.6 (21.6)
subtype1 28 35.0 (18.8)
subtype2 29 28.6 (16.1)
subtype3 18 46.5 (28.0)
subtype4 3 43.3 (30.6)

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S70.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 36 1 91
subtype1 1 0 44
subtype2 28 1 16
subtype3 6 0 25
subtype4 1 0 6

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S71.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 57 20 69
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0772 (logrank test), Q value = 1

Table S72.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 138 55 0.0 - 122.1 (7.6)
subtype1 57 29 0.3 - 122.1 (8.7)
subtype2 20 5 3.0 - 83.2 (14.4)
subtype3 61 21 0.0 - 68.0 (2.6)

Figure S64.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 1.13e-05 (Kruskal-Wallis (anova)), Q value = 0.00072

Table S73.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 146 63.3 (12.5)
subtype1 57 65.0 (12.6)
subtype2 20 73.0 (9.1)
subtype3 69 59.2 (11.6)

Figure S65.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0019

Table S74.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 7 4 5 1 34 25 23 11 8 6 3 2
subtype1 6 2 2 0 1 11 12 1 3 3 1 2
subtype2 1 0 0 0 4 4 2 2 1 2 1 0
subtype3 0 2 3 1 29 10 9 8 4 1 1 0

Figure S66.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00038 (Fisher's exact test), Q value = 0.021

Table S75.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 25 32 72 4
subtype1 18 8 21 0
subtype2 2 2 14 0
subtype3 5 22 37 4

Figure S67.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0126 (Fisher's exact test), Q value = 0.5

Table S76.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 60 54 11 6
subtype1 13 27 4 3
subtype2 8 6 2 2
subtype3 39 21 5 1

Figure S68.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0923 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 108 2 3 16
subtype1 32 1 2 9
subtype2 15 0 1 2
subtype3 61 1 0 5

Figure S69.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.945 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 19 127
subtype1 7 50
subtype2 3 17
subtype3 9 60

Figure S70.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.134 (Kruskal-Wallis (anova)), Q value = 1

Table S79.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 78 35.6 (21.6)
subtype1 25 34.0 (18.9)
subtype2 9 53.7 (31.7)
subtype3 44 32.8 (19.2)

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00072

Table S80.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 36 1 91
subtype1 1 0 41
subtype2 0 0 18
subtype3 35 1 32

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

Methods & Data
Input
  • Cluster data file = ESCA-TP.mergedcluster.txt

  • Clinical data file = ESCA-TP.merged_data.txt

  • Number of patients = 151

  • Number of clustering approaches = 8

  • Number of selected clinical features = 9

  • 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

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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] 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)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)