Correlation between aggregated molecular cancer subtypes and selected clinical features
Esophageal Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
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/C1M907D1
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 127 patients, 25 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 '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 4 subtypes that correlate to 'AGE' and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE' 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 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'RACE'.

  • 3 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, 25 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 0.528
(1.00)
0.393
(1.00)
0.638
(1.00)
0.666
(1.00)
0.413
(1.00)
0.237
(1.00)
0.455
(1.00)
0.16
(1.00)
AGE Kruskal-Wallis (anova) 0.0216
(0.952)
0.000234
(0.0129)
0.000228
(0.0128)
0.0017
(0.0817)
9.65e-06
(0.000695)
0.000158
(0.00918)
2.45e-05
(0.00154)
0.000356
(0.0192)
NEOPLASM DISEASESTAGE Fisher's exact test 0.324
(1.00)
0.00015
(0.00885)
0.022
(0.952)
0.0228
(0.959)
5e-05
(0.0031)
0.0015
(0.0735)
0.00066
(0.0343)
5e-05
(0.0031)
PATHOLOGY T STAGE Fisher's exact test 0.0676
(1.00)
0.00036
(0.0192)
0.0168
(0.757)
0.00688
(0.323)
0.00142
(0.071)
0.00017
(0.00969)
0.00092
(0.0469)
6e-05
(0.0036)
PATHOLOGY N STAGE Fisher's exact test 0.466
(1.00)
0.263
(1.00)
0.0681
(1.00)
0.251
(1.00)
0.129
(1.00)
0.184
(1.00)
0.135
(1.00)
0.0261
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.174
(1.00)
0.157
(1.00)
0.0875
(1.00)
0.127
(1.00)
0.0507
(1.00)
0.0805
(1.00)
0.0354
(1.00)
0.0131
(0.604)
GENDER Fisher's exact test 0.0979
(1.00)
0.846
(1.00)
0.0892
(1.00)
0.138
(1.00)
0.587
(1.00)
0.55
(1.00)
0.3
(1.00)
0.322
(1.00)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.042
(1.00)
0.36
(1.00)
0.602
(1.00)
0.675
(1.00)
0.318
(1.00)
0.698
(1.00)
0.132
(1.00)
0.251
(1.00)
RACE Fisher's exact test 1e-05
(0.00071)
1e-05
(0.00071)
1e-05
(0.00071)
1e-05
(0.00071)
1e-05
(0.00071)
1e-05
(0.00071)
1e-05
(0.00071)
1e-05
(0.00071)
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 37 36 32
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.528 (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 98 31 0.0 - 122.1 (4.2)
subtype1 35 12 0.1 - 122.1 (7.0)
subtype2 32 6 0.0 - 44.8 (0.2)
subtype3 31 13 0.1 - 60.4 (5.1)

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 = 0.0216 (Kruskal-Wallis (anova)), Q value = 0.95

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

nPatients Mean (Std.Dev)
ALL 105 62.6 (12.6)
subtype1 37 67.1 (11.8)
subtype2 36 59.1 (11.3)
subtype3 32 61.5 (13.6)

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 = 0.324 (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
ALL 7 4 4 1 30 16 11 8 6 3 1
subtype1 4 2 1 0 10 4 2 4 1 2 0
subtype2 0 1 2 1 16 7 5 2 2 0 0
subtype3 3 1 1 0 4 5 4 2 3 1 1

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 = 0.0676 (Fisher's exact test), Q value = 1

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

nPatients T0+T1 T2 T3+T4
ALL 19 26 50
subtype1 9 5 18
subtype2 3 15 18
subtype3 7 6 14

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 = 0.466 (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 50 33 8 3
subtype1 18 9 2 2
subtype2 21 13 2 0
subtype3 11 11 4 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 = 0.174 (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 M1A MX
ALL 75 1 15
subtype1 25 0 6
subtype2 32 0 3
subtype3 18 1 6

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 = 0.0979 (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 15 90
subtype1 9 28
subtype2 4 32
subtype3 2 30

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 = 0.042 (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 63 36.5 (19.6)
subtype1 22 43.8 (22.4)
subtype2 23 35.2 (18.4)
subtype3 18 29.3 (14.6)

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 = 0.00071

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

nPatients ASIAN WHITE
ALL 35 66
subtype1 5 29
subtype2 24 12
subtype3 6 25

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 55 24 48
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.393 (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 119 38 0.0 - 122.1 (5.4)
subtype1 55 19 0.3 - 122.1 (8.0)
subtype2 22 7 0.0 - 83.2 (4.8)
subtype3 42 12 0.1 - 53.9 (0.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.000234 (Kruskal-Wallis (anova)), Q value = 0.013

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

nPatients Mean (Std.Dev)
ALL 127 63.6 (12.7)
subtype1 55 67.5 (11.8)
subtype2 24 65.6 (15.2)
subtype3 48 58.2 (10.5)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

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

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 7 4 5 1 34 22 16 8 6 4 2 1
subtype1 7 2 2 0 3 11 7 1 3 3 2 1
subtype2 0 1 2 0 7 3 3 4 0 1 0 0
subtype3 0 1 1 1 24 8 6 3 3 0 0 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 = 0.00036 (Fisher's exact test), Q value = 0.019

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

nPatients T0+T1 T2 T3+T4
ALL 21 30 63
subtype1 17 6 22
subtype2 1 7 14
subtype3 3 17 27

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 = 0.263 (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 59 41 8 4
subtype1 19 20 3 3
subtype2 10 7 2 1
subtype3 30 14 3 0

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 = 0.157 (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 90 1 2 17
subtype1 30 1 2 9
subtype2 18 0 0 4
subtype3 42 0 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 = 0.846 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 18 109
subtype1 9 46
subtype2 3 21
subtype3 6 42

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.36 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 75 36.7 (21.2)
subtype1 32 40.5 (23.9)
subtype2 15 36.8 (19.5)
subtype3 28 32.3 (18.6)

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 = 0.00071

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 36 2 84
subtype1 0 0 51
subtype2 7 0 16
subtype3 29 2 17

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
Number of samples 13 23 6 24
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.638 (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 64 20 0.0 - 33.3 (3.2)
subtype1 12 5 0.1 - 33.3 (0.3)
subtype2 22 4 0.1 - 20.1 (0.1)
subtype3 6 1 0.0 - 20.1 (2.0)
subtype4 24 10 0.3 - 30.7 (6.8)

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 = 0.000228 (Kruskal-Wallis (anova)), Q value = 0.013

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

nPatients Mean (Std.Dev)
ALL 66 62.1 (13.2)
subtype1 13 58.2 (10.7)
subtype2 23 57.9 (11.6)
subtype3 6 50.5 (13.4)
subtype4 24 71.1 (10.7)

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 = 0.022 (Fisher's exact test), Q value = 0.95

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 4 3 2 1 21 10 6 6 5 1 1
subtype1 0 1 0 0 9 0 1 1 1 0 0
subtype2 0 0 2 1 9 4 4 2 1 0 0
subtype3 0 0 0 0 1 1 1 1 1 0 0
subtype4 4 2 0 0 2 5 0 2 2 1 1

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 = 0.0168 (Fisher's exact test), Q value = 0.76

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

nPatients T0+T1 T2 T3+T4
ALL 9 16 36
subtype1 1 3 9
subtype2 0 8 15
subtype3 0 1 4
subtype4 8 4 8

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 = 0.0681 (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 35 18 7 1
subtype1 11 1 1 0
subtype2 15 7 1 0
subtype3 1 3 1 0
subtype4 8 7 4 1

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 = 0.0875 (Fisher's exact test), Q value = 1

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

nPatients M0 M1A MX
ALL 49 1 7
subtype1 12 0 0
subtype2 21 0 2
subtype3 5 0 0
subtype4 11 1 5

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 = 0.0892 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 10 56
subtype1 2 11
subtype2 1 22
subtype3 0 6
subtype4 7 17

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

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.602 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 41 36.8 (20.8)
subtype1 6 49.1 (29.6)
subtype2 15 35.7 (16.4)
subtype3 5 37.2 (24.6)
subtype4 15 33.0 (20.0)

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 = 0.00071

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

nPatients ASIAN WHITE
ALL 27 36
subtype1 8 5
subtype2 17 6
subtype3 2 4
subtype4 0 21

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 4
Number of samples 12 20 26 8
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.666 (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 64 20 0.0 - 33.3 (3.2)
subtype1 11 4 0.0 - 33.3 (0.3)
subtype2 19 4 0.1 - 20.1 (0.2)
subtype3 26 10 0.3 - 30.7 (6.8)
subtype4 8 2 0.1 - 10.4 (0.1)

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 = 0.0017 (Kruskal-Wallis (anova)), Q value = 0.082

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

nPatients Mean (Std.Dev)
ALL 66 62.1 (13.2)
subtype1 12 58.8 (8.9)
subtype2 20 56.6 (11.8)
subtype3 26 69.0 (13.6)
subtype4 8 58.1 (12.1)

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 = 0.0228 (Fisher's exact test), Q value = 0.96

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 4 3 2 1 21 10 6 6 5 1 1
subtype1 0 1 0 0 4 1 3 2 1 0 0
subtype2 0 0 2 0 9 4 2 2 1 0 0
subtype3 4 2 0 0 2 5 0 2 3 1 1
subtype4 0 0 0 1 6 0 1 0 0 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 = 0.00688 (Fisher's exact test), Q value = 0.32

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

nPatients T0+T1 T2 T3+T4
ALL 9 16 36
subtype1 1 1 10
subtype2 0 7 13
subtype3 8 4 9
subtype4 0 4 4

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 = 0.251 (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 35 18 7 1
subtype1 6 5 1 0
subtype2 14 5 1 0
subtype3 8 7 5 1
subtype4 7 1 0 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 = 0.127 (Fisher's exact test), Q value = 1

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

nPatients M0 M1A MX
ALL 49 1 7
subtype1 11 0 0
subtype2 18 0 2
subtype3 12 1 5
subtype4 8 0 0

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 = 0.138 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 10 56
subtype1 2 10
subtype2 1 19
subtype3 7 19
subtype4 0 8

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

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.675 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 41 36.8 (20.8)
subtype1 8 44.3 (24.3)
subtype2 12 36.7 (17.8)
subtype3 17 34.2 (22.1)
subtype4 4 33.8 (20.6)

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 = 0.00071

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

nPatients ASIAN WHITE
ALL 27 36
subtype1 6 6
subtype2 14 6
subtype3 0 23
subtype4 7 1

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 60 54 10
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.413 (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 116 37 0.0 - 122.1 (5.8)
subtype1 60 21 0.3 - 122.1 (7.9)
subtype2 47 13 0.0 - 53.9 (0.4)
subtype3 9 3 0.3 - 83.2 (6.2)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 9.65e-06 (Kruskal-Wallis (anova)), Q value = 0.00069

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

nPatients Mean (Std.Dev)
ALL 124 63.8 (12.7)
subtype1 60 68.0 (11.9)
subtype2 54 57.8 (10.9)
subtype3 10 70.2 (14.1)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.0031

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 7 4 5 1 33 22 16 8 6 4 2 1
subtype1 7 2 2 0 3 12 7 2 3 4 2 1
subtype2 0 1 2 1 27 9 8 3 3 0 0 0
subtype3 0 1 1 0 3 1 1 3 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 = 0.00142 (Fisher's exact test), Q value = 0.071

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

nPatients T0+T1 T2 T3+T4
ALL 21 30 62
subtype1 17 8 24
subtype2 3 20 31
subtype3 1 2 7

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 = 0.129 (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 58 41 8 4
subtype1 19 21 5 4
subtype2 33 17 3 0
subtype3 6 3 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 = 0.0507 (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 89 1 2 17
subtype1 32 1 2 11
subtype2 49 0 0 4
subtype3 8 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 = 0.587 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 18 106
subtype1 11 49
subtype2 6 48
subtype3 1 9

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

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.318 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 73 37.2 (21.3)
subtype1 34 39.2 (24.1)
subtype2 32 32.8 (15.4)
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 = 0.00071

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 35 1 83
subtype1 0 0 55
subtype2 34 1 19
subtype3 1 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 44 20 35 25
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.237 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 116 37 0.0 - 122.1 (5.8)
subtype1 44 17 0.3 - 122.1 (7.3)
subtype2 20 6 1.0 - 83.2 (8.7)
subtype3 30 8 0.0 - 33.3 (0.3)
subtype4 22 6 0.1 - 53.9 (1.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000158 (Kruskal-Wallis (anova)), Q value = 0.0092

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

nPatients Mean (Std.Dev)
ALL 124 63.8 (12.7)
subtype1 44 66.3 (12.7)
subtype2 20 72.1 (10.5)
subtype3 35 58.4 (12.8)
subtype4 25 60.1 (9.9)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0015 (Fisher's exact test), Q value = 0.073

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 7 4 5 1 33 22 16 8 6 4 2 1
subtype1 6 2 2 0 1 9 5 1 2 3 1 1
subtype2 1 0 0 0 4 4 2 2 1 1 1 0
subtype3 0 1 2 1 12 7 7 3 2 0 0 0
subtype4 0 1 1 0 16 2 2 2 1 0 0 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 = 0.00017 (Fisher's exact test), Q value = 0.0097

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

nPatients T0+T1 T2 T3+T4
ALL 21 30 62
subtype1 16 8 12
subtype2 1 2 14
subtype3 2 10 23
subtype4 2 10 13

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 = 0.184 (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 58 41 8 4
subtype1 13 17 3 3
subtype2 8 5 2 1
subtype3 19 13 2 0
subtype4 18 6 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 = 0.0805 (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 89 1 2 17
subtype1 22 1 1 9
subtype2 13 0 1 3
subtype3 31 0 0 4
subtype4 23 0 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 = 0.55 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 18 106
subtype1 8 36
subtype2 4 16
subtype3 3 32
subtype4 3 22

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.698 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 73 37.2 (21.3)
subtype1 24 35.0 (18.5)
subtype2 12 47.4 (31.6)
subtype3 19 36.8 (20.1)
subtype4 18 33.9 (17.0)

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 = 0.00071

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 35 1 83
subtype1 0 0 40
subtype2 0 0 19
subtype3 18 1 16
subtype4 17 0 8

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
Number of samples 56 50 18
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.455 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 116 37 0.0 - 122.1 (5.8)
subtype1 56 21 0.3 - 122.1 (7.9)
subtype2 43 12 0.0 - 53.9 (0.5)
subtype3 17 4 0.1 - 83.2 (3.8)

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 = 2.45e-05 (Kruskal-Wallis (anova)), Q value = 0.0015

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

nPatients Mean (Std.Dev)
ALL 124 63.8 (12.7)
subtype1 56 67.9 (12.2)
subtype2 50 57.6 (10.8)
subtype3 18 68.1 (12.6)

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 = 0.00066 (Fisher's exact test), Q value = 0.034

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 33 22 16 8 6 4 2 1
subtype1 7 2 2 0 3 10 7 2 2 3 2 1
subtype2 0 1 2 1 25 8 7 3 3 0 0 0
subtype3 0 1 1 0 5 4 2 3 1 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 = 0.00092 (Fisher's exact test), Q value = 0.047

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

nPatients T0+T1 T2 T3+T4
ALL 21 30 62
subtype1 17 8 20
subtype2 3 17 30
subtype3 1 5 12

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 = 0.135 (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 58 41 8 4
subtype1 17 21 4 3
subtype2 31 15 3 0
subtype3 10 5 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 = 0.0354 (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 89 1 2 17
subtype1 28 1 2 11
subtype2 45 0 0 4
subtype3 16 0 0 2

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 = 0.3 (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 18 106
subtype1 11 45
subtype2 6 44
subtype3 1 17

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 = 0.132 (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 73 37.2 (21.3)
subtype1 30 38.3 (22.5)
subtype2 30 31.6 (14.9)
subtype3 13 47.7 (27.4)

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 = 0.00071

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 35 1 83
subtype1 0 0 51
subtype2 31 1 18
subtype3 4 0 14

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 48 15 61
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.16 (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 116 37 0.0 - 122.1 (5.8)
subtype1 48 20 0.3 - 122.1 (7.3)
subtype2 15 3 1.0 - 83.2 (13.2)
subtype3 53 14 0.0 - 53.9 (0.5)

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 = 0.000356 (Kruskal-Wallis (anova)), Q value = 0.019

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

nPatients Mean (Std.Dev)
ALL 124 63.8 (12.7)
subtype1 48 66.8 (12.5)
subtype2 15 71.1 (10.6)
subtype3 61 59.6 (12.0)

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 = 5e-05 (Fisher's exact test), Q value = 0.0031

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 33 22 16 8 6 4 2 1
subtype1 6 2 2 0 1 9 5 1 2 3 2 1
subtype2 1 0 0 0 4 3 2 2 1 1 0 0
subtype3 0 2 3 1 28 10 9 5 3 0 0 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 = 6e-05 (Fisher's exact test), Q value = 0.0036

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

nPatients T0+T1 T2 T3+T4
ALL 21 30 62
subtype1 16 8 13
subtype2 1 1 13
subtype3 4 21 36

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 = 0.0261 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 58 41 8 4
subtype1 13 18 3 3
subtype2 8 3 2 1
subtype3 37 20 3 0

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 = 0.0131 (Fisher's exact test), Q value = 0.6

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

nPatients M0 M1 M1A MX
ALL 89 1 2 17
subtype1 22 1 2 9
subtype2 12 0 0 3
subtype3 55 0 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 = 0.322 (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 18 106
subtype1 9 39
subtype2 3 12
subtype3 6 55

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 = 0.251 (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 73 37.2 (21.3)
subtype1 27 35.8 (20.0)
subtype2 8 53.2 (32.2)
subtype3 38 34.9 (18.4)

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 = 0.00071

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 35 1 83
subtype1 0 0 44
subtype2 0 0 14
subtype3 35 1 25

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 = 127

  • 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)