Correlation between aggregated molecular cancer subtypes and selected clinical features
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 10 different clustering approaches and 8 clinical features across 124 patients, 29 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 'YEARS_TO_BIRTH' and 'HISTOLOGICAL_TYPE'.

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH', and 'HISTOLOGICAL_TYPE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'HISTOLOGICAL_TYPE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • 2 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • 6 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 29 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		TUMOR
TISSUE
SITE 		GENDER RADIATION
THERAPY 		HISTOLOGICAL
TYPE 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
Copy Number Ratio CNMF subtypes 0.114
(0.227) 		0.00975
(0.0349) 		0.0605
(0.151) 		0.788
(0.9) 		0.0911
(0.203) 		5e-05
(0.000364) 		0.668
(0.81) 		0.158
(0.286)
METHLYATION CNMF 0.01
(0.0349) 		0.00205
(0.00963) 		0.111
(0.227) 		0.668
(0.81) 		0.29
(0.429) 		1e-05
(8.89e-05) 		0.187
(0.326) 		0.255
(0.418)
RPPA CNMF subtypes 0.434
(0.569) 		4.97e-05
(0.000364) 		0.58
(0.74) 		0.323
(0.461) 		0.427
(0.569) 		1e-05
(8.89e-05) 		0.263
(0.418) 		0.583
(0.74)
RPPA cHierClus subtypes 0.0913
(0.203) 		0.00733
(0.0309) 		0.933
(0.974) 		0.748
(0.88) 		0.0456
(0.126) 		1e-05
(8.89e-05) 		0.779
(0.9) 		0.266
(0.418)
RNAseq CNMF subtypes 0.105
(0.227) 		0.00984
(0.0349) 		0.938
(0.974) 		0.303
(0.441) 		0.0144
(0.0461) 		1e-05
(8.89e-05) 		0.91
(0.973) 		0.127
(0.247)
RNAseq cHierClus subtypes 0.267
(0.418) 		0.0033
(0.0147) 		0.973
(0.998) 		0.818
(0.916) 		0.00163
(0.00815) 		1e-05
(8.89e-05) 		0.414
(0.569) 		0.275
(0.419)
MIRSEQ CNMF 0.0792
(0.192) 		0.0113
(0.0376) 		0.278
(0.419) 		1
(1.00) 		8.67e-05
(0.000554) 		1e-05
(8.89e-05) 		1
(1.00) 		0.0522
(0.135)
MIRSEQ CHIERARCHICAL 0.0189
(0.055) 		0.00796
(0.0318) 		0.387
(0.543) 		0.163
(0.29) 		0.00062
(0.00331) 		1e-05
(8.89e-05) 		0.825
(0.916) 		0.139
(0.265)
MIRseq Mature CNMF subtypes 0.0158
(0.0485) 		0.0866
(0.203) 		0.697
(0.832) 		0.911
(0.973) 		9e-05
(0.000554) 		1e-05
(8.89e-05) 		0.626
(0.783) 		0.108
(0.227)
MIRseq Mature cHierClus subtypes 0.147
(0.274) 		0.0193
(0.055) 		0.421
(0.569) 		0.0509
(0.135) 		0.00029
(0.00166) 		1e-05
(8.89e-05) 		0.912
(0.973) 		0.209
(0.355)
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 65 31 27
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.114 (logrank test), Q value = 0.23

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

nPatients nDeath Duration Range (Median), Month
ALL 122 9 0.5 - 150.0 (40.6)
subtype1 65 2 0.5 - 150.0 (37.6)
subtype2 31 5 1.6 - 123.7 (49.8)
subtype3 26 2 4.1 - 120.4 (45.8)

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 'YEARS_TO_BIRTH'

P value = 0.00975 (Kruskal-Wallis (anova)), Q value = 0.035

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

nPatients Mean (Std.Dev)
ALL 122 58.3 (13.0)
subtype1 65 58.4 (11.7)
subtype2 31 53.0 (15.1)
subtype3 26 64.1 (11.4)

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.0605 (Fisher's exact test), Q value = 0.15

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

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 96
subtype1 9 56
subtype2 10 21
subtype3 8 19

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.788 (Fisher's exact test), Q value = 0.9

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 59 64
subtype1 29 36
subtype2 16 15
subtype3 14 13

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0911 (Fisher's exact test), Q value = 0.2

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

nPatients NO YES
ALL 81 42
subtype1 48 17
subtype2 16 15
subtype3 17 10

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00036

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

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 17 38 15 30 12 11
subtype1 10 24 13 11 2 5
subtype2 0 4 1 15 7 4
subtype3 7 10 1 4 3 2

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.668 (Fisher's exact test), Q value = 0.81

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 13 6 102
subtype1 6 4 54
subtype2 4 0 26
subtype3 3 2 22

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.158 (Fisher's exact test), Q value = 0.29

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 100
subtype1 2 54
subtype2 4 23
subtype3 3 23

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S10.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 39 36 25 24
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.01 (logrank test), Q value = 0.035

Table S11.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 123 9 0.5 - 150.0 (41.2)
subtype1 39 0 10.7 - 138.9 (50.8)
subtype2 36 4 1.6 - 120.4 (36.9)
subtype3 25 5 5.9 - 133.8 (48.9)
subtype4 23 0 0.5 - 150.0 (35.2)

Figure S9.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00205 (Kruskal-Wallis (anova)), Q value = 0.0096

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 123 58.2 (13.0)
subtype1 39 54.2 (12.5)
subtype2 36 64.9 (10.5)
subtype3 25 54.5 (14.2)
subtype4 23 58.3 (12.4)

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.111 (Fisher's exact test), Q value = 0.23

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 97
subtype1 5 34
subtype2 6 30
subtype3 9 16
subtype4 7 17

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.668 (Fisher's exact test), Q value = 0.81

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 60 64
subtype1 18 21
subtype2 15 21
subtype3 14 11
subtype4 13 11

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.29 (Fisher's exact test), Q value = 0.43

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 81 43
subtype1 24 15
subtype2 26 10
subtype3 13 12
subtype4 18 6

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 17 38 15 31 12 11
subtype1 0 7 14 15 3 0
subtype2 14 11 0 0 3 8
subtype3 2 2 1 11 6 3
subtype4 1 18 0 5 0 0

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.187 (Fisher's exact test), Q value = 0.33

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 13 6 103
subtype1 2 5 32
subtype2 5 1 30
subtype3 2 0 22
subtype4 4 0 19

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.255 (Fisher's exact test), Q value = 0.42

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 100
subtype1 1 34
subtype2 3 30
subtype3 4 18
subtype4 2 18

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S19.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 31 25 34
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.434 (logrank test), Q value = 0.57

Table S20.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 89 8 1.6 - 150.0 (34.6)
subtype1 30 4 1.6 - 150.0 (33.5)
subtype2 25 2 4.1 - 97.4 (38.3)
subtype3 34 2 2.4 - 138.9 (32.9)

Figure S17.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 4.97e-05 (Kruskal-Wallis (anova)), Q value = 0.00036

Table S21.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 89 58.1 (12.9)
subtype1 30 57.2 (14.3)
subtype2 25 67.0 (9.0)
subtype3 34 52.4 (10.5)

Figure S18.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.58 (Fisher's exact test), Q value = 0.74

Table S22.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 22 68
subtype1 6 25
subtype2 8 17
subtype3 8 26

Figure S19.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.323 (Fisher's exact test), Q value = 0.46

Table S23.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 43 47
subtype1 14 17
subtype2 15 10
subtype3 14 20

Figure S20.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.427 (Fisher's exact test), Q value = 0.57

Table S24.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 62 28
subtype1 19 12
subtype2 17 8
subtype3 26 8

Figure S21.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S25.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 11 28 12 24 5 10
subtype1 4 5 1 10 4 7
subtype2 7 14 0 1 1 2
subtype3 0 9 11 13 0 1

Figure S22.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.263 (Fisher's exact test), Q value = 0.42

Table S26.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 11 4 73
subtype1 6 0 24
subtype2 3 1 21
subtype3 2 3 28

Figure S23.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.583 (Fisher's exact test), Q value = 0.74

Table S27.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 72
subtype1 4 23
subtype2 2 20
subtype3 2 29

Figure S24.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S28.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 16 23 31 20
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0913 (logrank test), Q value = 0.2

Table S29.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 89 8 1.6 - 150.0 (34.6)
subtype1 16 1 13.9 - 107.4 (30.3)
subtype2 22 2 4.1 - 95.7 (35.3)
subtype3 31 1 2.4 - 150.0 (49.2)
subtype4 20 4 1.6 - 120.4 (27.7)

Figure S25.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00733 (Kruskal-Wallis (anova)), Q value = 0.031

Table S30.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 89 58.1 (12.9)
subtype1 16 57.9 (9.8)
subtype2 22 65.3 (11.1)
subtype3 31 53.1 (11.8)
subtype4 20 58.1 (15.4)

Figure S26.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.933 (Fisher's exact test), Q value = 0.97

Table S31.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 22 68
subtype1 5 11
subtype2 5 18
subtype3 7 24
subtype4 5 15

Figure S27.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.748 (Fisher's exact test), Q value = 0.88

Table S32.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 43 47
subtype1 7 9
subtype2 13 10
subtype3 13 18
subtype4 10 10

Figure S28.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0456 (Fisher's exact test), Q value = 0.13

Table S33.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 62 28
subtype1 13 3
subtype2 19 4
subtype3 21 10
subtype4 9 11

Figure S29.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S34.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 11 28 12 24 5 10
subtype1 0 9 2 5 0 0
subtype2 10 9 0 2 1 1
subtype3 0 9 10 12 0 0
subtype4 1 1 0 5 4 9

Figure S30.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.779 (Fisher's exact test), Q value = 0.9

Table S35.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 11 4 73
subtype1 2 1 13
subtype2 3 1 19
subtype3 2 2 26
subtype4 4 0 15

Figure S31.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.266 (Fisher's exact test), Q value = 0.42

Table S36.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 72
subtype1 1 13
subtype2 1 20
subtype3 2 26
subtype4 4 13

Figure S32.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S37.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 46 31 29 14
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.105 (logrank test), Q value = 0.23

Table S38.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 119 9 0.5 - 150.0 (40.1)
subtype1 46 5 1.6 - 138.5 (50.3)
subtype2 31 0 2.4 - 150.0 (38.3)
subtype3 28 2 0.5 - 120.4 (41.7)
subtype4 14 2 11.6 - 58.8 (29.3)

Figure S33.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00984 (Kruskal-Wallis (anova)), Q value = 0.035

Table S39.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 119 57.9 (13.1)
subtype1 46 53.1 (14.3)
subtype2 31 58.2 (10.7)
subtype3 28 63.8 (11.9)
subtype4 14 61.1 (10.6)

Figure S34.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.938 (Fisher's exact test), Q value = 0.97

Table S40.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 93
subtype1 12 34
subtype2 6 25
subtype3 6 23
subtype4 3 11

Figure S35.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.303 (Fisher's exact test), Q value = 0.44

Table S41.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 57 63
subtype1 20 26
subtype2 14 17
subtype3 13 16
subtype4 10 4

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0144 (Fisher's exact test), Q value = 0.046

Table S42.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 78 42
subtype1 24 22
subtype2 26 5
subtype3 21 8
subtype4 7 7

Figure S37.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S43.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 17 35 15 31 11 11
subtype1 1 3 12 23 7 0
subtype2 5 16 3 7 0 0
subtype3 10 16 0 0 1 2
subtype4 1 0 0 1 3 9

Figure S38.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.91 (Fisher's exact test), Q value = 0.97

Table S44.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 12 6 100
subtype1 3 3 39
subtype2 3 2 25
subtype3 4 1 24
subtype4 2 0 12

Figure S39.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.127 (Fisher's exact test), Q value = 0.25

Table S45.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 96
subtype1 4 35
subtype2 3 25
subtype3 0 25
subtype4 3 11

Figure S40.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S46.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 48 42 20 10
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.267 (logrank test), Q value = 0.42

Table S47.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 119 9 0.5 - 150.0 (40.1)
subtype1 48 5 1.6 - 138.5 (50.2)
subtype2 41 1 0.5 - 150.0 (36.6)
subtype3 20 1 12.7 - 112.1 (47.0)
subtype4 10 2 11.6 - 120.4 (27.7)

Figure S41.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0033 (Kruskal-Wallis (anova)), Q value = 0.015

Table S48.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 119 57.9 (13.1)
subtype1 48 53.0 (14.2)
subtype2 41 58.6 (10.5)
subtype3 20 65.4 (11.7)
subtype4 10 63.1 (11.0)

Figure S42.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.973 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 93
subtype1 12 36
subtype2 9 33
subtype3 4 16
subtype4 2 8

Figure S43.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.818 (Fisher's exact test), Q value = 0.92

Table S50.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 57 63
subtype1 21 27
subtype2 20 22
subtype3 10 10
subtype4 6 4

Figure S44.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00163 (Fisher's exact test), Q value = 0.0081

Table S51.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 78 42
subtype1 24 24
subtype2 35 7
subtype3 15 5
subtype4 4 6

Figure S45.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S52.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 17 35 15 31 11 11
subtype1 1 3 12 23 9 0
subtype2 5 28 3 6 0 0
subtype3 11 4 0 2 2 1
subtype4 0 0 0 0 0 10

Figure S46.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.414 (Fisher's exact test), Q value = 0.57

Table S53.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 12 6 100
subtype1 3 3 41
subtype2 4 3 34
subtype3 2 0 18
subtype4 3 0 7

Figure S47.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.275 (Fisher's exact test), Q value = 0.42

Table S54.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 96
subtype1 5 36
subtype2 3 34
subtype3 0 18
subtype4 2 8

Figure S48.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S55.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 59 61 1 2 1
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0792 (logrank test), Q value = 0.19

Table S56.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 119 7 0.5 - 150.0 (40.1)
subtype1 59 6 1.6 - 138.5 (48.7)
subtype2 60 1 0.5 - 150.0 (39.2)

Figure S49.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0113 (Wilcoxon-test), Q value = 0.038

Table S57.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 119 57.8 (13.0)
subtype1 59 54.5 (14.0)
subtype2 60 61.1 (11.2)

Figure S50.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.278 (Fisher's exact test), Q value = 0.42

Table S58.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 93
subtype1 16 43
subtype2 11 50

Figure S51.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'GENDER'

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

Table S59.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 58 62
subtype1 29 30
subtype2 29 32

Figure S52.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 8.67e-05 (Fisher's exact test), Q value = 0.00055

Table S60.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 80 40
subtype1 29 30
subtype2 51 10

Figure S53.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S61.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 16 37 14 31 11 11
subtype1 2 5 11 23 8 10
subtype2 14 32 3 8 3 1

Figure S54.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RACE'

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

Table S62.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 13 6 99
subtype1 6 3 49
subtype2 7 3 50

Figure S55.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0522 (Fisher's exact test), Q value = 0.13

Table S63.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 97
subtype1 8 45
subtype2 2 52

Figure S56.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S64.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5 6
Number of samples 27 18 29 7 16 27
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0189 (logrank test), Q value = 0.055

Table S65.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 123 9 0.5 - 150.0 (41.2)
subtype1 27 3 12.7 - 123.7 (52.9)
subtype2 17 1 0.5 - 150.0 (34.6)
subtype3 29 3 1.6 - 138.5 (37.6)
subtype4 7 2 11.6 - 58.8 (28.0)
subtype5 16 0 12.7 - 112.1 (43.8)
subtype6 27 0 2.4 - 138.9 (38.3)

Figure S57.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00796 (Kruskal-Wallis (anova)), Q value = 0.032

Table S66.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 123 58.2 (13.0)
subtype1 27 51.2 (14.0)
subtype2 17 58.4 (10.4)
subtype3 29 57.5 (13.8)
subtype4 7 62.6 (11.4)
subtype5 16 67.8 (8.3)
subtype6 27 58.9 (11.9)

Figure S58.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 0.387 (Fisher's exact test), Q value = 0.54

Table S67.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 97
subtype1 4 23
subtype2 3 15
subtype3 11 18
subtype4 1 6
subtype5 3 13
subtype6 5 22

Figure S59.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.163 (Fisher's exact test), Q value = 0.29

Table S68.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 60 64
subtype1 8 19
subtype2 8 10
subtype3 19 10
subtype4 4 3
subtype5 7 9
subtype6 14 13

Figure S60.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.00062 (Fisher's exact test), Q value = 0.0033

Table S69.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 81 43
subtype1 12 15
subtype2 14 4
subtype3 15 14
subtype4 3 4
subtype5 12 4
subtype6 25 2

Figure S61.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S70.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 17 38 15 31 12 11
subtype1 0 4 3 15 4 1
subtype2 4 13 0 1 0 0
subtype3 3 2 9 8 5 2
subtype4 0 0 0 0 0 7
subtype5 8 3 0 1 3 1
subtype6 2 16 3 6 0 0

Figure S62.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.825 (Fisher's exact test), Q value = 0.92

Table S71.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 13 6 103
subtype1 3 1 22
subtype2 2 1 15
subtype3 1 2 26
subtype4 2 0 5
subtype5 2 0 14
subtype6 3 2 21

Figure S63.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.139 (Fisher's exact test), Q value = 0.27

Table S72.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 100
subtype1 1 20
subtype2 1 14
subtype3 5 23
subtype4 2 5
subtype5 0 14
subtype6 1 24

Figure S64.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S73.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 18 59 47
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0158 (logrank test), Q value = 0.048

Table S74.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 123 9 0.5 - 150.0 (41.2)
subtype1 18 0 10.7 - 138.5 (44.6)
subtype2 58 1 0.5 - 150.0 (39.2)
subtype3 47 8 1.6 - 133.8 (48.9)

Figure S65.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0866 (Kruskal-Wallis (anova)), Q value = 0.2

Table S75.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 123 58.2 (13.0)
subtype1 18 54.2 (13.9)
subtype2 58 61.1 (11.4)
subtype3 47 56.1 (14.0)

Figure S66.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.697 (Fisher's exact test), Q value = 0.83

Table S76.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 97
subtype1 4 14
subtype2 11 48
subtype3 12 35

Figure S67.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.911 (Fisher's exact test), Q value = 0.97

Table S77.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 60 64
subtype1 8 10
subtype2 28 31
subtype3 24 23

Figure S68.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 9e-05 (Fisher's exact test), Q value = 0.00055

Table S78.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 81 43
subtype1 10 8
subtype2 50 9
subtype3 21 26

Figure S69.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S79.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 17 38 15 31 12 11
subtype1 0 1 10 5 2 0
subtype2 14 32 3 7 2 1
subtype3 3 5 2 19 8 10

Figure S70.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.626 (Fisher's exact test), Q value = 0.78

Table S80.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 13 6 103
subtype1 1 2 15
subtype2 7 3 48
subtype3 5 1 40

Figure S71.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.108 (Fisher's exact test), Q value = 0.23

Table S81.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 100
subtype1 1 14
subtype2 2 50
subtype3 7 36

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

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S82.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 29 33 34 28
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.147 (logrank test), Q value = 0.27

Table S83.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 123 9 0.5 - 150.0 (41.2)
subtype1 29 3 10.4 - 123.7 (52.8)
subtype2 32 1 0.5 - 112.1 (36.9)
subtype3 34 5 1.6 - 138.5 (32.6)
subtype4 28 0 2.4 - 150.0 (43.0)

Figure S73.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0193 (Kruskal-Wallis (anova)), Q value = 0.055

Table S84.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 123 58.2 (13.0)
subtype1 29 52.1 (14.0)
subtype2 32 63.3 (10.4)
subtype3 34 58.1 (13.7)
subtype4 28 58.6 (11.7)

Figure S74.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.421 (Fisher's exact test), Q value = 0.57

Table S85.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients ANTERIOR MEDIASTINUM THYMUS
ALL 27 97
subtype1 5 24
subtype2 6 27
subtype3 11 23
subtype4 5 23

Figure S75.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0509 (Fisher's exact test), Q value = 0.13

Table S86.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 60 64
subtype1 9 20
subtype2 14 19
subtype3 22 12
subtype4 15 13

Figure S76.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00029 (Fisher's exact test), Q value = 0.0017

Table S87.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients NO YES
ALL 81 43
subtype1 13 16
subtype2 27 6
subtype3 17 17
subtype4 24 4

Figure S77.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 8.9e-05

Table S88.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients THYMOMA; TYPE A THYMOMA; TYPE AB THYMOMA; TYPE B1 THYMOMA; TYPE B2 THYMOMA; TYPE B3 THYMOMA; TYPE C
ALL 17 38 15 31 12 11
subtype1 0 4 3 15 5 2
subtype2 12 16 0 1 3 1
subtype3 3 2 9 8 4 8
subtype4 2 16 3 7 0 0

Figure S78.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.912 (Fisher's exact test), Q value = 0.97

Table S89.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 13 6 103
subtype1 4 1 23
subtype2 4 1 28
subtype3 2 2 30
subtype4 3 2 22

Figure S79.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.209 (Fisher's exact test), Q value = 0.36

Table S90.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 100
subtype1 2 21
subtype2 1 28
subtype3 6 27
subtype4 1 24

Figure S80.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/THYM-TP/20140903/THYM-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/THYM-TP/19775615/THYM-TP.merged_data.txt

  • Number of patients = 124

  • Number of clustering approaches = 10

  • Number of selected clinical features = 8

  • 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.

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)
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