Correlation between aggregated molecular cancer subtypes and selected clinical features
Mesothelioma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1WW7GXN
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WW7GXN
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 11 clinical features across 87 patients, 14 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death'.

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

  • CNMF clustering analysis on RPPA data identified 6 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death'.

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL_TYPE'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death'.

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

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death' 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 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 14 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.00122
(0.0192) 		0.00809
(0.0989) 		0.0523
(0.288) 		5.63e-06
(0.000531) 		0.000417
(0.00918) 		9.66e-06
(0.000531) 		0.0349
(0.256) 		0.0298
(0.235) 		0.0243
(0.211) 		0.00396
(0.0544)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.545
(0.788) 		0.623
(0.836) 		0.974
(1.00) 		0.671
(0.839) 		0.375
(0.676) 		0.242
(0.591) 		0.374
(0.676) 		0.653
(0.836) 		0.136
(0.483) 		0.0916
(0.376)
PATHOLOGIC STAGE Fisher's exact test 0.691
(0.844) 		0.252
(0.591) 		0.964
(1.00) 		0.562
(0.803) 		0.282
(0.607) 		0.161
(0.537) 		0.992
(1.00) 		0.615
(0.836) 		0.0249
(0.211) 		0.495
(0.766)
PATHOLOGY T STAGE Fisher's exact test 0.456
(0.747) 		0.651
(0.836) 		0.688
(0.844) 		0.49
(0.766) 		0.788
(0.893) 		0.208
(0.591) 		0.94
(1.00) 		0.977
(1.00) 		0.335
(0.641) 		0.916
(1.00)
PATHOLOGY N STAGE Fisher's exact test 0.956
(1.00) 		0.533
(0.785) 		0.755
(0.883) 		0.0924
(0.376) 		0.0407
(0.28) 		0.0512
(0.288) 		0.207
(0.591) 		0.591
(0.822) 		0.0802
(0.367) 		0.515
(0.778)
PATHOLOGY M STAGE Fisher's exact test 0.462
(0.747) 		0.661
(0.836) 		0.413
(0.705) 		0.238
(0.591) 		0.628
(0.836) 		0.181
(0.554) 		0.784
(0.893) 		0.109
(0.419)
GENDER Fisher's exact test 0.252
(0.591) 		0.66
(0.836) 		0.0497
(0.288) 		0.316
(0.641) 		0.238
(0.591) 		0.178
(0.554) 		0.225
(0.591) 		0.0585
(0.307) 		0.413
(0.705) 		0.256
(0.591)
RADIATION THERAPY Fisher's exact test 0.281
(0.607) 		0.0697
(0.349) 		0.258
(0.591) 		0.469
(0.748) 		0.716
(0.866) 		0.64
(0.836) 		0.333
(0.641) 		0.776
(0.893) 		0.75
(0.883) 		0.746
(0.883)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.0459
(0.288) 		0.275
(0.607) 		0.0814
(0.367) 		0.0834
(0.367) 		0.333
(0.641) 		0.11
(0.419) 		0.865
(0.961) 		0.148
(0.507)
HISTOLOGICAL TYPE Fisher's exact test 0.394
(0.699) 		0.423
(0.705) 		0.17
(0.549) 		0.129
(0.474) 		0.00033
(0.00907) 		6e-05
(0.0022) 		0.0148
(0.163) 		0.221
(0.591) 		0.00075
(0.0137) 		0.0192
(0.192)
RESIDUAL TUMOR Fisher's exact test 0.321
(0.641) 		0.422
(0.705) 		0.535
(0.785) 		0.862
(0.961) 		0.295
(0.623) 		0.348
(0.649) 		0.246
(0.591) 		0.584
(0.822) 		0.516
(0.778) 		0.338
(0.641)
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 4
Number of samples 23 26 24 14
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.00122 (logrank test), Q value = 0.019

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

nPatients nDeath Duration Range (Median), Month
ALL 86 71 0.7 - 91.7 (16.9)
subtype1 23 17 1.6 - 91.7 (31.2)
subtype2 26 23 1.9 - 41.5 (14.6)
subtype3 23 20 0.7 - 29.0 (13.6)
subtype4 14 11 1.3 - 62.3 (11.6)

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 = 0.545 (Kruskal-Wallis (anova)), Q value = 0.79

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

nPatients Mean (Std.Dev)
ALL 87 63.0 (9.8)
subtype1 23 65.5 (8.1)
subtype2 26 62.7 (8.9)
subtype3 24 62.0 (10.2)
subtype4 14 61.1 (13.0)

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 'PATHOLOGIC_STAGE'

P value = 0.691 (Fisher's exact test), Q value = 0.84

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 45 16
subtype1 1 1 0 6 9 6
subtype2 3 0 0 4 17 2
subtype3 2 0 1 4 12 5
subtype4 1 1 0 2 7 3

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.456 (Fisher's exact test), Q value = 0.75

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

nPatients T1 T2 T3 T4
ALL 14 26 32 13
subtype1 3 9 5 5
subtype2 4 6 13 2
subtype3 3 9 8 4
subtype4 4 2 6 2

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 = 0.956 (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 44 10 29
subtype1 13 2 8
subtype2 14 3 9
subtype3 11 2 7
subtype4 6 3 5

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 = 0.462 (Fisher's exact test), Q value = 0.75

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

nPatients 0 1
ALL 57 3
subtype1 12 1
subtype2 18 0
subtype3 16 2
subtype4 11 0

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 = 0.252 (Fisher's exact test), Q value = 0.59

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

nPatients FEMALE MALE
ALL 16 71
subtype1 2 21
subtype2 5 21
subtype3 4 20
subtype4 5 9

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 'RADIATION_THERAPY'

P value = 0.281 (Fisher's exact test), Q value = 0.61

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

nPatients NO YES
ALL 57 25
subtype1 18 4
subtype2 16 9
subtype3 12 9
subtype4 11 3

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0459 (Kruskal-Wallis (anova)), Q value = 0.29

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

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
subtype1 3 96.7 (5.8)
subtype2 4 77.5 (18.9)
subtype3 6 91.7 (7.5)
subtype4 4 42.5 (49.2)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.394 (Fisher's exact test), Q value = 0.7

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 23 5 57 2
subtype1 6 2 14 1
subtype2 6 0 20 0
subtype3 8 3 13 0
subtype4 3 0 10 1

Figure S10.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.321 (Fisher's exact test), Q value = 0.64

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 17 3 15 11
subtype1 4 1 3 0
subtype2 3 2 5 4
subtype3 8 0 4 3
subtype4 2 0 3 4

Figure S11.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #2: 'METHLYATION CNMF'

Table S13.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 14 20 26 27
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00809 (logrank test), Q value = 0.099

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 86 71 0.7 - 91.7 (16.9)
subtype1 14 9 1.9 - 91.7 (26.9)
subtype2 19 17 0.7 - 41.5 (12.7)
subtype3 26 21 1.3 - 49.0 (19.3)
subtype4 27 24 1.6 - 80.8 (15.4)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.623 (Kruskal-Wallis (anova)), Q value = 0.84

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

nPatients Mean (Std.Dev)
ALL 87 63.0 (9.8)
subtype1 14 60.2 (8.7)
subtype2 20 64.0 (8.9)
subtype3 26 62.1 (11.5)
subtype4 27 64.5 (9.2)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.252 (Fisher's exact test), Q value = 0.59

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 45 16
subtype1 0 0 0 7 4 3
subtype2 2 0 1 3 11 3
subtype3 3 0 0 3 15 5
subtype4 2 2 0 3 15 5

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.651 (Fisher's exact test), Q value = 0.84

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 14 26 32 13
subtype1 2 7 2 3
subtype2 3 6 7 3
subtype3 3 7 12 4
subtype4 6 6 11 3

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.533 (Fisher's exact test), Q value = 0.79

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 44 10 29
subtype1 10 0 4
subtype2 9 1 8
subtype3 13 4 8
subtype4 12 5 9

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.661 (Fisher's exact test), Q value = 0.84

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 57 3
subtype1 10 0
subtype2 11 0
subtype3 17 2
subtype4 19 1

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.66 (Fisher's exact test), Q value = 0.84

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 71
subtype1 2 12
subtype2 2 18
subtype3 6 20
subtype4 6 21

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.0697 (Fisher's exact test), Q value = 0.35

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 25
subtype1 6 7
subtype2 16 2
subtype3 16 9
subtype4 19 7

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.275 (Kruskal-Wallis (anova)), Q value = 0.61

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
subtype1 4 95.0 (5.8)
subtype2 3 33.3 (57.7)
subtype3 5 80.0 (17.3)
subtype4 5 88.0 (8.4)

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.423 (Fisher's exact test), Q value = 0.71

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 23 5 57 2
subtype1 1 2 11 0
subtype2 7 1 12 0
subtype3 7 1 18 0
subtype4 8 1 16 2

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.422 (Fisher's exact test), Q value = 0.71

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 17 3 15 11
subtype1 2 1 4 2
subtype2 1 1 3 0
subtype3 5 1 4 5
subtype4 9 0 4 4

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S25.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 14 7 13 8 7 14
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0523 (logrank test), Q value = 0.29

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

nPatients nDeath Duration Range (Median), Month
ALL 63 56 1.3 - 80.8 (17.6)
subtype1 14 13 4.4 - 80.8 (22.7)
subtype2 7 6 1.6 - 41.5 (11.9)
subtype3 13 12 4.7 - 32.3 (19.4)
subtype4 8 8 1.3 - 42.8 (14.2)
subtype5 7 7 3.5 - 23.6 (6.5)
subtype6 14 10 1.9 - 55.4 (17.5)

Figure S23.  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 = 0.974 (Kruskal-Wallis (anova)), Q value = 1

Table S27.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 63 62.5 (10.0)
subtype1 14 62.8 (8.4)
subtype2 7 63.0 (12.7)
subtype3 13 63.1 (10.5)
subtype4 8 61.4 (14.9)
subtype5 7 60.6 (10.8)
subtype6 14 62.9 (7.1)

Figure S24.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.964 (Fisher's exact test), Q value = 1

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE III STAGE IV
ALL 5 1 9 35 13
subtype1 1 0 3 6 4
subtype2 0 1 0 5 1
subtype3 1 0 2 9 1
subtype4 1 0 1 4 2
subtype5 1 0 1 4 1
subtype6 1 0 2 7 4

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.688 (Fisher's exact test), Q value = 0.84

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 8 17 24 12
subtype1 2 5 2 4
subtype2 2 1 3 0
subtype3 1 3 8 1
subtype4 1 2 3 2
subtype5 1 3 2 1
subtype6 1 3 6 4

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.755 (Fisher's exact test), Q value = 0.88

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 28 8 25
subtype1 6 3 5
subtype2 3 0 4
subtype3 4 3 6
subtype4 3 0 4
subtype5 3 1 2
subtype6 9 1 4

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0497 (Fisher's exact test), Q value = 0.29

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 13 50
subtype1 0 14
subtype2 1 6
subtype3 4 9
subtype4 3 5
subtype5 0 7
subtype6 5 9

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.258 (Fisher's exact test), Q value = 0.59

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 45 16
subtype1 11 2
subtype2 6 1
subtype3 10 3
subtype4 3 5
subtype5 6 1
subtype6 9 4

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.17 (Fisher's exact test), Q value = 0.55

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 17 4 40 2
subtype1 4 2 8 0
subtype2 0 0 5 2
subtype3 3 1 9 0
subtype4 1 0 7 0
subtype5 3 1 3 0
subtype6 6 0 8 0

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.535 (Fisher's exact test), Q value = 0.79

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 12 2 11 6
subtype1 5 0 1 1
subtype2 1 1 1 0
subtype3 1 0 2 3
subtype4 1 0 3 0
subtype5 2 0 1 1
subtype6 2 1 3 1

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S35.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 9 11 10 12 9 12
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 5.63e-06 (logrank test), Q value = 0.00053

Table S36.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 63 56 1.3 - 80.8 (17.6)
subtype1 9 9 4.4 - 80.8 (23.9)
subtype2 11 11 1.3 - 42.8 (13.6)
subtype3 10 9 4.7 - 29.4 (17.7)
subtype4 12 10 4.7 - 32.3 (21.7)
subtype5 9 5 5.5 - 55.4 (19.8)
subtype6 12 12 1.6 - 18.8 (6.3)

Figure S32.  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 = 0.671 (Kruskal-Wallis (anova)), Q value = 0.84

Table S37.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 63 62.5 (10.0)
subtype1 9 63.8 (8.4)
subtype2 11 62.3 (12.8)
subtype3 10 65.4 (11.3)
subtype4 12 62.3 (8.8)
subtype5 9 62.7 (9.5)
subtype6 12 59.2 (9.4)

Figure S33.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.562 (Fisher's exact test), Q value = 0.8

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE III STAGE IV
ALL 5 1 9 35 13
subtype1 1 0 2 4 2
subtype2 1 0 1 7 2
subtype3 1 0 3 6 0
subtype4 0 0 0 8 4
subtype5 1 0 0 5 3
subtype6 1 1 3 5 2

Figure S34.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.49 (Fisher's exact test), Q value = 0.77

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 8 17 24 12
subtype1 2 2 2 2
subtype2 1 4 3 2
subtype3 2 4 4 0
subtype4 0 3 5 4
subtype5 1 0 5 3
subtype6 2 4 5 1

Figure S35.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0924 (Fisher's exact test), Q value = 0.38

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 28 8 25
subtype1 4 1 4
subtype2 3 1 6
subtype3 4 2 4
subtype4 2 4 6
subtype5 6 0 3
subtype6 9 0 2

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.316 (Fisher's exact test), Q value = 0.64

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 13 50
subtype1 0 9
subtype2 4 7
subtype3 3 7
subtype4 1 11
subtype5 2 7
subtype6 3 9

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.469 (Fisher's exact test), Q value = 0.75

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 45 16
subtype1 6 3
subtype2 6 5
subtype3 7 3
subtype4 10 1
subtype5 6 2
subtype6 10 2

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.129 (Fisher's exact test), Q value = 0.47

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 17 4 40 2
subtype1 2 2 5 0
subtype2 1 1 9 0
subtype3 1 0 9 0
subtype4 5 1 6 0
subtype5 5 0 4 0
subtype6 3 0 7 2

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.862 (Fisher's exact test), Q value = 0.96

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 12 2 11 6
subtype1 2 0 2 1
subtype2 2 0 4 0
subtype3 2 0 2 1
subtype4 2 0 1 2
subtype5 1 0 0 1
subtype6 3 2 2 1

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S45.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 26 22 17 21
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000417 (logrank test), Q value = 0.0092

Table S46.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 70 0.7 - 91.7 (16.4)
subtype1 26 21 3.7 - 91.7 (24.6)
subtype2 21 15 1.3 - 62.3 (19.8)
subtype3 17 16 0.7 - 42.8 (5.2)
subtype4 21 18 2.5 - 42.7 (16.4)

Figure S41.  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 = 0.375 (Kruskal-Wallis (anova)), Q value = 0.68

Table S47.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 86 62.9 (9.8)
subtype1 26 63.0 (8.9)
subtype2 22 59.8 (12.0)
subtype3 17 64.6 (9.2)
subtype4 21 64.6 (8.5)

Figure S42.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.282 (Fisher's exact test), Q value = 0.61

Table S48.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 45 15
subtype1 2 1 0 7 10 6
subtype2 0 0 0 3 16 3
subtype3 1 1 1 3 7 4
subtype4 4 0 0 3 12 2

Figure S43.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.788 (Fisher's exact test), Q value = 0.89

Table S49.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 14 26 32 12
subtype1 4 10 6 5
subtype2 2 6 10 3
subtype3 4 5 6 2
subtype4 4 5 10 2

Figure S44.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0407 (Fisher's exact test), Q value = 0.28

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 43 10 29
subtype1 15 1 10
subtype2 5 5 11
subtype3 11 1 3
subtype4 12 3 5

Figure S45.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.413 (Fisher's exact test), Q value = 0.71

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 56 3
subtype1 16 2
subtype2 16 0
subtype3 11 1
subtype4 13 0

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.238 (Fisher's exact test), Q value = 0.59

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 70
subtype1 4 22
subtype2 7 15
subtype3 1 16
subtype4 4 17

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.716 (Fisher's exact test), Q value = 0.87

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 56 25
subtype1 17 8
subtype2 13 7
subtype3 13 3
subtype4 13 7

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0814 (Kruskal-Wallis (anova)), Q value = 0.37

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
subtype1 5 96.0 (5.5)
subtype2 5 64.0 (40.4)
subtype3 2 85.0 (7.1)
subtype4 5 70.0 (39.4)

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00033 (Fisher's exact test), Q value = 0.0091

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 22 5 57 2
subtype1 3 3 20 0
subtype2 2 0 20 0
subtype3 9 1 5 2
subtype4 8 1 12 0

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.295 (Fisher's exact test), Q value = 0.62

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 16 3 15 11
subtype1 7 2 4 2
subtype2 3 1 5 2
subtype3 4 0 4 1
subtype4 2 0 2 6

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S57.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 22 12 22 13 17
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 9.66e-06 (logrank test), Q value = 0.00053

Table S58.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 70 0.7 - 91.7 (16.4)
subtype1 22 16 3.7 - 91.7 (27.8)
subtype2 12 12 1.9 - 23.6 (12.7)
subtype3 21 15 1.3 - 62.3 (19.8)
subtype4 13 13 0.7 - 42.8 (4.7)
subtype5 17 14 2.5 - 42.7 (17.3)

Figure S52.  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 = 0.242 (Kruskal-Wallis (anova)), Q value = 0.59

Table S59.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 86 62.9 (9.8)
subtype1 22 62.6 (9.5)
subtype2 12 61.4 (9.2)
subtype3 22 59.5 (11.8)
subtype4 13 66.6 (8.6)
subtype5 17 65.9 (7.4)

Figure S53.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.161 (Fisher's exact test), Q value = 0.54

Table S60.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 45 15
subtype1 1 1 0 6 8 6
subtype2 2 0 0 4 6 0
subtype3 0 0 0 3 14 5
subtype4 1 1 1 2 6 2
subtype5 3 0 0 1 11 2

Figure S54.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.208 (Fisher's exact test), Q value = 0.59

Table S61.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 14 26 32 12
subtype1 4 8 5 5
subtype2 2 6 3 0
subtype3 1 6 10 4
subtype4 4 4 4 1
subtype5 3 2 10 2

Figure S55.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0512 (Fisher's exact test), Q value = 0.29

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 43 10 29
subtype1 12 1 9
subtype2 8 1 3
subtype3 5 4 12
subtype4 9 1 1
subtype5 9 3 4

Figure S56.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.238 (Fisher's exact test), Q value = 0.59

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 56 3
subtype1 12 2
subtype2 10 0
subtype3 15 0
subtype4 8 1
subtype5 11 0

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.178 (Fisher's exact test), Q value = 0.55

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 70
subtype1 3 19
subtype2 2 10
subtype3 7 15
subtype4 0 13
subtype5 4 13

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.64 (Fisher's exact test), Q value = 0.84

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 56 25
subtype1 13 8
subtype2 7 5
subtype3 15 5
subtype4 10 2
subtype5 11 5

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0834 (Kruskal-Wallis (anova)), Q value = 0.37

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
subtype1 6 95.0 (5.5)
subtype3 4 57.5 (43.5)
subtype4 2 85.0 (7.1)
subtype5 5 70.0 (39.4)

Figure S60.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 6e-05 (Fisher's exact test), Q value = 0.0022

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 22 5 57 2
subtype1 2 2 18 0
subtype2 1 1 10 0
subtype3 3 0 19 0
subtype4 8 1 2 2
subtype5 8 1 8 0

Figure S61.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.348 (Fisher's exact test), Q value = 0.65

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 16 3 15 11
subtype1 7 1 3 1
subtype2 1 1 4 2
subtype3 2 1 4 2
subtype4 4 0 2 1
subtype5 2 0 2 5

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S69.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 36 29 22
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0349 (logrank test), Q value = 0.26

Table S70.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 86 71 0.7 - 91.7 (16.9)
subtype1 36 30 0.7 - 56.4 (17.0)
subtype2 28 21 1.3 - 91.7 (19.9)
subtype3 22 20 1.9 - 49.0 (10.9)

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.374 (Kruskal-Wallis (anova)), Q value = 0.68

Table S71.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 87 63.0 (9.8)
subtype1 36 64.0 (7.7)
subtype2 29 60.3 (12.0)
subtype3 22 64.9 (9.2)

Figure S64.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.992 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 45 16
subtype1 4 1 1 7 17 6
subtype2 2 0 0 5 16 6
subtype3 1 1 0 4 12 4

Figure S65.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.94 (Fisher's exact test), Q value = 1

Table S73.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 14 26 32 13
subtype1 7 10 13 5
subtype2 5 9 9 5
subtype3 2 7 10 3

Figure S66.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.207 (Fisher's exact test), Q value = 0.59

Table S74.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 44 10 29
subtype1 19 1 13
subtype2 12 6 10
subtype3 13 3 6

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.628 (Fisher's exact test), Q value = 0.84

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 57 3
subtype1 21 1
subtype2 19 2
subtype3 17 0

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.225 (Fisher's exact test), Q value = 0.59

Table S76.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 71
subtype1 4 32
subtype2 8 21
subtype3 4 18

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.333 (Fisher's exact test), Q value = 0.64

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 25
subtype1 23 12
subtype2 16 9
subtype3 18 4

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.333 (Kruskal-Wallis (anova)), Q value = 0.64

Table S78.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
subtype1 6 90.0 (6.3)
subtype2 7 85.7 (17.2)
subtype3 4 45.0 (52.6)

Figure S71.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0148 (Fisher's exact test), Q value = 0.16

Table S79.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 23 5 57 2
subtype1 14 4 17 1
subtype2 3 1 25 0
subtype3 6 0 15 1

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.246 (Fisher's exact test), Q value = 0.59

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 17 3 15 11
subtype1 5 0 7 5
subtype2 10 2 3 4
subtype3 2 1 5 2

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S81.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 29 25 6 13 14
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0298 (logrank test), Q value = 0.23

Table S82.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 86 71 0.7 - 91.7 (16.9)
subtype1 29 24 0.7 - 56.4 (16.4)
subtype2 24 20 2.5 - 62.3 (15.2)
subtype3 6 6 1.9 - 42.8 (9.7)
subtype4 13 11 1.3 - 49.0 (14.7)
subtype5 14 10 3.7 - 91.7 (28.7)

Figure S74.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.653 (Kruskal-Wallis (anova)), Q value = 0.84

Table S83.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 87 63.0 (9.8)
subtype1 29 64.7 (7.6)
subtype2 25 61.8 (10.0)
subtype3 6 67.2 (10.4)
subtype4 13 59.5 (12.7)
subtype5 14 63.0 (10.0)

Figure S75.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.615 (Fisher's exact test), Q value = 0.84

Table S84.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 45 16
subtype1 4 0 1 5 13 6
subtype2 0 0 0 5 16 4
subtype3 0 1 0 1 3 1
subtype4 1 1 0 3 7 1
subtype5 2 0 0 2 6 4

Figure S76.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.977 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 14 26 32 13
subtype1 6 8 9 5
subtype2 3 9 8 4
subtype3 1 1 4 0
subtype4 2 4 6 1
subtype5 2 4 5 3

Figure S77.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.591 (Fisher's exact test), Q value = 0.82

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 44 10 29
subtype1 15 2 10
subtype2 9 6 9
subtype3 5 0 1
subtype4 7 1 5
subtype5 8 1 4

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.181 (Fisher's exact test), Q value = 0.55

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 57 3
subtype1 17 1
subtype2 19 0
subtype3 5 0
subtype4 8 0
subtype5 8 2

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0585 (Fisher's exact test), Q value = 0.31

Table S88.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 16 71
subtype1 1 28
subtype2 7 18
subtype3 2 4
subtype4 3 10
subtype5 3 11

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.776 (Fisher's exact test), Q value = 0.89

Table S89.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 25
subtype1 20 8
subtype2 17 6
subtype3 5 1
subtype4 8 5
subtype5 7 5

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.11 (Kruskal-Wallis (anova)), Q value = 0.42

Table S90.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 17 77.6 (31.5)
subtype1 6 88.3 (7.5)
subtype2 6 53.3 (44.6)
subtype5 5 94.0 (5.5)

Figure S82.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.221 (Fisher's exact test), Q value = 0.59

Table S91.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 23 5 57 2
subtype1 12 3 13 1
subtype2 6 1 18 0
subtype3 1 0 4 1
subtype4 2 1 10 0
subtype5 2 0 12 0

Figure S83.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.584 (Fisher's exact test), Q value = 0.82

Table S92.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 17 3 15 11
subtype1 4 0 6 2
subtype2 6 1 5 4
subtype3 1 1 1 0
subtype4 2 0 1 4
subtype5 4 1 2 1

Figure S84.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S93.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 37 27 21
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0243 (logrank test), Q value = 0.21

Table S94.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 84 69 0.7 - 91.7 (17.5)
subtype1 36 27 2.5 - 91.7 (19.9)
subtype2 27 22 0.7 - 42.7 (18.5)
subtype3 21 20 1.6 - 42.8 (10.9)

Figure S85.  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 = 0.136 (Kruskal-Wallis (anova)), Q value = 0.48

Table S95.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 85 63.0 (9.9)
subtype1 37 60.5 (9.7)
subtype2 27 63.6 (10.0)
subtype3 21 66.6 (9.1)

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0249 (Fisher's exact test), Q value = 0.21

Table S96.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 43 16
subtype1 2 0 0 12 17 6
subtype2 5 0 1 2 13 6
subtype3 0 2 0 2 13 4

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.335 (Fisher's exact test), Q value = 0.64

Table S97.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 14 26 30 13
subtype1 4 16 12 5
subtype2 7 4 10 5
subtype3 3 6 8 3

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0802 (Fisher's exact test), Q value = 0.37

Table S98.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 42 10 29
subtype1 23 5 8
subtype2 12 1 11
subtype3 7 4 10

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.784 (Fisher's exact test), Q value = 0.89

Table S99.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 55 3
subtype1 24 2
subtype2 16 1
subtype3 15 0

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.413 (Fisher's exact test), Q value = 0.71

Table S100.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 15 70
subtype1 9 28
subtype2 3 24
subtype3 3 18

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.75 (Fisher's exact test), Q value = 0.88

Table S101.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 55 25
subtype1 22 12
subtype2 18 8
subtype3 15 5

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.865 (Kruskal-Wallis (anova)), Q value = 0.96

Table S102.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 16 82.5 (25.2)
subtype1 8 76.2 (35.0)
subtype2 5 90.0 (7.1)
subtype3 3 86.7 (5.8)

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00075 (Fisher's exact test), Q value = 0.014

Table S103.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 21 5 57 2
subtype1 3 1 33 0
subtype2 11 3 13 0
subtype3 7 1 11 2

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.516 (Fisher's exact test), Q value = 0.78

Table S104.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 16 3 14 11
subtype1 8 3 4 5
subtype2 4 0 7 3
subtype3 4 0 3 3

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S105.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 16 17 21 31
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00396 (logrank test), Q value = 0.054

Table S106.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 84 69 0.7 - 91.7 (17.5)
subtype1 16 11 3.7 - 91.7 (29.7)
subtype2 16 13 2.5 - 62.3 (17.1)
subtype3 21 18 1.9 - 49.0 (13.3)
subtype4 31 27 0.7 - 42.7 (15.0)

Figure S96.  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 = 0.0916 (Kruskal-Wallis (anova)), Q value = 0.38

Table S107.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 85 63.0 (9.9)
subtype1 16 62.1 (9.8)
subtype2 17 58.8 (8.4)
subtype3 21 66.4 (9.5)
subtype4 31 63.4 (10.4)

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.495 (Fisher's exact test), Q value = 0.77

Table S108.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE III STAGE IV
ALL 7 2 1 16 43 16
subtype1 2 0 0 5 5 4
subtype2 0 0 0 3 12 2
subtype3 1 2 0 4 10 4
subtype4 4 0 1 4 16 6

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.916 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 14 26 30 13
subtype1 2 7 3 3
subtype2 2 5 8 2
subtype3 4 6 7 3
subtype4 6 8 12 5

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.515 (Fisher's exact test), Q value = 0.78

Table S110.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2+N3
ALL 42 10 29
subtype1 11 1 4
subtype2 6 4 6
subtype3 11 3 7
subtype4 14 2 12

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.109 (Fisher's exact test), Q value = 0.42

Table S111.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 55 3
subtype1 7 2
subtype2 12 0
subtype3 17 0
subtype4 19 1

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.256 (Fisher's exact test), Q value = 0.59

Table S112.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 15 70
subtype1 2 14
subtype2 6 11
subtype3 3 18
subtype4 4 27

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.746 (Fisher's exact test), Q value = 0.88

Table S113.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 55 25
subtype1 10 5
subtype2 12 3
subtype3 13 8
subtype4 20 9

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 0.51

Table S114.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 16 82.5 (25.2)
subtype1 4 95.0 (5.8)
subtype2 4 57.5 (43.5)
subtype3 3 93.3 (5.8)
subtype4 5 86.0 (5.5)

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0192 (Fisher's exact test), Q value = 0.19

Table S115.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients BIPHASIC MESOTHELIOMA DIFFUSE MALIGNANT MESOTHELIOMA - NOS EPITHELIOID MESOTHELIOMA SARCOMATOID MESOTHELIOMA
ALL 21 5 57 2
subtype1 1 1 14 0
subtype2 3 0 14 0
subtype3 3 1 16 1
subtype4 14 3 13 1

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.338 (Fisher's exact test), Q value = 0.64

Table S116.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 16 3 14 11
subtype1 4 1 1 1
subtype2 4 1 1 3
subtype3 5 1 4 2
subtype4 3 0 8 5

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/MESO-TP/20139528/MESO-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/MESO-TP/19775385/MESO-TP.merged_data.txt

  • Number of patients = 87

  • Number of clustering approaches = 10

  • Number of selected clinical features = 11

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

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)
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