Correlation between aggregated molecular cancer subtypes and selected clinical features
Skin Cutaneous Melanoma (Metastatic)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1M32V17
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/C1M32V17
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 14 clinical features across 368 patients, 27 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 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGY_N_STAGE', and 'RADIATION_THERAPY'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time from Specimen Diagnosis to Death',  'Time to Death', and 'PATHOLOGY_T_STAGE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time from Specimen Diagnosis to Death'.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time from Specimen Diagnosis to Death',  'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE', and 'BRESLOW_THICKNESS'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time from Specimen Diagnosis to Death',  'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'BRESLOW_THICKNESS'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGY_T_STAGE'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'BRESLOW_THICKNESS'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'MELANOMA_ULCERATION', and 'BRESLOW_THICKNESS'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'BRESLOW_THICKNESS'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 27 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 from Specimen Diagnosis to Death logrank test 0.148
(0.473) 		0.00595
(0.064) 		0.0276
(0.196) 		0.427
(0.704) 		8.14e-05
(0.00228) 		1.12e-06
(7.18e-05) 		0.0922
(0.403) 		0.151
(0.473) 		0.893
(0.985) 		0.13
(0.473)
Time to Death logrank test 0.00349
(0.0407) 		0.00321
(0.0407) 		0.161
(0.473) 		0.512
(0.754) 		1.54e-06
(7.18e-05) 		3.66e-08
(5.12e-06) 		0.725
(0.89) 		0.214
(0.544) 		0.422
(0.704) 		0.109
(0.463)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.0192
(0.158) 		0.0584
(0.282) 		0.285
(0.604) 		0.548
(0.774) 		0.00303
(0.0407) 		0.000399
(0.00798) 		0.168
(0.481) 		0.879
(0.985) 		0.0336
(0.196) 		0.367
(0.674)
PATHOLOGIC STAGE Fisher's exact test 0.61
(0.808) 		0.162
(0.473) 		0.525
(0.756) 		0.238
(0.568) 		0.196
(0.527) 		0.0392
(0.211) 		0.157
(0.473) 		0.245
(0.568) 		0.00089
(0.0156) 		0.523
(0.756)
PATHOLOGY T STAGE Fisher's exact test 0.833
(0.964) 		0.0456
(0.237) 		0.439
(0.704) 		0.301
(0.611) 		0.0313
(0.196) 		0.00224
(0.0348) 		0.0301
(0.196) 		0.206
(0.544) 		0.00913
(0.0852) 		0.37
(0.674)
PATHOLOGY N STAGE Fisher's exact test 0.0319
(0.196) 		0.243
(0.568) 		0.15
(0.473) 		0.713
(0.89) 		0.587
(0.806) 		0.37
(0.674) 		0.26
(0.578) 		0.177
(0.494) 		0.324
(0.647) 		0.575
(0.796)
PATHOLOGY M STAGE Fisher's exact test 0.6
(0.808) 		0.728
(0.89) 		0.681
(0.859) 		0.462
(0.704) 		0.431
(0.704) 		0.883
(0.985) 		0.213
(0.544) 		0.333
(0.657) 		0.757
(0.914) 		0.372
(0.674)
MELANOMA ULCERATION Fisher's exact test 0.807
(0.957) 		0.0553
(0.277) 		0.258
(0.578) 		0.842
(0.966) 		0.379
(0.674) 		0.638
(0.812) 		0.121
(0.473) 		0.446
(0.704) 		0.0181
(0.158) 		0.38
(0.674)
MELANOMA PRIMARY KNOWN Fisher's exact test 0.923
(0.994) 		0.338
(0.658) 		0.594
(0.807) 		0.132
(0.473) 		0.455
(0.704) 		0.293
(0.604) 		0.45
(0.704) 		0.469
(0.704) 		0.833
(0.964) 		0.44
(0.704)
BRESLOW THICKNESS Kruskal-Wallis (anova) 0.633
(0.812) 		0.0888
(0.401) 		0.138
(0.473) 		0.882
(0.985) 		0.000224
(0.00523) 		6.9e-06
(0.000242) 		0.0765
(0.357) 		0.0335
(0.196) 		0.00821
(0.0821) 		0.0366
(0.205)
GENDER Fisher's exact test 0.283
(0.604) 		0.248
(0.568) 		0.911
(0.994) 		0.274
(0.6) 		0.39
(0.679) 		0.817
(0.962) 		0.467
(0.704) 		0.362
(0.674) 		0.404
(0.69) 		0.918
(0.994)
RADIATION THERAPY Fisher's exact test 0.0277
(0.196) 		0.473
(0.704) 		0.985
(1.00) 		0.122
(0.473) 		0.155
(0.473) 		0.393
(0.679) 		0.188
(0.515) 		0.563
(0.788) 		0.931
(0.995) 		0.145
(0.473)
RACE Fisher's exact test 0.622
(0.812) 		0.765
(0.916) 		0.529
(0.756) 		0.122
(0.473) 		0.16
(0.473) 		0.235
(0.568) 		0.612
(0.808) 		0.731
(0.89)
ETHNICITY Fisher's exact test 0.892
(0.985) 		1
(1.00) 		0.636
(0.812) 		0.223
(0.558) 		0.29
(0.604) 		1
(1.00) 		1
(1.00) 		1
(1.00)
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 101 139 126
'Copy Number Ratio CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.148 (logrank test), Q value = 0.47

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

nPatients nDeath Duration Range (Median), Month
ALL 245 123 0.0 - 346.5 (47.2)
subtype1 64 36 0.0 - 196.0 (45.8)
subtype2 95 41 0.2 - 239.1 (46.0)
subtype3 86 46 0.3 - 346.5 (58.9)

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

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.00349 (logrank test), Q value = 0.041

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

nPatients nDeath Duration Range (Median), Month
ALL 357 187 0.2 - 369.9 (53.2)
subtype1 99 61 0.2 - 368.8 (47.0)
subtype2 136 59 0.2 - 340.1 (55.2)
subtype3 122 67 0.2 - 369.9 (56.0)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0192 (Kruskal-Wallis (anova)), Q value = 0.16

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

nPatients Mean (Std.Dev)
ALL 358 56.3 (15.7)
subtype1 99 59.8 (17.5)
subtype2 137 55.9 (14.6)
subtype3 122 54.1 (15.1)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.61 (Fisher's exact test), Q value = 0.81

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

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 29 18 28 25 14 19 15 40 15 33 55 21
subtype1 1 2 6 6 9 4 5 4 5 12 4 11 19 5
subtype2 6 3 15 6 10 11 6 10 2 12 4 9 23 6
subtype3 6 2 8 6 9 10 3 5 8 16 7 13 13 10

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.833 (Fisher's exact test), Q value = 0.96

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

nPatients T0+T1 T2 T3 T4
ALL 64 73 78 70
subtype1 20 22 20 22
subtype2 19 26 33 26
subtype3 25 25 25 22

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0319 (Fisher's exact test), Q value = 0.2

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

nPatients N0 N1 N2 N3
ALL 177 65 39 45
subtype1 45 16 18 11
subtype2 70 18 11 22
subtype3 62 31 10 12

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.6 (Fisher's exact test), Q value = 0.81

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

nPatients 0 1
ALL 318 22
subtype1 90 5
subtype2 119 7
subtype3 109 10

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

'Copy Number Ratio CNMF subtypes' versus 'MELANOMA_ULCERATION'

P value = 0.807 (Fisher's exact test), Q value = 0.96

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

nPatients NO YES
ALL 134 90
subtype1 40 29
subtype2 52 31
subtype3 42 30

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

'Copy Number Ratio CNMF subtypes' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.923 (Fisher's exact test), Q value = 0.99

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

nPatients NO YES
ALL 47 319
subtype1 14 87
subtype2 17 122
subtype3 16 110

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

'Copy Number Ratio CNMF subtypes' versus 'BRESLOW_THICKNESS'

P value = 0.633 (Kruskal-Wallis (anova)), Q value = 0.81

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

nPatients Mean (Std.Dev)
ALL 266 3.5 (4.7)
subtype1 79 3.4 (3.3)
subtype2 98 3.9 (6.1)
subtype3 89 3.1 (3.9)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.283 (Fisher's exact test), Q value = 0.6

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

nPatients FEMALE MALE
ALL 137 229
subtype1 40 61
subtype2 45 94
subtype3 52 74

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0277 (Fisher's exact test), Q value = 0.2

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 317 47
subtype1 94 6
subtype2 114 24
subtype3 109 17

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.622 (Fisher's exact test), Q value = 0.81

Table S14.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 352
subtype1 1 0 97
subtype2 1 1 136
subtype3 3 0 119

Figure S13.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.892 (Fisher's exact test), Q value = 0.98

Table S15.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 351
subtype1 2 97
subtype2 2 136
subtype3 3 118

Figure S14.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S16.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 129 144 94
'METHLYATION CNMF' versus 'Time from Specimen Diagnosis to Death'

P value = 0.00595 (logrank test), Q value = 0.064

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 246 124 0.0 - 346.5 (47.1)
subtype1 88 48 3.1 - 312.1 (56.3)
subtype2 93 34 0.3 - 346.5 (44.3)
subtype3 65 42 0.0 - 209.8 (36.3)

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00321 (logrank test), Q value = 0.041

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 358 188 0.2 - 369.9 (52.3)
subtype1 124 74 0.7 - 357.4 (61.5)
subtype2 141 54 0.2 - 369.9 (49.5)
subtype3 93 60 0.2 - 368.8 (45.3)

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0584 (Kruskal-Wallis (anova)), Q value = 0.28

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 359 56.3 (15.7)
subtype1 124 58.6 (15.9)
subtype2 142 54.8 (15.4)
subtype3 93 55.5 (15.9)

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.162 (Fisher's exact test), Q value = 0.47

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 29 18 28 25 14 19 15 40 15 33 56 21
subtype1 7 3 9 9 11 6 6 9 8 13 8 9 12 8
subtype2 5 2 14 7 9 9 4 3 1 18 5 14 27 9
subtype3 1 2 6 2 8 10 4 7 6 9 2 10 17 4

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0456 (Fisher's exact test), Q value = 0.24

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 64 73 78 71
subtype1 23 24 26 26
subtype2 33 25 32 21
subtype3 8 24 20 24

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.243 (Fisher's exact test), Q value = 0.57

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 177 66 39 45
subtype1 70 19 15 11
subtype2 59 29 15 24
subtype3 48 18 9 10

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.728 (Fisher's exact test), Q value = 0.89

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 319 22
subtype1 114 8
subtype2 123 10
subtype3 82 4

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'MELANOMA_ULCERATION'

P value = 0.0553 (Fisher's exact test), Q value = 0.28

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 134 91
subtype1 54 35
subtype2 56 28
subtype3 24 28

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'METHLYATION CNMF' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.338 (Fisher's exact test), Q value = 0.66

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 47 320
subtype1 15 114
subtype2 23 121
subtype3 9 85

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'METHLYATION CNMF' versus 'BRESLOW_THICKNESS'

P value = 0.0888 (Kruskal-Wallis (anova)), Q value = 0.4

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 267 3.5 (4.7)
subtype1 95 3.3 (2.9)
subtype2 100 3.4 (6.0)
subtype3 72 3.8 (4.6)

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.248 (Fisher's exact test), Q value = 0.57

Table S27.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 137 230
subtype1 42 87
subtype2 54 90
subtype3 41 53

Figure S25.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.473 (Fisher's exact test), Q value = 0.7

Table S28.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 317 48
subtype1 114 15
subtype2 121 23
subtype3 82 10

Figure S26.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'RACE'

P value = 0.765 (Fisher's exact test), Q value = 0.92

Table S29.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 353
subtype1 2 0 125
subtype2 1 1 139
subtype3 2 0 89

Figure S27.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

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

Table S30.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 352
subtype1 2 122
subtype2 3 139
subtype3 2 91

Figure S28.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S31.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 50 51 42 26
'RPPA CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.0276 (logrank test), Q value = 0.2

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 121 71 0.0 - 346.5 (53.3)
subtype1 34 15 0.3 - 346.5 (61.4)
subtype2 38 23 4.5 - 312.1 (46.9)
subtype3 31 19 0.0 - 150.6 (54.5)
subtype4 18 14 4.4 - 150.1 (48.4)

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.161 (logrank test), Q value = 0.47

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 165 98 0.2 - 369.9 (56.4)
subtype1 48 26 9.9 - 369.9 (64.1)
subtype2 51 31 0.2 - 357.4 (59.6)
subtype3 40 25 2.6 - 176.6 (49.1)
subtype4 26 16 3.6 - 162.1 (48.8)

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.285 (Kruskal-Wallis (anova)), Q value = 0.6

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

nPatients Mean (Std.Dev)
ALL 166 55.3 (15.8)
subtype1 49 51.7 (13.1)
subtype2 51 56.0 (15.9)
subtype3 40 58.0 (17.6)
subtype4 26 56.5 (17.2)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.525 (Fisher's exact test), Q value = 0.76

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 8 3 15 8 17 7 5 6 4 22 5 15 23 9
subtype1 2 1 8 3 7 1 2 1 1 3 3 5 7 2
subtype2 1 2 5 1 6 2 2 2 3 7 0 2 6 5
subtype3 4 0 1 3 3 4 1 2 0 7 1 4 5 2
subtype4 1 0 1 1 1 0 0 1 0 5 1 4 5 0

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.439 (Fisher's exact test), Q value = 0.7

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 36 37 28 31
subtype1 10 13 10 9
subtype2 7 11 6 13
subtype3 13 6 7 7
subtype4 6 7 5 2

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.15 (Fisher's exact test), Q value = 0.47

Table S37.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 87 25 26 18
subtype1 27 7 6 6
subtype2 32 6 4 5
subtype3 21 6 9 3
subtype4 7 6 7 4

Figure S34.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.681 (Fisher's exact test), Q value = 0.86

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 149 10
subtype1 45 2
subtype2 42 5
subtype3 38 2
subtype4 24 1

Figure S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'MELANOMA_ULCERATION'

P value = 0.258 (Fisher's exact test), Q value = 0.58

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 65 37
subtype1 23 7
subtype2 19 15
subtype3 12 10
subtype4 11 5

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'RPPA CNMF subtypes' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.594 (Fisher's exact test), Q value = 0.81

Table S40.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 25 144
subtype1 6 44
subtype2 7 44
subtype3 9 33
subtype4 3 23

Figure S37.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'RPPA CNMF subtypes' versus 'BRESLOW_THICKNESS'

P value = 0.138 (Kruskal-Wallis (anova)), Q value = 0.47

Table S41.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 119 3.4 (5.3)
subtype1 39 3.4 (7.9)
subtype2 36 3.5 (3.1)
subtype3 25 3.6 (3.4)
subtype4 19 2.7 (3.9)

Figure S38.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.911 (Fisher's exact test), Q value = 0.99

Table S42.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 64 105
subtype1 18 32
subtype2 18 33
subtype3 17 25
subtype4 11 15

Figure S39.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.985 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 150 18
subtype1 44 6
subtype2 45 5
subtype3 38 4
subtype4 23 3

Figure S40.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S44.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 27 58 35 49
'RPPA cHierClus subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.427 (logrank test), Q value = 0.7

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 121 71 0.0 - 346.5 (53.3)
subtype1 18 11 0.0 - 225.1 (27.1)
subtype2 44 21 4.5 - 312.1 (50.6)
subtype3 25 18 0.2 - 346.5 (62.6)
subtype4 34 21 4.4 - 227.0 (60.6)

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.512 (logrank test), Q value = 0.75

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 165 98 0.2 - 369.9 (56.4)
subtype1 25 17 9.9 - 226.0 (53.3)
subtype2 57 32 6.4 - 357.4 (56.4)
subtype3 34 22 13.9 - 369.9 (61.2)
subtype4 49 27 0.2 - 248.7 (50.9)

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.548 (Kruskal-Wallis (anova)), Q value = 0.77

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 166 55.3 (15.8)
subtype1 26 58.5 (15.9)
subtype2 57 53.5 (15.2)
subtype3 34 56.4 (15.9)
subtype4 49 54.9 (16.6)

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.238 (Fisher's exact test), Q value = 0.57

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 8 3 15 8 17 7 5 6 4 22 5 15 23 9
subtype1 1 1 3 1 3 0 1 0 0 1 2 5 6 2
subtype2 2 1 7 3 5 0 3 4 4 11 0 2 7 2
subtype3 4 1 2 1 4 3 1 0 0 4 2 3 4 1
subtype4 1 0 3 3 5 4 0 2 0 6 1 5 6 4

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.301 (Fisher's exact test), Q value = 0.61

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 36 37 28 31
subtype1 4 6 7 7
subtype2 16 12 4 11
subtype3 6 9 8 3
subtype4 10 10 9 10

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.713 (Fisher's exact test), Q value = 0.89

Table S50.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 87 25 26 18
subtype1 11 8 4 3
subtype2 33 5 9 7
subtype3 18 5 6 3
subtype4 25 7 7 5

Figure S46.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.462 (Fisher's exact test), Q value = 0.7

Table S51.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 149 10
subtype1 24 2
subtype2 51 2
subtype3 32 1
subtype4 42 5

Figure S47.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'MELANOMA_ULCERATION'

P value = 0.842 (Fisher's exact test), Q value = 0.97

Table S52.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 65 37
subtype1 11 6
subtype2 19 14
subtype3 12 5
subtype4 23 12

Figure S48.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'RPPA cHierClus subtypes' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.132 (Fisher's exact test), Q value = 0.47

Table S53.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 25 144
subtype1 3 24
subtype2 14 44
subtype3 3 32
subtype4 5 44

Figure S49.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'RPPA cHierClus subtypes' versus 'BRESLOW_THICKNESS'

P value = 0.882 (Kruskal-Wallis (anova)), Q value = 0.98

Table S54.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 119 3.4 (5.3)
subtype1 21 5.4 (10.7)
subtype2 36 3.1 (3.0)
subtype3 24 2.2 (1.8)
subtype4 38 3.2 (3.5)

Figure S50.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.274 (Fisher's exact test), Q value = 0.6

Table S55.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 64 105
subtype1 6 21
subtype2 25 33
subtype3 15 20
subtype4 18 31

Figure S51.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.122 (Fisher's exact test), Q value = 0.47

Table S56.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 150 18
subtype1 26 1
subtype2 47 11
subtype3 32 3
subtype4 45 3

Figure S52.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S57.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 138 104 125
'RNAseq CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 8.14e-05 (logrank test), Q value = 0.0023

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 246 124 0.0 - 346.5 (47.1)
subtype1 95 56 0.0 - 174.1 (36.5)
subtype2 68 21 0.3 - 209.8 (50.6)
subtype3 83 47 0.8 - 346.5 (59.7)

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.54e-06 (logrank test), Q value = 7.2e-05

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 358 188 0.2 - 369.9 (52.3)
subtype1 135 83 0.2 - 247.0 (43.2)
subtype2 103 34 0.3 - 268.9 (56.4)
subtype3 120 71 0.2 - 369.9 (60.6)

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00303 (Kruskal-Wallis (anova)), Q value = 0.041

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 359 56.3 (15.7)
subtype1 135 59.4 (15.0)
subtype2 104 56.9 (15.7)
subtype3 120 52.4 (15.9)

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.196 (Fisher's exact test), Q value = 0.53

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 29 18 28 25 14 19 15 40 15 33 56 21
subtype1 3 3 6 7 10 7 4 9 10 13 8 13 27 7
subtype2 4 1 11 9 7 7 5 4 2 11 1 11 17 4
subtype3 6 3 12 2 11 11 5 6 3 16 6 9 12 10

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0313 (Fisher's exact test), Q value = 0.2

Table S62.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 64 73 78 71
subtype1 19 23 29 37
subtype2 25 17 22 17
subtype3 20 33 27 17

Figure S57.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.587 (Fisher's exact test), Q value = 0.81

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 177 66 39 45
subtype1 58 25 17 21
subtype2 54 18 8 12
subtype3 65 23 14 12

Figure S58.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.431 (Fisher's exact test), Q value = 0.7

Table S64.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 319 22
subtype1 121 8
subtype2 92 4
subtype3 106 10

Figure S59.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'MELANOMA_ULCERATION'

P value = 0.379 (Fisher's exact test), Q value = 0.67

Table S65.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 134 91
subtype1 56 42
subtype2 44 22
subtype3 34 27

Figure S60.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'RNAseq CNMF subtypes' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.455 (Fisher's exact test), Q value = 0.7

Table S66.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 47 320
subtype1 14 124
subtype2 16 88
subtype3 17 108

Figure S61.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'RNAseq CNMF subtypes' versus 'BRESLOW_THICKNESS'

P value = 0.000224 (Kruskal-Wallis (anova)), Q value = 0.0052

Table S67.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 267 3.5 (4.7)
subtype1 105 4.3 (4.3)
subtype2 73 2.8 (3.6)
subtype3 89 3.1 (5.7)

Figure S62.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.39 (Fisher's exact test), Q value = 0.68

Table S68.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 137 230
subtype1 46 92
subtype2 39 65
subtype3 52 73

Figure S63.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.155 (Fisher's exact test), Q value = 0.47

Table S69.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 317 48
subtype1 120 17
subtype2 85 19
subtype3 112 12

Figure S64.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.529 (Fisher's exact test), Q value = 0.76

Table S70.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 353
subtype1 3 0 131
subtype2 1 1 101
subtype3 1 0 121

Figure S65.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.636 (Fisher's exact test), Q value = 0.81

Table S71.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 352
subtype1 4 131
subtype2 1 102
subtype3 2 119

Figure S66.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S72.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 108 105 96 58
'RNAseq cHierClus subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 1.12e-06 (logrank test), Q value = 7.2e-05

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 246 124 0.0 - 346.5 (47.1)
subtype1 73 51 0.0 - 297.0 (36.3)
subtype2 73 25 0.8 - 239.1 (59.4)
subtype3 61 34 1.9 - 346.5 (62.4)
subtype4 39 14 0.3 - 174.1 (47.3)

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.66e-08 (logrank test), Q value = 5.1e-06

Table S74.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 358 188 0.2 - 369.9 (52.3)
subtype1 105 75 0.2 - 297.9 (42.8)
subtype2 104 37 0.7 - 340.1 (61.2)
subtype3 92 52 0.2 - 369.9 (60.9)
subtype4 57 24 0.3 - 228.2 (49.0)

Figure S68.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000399 (Kruskal-Wallis (anova)), Q value = 0.008

Table S75.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 359 56.3 (15.7)
subtype1 105 59.5 (15.0)
subtype2 104 56.1 (16.2)
subtype3 92 50.6 (15.6)
subtype4 58 60.1 (13.8)

Figure S69.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0392 (Fisher's exact test), Q value = 0.21

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 29 18 28 25 14 19 15 40 15 33 56 21
subtype1 2 2 3 4 7 5 5 9 10 9 7 8 22 6
subtype2 2 0 14 6 10 5 5 4 2 13 3 14 12 6
subtype3 6 3 10 3 7 11 3 2 2 13 5 6 10 5
subtype4 3 2 2 5 4 4 1 4 1 5 0 5 12 4

Figure S70.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00224 (Fisher's exact test), Q value = 0.035

Table S77.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 64 73 78 71
subtype1 11 16 22 36
subtype2 25 26 21 15
subtype3 17 23 23 10
subtype4 11 8 12 10

Figure S71.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.37 (Fisher's exact test), Q value = 0.67

Table S78.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 177 66 39 45
subtype1 47 18 13 15
subtype2 50 18 15 9
subtype3 50 21 7 9
subtype4 30 9 4 12

Figure S72.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.883 (Fisher's exact test), Q value = 0.98

Table S79.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 319 22
subtype1 92 6
subtype2 91 6
subtype3 84 5
subtype4 52 5

Figure S73.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'MELANOMA_ULCERATION'

P value = 0.638 (Fisher's exact test), Q value = 0.81

Table S80.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 134 91
subtype1 40 33
subtype2 42 25
subtype3 30 16
subtype4 22 17

Figure S74.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'RNAseq cHierClus subtypes' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.293 (Fisher's exact test), Q value = 0.6

Table S81.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 47 320
subtype1 12 96
subtype2 18 87
subtype3 13 83
subtype4 4 54

Figure S75.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'RNAseq cHierClus subtypes' versus 'BRESLOW_THICKNESS'

P value = 6.9e-06 (Kruskal-Wallis (anova)), Q value = 0.00024

Table S82.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 267 3.5 (4.7)
subtype1 82 4.7 (4.5)
subtype2 79 2.6 (3.5)
subtype3 67 2.9 (6.3)
subtype4 39 3.6 (3.6)

Figure S76.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.817 (Fisher's exact test), Q value = 0.96

Table S83.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 137 230
subtype1 37 71
subtype2 40 65
subtype3 39 57
subtype4 21 37

Figure S77.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.393 (Fisher's exact test), Q value = 0.68

Table S84.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 317 48
subtype1 94 12
subtype2 86 19
subtype3 86 10
subtype4 51 7

Figure S78.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.122 (Fisher's exact test), Q value = 0.47

Table S85.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 353
subtype1 2 0 104
subtype2 0 0 102
subtype3 1 0 93
subtype4 2 1 54

Figure S79.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.223 (Fisher's exact test), Q value = 0.56

Table S86.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 352
subtype1 4 102
subtype2 0 103
subtype3 2 91
subtype4 1 56

Figure S80.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S87.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 74 94 119 21 44
'MIRSEQ CNMF' versus 'Time from Specimen Diagnosis to Death'

P value = 0.0922 (logrank test), Q value = 0.4

Table S88.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 239 123 0.0 - 346.5 (47.0)
subtype1 48 20 0.2 - 346.5 (45.2)
subtype2 61 34 0.8 - 239.1 (49.4)
subtype3 90 54 0.2 - 209.8 (42.9)
subtype4 12 4 5.3 - 227.0 (53.3)
subtype5 28 11 0.0 - 312.1 (41.0)

Figure S81.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.725 (logrank test), Q value = 0.89

Table S89.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 343 182 0.2 - 369.9 (53.2)
subtype1 71 33 0.2 - 369.9 (46.5)
subtype2 91 50 0.2 - 368.8 (61.0)
subtype3 118 68 0.3 - 268.7 (53.2)
subtype4 20 9 10.5 - 248.7 (56.7)
subtype5 43 22 0.2 - 357.4 (45.3)

Figure S82.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.168 (Kruskal-Wallis (anova)), Q value = 0.48

Table S90.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 344 56.4 (15.7)
subtype1 71 57.2 (14.8)
subtype2 91 54.3 (15.2)
subtype3 118 57.9 (16.0)
subtype4 20 50.4 (16.5)
subtype5 44 57.9 (16.0)

Figure S83.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.157 (Fisher's exact test), Q value = 0.47

Table S91.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 28 16 28 24 12 18 14 38 14 31 55 20
subtype1 4 5 9 8 3 5 2 0 3 6 1 4 15 2
subtype2 3 1 9 2 10 4 5 5 2 13 6 7 16 3
subtype3 3 1 7 5 12 8 2 10 6 12 5 10 17 9
subtype4 2 0 1 0 1 1 0 2 1 3 0 3 3 2
subtype5 1 0 2 1 2 6 3 1 2 4 2 7 4 4

Figure S84.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0301 (Fisher's exact test), Q value = 0.2

Table S92.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 60 73 74 66
subtype1 20 12 9 13
subtype2 8 29 22 16
subtype3 18 23 26 25
subtype4 5 4 5 2
subtype5 9 5 12 10

Figure S85.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.26 (Fisher's exact test), Q value = 0.58

Table S93.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 170 62 39 43
subtype1 39 8 6 12
subtype2 43 22 10 9
subtype3 59 17 13 16
subtype4 9 7 1 3
subtype5 20 8 9 3

Figure S86.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.213 (Fisher's exact test), Q value = 0.54

Table S94.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 307 21
subtype1 67 2
subtype2 84 3
subtype3 101 10
subtype4 18 2
subtype5 37 4

Figure S87.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'MELANOMA_ULCERATION'

P value = 0.121 (Fisher's exact test), Q value = 0.47

Table S95.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 127 87
subtype1 27 16
subtype2 39 19
subtype3 47 32
subtype4 2 6
subtype5 12 14

Figure S88.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'MIRSEQ CNMF' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.45 (Fisher's exact test), Q value = 0.7

Table S96.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 45 307
subtype1 9 65
subtype2 9 85
subtype3 15 104
subtype4 5 16
subtype5 7 37

Figure S89.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'MIRSEQ CNMF' versus 'BRESLOW_THICKNESS'

P value = 0.0765 (Kruskal-Wallis (anova)), Q value = 0.36

Table S97.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 254 3.4 (4.7)
subtype1 49 3.3 (4.7)
subtype2 74 3.3 (6.2)
subtype3 89 3.7 (4.1)
subtype4 12 3.2 (2.0)
subtype5 30 3.6 (3.1)

Figure S90.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.467 (Fisher's exact test), Q value = 0.7

Table S98.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 131 221
subtype1 30 44
subtype2 30 64
subtype3 44 75
subtype4 11 10
subtype5 16 28

Figure S91.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.188 (Fisher's exact test), Q value = 0.52

Table S99.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 307 43
subtype1 63 10
subtype2 85 9
subtype3 106 13
subtype4 20 1
subtype5 33 10

Figure S92.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.16 (Fisher's exact test), Q value = 0.47

Table S100.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 338
subtype1 3 1 70
subtype2 1 0 89
subtype3 0 0 115
subtype4 0 0 21
subtype5 1 0 43

Figure S93.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.29 (Fisher's exact test), Q value = 0.6

Table S101.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 337
subtype1 0 73
subtype2 2 88
subtype3 4 112
subtype4 1 20
subtype5 0 44

Figure S94.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S102.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 132 93 89 38
'MIRSEQ CHIERARCHICAL' versus 'Time from Specimen Diagnosis to Death'

P value = 0.151 (logrank test), Q value = 0.47

Table S103.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 239 123 0.0 - 346.5 (47.0)
subtype1 92 45 0.8 - 297.0 (53.3)
subtype2 60 32 1.9 - 346.5 (48.7)
subtype3 63 36 0.2 - 196.0 (39.8)
subtype4 24 10 0.0 - 312.1 (41.0)

Figure S95.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.214 (logrank test), Q value = 0.54

Table S104.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 343 182 0.2 - 369.9 (53.2)
subtype1 126 67 0.7 - 340.1 (58.7)
subtype2 91 45 0.2 - 369.9 (56.4)
subtype3 89 52 0.2 - 247.0 (44.9)
subtype4 37 18 0.2 - 357.4 (45.3)

Figure S96.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.879 (Kruskal-Wallis (anova)), Q value = 0.98

Table S105.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 344 56.4 (15.7)
subtype1 126 56.7 (16.5)
subtype2 91 55.3 (14.9)
subtype3 89 56.9 (15.0)
subtype4 38 56.9 (16.5)

Figure S97.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.245 (Fisher's exact test), Q value = 0.57

Table S106.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 28 16 28 24 12 18 14 38 14 31 55 20
subtype1 8 0 11 9 14 6 5 8 5 11 4 9 22 10
subtype2 3 3 11 4 6 7 1 4 3 11 4 6 16 3
subtype3 1 4 4 2 7 7 3 5 5 13 5 7 14 3
subtype4 1 0 2 1 1 4 3 1 1 3 1 9 3 4

Figure S98.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.206 (Fisher's exact test), Q value = 0.54

Table S107.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 60 73 74 66
subtype1 21 36 23 25
subtype2 18 19 15 14
subtype3 13 14 25 19
subtype4 8 4 11 8

Figure S99.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.177 (Fisher's exact test), Q value = 0.49

Table S108.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 170 62 39 43
subtype1 72 19 11 18
subtype2 47 19 6 12
subtype3 36 16 14 10
subtype4 15 8 8 3

Figure S100.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.333 (Fisher's exact test), Q value = 0.66

Table S109.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 307 21
subtype1 115 10
subtype2 82 3
subtype3 78 4
subtype4 32 4

Figure S101.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'MELANOMA_ULCERATION'

P value = 0.446 (Fisher's exact test), Q value = 0.7

Table S110.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 127 87
subtype1 50 33
subtype2 29 18
subtype3 38 23
subtype4 10 13

Figure S102.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'MIRSEQ CHIERARCHICAL' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.469 (Fisher's exact test), Q value = 0.7

Table S111.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 45 307
subtype1 16 116
subtype2 15 78
subtype3 8 81
subtype4 6 32

Figure S103.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'MIRSEQ CHIERARCHICAL' versus 'BRESLOW_THICKNESS'

P value = 0.0335 (Kruskal-Wallis (anova)), Q value = 0.2

Table S112.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 254 3.4 (4.7)
subtype1 102 3.3 (4.0)
subtype2 61 3.7 (7.4)
subtype3 65 3.5 (3.0)
subtype4 26 3.2 (2.8)

Figure S104.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.362 (Fisher's exact test), Q value = 0.67

Table S113.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 131 221
subtype1 57 75
subtype2 32 61
subtype3 30 59
subtype4 12 26

Figure S105.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.563 (Fisher's exact test), Q value = 0.79

Table S114.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 307 43
subtype1 118 14
subtype2 81 12
subtype3 78 10
subtype4 30 7

Figure S106.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.235 (Fisher's exact test), Q value = 0.57

Table S115.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 338
subtype1 1 0 129
subtype2 1 0 90
subtype3 1 1 83
subtype4 2 0 36

Figure S107.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

Table S116.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 337
subtype1 3 128
subtype2 2 87
subtype3 2 85
subtype4 0 37

Figure S108.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S117.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 94 108 136
'MIRseq Mature CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.893 (logrank test), Q value = 0.98

Table S118.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 229 120 0.0 - 346.5 (48.0)
subtype1 58 26 1.6 - 185.1 (47.1)
subtype2 74 42 0.0 - 312.1 (55.7)
subtype3 97 52 0.2 - 346.5 (45.9)

Figure S109.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.422 (logrank test), Q value = 0.7

Table S119.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 329 175 0.2 - 369.9 (53.5)
subtype1 88 38 0.2 - 268.9 (47.8)
subtype2 106 60 4.0 - 368.8 (61.4)
subtype3 135 77 0.2 - 369.9 (49.5)

Figure S110.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0336 (Kruskal-Wallis (anova)), Q value = 0.2

Table S120.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 330 56.5 (15.8)
subtype1 89 57.6 (14.0)
subtype2 106 53.2 (16.8)
subtype3 135 58.4 (15.7)

Figure S111.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00089 (Fisher's exact test), Q value = 0.016

Table S121.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 28 16 27 24 12 17 14 38 13 29 50 18
subtype1 7 2 5 11 4 6 3 0 0 15 1 9 17 3
subtype2 2 2 13 2 12 7 6 5 3 10 7 7 16 6
subtype3 4 3 10 3 11 11 3 12 11 13 5 13 17 9

Figure S112.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00913 (Fisher's exact test), Q value = 0.085

Table S122.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 58 72 71 61
subtype1 27 13 17 13
subtype2 14 30 25 17
subtype3 17 29 29 31

Figure S113.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.324 (Fisher's exact test), Q value = 0.65

Table S123.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 168 61 35 38
subtype1 43 13 13 16
subtype2 56 21 11 10
subtype3 69 27 11 12

Figure S114.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.757 (Fisher's exact test), Q value = 0.91

Table S124.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 297 19
subtype1 84 4
subtype2 94 6
subtype3 119 9

Figure S115.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'MELANOMA_ULCERATION'

P value = 0.0181 (Fisher's exact test), Q value = 0.16

Table S125.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 122 82
subtype1 40 14
subtype2 39 25
subtype3 43 43

Figure S116.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'MIRseq Mature CNMF subtypes' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.833 (Fisher's exact test), Q value = 0.96

Table S126.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 41 297
subtype1 13 81
subtype2 12 96
subtype3 16 120

Figure S117.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'MIRseq Mature CNMF subtypes' versus 'BRESLOW_THICKNESS'

P value = 0.00821 (Kruskal-Wallis (anova)), Q value = 0.082

Table S127.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 245 3.4 (4.8)
subtype1 57 3.2 (4.5)
subtype2 87 3.2 (6.0)
subtype3 101 3.6 (3.7)

Figure S118.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.404 (Fisher's exact test), Q value = 0.69

Table S128.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 123 215
subtype1 39 55
subtype2 35 73
subtype3 49 87

Figure S119.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.931 (Fisher's exact test), Q value = 0.99

Table S129.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 297 39
subtype1 82 12
subtype2 96 12
subtype3 119 15

Figure S120.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.612 (Fisher's exact test), Q value = 0.81

Table S130.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 325
subtype1 1 1 90
subtype2 1 0 104
subtype3 3 0 131

Figure S121.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

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

Table S131.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 324
subtype1 2 90
subtype2 2 103
subtype3 3 131

Figure S122.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S132.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 162 70 76 30
'MIRseq Mature cHierClus subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.13 (logrank test), Q value = 0.47

Table S133.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

nPatients nDeath Duration Range (Median), Month
ALL 229 120 0.0 - 346.5 (48.0)
subtype1 114 57 0.2 - 297.0 (43.4)
subtype2 46 29 0.8 - 239.1 (49.7)
subtype3 49 27 2.0 - 346.5 (64.8)
subtype4 20 7 0.0 - 312.1 (41.0)

Figure S123.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.109 (logrank test), Q value = 0.46

Table S134.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 329 175 0.2 - 369.9 (53.5)
subtype1 160 81 0.2 - 297.9 (49.2)
subtype2 67 44 4.0 - 340.1 (55.7)
subtype3 72 36 0.2 - 369.9 (66.0)
subtype4 30 14 0.2 - 357.4 (46.1)

Figure S124.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.367 (Kruskal-Wallis (anova)), Q value = 0.67

Table S135.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 330 56.5 (15.8)
subtype1 160 57.9 (15.5)
subtype2 68 55.0 (17.7)
subtype3 72 54.6 (13.6)
subtype4 30 57.3 (17.5)

Figure S125.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.523 (Fisher's exact test), Q value = 0.76

Table S136.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients I OR II NOS STAGE 0 STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 13 7 28 16 27 24 12 17 14 38 13 29 50 18
subtype1 6 4 10 10 14 13 5 10 9 19 5 12 23 8
subtype2 1 0 7 4 7 2 3 4 3 5 4 5 15 5
subtype3 5 3 10 1 5 5 1 2 1 12 3 8 9 2
subtype4 1 0 1 1 1 4 3 1 1 2 1 4 3 3

Figure S126.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.37 (Fisher's exact test), Q value = 0.67

Table S137.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 58 72 71 61
subtype1 28 32 33 34
subtype2 9 20 15 14
subtype3 15 17 15 7
subtype4 6 3 8 6

Figure S127.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.575 (Fisher's exact test), Q value = 0.8

Table S138.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 168 61 35 38
subtype1 84 26 15 18
subtype2 33 12 9 9
subtype3 37 19 5 9
subtype4 14 4 6 2

Figure S128.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.372 (Fisher's exact test), Q value = 0.67

Table S139.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 297 19
subtype1 142 9
subtype2 61 5
subtype3 69 2
subtype4 25 3

Figure S129.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'MELANOMA_ULCERATION'

P value = 0.38 (Fisher's exact test), Q value = 0.67

Table S140.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

nPatients NO YES
ALL 122 82
subtype1 63 41
subtype2 34 17
subtype3 17 14
subtype4 8 10

Figure S130.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'MELANOMA_ULCERATION'

'MIRseq Mature cHierClus subtypes' versus 'MELANOMA_PRIMARY_KNOWN'

P value = 0.44 (Fisher's exact test), Q value = 0.7

Table S141.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

nPatients NO YES
ALL 41 297
subtype1 18 144
subtype2 6 64
subtype3 12 64
subtype4 5 25

Figure S131.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA_PRIMARY_KNOWN'

'MIRseq Mature cHierClus subtypes' versus 'BRESLOW_THICKNESS'

P value = 0.0366 (Kruskal-Wallis (anova)), Q value = 0.2

Table S142.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

nPatients Mean (Std.Dev)
ALL 245 3.4 (4.8)
subtype1 119 3.6 (4.2)
subtype2 57 3.0 (2.8)
subtype3 49 3.3 (7.7)
subtype4 20 3.3 (3.1)

Figure S132.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'BRESLOW_THICKNESS'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.918 (Fisher's exact test), Q value = 0.99

Table S143.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'GENDER'

nPatients FEMALE MALE
ALL 123 215
subtype1 58 104
subtype2 27 43
subtype3 26 50
subtype4 12 18

Figure S133.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.145 (Fisher's exact test), Q value = 0.47

Table S144.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients NO YES
ALL 297 39
subtype1 143 18
subtype2 65 5
subtype3 67 9
subtype4 22 7

Figure S134.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.731 (Fisher's exact test), Q value = 0.89

Table S145.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 5 1 325
subtype1 3 1 155
subtype2 0 0 68
subtype3 1 0 73
subtype4 1 0 29

Figure S135.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

Table S146.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 324
subtype1 4 155
subtype2 1 69
subtype3 2 70
subtype4 0 30

Figure S136.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/SKCM-TM/20140641/SKCM-TM.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/SKCM-TM/19775519/SKCM-TM.merged_data.txt

  • Number of patients = 368

  • Number of clustering approaches = 10

  • Number of selected clinical features = 14

  • 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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle