Correlation between aggregated molecular cancer subtypes and selected clinical features
Adrenocortical Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1VQ321G
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1VQ321G
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 12 clinical features across 92 patients, 33 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 7 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.

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

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death' and 'PATHOLOGIC_STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 8 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',  'PATHOLOGIC_STAGE', and 'RESIDUAL_TUMOR'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

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

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE', and 'RESIDUAL_TUMOR'.

  • 6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'PATHOLOGIC_STAGE', and 'RESIDUAL_TUMOR'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 33 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 0.00605
(0.0454) 		0.000226
(0.00387) 		0.0204
(0.102) 		0.0306
(0.127) 		7.01e-05
(0.0028) 		0.000178
(0.0036) 		0.00161
(0.0163) 		0.0165
(0.0936) 		0.00119
(0.0145) 		5.91e-06
(0.000709)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.723
(0.819) 		0.204
(0.422) 		0.528
(0.667) 		0.491
(0.655) 		0.954
(0.971) 		0.623
(0.748) 		0.127
(0.318) 		0.265
(0.441) 		0.318
(0.515) 		0.536
(0.67)
PATHOLOGIC STAGE Fisher's exact test 0.0294
(0.126) 		0.0673
(0.202) 		0.0446
(0.162) 		0.0714
(0.204) 		0.0197
(0.102) 		0.0042
(0.036) 		3e-05
(0.0018) 		0.00121
(0.0145) 		0.00012
(0.0036) 		0.00038
(0.0057)
PATHOLOGY T STAGE Fisher's exact test 0.199
(0.422) 		0.327
(0.523) 		0.183
(0.398) 		0.137
(0.336) 		0.105
(0.27) 		0.037
(0.143) 		0.00844
(0.0533) 		0.156
(0.354) 		0.00668
(0.0472) 		0.0655
(0.202)
PATHOLOGY N STAGE Fisher's exact test 0.0766
(0.214) 		0.0537
(0.184) 		0.254
(0.441) 		0.466
(0.63) 		0.067
(0.202) 		0.0361
(0.143) 		0.00504
(0.0403) 		0.255
(0.441) 		0.0259
(0.115) 		0.501
(0.657)
GENDER Fisher's exact test 0.575
(0.704) 		0.00789
(0.0526) 		0.182
(0.398) 		0.467
(0.63) 		0.433
(0.628) 		0.363
(0.563) 		0.0857
(0.229) 		0.331
(0.523) 		0.0713
(0.204) 		0.711
(0.812)
RADIATION THERAPY Fisher's exact test 0.94
(0.964) 		0.828
(0.891) 		0.148
(0.345) 		0.435
(0.628) 		0.526
(0.667) 		0.384
(0.583) 		0.238
(0.441) 		0.252
(0.441) 		0.141
(0.338) 		0.509
(0.657)
HISTOLOGICAL TYPE Fisher's exact test 0.00163
(0.0163) 		0.2
(0.422) 		0.452
(0.63) 		0.839
(0.891) 		0.398
(0.597) 		0.211
(0.426) 		0.0226
(0.105) 		0.88
(0.921) 		0.264
(0.441) 		0.569
(0.704)
RESIDUAL TUMOR Fisher's exact test 0.0628
(0.202) 		0.789
(0.861) 		0.26
(0.441) 		0.263
(0.441) 		0.0222
(0.105) 		0.303
(0.497) 		0.00018
(0.0036) 		0.462
(0.63) 		0.00412
(0.036) 		0.0423
(0.159)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.0172
(0.0936) 		0.238
(0.441) 		0.768
(0.846) 		0.447
(0.63) 		0.213
(0.426) 		0.053
(0.184) 		0.0811
(0.221) 		0.706
(0.812) 		0.15
(0.345) 		0.468
(0.63)
RACE Fisher's exact test 0.648
(0.77) 		0.6
(0.728) 		0.366
(0.563) 		0.263
(0.441) 		0.416
(0.616) 		0.00902
(0.0541) 		1
(1.00) 		0.106
(0.27) 		0.766
(0.846) 		0.234
(0.441)
ETHNICITY Fisher's exact test 0.926
(0.958) 		0.883
(0.921) 		1
(1.00) 		0.758
(0.846) 		0.838
(0.891) 		0.24
(0.441) 		0.504
(0.657) 		0.7
(0.812) 		0.0569
(0.19) 		0.657
(0.773)
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 5 6 7
Number of samples 19 18 13 18 11 6 5
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.00605 (logrank test), Q value = 0.045

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

nPatients nDeath Duration Range (Median), Month
ALL 90 33 0.0 - 153.6 (38.9)
subtype1 19 8 9.5 - 152.2 (39.2)
subtype2 18 1 14.6 - 121.2 (33.7)
subtype3 13 5 12.0 - 83.8 (29.1)
subtype4 18 12 0.0 - 79.1 (34.5)
subtype5 11 5 4.1 - 91.3 (42.5)
subtype6 6 0 18.1 - 99.9 (73.0)
subtype7 5 2 39.6 - 153.6 (78.4)

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 = 0.723 (Kruskal-Wallis (anova)), Q value = 0.82

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

nPatients Mean (Std.Dev)
ALL 90 47.1 (16.5)
subtype1 19 47.0 (19.4)
subtype2 18 51.8 (14.2)
subtype3 13 45.7 (17.1)
subtype4 18 48.5 (17.3)
subtype5 11 46.2 (13.8)
subtype6 6 37.5 (15.5)
subtype7 5 43.4 (16.6)

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 = 0.0294 (Fisher's exact test), Q value = 0.13

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 43 18 18
subtype1 3 10 3 3
subtype2 2 11 5 0
subtype3 0 5 2 6
subtype4 0 5 6 7
subtype5 3 4 1 1
subtype6 1 4 1 0
subtype7 0 4 0 1

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 = 0.199 (Fisher's exact test), Q value = 0.42

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

nPatients T1 T2 T3 T4
ALL 9 48 11 20
subtype1 3 11 3 2
subtype2 2 12 2 2
subtype3 0 6 2 5
subtype4 0 7 3 8
subtype5 3 4 0 2
subtype6 1 4 1 0
subtype7 0 4 0 1

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 = 0.0766 (Fisher's exact test), Q value = 0.21

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

nPatients 0 1
ALL 78 10
subtype1 18 1
subtype2 17 1
subtype3 8 5
subtype4 15 3
subtype5 9 0
subtype6 6 0
subtype7 5 0

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 'GENDER'

P value = 0.575 (Fisher's exact test), Q value = 0.7

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

nPatients FEMALE MALE
ALL 59 31
subtype1 15 4
subtype2 12 6
subtype3 7 6
subtype4 13 5
subtype5 6 5
subtype6 4 2
subtype7 2 3

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.94 (Fisher's exact test), Q value = 0.96

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

nPatients NO YES
ALL 69 18
subtype1 14 4
subtype2 14 4
subtype3 10 2
subtype4 13 5
subtype5 8 2
subtype6 6 0
subtype7 4 1

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00163 (Fisher's exact test), Q value = 0.016

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

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 4 85
subtype1 0 0 19
subtype2 0 1 17
subtype3 0 0 13
subtype4 0 0 18
subtype5 0 0 11
subtype6 1 2 3
subtype7 0 1 4

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0628 (Fisher's exact test), Q value = 0.2

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

nPatients R0 R1 R2 RX
ALL 63 7 12 5
subtype1 16 1 2 0
subtype2 15 1 0 2
subtype3 7 1 4 0
subtype4 8 2 6 2
subtype5 6 2 0 1
subtype6 6 0 0 0
subtype7 5 0 0 0

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0172 (Kruskal-Wallis (anova)), Q value = 0.094

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

nPatients Mean (Std.Dev)
ALL 31 2.5 (9.4)
subtype1 6 0.2 (0.4)
subtype2 5 1.6 (3.6)
subtype3 6 11.5 (20.0)
subtype4 7 0.1 (0.4)
subtype5 3 0.0 (0.0)
subtype6 2 0.0 (0.0)
subtype7 2 0.0 (0.0)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.648 (Fisher's exact test), Q value = 0.77

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 1 76
subtype1 1 1 16
subtype2 0 0 16
subtype3 0 0 12
subtype4 0 0 17
subtype5 1 0 8
subtype6 0 0 4
subtype7 0 0 3

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.926 (Fisher's exact test), Q value = 0.96

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 38
subtype1 3 9
subtype2 1 9
subtype3 2 6
subtype4 2 8
subtype5 0 3
subtype6 0 0
subtype7 0 3

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S14.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 16 14 8 18 9 13 2
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000226 (logrank test), Q value = 0.0039

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 78 28 4.1 - 153.6 (38.9)
subtype1 16 12 4.1 - 127.5 (20.1)
subtype2 14 4 12.6 - 152.2 (46.3)
subtype3 8 3 5.2 - 68.8 (50.7)
subtype4 18 8 6.8 - 153.6 (39.5)
subtype5 9 1 23.6 - 99.9 (66.2)
subtype6 13 0 18.1 - 121.2 (31.2)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.204 (Kruskal-Wallis (anova)), Q value = 0.42

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

nPatients Mean (Std.Dev)
ALL 78 46.4 (16.1)
subtype1 16 39.5 (19.2)
subtype2 14 49.6 (15.4)
subtype3 8 55.5 (16.3)
subtype4 18 44.1 (16.2)
subtype5 9 42.2 (14.0)
subtype6 13 51.9 (9.7)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0673 (Fisher's exact test), Q value = 0.2

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 37 16 14
subtype1 1 6 6 3
subtype2 4 5 1 4
subtype3 0 3 3 2
subtype4 2 7 2 5
subtype5 1 8 0 0
subtype6 1 8 4 0

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.327 (Fisher's exact test), Q value = 0.52

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 41 9 17
subtype1 1 8 3 4
subtype2 4 5 2 3
subtype3 0 4 2 2
subtype4 2 8 0 6
subtype5 1 8 0 0
subtype6 1 8 2 2

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0537 (Fisher's exact test), Q value = 0.18

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 67 9
subtype1 12 4
subtype2 14 0
subtype3 7 1
subtype4 12 4
subtype5 9 0
subtype6 13 0

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00789 (Fisher's exact test), Q value = 0.053

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 47 31
subtype1 11 5
subtype2 8 6
subtype3 7 1
subtype4 7 11
subtype5 9 0
subtype6 5 8

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.828 (Fisher's exact test), Q value = 0.89

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 58 17
subtype1 11 5
subtype2 11 2
subtype3 6 2
subtype4 13 3
subtype5 8 1
subtype6 9 4

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.2 (Fisher's exact test), Q value = 0.42

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 3 74
subtype1 0 0 16
subtype2 0 1 13
subtype3 0 0 8
subtype4 0 0 18
subtype5 1 1 7
subtype6 0 1 12

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.789 (Fisher's exact test), Q value = 0.86

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 54 6 9 6
subtype1 10 2 2 2
subtype2 10 1 3 0
subtype3 5 1 1 1
subtype4 10 1 3 1
subtype5 9 0 0 0
subtype6 10 1 0 2

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.238 (Kruskal-Wallis (anova)), Q value = 0.44

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 28 2.8 (9.9)
subtype1 6 0.7 (0.8)
subtype2 4 0.0 (0.0)
subtype3 4 2.0 (4.0)
subtype4 8 8.4 (17.8)
subtype5 2 0.0 (0.0)
subtype6 4 0.0 (0.0)

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.6 (Fisher's exact test), Q value = 0.73

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 65
subtype1 0 0 16
subtype2 1 1 11
subtype3 0 0 7
subtype4 0 0 13
subtype5 0 0 7
subtype6 0 0 11

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.883 (Fisher's exact test), Q value = 0.92

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 28
subtype1 2 5
subtype2 3 6
subtype3 1 4
subtype4 2 6
subtype5 0 3
subtype6 0 4

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 10 11 11 14
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0204 (logrank test), Q value = 0.1

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 46 14 4.1 - 153.6 (42.0)
subtype1 10 4 12.6 - 153.6 (34.2)
subtype2 11 2 15.2 - 72.4 (49.1)
subtype3 11 1 24.9 - 121.2 (78.4)
subtype4 14 7 4.1 - 79.1 (35.8)

Figure S25.  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 = 0.528 (Kruskal-Wallis (anova)), Q value = 0.67

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 46 47.2 (14.4)
subtype1 10 52.5 (14.3)
subtype2 11 47.1 (14.7)
subtype3 11 44.5 (10.7)
subtype4 14 45.5 (17.1)

Figure S26.  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 = 0.0446 (Fisher's exact test), Q value = 0.16

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 2 26 10 8
subtype1 0 4 2 4
subtype2 0 6 4 1
subtype3 2 9 0 0
subtype4 0 7 4 3

Figure S27.  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 = 0.183 (Fisher's exact test), Q value = 0.4

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 2 30 7 7
subtype1 0 6 2 2
subtype2 0 7 3 1
subtype3 2 9 0 0
subtype4 0 8 2 4

Figure S28.  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 = 0.254 (Fisher's exact test), Q value = 0.44

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 40 6
subtype1 7 3
subtype2 10 1
subtype3 11 0
subtype4 12 2

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.182 (Fisher's exact test), Q value = 0.4

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 28 18
subtype1 7 3
subtype2 6 5
subtype3 4 7
subtype4 11 3

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.148 (Fisher's exact test), Q value = 0.35

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 37 9
subtype1 6 4
subtype2 9 2
subtype3 11 0
subtype4 11 3

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.452 (Fisher's exact test), Q value = 0.63

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 2 44
subtype1 1 9
subtype2 0 11
subtype3 1 10
subtype4 0 14

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.26 (Fisher's exact test), Q value = 0.44

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 33 3 5 4
subtype1 6 0 3 0
subtype2 9 1 0 1
subtype3 10 0 0 1
subtype4 8 2 2 2

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.768 (Kruskal-Wallis (anova)), Q value = 0.85

Table S37.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 21 3.0 (11.4)
subtype1 7 0.6 (1.1)
subtype2 5 1.6 (3.6)
subtype3 4 0.0 (0.0)
subtype4 5 10.4 (23.3)

Figure S34.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.366 (Fisher's exact test), Q value = 0.56

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 35
subtype1 0 0 9
subtype2 1 0 8
subtype3 0 1 7
subtype4 0 0 11

Figure S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

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

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 17
subtype1 2 4
subtype2 1 5
subtype3 1 3
subtype4 2 5

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

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

P value = 0.0306 (logrank test), Q value = 0.13

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 46 14 4.1 - 153.6 (42.0)
subtype1 8 5 4.1 - 79.1 (37.8)
subtype2 7 3 12.8 - 68.8 (30.3)
subtype3 7 0 24.9 - 90.1 (39.5)
subtype4 3 1 15.2 - 44.8 (16.1)
subtype5 4 3 12.6 - 153.6 (18.6)
subtype6 4 0 18.1 - 121.2 (92.6)
subtype7 7 1 12.6 - 119.1 (66.5)
subtype8 6 1 20.2 - 72.4 (50.7)

Figure S37.  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 = 0.491 (Kruskal-Wallis (anova)), Q value = 0.65

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 46 47.2 (14.4)
subtype1 8 47.2 (15.0)
subtype2 7 41.6 (14.8)
subtype3 7 52.4 (14.2)
subtype4 3 31.7 (7.6)
subtype5 4 54.0 (15.4)
subtype6 4 45.2 (10.8)
subtype7 7 48.3 (15.4)
subtype8 6 50.7 (15.6)

Figure S38.  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 = 0.0714 (Fisher's exact test), Q value = 0.2

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 2 26 10 8
subtype1 0 5 1 2
subtype2 0 1 3 3
subtype3 1 6 0 0
subtype4 0 1 2 0
subtype5 0 1 1 2
subtype6 1 2 1 0
subtype7 0 6 1 0
subtype8 0 4 1 1

Figure S39.  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 = 0.137 (Fisher's exact test), Q value = 0.34

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

nPatients T1 T2 T3 T4
ALL 2 30 7 7
subtype1 0 6 1 1
subtype2 0 2 2 3
subtype3 1 6 0 0
subtype4 0 2 1 0
subtype5 0 1 1 2
subtype6 1 2 1 0
subtype7 0 6 0 1
subtype8 0 5 1 0

Figure S40.  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 = 0.466 (Fisher's exact test), Q value = 0.63

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 40 6
subtype1 7 1
subtype2 5 2
subtype3 7 0
subtype4 2 1
subtype5 3 1
subtype6 4 0
subtype7 7 0
subtype8 5 1

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.467 (Fisher's exact test), Q value = 0.63

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 28 18
subtype1 6 2
subtype2 6 1
subtype3 3 4
subtype4 2 1
subtype5 3 1
subtype6 1 3
subtype7 3 4
subtype8 4 2

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.435 (Fisher's exact test), Q value = 0.63

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 37 9
subtype1 8 0
subtype2 5 2
subtype3 5 2
subtype4 3 0
subtype5 2 2
subtype6 4 0
subtype7 5 2
subtype8 5 1

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.839 (Fisher's exact test), Q value = 0.89

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 2 44
subtype1 0 8
subtype2 0 7
subtype3 1 6
subtype4 0 3
subtype5 0 4
subtype6 0 4
subtype7 1 6
subtype8 0 6

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.263 (Fisher's exact test), Q value = 0.44

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 33 3 5 4
subtype1 4 0 2 2
subtype2 5 1 1 0
subtype3 7 0 0 0
subtype4 2 1 0 0
subtype5 2 0 2 0
subtype6 3 0 0 1
subtype7 6 1 0 0
subtype8 4 0 0 1

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.447 (Kruskal-Wallis (anova)), Q value = 0.63

Table S50.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 21 3.0 (11.4)
subtype1 2 0.0 (0.0)
subtype2 4 15.0 (25.0)
subtype3 3 0.0 (0.0)
subtype4 1 0.0 (NA)
subtype5 4 0.2 (0.5)
subtype6 2 0.0 (0.0)
subtype7 2 0.0 (0.0)
subtype8 3 1.0 (1.7)

Figure S46.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.263 (Fisher's exact test), Q value = 0.44

Table S51.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 35
subtype1 0 0 6
subtype2 0 0 7
subtype3 0 1 3
subtype4 0 0 3
subtype5 0 0 4
subtype6 0 0 2
subtype7 0 0 6
subtype8 1 0 4

Figure S47.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.758 (Fisher's exact test), Q value = 0.85

Table S52.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 17
subtype1 2 3
subtype2 1 4
subtype3 1 1
subtype4 1 1
subtype5 1 2
subtype6 0 0
subtype7 0 2
subtype8 0 4

Figure S48.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S53.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 24 15 17 23
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 7.01e-05 (logrank test), Q value = 0.0028

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 79 28 4.1 - 153.6 (39.2)
subtype1 24 15 4.1 - 152.2 (20.4)
subtype2 15 8 6.8 - 153.6 (36.3)
subtype3 17 4 17.8 - 119.1 (61.1)
subtype4 23 1 18.1 - 121.2 (39.5)

Figure S49.  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 = 0.954 (Kruskal-Wallis (anova)), Q value = 0.97

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 79 46.7 (15.8)
subtype1 24 46.7 (18.2)
subtype2 15 46.1 (15.1)
subtype3 17 45.5 (12.6)
subtype4 23 47.9 (16.5)

Figure S50.  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 = 0.0197 (Fisher's exact test), Q value = 0.1

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 37 16 15
subtype1 3 8 8 5
subtype2 0 6 3 6
subtype3 1 7 4 3
subtype4 5 16 1 1

Figure S51.  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 = 0.105 (Fisher's exact test), Q value = 0.27

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 42 8 18
subtype1 3 10 4 7
subtype2 0 8 2 5
subtype3 1 8 1 5
subtype4 5 16 1 1

Figure S52.  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 = 0.067 (Fisher's exact test), Q value = 0.2

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 68 9
subtype1 21 3
subtype2 11 4
subtype3 13 2
subtype4 23 0

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.433 (Fisher's exact test), Q value = 0.63

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 48 31
subtype1 16 8
subtype2 11 4
subtype3 8 9
subtype4 13 10

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.526 (Fisher's exact test), Q value = 0.67

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

nPatients NO YES
ALL 59 17
subtype1 16 7
subtype2 12 3
subtype3 11 4
subtype4 20 3

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.398 (Fisher's exact test), Q value = 0.6

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 3 75
subtype1 0 0 24
subtype2 0 0 15
subtype3 0 1 16
subtype4 1 2 20

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0222 (Fisher's exact test), Q value = 0.1

Table S62.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 55 6 9 6
subtype1 15 3 3 3
subtype2 8 0 5 1
subtype3 11 3 0 1
subtype4 21 0 1 1

Figure S57.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.213 (Kruskal-Wallis (anova)), Q value = 0.43

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 27 2.9 (10.1)
subtype1 8 0.2 (0.5)
subtype2 7 8.7 (19.2)
subtype3 5 2.8 (3.9)
subtype4 7 0.0 (0.0)

Figure S58.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.416 (Fisher's exact test), Q value = 0.62

Table S64.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 66
subtype1 0 0 24
subtype2 0 0 13
subtype3 0 0 15
subtype4 1 1 14

Figure S59.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.838 (Fisher's exact test), Q value = 0.89

Table S65.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 30
subtype1 2 11
subtype2 3 7
subtype3 1 6
subtype4 1 6

Figure S60.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 23 24 7 25
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000178 (logrank test), Q value = 0.0036

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 79 28 4.1 - 153.6 (39.2)
subtype1 23 15 4.1 - 152.2 (33.8)
subtype2 24 9 6.8 - 153.6 (41.0)
subtype3 7 3 12.6 - 95.2 (20.2)
subtype4 25 1 18.1 - 121.2 (43.3)

Figure S61.  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 = 0.623 (Kruskal-Wallis (anova)), Q value = 0.75

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 79 46.7 (15.8)
subtype1 23 47.3 (19.2)
subtype2 24 45.1 (14.7)
subtype3 7 41.3 (14.9)
subtype4 25 49.2 (13.8)

Figure S62.  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 = 0.0042 (Fisher's exact test), Q value = 0.036

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 37 16 15
subtype1 2 9 7 5
subtype2 0 9 5 8
subtype3 2 2 1 2
subtype4 5 17 3 0

Figure S63.  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 = 0.037 (Fisher's exact test), Q value = 0.14

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 42 8 18
subtype1 2 12 4 5
subtype2 0 11 2 9
subtype3 2 2 1 2
subtype4 5 17 1 2

Figure S64.  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 = 0.0361 (Fisher's exact test), Q value = 0.14

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 68 9
subtype1 19 4
subtype2 17 5
subtype3 7 0
subtype4 25 0

Figure S65.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.363 (Fisher's exact test), Q value = 0.56

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 48 31
subtype1 17 6
subtype2 13 11
subtype3 5 2
subtype4 13 12

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.384 (Fisher's exact test), Q value = 0.58

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 59 17
subtype1 17 6
subtype2 15 7
subtype3 5 1
subtype4 22 3

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.211 (Fisher's exact test), Q value = 0.43

Table S74.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 3 75
subtype1 0 0 23
subtype2 0 0 24
subtype3 0 0 7
subtype4 1 3 21

Figure S68.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.303 (Fisher's exact test), Q value = 0.5

Table S75.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 55 6 9 6
subtype1 14 2 4 3
subtype2 14 2 4 1
subtype3 5 1 1 0
subtype4 22 1 0 2

Figure S69.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.053 (Kruskal-Wallis (anova)), Q value = 0.18

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 27 2.9 (10.1)
subtype1 8 0.2 (0.5)
subtype2 10 7.5 (15.9)
subtype3 2 0.0 (0.0)
subtype4 7 0.0 (0.0)

Figure S70.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.00902 (Fisher's exact test), Q value = 0.054

Table S77.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 66
subtype1 0 0 22
subtype2 0 0 20
subtype3 1 1 5
subtype4 0 0 19

Figure S71.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.24 (Fisher's exact test), Q value = 0.44

Table S78.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 30
subtype1 2 10
subtype2 3 9
subtype3 2 3
subtype4 0 8

Figure S72.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 31 24 25
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00161 (logrank test), Q value = 0.016

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 29 4.1 - 153.6 (38.9)
subtype1 31 13 4.1 - 119.1 (36.0)
subtype2 24 14 5.2 - 153.6 (35.1)
subtype3 25 2 18.1 - 121.2 (43.3)

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.127 (Kruskal-Wallis (anova)), Q value = 0.32

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 80 46.4 (15.9)
subtype1 31 42.0 (14.8)
subtype2 24 50.4 (18.2)
subtype3 25 48.0 (14.1)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0018

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 37 16 16
subtype1 2 14 11 4
subtype2 2 6 3 12
subtype3 5 17 2 0

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00844 (Fisher's exact test), Q value = 0.053

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 42 9 18
subtype1 2 16 3 10
subtype2 2 9 4 8
subtype3 5 17 2 0

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00504 (Fisher's exact test), Q value = 0.04

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 68 10
subtype1 28 3
subtype2 16 7
subtype3 24 0

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0857 (Fisher's exact test), Q value = 0.23

Table S85.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 49 31
subtype1 20 11
subtype2 18 6
subtype3 11 14

Figure S78.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.238 (Fisher's exact test), Q value = 0.44

Table S86.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 60 17
subtype1 25 5
subtype2 15 8
subtype3 20 4

Figure S79.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0226 (Fisher's exact test), Q value = 0.1

Table S87.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 3 76
subtype1 0 0 31
subtype2 0 0 24
subtype3 1 3 21

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.00018 (Fisher's exact test), Q value = 0.0036

Table S88.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 55 6 10 6
subtype1 20 4 2 5
subtype2 12 2 8 0
subtype3 23 0 0 1

Figure S81.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0811 (Kruskal-Wallis (anova)), Q value = 0.22

Table S89.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 28 2.8 (9.9)
subtype1 8 1.8 (3.3)
subtype2 12 5.4 (14.8)
subtype3 8 0.0 (0.0)

Figure S82.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

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

Table S90.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 67
subtype1 1 1 26
subtype2 0 0 22
subtype3 0 0 19

Figure S83.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.504 (Fisher's exact test), Q value = 0.66

Table S91.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 30
subtype1 4 13
subtype2 4 11
subtype3 0 6

Figure S84.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 17 27 12 20 4
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0165 (logrank test), Q value = 0.094

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 80 29 4.1 - 153.6 (38.9)
subtype1 17 9 4.1 - 113.9 (44.5)
subtype2 27 7 12.6 - 153.6 (36.3)
subtype3 12 0 18.1 - 119.1 (44.9)
subtype4 20 11 5.2 - 152.2 (30.9)
subtype5 4 2 38.5 - 53.0 (44.3)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.265 (Kruskal-Wallis (anova)), Q value = 0.44

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 80 46.4 (15.9)
subtype1 17 46.3 (14.3)
subtype2 27 47.7 (14.7)
subtype3 12 40.5 (15.9)
subtype4 20 45.4 (18.8)
subtype5 4 60.8 (9.8)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.00121 (Fisher's exact test), Q value = 0.015

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 37 16 16
subtype1 0 5 7 4
subtype2 2 19 0 6
subtype3 4 5 2 1
subtype4 3 6 7 3
subtype5 0 2 0 2

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.156 (Fisher's exact test), Q value = 0.35

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 42 9 18
subtype1 0 6 4 6
subtype2 2 19 1 5
subtype3 4 5 1 2
subtype4 3 9 3 4
subtype5 0 3 0 1

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.255 (Fisher's exact test), Q value = 0.44

Table S97.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 68 10
subtype1 13 3
subtype2 25 2
subtype3 12 0
subtype4 15 4
subtype5 3 1

Figure S89.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.331 (Fisher's exact test), Q value = 0.52

Table S98.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 49 31
subtype1 7 10
subtype2 16 11
subtype3 9 3
subtype4 14 6
subtype5 3 1

Figure S90.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.252 (Fisher's exact test), Q value = 0.44

Table S99.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 60 17
subtype1 12 4
subtype2 20 6
subtype3 12 0
subtype4 13 6
subtype5 3 1

Figure S91.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.88 (Fisher's exact test), Q value = 0.92

Table S100.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 3 76
subtype1 0 1 16
subtype2 1 2 24
subtype3 0 0 12
subtype4 0 0 20
subtype5 0 0 4

Figure S92.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.462 (Fisher's exact test), Q value = 0.63

Table S101.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 55 6 10 6
subtype1 7 2 4 3
subtype2 20 2 4 1
subtype3 11 0 0 1
subtype4 14 2 2 1
subtype5 3 0 0 0

Figure S93.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.706 (Kruskal-Wallis (anova)), Q value = 0.81

Table S102.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 28 2.8 (9.9)
subtype1 8 2.0 (3.2)
subtype2 10 5.3 (16.4)
subtype3 2 0.0 (0.0)
subtype4 6 1.2 (2.4)
subtype5 2 1.5 (2.1)

Figure S94.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.106 (Fisher's exact test), Q value = 0.27

Table S103.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 67
subtype1 0 0 15
subtype2 0 0 23
subtype3 1 1 8
subtype4 0 0 18
subtype5 0 0 3

Figure S95.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.7 (Fisher's exact test), Q value = 0.81

Table S104.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 30
subtype1 3 4
subtype2 2 8
subtype3 1 6
subtype4 2 9
subtype5 0 3

Figure S96.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 16 19 17 26
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00119 (logrank test), Q value = 0.015

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 78 29 4.1 - 153.6 (37.4)
subtype1 16 10 4.1 - 127.5 (27.2)
subtype2 19 9 5.2 - 153.6 (38.5)
subtype3 17 8 6.8 - 88.9 (35.6)
subtype4 26 2 18.1 - 121.2 (41.4)

Figure S97.  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 = 0.318 (Kruskal-Wallis (anova)), Q value = 0.52

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 78 46.8 (15.9)
subtype1 16 42.2 (19.9)
subtype2 19 52.6 (14.1)
subtype3 17 43.9 (14.9)
subtype4 26 47.2 (14.4)

Figure S98.  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 = 0.00012 (Fisher's exact test), Q value = 0.0036

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 35 16 16
subtype1 0 6 6 3
subtype2 2 8 0 9
subtype3 2 4 7 4
subtype4 5 17 3 0

Figure S99.  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 = 0.00668 (Fisher's exact test), Q value = 0.047

Table S109.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 40 9 18
subtype1 0 8 3 4
subtype2 2 10 1 6
subtype3 2 5 2 8
subtype4 5 17 3 0

Figure S100.  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 = 0.0259 (Fisher's exact test), Q value = 0.12

Table S110.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 66 10
subtype1 11 4
subtype2 15 4
subtype3 15 2
subtype4 25 0

Figure S101.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0713 (Fisher's exact test), Q value = 0.2

Table S111.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 49 29
subtype1 9 7
subtype2 14 5
subtype3 14 3
subtype4 12 14

Figure S102.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.141 (Fisher's exact test), Q value = 0.34

Table S112.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 58 17
subtype1 9 7
subtype2 14 4
subtype3 13 3
subtype4 22 3

Figure S103.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.264 (Fisher's exact test), Q value = 0.44

Table S113.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 3 74
subtype1 0 0 16
subtype2 0 0 19
subtype3 0 0 17
subtype4 1 3 22

Figure S104.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00412 (Fisher's exact test), Q value = 0.036

Table S114.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 53 6 10 6
subtype1 10 1 2 2
subtype2 11 1 6 0
subtype3 9 4 2 2
subtype4 23 0 0 2

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.15 (Kruskal-Wallis (anova)), Q value = 0.35

Table S115.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 27 2.9 (10.0)
subtype1 6 0.7 (0.8)
subtype2 7 8.7 (19.2)
subtype3 5 2.8 (3.9)
subtype4 9 0.0 (0.0)

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.766 (Fisher's exact test), Q value = 0.85

Table S116.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 66
subtype1 0 0 15
subtype2 0 0 17
subtype3 0 1 15
subtype4 1 0 19

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.0569 (Fisher's exact test), Q value = 0.19

Table S117.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 29
subtype1 3 5
subtype2 1 11
subtype3 4 5
subtype4 0 8

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 7 24 11 14 11 11
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 5.91e-06 (logrank test), Q value = 0.00071

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 78 29 4.1 - 153.6 (37.4)
subtype1 7 7 4.1 - 79.1 (13.9)
subtype2 24 5 12.8 - 153.6 (37.9)
subtype3 11 2 17.8 - 88.9 (49.2)
subtype4 14 8 5.2 - 127.5 (27.2)
subtype5 11 6 12.6 - 152.2 (38.5)
subtype6 11 1 18.1 - 113.9 (43.3)

Figure S109.  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 = 0.536 (Kruskal-Wallis (anova)), Q value = 0.67

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 78 46.8 (15.9)
subtype1 7 47.7 (16.3)
subtype2 24 47.2 (15.4)
subtype3 11 45.7 (12.9)
subtype4 14 41.4 (20.0)
subtype5 11 55.2 (11.3)
subtype6 11 44.5 (17.1)

Figure S110.  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 = 0.00038 (Fisher's exact test), Q value = 0.0057

Table S121.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 9 35 16 16
subtype1 0 3 2 2
subtype2 2 18 0 4
subtype3 1 1 5 4
subtype4 1 5 6 1
subtype5 2 3 1 5
subtype6 3 5 2 0

Figure S111.  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 = 0.0655 (Fisher's exact test), Q value = 0.2

Table S122.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 40 9 18
subtype1 0 3 1 3
subtype2 2 18 1 3
subtype3 1 2 2 6
subtype4 1 7 2 3
subtype5 2 5 1 3
subtype6 3 5 2 0

Figure S112.  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 = 0.501 (Fisher's exact test), Q value = 0.66

Table S123.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 66 10
subtype1 5 2
subtype2 22 2
subtype3 9 2
subtype4 11 2
subtype5 9 2
subtype6 10 0

Figure S113.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.711 (Fisher's exact test), Q value = 0.81

Table S124.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 49 29
subtype1 3 4
subtype2 14 10
subtype3 8 3
subtype4 10 4
subtype5 8 3
subtype6 6 5

Figure S114.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.509 (Fisher's exact test), Q value = 0.66

Table S125.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 58 17
subtype1 7 0
subtype2 18 6
subtype3 7 3
subtype4 9 5
subtype5 8 2
subtype6 9 1

Figure S115.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.569 (Fisher's exact test), Q value = 0.7

Table S126.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients ADRENOCORTICAL CARCINOMA- MYXOID TYPE ADRENOCORTICAL CARCINOMA- ONCOCYTIC TYPE ADRENOCORTICAL CARCINOMA- USUAL TYPE
ALL 1 3 74
subtype1 0 0 7
subtype2 1 1 22
subtype3 0 0 11
subtype4 0 0 14
subtype5 0 0 11
subtype6 0 2 9

Figure S116.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0423 (Fisher's exact test), Q value = 0.16

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

nPatients R0 R1 R2 RX
ALL 53 6 10 6
subtype1 2 0 3 2
subtype2 19 1 3 1
subtype3 5 3 1 2
subtype4 10 1 1 1
subtype5 7 1 2 0
subtype6 10 0 0 0

Figure S117.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.468 (Kruskal-Wallis (anova)), Q value = 0.63

Table S128.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 27 2.9 (10.0)
subtype1 3 2.7 (3.1)
subtype2 9 5.9 (17.3)
subtype3 4 3.5 (4.1)
subtype4 4 0.2 (0.5)
subtype5 4 0.8 (1.5)
subtype6 3 0.0 (0.0)

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.234 (Fisher's exact test), Q value = 0.44

Table S129.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 66
subtype1 0 0 6
subtype2 0 0 20
subtype3 0 1 10
subtype4 0 0 12
subtype5 0 0 10
subtype6 1 0 8

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.657 (Fisher's exact test), Q value = 0.77

Table S130.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 29
subtype1 2 2
subtype2 2 7
subtype3 2 4
subtype4 1 5
subtype5 1 8
subtype6 0 3

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/ACC-TP/22541000/ACC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/ACC-TP/22489368/ACC-TP.merged_data.txt

  • Number of patients = 92

  • Number of clustering approaches = 10

  • Number of selected clinical features = 12

  • 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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