Correlation between aggregated molecular cancer subtypes and selected clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1VD6XK1
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/C1VD6XK1
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 12 different clustering approaches and 7 clinical features across 536 patients, 38 significant findings detected with P value < 0.05 and Q value < 0.25.

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

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

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.

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

  • CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'HISTOLOGICAL_TYPE'.

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

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION',  'COMPLETENESS_OF_RESECTION', and 'RACE'.

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

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 38 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION 		COMPLETENESS
OF
RESECTION 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.836
(0.889) 		0.00418
(0.0125) 		0.0003
(0.00126) 		0.0369
(0.0837) 		0.891
(0.935) 		0.148
(0.239) 		0.614
(0.679)
mRNA cHierClus subtypes 0.095
(0.185) 		0.0314
(0.0789) 		1e-05
(5.25e-05) 		0.103
(0.19) 		0.488
(0.577) 		0.571
(0.648) 		1
(1.00)
Copy Number Ratio CNMF subtypes 2.19e-06
(3.68e-05) 		3.09e-12
(2.59e-10) 		1e-05
(5.25e-05) 		0.0218
(0.059) 		0.313
(0.404) 		0.00269
(0.00837) 		0.794
(0.855)
METHLYATION CNMF 0.0348
(0.0836) 		0.00191
(0.00617) 		1e-05
(5.25e-05) 		0.248
(0.353) 		0.0319
(0.0789) 		0.0667
(0.144) 		0.483
(0.577)
RPPA CNMF subtypes 0.228
(0.342) 		0.148
(0.239) 		3e-05
(0.000148) 		0.471
(0.577) 		0.113
(0.193) 		0.0727
(0.149) 		0.478
(0.577)
RPPA cHierClus subtypes 0.278
(0.377) 		0.108
(0.19) 		1e-05
(5.25e-05) 		0.983
(1.00) 		0.107
(0.19) 		0.287
(0.377) 		0.498
(0.581)
RNAseq CNMF subtypes 7.24e-05
(0.00032) 		1.27e-06
(2.67e-05) 		1e-05
(5.25e-05) 		0.00432
(0.0125) 		0.108
(0.19) 		0.0827
(0.165) 		1
(1.00)
RNAseq cHierClus subtypes 0.000338
(0.00132) 		2.77e-08
(7.77e-07) 		1e-05
(5.25e-05) 		0.0441
(0.0975) 		0.0276
(0.0726) 		0.0359
(0.0837) 		0.227
(0.342)
MIRSEQ CNMF 0.000656
(0.0023) 		3.07e-06
(4.3e-05) 		1e-05
(5.25e-05) 		0.587
(0.657) 		0.246
(0.353) 		0.274
(0.377) 		0.105
(0.19)
MIRSEQ CHIERARCHICAL 5.04e-05
(0.000235) 		1.02e-09
(4.29e-08) 		1e-05
(5.25e-05) 		0.287
(0.377) 		0.392
(0.491) 		0.139
(0.234) 		0.976
(1.00)
MIRseq Mature CNMF subtypes 0.0723
(0.149) 		0.000821
(0.00276) 		1e-05
(5.25e-05) 		0.00572
(0.016) 		0.694
(0.757) 		0.00044
(0.00161) 		0.168
(0.266)
MIRseq Mature cHierClus subtypes 0.239
(0.352) 		0.000345
(0.00132) 		1e-05
(5.25e-05) 		0.282
(0.377) 		0.181
(0.282) 		0.366
(0.466) 		0.548
(0.631)
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 14 18 12 10
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.836 (logrank test), Q value = 0.89

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

nPatients nDeath Duration Range (Median), Month
ALL 54 9 6.0 - 149.6 (37.4)
subtype1 14 2 13.6 - 149.6 (41.7)
subtype2 18 4 6.0 - 125.4 (47.9)
subtype3 12 1 19.8 - 105.4 (33.8)
subtype4 10 2 18.6 - 82.5 (28.3)

Figure S1.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00418 (Kruskal-Wallis (anova)), Q value = 0.013

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

nPatients Mean (Std.Dev)
ALL 54 62.9 (11.8)
subtype1 14 65.6 (11.7)
subtype2 18 68.2 (9.3)
subtype3 12 58.7 (11.9)
subtype4 10 54.9 (11.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 3e-04 (Fisher's exact test), Q value = 0.0013

Table S4.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 41 1 12
subtype1 12 0 2
subtype2 8 0 10
subtype3 11 1 0
subtype4 10 0 0

Figure S3.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0369 (Fisher's exact test), Q value = 0.084

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 25 29
subtype1 8 6
subtype2 12 6
subtype3 3 9
subtype4 2 8

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.891 (Fisher's exact test), Q value = 0.94

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 41 4 2 1
subtype1 8 2 1 0
subtype2 14 1 1 1
subtype3 10 1 0 0
subtype4 9 0 0 0

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.148 (Fisher's exact test), Q value = 0.24

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 6 40
subtype1 1 1 1 10
subtype2 0 0 4 13
subtype3 1 3 0 8
subtype4 0 0 1 9

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.614 (Fisher's exact test), Q value = 0.68

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 24
subtype1 1 6
subtype2 1 6
subtype3 0 9
subtype4 0 3

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 14 10 7 7 16
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.095 (logrank test), Q value = 0.19

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 54 9 6.0 - 149.6 (37.4)
subtype1 14 1 10.2 - 149.6 (57.2)
subtype2 10 3 6.0 - 125.4 (46.2)
subtype3 7 3 13.6 - 68.3 (36.4)
subtype4 7 0 28.6 - 83.7 (49.2)
subtype5 16 2 18.6 - 105.4 (29.9)

Figure S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0314 (Kruskal-Wallis (anova)), Q value = 0.079

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 54 62.9 (11.8)
subtype1 14 62.4 (8.6)
subtype2 10 70.7 (9.3)
subtype3 7 63.4 (15.2)
subtype4 7 66.3 (7.1)
subtype5 16 56.9 (13.6)

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 41 1 12
subtype1 11 0 3
subtype2 1 0 9
subtype3 6 1 0
subtype4 7 0 0
subtype5 16 0 0

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.103 (Fisher's exact test), Q value = 0.19

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 25 29
subtype1 7 7
subtype2 7 3
subtype3 2 5
subtype4 5 2
subtype5 4 12

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.488 (Fisher's exact test), Q value = 0.58

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 41 4 2 1
subtype1 11 1 0 1
subtype2 9 0 1 0
subtype3 4 1 1 0
subtype4 4 1 0 0
subtype5 13 1 0 0

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.571 (Fisher's exact test), Q value = 0.65

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 6 40
subtype1 0 2 2 9
subtype2 0 0 3 7
subtype3 0 1 0 5
subtype4 1 0 0 6
subtype5 1 1 1 13

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

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

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 24
subtype1 1 9
subtype2 0 2
subtype3 1 5
subtype4 0 2
subtype5 0 6

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S17.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 164 299 19 9 37
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 2.19e-06 (logrank test), Q value = 3.7e-05

Table S18.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 526 69 0.1 - 191.8 (23.3)
subtype1 163 37 0.1 - 125.4 (21.2)
subtype2 298 22 0.2 - 191.8 (24.8)
subtype3 19 3 0.2 - 149.6 (30.8)
subtype4 9 0 8.6 - 60.5 (21.2)
subtype5 37 7 0.1 - 73.2 (17.9)

Figure S15.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.09e-12 (Kruskal-Wallis (anova)), Q value = 2.6e-10

Table S19.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 526 64.0 (11.2)
subtype1 163 69.2 (8.4)
subtype2 298 61.4 (11.2)
subtype3 19 61.9 (13.8)
subtype4 9 68.1 (12.9)
subtype5 37 61.6 (12.8)

Figure S16.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S20.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 395 21 112
subtype1 48 11 105
subtype2 286 8 5
subtype3 17 0 2
subtype4 8 1 0
subtype5 36 1 0

Figure S17.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0218 (Fisher's exact test), Q value = 0.059

Table S21.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 141 387
subtype1 32 132
subtype2 95 204
subtype3 2 17
subtype4 3 6
subtype5 9 28

Figure S18.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.313 (Fisher's exact test), Q value = 0.4

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 363 23 17 35
subtype1 103 9 10 14
subtype2 211 14 5 19
subtype3 12 0 1 1
subtype4 9 0 0 0
subtype5 28 0 1 1

Figure S19.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00269 (Fisher's exact test), Q value = 0.0084

Table S23.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 4 20 101 9 366
subtype1 1 2 50 3 95
subtype2 3 15 43 5 219
subtype3 0 3 1 0 15
subtype4 0 0 1 0 8
subtype5 0 0 6 1 29

Figure S20.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.794 (Fisher's exact test), Q value = 0.85

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 14 367
subtype1 6 112
subtype2 8 201
subtype3 0 15
subtype4 0 9
subtype5 0 30

Figure S21.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S25.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 162 87 171
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0348 (logrank test), Q value = 0.084

Table S26.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 418 54 0.1 - 191.8 (19.4)
subtype1 160 28 0.1 - 191.8 (18.8)
subtype2 87 9 0.3 - 116.2 (24.3)
subtype3 171 17 0.1 - 185.8 (18.9)

Figure S22.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00191 (Kruskal-Wallis (anova)), Q value = 0.0062

Table S27.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 418 64.2 (11.2)
subtype1 160 66.3 (10.1)
subtype2 87 63.2 (12.3)
subtype3 171 62.7 (11.4)

Figure S23.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S28.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 303 20 97
subtype1 66 15 81
subtype2 69 2 16
subtype3 168 3 0

Figure S24.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.248 (Fisher's exact test), Q value = 0.35

Table S29.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 90 330
subtype1 37 125
subtype2 13 74
subtype3 40 131

Figure S25.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0319 (Fisher's exact test), Q value = 0.079

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 277 19 13 34
subtype1 102 10 8 12
subtype2 54 3 4 12
subtype3 121 6 1 10

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0667 (Fisher's exact test), Q value = 0.14

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 8 93 7 286
subtype1 1 2 42 3 101
subtype2 0 2 20 4 57
subtype3 1 4 31 0 128

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.483 (Fisher's exact test), Q value = 0.58

Table S32.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 304
subtype1 6 114
subtype2 1 64
subtype3 4 126

Figure S28.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S33.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 42 39 40 12 41 26
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.228 (logrank test), Q value = 0.34

Table S34.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 200 20 0.6 - 185.8 (29.5)
subtype1 42 4 0.6 - 106.9 (29.5)
subtype2 39 6 6.0 - 149.6 (28.1)
subtype3 40 1 1.8 - 185.8 (30.0)
subtype4 12 4 4.0 - 112.5 (48.8)
subtype5 41 2 0.7 - 113.4 (25.1)
subtype6 26 3 0.7 - 78.2 (29.6)

Figure S29.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 0.24

Table S35.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 200 62.7 (11.1)
subtype1 42 62.3 (12.7)
subtype2 39 64.2 (9.6)
subtype3 40 62.2 (11.2)
subtype4 12 69.8 (8.5)
subtype5 41 60.7 (9.4)
subtype6 26 62.0 (12.8)

Figure S30.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00015

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 174 3 23
subtype1 35 1 6
subtype2 28 0 11
subtype3 39 1 0
subtype4 12 0 0
subtype5 41 0 0
subtype6 19 1 6

Figure S31.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.471 (Fisher's exact test), Q value = 0.58

Table S37.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 80 120
subtype1 20 22
subtype2 19 20
subtype3 13 27
subtype4 4 8
subtype5 13 28
subtype6 11 15

Figure S32.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.113 (Fisher's exact test), Q value = 0.19

Table S38.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 143 7 5 16
subtype1 24 3 3 6
subtype2 29 1 1 2
subtype3 26 0 0 5
subtype4 10 1 1 0
subtype5 32 2 0 1
subtype6 22 0 0 2

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0727 (Fisher's exact test), Q value = 0.15

Table S39.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 9 18 4 162
subtype1 0 1 6 2 30
subtype2 0 3 6 1 28
subtype3 0 0 2 0 38
subtype4 1 2 1 0 8
subtype5 1 2 3 1 33
subtype6 0 1 0 0 25

Figure S34.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.478 (Fisher's exact test), Q value = 0.58

Table S40.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 135
subtype1 2 29
subtype2 2 21
subtype3 0 30
subtype4 0 10
subtype5 1 23
subtype6 0 22

Figure S35.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S41.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 70 67 44 19
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.278 (logrank test), Q value = 0.38

Table S42.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 200 20 0.6 - 185.8 (29.5)
subtype1 70 11 0.6 - 149.6 (28.6)
subtype2 67 4 0.7 - 113.4 (25.1)
subtype3 44 4 1.8 - 185.8 (35.1)
subtype4 19 1 0.7 - 78.2 (29.9)

Figure S36.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.108 (Kruskal-Wallis (anova)), Q value = 0.19

Table S43.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 200 62.7 (11.1)
subtype1 70 64.9 (11.2)
subtype2 67 60.1 (10.9)
subtype3 44 62.9 (9.2)
subtype4 19 63.4 (13.8)

Figure S37.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S44.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 174 3 23
subtype1 52 2 16
subtype2 64 1 2
subtype3 44 0 0
subtype4 14 0 5

Figure S38.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.983 (Fisher's exact test), Q value = 1

Table S45.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 80 120
subtype1 29 41
subtype2 26 41
subtype3 17 27
subtype4 8 11

Figure S39.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.107 (Fisher's exact test), Q value = 0.19

Table S46.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 143 7 5 16
subtype1 43 4 5 9
subtype2 50 1 0 3
subtype3 34 2 0 3
subtype4 16 0 0 1

Figure S40.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.287 (Fisher's exact test), Q value = 0.38

Table S47.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 9 18 4 162
subtype1 0 2 9 4 54
subtype2 1 3 7 0 52
subtype3 1 3 2 0 38
subtype4 0 1 0 0 18

Figure S41.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.498 (Fisher's exact test), Q value = 0.58

Table S48.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 135
subtype1 3 46
subtype2 2 37
subtype3 0 37
subtype4 0 15

Figure S42.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S49.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 230 151 153
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 7.24e-05 (logrank test), Q value = 0.00032

Table S50.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 532 69 0.1 - 191.8 (23.3)
subtype1 228 45 0.1 - 191.8 (20.1)
subtype2 151 12 0.1 - 185.8 (27.2)
subtype3 153 12 0.4 - 116.2 (24.9)

Figure S43.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.27e-06 (Kruskal-Wallis (anova)), Q value = 2.7e-05

Table S51.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 532 64.0 (11.2)
subtype1 228 66.5 (10.5)
subtype2 151 60.9 (11.6)
subtype3 153 63.1 (11.0)

Figure S44.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S52.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 400 21 113
subtype1 102 16 112
subtype2 150 1 0
subtype3 148 4 1

Figure S45.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00432 (Fisher's exact test), Q value = 0.013

Table S53.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 141 393
subtype1 54 176
subtype2 55 96
subtype3 32 121

Figure S46.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.108 (Fisher's exact test), Q value = 0.19

Table S54.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 366 23 17 35
subtype1 150 14 11 16
subtype2 109 6 4 6
subtype3 107 3 2 13

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0827 (Fisher's exact test), Q value = 0.17

Table S55.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 4 20 102 9 371
subtype1 1 5 54 6 146
subtype2 2 7 20 2 114
subtype3 1 8 28 1 111

Figure S48.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

Table S56.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 14 370
subtype1 6 163
subtype2 4 99
subtype3 4 108

Figure S49.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S57.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 122 51 46 63 107 68 77
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000338 (logrank test), Q value = 0.0013

Table S58.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 532 69 0.1 - 191.8 (23.3)
subtype1 122 26 0.1 - 125.4 (20.5)
subtype2 51 4 0.7 - 185.8 (25.1)
subtype3 45 9 0.2 - 191.8 (27.4)
subtype4 63 2 0.1 - 110.4 (28.0)
subtype5 106 17 0.2 - 86.1 (18.6)
subtype6 68 8 0.4 - 116.2 (24.3)
subtype7 77 3 0.6 - 112.5 (27.2)

Figure S50.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.77e-08 (Kruskal-Wallis (anova)), Q value = 7.8e-07

Table S59.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 532 64.0 (11.2)
subtype1 121 69.3 (8.4)
subtype2 51 59.6 (10.4)
subtype3 45 63.9 (9.9)
subtype4 63 61.3 (13.0)
subtype5 107 62.7 (12.1)
subtype6 68 62.4 (11.8)
subtype7 77 63.8 (10.1)

Figure S51.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S60.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 400 21 113
subtype1 24 9 89
subtype2 51 0 0
subtype3 20 5 21
subtype4 61 2 0
subtype5 102 3 2
subtype6 67 1 0
subtype7 75 1 1

Figure S52.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0441 (Fisher's exact test), Q value = 0.097

Table S61.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 141 393
subtype1 26 96
subtype2 13 38
subtype3 16 30
subtype4 26 37
subtype5 29 78
subtype6 12 56
subtype7 19 58

Figure S53.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0276 (Fisher's exact test), Q value = 0.073

Table S62.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 366 23 17 35
subtype1 79 7 3 10
subtype2 39 2 0 3
subtype3 32 1 7 3
subtype4 43 5 1 1
subtype5 72 7 4 5
subtype6 44 1 1 5
subtype7 57 0 1 8

Figure S54.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0359 (Fisher's exact test), Q value = 0.084

Table S63.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 4 20 102 9 371
subtype1 0 3 35 1 73
subtype2 0 2 8 0 39
subtype3 1 3 10 1 26
subtype4 2 1 8 3 47
subtype5 0 4 15 4 77
subtype6 1 3 15 0 48
subtype7 0 4 11 0 61

Figure S55.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.227 (Fisher's exact test), Q value = 0.34

Table S64.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 14 370
subtype1 3 80
subtype2 1 36
subtype3 4 33
subtype4 2 40
subtype5 3 76
subtype6 1 45
subtype7 0 60

Figure S56.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S65.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 218 148 161
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.000656 (logrank test), Q value = 0.0023

Table S66.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 525 66 0.1 - 191.8 (23.3)
subtype1 216 39 0.1 - 191.8 (20.0)
subtype2 148 17 0.1 - 185.8 (27.1)
subtype3 161 10 0.4 - 116.2 (24.9)

Figure S57.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 3.07e-06 (Kruskal-Wallis (anova)), Q value = 4.3e-05

Table S67.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 525 63.9 (11.2)
subtype1 216 66.7 (10.0)
subtype2 148 61.0 (12.3)
subtype3 161 63.0 (11.0)

Figure S58.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S68.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 398 21 108
subtype1 98 16 104
subtype2 143 3 2
subtype3 157 2 2

Figure S59.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.587 (Fisher's exact test), Q value = 0.66

Table S69.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 137 390
subtype1 53 165
subtype2 43 105
subtype3 41 120

Figure S60.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.246 (Fisher's exact test), Q value = 0.35

Table S70.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 361 23 16 35
subtype1 142 12 11 16
subtype2 102 7 3 8
subtype3 117 4 2 11

Figure S61.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.274 (Fisher's exact test), Q value = 0.38

Table S71.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 4 20 102 9 364
subtype1 1 9 49 6 134
subtype2 2 4 28 2 105
subtype3 1 7 25 1 125

Figure S62.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.105 (Fisher's exact test), Q value = 0.19

Table S72.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 14 369
subtype1 10 151
subtype2 2 98
subtype3 2 120

Figure S63.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S73.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 214 56 142 115
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 5.04e-05 (logrank test), Q value = 0.00024

Table S74.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 525 66 0.1 - 191.8 (23.3)
subtype1 213 26 0.1 - 191.8 (20.6)
subtype2 56 3 0.7 - 185.8 (31.3)
subtype3 142 10 0.4 - 116.2 (24.5)
subtype4 114 27 0.3 - 149.6 (22.0)

Figure S64.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.02e-09 (Kruskal-Wallis (anova)), Q value = 4.3e-08

Table S75.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 525 63.9 (11.2)
subtype1 213 63.6 (12.3)
subtype2 56 56.7 (10.2)
subtype3 142 63.8 (10.2)
subtype4 114 68.2 (8.8)

Figure S65.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S76.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 398 21 108
subtype1 185 10 19
subtype2 56 0 0
subtype3 139 1 2
subtype4 18 10 87

Figure S66.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.287 (Fisher's exact test), Q value = 0.38

Table S77.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 137 390
subtype1 64 150
subtype2 12 44
subtype3 37 105
subtype4 24 91

Figure S67.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.392 (Fisher's exact test), Q value = 0.49

Table S78.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 361 23 16 35
subtype1 148 10 6 11
subtype2 36 4 1 4
subtype3 101 3 2 11
subtype4 76 6 7 9

Figure S68.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.139 (Fisher's exact test), Q value = 0.23

Table S79.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 4 20 102 9 364
subtype1 2 8 38 4 146
subtype2 1 3 8 2 40
subtype3 0 6 22 2 110
subtype4 1 3 34 1 68

Figure S69.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.976 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 14 369
subtype1 7 148
subtype2 1 36
subtype3 3 103
subtype4 3 82

Figure S70.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S81.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 165 92 134
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0723 (logrank test), Q value = 0.15

Table S82.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 389 52 0.1 - 191.8 (19.3)
subtype1 163 27 0.1 - 191.8 (17.8)
subtype2 92 14 0.3 - 87.3 (21.0)
subtype3 134 11 0.7 - 116.2 (20.2)

Figure S71.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000821 (Kruskal-Wallis (anova)), Q value = 0.0028

Table S83.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 389 64.3 (11.1)
subtype1 163 66.2 (10.7)
subtype2 92 64.7 (12.3)
subtype3 134 61.7 (10.1)

Figure S72.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S84.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 278 19 94
subtype1 89 8 68
subtype2 63 8 21
subtype3 126 3 5

Figure S73.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00572 (Fisher's exact test), Q value = 0.016

Table S85.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 88 303
subtype1 26 139
subtype2 20 72
subtype3 42 92

Figure S74.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.694 (Fisher's exact test), Q value = 0.76

Table S86.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 261 17 13 29
subtype1 102 7 8 11
subtype2 70 4 1 6
subtype3 89 6 4 12

Figure S75.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.00044 (Fisher's exact test), Q value = 0.0016

Table S87.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 7 88 5 265
subtype1 1 1 44 0 102
subtype2 1 4 24 4 54
subtype3 0 2 20 1 109

Figure S76.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.168 (Fisher's exact test), Q value = 0.27

Table S88.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 280
subtype1 8 115
subtype2 1 64
subtype3 2 101

Figure S77.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S89.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 140 114 137
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.239 (logrank test), Q value = 0.35

Table S90.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 389 52 0.1 - 191.8 (19.3)
subtype1 138 22 0.1 - 191.8 (17.8)
subtype2 114 10 0.4 - 116.2 (20.2)
subtype3 137 20 0.2 - 110.1 (20.0)

Figure S78.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000345 (Kruskal-Wallis (anova)), Q value = 0.0013

Table S91.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 389 64.3 (11.1)
subtype1 138 67.3 (9.0)
subtype2 114 62.5 (10.7)
subtype3 137 62.8 (12.5)

Figure S79.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

Table S92.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 278 19 94
subtype1 70 11 59
subtype2 110 2 2
subtype3 98 6 33

Figure S80.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.282 (Fisher's exact test), Q value = 0.38

Table S93.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 88 303
subtype1 27 113
subtype2 24 90
subtype3 37 100

Figure S81.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.181 (Fisher's exact test), Q value = 0.28

Table S94.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 261 17 13 29
subtype1 90 7 8 9
subtype2 72 4 1 13
subtype3 99 6 4 7

Figure S82.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.366 (Fisher's exact test), Q value = 0.47

Table S95.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 7 88 5 265
subtype1 1 1 33 0 96
subtype2 1 3 23 1 83
subtype3 0 3 32 4 86

Figure S83.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.548 (Fisher's exact test), Q value = 0.63

Table S96.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 280
subtype1 6 106
subtype2 2 84
subtype3 3 90

Figure S84.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/UCEC-TP/15120652/UCEC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCEC-TP/15094079/UCEC-TP.merged_data.txt

  • Number of patients = 536

  • Number of clustering approaches = 12

  • Number of selected clinical features = 7

  • 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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