Correlation between aggregated molecular cancer subtypes and selected clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1R78D60
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1R78D60
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 513 patients, 29 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 'AGE' and 'HISTOLOGICAL.TYPE'.

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

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'AGE',  'HISTOLOGICAL.TYPE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

  • 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',  'AGE',  'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'AGE', and 'HISTOLOGICAL.TYPE'.

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

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE',  'HISTOLOGICAL.TYPE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE' 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, 29 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE 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 0.838
(1.00) 		0.00418
(0.234) 		0.0003
(0.0192) 		0.0374
(1.00) 		0.889
(1.00) 		0.149
(1.00) 		0.615
(1.00)
mRNA cHierClus subtypes 0.126
(1.00) 		0.0314
(1.00) 		1e-05
(0.00079) 		0.104
(1.00) 		0.49
(1.00) 		0.571
(1.00) 		1
(1.00)
Copy Number Ratio CNMF subtypes 4.72e-05
(0.00316) 		4.02e-12
(3.37e-10) 		1e-05
(0.00079) 		0.037
(1.00) 		0.278
(1.00) 		0.00372
(0.212) 		0.746
(1.00)
METHLYATION CNMF 0.0544
(1.00) 		0.00189
(0.114) 		1e-05
(0.00079) 		0.182
(1.00) 		0.0243
(1.00) 		0.0685
(1.00) 		0.408
(1.00)
RPPA CNMF subtypes 0.0768
(1.00) 		0.148
(1.00) 		1e-05
(0.00079) 		0.471
(1.00) 		0.112
(1.00) 		0.0711
(1.00) 		0.479
(1.00)
RPPA cHierClus subtypes 0.274
(1.00) 		0.108
(1.00) 		2e-05
(0.00138) 		0.983
(1.00) 		0.106
(1.00) 		0.289
(1.00) 		0.495
(1.00)
RNAseq CNMF subtypes 4.5e-05
(0.00306) 		1.93e-06
(0.000154) 		1e-05
(0.00079) 		0.00357
(0.207) 		0.116
(1.00) 		0.0978
(1.00) 		1
(1.00)
RNAseq cHierClus subtypes 8.68e-05
(0.00573) 		1.97e-08
(1.64e-06) 		1e-05
(0.00079) 		0.191
(1.00) 		0.0447
(1.00) 		0.0608
(1.00) 		0.241
(1.00)
MIRSEQ CNMF 0.000521
(0.0328) 		2.94e-07
(2.38e-05) 		1e-05
(0.00079) 		0.803
(1.00) 		0.19
(1.00) 		0.382
(1.00) 		0.153
(1.00)
MIRSEQ CHIERARCHICAL 0.000198
(0.0129) 		2.21e-08
(1.81e-06) 		1e-05
(0.00079) 		0.192
(1.00) 		0.191
(1.00) 		0.105
(1.00) 		0.933
(1.00)
MIRseq Mature CNMF subtypes 0.0201
(1.00) 		0.00196
(0.116) 		1e-05
(0.00079) 		0.0117
(0.643) 		0.65
(1.00) 		0.00162
(0.0988) 		0.24
(1.00)
MIRseq Mature cHierClus subtypes 0.0749
(1.00) 		0.000882
(0.0547) 		1e-05
(0.00079) 		0.409
(1.00) 		0.201
(1.00) 		0.391
(1.00) 		0.723
(1.00)
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 = 0.838 (logrank test), Q value = 1

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 (46.2)
subtype3 12 1 19.8 - 105.4 (33.8)
subtype4 10 2 7.9 - 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 'AGE'

P value = 0.00418 (Kruskal-Wallis (anova)), Q value = 0.23

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

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: 'AGE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3e-04 (Fisher's exact test), Q value = 0.019

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 = 0.0374 (Fisher's exact test), Q value = 1

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 = 0.889 (Fisher's exact test), Q value = 1

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 = 0.149 (Fisher's exact test), Q value = 1

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 = 0.615 (Fisher's exact test), Q value = 1

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 = 0.126 (logrank test), Q value = 1

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 (42.5)
subtype3 7 3 13.6 - 68.3 (36.4)
subtype4 7 0 28.6 - 83.7 (49.2)
subtype5 16 2 7.9 - 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 'AGE'

P value = 0.0314 (Kruskal-Wallis (anova)), Q value = 1

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

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: 'AGE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 = 0.104 (Fisher's exact test), Q value = 1

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 = 0.49 (Fisher's exact test), Q value = 1

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 = 1

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 152 290 19 9 35
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 4.72e-05 (logrank test), Q value = 0.0032

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

nPatients nDeath Duration Range (Median), Month
ALL 503 60 0.0 - 191.8 (22.8)
subtype1 151 30 0.0 - 125.4 (18.7)
subtype2 289 21 0.1 - 191.8 (25.0)
subtype3 19 3 0.2 - 149.6 (30.8)
subtype4 9 0 8.6 - 60.5 (21.2)
subtype5 35 6 0.1 - 73.2 (17.6)

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 'AGE'

P value = 4.02e-12 (Kruskal-Wallis (anova)), Q value = 3.4e-10

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

nPatients Mean (Std.Dev)
ALL 504 63.8 (11.2)
subtype1 152 69.2 (8.4)
subtype2 289 61.4 (11.2)
subtype3 19 61.9 (13.8)
subtype4 9 68.1 (12.9)
subtype5 35 60.5 (12.2)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 380 21 104
subtype1 44 11 97
subtype2 277 8 5
subtype3 17 0 2
subtype4 8 1 0
subtype5 34 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 = 0.037 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 138 367
subtype1 31 121
subtype2 93 197
subtype3 2 17
subtype4 3 6
subtype5 9 26

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 = 0.278 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 351 22 17 30
subtype1 96 9 10 12
subtype2 208 13 5 16
subtype3 12 0 1 1
subtype4 9 0 0 0
subtype5 26 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 = 0.00372 (Fisher's exact test), Q value = 0.21

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 19 90 9 357
subtype1 1 2 45 3 90
subtype2 3 14 38 5 217
subtype3 0 3 1 0 15
subtype4 0 0 1 0 8
subtype5 0 0 5 1 27

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 = 0.746 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 350
subtype1 6 102
subtype2 7 197
subtype3 0 15
subtype4 0 9
subtype5 0 27

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 150 82 165
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0544 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 395 45 0.0 - 191.8 (18.2)
subtype1 148 24 0.0 - 191.8 (17.8)
subtype2 82 7 0.1 - 116.2 (24.1)
subtype3 165 14 0.1 - 185.8 (17.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00189 (Kruskal-Wallis (anova)), Q value = 0.11

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

nPatients Mean (Std.Dev)
ALL 396 64.0 (11.2)
subtype1 149 66.2 (10.2)
subtype2 82 63.1 (12.5)
subtype3 165 62.5 (11.2)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 288 20 89
subtype1 61 15 74
subtype2 65 2 15
subtype3 162 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 = 0.182 (Fisher's exact test), Q value = 1

Table S29.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 87 310
subtype1 37 113
subtype2 12 70
subtype3 38 127

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 = 0.0243 (Fisher's exact test), Q value = 1

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 265 18 13 29
subtype1 96 9 8 10
subtype2 51 3 4 11
subtype3 118 6 1 8

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.0685 (Fisher's exact test), Q value = 1

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 7 82 7 277
subtype1 1 2 37 3 97
subtype2 0 2 18 4 55
subtype3 1 3 27 0 125

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.408 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 287
subtype1 6 105
subtype2 1 61
subtype3 3 121

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 = 0.0768 (logrank test), Q value = 1

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 (29.2)
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 1.4 - 87.3 (35.6)
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 'AGE'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 1

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

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: 'AGE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 = 1

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 = 0.112 (Fisher's exact test), Q value = 1

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 = 0.0711 (Fisher's exact test), Q value = 1

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 = 0.479 (Fisher's exact test), Q value = 1

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 = 0.274 (logrank test), Q value = 1

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 (29.2)
subtype1 70 11 0.6 - 149.6 (28.6)
subtype2 67 4 0.7 - 113.4 (25.1)
subtype3 44 4 1.4 - 185.8 (35.0)
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 'AGE'

P value = 0.108 (Kruskal-Wallis (anova)), Q value = 1

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

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: 'AGE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.0014

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 = 0.106 (Fisher's exact test), Q value = 1

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 = 0.289 (Fisher's exact test), Q value = 1

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 = 0.495 (Fisher's exact test), Q value = 1

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 147 214 146
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 4.5e-05 (logrank test), Q value = 0.0031

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

nPatients nDeath Duration Range (Median), Month
ALL 505 60 0.0 - 191.8 (22.8)
subtype1 147 12 0.1 - 185.8 (27.2)
subtype2 212 40 0.0 - 191.8 (18.5)
subtype3 146 8 0.1 - 116.2 (24.1)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 1.93e-06 (Kruskal-Wallis (anova)), Q value = 0.00015

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

nPatients Mean (Std.Dev)
ALL 506 63.8 (11.2)
subtype1 147 60.9 (11.4)
subtype2 213 66.4 (10.6)
subtype3 146 63.1 (11.1)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 382 20 105
subtype1 146 1 0
subtype2 95 15 104
subtype3 141 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 = 0.00357 (Fisher's exact test), Q value = 0.21

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

nPatients NO YES
ALL 137 370
subtype1 55 92
subtype2 51 163
subtype3 31 115

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 = 0.116 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 351 22 17 30
subtype1 108 6 4 5
subtype2 140 13 11 14
subtype3 103 3 2 11

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 = 0.0978 (Fisher's exact test), Q value = 1

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 19 90 9 359
subtype1 2 6 18 2 112
subtype2 1 5 48 6 139
subtype3 1 8 24 1 108

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 13 350
subtype1 3 98
subtype2 6 150
subtype3 4 102

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
Number of samples 206 158 143
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 8.68e-05 (logrank test), Q value = 0.0057

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

nPatients nDeath Duration Range (Median), Month
ALL 505 60 0.0 - 191.8 (22.8)
subtype1 206 20 0.1 - 185.8 (23.3)
subtype2 156 32 0.0 - 191.8 (19.4)
subtype3 143 8 0.1 - 116.2 (24.3)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 1.97e-08 (Kruskal-Wallis (anova)), Q value = 1.6e-06

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

nPatients Mean (Std.Dev)
ALL 506 63.8 (11.2)
subtype1 206 61.3 (11.7)
subtype2 157 67.9 (9.4)
subtype3 143 63.0 (11.1)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 382 20 105
subtype1 201 3 2
subtype2 42 14 102
subtype3 139 3 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 = 0.191 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 137 370
subtype1 64 142
subtype2 41 117
subtype3 32 111

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 = 0.0447 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 351 22 17 30
subtype1 146 11 4 8
subtype2 104 9 10 11
subtype3 101 2 3 11

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 = 0.0608 (Fisher's exact test), Q value = 1

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 19 90 9 359
subtype1 2 6 28 6 153
subtype2 1 6 40 2 96
subtype3 1 7 22 1 110

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.241 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 350
subtype1 4 143
subtype2 7 104
subtype3 2 103

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 204 139 161
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.000521 (logrank test), Q value = 0.033

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

nPatients nDeath Duration Range (Median), Month
ALL 502 57 0.0 - 191.8 (22.9)
subtype1 202 35 0.0 - 191.8 (18.9)
subtype2 139 14 0.1 - 185.8 (27.1)
subtype3 161 8 0.1 - 116.2 (23.9)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 2.94e-07 (Kruskal-Wallis (anova)), Q value = 2.4e-05

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

nPatients Mean (Std.Dev)
ALL 503 63.7 (11.2)
subtype1 203 66.9 (9.9)
subtype2 139 60.4 (12.2)
subtype3 161 62.7 (11.0)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 383 21 100
subtype1 91 16 97
subtype2 135 3 1
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 = 0.803 (Fisher's exact test), Q value = 1

Table S69.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 134 370
subtype1 51 153
subtype2 39 100
subtype3 44 117

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 = 0.19 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 349 22 16 30
subtype1 132 12 11 13
subtype2 99 6 3 7
subtype3 118 4 2 10

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.382 (Fisher's exact test), Q value = 1

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 19 91 9 355
subtype1 2 7 44 6 130
subtype2 1 5 24 2 99
subtype3 1 7 23 1 126

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.153 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 352
subtype1 9 142
subtype2 2 91
subtype3 2 119

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 140 58 112 194
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.000198 (logrank test), Q value = 0.013

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

nPatients nDeath Duration Range (Median), Month
ALL 502 57 0.0 - 191.8 (22.9)
subtype1 140 9 0.1 - 116.2 (23.7)
subtype2 58 3 0.7 - 185.8 (29.0)
subtype3 111 24 0.0 - 149.6 (20.3)
subtype4 193 21 0.1 - 191.8 (20.2)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 2.21e-08 (Kruskal-Wallis (anova)), Q value = 1.8e-06

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

nPatients Mean (Std.Dev)
ALL 503 63.7 (11.2)
subtype1 140 63.8 (10.2)
subtype2 58 57.6 (10.7)
subtype3 111 68.1 (8.9)
subtype4 194 63.0 (12.3)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 383 21 100
subtype1 136 1 3
subtype2 58 0 0
subtype3 21 10 81
subtype4 168 10 16

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 = 0.192 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 134 370
subtype1 37 103
subtype2 12 46
subtype3 24 88
subtype4 61 133

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 = 0.191 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 349 22 16 30
subtype1 100 3 3 10
subtype2 38 4 1 5
subtype3 74 6 7 9
subtype4 137 9 5 6

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.105 (Fisher's exact test), Q value = 1

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 19 91 9 355
subtype1 0 6 19 2 110
subtype2 1 3 7 2 43
subtype3 1 3 32 1 68
subtype4 2 7 33 4 134

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.933 (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 13 352
subtype1 3 101
subtype2 1 38
subtype3 4 78
subtype4 5 135

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 150 85 133
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0201 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 366 43 0.0 - 191.8 (18.1)
subtype1 148 23 0.0 - 191.8 (16.3)
subtype2 85 12 0.1 - 87.3 (17.9)
subtype3 133 8 0.1 - 116.2 (19.8)

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 'AGE'

P value = 0.00196 (Kruskal-Wallis (anova)), Q value = 0.12

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

nPatients Mean (Std.Dev)
ALL 367 64.1 (11.0)
subtype1 149 66.0 (10.9)
subtype2 85 64.4 (12.1)
subtype3 133 61.7 (10.1)

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 263 19 86
subtype1 79 8 63
subtype2 59 8 18
subtype3 125 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 = 0.0117 (Fisher's exact test), Q value = 0.64

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

nPatients NO YES
ALL 85 283
subtype1 25 125
subtype2 18 67
subtype3 42 91

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 = 0.65 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 249 16 13 24
subtype1 95 6 8 8
subtype2 65 4 1 5
subtype3 89 6 4 11

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 = 0.00162 (Fisher's exact test), Q value = 0.099

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 6 77 5 256
subtype1 1 1 37 0 97
subtype2 1 3 21 4 50
subtype3 0 2 19 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 = 0.24 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 263
subtype1 7 106
subtype2 1 57
subtype3 2 100

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 130 109 129
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0749 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 366 43 0.0 - 191.8 (18.1)
subtype1 128 20 0.0 - 191.8 (16.9)
subtype2 109 6 0.1 - 116.2 (18.8)
subtype3 129 17 0.1 - 92.0 (19.4)

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 'AGE'

P value = 0.000882 (Kruskal-Wallis (anova)), Q value = 0.055

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

nPatients Mean (Std.Dev)
ALL 367 64.1 (11.0)
subtype1 129 67.1 (9.1)
subtype2 109 62.3 (10.8)
subtype3 129 62.6 (12.4)

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

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 263 19 86
subtype1 64 11 55
subtype2 105 2 2
subtype3 94 6 29

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 = 0.409 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 85 283
subtype1 27 103
subtype2 23 86
subtype3 35 94

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 = 0.201 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 249 16 13 24
subtype1 85 7 8 7
subtype2 70 4 1 11
subtype3 94 5 4 6

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 = 0.391 (Fisher's exact test), Q value = 1

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 6 77 5 256
subtype1 1 1 28 0 91
subtype2 1 3 20 1 81
subtype3 0 2 29 4 84

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 = 0.723 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 263
subtype1 5 98
subtype2 2 80
subtype3 3 85

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

Methods & Data
Input

  • Cluster data file = UCEC-TP.mergedcluster.txt

  • Clinical data file = UCEC-TP.merged_data.txt

  • Number of patients = 513

  • 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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