Correlation between aggregated molecular cancer subtypes and selected clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1125S0G
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/C1125S0G
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 545 patients, 39 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' and 'HISTOLOGICAL_TYPE'.

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

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH', and '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 'RESIDUAL_TUMOR'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'RADIATION_THERAPY', 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 'Time to Death',  'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE', 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, 39 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		RADIATION
THERAPY 		HISTOLOGICAL
TYPE 		RESIDUAL
TUMOR 		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 0.777
(0.826) 		0.00418
(0.0121) 		0.211
(0.301) 		0.00023
(0.000805) 		0.681
(0.752) 		0.15
(0.243) 		0.612
(0.686)
mRNA cHierClus subtypes 0.0472
(0.102) 		0.0314
(0.0713) 		0.487
(0.577) 		1e-05
(4.67e-05) 		0.179
(0.274) 		0.572
(0.658) 		1
(1.00)
Copy Number Ratio CNMF subtypes 5.53e-08
(1.16e-06) 		1.08e-12
(9.1e-11) 		0.161
(0.25) 		1e-05
(4.67e-05) 		0.238
(0.322) 		0.00522
(0.0146) 		0.693
(0.756)
METHLYATION CNMF 0.0135
(0.0365) 		0.0014
(0.00436) 		0.0289
(0.0673) 		1e-05
(4.67e-05) 		0.0149
(0.0392) 		0.0819
(0.156) 		0.481
(0.577)
RPPA CNMF subtypes 0.000737
(0.00248) 		0.0037
(0.0111) 		0.227
(0.318) 		1e-05
(4.67e-05) 		0.422
(0.521) 		0.21
(0.301) 		0.061
(0.125)
RPPA cHierClus subtypes 0.136
(0.243) 		0.872
(0.915) 		0.149
(0.243) 		1e-05
(4.67e-05) 		0.897
(0.93) 		0.353
(0.45) 		0.105
(0.195)
RNAseq CNMF subtypes 1.58e-05
(6.99e-05) 		4.01e-07
(6.74e-06) 		0.0627
(0.125) 		1e-05
(4.67e-05) 		0.0439
(0.097) 		0.16
(0.25) 		1
(1.00)
RNAseq cHierClus subtypes 3.12e-05
(0.000131) 		1.3e-08
(3.64e-07) 		0.441
(0.537) 		1e-05
(4.67e-05) 		0.0182
(0.0465) 		0.0553
(0.116) 		0.234
(0.322)
MIRSEQ CNMF 8.94e-05
(0.000357) 		9.68e-07
(1.36e-05) 		0.0225
(0.054) 		1e-05
(4.67e-05) 		0.145
(0.243) 		0.321
(0.426) 		0.0666
(0.13)
MIRSEQ CHIERARCHICAL 3.62e-06
(4.35e-05) 		2.71e-10
(1.14e-08) 		0.133
(0.243) 		1e-05
(4.67e-05) 		0.599
(0.68) 		0.203
(0.299) 		0.923
(0.945)
MIRseq Mature CNMF subtypes 0.0217
(0.0536) 		0.000881
(0.00285) 		0.324
(0.426) 		1e-05
(4.67e-05) 		0.744
(0.801) 		0.00015
(0.000548) 		0.197
(0.296)
MIRseq Mature cHierClus subtypes 0.145
(0.243) 		0.000102
(0.000388) 		0.349
(0.45) 		1e-05
(4.67e-05) 		0.146
(0.243) 		0.402
(0.504) 		0.55
(0.642)
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 = 0.777 (logrank test), Q value = 0.83

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

nPatients nDeath Duration Range (Median), Month
ALL 54 10 10.2 - 149.6 (46.6)
subtype1 14 2 13.6 - 149.6 (47.8)
subtype2 18 5 10.2 - 125.4 (55.8)
subtype3 12 1 20.9 - 105.4 (36.4)
subtype4 10 2 18.6 - 82.5 (37.1)

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 = 0.012

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 'RADIATION_THERAPY'

P value = 0.211 (Fisher's exact test), Q value = 0.3

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

nPatients NO YES
ALL 37 17
subtype1 7 7
subtype2 12 6
subtype3 9 3
subtype4 9 1

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00023 (Fisher's exact test), Q value = 8e-04

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: '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 S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.681 (Fisher's exact test), Q value = 0.75

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

nPatients R0 R1 R2 RX
ALL 42 3 2 1
subtype1 8 2 1 0
subtype2 14 1 1 1
subtype3 11 0 0 0
subtype4 9 0 0 0

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.15 (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 = 0.612 (Fisher's exact test), Q value = 0.69

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 = 0.0472 (logrank test), Q value = 0.1

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

nPatients nDeath Duration Range (Median), Month
ALL 54 10 10.2 - 149.6 (46.6)
subtype1 14 1 10.2 - 149.6 (59.1)
subtype2 10 4 22.0 - 125.4 (47.9)
subtype3 7 3 13.6 - 80.5 (36.4)
subtype4 7 0 32.8 - 129.8 (56.9)
subtype5 16 2 18.6 - 105.4 (34.6)

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 = 0.071

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 'RADIATION_THERAPY'

P value = 0.487 (Fisher's exact test), Q value = 0.58

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

nPatients NO YES
ALL 37 17
subtype1 9 5
subtype2 7 3
subtype3 5 2
subtype4 3 4
subtype5 13 3

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: '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 S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.179 (Fisher's exact test), Q value = 0.27

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

nPatients R0 R1 R2 RX
ALL 42 3 2 1
subtype1 11 1 0 1
subtype2 9 0 1 0
subtype3 4 1 1 0
subtype4 4 1 0 0
subtype5 14 0 0 0

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

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.572 (Fisher's exact test), Q value = 0.66

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 168 303 19 9 37
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 5.53e-08 (logrank test), Q value = 1.2e-06

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

nPatients nDeath Duration Range (Median), Month
ALL 534 81 0.0 - 225.5 (27.4)
subtype1 167 46 0.1 - 125.4 (23.9)
subtype2 302 24 0.0 - 225.5 (29.2)
subtype3 19 3 0.1 - 149.6 (52.0)
subtype4 9 0 8.6 - 60.5 (21.2)
subtype5 37 8 0.1 - 77.2 (25.5)

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 = 1.08e-12 (Kruskal-Wallis (anova)), Q value = 9.1e-11

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

nPatients Mean (Std.Dev)
ALL 534 64.0 (11.2)
subtype1 167 69.2 (8.4)
subtype2 302 61.4 (11.1)
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 'RADIATION_THERAPY'

P value = 0.161 (Fisher's exact test), Q value = 0.25

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

nPatients NO YES
ALL 284 218
subtype1 80 69
subtype2 165 126
subtype3 15 3
subtype4 4 5
subtype5 20 15

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 401 22 113
subtype1 50 12 106
subtype2 290 8 5
subtype3 17 0 2
subtype4 8 1 0
subtype5 36 1 0

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.238 (Fisher's exact test), Q value = 0.32

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

nPatients R0 R1 R2 RX
ALL 370 22 17 37
subtype1 105 9 10 16
subtype2 216 13 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: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00522 (Fisher's exact test), Q value = 0.015

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 20 106 9 368
subtype1 1 2 51 3 97
subtype2 3 15 47 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 = 0.693 (Fisher's exact test), Q value = 0.76

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 15 372
subtype1 7 114
subtype2 8 204
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 164 91 173
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0135 (logrank test), Q value = 0.037

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

nPatients nDeath Duration Range (Median), Month
ALL 426 65 0.0 - 225.5 (23.3)
subtype1 162 34 0.1 - 225.5 (22.0)
subtype2 91 11 0.3 - 136.6 (26.7)
subtype3 173 20 0.0 - 185.8 (22.8)

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 = 0.0014 (Kruskal-Wallis (anova)), Q value = 0.0044

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

nPatients Mean (Std.Dev)
ALL 426 64.2 (11.2)
subtype1 162 66.3 (10.1)
subtype2 91 63.2 (12.4)
subtype3 173 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 'RADIATION_THERAPY'

P value = 0.0289 (Fisher's exact test), Q value = 0.067

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

nPatients NO YES
ALL 217 177
subtype1 66 76
subtype2 54 32
subtype3 97 69

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 309 21 98
subtype1 66 16 82
subtype2 73 2 16
subtype3 170 3 0

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

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0149 (Fisher's exact test), Q value = 0.039

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

nPatients R0 R1 R2 RX
ALL 283 19 13 36
subtype1 104 10 8 12
subtype2 56 3 4 14
subtype3 123 6 1 10

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.0819 (Fisher's exact test), Q value = 0.16

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 98 7 287
subtype1 1 2 43 3 102
subtype2 0 2 22 4 57
subtype3 1 4 33 0 128

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.481 (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 11 309
subtype1 6 116
subtype2 1 66
subtype3 4 127

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
Number of samples 129 150 158
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.000737 (logrank test), Q value = 0.0025

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

nPatients nDeath Duration Range (Median), Month
ALL 435 69 0.0 - 185.8 (27.2)
subtype1 129 16 0.2 - 185.8 (28.8)
subtype2 150 16 0.4 - 149.6 (31.9)
subtype3 156 37 0.0 - 129.8 (22.4)

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 = 0.0037 (Kruskal-Wallis (anova)), Q value = 0.011

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

nPatients Mean (Std.Dev)
ALL 436 63.9 (11.2)
subtype1 129 64.7 (11.4)
subtype2 150 61.7 (10.6)
subtype3 157 65.2 (11.4)

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.227 (Fisher's exact test), Q value = 0.32

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

nPatients NO YES
ALL 224 188
subtype1 71 53
subtype2 71 75
subtype3 82 60

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 326 17 94
subtype1 113 4 12
subtype2 123 7 20
subtype3 90 6 62

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

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.422 (Fisher's exact test), Q value = 0.52

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

nPatients R0 R1 R2 RX
ALL 301 14 11 34
subtype1 91 7 2 9
subtype2 100 5 3 10
subtype3 110 2 6 15

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.21 (Fisher's exact test), Q value = 0.3

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 3 14 87 5 301
subtype1 1 5 21 1 99
subtype2 1 7 26 3 102
subtype3 1 2 40 1 100

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.061 (Fisher's exact test), Q value = 0.12

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 15 295
subtype1 2 102
subtype2 9 89
subtype3 4 104

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 110 183 93 51
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.136 (logrank test), Q value = 0.24

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

nPatients nDeath Duration Range (Median), Month
ALL 435 69 0.0 - 185.8 (27.2)
subtype1 110 10 0.2 - 185.8 (30.1)
subtype2 181 36 0.0 - 149.6 (26.1)
subtype3 93 16 0.1 - 118.0 (24.7)
subtype4 51 7 0.7 - 89.3 (28.7)

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 = 0.872 (Kruskal-Wallis (anova)), Q value = 0.92

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

nPatients Mean (Std.Dev)
ALL 436 63.9 (11.2)
subtype1 110 63.3 (11.2)
subtype2 182 64.2 (11.2)
subtype3 93 63.5 (11.6)
subtype4 51 64.5 (10.9)

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.149 (Fisher's exact test), Q value = 0.24

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

nPatients NO YES
ALL 224 188
subtype1 68 42
subtype2 91 74
subtype3 43 45
subtype4 22 27

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 326 17 94
subtype1 107 1 2
subtype2 109 7 67
subtype3 73 5 15
subtype4 37 4 10

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

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.897 (Fisher's exact test), Q value = 0.93

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

nPatients R0 R1 R2 RX
ALL 301 14 11 34
subtype1 83 3 2 7
subtype2 125 6 4 13
subtype3 58 4 4 9
subtype4 35 1 1 5

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.353 (Fisher's exact test), Q value = 0.45

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 3 14 87 5 301
subtype1 2 3 20 1 81
subtype2 1 5 45 1 119
subtype3 0 4 16 3 61
subtype4 0 2 6 0 40

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.105 (Fisher's exact test), Q value = 0.2

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 15 295
subtype1 2 90
subtype2 10 107
subtype3 3 62
subtype4 0 36

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 233 154 155
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.58e-05 (logrank test), Q value = 7e-05

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

nPatients nDeath Duration Range (Median), Month
ALL 540 82 0.0 - 225.5 (27.5)
subtype1 231 53 0.0 - 225.5 (22.8)
subtype2 154 13 0.1 - 185.8 (31.5)
subtype3 155 16 0.4 - 136.6 (30.1)

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 = 4.01e-07 (Kruskal-Wallis (anova)), Q value = 6.7e-06

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

nPatients Mean (Std.Dev)
ALL 540 63.9 (11.2)
subtype1 231 66.6 (10.5)
subtype2 154 60.9 (11.5)
subtype3 155 63.1 (10.9)

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0627 (Fisher's exact test), Q value = 0.13

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

nPatients NO YES
ALL 288 220
subtype1 110 97
subtype2 81 70
subtype3 97 53

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 406 22 114
subtype1 103 17 113
subtype2 153 1 0
subtype3 150 4 1

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

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0439 (Fisher's exact test), Q value = 0.097

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

nPatients R0 R1 R2 RX
ALL 373 22 17 37
subtype1 152 14 11 17
subtype2 112 6 4 6
subtype3 109 2 2 14

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.16 (Fisher's exact test), Q value = 0.25

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 20 107 9 373
subtype1 1 5 55 6 148
subtype2 2 7 23 2 114
subtype3 1 8 29 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 15 375
subtype1 7 165
subtype2 4 102
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 124 53 46 64 108 69 78
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.12e-05 (logrank test), Q value = 0.00013

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

nPatients nDeath Duration Range (Median), Month
ALL 540 82 0.0 - 225.5 (27.5)
subtype1 124 31 0.1 - 125.4 (22.3)
subtype2 53 4 0.4 - 185.8 (31.8)
subtype3 45 11 0.2 - 225.5 (32.0)
subtype4 64 2 0.1 - 129.8 (31.0)
subtype5 107 19 0.0 - 92.9 (23.2)
subtype6 69 11 0.4 - 136.6 (30.1)
subtype7 78 4 0.6 - 123.7 (30.1)

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 = 1.3e-08 (Kruskal-Wallis (anova)), Q value = 3.6e-07

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

nPatients Mean (Std.Dev)
ALL 540 63.9 (11.2)
subtype1 123 69.3 (8.4)
subtype2 53 59.3 (10.3)
subtype3 45 63.9 (9.9)
subtype4 64 61.3 (12.9)
subtype5 108 62.9 (12.3)
subtype6 69 62.3 (11.7)
subtype7 78 63.8 (10.0)

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.441 (Fisher's exact test), Q value = 0.54

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

nPatients NO YES
ALL 288 220
subtype1 56 54
subtype2 31 21
subtype3 20 21
subtype4 33 30
subtype5 58 42
subtype6 42 25
subtype7 48 27

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 406 22 114
subtype1 24 10 90
subtype2 53 0 0
subtype3 20 5 21
subtype4 62 2 0
subtype5 103 3 2
subtype6 68 1 0
subtype7 76 1 1

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

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0182 (Fisher's exact test), Q value = 0.046

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

nPatients R0 R1 R2 RX
ALL 373 22 17 37
subtype1 81 7 3 10
subtype2 42 1 0 3
subtype3 32 1 7 3
subtype4 44 5 1 1
subtype5 72 7 4 6
subtype6 44 1 1 6
subtype7 58 0 1 8

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0553 (Fisher's exact test), Q value = 0.12

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 20 107 9 373
subtype1 0 3 36 1 75
subtype2 0 2 10 0 39
subtype3 1 3 10 1 26
subtype4 2 1 9 3 47
subtype5 0 4 15 4 77
subtype6 1 3 15 0 48
subtype7 0 4 12 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 = 0.234 (Fisher's exact test), Q value = 0.32

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 15 375
subtype1 4 82
subtype2 1 38
subtype3 4 33
subtype4 2 41
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 221 151 163
'MIRSEQ CNMF' versus 'Time to Death'

P value = 8.94e-05 (logrank test), Q value = 0.00036

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

nPatients nDeath Duration Range (Median), Month
ALL 533 79 0.0 - 225.5 (27.4)
subtype1 219 48 0.1 - 225.5 (23.7)
subtype2 151 18 0.0 - 185.8 (31.3)
subtype3 163 13 0.4 - 136.6 (28.8)

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 = 9.68e-07 (Kruskal-Wallis (anova)), Q value = 1.4e-05

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

nPatients Mean (Std.Dev)
ALL 533 63.9 (11.2)
subtype1 219 66.7 (10.0)
subtype2 151 60.9 (12.2)
subtype3 163 62.9 (10.9)

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.0225 (Fisher's exact test), Q value = 0.054

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

nPatients NO YES
ALL 281 220
subtype1 96 102
subtype2 88 57
subtype3 97 61

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 404 22 109
subtype1 99 17 105
subtype2 146 3 2
subtype3 159 2 2

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.145 (Fisher's exact test), Q value = 0.24

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

nPatients R0 R1 R2 RX
ALL 368 22 16 37
subtype1 144 12 11 17
subtype2 105 7 3 8
subtype3 119 3 2 12

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.321 (Fisher's exact test), Q value = 0.43

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 20 107 9 366
subtype1 1 9 50 6 136
subtype2 2 4 31 2 105
subtype3 1 7 26 1 125

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0666 (Fisher's exact test), Q value = 0.13

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 15 374
subtype1 11 153
subtype2 2 101
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 217 58 143 117
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 3.62e-06 (logrank test), Q value = 4.3e-05

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

nPatients nDeath Duration Range (Median), Month
ALL 533 79 0.0 - 225.5 (27.4)
subtype1 216 30 0.0 - 225.5 (24.9)
subtype2 58 3 0.4 - 185.8 (36.1)
subtype3 143 13 0.4 - 136.6 (29.3)
subtype4 116 33 0.3 - 149.6 (25.1)

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 = 2.71e-10 (Kruskal-Wallis (anova)), Q value = 1.1e-08

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

nPatients Mean (Std.Dev)
ALL 533 63.9 (11.2)
subtype1 216 63.7 (12.3)
subtype2 58 56.6 (10.0)
subtype3 143 63.7 (10.2)
subtype4 116 68.2 (8.7)

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.133 (Fisher's exact test), Q value = 0.24

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

nPatients NO YES
ALL 281 220
subtype1 103 99
subtype2 38 20
subtype3 84 54
subtype4 56 47

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 404 22 109
subtype1 188 10 19
subtype2 58 0 0
subtype3 140 1 2
subtype4 18 11 88

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

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.599 (Fisher's exact test), Q value = 0.68

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

nPatients R0 R1 R2 RX
ALL 368 22 16 37
subtype1 149 10 6 13
subtype2 39 3 1 4
subtype3 102 3 2 11
subtype4 78 6 7 9

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.203 (Fisher's exact test), Q value = 0.3

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 20 107 9 366
subtype1 2 8 39 4 146
subtype2 1 3 10 2 40
subtype3 0 6 23 2 110
subtype4 1 3 35 1 70

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.923 (Fisher's exact test), Q value = 0.95

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 15 374
subtype1 7 149
subtype2 1 38
subtype3 3 103
subtype4 4 84

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 170 95 134
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0217 (logrank test), Q value = 0.054

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

nPatients nDeath Duration Range (Median), Month
ALL 397 61 0.0 - 225.5 (23.3)
subtype1 168 32 0.0 - 225.5 (22.1)
subtype2 95 18 0.3 - 123.7 (24.1)
subtype3 134 11 0.7 - 136.6 (24.7)

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 = 0.000881 (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 397 64.3 (11.0)
subtype1 168 66.2 (10.7)
subtype2 95 64.5 (12.2)
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 'RADIATION_THERAPY'

P value = 0.324 (Fisher's exact test), Q value = 0.43

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

nPatients NO YES
ALL 196 170
subtype1 75 72
subtype2 44 44
subtype3 77 54

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 284 20 95
subtype1 93 9 68
subtype2 65 8 22
subtype3 126 3 5

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

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.744 (Fisher's exact test), Q value = 0.8

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

nPatients R0 R1 R2 RX
ALL 267 17 13 31
subtype1 106 7 8 12
subtype2 72 4 1 7
subtype3 89 6 4 12

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.00015 (Fisher's exact test), Q value = 0.00055

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 93 5 266
subtype1 1 1 48 0 102
subtype2 1 4 25 4 55
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 = 0.197 (Fisher's exact test), Q value = 0.3

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 285
subtype1 8 118
subtype2 1 66
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 143 117 139
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.145 (logrank test), Q value = 0.24

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

nPatients nDeath Duration Range (Median), Month
ALL 397 61 0.0 - 225.5 (23.3)
subtype1 141 25 0.1 - 225.5 (22.0)
subtype2 117 11 0.4 - 136.6 (24.4)
subtype3 139 25 0.0 - 110.1 (23.2)

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 = 0.000102 (Kruskal-Wallis (anova)), Q value = 0.00039

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

nPatients Mean (Std.Dev)
ALL 397 64.3 (11.0)
subtype1 141 67.5 (9.0)
subtype2 117 62.4 (10.6)
subtype3 139 62.7 (12.4)

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 'RADIATION_THERAPY'

P value = 0.349 (Fisher's exact test), Q value = 0.45

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

nPatients NO YES
ALL 196 170
subtype1 63 61
subtype2 67 46
subtype3 66 63

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.7e-05

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 284 20 95
subtype1 72 12 59
subtype2 113 2 2
subtype3 99 6 34

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

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.146 (Fisher's exact test), Q value = 0.24

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

nPatients R0 R1 R2 RX
ALL 267 17 13 31
subtype1 92 7 8 10
subtype2 74 4 1 14
subtype3 101 6 4 7

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.402 (Fisher's exact test), Q value = 0.5

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 93 5 266
subtype1 1 1 35 0 96
subtype2 1 3 25 1 83
subtype3 0 3 33 4 87

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 = 0.55 (Fisher's exact test), Q value = 0.64

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 285
subtype1 6 108
subtype2 2 85
subtype3 3 92

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/20144021/UCEC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCEC-TP/19775642/UCEC-TP.merged_data.txt

  • Number of patients = 545

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