Uterine Corpus Endometrioid Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 5 clinical features across 430 patients, 34 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'AGE',  'HISTOLOGICAL.TYPE',  'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE' and 'NEOADJUVANT.THERAPY'.

  • 5 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death',  'AGE',  'HISTOLOGICAL.TYPE', and 'NEOADJUVANT.THERAPY'.

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

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

  • Consensus hierarchical clustering analysis on RPPA data identified 6 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',  'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

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

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death',  'AGE',  'HISTOLOGICAL.TYPE', and 'NEOADJUVANT.THERAPY'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE',  'HISTOLOGICAL.TYPE',  'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 5 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 34 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
Statistical Tests logrank test ANOVA Chi-square test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.53 0.0107 0.000456 0.0317 0.0124
mRNA cHierClus subtypes 0.73 0.0598 0.00134 0.0666 0.0114
CN CNMF 4.81e-05 3.14e-09 1.39e-32 0.107 0.0103
METHLYATION CNMF 0.00798 0.0123 4.28e-17 0.0786 0.0132
RPPA CNMF subtypes 0.114 0.343 3.91e-06 0.327 0.0533
RPPA cHierClus subtypes 0.294 0.463 0.00421 0.584 0.157
RNAseq CNMF subtypes 0.00873 0.00104 8.73e-18 0.0148 0.000242
RNAseq cHierClus subtypes 0.00985 0.00202 8.69e-18 0.0325 1.45e-05
MIRseq CNMF subtypes 0.00602 0.000193 5.4e-20 0.0958 0.00481
MIRseq cHierClus subtypes 0.0652 3.03e-05 1.14e-13 0.0303 0.0281
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 13 19 14 8
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.53 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 54 7 6.0 - 133.2 (35.4)
subtype1 13 2 9.0 - 133.2 (39.0)
subtype2 19 3 6.0 - 113.2 (37.7)
subtype3 14 1 8.6 - 89.3 (30.9)
subtype4 8 1 6.4 - 65.5 (22.9)

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 = 0.0107 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 54 62.9 (11.8)
subtype1 13 65.1 (12.0)
subtype2 19 68.4 (9.1)
subtype3 14 58.2 (11.0)
subtype4 8 54.8 (12.9)

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 = 0.000456 (Chi-square test)

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 1
subtype2 8 0 11
subtype3 13 1 0
subtype4 8 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 = 0.0317 (Fisher's exact test)

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

nPatients NO YES
ALL 25 29
subtype1 7 6
subtype2 13 6
subtype3 3 11
subtype4 2 6

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0124 (Fisher's exact test)

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

nPatients NO YES
ALL 18 36
subtype1 6 7
subtype2 10 9
subtype3 2 12
subtype4 0 8

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 20 15 19
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.73 (logrank test)

Table S8.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 54 7 6.0 - 133.2 (35.4)
subtype1 20 2 6.4 - 89.3 (29.8)
subtype2 15 2 9.0 - 133.2 (39.0)
subtype3 19 3 6.0 - 113.2 (37.7)

Figure S6.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0598 (ANOVA)

Table S9.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 54 62.9 (11.8)
subtype1 20 58.2 (13.3)
subtype2 15 64.0 (10.6)
subtype3 19 67.1 (9.9)

Figure S7.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00134 (Chi-square test)

Table S10.  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 19 1 0
subtype2 13 0 2
subtype3 9 0 10

Figure S8.  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 = 0.0666 (Fisher's exact test)

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 25 29
subtype1 5 15
subtype2 9 6
subtype3 11 8

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0114 (Fisher's exact test)

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 18 36
subtype1 2 18
subtype2 6 9
subtype3 10 9

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S13.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 255 11 119 17 16 2 2
'CN CNMF' versus 'Time to Death'

P value = 4.81e-05 (logrank test)

Table S14.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 414 40 0.0 - 187.1 (16.6)
subtype1 252 17 0.0 - 187.1 (18.2)
subtype2 11 0 1.4 - 133.2 (11.4)
subtype3 118 16 0.0 - 113.2 (13.0)
subtype4 17 5 0.5 - 68.3 (13.2)
subtype5 16 2 0.7 - 33.5 (13.2)

Figure S11.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 3.14e-09 (ANOVA)

Table S15.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 417 63.4 (11.2)
subtype1 254 60.9 (11.3)
subtype2 11 65.3 (13.1)
subtype3 119 69.0 (8.3)
subtype4 17 63.7 (12.5)
subtype5 16 60.9 (11.4)

Figure S12.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.39e-32 (Chi-square test)

Table S16.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 308 3 8 2 7 16 74
subtype1 229 3 8 1 4 6 4
subtype2 10 0 0 0 0 0 1
subtype3 39 0 0 0 2 10 68
subtype4 15 0 0 1 0 0 1
subtype5 15 0 0 0 1 0 0

Figure S13.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.107 (Chi-square test)

Table S17.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 129 289
subtype1 91 164
subtype2 2 9
subtype3 29 90
subtype4 3 14
subtype5 4 12

Figure S14.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.0103 (Chi-square test)

Table S18.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 102 316
subtype1 52 203
subtype2 1 10
subtype3 43 76
subtype4 3 14
subtype5 3 13

Figure S15.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S19.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 119 71 123
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00798 (logrank test)

Table S20.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 309 30 0.0 - 187.1 (12.8)
subtype1 117 18 0.0 - 187.1 (11.5)
subtype2 71 4 0.0 - 92.0 (17.5)
subtype3 121 8 0.1 - 173.6 (13.0)

Figure S16.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0123 (ANOVA)

Table S21.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 312 63.5 (11.1)
subtype1 118 65.8 (10.0)
subtype2 71 63.0 (12.9)
subtype3 123 61.6 (10.8)

Figure S17.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 4.28e-17 (Chi-square test)

Table S22.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 229 6 1 3 16 58
subtype1 54 1 0 1 13 50
subtype2 58 2 1 0 2 8
subtype3 117 3 0 2 1 0

Figure S18.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0786 (Fisher's exact test)

Table S23.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 79 234
subtype1 32 87
subtype2 11 60
subtype3 36 87

Figure S19.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.0132 (Fisher's exact test)

Table S24.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 70 243
subtype1 37 82
subtype2 10 61
subtype3 23 100

Figure S20.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S25.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 41 38 41 16 38 26
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.114 (logrank test)

Table S26.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 200 13 0.0 - 173.6 (21.7)
subtype1 41 4 0.6 - 106.9 (21.0)
subtype2 38 2 1.3 - 133.2 (24.7)
subtype3 41 2 1.8 - 173.6 (22.6)
subtype4 16 2 1.4 - 82.7 (26.6)
subtype5 38 1 0.0 - 101.1 (12.4)
subtype6 26 2 0.7 - 66.9 (20.8)

Figure S21.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.343 (ANOVA)

Table S27.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 200 62.7 (11.1)
subtype1 41 62.9 (12.2)
subtype2 38 63.4 (10.6)
subtype3 41 61.9 (10.8)
subtype4 16 68.1 (8.2)
subtype5 38 60.6 (9.6)
subtype6 26 62.6 (13.3)

Figure S22.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.91e-06 (Chi-square test)

Table S28.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 164 3 3 4 3 23
subtype1 33 0 0 1 1 6
subtype2 26 0 0 1 0 11
subtype3 40 0 1 0 0 0
subtype4 12 2 0 2 0 0
subtype5 36 0 2 0 0 0
subtype6 17 1 0 0 2 6

Figure S23.  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 = 0.327 (Chi-square test)

Table S29.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 80 120
subtype1 20 21
subtype2 18 20
subtype3 13 28
subtype4 4 12
subtype5 13 25
subtype6 12 14

Figure S24.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0533 (Chi-square test)

Table S30.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 60 140
subtype1 14 27
subtype2 15 23
subtype3 11 30
subtype4 5 11
subtype5 4 34
subtype6 11 15

Figure S25.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S31.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 7 33 39 39 56 26
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.294 (logrank test)

Table S32.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 200 13 0.0 - 173.6 (21.7)
subtype1 7 0 9.2 - 70.4 (15.1)
subtype2 33 2 0.0 - 89.3 (12.2)
subtype3 39 2 0.7 - 98.2 (16.3)
subtype4 39 4 1.3 - 133.2 (23.3)
subtype5 56 3 1.4 - 173.6 (23.7)
subtype6 26 2 0.7 - 101.1 (22.7)

Figure S26.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.463 (ANOVA)

Table S33.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 200 62.7 (11.1)
subtype1 7 65.0 (17.0)
subtype2 33 62.5 (11.5)
subtype3 39 59.8 (9.3)
subtype4 39 65.0 (11.3)
subtype5 56 63.1 (9.8)
subtype6 26 62.5 (13.2)

Figure S27.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00421 (Chi-square test)

Table S34.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 164 3 3 4 3 23
subtype1 7 0 0 0 0 0
subtype2 29 0 0 0 1 3
subtype3 35 0 1 0 1 2
subtype4 25 0 0 1 0 13
subtype5 50 1 1 3 1 0
subtype6 18 2 1 0 0 5

Figure S28.  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 = 0.584 (Chi-square test)

Table S35.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 80 120
subtype1 2 5
subtype2 10 23
subtype3 15 24
subtype4 19 20
subtype5 25 31
subtype6 9 17

Figure S29.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.157 (Chi-square test)

Table S36.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 60 140
subtype1 0 7
subtype2 7 26
subtype3 11 28
subtype4 16 23
subtype5 20 36
subtype6 6 20

Figure S30.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S37.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 96 83 87
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00873 (logrank test)

Table S38.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 266 20 0.0 - 173.6 (20.2)
subtype1 96 13 0.5 - 133.2 (17.8)
subtype2 83 2 0.6 - 101.1 (21.4)
subtype3 87 5 0.0 - 173.6 (22.6)

Figure S31.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00104 (ANOVA)

Table S39.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 266 63.1 (10.8)
subtype1 96 66.2 (10.4)
subtype2 83 62.4 (10.6)
subtype3 87 60.4 (10.7)

Figure S32.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.73e-18 (Chi-square test)

Table S40.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 205 3 4 1 5 7 41
subtype1 45 0 0 0 4 6 41
subtype2 78 2 2 0 0 1 0
subtype3 82 1 2 1 1 0 0

Figure S33.  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 = 0.0148 (Fisher's exact test)

Table S41.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 99 167
subtype1 44 52
subtype2 21 62
subtype3 34 53

Figure S34.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.000242 (Fisher's exact test)

Table S42.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 83 183
subtype1 45 51
subtype2 19 64
subtype3 19 68

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S43.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 96 82 88
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00985 (logrank test)

Table S44.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 266 20 0.0 - 173.6 (20.2)
subtype1 96 12 0.6 - 133.2 (17.7)
subtype2 82 1 0.6 - 101.1 (22.7)
subtype3 88 7 0.0 - 173.6 (22.5)

Figure S36.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00202 (ANOVA)

Table S45.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 266 63.1 (10.8)
subtype1 96 66.0 (10.4)
subtype2 82 62.5 (10.4)
subtype3 88 60.5 (11.0)

Figure S37.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.69e-18 (Chi-square test)

Table S46.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 205 3 4 1 5 7 41
subtype1 45 0 0 0 4 6 41
subtype2 77 2 2 0 0 1 0
subtype3 83 1 2 1 1 0 0

Figure S38.  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 = 0.0325 (Fisher's exact test)

Table S47.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 99 167
subtype1 44 52
subtype2 22 60
subtype3 33 55

Figure S39.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1.45e-05 (Fisher's exact test)

Table S48.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 83 183
subtype1 47 49
subtype2 15 67
subtype3 21 67

Figure S40.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S49.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 147 124 101
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00602 (logrank test)

Table S50.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 370 30 0.0 - 187.1 (17.4)
subtype1 146 18 0.0 - 187.1 (15.5)
subtype2 123 3 0.1 - 101.1 (17.3)
subtype3 101 9 0.5 - 173.6 (23.3)

Figure S41.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.000193 (ANOVA)

Table S51.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 372 63.2 (11.2)
subtype1 147 65.9 (10.6)
subtype2 124 62.5 (11.6)
subtype3 101 60.1 (10.8)

Figure S42.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 5.4e-20 (Chi-square test)

Table S52.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 284 3 9 2 7 13 54
subtype1 77 1 1 0 4 11 53
subtype2 117 2 3 0 1 1 0
subtype3 90 0 5 2 2 1 1

Figure S43.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0958 (Fisher's exact test)

Table S53.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 123 249
subtype1 58 89
subtype2 34 90
subtype3 31 70

Figure S44.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00481 (Fisher's exact test)

Table S54.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 96 276
subtype1 51 96
subtype2 22 102
subtype3 23 78

Figure S45.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S55.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 114 169 89
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0652 (logrank test)

Table S56.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 370 30 0.0 - 187.1 (17.4)
subtype1 113 4 0.1 - 101.1 (17.3)
subtype2 168 18 0.0 - 187.1 (17.8)
subtype3 89 8 0.6 - 173.6 (16.8)

Figure S46.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 3.03e-05 (ANOVA)

Table S57.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 372 63.2 (11.2)
subtype1 114 63.8 (10.5)
subtype2 169 65.2 (10.8)
subtype3 89 58.6 (11.7)

Figure S47.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.14e-13 (Chi-square test)

Table S58.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 284 3 9 2 7 13 54
subtype1 103 2 4 0 1 1 3
subtype2 100 0 2 0 4 12 51
subtype3 81 1 3 2 2 0 0

Figure S48.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0303 (Fisher's exact test)

Table S59.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 123 249
subtype1 31 83
subtype2 68 101
subtype3 24 65

Figure S49.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0281 (Fisher's exact test)

Table S60.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 96 276
subtype1 23 91
subtype2 55 114
subtype3 18 71

Figure S50.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = UCEC.mergedcluster.txt

  • Clinical data file = UCEC.clin.merged.picked.txt

  • Number of patients = 430

  • Number of clustering approaches = 10

  • Number of selected clinical features = 5

  • 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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' 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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] 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)
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