Correlation between aggregated molecular cancer subtypes and selected clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1PN9543
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PN9543
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 4 clinical features across 548 patients, 23 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 'HISTOLOGICAL_TYPE'.

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

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

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL_TYPE' and 'RESIDUAL_TUMOR'.

  • CNMF clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death' 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 4 subtypes that correlate to 'Time to Death',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

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

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

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

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death' and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to '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 4 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 23 significant findings detected.

Clinical
Features 		Time
to
Death 		RADIATION
THERAPY 		HISTOLOGICAL
TYPE 		RESIDUAL
TUMOR
Statistical Tests logrank test Fisher's exact test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.466
(0.559) 		0.0656
(0.122) 		0.00053
(0.00159) 		0.696
(0.777)
mRNA cHierClus subtypes 0.0761
(0.129) 		0.51
(0.583) 		1e-05
(4e-05) 		0.178
(0.251)
Copy Number Ratio CNMF subtypes 1.88e-05
(6.46e-05) 		0.783
(0.8) 		1e-05
(4e-05) 		0.0335
(0.0732)
METHLYATION CNMF 0.15
(0.225) 		0.357
(0.463) 		1e-05
(4e-05) 		0.0403
(0.084)
RPPA CNMF subtypes 0.00532
(0.0142) 		0.719
(0.782) 		1e-05
(4e-05) 		0.822
(0.822)
RPPA cHierClus subtypes 0.233
(0.311) 		0.0923
(0.148) 		1e-05
(4e-05) 		0.733
(0.782)
RNAseq CNMF subtypes 1.41e-05
(5.22e-05) 		0.0663
(0.122) 		1e-05
(4e-05) 		0.00586
(0.0148)
RNAseq cHierClus subtypes 6.74e-05
(0.000216) 		0.462
(0.559) 		1e-05
(4e-05) 		0.0174
(0.0419)
MIRSEQ CNMF 0.00248
(0.00699) 		0.158
(0.23) 		1e-05
(4e-05) 		0.121
(0.187)
MIRSEQ CHIERARCHICAL 3.06e-06
(4e-05) 		0.183
(0.251) 		1e-05
(4e-05) 		0.0608
(0.122)
MIRseq Mature CNMF subtypes 0.0287
(0.0656) 		0.433
(0.547) 		1e-05
(4e-05) 		0.482
(0.564)
MIRseq Mature cHierClus subtypes 0.0698
(0.124) 		0.784
(0.8) 		1e-05
(4e-05) 		0.078
(0.129)
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 15 18 14 7
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.466 (logrank test), Q value = 0.56

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

nPatients nDeath Duration Range (Median), Month
ALL 54 10 13.6 - 149.6 (47.8)
subtype1 15 2 13.6 - 149.6 (56.9)
subtype2 18 5 22.0 - 125.4 (57.8)
subtype3 14 1 20.9 - 105.4 (36.4)
subtype4 7 2 18.6 - 82.5 (25.1)

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0656 (Fisher's exact test), Q value = 0.12

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

nPatients NO YES
ALL 38 16
subtype1 7 8
subtype2 13 5
subtype3 11 3
subtype4 7 0

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00053 (Fisher's exact test), Q value = 0.0016

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 13 0 2
subtype2 8 0 10
subtype3 13 1 0
subtype4 7 0 0

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

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.696 (Fisher's exact test), Q value = 0.78

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

nPatients R0 R1 R2 RX
ALL 42 3 2 1
subtype1 9 2 1 0
subtype2 14 1 1 1
subtype3 13 0 0 0
subtype4 6 0 0 0

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S6.  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 = 0.0761 (logrank test), Q value = 0.13

Table S7.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 54 10 13.6 - 149.6 (47.8)
subtype1 14 1 20.9 - 149.6 (64.1)
subtype2 10 4 22.0 - 125.4 (47.9)
subtype3 7 3 13.6 - 80.5 (46.5)
subtype4 7 0 32.8 - 129.8 (56.9)
subtype5 16 2 18.6 - 105.4 (34.6)

Figure S5.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.51 (Fisher's exact test), Q value = 0.58

Table S8.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 38 16
subtype1 10 4
subtype2 7 3
subtype3 5 2
subtype4 3 4
subtype5 13 3

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S9.  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 S7.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.178 (Fisher's exact test), Q value = 0.25

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: '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 S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

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

Table S11.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 208 178 99 54
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 1.88e-05 (logrank test), Q value = 6.5e-05

Table S12.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 537 90 0.1 - 225.5 (29.9)
subtype1 207 54 0.1 - 225.5 (27.4)
subtype2 178 14 0.4 - 185.8 (33.8)
subtype3 98 16 0.1 - 118.0 (28.3)
subtype4 54 6 0.6 - 129.8 (31.9)

Figure S9.  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 'RADIATION_THERAPY'

P value = 0.783 (Fisher's exact test), Q value = 0.8

Table S13.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 290 224
subtype1 107 86
subtype2 95 77
subtype3 54 41
subtype4 34 20

Figure S10.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S14.  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 404 22 113
subtype1 88 12 108
subtype2 169 7 2
subtype3 95 2 2
subtype4 52 1 1

Figure S11.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0335 (Fisher's exact test), Q value = 0.073

Table S15.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 371 22 16 40
subtype1 132 9 11 19
subtype2 122 11 2 16
subtype3 75 2 2 3
subtype4 42 0 1 2

Figure S12.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #4: 'METHLYATION CNMF'

Table S16.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 155 80 196
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.15 (logrank test), Q value = 0.22

Table S17.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 429 73 0.1 - 225.5 (25.9)
subtype1 153 33 0.1 - 225.5 (27.1)
subtype2 80 15 0.3 - 136.6 (26.2)
subtype3 196 25 0.1 - 185.8 (24.3)

Figure S13.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.357 (Fisher's exact test), Q value = 0.46

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

nPatients NO YES
ALL 222 184
subtype1 69 69
subtype2 45 31
subtype3 108 84

Figure S14.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S19.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 312 21 98
subtype1 74 10 71
subtype2 46 7 27
subtype3 192 4 0

Figure S15.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0403 (Fisher's exact test), Q value = 0.084

Table S20.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 284 19 12 39
subtype1 100 8 8 13
subtype2 45 5 2 12
subtype3 139 6 2 14

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

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S21.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 41 82 53 56 86 68 54
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00532 (logrank test), Q value = 0.014

Table S22.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 438 75 0.1 - 185.8 (28.9)
subtype1 41 6 0.2 - 123.7 (30.1)
subtype2 82 6 0.7 - 185.8 (30.3)
subtype3 53 6 6.6 - 113.4 (36.1)
subtype4 56 14 0.6 - 149.6 (30.4)
subtype5 84 19 0.1 - 118.0 (21.8)
subtype6 68 10 0.6 - 105.4 (28.6)
subtype7 54 14 0.2 - 96.3 (27.9)

Figure S17.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.719 (Fisher's exact test), Q value = 0.78

Table S23.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 228 192
subtype1 26 13
subtype2 43 39
subtype3 27 24
subtype4 29 24
subtype5 42 32
subtype6 33 35
subtype7 28 25

Figure S18.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S24.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 329 17 94
subtype1 39 2 0
subtype2 81 0 1
subtype3 46 2 5
subtype4 34 3 19
subtype5 46 3 37
subtype6 52 2 14
subtype7 31 5 18

Figure S19.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.822 (Fisher's exact test), Q value = 0.82

Table S25.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 301 14 11 37
subtype1 31 1 1 2
subtype2 61 1 0 6
subtype3 32 2 0 5
subtype4 37 3 2 5
subtype5 58 4 3 9
subtype6 47 1 4 5
subtype7 35 2 1 5

Figure S20.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S26.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 110 183 95 52
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.233 (logrank test), Q value = 0.31

Table S27.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 438 75 0.1 - 185.8 (28.9)
subtype1 110 12 0.2 - 185.8 (31.5)
subtype2 181 37 0.1 - 149.6 (28.1)
subtype3 95 17 0.2 - 118.0 (27.0)
subtype4 52 9 0.7 - 89.3 (28.7)

Figure S21.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0923 (Fisher's exact test), Q value = 0.15

Table S28.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 228 192
subtype1 69 41
subtype2 94 77
subtype3 43 47
subtype4 22 27

Figure S22.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S29.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 329 17 94
subtype1 107 1 2
subtype2 109 7 67
subtype3 75 5 15
subtype4 38 4 10

Figure S23.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.733 (Fisher's exact test), Q value = 0.78

Table S30.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 301 14 11 37
subtype1 83 3 2 7
subtype2 125 6 4 13
subtype3 58 4 4 11
subtype4 35 1 1 6

Figure S24.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S31.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 189 124 94 138
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.41e-05 (logrank test), Q value = 5.2e-05

Table S32.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 543 91 0.1 - 225.5 (29.9)
subtype1 188 50 0.1 - 225.5 (25.4)
subtype2 124 13 0.1 - 185.8 (36.1)
subtype3 93 13 0.2 - 149.6 (30.7)
subtype4 138 15 0.6 - 136.6 (31.8)

Figure S25.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0663 (Fisher's exact test), Q value = 0.12

Table S33.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 294 226
subtype1 96 78
subtype2 73 50
subtype3 40 48
subtype4 85 50

Figure S26.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S34.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 409 22 114
subtype1 71 15 103
subtype2 124 0 0
subtype3 80 4 10
subtype4 134 3 1

Figure S27.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00586 (Fisher's exact test), Q value = 0.015

Table S35.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 374 22 16 40
subtype1 119 13 9 15
subtype2 96 2 3 5
subtype3 61 5 4 6
subtype4 98 2 0 14

Figure S28.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S36.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 124 53 47 65 109 69 78
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 6.74e-05 (logrank test), Q value = 0.00022

Table S37.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 543 91 0.1 - 225.5 (29.9)
subtype1 124 33 0.1 - 125.4 (26.0)
subtype2 53 5 0.4 - 185.8 (34.3)
subtype3 46 12 0.2 - 225.5 (33.5)
subtype4 65 4 0.1 - 129.8 (32.8)
subtype5 108 20 0.2 - 109.1 (23.8)
subtype6 69 13 0.7 - 136.6 (31.0)
subtype7 78 4 0.6 - 123.7 (32.0)

Figure S29.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.462 (Fisher's exact test), Q value = 0.56

Table S38.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 294 226
subtype1 59 56
subtype2 33 20
subtype3 21 21
subtype4 32 32
subtype5 60 42
subtype6 42 27
subtype7 47 28

Figure S30.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S39.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 409 22 114
subtype1 24 10 90
subtype2 53 0 0
subtype3 21 5 21
subtype4 63 2 0
subtype5 104 3 2
subtype6 68 1 0
subtype7 76 1 1

Figure S31.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0174 (Fisher's exact test), Q value = 0.042

Table S40.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 374 22 16 40
subtype1 81 7 3 10
subtype2 42 1 0 3
subtype3 32 1 7 4
subtype4 44 5 1 2
subtype5 72 7 4 7
subtype6 44 1 1 6
subtype7 59 0 0 8

Figure S32.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S41.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 165 81 107 138 47
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00248 (logrank test), Q value = 0.007

Table S42.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 536 88 0.1 - 225.5 (29.9)
subtype1 164 39 0.1 - 125.4 (24.0)
subtype2 81 13 0.1 - 185.8 (30.7)
subtype3 107 18 3.7 - 225.5 (40.1)
subtype4 138 11 0.6 - 136.6 (30.3)
subtype5 46 7 1.0 - 123.7 (27.3)

Figure S33.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.158 (Fisher's exact test), Q value = 0.23

Table S43.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 287 226
subtype1 82 66
subtype2 49 28
subtype3 54 53
subtype4 82 54
subtype5 20 25

Figure S34.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S44.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 407 22 109
subtype1 69 11 85
subtype2 79 2 0
subtype3 93 4 10
subtype4 134 2 2
subtype5 32 3 12

Figure S35.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.121 (Fisher's exact test), Q value = 0.19

Table S45.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 369 22 15 40
subtype1 101 10 8 15
subtype2 58 2 1 5
subtype3 73 5 5 7
subtype4 101 2 0 10
subtype5 36 3 1 3

Figure S36.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S46.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 219 173 146
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 3.06e-06 (logrank test), Q value = 4e-05

Table S47.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 536 88 0.1 - 225.5 (29.9)
subtype1 218 27 0.1 - 185.8 (31.5)
subtype2 172 47 0.1 - 225.5 (27.2)
subtype3 146 14 0.6 - 136.6 (30.6)

Figure S37.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.183 (Fisher's exact test), Q value = 0.25

Table S48.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 287 226
subtype1 124 86
subtype2 80 80
subtype3 83 60

Figure S38.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S49.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 407 22 109
subtype1 207 6 6
subtype2 57 15 101
subtype3 143 1 2

Figure S39.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.0608 (Fisher's exact test), Q value = 0.12

Table S50.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 369 22 15 40
subtype1 152 9 6 16
subtype2 112 10 9 13
subtype3 105 3 0 11

Figure S40.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S51.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 116 101 93 92
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0287 (logrank test), Q value = 0.066

Table S52.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 400 68 0.1 - 225.5 (25.9)
subtype1 115 27 0.1 - 110.1 (22.3)
subtype2 101 10 0.4 - 136.6 (25.9)
subtype3 93 14 0.3 - 123.7 (27.2)
subtype4 91 17 1.3 - 225.5 (32.0)

Figure S41.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.433 (Fisher's exact test), Q value = 0.55

Table S53.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 200 178
subtype1 53 48
subtype2 59 40
subtype3 43 46
subtype4 45 44

Figure S42.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S54.  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 287 20 95
subtype1 59 6 51
subtype2 96 2 3
subtype3 69 6 18
subtype4 63 6 23

Figure S43.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.482 (Fisher's exact test), Q value = 0.56

Table S55.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 268 17 12 34
subtype1 72 5 4 9
subtype2 65 4 1 13
subtype3 70 4 2 4
subtype4 61 4 5 8

Figure S44.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S56.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 163 136 103
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0698 (logrank test), Q value = 0.12

Table S57.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 400 68 0.1 - 225.5 (25.9)
subtype1 161 33 0.1 - 225.5 (23.7)
subtype2 136 15 0.4 - 136.6 (27.3)
subtype3 103 20 0.3 - 110.1 (25.9)

Figure S45.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.784 (Fisher's exact test), Q value = 0.8

Table S58.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

nPatients NO YES
ALL 200 178
subtype1 80 67
subtype2 70 61
subtype3 50 50

Figure S46.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4e-05

Table S59.  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 287 20 95
subtype1 92 11 60
subtype2 130 3 3
subtype3 65 6 32

Figure S47.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.078 (Fisher's exact test), Q value = 0.13

Table S60.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 268 17 12 34
subtype1 103 8 9 12
subtype2 89 5 1 17
subtype3 76 4 2 5

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

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/UCEC-TP/22553827/UCEC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCEC-TP/22507145/UCEC-TP.merged_data.txt

  • Number of patients = 548

  • Number of clustering approaches = 12

  • Number of selected clinical features = 4

  • 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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