Correlation between aggregated molecular cancer subtypes and selected clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1H993PF
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1H993PF
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 5 clinical features across 480 patients, 27 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 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

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

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

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

  • Consensus hierarchical clustering analysis on RPPA data identified 2 subtypes that correlate to 'HISTOLOGICAL.TYPE' and 'COMPLETENESS.OF.RESECTION'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death',  'AGE', and 'HISTOLOGICAL.TYPE'.

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

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

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

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

  • 4 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 5 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 27 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION 		COMPLETENESS
OF
RESECTION
Statistical Tests logrank test ANOVA Chi-square test Fisher's exact test Chi-square test
mRNA CNMF subtypes 0.858
(1.00) 		0.0116
(0.383) 		0.00151
(0.0605) 		0.0371
(1.00) 		0.855
(1.00)
mRNA cHierClus subtypes 0.727
(1.00) 		0.00301
(0.114) 		9.4e-05
(0.0046) 		0.0215
(0.688) 		0.677
(1.00)
Copy Number Ratio CNMF subtypes 0.00251
(0.0977) 		2.16e-09
(1.1e-07) 		2.4e-45
(1.44e-43) 		0.0406
(1.00) 		0.298
(1.00)
METHLYATION CNMF 0.0054
(0.194) 		0.0262
(0.813) 		9.22e-24
(5.17e-22) 		0.176
(1.00) 		0.0411
(1.00)
RPPA CNMF subtypes 0.0702
(1.00) 		0.203
(1.00) 		0.00103
(0.0433) 		0.461
(1.00) 		0.102
(1.00)
RPPA cHierClus subtypes 0.089
(1.00) 		0.236
(1.00) 		0.000814
(0.035) 		0.46
(1.00) 		0.00347
(0.128)
RNAseq CNMF subtypes 0.000165
(0.00761) 		1.38e-09
(7.18e-08) 		7.36e-43
(4.34e-41) 		0.0386
(1.00) 		0.0605
(1.00)
RNAseq cHierClus subtypes 0.000178
(0.00801) 		0.00015
(0.00703) 		8.62e-31
(5e-29) 		0.006
(0.204) 		0.051
(1.00)
MIRSEQ CNMF 0.00139
(0.057) 		4.52e-09
(2.26e-07) 		9.68e-28
(5.52e-26) 		0.000332
(0.0146) 		0.0929
(1.00)
MIRSEQ CHIERARCHICAL 9.38e-05
(0.0046) 		0.00576
(0.202) 		5.72e-20
(3.09e-18) 		0.105
(1.00) 		0.442
(1.00)
MIRseq Mature CNMF subtypes 0.0515
(1.00) 		0.0583
(1.00) 		7.23e-11
(3.83e-09) 		0.431
(1.00) 		0.0294
(0.854)
MIRseq Mature cHierClus subtypes 0.0281
(0.842) 		0.0425
(1.00) 		2.2e-23
(1.21e-21) 		0.296
(1.00) 		0.0642
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

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

P value = 0.858 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 54 9 6.0 - 149.5 (37.4)
subtype1 14 2 13.6 - 149.5 (41.7)
subtype2 18 4 6.0 - 125.4 (46.2)
subtype3 12 1 12.7 - 105.4 (31.1)
subtype4 10 2 7.9 - 82.5 (28.3)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.0116 (ANOVA), Q value = 0.38

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00151 (Chi-square test), Q value = 0.06

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

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

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

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

P value = 0.0371 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.855 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 39 6 2 1
subtype1 8 2 1 0
subtype2 14 1 1 1
subtype3 9 2 0 0
subtype4 8 1 0 0

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 19 19 16
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.727 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 54 9 6.0 - 149.5 (37.4)
subtype1 19 3 13.6 - 149.5 (46.5)
subtype2 19 2 7.9 - 105.4 (28.3)
subtype3 16 4 6.0 - 125.4 (46.2)

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 = 0.00301 (ANOVA), Q value = 0.11

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

nPatients Mean (Std.Dev)
ALL 54 62.9 (11.8)
subtype1 19 63.9 (10.5)
subtype2 19 56.4 (12.5)
subtype3 16 69.5 (8.7)

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 = 9.4e-05 (Chi-square test), Q value = 0.0046

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 16 1 2
subtype2 19 0 0
subtype3 6 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 = 0.0215 (Fisher's exact test), Q value = 0.69

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

nPatients NO YES
ALL 25 29
subtype1 11 8
subtype2 4 15
subtype3 10 6

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

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.677 (Chi-square test), Q value = 1

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 39 6 2 1
subtype1 13 2 1 0
subtype2 13 3 0 0
subtype3 13 1 1 1

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

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

Table S13.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 280 22 134 20 15 1
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.00251 (logrank test), Q value = 0.098

Table S14.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 468 57 0.0 - 191.8 (20.9)
subtype1 278 22 0.1 - 191.8 (24.3)
subtype2 22 5 0.5 - 149.5 (22.1)
subtype3 133 24 0.0 - 125.4 (16.1)
subtype4 20 3 0.2 - 46.3 (12.0)
subtype5 15 3 1.7 - 73.2 (25.0)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 2.16e-09 (ANOVA), Q value = 1.1e-07

Table S15.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 470 63.8 (11.3)
subtype1 279 61.5 (11.3)
subtype2 22 62.9 (10.8)
subtype3 134 69.3 (8.7)
subtype4 20 61.6 (14.7)
subtype5 15 61.8 (12.3)

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

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

P value = 2.4e-45 (Chi-square test), Q value = 1.4e-43

Table S16.  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 359 19 93
subtype1 268 7 5
subtype2 17 1 4
subtype3 41 10 83
subtype4 19 0 1
subtype5 14 1 0

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0406 (Chi-square test), Q value = 1

Table S17.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 135 336
subtype1 94 186
subtype2 6 16
subtype3 29 105
subtype4 2 18
subtype5 4 11

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.298 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 324 24 16 27
subtype1 200 14 4 15
subtype2 14 1 2 1
subtype3 86 8 9 9
subtype4 13 1 1 2
subtype5 11 0 0 0

Figure S15.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 131 78 155
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0054 (logrank test), Q value = 0.19

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

nPatients nDeath Duration Range (Median), Month
ALL 361 42 0.0 - 191.8 (16.9)
subtype1 129 24 0.0 - 191.8 (14.4)
subtype2 78 5 0.1 - 102.0 (23.2)
subtype3 154 13 0.1 - 185.8 (17.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0262 (ANOVA), Q value = 0.81

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

nPatients Mean (Std.Dev)
ALL 363 64.0 (11.3)
subtype1 130 65.9 (10.6)
subtype2 78 64.1 (12.5)
subtype3 155 62.3 (11.1)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 9.22e-24 (Chi-square test), Q value = 5.2e-22

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 268 18 78
subtype1 56 13 62
subtype2 60 2 16
subtype3 152 3 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 = 0.176 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 84 280
subtype1 34 97
subtype2 12 66
subtype3 38 117

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

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0411 (Chi-square test), Q value = 1

Table S24.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 241 18 12 26
subtype1 83 8 7 10
subtype2 48 4 4 10
subtype3 110 6 1 6

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

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

P value = 0.0702 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 200 20 0.6 - 185.8 (28.9)
subtype1 42 4 0.6 - 106.9 (29.5)
subtype2 39 6 6.0 - 149.5 (28.1)
subtype3 40 1 1.8 - 185.8 (29.9)
subtype4 12 4 1.4 - 87.3 (35.2)
subtype5 41 2 0.7 - 113.3 (24.6)
subtype6 26 3 0.7 - 78.2 (29.6)

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 = 0.203 (ANOVA), Q value = 1

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

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

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00103 (Chi-square test), Q value = 0.043

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

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

Figure 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 = 0.461 (Chi-square test), Q value = 1

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

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

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

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.102 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 141 9 5 16
subtype1 24 3 3 6
subtype2 29 1 1 2
subtype3 26 0 0 5
subtype4 10 1 1 0
subtype5 30 4 0 1
subtype6 22 0 0 2

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2
Number of samples 124 76
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.089 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 200 20 0.6 - 185.8 (28.9)
subtype1 124 9 0.7 - 185.8 (29.7)
subtype2 76 11 0.6 - 149.5 (23.9)

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 = 0.236 (t-test), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 200 62.7 (11.1)
subtype1 124 62.0 (10.9)
subtype2 76 63.9 (11.3)

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 = 0.000814 (Chi-square test), Q value = 0.035

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 174 3 23
subtype1 116 2 6
subtype2 58 1 17

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 = 0.46 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 80 120
subtype1 47 77
subtype2 33 43

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

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.00347 (Chi-square test), Q value = 0.13

Table S36.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 141 9 5 16
subtype1 94 6 0 6
subtype2 47 3 5 10

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 157 78 112 124
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000165 (logrank test), Q value = 0.0076

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

nPatients nDeath Duration Range (Median), Month
ALL 468 57 0.0 - 191.8 (21.0)
subtype1 155 32 0.0 - 191.8 (17.5)
subtype2 77 7 0.1 - 106.9 (22.0)
subtype3 112 12 0.4 - 185.8 (27.2)
subtype4 124 6 0.1 - 113.3 (23.7)

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 = 1.38e-09 (ANOVA), Q value = 7.2e-08

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

nPatients Mean (Std.Dev)
ALL 470 63.7 (11.3)
subtype1 157 67.6 (10.0)
subtype2 77 62.9 (11.2)
subtype3 112 58.6 (11.3)
subtype4 124 63.8 (11.0)

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 = 7.36e-43 (Chi-square test), Q value = 4.3e-41

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 360 17 94
subtype1 58 12 87
subtype2 69 3 6
subtype3 112 0 0
subtype4 121 2 1

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 = 0.0386 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 134 337
subtype1 37 120
subtype2 31 47
subtype3 36 76
subtype4 30 94

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

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0605 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 325 23 16 27
subtype1 98 12 9 11
subtype2 52 5 3 2
subtype3 87 2 2 4
subtype4 88 4 2 10

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 216 79 176
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000178 (logrank test), Q value = 0.008

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

nPatients nDeath Duration Range (Median), Month
ALL 468 57 0.0 - 191.8 (21.0)
subtype1 213 39 0.0 - 191.8 (17.9)
subtype2 79 9 1.0 - 110.4 (27.1)
subtype3 176 9 0.1 - 185.8 (23.7)

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 = 0.00015 (ANOVA), Q value = 0.007

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

nPatients Mean (Std.Dev)
ALL 470 63.7 (11.3)
subtype1 215 66.0 (10.7)
subtype2 79 60.8 (11.3)
subtype3 176 62.2 (11.4)

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 = 8.62e-31 (Chi-square test), Q value = 5e-29

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 360 17 94
subtype1 110 13 93
subtype2 78 1 0
subtype3 172 3 1

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 = 0.006 (Fisher's exact test), Q value = 0.2

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

nPatients NO YES
ALL 134 337
subtype1 59 157
subtype2 34 45
subtype3 41 135

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

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.051 (Chi-square test), Q value = 1

Table S48.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 325 23 16 27
subtype1 145 11 11 13
subtype2 54 7 3 1
subtype3 126 5 2 13

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

Clustering Approach #9: 'MIRSEQ CNMF'

Table S49.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 128 133 45 52 113
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00139 (logrank test), Q value = 0.057

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

nPatients nDeath Duration Range (Median), Month
ALL 468 54 0.0 - 191.8 (20.9)
subtype1 126 22 0.0 - 125.4 (15.0)
subtype2 133 5 0.1 - 113.3 (24.3)
subtype3 44 7 0.9 - 88.3 (20.6)
subtype4 52 7 0.2 - 96.5 (17.5)
subtype5 113 13 1.0 - 191.8 (30.8)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 4.52e-09 (ANOVA), Q value = 2.3e-07

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

nPatients Mean (Std.Dev)
ALL 470 63.7 (11.3)
subtype1 128 68.7 (9.7)
subtype2 133 63.4 (10.7)
subtype3 44 63.4 (11.2)
subtype4 52 59.4 (12.3)
subtype5 113 60.3 (11.2)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 9.68e-28 (Chi-square test), Q value = 5.5e-26

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

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 363 19 89
subtype1 54 8 66
subtype2 129 2 2
subtype3 31 2 12
subtype4 48 3 1
subtype5 101 4 8

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

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.000332 (Chi-square test), Q value = 0.015

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

nPatients NO YES
ALL 131 340
subtype1 26 102
subtype2 32 101
subtype3 12 33
subtype4 11 41
subtype5 50 63

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

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0929 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 323 24 15 27
subtype1 75 10 9 8
subtype2 98 4 1 8
subtype3 36 2 0 2
subtype4 36 2 1 5
subtype5 78 6 4 4

Figure S45.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S55.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 13 207 251
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 9.38e-05 (logrank test), Q value = 0.0046

Table S56.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 468 54 0.0 - 191.8 (20.9)
subtype1 13 2 1.4 - 73.2 (23.9)
subtype2 207 10 0.1 - 185.8 (25.0)
subtype3 248 42 0.0 - 191.8 (17.8)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00576 (ANOVA), Q value = 0.2

Table S57.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 470 63.7 (11.3)
subtype1 13 65.3 (13.5)
subtype2 207 61.8 (11.3)
subtype3 250 65.2 (11.0)

Figure S47.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 5.72e-20 (Chi-square test), Q value = 3.1e-18

Table S58.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA MIXED SEROUS AND ENDOMETRIOID SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL 363 19 89
subtype1 13 0 0
subtype2 201 3 3
subtype3 149 16 86

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S59.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 131 340
subtype1 7 6
subtype2 58 149
subtype3 66 185

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

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.442 (Chi-square test), Q value = 1

Table S60.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 323 24 15 27
subtype1 8 1 1 0
subtype2 143 9 3 14
subtype3 172 14 11 13

Figure S50.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S61.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 149 83 112
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0515 (logrank test), Q value = 1

Table S62.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 341 43 0.0 - 191.8 (16.7)
subtype1 146 23 0.0 - 191.8 (14.2)
subtype2 83 13 0.1 - 92.0 (24.1)
subtype3 112 7 0.1 - 113.3 (19.4)

Figure S51.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0583 (ANOVA), Q value = 1

Table S63.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 343 64.3 (11.3)
subtype1 148 65.9 (11.1)
subtype2 83 62.5 (12.6)
subtype3 112 63.5 (10.1)

Figure S52.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 7.23e-11 (Chi-square test), Q value = 3.8e-09

Table S64.  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 250 17 77
subtype1 86 8 55
subtype2 56 7 20
subtype3 108 2 2

Figure S53.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.431 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 82 262
subtype1 32 117
subtype2 24 59
subtype3 26 86

Figure S54.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0294 (Chi-square test), Q value = 0.85

Table S66.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 232 17 12 21
subtype1 96 9 10 6
subtype2 64 4 0 4
subtype3 72 4 2 11

Figure S55.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S67.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 90 85 140 29
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0281 (logrank test), Q value = 0.84

Table S68.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 341 43 0.0 - 191.8 (16.7)
subtype1 89 10 0.1 - 92.0 (16.7)
subtype2 83 17 0.0 - 191.8 (15.0)
subtype3 140 10 0.1 - 113.3 (18.8)
subtype4 29 6 0.2 - 88.3 (14.4)

Figure S56.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0425 (ANOVA), Q value = 1

Table S69.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 343 64.3 (11.3)
subtype1 89 64.6 (11.1)
subtype2 85 67.0 (9.7)
subtype3 140 63.0 (11.3)
subtype4 29 62.0 (14.2)

Figure S57.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 2.2e-23 (Chi-square test), Q value = 1.2e-21

Table S70.  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 250 17 77
subtype1 59 5 26
subtype2 28 9 48
subtype3 135 3 2
subtype4 28 0 1

Figure S58.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.296 (Fisher's exact test), Q value = 1

Table S71.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 82 262
subtype1 27 63
subtype2 15 70
subtype3 33 107
subtype4 7 22

Figure S59.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0642 (Chi-square test), Q value = 1

Table S72.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 232 17 12 21
subtype1 68 4 1 4
subtype2 51 7 7 4
subtype3 95 5 2 12
subtype4 18 1 2 1

Figure S60.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Methods & Data
Input

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

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

  • Number of patients = 480

  • Number of clustering approaches = 12

  • 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] 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)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[8] 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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