Correlation between aggregated molecular cancer subtypes and selected clinical features
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
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/C1R20ZVC
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 8 different clustering approaches and 9 clinical features across 84 patients, 6 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'PATHOLOGY.M.STAGE', and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 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 8 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 6 significant findings detected.

Clinical
Features
Statistical
Tests
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.0568
(1.00)
0.00168
(0.114)
0.262
(1.00)
0.721
(1.00)
0.567
(1.00)
0.208
(1.00)
0.455
(1.00)
0.704
(1.00)
AGE ANOVA 0.878
(1.00)
0.264
(1.00)
0.246
(1.00)
0.235
(1.00)
0.819
(1.00)
0.202
(1.00)
0.6
(1.00)
0.957
(1.00)
PATHOLOGY T STAGE Fisher's exact test 0.928
(1.00)
0.465
(1.00)
0.812
(1.00)
0.892
(1.00)
0.931
(1.00)
0.195
(1.00)
0.396
(1.00)
0.575
(1.00)
PATHOLOGY N STAGE Fisher's exact test 0.48
(1.00)
0.641
(1.00)
0.569
(1.00)
0.928
(1.00)
0.802
(1.00)
0.825
(1.00)
0.635
(1.00)
0.319
(1.00)
PATHOLOGY M STAGE Chi-square test 0.93
(1.00)
0.00172
(0.115)
0.0399
(1.00)
0.0875
(1.00)
0.15
(1.00)
0.424
(1.00)
0.122
(1.00)
0.183
(1.00)
HISTOLOGICAL TYPE Chi-square test 0.656
(1.00)
0.000388
(0.0268)
0.00432
(0.281)
8.92e-08
(6.43e-06)
0.00809
(0.518)
4.21e-06
(0.000299)
0.0039
(0.257)
2.96e-05
(0.00207)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.437
(1.00)
0.222
(1.00)
0.0832
(1.00)
0.317
(1.00)
0.297
(1.00)
0.0816
(1.00)
0.113
(1.00)
0.406
(1.00)
NUMBERPACKYEARSSMOKED ANOVA 0.725
(1.00)
0.464
(1.00)
0.872
(1.00)
0.598
(1.00)
0.871
(1.00)
0.496
(1.00)
0.939
(1.00)
0.607
(1.00)
NUMBER OF LYMPH NODES ANOVA 0.266
(1.00)
0.0844
(1.00)
0.392
(1.00)
0.552
(1.00)
0.602
(1.00)
0.514
(1.00)
0.551
(1.00)
0.478
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 27 14 37
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0568 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 76 11 0.0 - 177.0 (6.0)
subtype1 25 2 0.0 - 117.4 (6.0)
subtype2 14 6 1.2 - 87.8 (18.1)
subtype3 37 3 0.1 - 177.0 (3.7)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.878 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 77 47.6 (13.0)
subtype1 26 48.4 (11.7)
subtype2 14 46.2 (12.6)
subtype3 37 47.5 (14.2)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.928 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 49 23
subtype1 17 8
subtype2 7 4
subtype3 25 11

Figure S3.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.48 (Fisher's exact test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 49 21
subtype1 18 6
subtype2 6 5
subtype3 25 10

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.93 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 45 2 26
subtype1 17 1 8
subtype2 7 0 5
subtype3 21 1 13

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

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

P value = 0.656 (Chi-square test), Q value = 1

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 66 1 8 1 2
subtype1 25 0 1 0 1
subtype2 13 0 1 0 0
subtype3 28 1 6 1 1

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.437 (Fisher's exact test), Q value = 1

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 13 65
subtype1 4 23
subtype2 4 10
subtype3 5 32

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.725 (ANOVA), Q value = 1

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 21 18.0 (12.8)
subtype1 7 16.4 (11.0)
subtype2 4 22.8 (10.0)
subtype3 10 17.2 (15.4)

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.266 (ANOVA), Q value = 1

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 64 0.6 (1.3)
subtype1 23 0.4 (0.9)
subtype2 10 1.2 (1.6)
subtype3 31 0.5 (1.3)

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'METHLYATION CNMF'

Table S11.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 17 12 15 16 13 7 4
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00168 (logrank test), Q value = 0.11

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 82 14 0.0 - 177.0 (6.9)
subtype1 17 0 0.1 - 177.0 (13.9)
subtype2 12 3 0.1 - 95.1 (11.9)
subtype3 14 1 0.0 - 67.5 (5.0)
subtype4 15 8 0.1 - 69.9 (5.5)
subtype5 13 2 0.3 - 124.3 (3.7)
subtype6 7 0 1.7 - 147.3 (29.7)
subtype7 4 0 0.6 - 53.1 (11.7)

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.264 (ANOVA), Q value = 1

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 83 48.2 (13.2)
subtype1 17 43.6 (10.3)
subtype2 12 45.4 (13.0)
subtype3 15 55.6 (12.8)
subtype4 15 48.0 (16.4)
subtype5 13 47.0 (11.8)
subtype6 7 49.1 (10.0)
subtype7 4 51.5 (19.5)

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.465 (Chi-square test), Q value = 1

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 53 25
subtype1 11 6
subtype2 8 3
subtype3 7 6
subtype4 11 2
subtype5 10 3
subtype6 3 4
subtype7 3 1

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.641 (Chi-square test), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 52 24
subtype1 12 5
subtype2 8 3
subtype3 8 4
subtype4 6 7
subtype5 10 3
subtype6 5 1
subtype7 3 1

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.00172 (Chi-square test), Q value = 0.12

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 51 2 26
subtype1 12 0 5
subtype2 10 0 2
subtype3 5 0 9
subtype4 10 0 2
subtype5 8 0 5
subtype6 4 2 1
subtype7 2 0 2

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000388 (Chi-square test), Q value = 0.027

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 72 1 8 1 2
subtype1 17 0 0 0 0
subtype2 12 0 0 0 0
subtype3 15 0 0 0 0
subtype4 16 0 0 0 0
subtype5 4 1 5 1 2
subtype6 4 0 3 0 0
subtype7 4 0 0 0 0

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.222 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 17 67
subtype1 3 14
subtype2 4 8
subtype3 2 13
subtype4 5 11
subtype5 1 12
subtype6 0 7
subtype7 2 2

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.464 (ANOVA), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 21 18.0 (12.8)
subtype1 5 14.0 (4.2)
subtype2 1 20.0 (NA)
subtype3 6 22.4 (17.8)
subtype4 4 14.0 (8.4)
subtype5 2 18.8 (23.0)
subtype6 2 25.0 (21.2)
subtype7 1 10.0 (NA)

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0844 (ANOVA), Q value = 1

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 70 0.9 (2.4)
subtype1 12 0.2 (0.6)
subtype2 11 0.9 (1.9)
subtype3 13 0.7 (1.3)
subtype4 12 2.9 (4.8)
subtype5 12 0.5 (1.2)
subtype6 6 0.2 (0.4)
subtype7 4 0.2 (0.5)

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S21.  Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 23 13 10 15 15
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.262 (logrank test), Q value = 1

Table S22.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 74 12 0.1 - 177.0 (9.7)
subtype1 23 3 0.1 - 177.0 (2.4)
subtype2 12 3 0.3 - 36.8 (10.3)
subtype3 10 1 0.1 - 101.8 (14.4)
subtype4 14 4 0.1 - 95.1 (13.4)
subtype5 15 1 1.2 - 147.3 (29.7)

Figure S19.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.246 (ANOVA), Q value = 1

Table S23.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 75 47.3 (12.8)
subtype1 23 49.1 (15.5)
subtype2 13 51.5 (9.9)
subtype3 10 49.1 (11.2)
subtype4 14 41.2 (12.1)
subtype5 15 45.6 (11.2)

Figure S20.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.812 (Chi-square test), Q value = 1

Table S24.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 48 24
subtype1 14 8
subtype2 7 5
subtype3 6 4
subtype4 10 3
subtype5 11 4

Figure S21.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.569 (Chi-square test), Q value = 1

Table S25.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 48 23
subtype1 16 6
subtype2 7 5
subtype3 5 5
subtype4 9 4
subtype5 11 3

Figure S22.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0399 (Chi-square test), Q value = 1

Table S26.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 46 2 23
subtype1 14 0 7
subtype2 9 0 3
subtype3 4 0 6
subtype4 12 0 1
subtype5 7 2 6

Figure S23.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00432 (Chi-square test), Q value = 0.28

Table S27.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 65 1 8 1 1
subtype1 23 0 0 0 0
subtype2 11 0 2 0 0
subtype3 10 0 0 0 0
subtype4 15 0 0 0 0
subtype5 6 1 6 1 1

Figure S24.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.0832 (Chi-square test), Q value = 1

Table S28.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 16 60
subtype1 2 21
subtype2 4 9
subtype3 4 6
subtype4 5 10
subtype5 1 14

Figure S25.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.872 (ANOVA), Q value = 1

Table S29.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 19 19.2 (12.8)
subtype1 7 21.4 (15.7)
subtype2 3 24.0 (11.5)
subtype3 2 12.5 (3.5)
subtype4 3 18.7 (7.1)
subtype5 4 15.6 (16.6)

Figure S26.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.392 (ANOVA), Q value = 1

Table S30.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 64 0.9 (2.3)
subtype1 20 0.7 (1.6)
subtype2 11 2.1 (4.8)
subtype3 8 1.1 (1.5)
subtype4 11 0.5 (0.8)
subtype5 14 0.4 (1.1)

Figure S27.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S31.  Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 16 9 51
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.721 (logrank test), Q value = 1

Table S32.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 74 12 0.1 - 177.0 (9.7)
subtype1 16 2 0.3 - 124.3 (11.2)
subtype2 9 1 0.1 - 78.7 (11.4)
subtype3 49 9 0.1 - 177.0 (6.8)

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.235 (ANOVA), Q value = 1

Table S33.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 75 47.3 (12.8)
subtype1 16 48.9 (11.0)
subtype2 9 40.6 (9.8)
subtype3 50 48.1 (13.6)

Figure S29.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.892 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 48 24
subtype1 10 6
subtype2 6 3
subtype3 32 15

Figure S30.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.928 (Fisher's exact test), Q value = 1

Table S35.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 48 23
subtype1 11 4
subtype2 6 3
subtype3 31 16

Figure S31.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0875 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 46 2 23
subtype1 8 2 6
subtype2 7 0 2
subtype3 31 0 15

Figure S32.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.92e-08 (Chi-square test), Q value = 6.4e-06

Table S37.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 65 1 8 1 1
subtype1 5 1 8 1 1
subtype2 9 0 0 0 0
subtype3 51 0 0 0 0

Figure S33.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.317 (Fisher's exact test), Q value = 1

Table S38.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 16 60
subtype1 1 15
subtype2 2 7
subtype3 13 38

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

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.598 (ANOVA), Q value = 1

Table S39.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 19 19.2 (12.8)
subtype1 3 25.8 (20.4)
subtype2 2 15.0 (7.1)
subtype3 14 18.4 (12.0)

Figure S35.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.552 (ANOVA), Q value = 1

Table S40.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 64 0.9 (2.3)
subtype1 14 0.5 (1.1)
subtype2 8 0.4 (0.7)
subtype3 42 1.1 (2.7)

Figure S36.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S41.  Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 29 29 20
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.567 (logrank test), Q value = 1

Table S42.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 76 14 0.1 - 177.0 (10.1)
subtype1 28 7 0.1 - 177.0 (11.9)
subtype2 28 5 0.1 - 147.3 (7.4)
subtype3 20 2 0.1 - 124.3 (10.2)

Figure S37.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.819 (ANOVA), Q value = 1

Table S43.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 77 48.1 (13.5)
subtype1 28 46.9 (14.8)
subtype2 29 49.2 (14.4)
subtype3 20 48.1 (10.1)

Figure S38.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.931 (Fisher's exact test), Q value = 1

Table S44.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 49 25
subtype1 18 8
subtype2 18 10
subtype3 13 7

Figure S39.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.802 (Fisher's exact test), Q value = 1

Table S45.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 49 24
subtype1 17 9
subtype2 18 10
subtype3 14 5

Figure S40.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.15 (Chi-square test), Q value = 1

Table S46.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 48 2 23
subtype1 19 0 6
subtype2 18 0 10
subtype3 11 2 7

Figure S41.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00809 (Chi-square test), Q value = 0.52

Table S47.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 67 1 8 1 1
subtype1 29 0 0 0 0
subtype2 26 1 2 0 0
subtype3 12 0 6 1 1

Figure S42.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.297 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 17 61
subtype1 9 20
subtype2 4 25
subtype3 4 16

Figure S43.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.871 (ANOVA), Q value = 1

Table S49.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 19 19.2 (12.8)
subtype1 8 19.1 (13.8)
subtype2 8 20.6 (11.8)
subtype3 3 15.8 (17.0)

Figure S44.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.602 (ANOVA), Q value = 1

Table S50.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 66 1.0 (2.4)
subtype1 24 1.2 (3.3)
subtype2 25 1.1 (2.1)
subtype3 17 0.5 (1.0)

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

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S51.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 32 12 14 20
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.208 (logrank test), Q value = 1

Table S52.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 76 14 0.1 - 177.0 (10.1)
subtype1 32 6 0.3 - 177.0 (20.6)
subtype2 10 1 1.0 - 95.1 (20.3)
subtype3 14 2 0.3 - 124.3 (11.2)
subtype4 20 5 0.1 - 78.7 (2.2)

Figure S46.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.202 (ANOVA), Q value = 1

Table S53.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 77 48.1 (13.5)
subtype1 32 50.7 (16.1)
subtype2 11 50.5 (6.7)
subtype3 14 47.9 (11.0)
subtype4 20 42.8 (12.3)

Figure S47.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.195 (Fisher's exact test), Q value = 1

Table S54.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 49 25
subtype1 24 7
subtype2 6 5
subtype3 10 4
subtype4 9 9

Figure S48.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.825 (Fisher's exact test), Q value = 1

Table S55.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 49 24
subtype1 21 10
subtype2 7 4
subtype3 10 3
subtype4 11 7

Figure S49.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.424 (Chi-square test), Q value = 1

Table S56.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 48 2 23
subtype1 22 0 9
subtype2 8 1 2
subtype3 7 1 6
subtype4 11 0 6

Figure S50.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 4.21e-06 (Chi-square test), Q value = 3e-04

Table S57.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 67 1 8 1 1
subtype1 32 0 0 0 0
subtype2 11 0 1 0 0
subtype3 4 1 7 1 1
subtype4 20 0 0 0 0

Figure S51.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.0816 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 17 61
subtype1 9 23
subtype2 5 7
subtype3 1 13
subtype4 2 18

Figure S52.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.496 (ANOVA), Q value = 1

Table S59.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 19 19.2 (12.8)
subtype1 8 15.9 (8.5)
subtype2 1 10.0 (NA)
subtype3 3 25.8 (20.4)
subtype4 7 21.6 (14.5)

Figure S53.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.514 (ANOVA), Q value = 1

Table S60.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 66 1.0 (2.4)
subtype1 28 0.8 (1.8)
subtype2 10 2.0 (5.0)
subtype3 12 0.5 (1.2)
subtype4 16 1.0 (1.8)

Figure S54.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S61.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 28 30 20
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.455 (logrank test), Q value = 1

Table S62.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 76 14 0.1 - 177.0 (10.1)
subtype1 27 7 0.1 - 177.0 (20.4)
subtype2 29 5 0.3 - 147.3 (6.8)
subtype3 20 2 0.1 - 124.3 (9.4)

Figure S55.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.6 (ANOVA), Q value = 1

Table S63.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 77 48.1 (13.5)
subtype1 27 47.3 (12.8)
subtype2 30 50.0 (15.1)
subtype3 20 46.4 (12.0)

Figure S56.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.396 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 49 25
subtype1 19 6
subtype2 17 12
subtype3 13 7

Figure S57.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.635 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 49 24
subtype1 15 10
subtype2 20 9
subtype3 14 5

Figure S58.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.122 (Chi-square test), Q value = 1

Table S66.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 48 2 23
subtype1 19 0 6
subtype2 19 0 10
subtype3 10 2 7

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

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

P value = 0.0039 (Chi-square test), Q value = 0.26

Table S67.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 67 1 8 1 1
subtype1 28 0 0 0 0
subtype2 28 0 2 0 0
subtype3 11 1 6 1 1

Figure S60.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.113 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 17 61
subtype1 10 18
subtype2 4 26
subtype3 3 17

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.939 (ANOVA), Q value = 1

Table S69.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 19 19.2 (12.8)
subtype1 7 19.7 (14.8)
subtype2 7 17.9 (9.5)
subtype3 5 20.5 (16.2)

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.551 (ANOVA), Q value = 1

Table S70.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 66 1.0 (2.4)
subtype1 23 1.3 (3.4)
subtype2 27 1.0 (2.1)
subtype3 16 0.4 (1.0)

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S71.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 17 40 21
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.704 (logrank test), Q value = 1

Table S72.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 76 14 0.1 - 177.0 (10.1)
subtype1 16 1 0.1 - 78.3 (2.2)
subtype2 39 10 0.3 - 177.0 (14.9)
subtype3 21 3 0.1 - 124.3 (7.9)

Figure S64.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.957 (ANOVA), Q value = 1

Table S73.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 77 48.1 (13.5)
subtype1 16 48.9 (11.8)
subtype2 40 48.0 (15.7)
subtype3 21 47.6 (10.1)

Figure S65.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.575 (Fisher's exact test), Q value = 1

Table S74.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2+T3+T4
ALL 49 25
subtype1 12 5
subtype2 25 11
subtype3 12 9

Figure S66.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.319 (Fisher's exact test), Q value = 1

Table S75.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 49 24
subtype1 14 3
subtype2 22 14
subtype3 13 7

Figure S67.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.183 (Chi-square test), Q value = 1

Table S76.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 48 2 23
subtype1 11 0 5
subtype2 26 0 10
subtype3 11 2 8

Figure S68.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

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

P value = 2.96e-05 (Chi-square test), Q value = 0.0021

Table S77.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 67 1 8 1 1
subtype1 17 0 0 0 0
subtype2 40 0 0 0 0
subtype3 10 1 8 1 1

Figure S69.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

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

P value = 0.406 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 17 61
subtype1 2 15
subtype2 11 29
subtype3 4 17

Figure S70.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.607 (ANOVA), Q value = 1

Table S79.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 19 19.2 (12.8)
subtype1 3 23.3 (23.6)
subtype2 11 16.6 (7.9)
subtype3 5 22.5 (16.0)

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.478 (ANOVA), Q value = 1

Table S80.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 66 1.0 (2.4)
subtype1 15 0.3 (0.7)
subtype2 33 1.1 (2.0)
subtype3 18 1.3 (3.8)

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

Methods & Data
Input
  • Cluster data file = CESC-TP.mergedcluster.txt

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

  • Number of patients = 84

  • Number of clustering approaches = 8

  • Number of selected clinical features = 9

  • 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

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

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

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)
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