Correlation between aggregated molecular cancer subtypes and selected clinical features
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1571B84
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1571B84
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 14 different clustering approaches and 8 clinical features across 589 patients, 13 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH'.

  • CNMF clustering analysis on array-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based miR expression data identified 10 subtypes that correlate to 'YEARS_TO_BIRTH'.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH'.

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'ETHNICITY'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'TUMOR_TISSUE_SITE'.

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

  • 2 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 'Time to Death'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 14 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 13 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		TUMOR
TISSUE
SITE 		RADIATION
THERAPY 		KARNOFSKY
PERFORMANCE
SCORE 		RESIDUAL
TUMOR 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.0154
(0.191) 		0.00906
(0.145) 		0.433
(0.859) 		0.843
(1.00) 		0.14
(0.604) 		0.149
(0.618) 		0.0418
(0.312) 		0.166
(0.635)
mRNA cHierClus subtypes 0.17
(0.635) 		0.0233
(0.201) 		0.241
(0.676) 		1
(1.00) 		0.407
(0.859) 		0.155
(0.619) 		0.708
(0.973) 		0.0631
(0.321)
miR CNMF subtypes 0.063
(0.321) 		0.7
(0.973) 		0.219
(0.676) 		0.739
(0.973) 		0.669
(0.972) 		0.209
(0.669) 		0.449
(0.859) 		0.235
(0.676)
miR cHierClus subtypes 0.132
(0.59) 		0.000445
(0.0125) 		0.805
(0.997) 		0.255
(0.681) 		0.903
(1.00) 		0.267
(0.694) 		0.954
(1.00) 		0.44
(0.859)
Copy Number Ratio CNMF subtypes 0.544
(0.859) 		1.61e-09
(1.8e-07) 		0.231
(0.676) 		0.861
(1.00) 		0.84
(1.00) 		0.208
(0.669) 		0.345
(0.788) 		0.371
(0.824)
METHLYATION CNMF 0.459
(0.859) 		4.43e-09
(2.48e-07) 		0.526
(0.859) 		0.528
(0.859) 		0.728
(0.973) 		0.482
(0.859) 		0.542
(0.859) 		0.552
(0.859)
RPPA CNMF subtypes 0.0602
(0.321) 		0.453
(0.859) 		1
(1.00) 		0.676
(0.972) 		0.481
(0.859) 		0.315
(0.75) 		0.779
(0.988) 		0.721
(0.973)
RPPA cHierClus subtypes 0.00349
(0.0652) 		0.0379
(0.303) 		0.0504
(0.321) 		0.739
(0.973) 		0.938
(1.00) 		0.516
(0.859) 		0.307
(0.749) 		0.113
(0.529)
RNAseq CNMF subtypes 0.714
(0.973) 		0.00264
(0.0591) 		0.785
(0.988) 		0.198
(0.669) 		0.427
(0.859) 		0.509
(0.859) 		0.0602
(0.321) 		0.0172
(0.192)
RNAseq cHierClus subtypes 0.0581
(0.321) 		0.0145
(0.191) 		0.592
(0.896) 		0.467
(0.859) 		0.204
(0.669) 		0.588
(0.896) 		0.652
(0.972) 		0.325
(0.759)
MIRSEQ CNMF 0.375
(0.824) 		0.506
(0.859) 		0.0206
(0.192) 		0.779
(0.988) 		0.235
(0.676) 		0.549
(0.859) 		0.0471
(0.321)
MIRSEQ CHIERARCHICAL 0.28
(0.713) 		0.000116
(0.00434) 		0.871
(1.00) 		0.52
(0.859) 		0.677
(0.972) 		0.81
(0.997) 		0.774
(0.988)
MIRseq Mature CNMF subtypes 0.186
(0.669) 		0.434
(0.859) 		1
(1.00) 		0.0782
(0.381)
MIRseq Mature cHierClus subtypes 0.0195
(0.192) 		0.308
(0.749) 		0.253
(0.681) 		1
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 219 215 129
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0154 (logrank test), Q value = 0.19

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

nPatients nDeath Duration Range (Median), Month
ALL 559 300 0.3 - 180.2 (30.0)
subtype1 218 126 0.3 - 152.0 (30.9)
subtype2 212 95 0.4 - 180.2 (30.2)
subtype3 129 79 0.3 - 119.1 (28.9)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00906 (Kruskal-Wallis (anova)), Q value = 0.14

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

nPatients Mean (Std.Dev)
ALL 552 59.7 (11.6)
subtype1 213 61.4 (11.6)
subtype2 211 57.9 (11.4)
subtype3 128 60.0 (11.7)

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

'mRNA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.433 (Fisher's exact test), Q value = 0.86

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

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 559 2
subtype1 0 219 0
subtype2 1 213 1
subtype3 1 127 1

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.843 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 500 5
subtype1 193 3
subtype2 195 1
subtype3 112 1

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

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.14 (Kruskal-Wallis (anova)), Q value = 0.6

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

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 31 78.1 (13.0)
subtype2 28 76.4 (12.2)
subtype3 19 70.5 (12.2)

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

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.149 (Fisher's exact test), Q value = 0.62

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2
ALL 14 27 1
subtype1 6 9 0
subtype2 7 9 0
subtype3 1 9 1

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.0418 (Fisher's exact test), Q value = 0.31

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 20 24 1 486
subtype1 0 7 12 0 187
subtype2 0 12 9 1 181
subtype3 2 1 3 0 118

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.166 (Fisher's exact test), Q value = 0.64

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 330
subtype1 6 131
subtype2 5 121
subtype3 0 78

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 313 161 89
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.17 (logrank test), Q value = 0.64

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 559 300 0.3 - 180.2 (30.0)
subtype1 309 157 0.3 - 130.0 (30.2)
subtype2 161 91 0.3 - 180.2 (27.0)
subtype3 89 52 0.8 - 152.0 (34.1)

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0233 (Kruskal-Wallis (anova)), Q value = 0.2

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

nPatients Mean (Std.Dev)
ALL 552 59.7 (11.6)
subtype1 307 59.5 (11.4)
subtype2 158 58.4 (12.0)
subtype3 87 62.9 (11.3)

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

'mRNA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.241 (Fisher's exact test), Q value = 0.68

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 559 2
subtype1 1 312 0
subtype2 1 158 2
subtype3 0 89 0

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

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

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 500 5
subtype1 279 3
subtype2 143 1
subtype3 78 1

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.407 (Kruskal-Wallis (anova)), Q value = 0.86

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 43 76.7 (11.5)
subtype2 21 72.4 (13.4)
subtype3 14 77.1 (15.4)

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.155 (Fisher's exact test), Q value = 0.62

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2
ALL 14 27 1
subtype1 8 11 0
subtype2 2 12 1
subtype3 4 4 0

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.708 (Fisher's exact test), Q value = 0.97

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 20 24 1 486
subtype1 0 10 14 1 274
subtype2 2 7 6 0 136
subtype3 0 3 4 0 76

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0631 (Fisher's exact test), Q value = 0.32

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 330
subtype1 8 179
subtype2 0 94
subtype3 3 57

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S19.  Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 158 165 238
'miR CNMF subtypes' versus 'Time to Death'

P value = 0.063 (logrank test), Q value = 0.32

Table S20.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 557 300 0.3 - 180.2 (30.0)
subtype1 158 91 0.3 - 130.0 (30.0)
subtype2 161 93 0.3 - 115.9 (28.9)
subtype3 238 116 0.3 - 180.2 (31.3)

Figure S17.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.7 (Kruskal-Wallis (anova)), Q value = 0.97

Table S21.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 550 59.7 (11.6)
subtype1 154 59.3 (12.5)
subtype2 162 59.3 (11.6)
subtype3 234 60.3 (11.1)

Figure S18.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.219 (Fisher's exact test), Q value = 0.68

Table S22.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 557 2
subtype1 0 156 2
subtype2 1 164 0
subtype3 1 237 0

Figure S19.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'miR CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.739 (Fisher's exact test), Q value = 0.97

Table S23.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 498 5
subtype1 142 1
subtype2 146 1
subtype3 210 3

Figure S20.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'miR CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.669 (Kruskal-Wallis (anova)), Q value = 0.97

Table S24.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 24 76.7 (12.7)
subtype2 23 73.9 (15.3)
subtype3 31 76.1 (10.9)

Figure S21.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.209 (Fisher's exact test), Q value = 0.67

Table S25.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2
ALL 14 27 1
subtype1 4 12 0
subtype2 3 9 1
subtype3 7 6 0

Figure S22.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'miR CNMF subtypes' versus 'RACE'

P value = 0.449 (Fisher's exact test), Q value = 0.86

Table S26.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 20 23 1 485
subtype1 0 3 8 0 137
subtype2 2 6 5 0 143
subtype3 0 11 10 1 205

Figure S23.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'RACE'

'miR CNMF subtypes' versus 'ETHNICITY'

P value = 0.235 (Fisher's exact test), Q value = 0.68

Table S27.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 329
subtype1 5 88
subtype2 1 97
subtype3 5 144

Figure S24.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S28.  Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels 1 10 2 3 4 5 6 7 8 9
Number of samples 83 57 58 86 35 75 36 36 51 44
'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.132 (logrank test), Q value = 0.59

Table S29.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 557 300 0.3 - 180.2 (30.0)
subtype1 83 49 0.3 - 130.0 (25.9)
subtype10 56 28 2.0 - 115.9 (28.2)
subtype2 58 35 0.3 - 106.0 (26.0)
subtype3 86 43 0.3 - 125.8 (33.0)
subtype4 34 25 1.2 - 107.3 (27.9)
subtype5 74 38 1.7 - 180.2 (35.8)
subtype6 36 20 1.0 - 111.8 (33.5)
subtype7 36 13 6.1 - 98.0 (31.7)
subtype8 50 25 0.5 - 152.0 (24.8)
subtype9 44 24 1.0 - 118.4 (29.7)

Figure S25.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000445 (Kruskal-Wallis (anova)), Q value = 0.012

Table S30.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 550 59.7 (11.6)
subtype1 80 58.7 (12.2)
subtype10 56 55.6 (11.2)
subtype2 58 61.7 (12.3)
subtype3 85 59.3 (11.2)
subtype4 35 60.3 (11.7)
subtype5 74 60.0 (10.0)
subtype6 35 63.3 (10.9)
subtype7 35 62.9 (12.0)
subtype8 49 63.6 (11.4)
subtype9 43 54.3 (11.3)

Figure S26.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.805 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 557 2
subtype1 0 82 1
subtype10 0 57 0
subtype2 0 58 0
subtype3 1 85 0
subtype4 0 34 1
subtype5 1 74 0
subtype6 0 36 0
subtype7 0 36 0
subtype8 0 51 0
subtype9 0 44 0

Figure S27.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'miR cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.255 (Fisher's exact test), Q value = 0.68

Table S32.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 498 5
subtype1 71 1
subtype10 55 0
subtype2 53 1
subtype3 77 0
subtype4 32 0
subtype5 66 0
subtype6 31 0
subtype7 32 1
subtype8 43 2
subtype9 38 0

Figure S28.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'miR cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.903 (Kruskal-Wallis (anova)), Q value = 1

Table S33.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 13 73.8 (15.0)
subtype10 6 73.3 (16.3)
subtype2 4 75.0 (10.0)
subtype3 13 80.0 (11.5)
subtype4 3 73.3 (11.5)
subtype5 12 76.7 (16.7)
subtype6 7 77.1 (13.8)
subtype7 6 76.7 (8.2)
subtype8 5 68.0 (11.0)
subtype9 9 75.6 (8.8)

Figure S29.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.267 (Fisher's exact test), Q value = 0.69

Table S34.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2
ALL 14 27 1
subtype1 2 7 0
subtype10 2 1 0
subtype2 1 2 0
subtype3 2 4 0
subtype4 0 5 0
subtype5 1 6 0
subtype6 1 1 0
subtype7 2 1 0
subtype8 2 0 0
subtype9 1 0 1

Figure S30.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'miR cHierClus subtypes' versus 'RACE'

P value = 0.954 (Fisher's exact test), Q value = 1

Table S35.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 20 23 1 485
subtype1 0 2 3 0 71
subtype10 1 2 4 0 47
subtype2 0 2 5 0 48
subtype3 0 4 4 1 73
subtype4 0 0 1 0 33
subtype5 1 3 2 0 66
subtype6 0 1 0 0 34
subtype7 0 1 1 0 32
subtype8 0 4 1 0 42
subtype9 0 1 2 0 39

Figure S31.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'miR cHierClus subtypes' versus 'ETHNICITY'

P value = 0.44 (Fisher's exact test), Q value = 0.86

Table S36.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 329
subtype1 3 46
subtype10 0 36
subtype2 0 25
subtype3 3 56
subtype4 1 18
subtype5 1 44
subtype6 2 23
subtype7 1 19
subtype8 0 34
subtype9 0 28

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

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S37.  Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 170 202 201
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.544 (logrank test), Q value = 0.86

Table S38.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 559 298 0.3 - 180.2 (29.4)
subtype1 166 96 0.8 - 119.1 (30.5)
subtype2 197 99 0.3 - 130.0 (30.6)
subtype3 196 103 0.3 - 180.2 (25.0)

Figure S33.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.61e-09 (Kruskal-Wallis (anova)), Q value = 1.8e-07

Table S39.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 552 59.8 (11.6)
subtype1 163 60.2 (12.2)
subtype2 198 56.2 (10.9)
subtype3 191 63.3 (10.6)

Figure S34.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.231 (Fisher's exact test), Q value = 0.68

Table S40.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 3 558 2
subtype1 1 166 0
subtype2 0 199 0
subtype3 2 193 2

Figure S35.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.861 (Fisher's exact test), Q value = 1

Table S41.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 500 5
subtype1 145 2
subtype2 179 2
subtype3 176 1

Figure S36.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.84 (Kruskal-Wallis (anova)), Q value = 1

Table S42.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 82 75.7 (13.9)
subtype1 22 77.3 (11.2)
subtype2 31 74.8 (12.9)
subtype3 29 75.5 (16.8)

Figure S37.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.208 (Fisher's exact test), Q value = 0.67

Table S43.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 15 26 5 3
subtype1 2 11 0 1
subtype2 3 6 2 1
subtype3 10 9 3 1

Figure S38.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.345 (Fisher's exact test), Q value = 0.79

Table S44.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 3 19 34 1 476
subtype1 1 3 7 0 145
subtype2 0 10 15 1 163
subtype3 2 6 12 0 168

Figure S39.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.371 (Fisher's exact test), Q value = 0.82

Table S45.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 327
subtype1 1 101
subtype2 4 112
subtype3 5 114

Figure S40.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #6: 'METHLYATION CNMF'

Table S46.  Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 175 180 220
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.459 (logrank test), Q value = 0.86

Table S47.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 560 302 0.3 - 180.2 (29.9)
subtype1 171 103 0.3 - 152.0 (29.2)
subtype2 173 88 0.8 - 180.2 (30.6)
subtype3 216 111 0.3 - 130.0 (29.5)

Figure S41.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 4.43e-09 (Kruskal-Wallis (anova)), Q value = 2.5e-07

Table S48.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 554 59.8 (11.6)
subtype1 168 64.5 (10.4)
subtype2 170 57.8 (12.3)
subtype3 216 57.8 (10.8)

Figure S42.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.526 (Fisher's exact test), Q value = 0.86

Table S49.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 561 2
subtype1 0 170 1
subtype2 0 175 0
subtype3 2 216 1

Figure S43.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.528 (Fisher's exact test), Q value = 0.86

Table S50.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 501 5
subtype1 153 1
subtype2 155 3
subtype3 193 1

Figure S44.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.728 (Kruskal-Wallis (anova)), Q value = 0.97

Table S51.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 22 74.5 (12.6)
subtype2 21 75.2 (14.0)
subtype3 35 76.6 (12.4)

Figure S45.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.482 (Fisher's exact test), Q value = 0.86

Table S52.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2
ALL 14 26 2
subtype1 4 8 1
subtype2 4 12 0
subtype3 6 6 1

Figure S46.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'RACE'

P value = 0.542 (Fisher's exact test), Q value = 0.86

Table S53.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 3 19 24 1 487
subtype1 1 3 9 0 152
subtype2 1 8 4 0 151
subtype3 1 8 11 1 184

Figure S47.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.552 (Fisher's exact test), Q value = 0.86

Table S54.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 331
subtype1 5 103
subtype2 2 104
subtype3 4 124

Figure S48.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S55.  Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 150 192 78
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0602 (logrank test), Q value = 0.32

Table S56.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 416 221 0.3 - 180.2 (30.1)
subtype1 147 86 0.4 - 125.8 (31.3)
subtype2 191 88 0.3 - 180.2 (28.5)
subtype3 78 47 1.1 - 91.7 (27.7)

Figure S49.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.453 (Kruskal-Wallis (anova)), Q value = 0.86

Table S57.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 413 59.7 (11.8)
subtype1 148 59.9 (12.0)
subtype2 189 59.1 (11.6)
subtype3 76 60.8 (11.9)

Figure S50.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

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

Table S58.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 3 415 2
subtype1 1 148 1
subtype2 2 189 1
subtype3 0 78 0

Figure S51.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.676 (Fisher's exact test), Q value = 0.97

Table S59.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 371 4
subtype1 134 2
subtype2 165 1
subtype3 72 1

Figure S52.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.481 (Kruskal-Wallis (anova)), Q value = 0.86

Table S60.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 57 75.3 (13.5)
subtype1 25 76.8 (12.8)
subtype2 27 73.0 (14.1)
subtype3 5 80.0 (14.1)

Figure S53.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.315 (Fisher's exact test), Q value = 0.75

Table S61.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 14 29 5 3
subtype1 2 12 1 1
subtype2 9 8 3 1
subtype3 3 9 1 1

Figure S54.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.779 (Fisher's exact test), Q value = 0.99

Table S62.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 17 29 345
subtype1 2 6 10 121
subtype2 1 9 16 156
subtype3 0 2 3 68

Figure S55.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.721 (Fisher's exact test), Q value = 0.97

Table S63.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 215
subtype1 2 79
subtype2 5 98
subtype3 1 38

Figure S56.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S64.  Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 146 57 61 156
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00349 (logrank test), Q value = 0.065

Table S65.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 416 221 0.3 - 180.2 (30.1)
subtype1 145 81 0.4 - 180.2 (29.0)
subtype2 57 37 0.8 - 72.9 (33.1)
subtype3 61 24 0.8 - 107.3 (30.4)
subtype4 153 79 0.3 - 152.0 (32.9)

Figure S57.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0379 (Kruskal-Wallis (anova)), Q value = 0.3

Table S66.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 413 59.7 (11.8)
subtype1 144 59.9 (11.9)
subtype2 57 62.8 (10.7)
subtype3 61 57.0 (12.4)
subtype4 151 59.4 (11.6)

Figure S58.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.0504 (Fisher's exact test), Q value = 0.32

Table S67.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 3 415 2
subtype1 1 145 0
subtype2 2 54 1
subtype3 0 61 0
subtype4 0 155 1

Figure S59.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.739 (Fisher's exact test), Q value = 0.97

Table S68.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 371 4
subtype1 130 1
subtype2 55 0
subtype3 51 1
subtype4 135 2

Figure S60.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.938 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 57 75.3 (13.5)
subtype1 14 76.4 (16.5)
subtype2 9 73.3 (17.3)
subtype3 17 74.1 (11.8)
subtype4 17 76.5 (11.1)

Figure S61.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.516 (Fisher's exact test), Q value = 0.86

Table S70.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 14 29 5 3
subtype1 4 13 3 0
subtype2 3 6 0 1
subtype3 2 5 0 0
subtype4 5 5 2 2

Figure S62.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.307 (Fisher's exact test), Q value = 0.75

Table S71.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 17 29 345
subtype1 2 2 10 121
subtype2 0 1 6 48
subtype3 0 5 3 46
subtype4 1 9 10 130

Figure S63.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.113 (Fisher's exact test), Q value = 0.53

Table S72.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 215
subtype1 1 75
subtype2 2 25
subtype3 3 32
subtype4 2 83

Figure S64.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S73.  Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 79 109 115
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.714 (logrank test), Q value = 0.97

Table S74.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 302 172 0.3 - 180.2 (28.9)
subtype1 78 43 0.8 - 116.1 (34.2)
subtype2 109 65 0.3 - 152.0 (27.0)
subtype3 115 64 0.3 - 180.2 (28.2)

Figure S65.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00264 (Kruskal-Wallis (anova)), Q value = 0.059

Table S75.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 295 59.1 (10.9)
subtype1 76 55.7 (10.8)
subtype2 106 61.2 (10.6)
subtype3 113 59.5 (10.9)

Figure S66.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.785 (Fisher's exact test), Q value = 0.99

Table S76.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY
ALL 3 300
subtype1 0 79
subtype2 1 108
subtype3 2 113

Figure S67.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.198 (Fisher's exact test), Q value = 0.67

Table S77.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 269 2
subtype1 71 0
subtype2 97 2
subtype3 101 0

Figure S68.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.427 (Kruskal-Wallis (anova)), Q value = 0.86

Table S78.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 20 78.0 (16.1)
subtype1 4 72.5 (22.2)
subtype2 6 85.0 (12.2)
subtype3 10 76.0 (15.8)

Figure S69.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.509 (Fisher's exact test), Q value = 0.86

Table S79.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 3 5 2 3
subtype1 1 0 1 1
subtype2 2 2 1 2
subtype3 0 3 0 0

Figure S70.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0602 (Fisher's exact test), Q value = 0.32

Table S80.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 12 26 1 251
subtype1 1 2 7 1 66
subtype2 0 6 15 0 85
subtype3 1 4 4 0 100

Figure S71.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0172 (Fisher's exact test), Q value = 0.19

Table S81.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 163
subtype1 3 40
subtype2 0 60
subtype3 0 63

Figure S72.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S82.  Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 147 60 96
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0581 (logrank test), Q value = 0.32

Table S83.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 302 172 0.3 - 180.2 (28.9)
subtype1 146 78 0.3 - 180.2 (31.2)
subtype2 60 39 1.0 - 152.0 (25.0)
subtype3 96 55 0.3 - 101.8 (27.6)

Figure S73.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0145 (Kruskal-Wallis (anova)), Q value = 0.19

Table S84.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 295 59.1 (10.9)
subtype1 142 57.4 (10.6)
subtype2 59 62.4 (10.8)
subtype3 94 59.7 (11.0)

Figure S74.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.592 (Fisher's exact test), Q value = 0.9

Table S85.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY
ALL 3 300
subtype1 1 146
subtype2 0 60
subtype3 2 94

Figure S75.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.467 (Fisher's exact test), Q value = 0.86

Table S86.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 269 2
subtype1 130 1
subtype2 55 1
subtype3 84 0

Figure S76.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.204 (Kruskal-Wallis (anova)), Q value = 0.67

Table S87.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 20 78.0 (16.1)
subtype1 9 75.6 (14.2)
subtype2 3 93.3 (11.5)
subtype3 8 75.0 (17.7)

Figure S77.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.588 (Fisher's exact test), Q value = 0.9

Table S88.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 3 5 2 3
subtype1 1 3 1 2
subtype2 2 0 1 1
subtype3 0 2 0 0

Figure S78.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.652 (Fisher's exact test), Q value = 0.97

Table S89.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 12 26 1 251
subtype1 1 6 12 1 122
subtype2 0 2 9 0 48
subtype3 1 4 5 0 81

Figure S79.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.325 (Fisher's exact test), Q value = 0.76

Table S90.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 163
subtype1 3 73
subtype2 0 39
subtype3 0 51

Figure S80.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #11: 'MIRSEQ CNMF'

Table S91.  Description of clustering approach #11: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 124 185 144
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.375 (logrank test), Q value = 0.82

Table S92.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 451 254 0.3 - 180.2 (31.2)
subtype1 123 78 0.3 - 152.0 (30.2)
subtype2 185 99 0.3 - 180.2 (33.7)
subtype3 143 77 0.3 - 130.0 (28.8)

Figure S81.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.506 (Kruskal-Wallis (anova)), Q value = 0.86

Table S93.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 445 59.8 (11.5)
subtype1 123 60.7 (11.7)
subtype2 182 59.4 (11.3)
subtype3 140 59.4 (11.6)

Figure S82.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.0206 (Fisher's exact test), Q value = 0.19

Table S94.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 450 1
subtype1 2 121 1
subtype2 0 185 0
subtype3 0 144 0

Figure S83.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.779 (Fisher's exact test), Q value = 0.99

Table S95.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 397 3
subtype1 115 0
subtype2 152 2
subtype3 130 1

Figure S84.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.235 (Kruskal-Wallis (anova)), Q value = 0.68

Table S96.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 64 75.3 (13.2)
subtype1 11 81.8 (14.0)
subtype2 45 74.2 (12.5)
subtype3 8 72.5 (14.9)

Figure S85.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.549 (Fisher's exact test), Q value = 0.86

Table S97.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 15 21 401
subtype1 2 3 6 106
subtype2 0 6 10 167
subtype3 0 6 5 128

Figure S86.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0471 (Fisher's exact test), Q value = 0.32

Table S98.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 263
subtype1 0 66
subtype2 7 114
subtype3 1 83

Figure S87.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #12: 'MIRSEQ CHIERARCHICAL'

Table S99.  Description of clustering approach #12: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 105 73 169 106
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.28 (logrank test), Q value = 0.71

Table S100.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 451 254 0.3 - 180.2 (31.2)
subtype1 105 54 0.3 - 107.2 (34.6)
subtype2 73 46 0.8 - 89.3 (27.0)
subtype3 168 99 0.3 - 180.2 (30.1)
subtype4 105 55 0.3 - 130.0 (30.6)

Figure S88.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.000116 (Kruskal-Wallis (anova)), Q value = 0.0043

Table S101.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 445 59.8 (11.5)
subtype1 104 56.9 (11.5)
subtype2 73 62.5 (11.8)
subtype3 166 61.8 (11.1)
subtype4 102 57.4 (10.9)

Figure S89.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 0.871 (Fisher's exact test), Q value = 1

Table S102.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 450 1
subtype1 0 105 0
subtype2 0 73 0
subtype3 2 166 1
subtype4 0 106 0

Figure S90.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.52 (Fisher's exact test), Q value = 0.86

Table S103.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients NO YES
ALL 397 3
subtype1 86 0
subtype2 66 1
subtype3 146 2
subtype4 99 0

Figure S91.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.677 (Kruskal-Wallis (anova)), Q value = 0.97

Table S104.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 64 75.3 (13.2)
subtype1 28 76.4 (11.0)
subtype2 6 70.0 (21.0)
subtype3 26 74.6 (14.5)
subtype4 4 80.0 (0.0)

Figure S92.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.81 (Fisher's exact test), Q value = 1

Table S105.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 15 21 401
subtype1 0 6 4 94
subtype2 0 3 4 61
subtype3 1 3 8 152
subtype4 1 3 5 94

Figure S93.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.774 (Fisher's exact test), Q value = 0.99

Table S106.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 263
subtype1 3 70
subtype2 2 45
subtype3 2 91
subtype4 1 57

Figure S94.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #13: 'MIRseq Mature CNMF subtypes'

Table S107.  Description of clustering approach #13: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2
Number of samples 12 10
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.186 (logrank test), Q value = 0.67

Table S108.  Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 22 14 0.8 - 125.8 (30.4)
subtype1 12 8 0.8 - 125.8 (22.9)
subtype2 10 6 5.4 - 115.9 (59.9)

Figure S95.  Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.434 (Wilcoxon-test), Q value = 0.86

Table S109.  Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 21 60.5 (12.6)
subtype1 12 62.6 (13.1)
subtype2 9 57.7 (12.1)

Figure S96.  Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

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

Table S110.  Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY
ALL 1 21
subtype1 1 11
subtype2 0 10

Figure S97.  Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.0782 (Fisher's exact test), Q value = 0.38

Table S111.  Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 1 19
subtype1 0 0 0 12
subtype2 1 1 1 7

Figure S98.  Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

Clustering Approach #14: 'MIRseq Mature cHierClus subtypes'

Table S112.  Description of clustering approach #14: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 6 2 2 6 4 2
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0195 (logrank test), Q value = 0.19

Table S113.  Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 16 10 4.8 - 125.8 (34.0)
subtype1 6 5 4.8 - 42.0 (24.9)
subtype4 6 2 5.4 - 115.9 (59.9)
subtype5 4 3 11.1 - 125.8 (51.8)

Figure S99.  Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.308 (Kruskal-Wallis (anova)), Q value = 0.75

Table S114.  Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 15 57.6 (12.9)
subtype1 6 54.3 (17.4)
subtype4 5 55.2 (7.7)
subtype5 4 65.5 (9.1)

Figure S100.  Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.253 (Fisher's exact test), Q value = 0.68

Table S115.  Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients OMENTUM OVARY
ALL 1 15
subtype1 0 6
subtype4 0 6
subtype5 1 3

Figure S101.  Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

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

Table S116.  Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN WHITE
ALL 1 1 14
subtype1 1 0 5
subtype4 0 1 5
subtype5 0 0 4

Figure S102.  Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/OV-TP/20141012/OV-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/OV-TP/19775421/OV-TP.merged_data.txt

  • Number of patients = 589

  • Number of clustering approaches = 14

  • Number of selected clinical features = 8

  • 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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