Ovarian Serous Cystadenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 7 clinical features across 569 patients, 15 significant findings detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death' and 'TUMOR.STAGE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'TUMOR.STAGE', and 'NEOADJUVANT.THERAPY'.
CNMF clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.
3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'AGE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death' and 'AGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 7 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 15 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	KARNOFSKY PERFORMANCE SCORE	TUMOR STAGE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
Statistical Tests	logrank test	ANOVA	Chi-square test	ANOVA	Chi-square test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.00933	0.06	0.555	0.147	0.031	0.793	0.371
mRNA cHierClus subtypes	0.00116	0.00679	0.588	0.241	0.0228	0.619	0.0475
miR CNMF subtypes	0.00559	0.698	0.201	0.677	0.11	0.265	0.275
miR cHierClus subtypes	0.0418	0.504	0.0908	0.547	0.0541	0.533	0.641
CN CNMF	0.0985	3.19e-08	0.323	0.253	0.181	0.505	0.0759
METHLYATION CNMF	0.251	1.3e-11	0.656	0.509	0.0557	0.189	0.281
RPPA CNMF subtypes	0.00128	0.58	0.885	0.61	0.333	0.129	0.586
RPPA cHierClus subtypes	0.0258	0.0408	0.676	0.68	0.181	0.823	0.201
RNAseq CNMF subtypes	0.124	0.00539	1	0.104	0.648	1	0.449
RNAseq cHierClus subtypes	0.122	0.128	0.536	0.104	0.119	0.618	0.47
MIRseq CNMF subtypes	0.0121	0.594	0.0583	0.207	0.193	0.476	0.546
MIRseq cHierClus subtypes	0.284	0.757	0.812	0.577	0.49	0.281	0.753

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	225	207	132

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.00933 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	559	292	0.3 - 180.2 (28.3)
subtype1	222	127	0.3 - 152.0 (29.1)
subtype2	205	90	0.4 - 180.2 (27.9)
subtype3	132	75	0.3 - 119.1 (26.7)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.06 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	553	59.7 (11.6)
subtype1	219	61.0 (11.6)
subtype2	203	58.4 (11.3)
subtype3	131	59.5 (11.9)

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

'mRNA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.555 (Chi-square test)

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	560	2
subtype1	0	225	0
subtype2	1	205	1
subtype3	1	130	1

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

'mRNA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.147 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	31	78.1 (13.0)
subtype2	27	76.3 (12.4)
subtype3	20	71.0 (12.1)

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.031 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	16	26	433	85
subtype1	7	9	178	29
subtype2	9	15	150	32
subtype3	0	2	105	24

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

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

P value = 0.793 (Fisher's exact test)

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

nPatients	NO	YES
ALL	3	561
subtype1	2	223
subtype2	1	206
subtype3	0	132

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.371 (Fisher's exact test)

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

nPatients	NO	YES
ALL	456	108
subtype1	176	49
subtype2	173	34
subtype3	107	25

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	209	223	132

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.00116 (logrank test)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	559	292	0.3 - 180.2 (28.3)
subtype1	206	124	0.3 - 152.0 (29.6)
subtype2	221	87	0.5 - 116.1 (27.5)
subtype3	132	81	0.3 - 180.2 (27.6)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.00679 (ANOVA)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	553	59.7 (11.6)
subtype1	202	61.7 (11.5)
subtype2	221	58.5 (11.0)
subtype3	130	58.6 (12.4)

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

'mRNA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.588 (Chi-square test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	560	2
subtype1	0	209	0
subtype2	1	221	1
subtype3	1	130	1

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.241 (ANOVA)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	25	79.2 (13.5)
subtype2	33	73.9 (11.7)
subtype3	20	74.0 (13.1)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0228 (Chi-square test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	26	433	85
subtype1	7	10	165	25
subtype2	9	15	162	36
subtype3	0	1	106	24

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.619 (Fisher's exact test)

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	561
subtype1	2	207
subtype2	1	222
subtype3	0	132

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0475 (Fisher's exact test)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	456	108
subtype1	160	49
subtype2	191	32
subtype3	105	27

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S17. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	159	163	240

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00559 (logrank test)

Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	292	0.3 - 180.2 (28.2)
subtype1	158	86	0.3 - 130.0 (25.5)
subtype2	160	92	0.3 - 109.7 (24.7)
subtype3	239	114	0.3 - 180.2 (30.1)

Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.698 (ANOVA)

Table S19. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	155	59.0 (12.3)
subtype2	160	59.8 (11.8)
subtype3	236	60.1 (11.1)

Figure S16. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

'miR CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.201 (Chi-square test)

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	558	2
subtype1	0	157	2
subtype2	1	162	0
subtype3	1	239	0

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'miR CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.677 (ANOVA)

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	25	76.8 (12.5)
subtype2	22	73.6 (15.6)
subtype3	31	76.1 (10.9)

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.11 (Chi-square test)

Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	25	432	85
subtype1	3	6	115	33
subtype2	7	5	133	17
subtype3	6	14	184	35

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

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

P value = 0.265 (Fisher's exact test)

Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	0	159
subtype2	0	163
subtype3	3	237

Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.275 (Fisher's exact test)

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	454	108
subtype1	130	29
subtype2	125	38
subtype3	199	41

Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S25. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	200	186	176

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.0418 (logrank test)

Table S26. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	292	0.3 - 180.2 (28.2)
subtype1	197	95	0.3 - 180.2 (29.0)
subtype2	184	99	0.5 - 152.0 (29.0)
subtype3	176	98	0.3 - 130.0 (24.6)

Figure S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.504 (ANOVA)

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	198	60.0 (11.8)
subtype2	181	60.2 (11.0)
subtype3	172	58.8 (12.2)

Figure S23. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'miR cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0908 (Chi-square test)

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	558	2
subtype1	2	198	0
subtype2	0	186	0
subtype3	0	174	2

Figure S24. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'miR cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.547 (ANOVA)

Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	31	76.1 (13.1)
subtype2	20	73.0 (13.4)
subtype3	27	77.0 (12.0)

Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0541 (Chi-square test)

Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	25	432	85
subtype1	4	9	152	33
subtype2	9	10	149	17
subtype3	3	6	131	35

Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.533 (Fisher's exact test)

Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	1	199
subtype2	2	184
subtype3	0	176

Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.641 (Fisher's exact test)

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	454	108
subtype1	164	36
subtype2	146	40
subtype3	144	32

Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'CN CNMF'

Table S33. Get Full Table Description of clustering approach #5: 'CN CNMF'

Cluster Labels	1	2	3
Number of samples	157	195	202

'CN CNMF' versus 'Time to Death'

P value = 0.0985 (logrank test)

Table S34. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	548	285	0.3 - 180.2 (28.3)
subtype1	155	86	1.2 - 119.1 (29.5)
subtype2	194	94	0.3 - 130.0 (30.2)
subtype3	199	105	0.3 - 180.2 (22.3)

Figure S29. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 3.19e-08 (ANOVA)

Table S35. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	543	59.8 (11.6)
subtype1	153	59.6 (12.4)
subtype2	194	56.4 (11.0)
subtype3	196	63.2 (10.6)

Figure S30. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.323 (Chi-square test)

Table S36. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	550	2
subtype1	1	156	0
subtype2	1	194	0
subtype3	0	200	2

Figure S31. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.253 (ANOVA)

Table S37. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	76	75.5 (12.9)
subtype1	19	77.9 (11.3)
subtype2	29	72.4 (12.4)
subtype3	28	77.1 (14.1)

Figure S32. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.181 (Chi-square test)

Table S38. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	26	425	82
subtype1	7	7	118	23
subtype2	3	10	144	38
subtype3	6	9	163	21

Figure S33. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.505 (Fisher's exact test)

Table S39. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	551
subtype1	1	156
subtype2	0	195
subtype3	2	200

Figure S34. Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.0759 (Fisher's exact test)

Table S40. Clustering Approach #5: 'CN CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	450	104
subtype1	128	29
subtype2	167	28
subtype3	155	47

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

Clustering Approach #6: 'METHLYATION CNMF'

Table S41. Get Full Table Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	105	137	151	156

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.251 (logrank test)

Table S42. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	543	290	0.3 - 180.2 (29.0)
subtype1	102	48	0.4 - 130.0 (23.5)
subtype2	135	78	0.8 - 125.7 (29.1)
subtype3	150	80	0.3 - 180.2 (30.3)
subtype4	156	84	0.3 - 152.0 (29.4)

Figure S36. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 1.3e-11 (ANOVA)

Table S43. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	539	59.7 (11.7)
subtype1	104	58.3 (12.5)
subtype2	134	64.5 (11.0)
subtype3	147	54.9 (11.2)
subtype4	154	61.1 (10.2)

Figure S37. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.656 (Chi-square test)

Table S44. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	545	2
subtype1	1	104	0
subtype2	0	136	1
subtype3	0	151	0
subtype4	1	154	1

Figure S38. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.509 (ANOVA)

Table S45. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	19	72.6 (13.7)
subtype2	19	75.8 (15.7)
subtype3	19	78.9 (8.1)
subtype4	21	75.2 (12.5)

Figure S39. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0557 (Chi-square test)

Table S46. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	24	421	83
subtype1	2	5	77	19
subtype2	5	4	114	11
subtype3	5	4	109	33
subtype4	4	11	121	20

Figure S40. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.189 (Fisher's exact test)

Table S47. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	546
subtype1	1	104
subtype2	2	135
subtype3	0	151
subtype4	0	156

Figure S41. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.281 (Fisher's exact test)

Table S48. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	441	108
subtype1	84	21
subtype2	106	31
subtype3	129	22
subtype4	122	34

Figure S42. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	128	207	72

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00128 (logrank test)

Table S50. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	400	209	0.3 - 180.2 (28.6)
subtype1	125	71	0.4 - 125.7 (31.1)
subtype2	204	92	0.3 - 180.2 (25.2)
subtype3	71	46	0.5 - 89.3 (25.9)

Figure S43. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.58 (ANOVA)

Table S51. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	400	59.7 (11.8)
subtype1	126	60.0 (12.3)
subtype2	204	59.1 (11.7)
subtype3	70	60.7 (11.3)

Figure S44. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.885 (Chi-square test)

Table S52. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	403	2
subtype1	1	126	1
subtype2	1	205	1
subtype3	0	72	0

Figure S45. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.61 (ANOVA)

Table S53. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	51	74.9 (11.9)
subtype1	19	76.8 (10.0)
subtype2	27	73.3 (13.6)
subtype3	5	76.0 (8.9)

Figure S46. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.333 (Chi-square test)

Table S54. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	14	19	317	52
subtype1	4	7	96	20
subtype2	9	11	164	20
subtype3	1	1	57	12

Figure S47. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.129 (Fisher's exact test)

Table S55. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	405
subtype1	2	126
subtype2	0	207
subtype3	0	72

Figure S48. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.586 (Fisher's exact test)

Table S56. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	334	73
subtype1	106	22
subtype2	172	35
subtype3	56	16

Figure S49. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S57. Get Full Table Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	101	68	145	93

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0258 (logrank test)

Table S58. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	400	209	0.3 - 180.2 (28.6)
subtype1	97	45	0.3 - 115.9 (28.5)
subtype2	68	42	0.8 - 180.2 (35.5)
subtype3	143	84	0.4 - 125.7 (27.5)
subtype4	92	38	0.5 - 152.0 (22.6)

Figure S50. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0408 (ANOVA)

Table S59. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	400	59.7 (11.8)
subtype1	100	57.7 (11.8)
subtype2	67	61.5 (11.1)
subtype3	142	61.2 (12.1)
subtype4	91	58.2 (11.6)

Figure S51. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.676 (Chi-square test)

Table S60. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	403	2
subtype1	0	101	0
subtype2	1	67	0
subtype3	1	143	1
subtype4	0	92	1

Figure S52. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.68 (ANOVA)

Table S61. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	51	74.9 (11.9)
subtype1	5	80.0 (0.0)
subtype2	9	75.6 (16.7)
subtype3	13	72.3 (10.1)
subtype4	24	75.0 (12.2)

Figure S53. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.181 (Chi-square test)

Table S62. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	14	19	317	52
subtype1	8	5	76	9
subtype2	2	4	53	9
subtype3	3	5	111	24
subtype4	1	5	77	10

Figure S54. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.823 (Fisher's exact test)

Table S63. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	405
subtype1	0	101
subtype2	0	68
subtype3	1	144
subtype4	1	92

Figure S55. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.201 (Fisher's exact test)

Table S64. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	334	73
subtype1	80	21
subtype2	54	14
subtype3	117	28
subtype4	83	10

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

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S65. Get Full Table Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	103	69	91

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.124 (logrank test)

Table S66. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	261	147	0.3 - 180.2 (28.2)
subtype1	103	55	0.4 - 180.2 (28.2)
subtype2	68	36	1.0 - 106.0 (34.6)
subtype3	90	56	0.3 - 152.0 (25.0)

Figure S57. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00539 (ANOVA)

Table S67. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	256	59.1 (10.8)
subtype1	100	59.2 (10.3)
subtype2	68	55.8 (10.9)
subtype3	88	61.4 (10.9)

Figure S58. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 1 (Fisher's exact test)

Table S68. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY
ALL	1	262
subtype1	1	102
subtype2	0	69
subtype3	0	91

Figure S59. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.104 (ANOVA)

Table S69. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	15	74.7 (11.9)
subtype1	10	72.0 (14.0)
subtype2	3	80.0 (0.0)
subtype3	2	80.0 (0.0)

Figure S60. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.648 (Chi-square test)

Table S70. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	18	211	33
subtype1	8	81	14
subtype2	5	53	11
subtype3	5	77	8

Figure S61. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S71. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	261
subtype1	1	102
subtype2	0	69
subtype3	1	90

Figure S62. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.449 (Fisher's exact test)

Table S72. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	211	52
subtype1	83	20
subtype2	52	17
subtype3	76	15

Figure S63. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S73. Get Full Table Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	33	108	122

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.122 (logrank test)

Table S74. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	261	147	0.3 - 180.2 (28.2)
subtype1	33	16	0.3 - 116.1 (26.9)
subtype2	107	58	0.4 - 180.2 (27.5)
subtype3	121	73	0.4 - 152.0 (30.0)

Figure S64. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.128 (ANOVA)

Table S75. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	256	59.1 (10.8)
subtype1	32	56.1 (11.2)
subtype2	105	58.6 (10.4)
subtype3	119	60.3 (11.0)

Figure S65. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.536 (Fisher's exact test)

Table S76. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY
ALL	1	262
subtype1	0	33
subtype2	1	107
subtype3	0	122

Figure S66. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.104 (ANOVA)

Table S77. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	15	74.7 (11.9)
subtype2	11	72.7 (13.5)
subtype3	4	80.0 (0.0)

Figure S67. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.119 (Chi-square test)

Table S78. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	18	211	33
subtype1	4	22	7
subtype2	5	87	16
subtype3	9	102	10

Figure S68. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.618 (Fisher's exact test)

Table S79. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	261
subtype1	0	33
subtype2	0	108
subtype3	2	120

Figure S69. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.47 (Fisher's exact test)

Table S80. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	211	52
subtype1	24	9
subtype2	87	21
subtype3	100	22

Figure S70. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #11: 'MIRseq CNMF subtypes'

Table S81. Get Full Table Description of clustering approach #11: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	113	195	146

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0121 (logrank test)

Table S82. Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	451	252	0.3 - 180.2 (30.1)
subtype1	113	74	0.3 - 152.0 (27.0)
subtype2	194	101	0.3 - 180.2 (32.5)
subtype3	144	77	0.3 - 130.0 (28.7)

Figure S71. Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.594 (ANOVA)

Table S83. Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	446	59.8 (11.5)
subtype1	112	60.8 (11.7)
subtype2	192	59.5 (11.3)
subtype3	142	59.5 (11.6)

Figure S72. Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0583 (Chi-square test)

Table S84. Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	451	1
subtype1	2	110	1
subtype2	0	195	0
subtype3	0	146	0

Figure S73. Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.207 (ANOVA)

Table S85. Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	10	82.0 (14.8)
subtype2	46	74.3 (12.4)
subtype3	8	72.5 (14.9)

Figure S74. Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.193 (Chi-square test)

Table S86. Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	24	352	74
subtype1	4	91	17
subtype2	8	147	38
subtype3	12	114	19

Figure S75. Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.476 (Fisher's exact test)

Table S87. Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	451
subtype1	0	113
subtype2	1	194
subtype3	2	144

Figure S76. Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.546 (Fisher's exact test)

Table S88. Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	367	87
subtype1	89	24
subtype2	162	33
subtype3	116	30

Figure S77. Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #12: 'MIRseq cHierClus subtypes'

Table S89. Get Full Table Description of clustering approach #12: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	3	153	298

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.284 (logrank test)

Table S90. Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	451	252	0.3 - 180.2 (30.1)
subtype1	3	3	4.5 - 60.6 (33.7)
subtype2	151	82	0.3 - 130.0 (28.6)
subtype3	297	167	0.3 - 180.2 (31.0)

Figure S78. Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.757 (ANOVA)

Table S91. Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	446	59.8 (11.5)
subtype1	3	62.7 (1.5)
subtype2	149	59.3 (11.9)
subtype3	294	60.0 (11.4)

Figure S79. Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.812 (Chi-square test)

Table S92. Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	451	1
subtype1	0	3	0
subtype2	0	153	0
subtype3	2	295	1

Figure S80. Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.577 (ANOVA)

Table S93. Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype2	8	72.5 (14.9)
subtype3	56	75.7 (13.1)

Figure S81. Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.49 (Chi-square test)

Table S94. Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	24	352	74
subtype1	0	3	0
subtype2	11	120	21
subtype3	13	229	53

Figure S82. Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.281 (Fisher's exact test)

Table S95. Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	451
subtype1	0	3
subtype2	2	151
subtype3	1	297

Figure S83. Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.753 (Fisher's exact test)

Table S96. Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	367	87
subtype1	3	0
subtype2	121	32
subtype3	243	55

Figure S84. Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = OV.mergedcluster.txt
Clinical data file = OV.clin.merged.picked.txt
Number of patients = 569
Number of clustering approaches = 12
Number of selected clinical features = 7
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

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

Chi-square test

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

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Download Results

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

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

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