Correlation between molecular cancer subtypes and selected clinical features

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Ovarian Serous Cystadenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1VX0DHT

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 578 patients, 3 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 do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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 6 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
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 do not correlate to any clinical features.
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.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes 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 (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 3 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	KARNOFSKY PERFORMANCE SCORE	TUMOR STAGE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION
Statistical Tests	logrank test	ANOVA	Chi-square test	ANOVA	Chi-square test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0275 (1.00)	0.0107 (0.827)	0.568 (1.00)	0.151 (1.00)	0.0364 (1.00)	0.793 (1.00)	0.144 (1.00)
mRNA cHierClus subtypes	0.802 (1.00)	0.00529 (0.418)	0.224 (1.00)	0.172 (1.00)	0.266 (1.00)	0.611 (1.00)	0.211 (1.00)
miR CNMF subtypes	0.0543 (1.00)	0.622 (1.00)	0.198 (1.00)	0.737 (1.00)	0.126 (1.00)	0.266 (1.00)	0.197 (1.00)
miR cHierClus subtypes	0.607 (1.00)	0.00948 (0.74)	0.837 (1.00)	0.0822 (1.00)	0.00314 (0.251)	0.525 (1.00)	0.192 (1.00)
Copy Number Ratio CNMF subtypes	0.284 (1.00)	2.14e-09 (1.76e-07)	0.307 (1.00)	0.731 (1.00)	0.339 (1.00)	0.404 (1.00)	0.21 (1.00)
METHLYATION CNMF	0.198 (1.00)	3.66e-10 (3.04e-08)	0.387 (1.00)	0.637 (1.00)	0.29 (1.00)	1 (1.00)	0.387 (1.00)
RPPA CNMF subtypes	0.0637 (1.00)	0.705 (1.00)	0.718 (1.00)	0.54 (1.00)	0.294 (1.00)	0.134 (1.00)	0.212 (1.00)
RPPA cHierClus subtypes	0.163 (1.00)	0.0174 (1.00)	0.684 (1.00)	0.851 (1.00)	0.479 (1.00)	1 (1.00)	0.131 (1.00)
RNAseq CNMF subtypes	0.417 (1.00)	0.00127 (0.103)	1 (1.00)		0.527 (1.00)	1 (1.00)	0.333 (1.00)
RNAseq cHierClus subtypes	0.438 (1.00)	0.166 (1.00)	1 (1.00)	0.104 (1.00)	0.162 (1.00)	0.637 (1.00)	0.0476 (1.00)
MIRSEQ CNMF	0.301 (1.00)	0.535 (1.00)	0.0911 (1.00)	0.19 (1.00)	0.412 (1.00)	0.489 (1.00)	0.0357 (1.00)
MIRSEQ CHIERARCHICAL	0.639 (1.00)	0.676 (1.00)	0.782 (1.00)	0.901 (1.00)	0.395 (1.00)	0.317 (1.00)	0.0357 (1.00)

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	219	211	132

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0275 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	290	0.3 - 180.2 (28.3)
subtype1	216	125	0.3 - 152.0 (29.6)
subtype2	209	90	0.4 - 180.2 (27.0)
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 = 0.0107 (ANOVA), Q value = 0.83

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

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	213	61.4 (11.5)
subtype2	207	58.0 (11.4)
subtype3	131	59.7 (11.8)

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 = 0.568 (Chi-square test), Q value = 1

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

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	558	2
subtype1	0	219	0
subtype2	1	209	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 = 0.151 (ANOVA), Q value = 1

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	30	78.0 (13.2)
subtype2	28	76.4 (12.2)
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 = 0.0364 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	16	26	431	85
subtype1	7	9	173	28
subtype2	9	15	153	33
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), Q value = 1

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

nPatients	NO	YES
ALL	3	559
subtype1	2	217
subtype2	1	210
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 'COMPLETENESS.OF.RESECTION'

P value = 0.144 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1
ALL	14	28
subtype1	6	9
subtype2	7	9
subtype3	1	10

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

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	233	202	127

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.802 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	290	0.3 - 180.2 (28.3)
subtype1	232	111	0.3 - 119.1 (24.2)
subtype2	199	113	0.3 - 152.0 (30.0)
subtype3	126	66	0.3 - 180.2 (31.5)

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 = 0.00529 (ANOVA), Q value = 0.42

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

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	231	58.5 (11.6)
subtype2	195	61.9 (11.8)
subtype3	125	58.7 (11.0)

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 = 0.224 (Chi-square test), Q value = 1

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

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	558	2
subtype1	2	229	2
subtype2	0	202	0
subtype3	0	127	0

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 = 0.172 (ANOVA), Q value = 1

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	29	73.1 (12.3)
subtype2	20	80.0 (14.5)
subtype3	29	75.2 (11.5)

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 = 0.266 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	16	26	431	85
subtype1	4	13	179	36
subtype2	7	9	160	23
subtype3	5	4	92	26

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 = 0.611 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	3	559
subtype1	1	232
subtype2	2	200
subtype3	0	127

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

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

P value = 0.211 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	14	28
subtype1	5	18
subtype2	7	7
subtype3	2	3

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

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	157	165	238

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.0543 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	555	290	0.3 - 180.2 (28.2)
subtype1	156	86	0.3 - 130.0 (26.4)
subtype2	162	91	0.3 - 115.9 (24.7)
subtype3	237	113	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 = 0.622 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	549	59.7 (11.6)
subtype1	153	59.3 (12.5)
subtype2	162	59.3 (11.6)
subtype3	234	60.3 (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 = 0.198 (Chi-square test), Q value = 1

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

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	556	2
subtype1	0	155	2
subtype2	1	164	0
subtype3	1	237	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 = 0.737 (ANOVA), Q value = 1

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	24	76.7 (12.7)
subtype2	23	73.9 (15.3)
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 = 0.126 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	16	25	430	85
subtype1	3	6	114	32
subtype2	7	5	135	17
subtype3	6	14	181	36

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 = 0.266 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	3	557
subtype1	0	157
subtype2	0	165
subtype3	3	235

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

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

P value = 0.197 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	14	28
subtype1	4	12
subtype2	3	10
subtype3	7	6

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

Clustering Approach #4: 'miR cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5	6
Number of samples	66	120	122	102	56	94

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.607 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	555	290	0.3 - 180.2 (28.2)
subtype1	65	31	0.8 - 115.9 (26.9)
subtype2	119	65	1.0 - 180.2 (28.7)
subtype3	122	65	0.3 - 130.0 (25.5)
subtype4	102	46	0.3 - 125.7 (29.6)
subtype5	54	31	1.8 - 106.0 (22.4)
subtype6	93	52	0.5 - 152.0 (30.4)

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 = 0.00948 (ANOVA), Q value = 0.74

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

	nPatients	Mean (Std.Dev)
ALL	549	59.7 (11.6)
subtype1	66	56.5 (11.2)
subtype2	119	57.9 (11.2)
subtype3	118	59.9 (12.3)
subtype4	100	60.1 (10.8)
subtype5	56	62.2 (13.2)
subtype6	90	62.2 (10.6)

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 = 0.837 (Chi-square test), Q value = 1

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

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	556	2
subtype1	0	66	0
subtype2	1	118	1
subtype3	0	121	1
subtype4	1	101	0
subtype5	0	56	0
subtype6	0	94	0

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 = 0.0822 (ANOVA), Q value = 1

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	9	75.6 (13.3)
subtype2	19	75.8 (12.6)
subtype3	17	76.5 (14.6)
subtype4	17	76.5 (10.6)
subtype5	5	88.0 (11.0)
subtype6	11	67.3 (10.1)

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 = 0.00314 (Chi-square test), Q value = 0.25

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

nPatients	I	II	III	IV
ALL	16	25	430	85
subtype1	5	1	48	12
subtype2	0	0	98	21
subtype3	2	7	91	21
subtype4	5	7	73	17
subtype5	1	3	40	11
subtype6	3	7	80	3

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 = 0.525 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	3	557
subtype1	0	66
subtype2	0	120
subtype3	0	122
subtype4	1	101
subtype5	1	55
subtype6	1	93

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

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

P value = 0.192 (Chi-square test), Q value = 1

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	14	28
subtype1	1	2
subtype2	1	9
subtype3	4	10
subtype4	4	5
subtype5	0	0
subtype6	4	2

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

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

Table S33. Get Full Table Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	165	199	198

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.284 (logrank test), Q value = 1

Table S34. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	546	283	0.3 - 180.2 (28.3)
subtype1	159	89	0.8 - 119.1 (29.5)
subtype2	195	92	0.3 - 130.0 (30.2)
subtype3	192	102	0.3 - 180.2 (22.3)

Figure S29. 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 'AGE'

P value = 2.14e-09 (ANOVA), Q value = 1.8e-07

Table S35. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	541	59.8 (11.6)
subtype1	157	59.8 (12.4)
subtype2	196	56.2 (10.9)
subtype3	188	63.5 (10.5)

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

'Copy Number Ratio CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.307 (Chi-square test), Q value = 1

Table S36. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	548	2
subtype1	1	160	0
subtype2	1	196	0
subtype3	0	192	2

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.731 (ANOVA), Q value = 1

Table S37. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	76	75.5 (12.9)
subtype1	21	75.2 (12.5)
subtype2	28	74.3 (12.0)
subtype3	27	77.0 (14.4)

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.339 (Chi-square test), Q value = 1

Table S38. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	26	423	82
subtype1	7	7	120	26
subtype2	4	10	147	36
subtype3	5	9	156	20

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

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

P value = 0.404 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	1	164
subtype2	0	199
subtype3	2	196

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

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

P value = 0.21 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	14	27
subtype1	2	10
subtype2	3	7
subtype3	9	10

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

Clustering Approach #6: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	174	181	219

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.198 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	291	0.3 - 180.2 (28.3)
subtype1	168	100	0.3 - 152.0 (26.9)
subtype2	173	86	0.8 - 180.2 (28.6)
subtype3	216	105	0.3 - 130.0 (28.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 3.66e-10 (ANOVA), Q value = 3e-08

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

	nPatients	Mean (Std.Dev)
ALL	553	59.8 (11.6)
subtype1	167	64.6 (10.6)
subtype2	171	57.6 (12.0)
subtype3	215	57.8 (10.9)

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 = 0.387 (Chi-square test), Q value = 1

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

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	560	2
subtype1	0	169	1
subtype2	0	176	0
subtype3	2	215	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 = 0.637 (ANOVA), Q value = 1

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	23	74.8 (13.8)
subtype2	20	74.0 (13.1)
subtype3	35	77.1 (12.0)

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 = 0.29 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	16	26	430	86
subtype1	4	7	135	21
subtype2	3	5	134	32
subtype3	9	14	161	33

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

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

nPatients	NO	YES
ALL	3	571
subtype1	1	173
subtype2	1	180
subtype3	1	218

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

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

P value = 0.387 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	14	27
subtype1	4	8
subtype2	4	13
subtype3	6	6

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

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	123	199	85

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0637 (logrank test), Q value = 1

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	120	66	0.4 - 125.7 (31.1)
subtype2	196	91	0.3 - 180.2 (24.0)
subtype3	84	52	0.5 - 89.3 (27.2)

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 = 0.705 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	400	59.7 (11.8)
subtype1	121	60.0 (12.3)
subtype2	196	59.2 (11.7)
subtype3	83	60.4 (11.4)

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 = 0.718 (Chi-square test), Q value = 1

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	122	0
subtype2	1	197	1
subtype3	0	84	1

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 = 0.54 (ANOVA), Q value = 1

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	21	76.2 (10.2)
subtype2	24	75.0 (12.2)
subtype3	6	70.0 (16.7)

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 = 0.294 (Chi-square test), Q value = 1

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	93	18
subtype2	9	11	156	20
subtype3	1	1	68	14

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 = 0.134 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	2	405
subtype1	2	121
subtype2	0	199
subtype3	0	85

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

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

P value = 0.212 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	13	29
subtype1	2	10
subtype2	8	9
subtype3	3	10

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

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	60	86	109	152

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.163 (logrank test), Q value = 1

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	60	35	0.8 - 180.2 (34.8)
subtype2	83	40	0.3 - 115.9 (28.3)
subtype3	107	46	0.4 - 152.0 (25.9)
subtype4	150	88	0.4 - 125.7 (27.2)

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 = 0.0174 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	400	59.7 (11.8)
subtype1	59	62.5 (11.0)
subtype2	85	57.3 (11.5)
subtype3	107	58.2 (11.6)
subtype4	149	61.0 (12.1)

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 = 0.684 (Chi-square test), Q value = 1

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	1	59	0
subtype2	0	86	0
subtype3	0	108	1
subtype4	1	150	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 = 0.851 (ANOVA), Q value = 1

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	8	75.0 (17.7)
subtype2	3	80.0 (0.0)
subtype3	25	75.2 (11.9)
subtype4	15	73.3 (9.8)

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 = 0.479 (Chi-square test), Q value = 1

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	2	4	46	8
subtype2	6	3	67	8
subtype3	3	7	86	12
subtype4	3	5	118	24

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

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

nPatients	NO	YES
ALL	2	405
subtype1	0	60
subtype2	0	86
subtype3	1	108
subtype4	1	151

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

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

P value = 0.131 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	13	29
subtype1	3	4
subtype2	4	2
subtype3	3	9
subtype4	3	14

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

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	102	67	92

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.417 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	259	147	0.3 - 180.2 (28.2)
subtype1	102	55	0.4 - 180.2 (28.5)
subtype2	66	36	1.0 - 106.0 (34.6)
subtype3	91	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 = 0.00127 (ANOVA), Q value = 0.1

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

	nPatients	Mean (Std.Dev)
ALL	254	59.0 (10.8)
subtype1	99	59.2 (10.3)
subtype2	66	55.2 (10.6)
subtype3	89	61.5 (10.8)

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), Q value = 1

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

nPatients	OMENTUM	OVARY
ALL	1	260
subtype1	1	101
subtype2	0	67
subtype3	0	92

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.527 (Chi-square test), Q value = 1

Table S69. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	18	209	33
subtype1	7	81	14
subtype2	6	50	11
subtype3	5	78	8

Figure S60. 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), Q value = 1

Table S70. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	259
subtype1	1	101
subtype2	0	67
subtype3	1	91

Figure S61. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.333 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

Figure S62. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S72. Get Full Table Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	111	111	39

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.438 (logrank test), Q value = 1

Table S73. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	259	147	0.3 - 180.2 (28.2)
subtype1	110	69	0.4 - 152.0 (28.8)
subtype2	110	58	0.4 - 180.2 (28.0)
subtype3	39	20	0.3 - 116.1 (26.9)

Figure S63. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.166 (ANOVA), Q value = 1

Table S74. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	59.0 (10.8)
subtype1	108	59.7 (11.0)
subtype2	109	59.3 (10.6)
subtype3	37	55.9 (10.7)

Figure S64. 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 = 1 (Fisher's exact test), Q value = 1

Table S75. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY
ALL	1	260
subtype1	0	111
subtype2	1	110
subtype3	0	39

Figure S65. 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), Q value = 1

Table S76. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	14	74.3 (12.2)
subtype1	3	80.0 (0.0)
subtype2	10	72.0 (14.0)
subtype3	1	80.0 (NA)

Figure S66. 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 = 0.162 (Chi-square test), Q value = 1

Table S77. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	18	209	33
subtype1	8	92	10
subtype2	6	91	14
subtype3	4	26	9

Figure S67. 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 = 0.637 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	259
subtype1	2	109
subtype2	0	111
subtype3	0	39

Figure S68. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0476 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	2	5
subtype1	2	0
subtype2	0	5
subtype3	0	0

Figure S69. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #11: 'MIRSEQ CNMF'

Table S80. Get Full Table 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 = 0.301 (logrank test), Q value = 1

Table S81. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	124	78	0.3 - 152.0 (28.6)
subtype2	184	98	0.3 - 180.2 (32.0)
subtype3	142	76	0.3 - 130.0 (28.7)

Figure S70. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.535 (ANOVA), Q value = 1

Table S82. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	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 S71. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.0911 (Chi-square test), Q value = 1

Table S83. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

Figure S72. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.19 (ANOVA), Q value = 1

Table S84. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: '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 S73. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.412 (Chi-square test), Q value = 1

Table S85. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	24	351	74
subtype1	5	98	20
subtype2	8	140	35
subtype3	11	113	19

Figure S74. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.489 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	0	124
subtype2	1	184
subtype3	2	142

Figure S75. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0357 (Fisher's exact test), Q value = 1

Table S87. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	2	6
subtype1	0	3
subtype2	0	3
subtype3	2	0

Figure S76. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #12: 'MIRSEQ CHIERARCHICAL'

Table S88. Get Full Table Description of clustering approach #12: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	5	287	161

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.639 (logrank test), Q value = 1

Table S89. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	5	2	4.3 - 68.9 (45.6)
subtype2	286	163	0.3 - 180.2 (30.8)
subtype3	159	87	0.3 - 130.0 (28.7)

Figure S77. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.676 (ANOVA), Q value = 1

Table S90. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	5	60.6 (9.2)
subtype2	283	60.1 (11.5)
subtype3	157	59.1 (11.6)

Figure S78. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.782 (Chi-square test), Q value = 1

Table S91. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	450	1
subtype1	0	5	0
subtype2	2	284	1
subtype3	0	161	0

Figure S79. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.901 (ANOVA), Q value = 1

Table S92. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	1	100.0 (NA)
subtype2	54	74.8 (12.4)
subtype3	9	75.6 (16.7)

Figure S80. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.395 (Chi-square test), Q value = 1

Table S93. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients	II	III	IV
ALL	24	351	74
subtype1	0	4	1
subtype2	12	220	52
subtype3	12	127	21

Figure S81. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'TUMOR.STAGE'

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

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

Table S94. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	0	5
subtype2	1	286
subtype3	2	159

Figure S82. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0357 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1
ALL	2	6
subtype1	0	0
subtype2	0	6
subtype3	2	0

Figure S83. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = OV-TP.mergedcluster.txt
Clinical data file = OV-TP.clin.merged.picked.txt
Number of patients = 578
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

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

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)

[7] 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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