Correlation between aggregated molecular cancer subtypes and selected clinical features

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1JM289B

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 6 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.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes 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 14 different clustering approaches and 6 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	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION
Statistical Tests	logrank test	ANOVA	Chi-square test	ANOVA	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0159 (1.00)	0.00808 (0.59)	0.557 (1.00)	0.117 (1.00)	0.795 (1.00)	0.184 (1.00)
mRNA cHierClus subtypes	0.00642 (0.475)	0.0219 (1.00)	0.578 (1.00)	0.0722 (1.00)	0.624 (1.00)	0.144 (1.00)
miR CNMF subtypes	0.0391 (1.00)	0.611 (1.00)	0.198 (1.00)	0.737 (1.00)	0.266 (1.00)	0.23 (1.00)
miR cHierClus subtypes	0.579 (1.00)	0.00948 (0.683)	0.837 (1.00)	0.0822 (1.00)	0.525 (1.00)	0.363 (1.00)
Copy Number Ratio CNMF subtypes	0.203 (1.00)	1.2e-09 (9.2e-08)	0.285 (1.00)	0.592 (1.00)	0.411 (1.00)	0.328 (1.00)
METHLYATION CNMF	0.227 (1.00)	1.78e-09 (1.35e-07)	0.388 (1.00)	0.835 (1.00)	1 (1.00)	0.526 (1.00)
RPPA CNMF subtypes	0.0245 (1.00)	0.716 (1.00)	0.718 (1.00)	0.54 (1.00)	0.134 (1.00)	0.37 (1.00)
RPPA cHierClus subtypes	0.16 (1.00)	0.0181 (1.00)	0.684 (1.00)	0.851 (1.00)	1 (1.00)	0.38 (1.00)
RNAseq CNMF subtypes	0.416 (1.00)	0.00125 (0.094)	1 (1.00)		1 (1.00)
RNAseq cHierClus subtypes	0.438 (1.00)	0.17 (1.00)	1 (1.00)	0.104 (1.00)	0.637 (1.00)
MIRSEQ CNMF	0.301 (1.00)	0.538 (1.00)	0.0911 (1.00)	0.19 (1.00)	0.489 (1.00)
MIRSEQ CHIERARCHICAL	0.638 (1.00)	0.682 (1.00)	0.782 (1.00)	0.901 (1.00)	0.317 (1.00)
MIRseq Mature CNMF subtypes	0.0657 (1.00)	0.427 (1.00)	0.277 (1.00)	0.24 (1.00)	0.051 (1.00)
MIRseq Mature cHierClus subtypes	0.463 (1.00)	0.746 (1.00)	0.779 (1.00)	0.629 (1.00)	0.1 (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	214	129

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0159 (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	293	0.3 - 180.2 (28.6)
subtype1	216	124	0.3 - 152.0 (30.2)
subtype2	212	93	0.4 - 180.2 (28.2)
subtype3	129	76	0.3 - 119.1 (25.9)

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 = 0.00808 (ANOVA), Q value = 0.59

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 (11.6)
subtype2	210	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: 'AGE'

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

P value = 0.557 (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	212	1
subtype3	1	127	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 = 0.117 (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	31	78.1 (13.0)
subtype2	28	76.4 (12.2)
subtype3	19	70.5 (12.2)

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

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

P value = 0.795 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	3	559
subtype1	2	217
subtype2	1	213
subtype3	0	129

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

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

P value = 0.184 (Chi-square test), Q value = 1

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

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: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	132	214	216

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.00642 (logrank test), Q value = 0.48

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	293	0.3 - 180.2 (28.6)
subtype1	132	80	0.3 - 119.1 (26.4)
subtype2	211	126	0.3 - 152.0 (32.1)
subtype3	214	87	0.5 - 180.2 (25.5)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0219 (ANOVA), Q value = 1

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	131	59.0 (12.1)
subtype2	207	61.5 (11.9)
subtype3	213	58.5 (10.9)

Figure S8. 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 = 0.578 (Chi-square test), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	558	2
subtype1	1	130	1
subtype2	0	214	0
subtype3	1	214	1

Figure S9. 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 = 0.0722 (ANOVA), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	19	71.6 (13.8)
subtype2	26	80.0 (12.6)
subtype3	33	74.5 (11.5)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.624 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	0	132
subtype2	2	212
subtype3	1	215

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.144 (Chi-square test), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	1	8	1
subtype2	7	7	0
subtype3	6	12	0

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #3: 'miR CNMF subtypes'

Table S15. 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 = 0.0391 (logrank test), Q value = 1

Table S16. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	555	293	0.3 - 180.2 (28.5)
subtype1	156	88	0.3 - 130.0 (27.3)
subtype2	162	92	0.3 - 115.9 (24.7)
subtype3	237	113	0.3 - 180.2 (30.4)

Figure S13. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.611 (ANOVA), Q value = 1

Table S17. 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 S14. 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 S18. 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 S15. 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 S19. 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 S16. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.23 (Chi-square test), Q value = 1

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

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

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S22. 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 = 0.579 (logrank test), Q value = 1

Table S23. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	555	293	0.3 - 180.2 (28.5)
subtype1	65	32	0.8 - 115.9 (26.9)
subtype2	119	66	1.0 - 180.2 (28.7)
subtype3	122	66	0.3 - 130.0 (26.4)
subtype4	102	46	0.3 - 125.8 (30.0)
subtype5	54	31	1.8 - 106.0 (22.4)
subtype6	93	52	0.5 - 152.0 (30.4)

Figure S19. 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 = 0.68

Table S24. 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 (12.4)
subtype4	100	60.1 (10.8)
subtype5	56	62.2 (13.2)
subtype6	90	62.3 (10.7)

Figure S20. 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 S25. 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 S21. 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 S26. 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 S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: '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 S23. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.363 (Chi-square test), Q value = 1

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	1	2	0
subtype2	1	8	1
subtype3	4	10	0
subtype4	4	5	0
subtype5	0	0	0
subtype6	4	2	0

Figure S24. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

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

Table S29. Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	173	201	188

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

P value = 0.203 (logrank test), Q value = 1

Table S30. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	547	287	0.3 - 180.2 (28.5)
subtype1	168	97	0.8 - 119.1 (29.8)
subtype2	196	92	0.3 - 130.0 (30.1)
subtype3	183	98	0.3 - 180.2 (22.3)

Figure S25. 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 = 1.2e-09 (ANOVA), Q value = 9.2e-08

Table S31. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	541	59.8 (11.6)
subtype1	165	60.0 (12.1)
subtype2	197	56.1 (11.0)
subtype3	179	63.7 (10.6)

Figure S26. 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 = 0.285 (Chi-square test), Q value = 1

Table S32. 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	169	0
subtype2	1	197	0
subtype3	0	182	2

Figure S27. 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 = 0.592 (ANOVA), Q value = 1

Table S33. 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	76.2 (10.2)
subtype2	28	73.6 (13.4)
subtype3	27	77.0 (14.4)

Figure S28. 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 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.411 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	1	172
subtype2	0	201
subtype3	2	186

Figure S29. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.328 (Chi-square test), Q value = 1

Table S35. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	26	2
subtype1	2	11	0
subtype2	4	6	1
subtype3	8	9	1

Figure S30. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'METHLYATION CNMF'

Table S36. Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	175	180	219

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.227 (logrank test), Q value = 1

Table S37. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	558	295	0.3 - 180.2 (28.5)
subtype1	170	101	0.3 - 152.0 (26.9)
subtype2	172	86	0.8 - 180.2 (30.1)
subtype3	216	108	0.3 - 130.0 (28.4)

Figure S31. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 1.78e-09 (ANOVA), Q value = 1.4e-07

Table S38. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	553	59.8 (11.6)
subtype1	168	64.5 (10.4)
subtype2	170	57.8 (12.3)
subtype3	215	57.7 (10.9)

Figure S32. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.388 (Chi-square test), Q value = 1

Table S39. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	560	2
subtype1	0	170	1
subtype2	0	175	0
subtype3	2	215	1

Figure S33. 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 = 0.835 (ANOVA), Q value = 1

Table S40. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: '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 S34. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S41. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	571
subtype1	1	174
subtype2	1	179
subtype3	1	218

Figure S35. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.526 (Chi-square test), Q value = 1

Table S42. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

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

Figure S36. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S43. 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 = 0.0245 (logrank test), Q value = 1

Table S44. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	401	213	0.3 - 180.2 (28.7)
subtype1	120	66	0.4 - 125.8 (31.1)
subtype2	197	92	0.3 - 180.2 (25.1)
subtype3	84	55	0.5 - 89.3 (27.2)

Figure S37. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.716 (ANOVA), Q value = 1

Table S45. 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 (12.4)
subtype2	196	59.2 (11.7)
subtype3	83	60.4 (11.4)

Figure S38. 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 S46. 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 S39. 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 S47. 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 S40. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

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

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

Figure S41. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.37 (Chi-square test), Q value = 1

Table S49. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	13	28	2
subtype1	2	10	0
subtype2	8	9	1
subtype3	3	9	1

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

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S50. 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 = 0.16 (logrank test), Q value = 1

Table S51. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	401	213	0.3 - 180.2 (28.7)
subtype1	60	36	0.8 - 180.2 (35.0)
subtype2	83	40	0.3 - 115.9 (28.4)
subtype3	108	47	0.4 - 152.0 (25.8)
subtype4	150	90	0.4 - 125.8 (27.6)

Figure S43. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0181 (ANOVA), Q value = 1

Table S52. 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	60.9 (12.1)

Figure S44. 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 S53. 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 S45. 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 S54. 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 S46. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S55. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S47. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.38 (Chi-square test), Q value = 1

Table S56. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	13	28	2
subtype1	3	4	0
subtype2	4	2	0
subtype3	3	9	1
subtype4	3	13	1

Figure S48. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S57. 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 = 0.416 (logrank test), Q value = 1

Table S58. 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 S49. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00125 (ANOVA), Q value = 0.094

Table S59. 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 (10.9)

Figure S50. 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 S60. 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 S51. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

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

Table S61. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S52. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S62. 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 S63. 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 (27.0)

Figure S53. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.17 (ANOVA), Q value = 1

Table S64. 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 S54. 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 S65. 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 S55. 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 S66. 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 S56. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.637 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S57. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #11: 'MIRSEQ CNMF'

Table S68. 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 S69. 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 (32.1)
subtype3	142	76	0.3 - 130.0 (28.7)

Figure S58. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.538 (ANOVA), Q value = 1

Table S70. 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 S59. 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 S71. 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 S60. 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 S72. 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 S61. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S73. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S62. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #12: 'MIRSEQ CHIERARCHICAL'

Table S74. 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 = 0.638 (logrank test), Q value = 1

Table S75. 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 (28.8)

Figure S63. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.682 (ANOVA), Q value = 1

Table S76. 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 S64. 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 S77. 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 S65. 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 S78. 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 S66. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S79. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S67. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #13: 'MIRseq Mature CNMF subtypes'

Table S80. Description of clustering approach #13: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	98	198	157

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0657 (logrank test), Q value = 1

Table S81. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	98	69	0.3 - 152.0 (28.6)
subtype2	197	100	0.3 - 180.2 (31.8)
subtype3	155	83	0.3 - 130.0 (29.2)

Figure S68. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.427 (ANOVA), Q value = 1

Table S82. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	97	61.1 (12.0)
subtype2	195	59.4 (11.3)
subtype3	153	59.4 (11.5)

Figure S69. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.277 (Chi-square test), Q value = 1

Table S83. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	1	96	1
subtype2	1	197	0
subtype3	0	157	0

Figure S70. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.24 (ANOVA), Q value = 1

Table S84. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	8	82.5 (16.7)
subtype2	48	74.6 (12.2)
subtype3	8	72.5 (14.9)

Figure S71. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.051 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	0	98
subtype2	0	198
subtype3	3	154

Figure S72. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #14: 'MIRseq Mature cHierClus subtypes'

Table S86. Description of clustering approach #14: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	158	9	286

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.463 (logrank test), Q value = 1

Table S87. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	156	86	0.3 - 130.0 (28.7)
subtype2	9	5	1.0 - 76.9 (54.5)
subtype3	285	161	0.3 - 180.2 (30.2)

Figure S73. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.746 (ANOVA), Q value = 1

Table S88. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	154	59.2 (11.7)
subtype2	9	59.3 (11.3)
subtype3	282	60.1 (11.4)

Figure S74. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.779 (Chi-square test), Q value = 1

Table S89. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	0	158	0
subtype2	0	9	0
subtype3	2	283	1

Figure S75. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.629 (ANOVA), Q value = 1

Table S90. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	8	72.5 (14.9)
subtype2	1	100.0 (NA)
subtype3	55	75.3 (12.7)

Figure S76. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.1 (Fisher's exact test), Q value = 1

Table S91. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	3	155
subtype2	0	9
subtype3	0	286

Figure S77. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data

Input

Cluster data file = OV-TP.mergedcluster.txt
Clinical data file = OV-TP.merged_data.txt
Number of patients = 578
Number of clustering approaches = 14
Number of selected clinical features = 6
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

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)

[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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