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

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

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 9 different clustering approaches and 6 clinical features across 566 patients, 11 significant findings detected with P value < 0.05.

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

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 9 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 11 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	KARNOFSKY PERFORMANCE SCORE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
Statistical Tests	logrank test	ANOVA	Chi-square test	ANOVA	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.00682	0.0382	0.577	0.0882	0.79	0.385
mRNA cHierClus subtypes	0.000224	0.012	0.608	0.205	0.609	0.486
miR CNMF subtypes	0.0104	0.757	0.22	0.677	0.264	0.437
miR cHierClus subtypes	0.271	0.707	0.512	0.995	1	0.314
METHLYATION CNMF	0.0475	2.19e-09	0.739	0.368	0.321	0.671
RNAseq CNMF subtypes	0.274	0.00136	1		0.173	0.565
RNAseq cHierClus subtypes	0.58	0.00134	1		0.453	0.691
MIRseq CNMF subtypes	0.0208	0.53	0.385	0.113	0.476	0.704
MIRseq cHierClus subtypes	0.0354	0.353	0.809	0.382	0.284	0.461

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	222	205	137

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.00682 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	559	292	0.3 - 180.2 (28.3)
subtype1	219	126	0.3 - 152.0 (29.2)
subtype2	203	90	0.4 - 180.2 (28.6)
subtype3	137	76	0.3 - 119.1 (24.3)

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 = 0.0382 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	553	59.7 (11.6)
subtype1	216	61.2 (11.6)
subtype2	201	58.4 (10.9)
subtype3	136	59.2 (12.4)

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 = 0.577 (Chi-square test)

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

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	560	2
subtype1	0	222	0
subtype2	1	203	1
subtype3	1	135	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 = 0.0882 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	31	78.1 (13.0)
subtype2	26	76.9 (12.3)
subtype3	21	70.5 (12.0)

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 = 0.79 (Fisher's exact test)

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

nPatients	NO	YES
ALL	561	3
subtype1	220	2
subtype2	204	1
subtype3	137	0

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.385 (Fisher's exact test)

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

nPatients	NO	YES
ALL	456	108
subtype1	173	49
subtype2	170	35
subtype3	113	24

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	240	130	194

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.000224 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	559	292	0.3 - 180.2 (28.3)
subtype1	238	99	0.3 - 180.2 (27.3)
subtype2	130	79	0.3 - 119.1 (24.3)
subtype3	191	114	0.3 - 152.0 (31.2)

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 = 0.012 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	553	59.7 (11.6)
subtype1	237	58.7 (11.1)
subtype2	129	58.5 (12.2)
subtype3	187	61.7 (11.7)

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 = 0.608 (Chi-square test)

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

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	560	2
subtype1	1	238	1
subtype2	1	128	1
subtype3	0	194	0

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 = 0.205 (ANOVA)

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	34	74.7 (11.3)
subtype2	19	72.6 (13.7)
subtype3	25	79.2 (13.5)

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 = 0.609 (Fisher's exact test)

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

nPatients	NO	YES
ALL	561	3
subtype1	239	1
subtype2	130	0
subtype3	192	2

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

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.486 (Fisher's exact test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	456	108
subtype1	199	41
subtype2	105	25
subtype3	152	42

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

Clustering Approach #3: 'miR CNMF subtypes'

Table S15. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	167	154	241

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.0104 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	292	0.3 - 180.2 (28.2)
subtype1	166	91	0.3 - 130.0 (26.4)
subtype2	151	86	0.3 - 109.7 (25.1)
subtype3	240	115	0.3 - 180.2 (29.3)

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 = 0.757 (ANOVA)

Table S17. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	163	59.7 (12.4)
subtype2	152	59.1 (11.6)
subtype3	236	60.0 (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 = 0.22 (Chi-square test)

Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	558	2
subtype1	0	165	2
subtype2	1	153	0
subtype3	1	240	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 = 0.677 (ANOVA)

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	25	76.8 (12.5)
subtype2	22	73.6 (15.6)
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 = 0.264 (Fisher's exact test)

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

nPatients	NO	YES
ALL	559	3
subtype1	167	0
subtype2	154	0
subtype3	238	3

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

'miR CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.437 (Fisher's exact test)

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	454	108
subtype1	137	30
subtype2	119	35
subtype3	198	43

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

Clustering Approach #4: 'miR cHierClus subtypes'

Table S22. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	293	137	132

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.271 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	292	0.3 - 180.2 (28.2)
subtype1	289	146	0.3 - 152.0 (28.5)
subtype2	137	73	0.3 - 130.0 (29.8)
subtype3	131	73	1.0 - 180.2 (27.5)

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 = 0.707 (ANOVA)

Table S24. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	288	60.1 (11.5)
subtype2	132	59.4 (12.0)
subtype3	131	59.1 (11.5)

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 = 0.512 (Chi-square test)

Table S25. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	558	2
subtype1	1	292	0
subtype2	0	136	1
subtype3	1	130	1

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 = 0.995 (ANOVA)

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	38	75.8 (13.3)
subtype2	18	75.6 (12.9)
subtype3	22	75.5 (12.2)

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 = 1 (Fisher's exact test)

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	559	3
subtype1	291	2
subtype2	136	1
subtype3	132	0

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

'miR cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.314 (Fisher's exact test)

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	454	108
subtype1	236	57
subtype2	116	21
subtype3	102	30

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

Clustering Approach #5: 'METHLYATION CNMF'

Table S29. Get Full Table Description of clustering approach #5: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	186	177	186

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0475 (logrank test)

Table S30. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	543	290	0.3 - 180.2 (29.0)
subtype1	184	95	0.3 - 180.2 (30.1)
subtype2	174	95	0.4 - 125.7 (25.9)
subtype3	185	100	0.4 - 152.0 (29.5)

Figure S25. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 2.19e-09 (ANOVA)

Table S31. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	539	59.7 (11.7)
subtype1	184	55.4 (11.3)
subtype2	172	61.9 (12.1)
subtype3	183	62.1 (10.4)

Figure S26. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.739 (Chi-square test)

Table S32. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	545	2
subtype1	1	185	0
subtype2	0	176	1
subtype3	1	184	1

Figure S27. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.368 (ANOVA)

Table S33. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	26	78.5 (11.2)
subtype2	27	74.8 (15.3)
subtype3	25	73.6 (11.1)

Figure S28. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.321 (Fisher's exact test)

Table S34. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	546	3
subtype1	186	0
subtype2	175	2
subtype3	185	1

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.671 (Fisher's exact test)

Table S35. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	441	108
subtype1	149	37
subtype2	139	38
subtype3	153	33

Figure S30. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RNAseq CNMF subtypes'

Table S36. Get Full Table Description of clustering approach #6: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	72	48	87

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.274 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	207	128	0.3 - 180.2 (27.0)
subtype1	72	50	0.3 - 152.0 (26.4)
subtype2	48	28	1.0 - 116.1 (29.6)
subtype3	87	50	0.4 - 180.2 (25.9)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00136 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	200	59.7 (11.2)
subtype1	69	62.2 (11.2)
subtype2	47	54.8 (10.2)
subtype3	84	60.4 (10.9)

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

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

P value = 1 (Fisher's exact test)

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

nPatients	OMENTUM	OVARY
ALL	1	206
subtype1	0	72
subtype2	0	48
subtype3	1	86

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.173 (Fisher's exact test)

Table S40. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	205	2
subtype1	70	2
subtype2	48	0
subtype3	87	0

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.565 (Fisher's exact test)

Table S41. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	161	46
subtype1	59	13
subtype2	36	12
subtype3	66	21

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq cHierClus subtypes'

Table S42. Get Full Table Description of clustering approach #7: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	32	85	90

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.58 (logrank test)

Table S43. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	207	128	0.3 - 180.2 (27.0)
subtype1	32	20	1.2 - 89.3 (32.6)
subtype2	85	55	0.3 - 152.0 (25.1)
subtype3	90	53	0.4 - 180.2 (24.9)

Figure S36. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00134 (ANOVA)

Table S44. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	200	59.7 (11.2)
subtype1	32	53.7 (9.4)
subtype2	82	59.7 (11.3)
subtype3	86	62.0 (11.0)

Figure S37. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1 (Fisher's exact test)

Table S45. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY
ALL	1	206
subtype1	0	32
subtype2	0	85
subtype3	1	89

Figure S38. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.453 (Fisher's exact test)

Table S46. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	205	2
subtype1	32	0
subtype2	83	2
subtype3	90	0

Figure S39. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.691 (Fisher's exact test)

Table S47. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	161	46
subtype1	27	5
subtype2	65	20
subtype3	69	21

Figure S40. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'MIRseq CNMF subtypes'

Table S48. Get Full Table Description of clustering approach #8: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	114	195	145

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0208 (logrank test)

Table S49. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	451	252	0.3 - 180.2 (30.1)
subtype1	114	71	0.3 - 152.0 (24.3)
subtype2	194	102	0.3 - 180.2 (33.6)
subtype3	143	79	0.3 - 130.0 (28.6)

Figure S41. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.53 (ANOVA)

Table S50. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	446	59.8 (11.5)
subtype1	113	60.8 (11.8)
subtype2	192	59.5 (11.2)
subtype3	141	59.4 (11.8)

Figure S42. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.385 (Chi-square test)

Table S51. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	451	1
subtype1	1	112	1
subtype2	1	194	0
subtype3	0	145	0

Figure S43. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.113 (ANOVA)

Table S52. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	11	81.8 (14.0)
subtype2	48	74.6 (12.2)
subtype3	5	68.0 (17.9)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.476 (Fisher's exact test)

Table S53. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	451	3
subtype1	114	0
subtype2	194	1
subtype3	143	2

Figure S45. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.704 (Fisher's exact test)

Table S54. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	367	87
subtype1	91	23
subtype2	161	34
subtype3	115	30

Figure S46. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq cHierClus subtypes'

Table S55. Get Full Table Description of clustering approach #9: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	3	297	154

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0354 (logrank test)

Table S56. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	451	252	0.3 - 180.2 (30.1)
subtype1	3	2	4.5 - 33.7 (8.6)
subtype2	296	164	0.3 - 180.2 (31.0)
subtype3	152	86	0.3 - 130.0 (27.4)

Figure S47. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.353 (ANOVA)

Table S57. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	446	59.8 (11.5)
subtype1	3	69.0 (12.2)
subtype2	293	59.9 (11.4)
subtype3	150	59.4 (11.8)

Figure S48. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.809 (Chi-square test)

Table S58. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM (OVARY)
ALL	2	451	1
subtype1	0	3	0
subtype2	2	294	1
subtype3	0	154	0

Figure S49. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.382 (ANOVA)

Table S59. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype2	59	75.9 (12.7)
subtype3	5	68.0 (17.9)

Figure S50. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.284 (Fisher's exact test)

Table S60. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	451	3
subtype1	3	0
subtype2	296	1
subtype3	152	2

Figure S51. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.461 (Fisher's exact test)

Table S61. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	367	87
subtype1	2	1
subtype2	243	54
subtype3	122	32

Figure S52. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = OV.mergedcluster.txt
Clinical data file = OV.clin.merged.picked.txt
Number of patients = 566
Number of clustering approaches = 9
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

Download Results

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

References

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

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

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

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

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

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

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