Correlation between aggregated molecular cancer subtypes and selected clinical features

Prostate Adenocarcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1CR5RQG

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 10 different clustering approaches and 6 clinical features across 165 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE' and 'NUMBER.OF.LYMPH.NODES'.
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 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NUMBER.OF.LYMPH.NODES'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.T.STAGE'.
5 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 10 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, 5 significant findings detected.


Clinical Features	Time to Death	AGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES
Statistical Tests	logrank test	ANOVA	Chi-square test	Fisher's exact test	Chi-square test	ANOVA
Copy Number Ratio CNMF subtypes	100 (1.00)	0.146 (1.00)	0.00117 (0.0679)	0.0052 (0.286)	0.616 (1.00)	0.00163 (0.093)
METHLYATION CNMF	100 (1.00)	0.000924 (0.0545)	0.0284 (1.00)	0.097 (1.00)	0.474 (1.00)	0.0822 (1.00)
RPPA CNMF subtypes	100 (1.00)	0.017 (0.866)	0.0339 (1.00)	0.00581 (0.314)	0.147 (1.00)	0.0305 (1.00)
RPPA cHierClus subtypes	100 (1.00)	0.0273 (1.00)	0.114 (1.00)	0.00852 (0.452)	0.0243 (1.00)	0.0294 (1.00)
RNAseq CNMF subtypes	100 (1.00)	0.196 (1.00)	0.0365 (1.00)	0.206 (1.00)	0.552 (1.00)	0.18 (1.00)
RNAseq cHierClus subtypes	100 (1.00)	0.65 (1.00)	0.0273 (1.00)	0.0245 (1.00)	0.534 (1.00)	0.00369 (0.207)
MIRSEQ CNMF	100 (1.00)	0.221 (1.00)	0.0142 (0.74)	0.0713 (1.00)	0.372 (1.00)	0.0372 (1.00)
MIRSEQ CHIERARCHICAL	100 (1.00)	0.525 (1.00)	0.000413 (0.0248)	0.228 (1.00)	0.622 (1.00)	0.0172 (0.866)
MIRseq Mature CNMF subtypes	100 (1.00)	0.437 (1.00)	0.305 (1.00)	0.311 (1.00)	0.077 (1.00)	0.308 (1.00)
MIRseq Mature cHierClus subtypes	100 (1.00)	0.387 (1.00)	0.0728 (1.00)	0.389 (1.00)	0.499 (1.00)	0.0384 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	23	86	51

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

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	160	1	0.3 - 66.1 (15.0)
subtype1	23	0	0.3 - 61.1 (5.8)
subtype2	86	0	1.0 - 66.0 (17.5)
subtype3	51	1	0.8 - 66.1 (16.6)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.146 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	159	60.3 (6.9)
subtype1	23	60.8 (7.3)
subtype2	85	59.3 (7.2)
subtype3	51	61.7 (5.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00117 (Chi-square test), Q value = 0.068

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	69	85	5
subtype1	13	10	0
subtype2	46	36	3
subtype3	10	39	2

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0052 (Fisher's exact test), Q value = 0.29

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	126	15
subtype1	21	1
subtype2	68	3
subtype3	37	11

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.616 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	119	31	3
subtype1	17	5	0
subtype2	64	16	3
subtype3	38	10	0

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00163 (ANOVA), Q value = 0.093

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	141	0.2 (0.7)
subtype1	22	0.0 (0.2)
subtype2	71	0.0 (0.2)
subtype3	48	0.5 (1.2)

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'METHLYATION CNMF'

Table S8. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	49	52	64

'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	165	1	0.3 - 66.1 (14.8)
subtype1	49	0	0.3 - 65.9 (14.7)
subtype2	52	0	1.1 - 62.4 (13.5)
subtype3	64	1	1.0 - 66.1 (16.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.000924 (ANOVA), Q value = 0.055

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	164	60.2 (6.9)
subtype1	48	62.1 (6.2)
subtype2	52	57.4 (7.0)
subtype3	64	61.1 (6.6)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0284 (Chi-square test), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	70	89	5
subtype1	23	23	3
subtype2	28	23	0
subtype3	19	43	2

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.097 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	130	15
subtype1	41	5
subtype2	40	1
subtype3	49	9

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.474 (Chi-square test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	122	31	3
subtype1	34	12	0
subtype2	40	8	2
subtype3	48	11	1

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0822 (ANOVA), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	145	0.2 (0.7)
subtype1	46	0.2 (0.5)
subtype2	41	0.0 (0.2)
subtype3	58	0.3 (1.0)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S15. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	40	47	50

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	137	1	0.3 - 66.0 (16.6)
subtype1	40	0	1.0 - 44.0 (13.0)
subtype2	47	0	0.3 - 65.9 (17.4)
subtype3	50	1	0.9 - 66.0 (17.8)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.017 (ANOVA), Q value = 0.87

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

	nPatients	Mean (Std.Dev)
ALL	137	60.2 (7.1)
subtype1	40	62.9 (6.4)
subtype2	47	58.7 (7.8)
subtype3	50	59.5 (6.5)

Figure S14. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0339 (Chi-square test), Q value = 1

Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	53	79	5
subtype1	18	22	0
subtype2	23	23	1
subtype3	12	34	4

Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00581 (Fisher's exact test), Q value = 0.31

Table S19. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	109	13
subtype1	36	4
subtype2	38	0
subtype3	35	9

Figure S16. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.147 (Chi-square test), Q value = 1

Table S20. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	100	27	3
subtype1	34	4	1
subtype2	36	8	1
subtype3	30	15	1

Figure S17. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0305 (ANOVA), Q value = 1

Table S21. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	122	0.2 (0.8)
subtype1	40	0.1 (0.5)
subtype2	38	0.0 (0.0)
subtype3	44	0.4 (1.1)

Figure S18. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S22. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	49	51	37

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	137	1	0.3 - 66.0 (16.6)
subtype1	49	0	1.0 - 44.0 (13.0)
subtype2	51	1	0.9 - 66.0 (17.0)
subtype3	37	0	0.3 - 65.9 (17.5)

Figure S19. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0273 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	137	60.2 (7.1)
subtype1	49	62.2 (6.7)
subtype2	51	59.8 (6.7)
subtype3	37	58.2 (7.7)

Figure S20. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.114 (Chi-square test), Q value = 1

Table S25. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	53	79	5
subtype1	24	25	0
subtype2	15	32	4
subtype3	14	22	1

Figure S21. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00852 (Fisher's exact test), Q value = 0.45

Table S26. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	109	13
subtype1	44	4
subtype2	34	9
subtype3	31	0

Figure S22. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.0243 (Chi-square test), Q value = 1

Table S27. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	100	27	3
subtype1	44	4	0
subtype2	29	15	2
subtype3	27	8	1

Figure S23. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0294 (ANOVA), Q value = 1

Table S28. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	122	0.2 (0.8)
subtype1	48	0.1 (0.5)
subtype2	43	0.4 (1.1)
subtype3	31	0.0 (0.0)

Figure S24. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S29. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	54	51	56

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	161	1	0.3 - 66.1 (15.0)
subtype1	54	0	0.3 - 65.9 (15.5)
subtype2	51	0	1.0 - 62.4 (13.0)
subtype3	56	1	1.0 - 66.1 (16.9)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.196 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	160	60.3 (6.9)
subtype1	53	61.0 (6.7)
subtype2	51	58.9 (7.0)
subtype3	56	61.0 (6.8)

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0365 (Chi-square test), Q value = 1

Table S32. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	69	86	5
subtype1	26	25	3
subtype2	27	23	0
subtype3	16	38	2

Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.206 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	127	15
subtype1	42	5
subtype2	43	2
subtype3	42	8

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.552 (Chi-square test), Q value = 1

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	120	31	3
subtype1	41	13	0
subtype2	38	8	2
subtype3	41	10	1

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.18 (ANOVA), Q value = 1

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	142	0.2 (0.7)
subtype1	47	0.2 (0.9)
subtype2	45	0.0 (0.2)
subtype3	50	0.3 (0.8)

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S36. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	40	55	66

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	161	1	0.3 - 66.1 (15.0)
subtype1	40	0	1.0 - 65.9 (16.6)
subtype2	55	1	0.8 - 66.1 (13.0)
subtype3	66	0	0.3 - 62.4 (13.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.65 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	160	60.3 (6.9)
subtype1	39	60.6 (6.9)
subtype2	55	60.9 (6.9)
subtype3	66	59.7 (6.9)

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0273 (Chi-square test), Q value = 1

Table S39. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	69	86	5
subtype1	23	15	2
subtype2	15	38	2
subtype3	31	33	1

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0245 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	127	15
subtype1	32	2
subtype2	38	10
subtype3	57	3

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.534 (Chi-square test), Q value = 1

Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	120	31	3
subtype1	29	11	0
subtype2	40	10	1
subtype3	51	10	2

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00369 (ANOVA), Q value = 0.21

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	142	0.2 (0.7)
subtype1	34	0.1 (0.2)
subtype2	48	0.5 (1.2)
subtype3	60	0.1 (0.2)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S43. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	49	32	10	12	61

'MIRSEQ CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	164	1	0.3 - 66.1 (14.8)
subtype1	49	0	0.9 - 65.9 (19.5)
subtype2	32	0	1.9 - 66.1 (23.0)
subtype3	10	0	0.8 - 33.1 (9.4)
subtype4	12	0	1.7 - 64.1 (16.6)
subtype5	61	1	0.3 - 66.0 (5.3)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.221 (ANOVA), Q value = 1

Table S45. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	163	60.2 (6.9)
subtype1	49	62.0 (6.3)
subtype2	32	59.2 (7.0)
subtype3	10	57.9 (6.7)
subtype4	12	58.8 (7.8)
subtype5	60	59.9 (7.0)

Figure S38. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0142 (Chi-square test), Q value = 0.74

Table S46. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	70	89	4
subtype1	18	29	2
subtype2	11	20	0
subtype3	3	7	0
subtype4	7	3	2
subtype5	31	30	0

Figure S39. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0713 (Chi-square test), Q value = 1

Table S47. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	130	14
subtype1	36	9
subtype2	27	2
subtype3	9	0
subtype4	11	0
subtype5	47	3

Figure S40. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.372 (Chi-square test), Q value = 1

Table S48. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	122	30	3
subtype1	38	11	0
subtype2	22	8	1
subtype3	6	2	0
subtype4	8	4	0
subtype5	48	5	2

Figure S41. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0372 (ANOVA), Q value = 1

Table S49. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	144	0.2 (0.7)
subtype1	45	0.5 (1.2)
subtype2	29	0.1 (0.3)
subtype3	9	0.0 (0.0)
subtype4	11	0.0 (0.0)
subtype5	50	0.1 (0.3)

Figure S42. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S50. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	33	13	58	60

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S51. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	164	1	0.3 - 66.1 (14.8)
subtype1	33	0	0.3 - 64.1 (17.3)
subtype2	13	0	0.8 - 33.3 (16.5)
subtype3	58	1	1.0 - 66.0 (5.4)
subtype4	60	0	1.0 - 66.1 (18.1)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.525 (ANOVA), Q value = 1

Table S52. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	163	60.2 (6.9)
subtype1	32	58.8 (7.4)
subtype2	13	59.9 (6.4)
subtype3	58	61.1 (6.8)
subtype4	60	60.1 (6.9)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.000413 (Chi-square test), Q value = 0.025

Table S53. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	70	89	4
subtype1	19	10	4
subtype2	3	10	0
subtype3	26	32	0
subtype4	22	37	0

Figure S45. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.228 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	130	14
subtype1	25	1
subtype2	10	2
subtype3	49	3
subtype4	46	8

Figure S46. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

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

P value = 0.622 (Chi-square test), Q value = 1

Table S55. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	122	30	3
subtype1	25	8	0
subtype2	8	4	0
subtype3	41	8	2
subtype4	48	10	1

Figure S47. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0172 (ANOVA), Q value = 0.87

Table S56. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	144	0.2 (0.7)
subtype1	26	0.0 (0.2)
subtype2	12	0.8 (1.9)
subtype3	52	0.1 (0.3)
subtype4	54	0.2 (0.7)

Figure S48. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S57. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	73	9	13	6	63

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

P value = 100 (logrank test), Q value = 1

Table S58. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	164	1	0.3 - 66.1 (14.8)
subtype1	73	0	0.9 - 65.9 (23.0)
subtype2	9	0	5.3 - 34.9 (24.0)
subtype3	13	0	1.0 - 64.1 (10.7)
subtype4	6	0	0.8 - 36.5 (7.4)
subtype5	63	1	0.3 - 66.1 (5.6)

Figure S49. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.437 (ANOVA), Q value = 1

Table S59. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	163	60.2 (6.9)
subtype1	73	61.0 (6.6)
subtype2	9	61.4 (7.0)
subtype3	13	57.5 (8.3)
subtype4	6	60.2 (6.2)
subtype5	62	59.5 (6.9)

Figure S50. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.305 (Chi-square test), Q value = 1

Table S60. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	70	89	4
subtype1	26	43	3
subtype2	4	5	0
subtype3	7	5	1
subtype4	1	5	0
subtype5	32	31	0

Figure S51. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.311 (Chi-square test), Q value = 1

Table S61. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	130	14
subtype1	58	10
subtype2	6	1
subtype3	9	0
subtype4	6	0
subtype5	51	3

Figure S52. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.077 (Chi-square test), Q value = 1

Table S62. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	122	30	3
subtype1	57	16	0
subtype2	6	2	1
subtype3	6	5	0
subtype4	4	1	0
subtype5	49	6	2

Figure S53. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.308 (ANOVA), Q value = 1

Table S63. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	144	0.2 (0.7)
subtype1	68	0.3 (1.0)
subtype2	7	0.1 (0.4)
subtype3	9	0.0 (0.0)
subtype4	6	0.0 (0.0)
subtype5	54	0.1 (0.3)

Figure S54. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S64. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	78	74	12

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

P value = 100 (logrank test), Q value = 1

Table S65. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	164	1	0.3 - 66.1 (14.8)
subtype1	78	0	0.9 - 65.9 (23.4)
subtype2	74	1	0.3 - 66.1 (6.0)
subtype3	12	0	0.8 - 30.4 (9.2)

Figure S55. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.387 (ANOVA), Q value = 1

Table S66. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	163	60.2 (6.9)
subtype1	78	60.9 (6.7)
subtype2	73	59.7 (6.9)
subtype3	12	58.6 (7.7)

Figure S56. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0728 (Chi-square test), Q value = 1

Table S67. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients	T2	T3	T4
ALL	70	89	4
subtype1	29	44	4
subtype2	38	36	0
subtype3	3	9	0

Figure S57. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.389 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	130	14
subtype1	64	9
subtype2	59	4
subtype3	7	1

Figure S58. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.499 (Chi-square test), Q value = 1

Table S69. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	122	30	3
subtype1	58	19	1
subtype2	56	9	2
subtype3	8	2	0

Figure S59. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0384 (ANOVA), Q value = 1

Table S70. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	144	0.2 (0.7)
subtype1	73	0.2 (0.7)
subtype2	63	0.1 (0.3)
subtype3	8	0.8 (2.1)

Figure S60. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = PRAD-TP.mergedcluster.txt
Clinical data file = PRAD-TP.clin.merged.picked.txt
Number of patients = 165
Number of clustering approaches = 10
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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