Correlation between aggregated molecular cancer subtypes and selected clinical features

Uveal Melanoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1R78DC7

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 8 different clustering approaches and 6 clinical features across 80 patients, 13 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 'Time to Death', 'YEARS_TO_BIRTH', and 'PATHOLOGY_M_STAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death' and 'PATHOLOGY_M_STAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death' and 'NEOPLASM_DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death' and 'PATHOLOGY_M_STAGE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 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, 13 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY M STAGE	GENDER
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.00361 (0.0289)	0.0498 (0.182)	0.0738 (0.212)	0.465 (0.519)	0.0481 (0.182)	0.919 (0.919)
METHLYATION CNMF	0.00244 (0.0234)	0.166 (0.275)	0.165 (0.275)	0.269 (0.339)	0.108 (0.235)	0.505 (0.551)
RNAseq CNMF subtypes	2.62e-05 (0.00063)	0.198 (0.275)	0.105 (0.235)	0.132 (0.244)	0.0366 (0.176)	0.702 (0.733)
RNAseq cHierClus subtypes	1.08e-05 (0.000516)	0.0852 (0.224)	0.0405 (0.177)	0.114 (0.235)	0.0651 (0.208)	0.878 (0.896)
MIRSEQ CNMF	0.0285 (0.152)	0.559 (0.596)	0.0887 (0.224)	0.207 (0.275)	0.179 (0.275)	0.188 (0.275)
MIRSEQ CHIERARCHICAL	8.12e-05 (0.0013)	0.125 (0.24)	0.19 (0.275)	0.0954 (0.229)	0.118 (0.235)	0.2 (0.275)
MIRseq Mature CNMF subtypes	0.00917 (0.0629)	0.053 (0.182)	0.137 (0.244)	0.318 (0.391)	0.0232 (0.139)	0.075 (0.212)
MIRseq Mature cHierClus subtypes	0.000112 (0.00134)	0.42 (0.48)	0.207 (0.275)	0.395 (0.462)	0.235 (0.305)	0.381 (0.457)

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	22	36	22

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

P value = 0.00361 (logrank test), Q value = 0.029

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	13	0.1 - 74.5 (19.1)
subtype1	22	5	0.1 - 31.4 (15.3)
subtype2	36	1	0.1 - 74.5 (22.4)
subtype3	22	7	0.3 - 52.6 (14.0)

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 'YEARS_TO_BIRTH'

P value = 0.0498 (Kruskal-Wallis (anova)), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	80	61.6 (13.9)
subtype1	22	65.0 (12.5)
subtype2	36	57.1 (14.3)
subtype3	22	65.7 (13.1)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0738 (Fisher's exact test), Q value = 0.21

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	12	27	25	10	1	4
subtype1	1	8	5	3	1	3
subtype2	9	14	9	4	0	0
subtype3	2	5	11	3	0	1

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.465 (Fisher's exact test), Q value = 0.52

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

nPatients	T2	T3	T4
ALL	14	32	34
subtype1	2	8	12
subtype2	9	15	12
subtype3	3	9	10

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0481 (Fisher's exact test), Q value = 0.18

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

nPatients	0	1
ALL	51	4
subtype1	13	3
subtype2	25	0
subtype3	13	1

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.919 (Fisher's exact test), Q value = 0.92

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	45
subtype1	10	12
subtype2	15	21
subtype3	10	12

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	33	18	29

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00244 (logrank test), Q value = 0.023

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	13	0.1 - 74.5 (19.1)
subtype1	33	10	0.1 - 42.1 (13.6)
subtype2	18	2	0.2 - 74.5 (19.6)
subtype3	29	1	0.1 - 70.7 (22.8)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.166 (Kruskal-Wallis (anova)), Q value = 0.27

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

	nPatients	Mean (Std.Dev)
ALL	80	61.6 (13.9)
subtype1	33	65.2 (13.2)
subtype2	18	61.9 (11.9)
subtype3	29	57.4 (15.2)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.165 (Fisher's exact test), Q value = 0.27

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	12	27	25	10	1	4
subtype1	2	11	11	3	1	4
subtype2	2	5	7	4	0	0
subtype3	8	11	7	3	0	0

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.269 (Fisher's exact test), Q value = 0.34

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T2	T3	T4
ALL	14	32	34
subtype1	3	12	18
subtype2	3	8	7
subtype3	8	12	9

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

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.108 (Fisher's exact test), Q value = 0.23

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	51	4
subtype1	20	4
subtype2	13	0
subtype3	18	0

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.505 (Fisher's exact test), Q value = 0.55

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	45
subtype1	14	19
subtype2	10	8
subtype3	11	18

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	30	15	35

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 2.62e-05 (logrank test), Q value = 0.00063

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	13	0.1 - 74.5 (19.1)
subtype1	30	11	0.1 - 37.8 (13.4)
subtype2	15	1	0.2 - 52.6 (21.0)
subtype3	35	1	0.1 - 74.5 (22.0)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.198 (Kruskal-Wallis (anova)), Q value = 0.28

Table S17. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	80	61.6 (13.9)
subtype1	30	65.5 (13.2)
subtype2	15	61.0 (13.1)
subtype3	35	58.6 (14.5)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.105 (Fisher's exact test), Q value = 0.23

Table S18. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	12	27	25	10	1	4
subtype1	3	8	11	3	1	4
subtype2	0	6	5	3	0	0
subtype3	9	13	9	4	0	0

Figure S15. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.132 (Fisher's exact test), Q value = 0.24

Table S19. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T2	T3	T4
ALL	14	32	34
subtype1	3	9	18
subtype2	2	8	5
subtype3	9	15	11

Figure S16. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0366 (Fisher's exact test), Q value = 0.18

Table S20. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	51	4
subtype1	18	4
subtype2	10	0
subtype3	23	0

Figure S17. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.702 (Fisher's exact test), Q value = 0.73

Table S21. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	45
subtype1	13	17
subtype2	8	7
subtype3	14	21

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	29	33	18

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1.08e-05 (logrank test), Q value = 0.00052

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	13	0.1 - 74.5 (19.1)
subtype1	29	11	0.1 - 37.8 (13.2)
subtype2	33	1	0.1 - 74.5 (22.8)
subtype3	18	1	0.2 - 52.6 (20.3)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0852 (Kruskal-Wallis (anova)), Q value = 0.22

Table S24. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	80	61.6 (13.9)
subtype1	29	66.4 (12.5)
subtype2	33	57.8 (14.5)
subtype3	18	60.9 (13.4)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0405 (Fisher's exact test), Q value = 0.18

Table S25. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	12	27	25	10	1	4
subtype1	3	9	9	3	1	4
subtype2	9	13	8	3	0	0
subtype3	0	5	8	4	0	0

Figure S21. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.114 (Fisher's exact test), Q value = 0.24

Table S26. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T2	T3	T4
ALL	14	32	34
subtype1	4	9	16
subtype2	9	15	9
subtype3	1	8	9

Figure S22. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0651 (Fisher's exact test), Q value = 0.21

Table S27. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	51	4
subtype1	18	4
subtype2	22	0
subtype3	11	0

Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.878 (Fisher's exact test), Q value = 0.9

Table S28. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	45
subtype1	12	17
subtype2	14	19
subtype3	9	9

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	34	17	29

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0285 (logrank test), Q value = 0.15

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	13	0.1 - 74.5 (19.1)
subtype1	34	7	0.1 - 52.6 (16.4)
subtype2	17	5	0.1 - 70.7 (14.9)
subtype3	29	1	0.2 - 74.5 (20.2)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.559 (Kruskal-Wallis (anova)), Q value = 0.6

Table S31. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	80	61.6 (13.9)
subtype1	34	63.2 (12.4)
subtype2	17	63.6 (14.2)
subtype3	29	58.7 (15.5)

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

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0887 (Fisher's exact test), Q value = 0.22

Table S32. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	12	27	25	10	1	4
subtype1	1	14	10	5	1	2
subtype2	2	5	7	1	0	2
subtype3	9	8	8	4	0	0

Figure S27. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.207 (Fisher's exact test), Q value = 0.28

Table S33. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T2	T3	T4
ALL	14	32	34
subtype1	3	16	15
subtype2	2	6	9
subtype3	9	10	10

Figure S28. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.179 (Fisher's exact test), Q value = 0.28

Table S34. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	51	4
subtype1	22	2
subtype2	9	2
subtype3	20	0

Figure S29. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.188 (Fisher's exact test), Q value = 0.28

Table S35. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	45
subtype1	11	23
subtype2	10	7
subtype3	14	15

Figure S30. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S36. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	17	13	28	22

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 8.12e-05 (logrank test), Q value = 0.0013

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	13	0.1 - 74.5 (19.1)
subtype1	17	2	1.4 - 52.6 (21.7)
subtype2	13	0	0.1 - 70.7 (22.0)
subtype3	28	2	0.2 - 74.5 (20.0)
subtype4	22	9	0.1 - 37.8 (13.2)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.125 (Kruskal-Wallis (anova)), Q value = 0.24

Table S38. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	80	61.6 (13.9)
subtype1	17	60.9 (14.9)
subtype2	13	59.2 (12.2)
subtype3	28	58.4 (15.7)
subtype4	22	67.8 (10.2)

Figure S32. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.19 (Fisher's exact test), Q value = 0.28

Table S39. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	12	27	25	10	1	4
subtype1	1	7	5	1	1	1
subtype2	0	6	5	2	0	0
subtype3	9	8	7	4	0	0
subtype4	2	6	8	3	0	3

Figure S33. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.0954 (Fisher's exact test), Q value = 0.23

Table S40. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T2	T3	T4
ALL	14	32	34
subtype1	3	9	5
subtype2	0	6	7
subtype3	9	10	9
subtype4	2	7	13

Figure S34. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.118 (Fisher's exact test), Q value = 0.24

Table S41. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	51	4
subtype1	10	1
subtype2	9	0
subtype3	19	0
subtype4	13	3

Figure S35. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.2 (Fisher's exact test), Q value = 0.28

Table S42. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	45
subtype1	10	7
subtype2	3	10
subtype3	14	14
subtype4	8	14

Figure S36. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S43. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	32	28	14

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

P value = 0.00917 (logrank test), Q value = 0.063

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	74	13	0.1 - 74.5 (19.1)
subtype1	32	8	0.1 - 52.6 (13.4)
subtype2	28	1	0.1 - 74.5 (22.4)
subtype3	14	4	0.5 - 37.8 (13.2)

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.053 (Kruskal-Wallis (anova)), Q value = 0.18

Table S45. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	74	61.9 (14.1)
subtype1	32	63.2 (13.6)
subtype2	28	57.0 (15.4)
subtype3	14	68.9 (9.2)

Figure S38. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.137 (Fisher's exact test), Q value = 0.24

Table S46. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	25	24	9	1	4
subtype1	1	12	11	4	1	2
subtype2	4	10	10	4	0	0
subtype3	5	3	3	1	0	2

Figure S39. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.318 (Fisher's exact test), Q value = 0.39

Table S47. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T2	T3	T4
ALL	12	30	32
subtype1	3	14	15
subtype2	4	11	13
subtype3	5	5	4

Figure S40. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0232 (Fisher's exact test), Q value = 0.14

Table S48. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	48	4
subtype1	20	2
subtype2	23	0
subtype3	5	2

Figure S41. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.075 (Fisher's exact test), Q value = 0.21

Table S49. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	32	42
subtype1	12	20
subtype2	10	18
subtype3	10	4

Figure S42. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S50. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	5	12	16	16	15	10

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

P value = 0.000112 (logrank test), Q value = 0.0013

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	74	13	0.1 - 74.5 (19.1)
subtype1	5	2	1.4 - 23.3 (7.9)
subtype2	12	0	0.3 - 52.6 (20.3)
subtype3	16	0	0.1 - 70.7 (23.2)
subtype4	16	1	0.2 - 74.5 (21.5)
subtype5	15	6	0.1 - 37.8 (13.1)
subtype6	10	4	1.2 - 28.4 (18.3)

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

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.42 (Kruskal-Wallis (anova)), Q value = 0.48

Table S52. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	74	61.9 (14.1)
subtype1	5	68.2 (18.8)
subtype2	12	60.3 (13.3)
subtype3	16	58.2 (13.7)
subtype4	16	60.3 (17.4)
subtype5	15	67.9 (11.3)
subtype6	10	60.3 (10.6)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.207 (Fisher's exact test), Q value = 0.28

Table S53. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	25	24	9	1	4
subtype1	0	1	2	0	1	1
subtype2	0	5	3	3	0	0
subtype3	3	5	7	1	0	0
subtype4	5	6	3	2	0	0
subtype5	1	7	4	1	0	2
subtype6	1	1	5	2	0	1

Figure S45. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.395 (Fisher's exact test), Q value = 0.46

Table S54. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T2	T3	T4
ALL	12	30	32
subtype1	0	2	3
subtype2	2	5	5
subtype3	3	5	8
subtype4	5	8	3
subtype5	1	8	6
subtype6	1	2	7

Figure S46. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.235 (Fisher's exact test), Q value = 0.3

Table S55. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	48	4
subtype1	4	1
subtype2	5	0
subtype3	13	0
subtype4	11	0
subtype5	9	2
subtype6	6	1

Figure S47. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.381 (Fisher's exact test), Q value = 0.46

Table S56. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients	FEMALE	MALE
ALL	32	42
subtype1	4	1
subtype2	5	7
subtype3	6	10
subtype4	8	8
subtype5	7	8
subtype6	2	8

Figure S48. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/UVM-TP/15952300/UVM-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UVM-TP/15096076/UVM-TP.merged_data.txt
Number of patients = 80
Number of clustering approaches = 8
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

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] 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)

[5] 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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