Correlation between aggregated molecular cancer subtypes and selected clinical features

Skin Cutaneous Melanoma (Metastatic)

15 January 2014 | analyses__2014_01_15

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RV0M6N

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 12 clinical features across 260 patients, 9 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 do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time from Specimen Diagnosis to Death'.
CNMF clustering analysis on RPPA data identified 4 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 correlate to 'Time from Specimen Diagnosis to Death', 'Time to Death', and 'PRIMARY.SITE.OF.DISEASE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time from Specimen Diagnosis to Death', 'AGE', 'PRIMARY.SITE.OF.DISEASE', and 'PATHOLOGY.T.STAGE'.
3 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 do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE'.
4 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 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 9 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time from Specimen Diagnosis to Death	logrank test	0.0252 (1.00)	0.000154 (0.0181)	0.133 (1.00)	0.227 (1.00)	5.83e-06 (7e-04)	1.58e-05 (0.00187)	0.0322 (1.00)	0.111 (1.00)	0.0105 (1.00)	0.0602 (1.00)
Time to Death	logrank test	0.21 (1.00)	0.0182 (1.00)	0.17 (1.00)	0.812 (1.00)	0.00107 (0.121)	0.0233 (1.00)	0.0911 (1.00)	0.566 (1.00)	0.0545 (1.00)	0.339 (1.00)
AGE	ANOVA	0.0165 (1.00)	0.293 (1.00)	0.138 (1.00)	0.821 (1.00)	0.00727 (0.8)	0.000476 (0.0547)	0.358 (1.00)	0.0552 (1.00)	0.0576 (1.00)	0.721 (1.00)
PRIMARY SITE OF DISEASE	Chi-square test	0.487 (1.00)	0.0029 (0.322)	0.0775 (1.00)	0.515 (1.00)	0.00101 (0.116)	9.62e-06 (0.00115)	0.0376 (1.00)	0.713 (1.00)	0.0102 (1.00)	0.0826 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.435 (1.00)	0.381 (1.00)	0.511 (1.00)	0.157 (1.00)	0.111 (1.00)	0.0344 (1.00)	0.734 (1.00)	0.3 (1.00)	0.304 (1.00)	0.31 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.518 (1.00)	0.215 (1.00)	0.351 (1.00)	0.606 (1.00)	0.0887 (1.00)	0.00144 (0.161)	0.188 (1.00)	0.179 (1.00)	0.000469 (0.0544)	0.534 (1.00)
PATHOLOGY N STAGE	Chi-square test	0.585 (1.00)	0.0665 (1.00)	0.423 (1.00)	0.216 (1.00)	0.649 (1.00)	0.993 (1.00)	0.55 (1.00)	0.886 (1.00)	0.915 (1.00)	0.969 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.701 (1.00)	0.725 (1.00)	0.166 (1.00)	0.921 (1.00)	0.897 (1.00)	0.371 (1.00)	0.938 (1.00)	0.609 (1.00)	0.936 (1.00)	0.646 (1.00)
MELANOMA ULCERATION	Fisher's exact test	0.838 (1.00)	0.234 (1.00)	0.607 (1.00)	0.583 (1.00)	0.305 (1.00)	0.212 (1.00)	0.246 (1.00)	0.711 (1.00)	0.411 (1.00)	0.717 (1.00)
MELANOMA PRIMARY KNOWN	Fisher's exact test	0.316 (1.00)	0.287 (1.00)	0.581 (1.00)	0.164 (1.00)	0.0188 (1.00)	0.194 (1.00)	0.168 (1.00)	0.286 (1.00)	0.111 (1.00)	0.0415 (1.00)
BRESLOW THICKNESS	ANOVA	0.903 (1.00)	0.824 (1.00)	0.928 (1.00)	0.576 (1.00)	0.16 (1.00)	0.137 (1.00)	0.806 (1.00)	0.899 (1.00)	0.886 (1.00)	0.824 (1.00)
GENDER	Fisher's exact test	0.443 (1.00)	0.811 (1.00)	0.826 (1.00)	0.202 (1.00)	0.48 (1.00)	0.626 (1.00)	0.0743 (1.00)	0.793 (1.00)	0.277 (1.00)	0.58 (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	95	73	92

'Copy Number Ratio CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.0252 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	247	120	0.1 - 124.3 (13.9)
subtype1	91	48	0.1 - 122.7 (11.2)
subtype2	66	35	1.9 - 111.1 (13.2)
subtype3	90	37	0.1 - 124.3 (19.0)

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

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

P value = 0.21 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	254	122	0.2 - 357.4 (48.2)
subtype1	94	49	0.2 - 268.9 (45.7)
subtype2	69	36	3.2 - 357.4 (56.2)
subtype3	91	37	2.4 - 314.5 (48.8)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0165 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	256	55.6 (15.8)
subtype1	94	57.4 (17.2)
subtype2	70	51.0 (15.7)
subtype3	92	57.2 (13.7)

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

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

P value = 0.487 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	33	1	55	170
subtype1	16	1	18	59
subtype2	9	0	18	46
subtype3	8	0	19	65

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.435 (Chi-square test), Q value = 1

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

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	16	10	13	10	30	11	23	41	12
subtype1	1	1	5	3	11	7	3	5	6	10	4	11	12	5
subtype2	6	1	5	3	5	6	3	2	3	10	1	8	10	4
subtype3	3	3	11	4	8	3	4	6	1	10	6	4	19	3

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

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

P value = 0.518 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	51	57	50	53
subtype1	16	21	18	22
subtype2	20	16	12	11
subtype3	15	20	20	20

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.585 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	134	48	31	31
subtype1	49	14	15	10
subtype2	39	16	8	7
subtype3	46	18	8	14

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.701 (Chi-square test), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	233	4	2	2	5
subtype1	82	2	1	1	2
subtype2	67	1	0	0	3
subtype3	84	1	1	1	0

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'MELANOMA.ULCERATION'

P value = 0.838 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	98	64
subtype1	39	24
subtype2	22	17
subtype3	37	23

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'Copy Number Ratio CNMF subtypes' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.316 (Fisher's exact test), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	229
subtype1	11	84
subtype2	12	61
subtype3	8	84

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'Copy Number Ratio CNMF subtypes' versus 'BRESLOW.THICKNESS'

P value = 0.903 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	192	3.6 (5.1)
subtype1	74	3.4 (3.9)
subtype2	48	3.6 (7.4)
subtype3	70	3.8 (4.3)

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.443 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	159
subtype1	39	56
subtype2	31	42
subtype3	31	61

Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #2: 'METHLYATION CNMF'

Table S14. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	65	94	101

'METHLYATION CNMF' versus 'Time from Specimen Diagnosis to Death'

P value = 0.000154 (logrank test), Q value = 0.018

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	247	120	0.1 - 124.3 (13.9)
subtype1	64	40	0.1 - 111.1 (10.9)
subtype2	89	41	0.1 - 114.2 (13.9)
subtype3	94	39	0.1 - 124.3 (19.0)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0182 (logrank test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	254	122	0.2 - 357.4 (48.2)
subtype1	64	40	0.2 - 247.0 (40.9)
subtype2	92	43	0.9 - 357.4 (54.9)
subtype3	98	39	2.4 - 346.0 (47.0)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.293 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	256	55.6 (15.8)
subtype1	64	54.4 (16.1)
subtype2	92	57.7 (16.4)
subtype3	100	54.5 (14.9)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'AGE'

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

P value = 0.0029 (Chi-square test), Q value = 0.32

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	33	1	55	170
subtype1	15	1	15	34
subtype2	14	0	22	58
subtype3	4	0	18	78

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.381 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	16	10	13	10	30	11	23	41	12
subtype1	1	2	4	1	7	8	2	4	4	6	2	6	11	3
subtype2	6	1	6	6	9	5	5	7	5	8	6	7	12	3
subtype3	3	2	11	3	8	3	3	2	1	16	3	10	18	6

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.215 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	51	57	50	53
subtype1	7	20	11	15
subtype2	19	18	19	21
subtype3	25	19	20	17

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.0665 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	134	48	31	31
subtype1	36	14	6	5
subtype2	56	10	14	10
subtype3	42	24	11	16

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.725 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	233	4	2	2	5
subtype1	57	2	1	0	0
subtype2	88	1	0	1	2
subtype3	88	1	1	1	3

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'MELANOMA.ULCERATION'

P value = 0.234 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	98	64
subtype1	17	18
subtype2	42	26
subtype3	39	20

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'METHLYATION CNMF' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.287 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	229
subtype1	7	58
subtype2	8	86
subtype3	16	85

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'METHLYATION CNMF' versus 'BRESLOW.THICKNESS'

P value = 0.824 (ANOVA), Q value = 1

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	192	3.6 (5.1)
subtype1	50	3.6 (5.0)
subtype2	73	3.3 (2.7)
subtype3	69	3.8 (6.8)

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.811 (Fisher's exact test), Q value = 1

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	159
subtype1	24	41
subtype2	39	55
subtype3	38	63

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'GENDER'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	46	53	27	38

'RPPA CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.133 (logrank test), Q value = 1

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	155	82	0.2 - 124.3 (13.7)
subtype1	44	20	3.5 - 111.9 (14.2)
subtype2	50	28	0.2 - 114.2 (18.2)
subtype3	25	14	1.9 - 68.1 (6.6)
subtype4	36	20	1.1 - 124.3 (13.7)

Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.17 (logrank test), Q value = 1

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	159	83	0.2 - 357.4 (50.7)
subtype1	44	20	9.9 - 346.0 (55.3)
subtype2	53	29	0.2 - 357.4 (54.7)
subtype3	26	14	3.6 - 162.1 (40.3)
subtype4	36	20	2.6 - 176.6 (40.0)

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.138 (ANOVA), Q value = 1

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	161	55.3 (15.9)
subtype1	45	50.9 (13.2)
subtype2	53	55.9 (15.8)
subtype3	27	57.0 (16.9)
subtype4	36	58.7 (17.6)

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'AGE'

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

P value = 0.0775 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	21	1	28	113
subtype1	2	0	14	30
subtype2	10	1	6	36
subtype3	5	0	5	17
subtype4	4	0	3	30

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.511 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	8	2	13	8	16	7	5	6	3	20	5	15	26	9
subtype1	2	1	6	1	7	1	2	1	0	3	3	6	8	2
subtype2	2	1	5	1	6	2	2	2	3	8	1	1	6	5
subtype3	1	0	1	2	1	1	0	1	0	5	1	4	5	0
subtype4	3	0	1	4	2	3	1	2	0	4	0	4	7	2

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.351 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	36	35	28	31
subtype1	8	12	11	9
subtype2	8	11	6	13
subtype3	7	7	5	3
subtype4	13	5	6	6

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.423 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	82	25	24	21
subtype1	22	8	5	8
subtype2	32	5	6	6
subtype3	9	6	7	3
subtype4	19	6	6	4

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.166 (Chi-square test), Q value = 1

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	145	4	2	2	2
subtype1	42	1	1	0	0
subtype2	43	3	1	2	0
subtype3	26	0	0	0	0
subtype4	34	0	0	0	2

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'MELANOMA.ULCERATION'

P value = 0.607 (Fisher's exact test), Q value = 1

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	64	36
subtype1	19	8
subtype2	23	13
subtype3	11	5
subtype4	11	10

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'RPPA CNMF subtypes' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.581 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	24	140
subtype1	5	41
subtype2	8	45
subtype3	3	24
subtype4	8	30

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'RPPA CNMF subtypes' versus 'BRESLOW.THICKNESS'

P value = 0.928 (ANOVA), Q value = 1

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	116	3.4 (5.3)
subtype1	36	3.7 (8.2)
subtype2	36	3.5 (3.1)
subtype3	21	2.7 (3.8)
subtype4	23	3.6 (3.6)

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.826 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	64	100
subtype1	17	29
subtype2	19	34
subtype3	11	16
subtype4	17	21

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	65	56	43

'RPPA cHierClus subtypes' versus 'Time from Specimen Diagnosis to Death'

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

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	155	82	0.2 - 124.3 (13.7)
subtype1	60	32	0.2 - 111.9 (11.7)
subtype2	55	29	3.5 - 124.3 (16.0)
subtype3	40	21	1.4 - 84.7 (15.8)

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.812 (logrank test), Q value = 1

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	159	83	0.2 - 357.4 (50.7)
subtype1	64	33	0.2 - 248.6 (43.9)
subtype2	55	29	6.4 - 357.4 (53.5)
subtype3	40	21	9.9 - 346.0 (54.7)

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.821 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	161	55.3 (15.9)
subtype1	65	56.2 (16.8)
subtype2	55	54.5 (15.3)
subtype3	41	55.0 (15.3)

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'AGE'

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

P value = 0.515 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	21	1	28	113
subtype1	9	0	11	44
subtype2	9	1	7	39
subtype3	3	0	10	30

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.157 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	8	2	13	8	16	7	5	6	3	20	5	15	26	9
subtype1	2	0	5	5	6	6	0	2	0	8	2	7	8	4
subtype2	3	1	6	2	5	0	4	4	3	8	1	2	8	2
subtype3	3	1	2	1	5	1	1	0	0	4	2	6	10	3

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.606 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	36	35	28	31
subtype1	15	14	11	13
subtype2	14	10	6	11
subtype3	7	11	11	7

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.216 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	82	25	24	21
subtype1	33	11	10	5
subtype2	32	4	8	8
subtype3	17	10	6	8

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.921 (Chi-square test), Q value = 1

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	145	4	2	2	2
subtype1	57	2	1	1	1
subtype2	49	1	0	1	0
subtype3	39	1	1	0	1

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'MELANOMA.ULCERATION'

P value = 0.583 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	64	36
subtype1	31	14
subtype2	20	12
subtype3	13	10

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'RPPA cHierClus subtypes' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.164 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	24	140
subtype1	6	59
subtype2	12	44
subtype3	6	37

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'RPPA cHierClus subtypes' versus 'BRESLOW.THICKNESS'

P value = 0.576 (ANOVA), Q value = 1

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	116	3.4 (5.3)
subtype1	50	3.0 (3.3)
subtype2	35	3.2 (3.0)
subtype3	31	4.3 (8.9)

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.202 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	64	100
subtype1	29	36
subtype2	23	33
subtype3	12	31

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S53. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	89	71	98

'RNAseq CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 5.83e-06 (logrank test), Q value = 7e-04

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	246	120	0.1 - 124.3 (13.9)
subtype1	84	48	1.7 - 122.7 (15.3)
subtype2	69	20	0.4 - 124.3 (24.0)
subtype3	93	52	0.1 - 99.5 (10.0)

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00107 (logrank test), Q value = 0.12

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	252	122	0.2 - 357.4 (48.7)
subtype1	86	50	4.9 - 357.4 (57.1)
subtype2	70	20	2.4 - 268.9 (54.0)
subtype3	96	52	0.2 - 247.0 (40.9)

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00727 (ANOVA), Q value = 0.8

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	55.5 (15.8)
subtype1	86	51.3 (15.6)
subtype2	71	57.1 (15.3)
subtype3	97	58.2 (15.7)

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'AGE'

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

P value = 0.00101 (Chi-square test), Q value = 0.12

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	33	1	55	168
subtype1	9	0	30	50
subtype2	4	0	11	56
subtype3	20	1	14	62

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.111 (Chi-square test), Q value = 1

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	16	10	13	10	30	11	22	41	12
subtype1	4	3	9	1	8	7	3	3	1	13	5	6	11	7
subtype2	3	1	8	6	5	4	4	1	2	9	0	5	14	2
subtype3	3	1	4	3	11	5	3	9	7	8	6	11	16	3

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0887 (Chi-square test), Q value = 1

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	51	57	49	53
subtype1	18	25	15	12
subtype2	19	12	12	14
subtype3	14	20	22	27

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.649 (Chi-square test), Q value = 1

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	134	47	31	31
subtype1	45	17	12	10
subtype2	40	14	4	9
subtype3	49	16	15	12

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.897 (Chi-square test), Q value = 1

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	232	4	2	2	5
subtype1	78	2	1	1	3
subtype2	64	1	0	0	1
subtype3	90	1	1	1	1

Figure S56. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'MELANOMA.ULCERATION'

P value = 0.305 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	98	64
subtype1	27	18
subtype2	30	13
subtype3	41	33

Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'RNAseq CNMF subtypes' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.0188 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	227
subtype1	15	74
subtype2	11	60
subtype3	5	93

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'RNAseq CNMF subtypes' versus 'BRESLOW.THICKNESS'

P value = 0.16 (ANOVA), Q value = 1

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	192	3.6 (5.1)
subtype1	61	3.1 (6.6)
subtype2	50	2.8 (3.5)
subtype3	81	4.4 (4.5)

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.48 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	100	158
subtype1	39	50
subtype2	25	46
subtype3	36	62

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	71	111	76

'RNAseq cHierClus subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 1.58e-05 (logrank test), Q value = 0.0019

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	246	120	0.1 - 124.3 (13.9)
subtype1	68	39	1.7 - 122.7 (14.4)
subtype2	103	37	0.2 - 124.3 (22.2)
subtype3	75	44	0.1 - 95.4 (9.9)

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0233 (logrank test), Q value = 1

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	252	122	0.2 - 357.4 (48.7)
subtype1	69	40	4.9 - 357.4 (58.0)
subtype2	108	38	0.9 - 268.9 (47.7)
subtype3	75	44	0.2 - 314.5 (43.4)

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000476 (ANOVA), Q value = 0.055

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	55.5 (15.8)
subtype1	69	49.9 (15.1)
subtype2	110	56.0 (15.4)
subtype3	75	60.0 (15.6)

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'AGE'

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

P value = 9.62e-06 (Chi-square test), Q value = 0.0011

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	33	1	55	168
subtype1	9	0	23	39
subtype2	5	0	15	91
subtype3	19	1	17	38

Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0344 (Chi-square test), Q value = 1

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	16	10	13	10	30	11	22	41	12
subtype1	3	3	8	1	7	5	2	2	1	11	4	4	8	5
subtype2	5	2	10	7	12	4	4	3	2	15	1	9	20	4
subtype3	2	0	3	2	5	7	4	8	7	4	6	9	13	3

Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00144 (Chi-square test), Q value = 0.16

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	51	57	49	53
subtype1	13	21	13	8
subtype2	29	24	15	19
subtype3	9	12	21	26

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.993 (Chi-square test), Q value = 1

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	134	47	31	31
subtype1	38	14	8	7
subtype2	57	19	14	15
subtype3	39	14	9	9

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.371 (Chi-square test), Q value = 1

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	232	4	2	2	5
subtype1	62	1	1	1	2
subtype2	101	0	1	1	3
subtype3	69	3	0	0	0

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'MELANOMA.ULCERATION'

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

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	98	64
subtype1	21	13
subtype2	47	23
subtype3	30	28

Figure S69. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'RNAseq cHierClus subtypes' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.194 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	227
subtype1	11	60
subtype2	15	96
subtype3	5	71

Figure S70. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'RNAseq cHierClus subtypes' versus 'BRESLOW.THICKNESS'

P value = 0.137 (ANOVA), Q value = 1

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	192	3.6 (5.1)
subtype1	50	3.2 (7.2)
subtype2	77	3.0 (3.5)
subtype3	65	4.6 (4.5)

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.626 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	100	158
subtype1	31	40
subtype2	41	70
subtype3	28	48

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	69	104	78

'MIRSEQ CNMF' versus 'Time from Specimen Diagnosis to Death'

P value = 0.0322 (logrank test), Q value = 1

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	239	119	0.1 - 124.3 (14.2)
subtype1	66	23	0.1 - 122.7 (16.0)
subtype2	99	57	0.1 - 124.3 (12.9)
subtype3	74	39	1.0 - 111.1 (14.8)

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0911 (logrank test), Q value = 1

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	245	121	0.2 - 357.4 (47.8)
subtype1	67	24	0.2 - 357.4 (34.3)
subtype2	104	58	2.6 - 247.0 (47.5)
subtype3	74	39	4.9 - 314.5 (61.1)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.358 (ANOVA), Q value = 1

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	247	55.6 (15.6)
subtype1	68	56.4 (14.7)
subtype2	104	56.7 (16.8)
subtype3	75	53.5 (14.8)

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'AGE'

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

P value = 0.0376 (Chi-square test), Q value = 1

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	32	1	53	164
subtype1	6	1	14	48
subtype2	17	0	14	72
subtype3	9	0	25	44

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.734 (Chi-square test), Q value = 1

Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	14	9	13	9	28	11	22	40	12
subtype1	4	1	5	3	3	5	2	1	2	7	2	9	11	5
subtype2	3	2	7	6	11	7	4	8	4	10	4	10	14	5
subtype3	3	2	9	1	10	2	3	4	3	11	5	3	15	2

Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.188 (Chi-square test), Q value = 1

Table S85. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	50	57	46	50
subtype1	18	8	12	14
subtype2	21	25	20	21
subtype3	11	24	14	15

Figure S78. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.55 (Chi-square test), Q value = 1

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	130	45	31	30
subtype1	29	11	11	11
subtype2	59	17	11	11
subtype3	42	17	9	8

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.938 (Chi-square test), Q value = 1

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	226	4	2	2	5
subtype1	58	1	1	1	2
subtype2	95	2	1	1	2
subtype3	73	1	0	0	1

Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'MELANOMA.ULCERATION'

P value = 0.246 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	96	62
subtype1	19	19
subtype2	49	25
subtype3	28	18

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'MIRSEQ CNMF' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.168 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	220
subtype1	13	56
subtype2	11	93
subtype3	7	71

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'MIRSEQ CNMF' versus 'BRESLOW.THICKNESS'

P value = 0.806 (ANOVA), Q value = 1

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	186	3.6 (5.1)
subtype1	44	3.2 (2.9)
subtype2	82	3.5 (4.1)
subtype3	60	3.9 (7.3)

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0743 (Fisher's exact test), Q value = 1

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	95	156
subtype1	33	36
subtype2	39	65
subtype3	23	55

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'GENDER'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	29	25	69	128

'MIRSEQ CHIERARCHICAL' versus 'Time from Specimen Diagnosis to Death'

P value = 0.111 (logrank test), Q value = 1

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	239	119	0.1 - 124.3 (14.2)
subtype1	27	9	0.2 - 114.2 (20.4)
subtype2	23	12	3.5 - 47.4 (13.0)
subtype3	67	34	0.1 - 122.7 (18.6)
subtype4	122	64	0.1 - 124.3 (11.7)

Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.566 (logrank test), Q value = 1

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	245	121	0.2 - 357.4 (47.8)
subtype1	28	10	0.2 - 357.4 (33.0)
subtype2	23	12	4.9 - 182.0 (58.0)
subtype3	67	34	7.2 - 346.0 (56.2)
subtype4	127	65	0.9 - 314.5 (44.4)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0552 (ANOVA), Q value = 1

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	247	55.6 (15.6)
subtype1	29	55.4 (16.5)
subtype2	23	47.7 (16.1)
subtype3	67	55.1 (13.1)
subtype4	128	57.4 (16.3)

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'AGE'

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

P value = 0.713 (Chi-square test), Q value = 1

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	32	1	53	164
subtype1	3	0	9	17
subtype2	3	0	6	16
subtype3	6	0	17	46
subtype4	20	1	21	85

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.3 (Chi-square test), Q value = 1

Table S97. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	14	9	13	9	28	11	22	40	12
subtype1	1	0	1	1	1	3	2	0	1	2	1	7	3	3
subtype2	2	0	5	1	4	0	0	0	0	2	2	1	4	2
subtype3	2	3	8	1	6	3	1	4	2	10	3	3	13	2
subtype4	5	2	7	7	13	8	6	9	6	14	5	11	20	5

Figure S89. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.179 (Chi-square test), Q value = 1

Table S98. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	50	57	46	50
subtype1	6	3	6	8
subtype2	2	11	3	3
subtype3	15	15	10	12
subtype4	27	28	27	27

Figure S90. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.886 (Chi-square test), Q value = 1

Table S99. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	130	45	31	30
subtype1	12	6	6	3
subtype2	14	4	4	2
subtype3	34	13	7	10
subtype4	70	22	14	15

Figure S91. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.609 (Chi-square test), Q value = 1

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	226	4	2	2	5
subtype1	25	1	1	1	0
subtype2	21	1	0	0	1
subtype3	63	1	0	0	1
subtype4	117	1	1	1	3

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'MELANOMA.ULCERATION'

P value = 0.711 (Fisher's exact test), Q value = 1

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	96	62
subtype1	10	8
subtype2	10	4
subtype3	20	16
subtype4	56	34

Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'MIRSEQ CHIERARCHICAL' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.286 (Fisher's exact test), Q value = 1

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	220
subtype1	5	24
subtype2	2	23
subtype3	12	57
subtype4	12	116

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'MIRSEQ CHIERARCHICAL' versus 'BRESLOW.THICKNESS'

P value = 0.899 (ANOVA), Q value = 1

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	186	3.6 (5.1)
subtype1	19	3.5 (3.2)
subtype2	19	2.9 (3.8)
subtype3	45	4.0 (8.1)
subtype4	103	3.5 (3.9)

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.793 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	95	156
subtype1	11	18
subtype2	9	16
subtype3	23	46
subtype4	52	76

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'GENDER'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	86	93	72

'MIRseq Mature CNMF subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.0105 (logrank test), Q value = 1

Table S106. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	239	119	0.1 - 124.3 (14.2)
subtype1	81	31	0.1 - 122.7 (16.0)
subtype2	89	52	0.1 - 124.3 (11.2)
subtype3	69	36	1.0 - 111.1 (14.5)

Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

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

P value = 0.0545 (logrank test), Q value = 1

Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	245	121	0.2 - 357.4 (47.8)
subtype1	83	33	0.2 - 357.4 (47.5)
subtype2	93	52	2.6 - 248.6 (43.2)
subtype3	69	36	4.9 - 314.5 (61.0)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0576 (ANOVA), Q value = 1

Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	247	55.6 (15.6)
subtype1	84	55.4 (15.0)
subtype2	93	58.3 (16.8)
subtype3	70	52.4 (14.4)

Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'AGE'

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

P value = 0.0102 (Chi-square test), Q value = 1

Table S109. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	32	1	53	164
subtype1	10	1	16	59
subtype2	14	0	11	67
subtype3	8	0	26	38

Figure S100. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.304 (Chi-square test), Q value = 1

Table S110. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	14	9	13	9	28	11	22	40	12
subtype1	6	4	7	6	3	5	1	1	4	11	2	8	14	4
subtype2	2	0	6	3	12	5	3	8	3	10	4	9	14	4
subtype3	2	1	8	1	9	4	5	4	2	7	5	5	12	4

Figure S101. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.000469 (Chi-square test), Q value = 0.054

Table S111. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	50	57	46	50
subtype1	26	10	9	19
subtype2	17	21	20	20
subtype3	7	26	17	11

Figure S102. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.915 (Chi-square test), Q value = 1

Table S112. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	130	45	31	30
subtype1	41	13	11	13
subtype2	49	18	11	9
subtype3	40	14	9	8

Figure S103. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.936 (Chi-square test), Q value = 1

Table S113. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	226	4	2	2	5
subtype1	75	1	1	1	1
subtype2	85	1	1	1	2
subtype3	66	2	0	0	2

Figure S104. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'MELANOMA.ULCERATION'

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

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	96	62
subtype1	25	22
subtype2	44	23
subtype3	27	17

Figure S105. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'MIRseq Mature CNMF subtypes' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.111 (Fisher's exact test), Q value = 1

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	220
subtype1	16	70
subtype2	9	84
subtype3	6	66

Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'MIRseq Mature CNMF subtypes' versus 'BRESLOW.THICKNESS'

P value = 0.886 (ANOVA), Q value = 1

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	186	3.6 (5.1)
subtype1	53	3.5 (4.3)
subtype2	75	3.7 (4.2)
subtype3	58	3.3 (6.8)

Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.277 (Fisher's exact test), Q value = 1

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	95	156
subtype1	38	48
subtype2	34	59
subtype3	23	49

Figure S108. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	25	98	81	47

'MIRseq Mature cHierClus subtypes' versus 'Time from Specimen Diagnosis to Death'

P value = 0.0602 (logrank test), Q value = 1

Table S119. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	239	119	0.1 - 124.3 (14.2)
subtype1	23	8	0.2 - 114.2 (20.5)
subtype2	93	47	0.1 - 124.3 (13.1)
subtype3	78	37	0.1 - 122.7 (18.1)
subtype4	45	27	0.1 - 98.8 (11.1)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

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

P value = 0.339 (logrank test), Q value = 1

Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	245	121	0.2 - 357.4 (47.8)
subtype1	24	9	0.2 - 357.4 (40.6)
subtype2	98	48	0.9 - 248.6 (38.4)
subtype3	78	37	4.9 - 346.0 (58.2)
subtype4	45	27	6.4 - 314.5 (58.0)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.721 (ANOVA), Q value = 1

Table S121. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	247	55.6 (15.6)
subtype1	25	56.3 (17.2)
subtype2	98	56.7 (15.8)
subtype3	78	54.0 (14.1)
subtype4	46	55.9 (17.3)

Figure S111. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'AGE'

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

P value = 0.0826 (Chi-square test), Q value = 1

Table S122. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE	REGIONAL LYMPH NODE
ALL	32	1	53	164
subtype1	3	0	6	16
subtype2	13	1	10	74
subtype3	8	0	23	50
subtype4	8	0	14	24

Figure S112. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.31 (Chi-square test), Q value = 1

Table S123. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	STAGE 0	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	10	5	21	10	24	14	9	13	9	28	11	22	40	12
subtype1	1	0	1	1	1	3	2	0	1	1	1	5	3	2
subtype2	5	2	5	7	10	7	3	7	3	11	2	9	15	3
subtype3	3	3	10	1	6	3	2	4	2	14	3	4	15	3
subtype4	1	0	5	1	7	1	2	2	3	2	5	4	7	4

Figure S113. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

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

Table S124. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	50	57	46	50
subtype1	5	2	6	6
subtype2	21	21	19	20
subtype3	18	18	11	14
subtype4	6	16	10	10

Figure S114. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.969 (Chi-square test), Q value = 1

Table S125. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	130	45	31	30
subtype1	12	5	4	2
subtype2	56	16	11	10
subtype3	38	16	10	12
subtype4	24	8	6	6

Figure S115. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.646 (Chi-square test), Q value = 1

Table S126. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	226	4	2	2	5
subtype1	22	1	0	1	0
subtype2	89	1	1	1	1
subtype3	73	1	0	0	2
subtype4	42	1	1	0	2

Figure S116. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'MELANOMA.ULCERATION'

P value = 0.717 (Fisher's exact test), Q value = 1

Table S127. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	96	62
subtype1	8	7
subtype2	44	23
subtype3	23	17
subtype4	21	15

Figure S117. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

'MIRseq Mature cHierClus subtypes' versus 'MELANOMA.PRIMARY.KNOWN'

P value = 0.0415 (Fisher's exact test), Q value = 1

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	31	220
subtype1	4	21
subtype2	9	89
subtype3	16	65
subtype4	2	45

Figure S118. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'MIRseq Mature cHierClus subtypes' versus 'BRESLOW.THICKNESS'

P value = 0.824 (ANOVA), Q value = 1

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	186	3.6 (5.1)
subtype1	16	3.4 (3.4)
subtype2	77	3.5 (4.1)
subtype3	51	4.0 (7.9)
subtype4	42	3.0 (2.7)

Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.58 (Fisher's exact test), Q value = 1

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	95	156
subtype1	11	14
subtype2	40	58
subtype3	26	55
subtype4	18	29

Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

Methods & Data

Input

Cluster data file = SKCM-TM.mergedcluster.txt
Clinical data file = SKCM-TM.merged_data.txt
Number of patients = 260
Number of clustering approaches = 10
Number of selected clinical features = 12
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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