Correlation between aggregated molecular cancer subtypes and selected clinical features

Skin Cutaneous Melanoma (Metastatic)

17 October 2014 | analyses__2014_10_17

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/C1N878Q3

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 14 clinical features across 349 patients, 14 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 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time from Specimen Diagnosis to Death', 'Time to Death', 'PRIMARY.SITE.OF.DISEASE', and 'BRESLOW.THICKNESS'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time from Specimen Diagnosis to Death', 'Time to Death', 'AGE', 'PRIMARY.SITE.OF.DISEASE', 'PATHOLOGY.T.STAGE', and 'BRESLOW.THICKNESS'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE'.
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 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 14 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	0.00283 (0.343)	1.01e-05 (0.00134)	0.347 (1.00)	0.46 (1.00)	5.73e-06 (0.000774)	3.95e-09 (5.38e-07)	0.0339 (1.00)	0.339 (1.00)	0.267 (1.00)	0.353 (1.00)
Time to Death	logrank test	0.0209 (1.00)	0.019 (1.00)	0.0864 (1.00)	0.819 (1.00)	0.000139 (0.0179)	1e-05 (0.00134)	0.227 (1.00)	0.552 (1.00)	0.861 (1.00)	0.932 (1.00)
AGE	Kruskal-Wallis (anova)	0.0388 (1.00)	0.0346 (1.00)	0.305 (1.00)	0.548 (1.00)	0.00219 (0.268)	0.000814 (0.101)	0.262 (1.00)	0.897 (1.00)	0.0491 (1.00)	0.322 (1.00)
PRIMARY SITE OF DISEASE	Fisher's exact test	0.0131 (1.00)	0.0399 (1.00)	0.104 (1.00)	0.287 (1.00)	0.0003 (0.0381)	1e-05 (0.00134)	0.00016 (0.0205)	0.813 (1.00)	0.0039 (0.468)	0.00049 (0.0617)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.829 (1.00)	0.128 (1.00)	0.561 (1.00)	0.302 (1.00)	0.106 (1.00)	0.0189 (1.00)	0.0783 (1.00)	0.323 (1.00)	0.00181 (0.223)	0.871 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.797 (1.00)	0.034 (1.00)	0.457 (1.00)	0.299 (1.00)	0.0134 (1.00)	0.00057 (0.0712)	0.00601 (0.715)	0.285 (1.00)	0.0211 (1.00)	0.18 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.0857 (1.00)	0.136 (1.00)	0.31 (1.00)	0.466 (1.00)	0.579 (1.00)	0.821 (1.00)	0.0388 (1.00)	0.191 (1.00)	0.269 (1.00)	0.642 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.492 (1.00)	0.209 (1.00)	0.382 (1.00)	0.809 (1.00)	0.422 (1.00)	0.186 (1.00)	0.177 (1.00)	0.226 (1.00)	0.618 (1.00)	0.545 (1.00)
MELANOMA ULCERATION	Fisher's exact test	0.894 (1.00)	0.0882 (1.00)	0.331 (1.00)	0.841 (1.00)	0.498 (1.00)	0.647 (1.00)	0.289 (1.00)	0.421 (1.00)	0.0993 (1.00)	0.551 (1.00)
MELANOMA PRIMARY KNOWN	Fisher's exact test	0.856 (1.00)	0.192 (1.00)	0.675 (1.00)	0.131 (1.00)	0.19 (1.00)	0.249 (1.00)	0.0293 (1.00)	0.236 (1.00)	0.517 (1.00)	0.567 (1.00)
BRESLOW THICKNESS	Kruskal-Wallis (anova)	0.55 (1.00)	0.136 (1.00)	0.224 (1.00)	0.882 (1.00)	0.000114 (0.0148)	2.06e-05 (0.0027)	0.183 (1.00)	0.0707 (1.00)	0.035 (1.00)	0.127 (1.00)
GENDER	Fisher's exact test	0.248 (1.00)	0.536 (1.00)	0.84 (1.00)	0.277 (1.00)	0.346 (1.00)	0.333 (1.00)	0.382 (1.00)	0.63 (1.00)	0.546 (1.00)	0.841 (1.00)
RACE	Fisher's exact test	0.619 (1.00)	0.761 (1.00)			0.53 (1.00)	0.936 (1.00)	0.176 (1.00)	0.257 (1.00)	0.615 (1.00)	0.445 (1.00)
ETHNICITY	Fisher's exact test	0.893 (1.00)	1 (1.00)			0.141 (1.00)	0.132 (1.00)	0.0977 (1.00)	0.949 (1.00)	1 (1.00)	1 (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	94	133	121

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

P value = 0.00283 (logrank test), Q value = 0.34

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	333	165	0.1 - 185.2 (14.7)
subtype1	90	51	0.2 - 122.7 (13.0)
subtype2	126	54	0.2 - 185.2 (20.5)
subtype3	117	60	0.1 - 125.7 (13.3)

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 = 0.0209 (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	339	166	0.2 - 369.9 (50.1)
subtype1	92	52	0.2 - 357.0 (47.0)
subtype2	130	54	0.2 - 314.6 (51.7)
subtype3	117	60	0.3 - 369.9 (54.4)

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 = 0.0388 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	340	56.2 (15.7)
subtype1	92	59.2 (17.6)
subtype2	131	56.2 (14.6)
subtype3	117	53.8 (14.9)

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 = 0.0131 (Fisher's exact 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	61	5	72	209
subtype1	26	1	13	54
subtype2	14	1	29	88
subtype3	21	3	30	67

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 = 0.829 (Fisher's exact 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	12	7	28	17	27	22	14	19	12	35	15	32	58	19
subtype1	1	2	6	5	9	3	4	4	4	11	4	11	18	4
subtype2	5	3	15	6	9	10	6	10	2	11	4	9	23	5
subtype3	6	2	7	6	9	9	4	5	6	13	7	12	17	10

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 = 0.797 (Fisher's exact 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	63	71	74	67
subtype1	19	20	19	21
subtype2	19	25	32	25
subtype3	25	26	23	21

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 = 0.0857 (Fisher's exact 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	168	61	38	47
subtype1	42	16	17	10
subtype2	67	17	11	22
subtype3	59	28	10	15

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 = 0.492 (Fisher's exact 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	306	4	3	4	9
subtype1	84	1	1	0	2
subtype2	115	1	1	3	1
subtype3	107	2	1	1	6

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 = 0.894 (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	129	87
subtype1	38	27
subtype2	50	31
subtype3	41	29

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 = 0.856 (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	43	304
subtype1	13	81
subtype2	15	118
subtype3	15	105

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 = 0.55 (Kruskal-Wallis (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	257	3.5 (4.8)
subtype1	75	3.4 (3.4)
subtype2	96	3.9 (6.1)
subtype3	86	3.1 (4.0)

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 = 0.248 (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	131	217
subtype1	37	57
subtype2	43	90
subtype3	51	70

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.619 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	326
subtype1	1	0	88
subtype2	1	1	127
subtype3	3	0	111

Figure S13. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.893 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	334
subtype1	2	90
subtype2	2	130
subtype3	3	114

Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S16. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	118	139	89

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

P value = 1.01e-05 (logrank test), Q value = 0.0013

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	332	163	0.1 - 185.2 (14.8)
subtype1	113	62	0.1 - 164.2 (15.9)
subtype2	131	50	0.2 - 185.2 (21.1)
subtype3	88	51	0.1 - 111.1 (10.4)

Figure S15. 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 = 0.019 (logrank test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	338	164	0.2 - 369.9 (50.1)
subtype1	115	63	0.7 - 357.4 (58.5)
subtype2	135	50	0.2 - 369.9 (49.5)
subtype3	88	51	0.2 - 357.0 (43.6)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0346 (Kruskal-Wallis (anova)), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	339	56.1 (15.7)
subtype1	115	58.8 (15.6)
subtype2	136	54.7 (15.5)
subtype3	88	54.8 (15.7)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'AGE'

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

P value = 0.0399 (Fisher's exact test), Q value = 1

Table S20. 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	60	5	72	208
subtype1	24	2	26	66
subtype2	15	1	25	97
subtype3	21	2	21	45

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.128 (Fisher's exact test), Q value = 1

Table S21. 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	12	7	28	16	27	21	14	19	12	35	15	32	59	19
subtype1	6	3	9	9	10	4	6	9	6	10	8	8	14	7
subtype2	5	2	13	6	9	7	4	3	1	18	5	14	28	8
subtype3	1	2	6	1	8	10	4	7	5	7	2	10	17	4

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.034 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	62	71	73	68
subtype1	23	23	23	25
subtype2	32	25	30	20
subtype3	7	23	20	23

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.136 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	167	62	38	47
subtype1	64	15	15	13
subtype2	56	29	15	25
subtype3	47	18	8	9

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.209 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	305	4	3	4	9
subtype1	106	1	0	1	5
subtype2	120	1	1	3	4
subtype3	79	2	2	0	0

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'MELANOMA.ULCERATION'

P value = 0.0882 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	126	88
subtype1	50	33
subtype2	53	28
subtype3	23	27

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

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

P value = 0.192 (Fisher's exact test), Q value = 1

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	43	302
subtype1	12	106
subtype2	23	116
subtype3	8	80

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'METHLYATION CNMF' versus 'BRESLOW.THICKNESS'

P value = 0.136 (Kruskal-Wallis (anova)), Q value = 1

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	255	3.5 (4.8)
subtype1	90	3.3 (3.0)
subtype2	96	3.5 (6.1)
subtype3	69	3.9 (4.6)

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.536 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	129	217
subtype1	40	78
subtype2	52	87
subtype3	37	52

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'GENDER'

'METHLYATION CNMF' versus 'RACE'

P value = 0.761 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	323
subtype1	2	0	112
subtype2	1	1	130
subtype3	2	0	81

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

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

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	332
subtype1	2	112
subtype2	3	134
subtype3	2	86

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S31. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	47	58	26	38

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

P value = 0.347 (logrank test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	161	93	0.2 - 125.7 (17.6)
subtype1	45	23	3.5 - 111.9 (17.6)
subtype2	55	32	0.2 - 125.7 (20.4)
subtype3	25	15	0.2 - 81.2 (9.6)
subtype4	36	23	1.1 - 124.3 (26.3)

Figure S29. 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 = 0.0864 (logrank test), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	165	94	0.2 - 369.9 (53.9)
subtype1	45	23	9.9 - 369.9 (61.5)
subtype2	58	33	0.2 - 357.4 (56.0)
subtype3	26	15	0.2 - 162.1 (47.0)
subtype4	36	23	2.6 - 176.6 (46.1)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.305 (Kruskal-Wallis (anova)), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	166	55.3 (15.8)
subtype1	46	51.8 (13.4)
subtype2	58	55.3 (15.7)
subtype3	26	56.6 (17.1)
subtype4	36	58.8 (17.6)

Figure S31. 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 = 0.104 (Fisher's exact test), Q value = 1

Table S35. 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	22	1	32	113
subtype1	2	0	14	31
subtype2	11	1	10	36
subtype3	4	0	5	17
subtype4	5	0	3	29

Figure S32. 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 = 0.561 (Fisher's exact test), Q value = 1

Table S36. 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	3	14	8	17	7	5	6	3	20	5	15	27	9
subtype1	2	1	5	2	7	1	2	1	0	3	3	5	9	2
subtype2	2	2	7	1	7	2	2	2	3	8	1	2	6	5
subtype3	1	0	1	2	1	0	0	1	0	5	1	4	5	0
subtype4	3	0	1	3	2	4	1	2	0	4	0	4	7	2

Figure S33. 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 = 0.457 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	36	37	28	31
subtype1	8	12	10	9
subtype2	9	13	7	13
subtype3	7	7	5	2
subtype4	12	5	6	7

Figure S34. 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 = 0.31 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	85	24	26	21
subtype1	22	7	6	8
subtype2	36	6	6	6
subtype3	8	6	7	3
subtype4	19	5	7	4

Figure S35. 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 = 0.382 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	149	4	2	2	2
subtype1	42	1	1	0	0
subtype2	48	3	1	2	0
subtype3	25	0	0	0	0
subtype4	34	0	0	0	2

Figure S36. 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 = 0.331 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	65	37
subtype1	21	7
subtype2	23	15
subtype3	11	5
subtype4	10	10

Figure S37. 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 = 0.675 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	25	144
subtype1	6	41
subtype2	8	50
subtype3	3	23
subtype4	8	30

Figure S38. 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 = 0.224 (Kruskal-Wallis (anova)), Q value = 1

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	119	3.4 (5.3)
subtype1	36	3.6 (8.2)
subtype2	40	3.3 (3.0)
subtype3	20	2.6 (3.9)
subtype4	23	3.8 (3.5)

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.84 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	64	105
subtype1	17	30
subtype2	20	38
subtype3	11	15
subtype4	16	22

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S44. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	27	58	35	49

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

P value = 0.46 (logrank test), Q value = 1

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	161	93	0.2 - 125.7 (17.6)
subtype1	25	14	1.4 - 84.7 (11.3)
subtype2	57	31	1.5 - 125.7 (25.1)
subtype3	34	22	1.1 - 82.3 (16.8)
subtype4	45	26	0.2 - 111.9 (13.0)

Figure S41. 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 = 0.819 (logrank test), Q value = 1

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	165	94	0.2 - 369.9 (53.9)
subtype1	25	14	9.9 - 215.4 (47.8)
subtype2	57	31	6.4 - 357.4 (53.9)
subtype3	34	22	11.3 - 369.9 (58.2)
subtype4	49	27	0.2 - 248.7 (50.9)

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.548 (Kruskal-Wallis (anova)), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	166	55.3 (15.8)
subtype1	26	58.5 (15.9)
subtype2	57	53.5 (15.2)
subtype3	34	56.4 (15.9)
subtype4	49	54.9 (16.6)

Figure S43. 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 = 0.287 (Fisher's exact test), Q value = 1

Table S48. 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	22	1	32	113
subtype1	3	0	8	16
subtype2	9	1	8	40
subtype3	2	0	4	28
subtype4	8	0	12	29

Figure S44. 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 = 0.302 (Fisher's exact test), Q value = 1

Table S49. 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	3	14	8	17	7	5	6	3	20	5	15	27	9
subtype1	1	1	2	1	3	0	1	0	0	1	2	5	7	2
subtype2	2	1	7	3	5	0	3	4	3	10	0	2	9	2
subtype3	4	1	2	1	4	3	1	0	0	3	2	3	5	1
subtype4	1	0	3	3	5	4	0	2	0	6	1	5	6	4

Figure S45. 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 = 0.299 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	36	37	28	31
subtype1	4	6	7	7
subtype2	16	12	4	11
subtype3	6	9	8	3
subtype4	10	10	9	10

Figure S46. 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 = 0.466 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	85	24	26	21
subtype1	10	8	4	4
subtype2	32	4	9	9
subtype3	18	5	6	3
subtype4	25	7	7	5

Figure S47. 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 = 0.809 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	149	4	2	2	2
subtype1	24	1	1	0	0
subtype2	51	1	0	1	0
subtype3	32	0	0	0	1
subtype4	42	2	1	1	1

Figure S48. 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 = 0.841 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	65	37
subtype1	11	6
subtype2	19	14
subtype3	12	5
subtype4	23	12

Figure S49. 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 = 0.131 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	25	144
subtype1	3	24
subtype2	14	44
subtype3	3	32
subtype4	5	44

Figure S50. 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 = 0.882 (Kruskal-Wallis (anova)), Q value = 1

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	119	3.4 (5.3)
subtype1	21	5.4 (10.7)
subtype2	36	3.1 (3.0)
subtype3	24	2.2 (1.8)
subtype4	38	3.2 (3.5)

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RPPA cHierClus subtypes' versus 'GENDER'

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

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	64	105
subtype1	6	21
subtype2	25	33
subtype3	15	20
subtype4	18	31

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S57. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	132	100	116

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

P value = 5.73e-06 (logrank test), Q value = 0.00077

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	333	164	0.1 - 185.2 (14.6)
subtype1	127	71	0.1 - 164.2 (11.3)
subtype2	96	32	0.2 - 185.2 (26.6)
subtype3	110	61	0.2 - 125.7 (14.0)

Figure S53. 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 = 0.000139 (logrank test), Q value = 0.018

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	339	165	0.2 - 369.9 (50.1)
subtype1	130	71	0.2 - 247.0 (40.0)
subtype2	99	33	0.3 - 268.9 (55.7)
subtype3	110	61	0.2 - 369.9 (57.1)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00219 (Kruskal-Wallis (anova)), Q value = 0.27

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	340	56.1 (15.7)
subtype1	130	59.0 (14.7)
subtype2	100	57.2 (15.8)
subtype3	110	51.8 (15.7)

Figure S55. 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 = 3e-04 (Fisher's exact test), Q value = 0.038

Table S61. 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	60	5	72	210
subtype1	30	5	21	75
subtype2	12	0	14	74
subtype3	18	0	37	61

Figure S56. 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 = 0.106 (Fisher's exact test), Q value = 1

Table S62. 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	12	7	28	17	27	21	14	19	12	35	15	32	59	19
subtype1	3	3	6	7	10	5	4	10	9	12	8	13	26	6
subtype2	4	1	11	9	7	6	5	3	2	10	1	10	18	3
subtype3	5	3	11	1	10	10	5	6	1	13	6	9	15	10

Figure S57. 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 = 0.0134 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	63	71	73	68
subtype1	19	23	26	38
subtype2	25	17	21	15
subtype3	19	31	26	15

Figure S58. 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 = 0.579 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	167	62	38	47
subtype1	56	23	19	20
subtype2	52	17	7	13
subtype3	59	22	12	14

Figure S59. 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 = 0.422 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	306	4	3	4	9
subtype1	118	0	2	1	4
subtype2	89	1	0	0	2
subtype3	99	3	1	3	3

Figure S60. 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 = 0.498 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	129	88
subtype1	55	41
subtype2	42	22
subtype3	32	25

Figure S61. 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 = 0.19 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	43	304
subtype1	11	121
subtype2	15	85
subtype3	17	98

Figure S62. 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 = 0.000114 (Kruskal-Wallis (anova)), Q value = 0.015

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	257	3.5 (4.8)
subtype1	104	4.4 (4.4)
subtype2	71	2.8 (3.7)
subtype3	82	3.0 (5.9)

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.346 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	217
subtype1	44	88
subtype2	38	62
subtype3	49	67

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'GENDER'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.53 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	325
subtype1	3	0	120
subtype2	1	1	96
subtype3	1	0	109

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.141 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	334
subtype1	5	125
subtype2	0	99
subtype3	2	110

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S72. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	101	159	88

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

P value = 3.95e-09 (logrank test), Q value = 5.4e-07

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	333	164	0.1 - 185.2 (14.6)
subtype1	98	60	0.1 - 110.2 (8.7)
subtype2	151	59	0.2 - 185.2 (22.2)
subtype3	84	45	0.4 - 125.7 (15.2)

Figure S67. 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 = 1e-05 (logrank test), Q value = 0.0013

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	339	165	0.2 - 369.9 (50.1)
subtype1	99	60	0.2 - 294.8 (36.0)
subtype2	156	60	0.2 - 369.9 (54.4)
subtype3	84	45	2.0 - 357.4 (59.1)

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000814 (Kruskal-Wallis (anova)), Q value = 0.1

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	340	56.1 (15.7)
subtype1	99	59.4 (15.3)
subtype2	157	57.1 (15.3)
subtype3	84	50.4 (15.4)

Figure S69. 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 = 1e-05 (Fisher's exact test), Q value = 0.0013

Table S76. 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	60	5	72	210
subtype1	32	4	19	45
subtype2	16	1	28	114
subtype3	12	0	25	51

Figure S70. 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 = 0.0189 (Fisher's exact test), Q value = 1

Table S77. 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	12	7	28	17	27	21	14	19	12	35	15	32	59	19
subtype1	2	2	3	4	6	5	5	9	9	8	9	7	21	5
subtype2	6	2	15	11	14	8	6	8	2	14	2	19	27	8
subtype3	4	3	10	2	7	8	3	2	1	13	4	6	11	6

Figure S71. 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 = 0.00057 (Fisher's exact test), Q value = 0.071

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	63	71	73	68
subtype1	11	16	22	35
subtype2	36	31	31	24
subtype3	16	24	20	9

Figure S72. 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 = 0.821 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	167	62	38	47
subtype1	44	17	14	14
subtype2	79	26	15	23
subtype3	44	19	9	10

Figure S73. 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 = 0.186 (Fisher's exact test), Q value = 1

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	306	4	3	4	9
subtype1	89	3	1	0	1
subtype2	141	1	2	1	5
subtype3	76	0	0	3	3

Figure S74. 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 = 0.647 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	129	88
subtype1	39	32
subtype2	64	40
subtype3	26	16

Figure S75. 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 = 0.249 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	43	304
subtype1	8	93
subtype2	23	136
subtype3	12	75

Figure S76. 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 = 2.06e-05 (Kruskal-Wallis (anova)), Q value = 0.0027

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	257	3.5 (4.8)
subtype1	81	4.7 (4.5)
subtype2	113	3.0 (3.6)
subtype3	63	3.0 (6.5)

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.333 (Fisher's exact test), Q value = 1

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	217
subtype1	35	66
subtype2	57	102
subtype3	39	49

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.936 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	325
subtype1	2	0	93
subtype2	2	1	149
subtype3	1	0	83

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.132 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	334
subtype1	4	96
subtype2	1	155
subtype3	2	83

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S87. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	44	99	73	109	8

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

P value = 0.0339 (logrank test), Q value = 1

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	318	160	0.1 - 185.2 (15.2)
subtype1	42	17	0.2 - 122.7 (19.9)
subtype2	95	49	0.2 - 124.5 (12.6)
subtype3	70	31	0.1 - 185.2 (16.2)
subtype4	104	62	0.3 - 164.2 (13.7)
subtype5	7	1	5.6 - 59.6 (25.1)

Figure S81. 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 = 0.227 (logrank test), Q value = 1

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	324	161	0.2 - 369.9 (50.8)
subtype1	43	18	0.2 - 357.4 (42.1)
subtype2	95	49	0.2 - 357.0 (53.5)
subtype3	71	31	4.4 - 369.9 (47.5)
subtype4	108	62	0.3 - 268.7 (50.8)
subtype5	7	1	6.8 - 145.1 (53.9)

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.262 (Kruskal-Wallis (anova)), Q value = 1

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	325	56.2 (15.6)
subtype1	44	57.3 (16.5)
subtype2	95	53.9 (15.3)
subtype3	71	57.2 (13.5)
subtype4	108	57.7 (16.5)
subtype5	7	48.9 (17.7)

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'AGE'

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

P value = 0.00016 (Fisher's exact test), Q value = 0.02

Table S91. 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	57	4	69	202
subtype1	6	0	9	29
subtype2	16	0	35	48
subtype3	14	4	5	50
subtype4	21	0	17	70
subtype5	0	0	3	5

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0783 (Fisher's exact test), Q value = 1

Table S92. 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	12	7	27	15	27	20	12	18	11	33	14	30	58	18
subtype1	1	0	2	1	2	5	3	1	2	4	2	7	5	4
subtype2	3	1	9	1	11	3	5	5	2	12	6	8	21	4
subtype3	5	5	9	8	3	5	1	2	3	5	2	4	14	0
subtype4	2	1	7	5	11	7	3	10	4	10	4	10	15	9
subtype5	1	0	0	0	0	0	0	0	0	2	0	1	3	1

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00601 (Fisher's exact test), Q value = 0.72

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	59	71	69	63
subtype1	9	6	11	10
subtype2	7	31	23	17
subtype3	20	10	11	12
subtype4	19	23	24	22
subtype5	4	1	0	2

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.0388 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	161	58	38	45
subtype1	19	8	9	4
subtype2	44	24	10	13
subtype3	41	8	6	11
subtype4	56	15	13	13
subtype5	1	3	0	4

Figure S87. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.177 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	294	4	2	4	9
subtype1	37	1	1	1	1
subtype2	89	2	0	1	1
subtype3	68	0	0	0	0
subtype4	93	1	1	2	6
subtype5	7	0	0	0	1

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'MELANOMA.ULCERATION'

P value = 0.289 (Fisher's exact test), Q value = 1

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	122	84
subtype1	12	14
subtype2	37	22
subtype3	27	17
subtype4	46	29
subtype5	0	2

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

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

P value = 0.0293 (Fisher's exact test), Q value = 1

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	41	291
subtype1	7	37
subtype2	10	89
subtype3	7	66
subtype4	13	96
subtype5	4	3

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'MIRSEQ CNMF' versus 'BRESLOW.THICKNESS'

P value = 0.183 (Kruskal-Wallis (anova)), Q value = 1

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	245	3.5 (4.8)
subtype1	31	3.5 (3.1)
subtype2	76	3.3 (6.1)
subtype3	50	3.3 (4.6)
subtype4	85	3.7 (4.2)
subtype5	3	4.9 (3.4)

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.382 (Fisher's exact test), Q value = 1

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	124	209
subtype1	17	27
subtype2	31	68
subtype3	30	43
subtype4	41	68
subtype5	5	3

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'GENDER'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.176 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	311
subtype1	1	0	43
subtype2	1	0	90
subtype3	3	1	67
subtype4	0	0	104
subtype5	0	0	7

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0977 (Fisher's exact test), Q value = 1

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	319
subtype1	0	44
subtype2	2	93
subtype3	0	72
subtype4	4	103
subtype5	1	7

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S102. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	88	138	70	37

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

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

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	318	160	0.1 - 185.2 (15.2)
subtype1	85	48	0.1 - 164.2 (13.7)
subtype2	131	62	0.2 - 142.4 (14.2)
subtype3	67	35	0.1 - 185.2 (15.5)
subtype4	35	15	0.2 - 122.7 (20.4)

Figure S95. 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 = 0.552 (logrank test), Q value = 1

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	324	161	0.2 - 369.9 (50.8)
subtype1	88	48	0.3 - 314.6 (49.5)
subtype2	132	62	0.2 - 294.8 (50.2)
subtype3	68	35	6.3 - 369.9 (58.3)
subtype4	36	16	0.2 - 357.4 (43.7)

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.897 (Kruskal-Wallis (anova)), Q value = 1

Table S105. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	325	56.2 (15.6)
subtype1	88	57.3 (15.0)
subtype2	132	55.6 (16.8)
subtype3	68	55.9 (13.5)
subtype4	37	56.5 (16.5)

Figure S97. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'AGE'

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

P value = 0.813 (Fisher's exact test), Q value = 1

Table S106. 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	57	4	69	202
subtype1	19	2	16	51
subtype2	21	2	27	87
subtype3	12	0	15	43
subtype4	5	0	11	21

Figure S98. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.323 (Fisher's exact test), Q value = 1

Table S107. 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	12	7	27	15	27	20	12	18	11	33	14	30	58	18
subtype1	1	4	5	4	8	5	4	5	4	9	5	7	16	3
subtype2	8	0	11	7	14	6	5	10	4	13	5	9	25	10
subtype3	2	3	10	3	4	5	1	2	2	8	3	5	13	1
subtype4	1	0	1	1	1	4	2	1	1	3	1	9	4	4

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.285 (Fisher's exact test), Q value = 1

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	59	71	69	63
subtype1	15	18	22	17
subtype2	20	36	24	27
subtype3	16	14	12	11
subtype4	8	3	11	8

Figure S100. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

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

P value = 0.191 (Fisher's exact test), Q value = 1

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	161	58	38	45
subtype1	39	17	12	10
subtype2	72	18	15	20
subtype3	36	15	3	11
subtype4	14	8	8	4

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

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

P value = 0.226 (Fisher's exact test), Q value = 1

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	294	4	2	4	9
subtype1	78	0	1	1	2
subtype2	120	3	0	1	6
subtype3	65	0	0	1	0
subtype4	31	1	1	1	1

Figure S102. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'MELANOMA.ULCERATION'

P value = 0.421 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	122	84
subtype1	39	25
subtype2	54	31
subtype3	19	16
subtype4	10	12

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

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

P value = 0.236 (Fisher's exact test), Q value = 1

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	41	291
subtype1	6	82
subtype2	18	119
subtype3	11	59
subtype4	6	31

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

'MIRSEQ CHIERARCHICAL' versus 'BRESLOW.THICKNESS'

P value = 0.0707 (Kruskal-Wallis (anova)), Q value = 1

Table S113. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	245	3.5 (4.8)
subtype1	68	3.4 (3.0)
subtype2	104	3.5 (4.2)
subtype3	48	3.8 (8.0)
subtype4	25	3.3 (2.8)

Figure S105. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.63 (Fisher's exact test), Q value = 1

Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	124	209
subtype1	29	59
subtype2	56	82
subtype3	27	43
subtype4	12	25

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.257 (Fisher's exact test), Q value = 1

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	311
subtype1	1	1	82
subtype2	1	0	128
subtype3	1	0	66
subtype4	2	0	35

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.949 (Fisher's exact test), Q value = 1

Table S116. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	319
subtype1	2	84
subtype2	4	134
subtype3	1	65
subtype4	0	36

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S117. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	90	102	127

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

P value = 0.267 (logrank test), Q value = 1

Table S118. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	304	155	0.1 - 185.2 (15.2)
subtype1	81	36	0.1 - 185.2 (20.0)
subtype2	99	54	0.2 - 124.5 (15.7)
subtype3	124	65	0.1 - 164.2 (13.2)

Figure S109. 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 = 0.861 (logrank test), Q value = 1

Table S119. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	310	156	0.2 - 369.9 (51.8)
subtype1	85	36	3.6 - 268.9 (46.3)
subtype2	99	54	0.2 - 357.4 (56.4)
subtype3	126	66	0.2 - 369.9 (49.2)

Figure S110. 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 = 0.0491 (Kruskal-Wallis (anova)), Q value = 1

Table S120. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	311	56.4 (15.7)
subtype1	86	58.3 (14.3)
subtype2	99	53.0 (16.5)
subtype3	126	57.7 (15.7)

Figure S111. 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 = 0.0039 (Fisher's exact test), Q value = 0.47

Table S121. 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	56	4	66	192
subtype1	11	0	12	66
subtype2	19	0	31	52
subtype3	26	4	23	74

Figure S112. 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 = 0.00181 (Fisher's exact test), Q value = 0.22

Table S122. 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	12	7	27	15	26	20	12	17	11	33	13	28	53	16
subtype1	7	2	5	10	4	4	3	0	0	13	1	9	21	2
subtype2	2	2	11	1	12	6	6	5	3	9	6	7	16	6
subtype3	3	3	11	4	10	10	3	12	8	11	6	12	16	8

Figure S113. 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 = 0.0211 (Fisher's exact test), Q value = 1

Table S123. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	57	70	66	58
subtype1	26	13	17	12
subtype2	13	29	21	17
subtype3	18	28	28	29

Figure S114. 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 = 0.269 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	159	57	34	40
subtype1	40	14	12	18
subtype2	53	18	11	11
subtype3	66	25	11	11

Figure S115. 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 = 0.618 (Fisher's exact test), Q value = 1

Table S125. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	284	4	1	3	9
subtype1	83	0	0	1	2
subtype2	88	2	0	2	2
subtype3	113	2	1	0	5

Figure S116. 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 = 0.0993 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	117	79
subtype1	38	16
subtype2	36	23
subtype3	43	40

Figure S117. 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 = 0.517 (Fisher's exact test), Q value = 1

Table S127. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	37	282
subtype1	13	77
subtype2	12	90
subtype3	12	115

Figure S118. 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 = 0.035 (Kruskal-Wallis (anova)), Q value = 1

Table S128. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	236	3.4 (4.8)
subtype1	57	3.3 (4.4)
subtype2	80	3.3 (6.2)
subtype3	99	3.5 (3.7)

Figure S119. 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 = 0.546 (Fisher's exact test), Q value = 1

Table S129. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	116	203
subtype1	36	54
subtype2	33	69
subtype3	47	80

Figure S120. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.615 (Fisher's exact test), Q value = 1

Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	299
subtype1	1	1	85
subtype2	1	0	95
subtype3	3	0	119

Figure S121. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

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

Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	306
subtype1	2	87
subtype2	2	96
subtype3	3	123

Figure S122. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S132. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	157	133	29

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

P value = 0.353 (logrank test), Q value = 1

Table S133. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time from Specimen Diagnosis to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	304	155	0.1 - 185.2 (15.2)
subtype1	151	74	0.1 - 185.2 (14.5)
subtype2	125	68	0.2 - 124.5 (14.5)
subtype3	28	13	0.2 - 122.7 (28.7)

Figure S123. 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 = 0.932 (logrank test), Q value = 1

Table S134. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	310	156	0.2 - 369.9 (51.8)
subtype1	155	74	0.3 - 294.8 (47.5)
subtype2	126	68	0.2 - 369.9 (56.3)
subtype3	29	14	0.2 - 357.4 (45.3)

Figure S124. 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 = 0.322 (Kruskal-Wallis (anova)), Q value = 1

Table S135. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	311	56.4 (15.7)
subtype1	156	57.5 (15.7)
subtype2	126	54.8 (15.3)
subtype3	29	56.8 (17.6)

Figure S125. 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 = 0.00049 (Fisher's exact test), Q value = 0.062

Table S136. 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	56	4	66	192
subtype1	31	4	17	104
subtype2	20	0	43	70
subtype3	5	0	6	18

Figure S126. 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 = 0.871 (Fisher's exact test), Q value = 1

Table S137. 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	12	7	27	15	26	20	12	17	11	33	13	28	53	16
subtype1	5	4	11	11	13	10	6	10	7	17	6	13	25	7
subtype2	6	3	15	3	12	6	4	6	3	14	6	11	25	6
subtype3	1	0	1	1	1	4	2	1	1	2	1	4	3	3

Figure S127. 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 = 0.18 (Fisher's exact test), Q value = 1

Table S138. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	57	70	66	58
subtype1	31	32	32	33
subtype2	20	36	26	19
subtype3	6	2	8	6

Figure S128. 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 = 0.642 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	159	57	34	40
subtype1	80	27	16	19
subtype2	65	26	12	19
subtype3	14	4	6	2

Figure S129. 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 = 0.545 (Fisher's exact test), Q value = 1

Table S140. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	M1C
ALL	284	4	1	3	9
subtype1	141	2	1	1	4
subtype2	119	1	0	1	4
subtype3	24	1	0	1	1

Figure S130. 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 = 0.551 (Fisher's exact test), Q value = 1

Table S141. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'MELANOMA.ULCERATION'

nPatients	NO	YES
ALL	117	79
subtype1	64	41
subtype2	45	29
subtype3	8	9

Figure S131. 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 = 0.567 (Fisher's exact test), Q value = 1

Table S142. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'MELANOMA.PRIMARY.KNOWN'

nPatients	NO	YES
ALL	37	282
subtype1	17	140
subtype2	15	118
subtype3	5	24

Figure S132. 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 = 0.127 (Kruskal-Wallis (anova)), Q value = 1

Table S143. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

	nPatients	Mean (Std.Dev)
ALL	236	3.4 (4.8)
subtype1	120	3.6 (4.2)
subtype2	97	3.2 (5.8)
subtype3	19	3.3 (3.1)

Figure S133. 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 = 0.841 (Fisher's exact test), Q value = 1

Table S144. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

nPatients	FEMALE	MALE
ALL	116	203
subtype1	56	101
subtype2	48	85
subtype3	12	17

Figure S134. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.445 (Fisher's exact test), Q value = 1

Table S145. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	1	299
subtype1	3	1	147
subtype2	1	0	124
subtype3	1	0	28

Figure S135. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

Table S146. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	306
subtype1	4	151
subtype2	3	126
subtype3	0	29

Figure S136. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Methods & Data

Input

Cluster data file = SKCM-TM.mergedcluster.txt
Clinical data file = SKCM-TM.merged_data.txt
Number of patients = 349
Number of clustering approaches = 10
Number of selected clinical features = 14
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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