Correlation between aggregated molecular cancer subtypes and selected clinical features

Skin Cutaneous Melanoma (Metastatic)

16 April 2014 | analyses__2014_04_16

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/C10P0XP3

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 278 patients, 8 significant findings detected with P value < 0.05 and Q value < 0.25.

4 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', 'Time to Death', and 'PRIMARY.SITE.OF.DISEASE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 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, 8 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	0.109 (1.00)	0.000567 (0.0646)	0.186 (1.00)	0.289 (1.00)	1.61e-05 (0.0019)	1.19e-07 (1.42e-05)	0.0293 (1.00)	0.0973 (1.00)	0.0136 (1.00)	0.128 (1.00)
Time to Death	logrank test	0.107 (1.00)	0.0482 (1.00)	0.0694 (1.00)	0.231 (1.00)	0.000343 (0.0394)	0.0016 (0.181)	0.0742 (1.00)	0.301 (1.00)	0.0529 (1.00)	0.417 (1.00)
AGE	ANOVA	0.00831 (0.915)	0.364 (1.00)	0.204 (1.00)	0.429 (1.00)	0.012 (1.00)	0.167 (1.00)	0.368 (1.00)	0.797 (1.00)	0.205 (1.00)	0.738 (1.00)
PRIMARY SITE OF DISEASE	Chi-square test	0.0947 (1.00)	0.00756 (0.84)	0.164 (1.00)	0.626 (1.00)	6.22e-05 (0.00728)	3.13e-06 (0.000373)	0.0113 (1.00)	0.278 (1.00)	0.00705 (0.789)	0.0994 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.598 (1.00)	0.288 (1.00)	0.415 (1.00)	0.195 (1.00)	0.22 (1.00)	0.285 (1.00)	0.816 (1.00)	0.469 (1.00)	0.526 (1.00)	0.142 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.41 (1.00)	0.299 (1.00)	0.468 (1.00)	0.176 (1.00)	0.135 (1.00)	0.00884 (0.964)	0.161 (1.00)	0.735 (1.00)	6.59e-05 (0.00765)	0.47 (1.00)
PATHOLOGY N STAGE	Chi-square test	0.668 (1.00)	0.0421 (1.00)	0.291 (1.00)	0.202 (1.00)	0.578 (1.00)	0.765 (1.00)	0.837 (1.00)	0.877 (1.00)	0.719 (1.00)	0.966 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.762 (1.00)	0.739 (1.00)	0.206 (1.00)	0.895 (1.00)	0.663 (1.00)	0.683 (1.00)	0.917 (1.00)	0.305 (1.00)	0.931 (1.00)	0.631 (1.00)
MELANOMA ULCERATION	Fisher's exact test	0.752 (1.00)	0.143 (1.00)	0.323 (1.00)	0.773 (1.00)	0.277 (1.00)	0.276 (1.00)	0.219 (1.00)	0.69 (1.00)	0.703 (1.00)	0.815 (1.00)
MELANOMA PRIMARY KNOWN	Fisher's exact test	0.666 (1.00)	0.37 (1.00)	0.596 (1.00)	0.0474 (1.00)	0.0227 (1.00)	0.187 (1.00)	0.129 (1.00)	0.242 (1.00)	0.0723 (1.00)	0.161 (1.00)
BRESLOW THICKNESS	ANOVA	0.588 (1.00)	0.849 (1.00)	0.893 (1.00)	0.315 (1.00)	0.109 (1.00)	0.101 (1.00)	0.786 (1.00)	0.854 (1.00)	0.792 (1.00)	0.769 (1.00)
GENDER	Fisher's exact test	0.693 (1.00)	1 (1.00)	0.841 (1.00)	0.396 (1.00)	0.603 (1.00)	0.732 (1.00)	0.0683 (1.00)	0.5 (1.00)	0.328 (1.00)	0.687 (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	4
Number of samples	79	52	45	98

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

P value = 0.109 (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	263	127	0.0 - 124.3 (13.4)
subtype1	76	40	0.2 - 122.7 (12.9)
subtype2	51	28	0.1 - 111.8 (11.9)
subtype3	43	21	1.5 - 98.3 (15.9)
subtype4	93	38	0.0 - 124.3 (15.5)

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 = 0.107 (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	269	128	0.2 - 357.4 (47.5)
subtype1	78	41	0.2 - 268.9 (47.0)
subtype2	51	28	6.4 - 294.8 (47.5)
subtype3	44	21	3.2 - 357.4 (59.5)
subtype4	96	38	0.2 - 314.6 (47.8)

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 = 0.00831 (ANOVA), Q value = 0.91

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

	nPatients	Mean (Std.Dev)
ALL	270	55.5 (15.6)
subtype1	78	59.1 (16.5)
subtype2	51	51.5 (15.9)
subtype3	44	51.1 (15.7)
subtype4	97	56.7 (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 = 0.0947 (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	37	1	59	176
subtype1	16	0	13	49
subtype2	9	1	11	31
subtype3	6	0	13	26
subtype4	6	0	22	70

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 = 0.598 (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	6	23	10	25	17	10	14	10	30	11	23	46	12
subtype1	1	1	5	3	8	5	3	5	4	8	4	11	13	3
subtype2	2	0	4	2	7	4	0	2	4	7	1	1	10	3
subtype3	4	2	3	2	2	4	2	0	1	6	2	5	5	4
subtype4	3	3	11	3	8	4	5	7	1	9	4	6	18	2

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 = 0.41 (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	53	59	53	54
subtype1	17	17	18	17
subtype2	10	13	5	14
subtype3	12	9	8	5
subtype4	14	20	22	18

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 = 0.668 (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	140	48	31	35
subtype1	41	15	13	8
subtype2	28	9	3	7
subtype3	24	10	6	4
subtype4	47	14	9	16

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 = 0.762 (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	245	4	2	2	5
subtype1	74	1	0	0	2
subtype2	45	1	1	0	1
subtype3	41	1	0	1	2
subtype4	85	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 = 0.752 (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	101	69
subtype1	32	22
subtype2	16	15
subtype3	15	10
subtype4	38	22

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 = 0.666 (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	33	241
subtype1	10	69
subtype2	8	44
subtype3	6	39
subtype4	9	89

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 = 0.588 (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	200	3.5 (5.0)
subtype1	64	3.4 (4.2)
subtype2	36	3.3 (3.2)
subtype3	32	2.7 (3.3)
subtype4	68	4.1 (6.8)

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 = 0.693 (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	105	169
subtype1	33	46
subtype2	22	30
subtype3	16	29
subtype4	34	64

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	72	96	103

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

P value = 0.000567 (logrank test), Q value = 0.065

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	260	127	0.0 - 124.3 (13.7)
subtype1	71	43	0.1 - 111.1 (11.2)
subtype2	92	44	0.1 - 122.7 (15.3)
subtype3	97	40	0.0 - 124.3 (17.7)

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 = 0.0482 (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	266	128	0.2 - 357.4 (47.5)
subtype1	71	43	0.2 - 247.0 (43.4)
subtype2	94	45	2.6 - 357.4 (54.9)
subtype3	101	40	0.2 - 346.0 (45.1)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.364 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	267	55.6 (15.6)
subtype1	71	54.2 (16.1)
subtype2	94	57.4 (16.2)
subtype3	102	54.9 (14.7)

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 = 0.00756 (Chi-square test), Q value = 0.84

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	36	1	59	174
subtype1	15	1	19	37
subtype2	16	0	21	59
subtype3	5	0	19	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 = 0.288 (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	6	22	10	25	17	10	14	10	30	11	23	44	12
subtype1	1	2	4	1	9	9	2	6	4	6	2	7	11	3
subtype2	6	2	7	6	8	5	5	6	5	9	6	7	12	3
subtype3	3	2	11	3	8	3	3	2	1	15	3	9	21	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 = 0.299 (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	52	59	52	54
subtype1	8	21	14	16
subtype2	19	19	18	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 = 0.0421 (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	139	48	31	33
subtype1	42	13	7	5
subtype2	55	11	14	10
subtype3	42	24	10	18

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 = 0.739 (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	242	4	2	2	5
subtype1	64	2	1	0	0
subtype2	89	1	0	1	2
subtype3	89	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 = 0.143 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	100	68
subtype1	19	21
subtype2	40	27
subtype3	41	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 = 0.37 (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	32	239
subtype1	7	65
subtype2	9	87
subtype3	16	87

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 = 0.849 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	198	3.5 (5.0)
subtype1	57	3.4 (4.8)
subtype2	71	3.4 (2.7)
subtype3	70	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 = 1 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	103	168
subtype1	27	45
subtype2	37	59
subtype3	39	64

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	58	26	38

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

P value = 0.186 (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	160	85	0.2 - 124.3 (13.6)
subtype1	44	20	3.5 - 111.9 (13.3)
subtype2	55	30	0.2 - 114.2 (15.9)
subtype3	25	14	0.2 - 81.2 (6.6)
subtype4	36	21	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 = 0.0694 (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	164	86	0.2 - 357.4 (49.8)
subtype1	44	20	9.9 - 346.0 (61.3)
subtype2	58	31	0.2 - 357.4 (54.7)
subtype3	26	14	0.2 - 162.1 (40.3)
subtype4	36	21	2.6 - 176.6 (35.7)

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 = 0.204 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	165	55.2 (15.8)
subtype1	45	51.4 (13.3)
subtype2	58	55.3 (15.7)
subtype3	26	56.6 (17.1)
subtype4	36	58.8 (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 = 0.164 (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	22	1	31	113
subtype1	2	0	14	30
subtype2	11	1	9	37
subtype3	4	0	5	17
subtype4	5	0	3	29

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 = 0.415 (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	3	14	8	17	7	5	6	3	19	5	15	27	9
subtype1	2	1	5	2	7	1	2	1	0	2	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 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 = 0.468 (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	37	28	31
subtype1	8	12	10	9
subtype2	9	13	7	13
subtype3	7	7	5	2
subtype4	12	5	6	7

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 = 0.291 (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	85	25	24	21
subtype1	22	7	5	8
subtype2	36	6	6	6
subtype3	8	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 = 0.206 (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	148	4	2	2	2
subtype1	41	1	1	0	0
subtype2	48	3	1	2	0
subtype3	25	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 = 0.323 (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	65	37
subtype1	21	7
subtype2	23	15
subtype3	11	5
subtype4	10	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 = 0.596 (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	144
subtype1	5	41
subtype2	8	50
subtype3	3	23
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 = 0.893 (ANOVA), Q value = 1

Table S38. 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 S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'BRESLOW.THICKNESS'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.841 (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	104
subtype1	17	29
subtype2	20	38
subtype3	11	15
subtype4	16	22

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	38	71	59

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

P value = 0.289 (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	160	85	0.2 - 124.3 (13.6)
subtype1	35	21	1.4 - 84.7 (16.2)
subtype2	67	34	0.2 - 111.9 (11.4)
subtype3	58	30	1.5 - 124.3 (18.2)

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 = 0.231 (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	164	86	0.2 - 357.4 (49.8)
subtype1	35	21	9.9 - 215.4 (45.1)
subtype2	71	35	0.2 - 346.0 (47.3)
subtype3	58	30	6.4 - 357.4 (54.9)

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 = 0.429 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	165	55.2 (15.8)
subtype1	36	57.0 (15.4)
subtype2	71	56.0 (16.6)
subtype3	58	53.1 (15.1)

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 = 0.626 (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	22	1	31	113
subtype1	3	0	9	26
subtype2	10	0	14	46
subtype3	9	1	8	41

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 = 0.195 (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	3	14	8	17	7	5	6	3	19	5	15	27	9
subtype1	2	1	2	1	4	0	1	1	0	3	2	5	10	3
subtype2	2	0	5	4	8	7	1	3	0	7	2	8	8	4
subtype3	4	2	7	3	5	0	3	2	3	9	1	2	9	2

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 = 0.176 (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	37	28	31
subtype1	7	8	10	7
subtype2	13	16	15	13
subtype3	16	13	3	11

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 = 0.202 (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	85	25	24	21
subtype1	14	9	6	7
subtype2	38	11	10	5
subtype3	33	5	8	9

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 = 0.895 (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	148	4	2	2	2
subtype1	33	1	1	0	1
subtype2	63	2	1	1	1
subtype3	52	1	0	1	0

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 = 0.773 (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	65	37
subtype1	14	8
subtype2	32	16
subtype3	19	13

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 = 0.0474 (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	144
subtype1	6	32
subtype2	5	66
subtype3	13	46

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 = 0.315 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	119	3.4 (5.3)
subtype1	26	4.8 (9.7)
subtype2	56	3.0 (3.2)
subtype3	37	3.0 (3.0)

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 = 0.396 (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	104
subtype1	11	27
subtype2	28	43
subtype3	25	34

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	98	76	97

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

P value = 1.61e-05 (logrank test), Q value = 0.0019

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	260	127	0.0 - 124.3 (13.7)
subtype1	94	49	0.2 - 122.7 (14.4)
subtype2	73	23	0.0 - 124.3 (21.6)
subtype3	93	55	0.1 - 114.7 (10.3)

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 = 0.000343 (logrank test), Q value = 0.039

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	266	128	0.2 - 357.4 (47.5)
subtype1	95	50	0.2 - 357.4 (58.1)
subtype2	75	23	2.4 - 268.9 (53.2)
subtype3	96	55	0.2 - 247.0 (39.7)

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 = 0.012 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	267	55.6 (15.6)
subtype1	95	51.8 (15.4)
subtype2	76	56.8 (15.1)
subtype3	96	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 = 6.22e-05 (Chi-square test), Q value = 0.0073

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	36	1	59	174
subtype1	13	0	33	52
subtype2	2	0	12	62
subtype3	21	1	14	60

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 = 0.22 (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	6	22	10	25	17	10	14	10	30	11	23	44	12
subtype1	4	3	10	2	9	8	4	4	1	12	5	6	13	8
subtype2	3	1	8	5	6	4	4	1	2	9	0	7	14	2
subtype3	3	2	4	3	10	5	2	9	7	9	6	10	17	2

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 = 0.135 (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	52	59	52	54
subtype1	20	28	16	14
subtype2	17	13	15	13
subtype3	15	18	21	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 = 0.578 (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	139	48	31	33
subtype1	51	16	12	12
subtype2	40	16	4	9
subtype3	48	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 = 0.663 (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	242	4	2	2	5
subtype1	86	3	1	1	3
subtype2	66	1	0	0	1
subtype3	90	0	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 = 0.277 (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	100	68
subtype1	29	19
subtype2	32	15
subtype3	39	34

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 = 0.0227 (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	32	239
subtype1	17	81
subtype2	10	66
subtype3	5	92

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 = 0.109 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	198	3.5 (5.0)
subtype1	67	3.0 (6.3)
subtype2	52	2.8 (3.4)
subtype3	79	4.5 (4.6)

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 = 0.603 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	103	168
subtype1	41	57
subtype2	28	48
subtype3	34	63

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	72	131	68

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

P value = 1.19e-07 (logrank test), Q value = 1.4e-05

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	260	127	0.0 - 124.3 (13.7)
subtype1	71	42	0.8 - 122.7 (13.4)
subtype2	122	42	0.0 - 124.3 (21.2)
subtype3	67	43	0.1 - 95.4 (8.8)

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 = 0.0016 (logrank test), Q value = 0.18

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	266	128	0.2 - 357.4 (47.5)
subtype1	71	42	4.9 - 357.4 (55.6)
subtype2	128	43	0.2 - 346.0 (47.5)
subtype3	67	43	0.2 - 247.0 (43.2)

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 = 0.167 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	267	55.6 (15.6)
subtype1	71	53.3 (15.4)
subtype2	129	55.4 (15.6)
subtype3	67	58.3 (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 = 3.13e-06 (Chi-square test), Q value = 0.00037

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	36	1	59	174
subtype1	12	0	26	34
subtype2	7	0	20	104
subtype3	17	1	13	36

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 = 0.285 (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	6	22	10	25	17	10	14	10	30	11	23	44	12
subtype1	2	3	7	1	7	7	3	5	3	7	4	4	9	4
subtype2	7	2	11	7	12	6	5	3	2	18	1	13	20	6
subtype3	1	1	4	2	6	4	2	6	5	5	6	6	15	2

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 = 0.00884 (Chi-square test), Q value = 0.96

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

nPatients	T0+T1	T2	T3	T4
ALL	52	59	52	54
subtype1	11	20	16	11
subtype2	33	27	19	20
subtype3	8	12	17	23

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 = 0.765 (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	139	48	31	33
subtype1	41	13	6	7
subtype2	66	23	14	18
subtype3	32	12	11	8

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 = 0.683 (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	242	4	2	2	5
subtype1	64	1	1	1	1
subtype2	116	1	1	1	4
subtype3	62	2	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 = 0.276 (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	100	68
subtype1	21	19
subtype2	52	27
subtype3	27	22

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 = 0.187 (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	32	239
subtype1	9	63
subtype2	19	112
subtype3	4	64

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 = 0.101 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	198	3.5 (5.0)
subtype1	55	3.4 (6.9)
subtype2	87	2.9 (3.4)
subtype3	56	4.7 (4.8)

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 = 0.732 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	103	168
subtype1	30	42
subtype2	49	82
subtype3	24	44

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	68	112	85

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

P value = 0.0293 (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	252	126	0.0 - 124.3 (13.9)
subtype1	65	25	0.1 - 122.7 (16.0)
subtype2	106	61	0.0 - 124.3 (12.7)
subtype3	81	40	0.2 - 114.7 (14.6)

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 = 0.0742 (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	258	127	0.2 - 357.4 (47.5)
subtype1	66	26	0.2 - 357.4 (40.9)
subtype2	111	61	2.6 - 247.0 (44.4)
subtype3	81	40	0.2 - 314.6 (61.0)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.368 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	259	55.6 (15.4)
subtype1	67	56.4 (14.8)
subtype2	111	56.6 (16.4)
subtype3	81	53.6 (14.5)

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 = 0.0113 (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	37	1	58	168
subtype1	6	1	12	49
subtype2	20	0	17	74
subtype3	11	0	29	45

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 = 0.816 (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	6	22	10	25	15	9	13	9	28	11	23	45	12
subtype1	4	1	6	3	3	4	2	1	2	7	2	8	11	5
subtype2	3	3	6	6	11	8	4	8	4	11	4	11	17	5
subtype3	3	2	10	1	11	3	3	4	3	10	5	4	17	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 = 0.161 (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	51	59	50	52
subtype1	18	9	11	13
subtype2	22	24	23	23
subtype3	11	26	16	16

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 = 0.837 (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	135	46	33	33
subtype1	29	11	10	11
subtype2	60	19	13	13
subtype3	46	16	10	9

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 = 0.917 (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	236	4	2	2	5
subtype1	57	1	1	1	2
subtype2	101	2	1	1	2
subtype3	78	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 = 0.219 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	98	67
subtype1	17	19
subtype2	51	28
subtype3	30	20

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 = 0.129 (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	33	232
subtype1	13	55
subtype2	13	99
subtype3	7	78

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 = 0.786 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	194	3.6 (5.1)
subtype1	43	3.1 (2.9)
subtype2	85	3.6 (4.2)
subtype3	66	3.7 (7.0)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0683 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	99	166
subtype1	33	35
subtype2	40	72
subtype3	26	59

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
Number of samples	27	154	84

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

P value = 0.0973 (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	252	126	0.0 - 124.3 (13.9)
subtype1	25	9	0.2 - 114.2 (20.4)
subtype2	147	78	0.0 - 124.3 (12.9)
subtype3	80	39	0.1 - 122.7 (16.8)

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 = 0.301 (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	258	127	0.2 - 357.4 (47.5)
subtype1	26	10	0.2 - 357.4 (38.2)
subtype2	152	78	0.2 - 314.6 (45.1)
subtype3	80	39	4.9 - 346.0 (55.5)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.797 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	259	55.6 (15.4)
subtype1	27	55.9 (17.0)
subtype2	152	56.1 (16.1)
subtype3	80	54.7 (13.8)

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 = 0.278 (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	37	1	58	168
subtype1	3	0	7	17
subtype2	26	1	26	100
subtype3	8	0	25	51

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 = 0.469 (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	6	22	10	25	15	9	13	9	28	11	23	45	12
subtype1	1	0	1	1	1	3	2	0	1	1	1	6	3	3
subtype2	6	3	11	7	17	9	6	9	6	15	6	12	26	7
subtype3	3	3	10	2	7	3	1	4	2	12	4	5	16	2

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 = 0.735 (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	51	59	50	52
subtype1	5	3	6	7
subtype2	29	37	32	31
subtype3	17	19	12	14

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 = 0.877 (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	135	46	33	33
subtype1	12	5	5	3
subtype2	84	25	18	18
subtype3	39	16	10	12

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 = 0.305 (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	236	4	2	2	5
subtype1	23	1	1	1	0
subtype2	138	2	1	1	4
subtype3	75	1	0	0	1

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 = 0.69 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	98	67
subtype1	9	8
subtype2	66	41
subtype3	23	18

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 = 0.242 (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	33	232
subtype1	4	23
subtype2	15	139
subtype3	14	70

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 = 0.854 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	194	3.6 (5.1)
subtype1	18	3.4 (3.2)
subtype2	121	3.4 (3.7)
subtype3	55	3.9 (7.6)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.5 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	99	166
subtype1	11	16
subtype2	61	93
subtype3	27	57

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	82	107	76

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

P value = 0.0136 (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	252	126	0.0 - 124.3 (13.9)
subtype1	78	31	0.0 - 122.7 (17.0)
subtype2	101	58	0.1 - 124.3 (11.2)
subtype3	73	37	0.2 - 114.7 (13.0)

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 = 0.0529 (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	258	127	0.2 - 357.4 (47.5)
subtype1	79	32	0.2 - 357.4 (47.0)
subtype2	106	58	2.6 - 248.7 (42.4)
subtype3	73	37	0.2 - 314.6 (56.2)

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 = 0.205 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	259	55.6 (15.4)
subtype1	80	56.0 (14.6)
subtype2	106	57.2 (16.7)
subtype3	73	53.0 (14.3)

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 = 0.00705 (Chi-square test), Q value = 0.79

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	37	1	58	168
subtype1	9	1	15	57
subtype2	18	0	15	73
subtype3	10	0	28	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 = 0.526 (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	6	22	10	25	15	9	13	9	28	11	23	45	12
subtype1	4	4	7	5	3	4	1	1	3	11	2	8	14	4
subtype2	4	1	6	4	12	8	3	8	4	10	4	10	18	4
subtype3	2	1	9	1	10	3	5	4	2	7	5	5	13	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 = 6.59e-05 (Chi-square test), Q value = 0.0076

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

nPatients	T0+T1	T2	T3	T4
ALL	51	59	50	52
subtype1	26	9	8	17
subtype2	19	23	24	23
subtype3	6	27	18	12

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 = 0.719 (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	135	46	33	33
subtype1	36	12	11	14
subtype2	57	20	13	11
subtype3	42	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 = 0.931 (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	236	4	2	2	5
subtype1	71	1	1	1	1
subtype2	97	1	1	1	2
subtype3	68	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 = 0.703 (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	98	67
subtype1	24	20
subtype2	46	28
subtype3	28	19

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 = 0.0723 (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	33	232
subtype1	16	66
subtype2	11	96
subtype3	6	70

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 = 0.792 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	194	3.6 (5.1)
subtype1	49	3.4 (4.3)
subtype2	84	3.8 (4.2)
subtype3	61	3.3 (6.6)

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 = 0.328 (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	99	166
subtype1	36	46
subtype2	38	69
subtype3	25	51

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	81	46	113

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

P value = 0.128 (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	252	126	0.0 - 124.3 (13.9)
subtype1	23	8	0.2 - 114.2 (20.5)
subtype2	77	39	0.1 - 122.7 (17.6)
subtype3	45	25	0.1 - 110.8 (10.0)
subtype4	107	54	0.0 - 124.3 (13.1)

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 = 0.417 (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	258	127	0.2 - 357.4 (47.5)
subtype1	24	9	0.2 - 357.4 (44.5)
subtype2	77	39	4.9 - 346.0 (56.2)
subtype3	45	25	0.2 - 314.6 (53.3)
subtype4	112	54	2.6 - 248.7 (39.6)

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 = 0.738 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	259	55.6 (15.4)
subtype1	25	56.3 (17.2)
subtype2	77	54.6 (13.7)
subtype3	45	54.3 (16.5)
subtype4	112	56.7 (15.9)

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 = 0.0994 (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	37	1	58	168
subtype1	3	0	6	16
subtype2	9	0	23	49
subtype3	9	0	15	22
subtype4	16	1	14	81

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 = 0.142 (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	6	22	10	25	15	9	13	9	28	11	23	45	12
subtype1	1	0	1	1	1	3	2	0	1	1	1	5	3	2
subtype2	3	3	9	0	7	3	2	4	2	12	3	5	16	2
subtype3	1	0	5	1	7	1	2	1	3	3	6	2	8	4
subtype4	5	3	7	8	10	8	3	8	3	12	1	11	18	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 = 0.47 (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	51	59	50	52
subtype1	5	2	6	6
subtype2	15	18	12	14
subtype3	5	16	10	10
subtype4	26	23	22	22

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 = 0.966 (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	135	46	33	33
subtype1	12	5	4	2
subtype2	37	15	10	12
subtype3	23	7	7	6
subtype4	63	19	12	13

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 = 0.631 (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	236	4	2	2	5
subtype1	22	1	0	1	0
subtype2	72	1	0	0	1
subtype3	41	1	1	0	2
subtype4	101	1	1	1	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 = 0.815 (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	98	67
subtype1	8	7
subtype2	22	18
subtype3	21	14
subtype4	47	28

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 = 0.161 (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	33	232
subtype1	4	21
subtype2	14	67
subtype3	2	44
subtype4	13	100

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 = 0.769 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	194	3.6 (5.1)
subtype1	16	3.4 (3.4)
subtype2	52	4.0 (7.8)
subtype3	42	2.9 (2.5)
subtype4	84	3.6 (4.1)

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 = 0.687 (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	99	166
subtype1	11	14
subtype2	27	54
subtype3	16	30
subtype4	45	68

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 = 278
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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