Skin Cutaneous Melanoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(Regional

Skin Cutaneous Melanoma: Correlation between molecular cancer subtypes and selected clinical features
(Regional_Metastatic cohort)

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

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 8 clinical features across 152 patients, 3 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 do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 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 'PRIMARY.SITE.OF.DISEASE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'PRIMARY.SITE.OF.DISEASE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. 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 8 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 3 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	GENDER	DISTANT METASTASIS	LYMPH NODE METASTASIS	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Fisher's exact test	Chi-square test	Chi-square test	ANOVA	Chi-square test
Copy Number Ratio CNMF subtypes	0.104 (1.00)	0.124 (1.00)	0.338 (1.00)	0.575 (1.00)	0.535 (1.00)	0.527 (1.00)		0.395 (1.00)
METHLYATION CNMF	0.11 (1.00)	0.12 (1.00)	0.0552 (1.00)	0.081 (1.00)	0.699 (1.00)	0.418 (1.00)		0.403 (1.00)
RPPA CNMF subtypes	0.133 (1.00)	0.661 (1.00)	0.0295 (1.00)	0.766 (1.00)	0.513 (1.00)	0.165 (1.00)		0.036 (1.00)
RPPA cHierClus subtypes	0.221 (1.00)	0.897 (1.00)	0.0609 (1.00)	0.0632 (1.00)	0.427 (1.00)	0.0246 (1.00)		0.00868 (0.451)
RNAseq CNMF subtypes	0.0429 (1.00)	0.00657 (0.348)	1.6e-06 (8.98e-05)	0.703 (1.00)	0.782 (1.00)	0.625 (1.00)		0.0265 (1.00)
RNAseq cHierClus subtypes	0.204 (1.00)	0.0108 (0.552)	0.000359 (0.0198)	0.551 (1.00)	0.532 (1.00)	0.86 (1.00)		0.052 (1.00)
MIRSEQ CNMF	0.173 (1.00)	0.0885 (1.00)	0.00304 (0.164)	0.585 (1.00)	0.263 (1.00)	0.809 (1.00)		0.0444 (1.00)
MIRSEQ CHIERARCHICAL	0.283 (1.00)	0.964 (1.00)	0.373 (1.00)	1 (1.00)	0.274 (1.00)	0.548 (1.00)		0.296 (1.00)

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

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

Cluster Labels	1	2	3	4
Number of samples	24	26	41	56

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

P value = 0.104 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	83	44	1.0 - 98.8 (12.2)
subtype1	14	8	1.1 - 39.2 (7.9)
subtype2	16	9	2.9 - 45.7 (20.1)
subtype3	23	14	1.1 - 70.2 (11.1)
subtype4	30	13	1.0 - 98.8 (17.4)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.124 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	147	56.0 (16.0)
subtype1	24	61.0 (14.9)
subtype2	26	51.2 (15.6)
subtype3	41	58.0 (17.2)
subtype4	56	54.7 (15.2)

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

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

P value = 0.338 (Fisher's exact test), Q value = 1

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	35	112
subtype1	7	17
subtype2	8	18
subtype3	11	30
subtype4	9	47

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.575 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	52	95
subtype1	10	14
subtype2	10	16
subtype3	16	25
subtype4	16	40

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

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.535 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	M1C
ALL	128	1	2	2	1
subtype1	21	0	0	0	1
subtype2	22	0	0	1	0
subtype3	36	1	1	0	0
subtype4	49	0	1	1	0

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.527 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	77	2	6	12	1	4	11	4	16	2
subtype1	11	0	2	1	0	0	1	2	4	1
subtype2	12	0	1	3	0	2	1	0	3	1
subtype3	23	0	2	5	0	1	4	2	2	0
subtype4	31	2	1	3	1	1	5	0	7	0

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

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

P value = 0.395 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	16	8	13	17	7	8	4	8	5	16	21	4
subtype1	0	2	2	2	2	2	1	0	1	2	1	5	1
subtype2	1	1	2	2	4	0	0	2	0	1	5	3	0
subtype3	0	2	1	4	7	1	4	2	3	1	5	4	2
subtype4	0	11	3	5	4	4	3	0	4	1	5	9	1

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S9. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	43	57	52

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.11 (logrank test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	46	0.2 - 98.8 (11.8)
subtype1	26	15	0.2 - 45.7 (9.5)
subtype2	33	16	2.9 - 98.8 (17.6)
subtype3	29	15	0.2 - 53.8 (11.1)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.12 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	55.9 (15.9)
subtype1	43	55.5 (16.2)
subtype2	57	53.1 (13.9)
subtype3	52	59.4 (17.3)

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

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

P value = 0.0552 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	36	116
subtype1	16	27
subtype2	10	47
subtype3	10	42

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.081 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	55	97
subtype1	14	29
subtype2	16	41
subtype3	25	27

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.699 (Chi-square test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	133	1	2	2	1
subtype1	39	0	1	0	0
subtype2	50	0	1	1	1
subtype3	44	1	0	1	0

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.418 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	78	2	7	12	1	4	12	4	18	2
subtype1	23	0	4	4	1	1	4	2	2	0
subtype2	28	2	1	7	0	1	5	1	7	1
subtype3	27	0	2	1	0	2	3	1	9	1

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.403 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	16	8	14	17	7	8	4	9	5	17	23	4
subtype1	1	2	0	4	8	2	3	2	5	2	5	6	1
subtype2	0	10	3	5	5	3	1	0	4	1	7	8	2
subtype3	0	4	5	5	4	2	4	2	0	2	5	9	1

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S17. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	22	36	37

'RPPA CNMF subtypes' versus 'Time to Death'

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

Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	49	30	1.1 - 98.8 (13.3)
subtype1	17	9	3.5 - 84.7 (16.2)
subtype2	14	8	4.2 - 98.8 (19.3)
subtype3	18	13	1.1 - 78.4 (10.8)

Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.661 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	95	56.9 (15.9)
subtype1	22	55.3 (15.4)
subtype2	36	56.0 (15.9)
subtype3	37	58.8 (16.5)

Figure S16. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0295 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	15	80
subtype1	7	15
subtype2	2	34
subtype3	6	31

Figure S17. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.766 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	60
subtype1	9	13
subtype2	14	22
subtype3	12	25

Figure S18. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.513 (Chi-square test), Q value = 1

Table S22. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1B	M1C
ALL	78	1	2	1
subtype1	21	0	0	0
subtype2	23	1	1	1
subtype3	34	0	1	0

Figure S19. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.165 (Chi-square test), Q value = 1

Table S23. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	48	2	3	9	2	5	3	9	2
subtype1	9	0	3	3	0	2	0	3	1
subtype2	21	0	0	2	0	1	1	2	0
subtype3	18	2	0	4	2	2	2	4	1

Figure S20. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.036 (Chi-square test), Q value = 1

Table S24. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	11	7	7	9	5	4	2	5	2	9	11	3
subtype1	0	3	1	3	1	1	0	0	1	2	3	5	0
subtype2	1	7	2	1	3	3	1	2	1	0	2	0	3
subtype3	0	1	4	3	5	1	3	0	3	0	4	6	0

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S25. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	16	26	34	19

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.221 (logrank test), Q value = 1

Table S26. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	49	30	1.1 - 98.8 (13.3)
subtype1	12	7	8.4 - 84.7 (16.3)
subtype2	11	7	3.5 - 42.8 (14.7)
subtype3	15	12	1.1 - 78.4 (13.0)
subtype4	11	4	1.1 - 98.8 (12.0)

Figure S22. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.897 (ANOVA), Q value = 1

Table S27. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	95	56.9 (15.9)
subtype1	16	56.3 (16.6)
subtype2	26	56.1 (15.0)
subtype3	34	56.5 (16.3)
subtype4	19	59.5 (17.0)

Figure S23. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0609 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	15	80
subtype1	5	11
subtype2	4	22
subtype3	6	28
subtype4	0	19

Figure S24. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0632 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	60
subtype1	4	12
subtype2	7	19
subtype3	12	22
subtype4	12	7

Figure S25. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.427 (Chi-square test), Q value = 1

Table S30. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1B	M1C
ALL	78	1	2	1
subtype1	15	0	0	0
subtype2	15	1	1	1
subtype3	29	0	1	0
subtype4	19	0	0	0

Figure S26. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0246 (Chi-square test), Q value = 1

Table S31. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	48	2	3	9	2	5	3	9	2
subtype1	6	0	3	3	0	1	0	2	0
subtype2	14	0	0	1	0	0	1	3	0
subtype3	17	1	0	4	0	2	2	4	0
subtype4	11	1	0	1	2	2	0	0	2

Figure S27. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00868 (Chi-square test), Q value = 0.45

Table S32. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	11	7	7	9	5	4	2	5	2	9	11	3
subtype1	0	2	1	2	0	1	0	0	1	2	3	3	0
subtype2	1	5	0	1	0	4	1	1	0	0	1	1	3
subtype3	0	1	4	3	6	0	3	0	3	0	2	6	0
subtype4	0	3	2	1	3	0	0	1	1	0	3	1	0

Figure S28. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S33. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	56	38	51

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0429 (logrank test), Q value = 1

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	81	44	1.0 - 98.8 (12.2)
subtype1	30	19	1.7 - 70.2 (13.5)
subtype2	21	7	1.1 - 98.8 (21.1)
subtype3	30	18	1.0 - 53.8 (10.5)

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00657 (ANOVA), Q value = 0.35

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	145	56.0 (16.1)
subtype1	56	51.4 (15.9)
subtype2	38	55.9 (15.3)
subtype3	51	61.1 (15.6)

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1.6e-06 (Fisher's exact test), Q value = 9e-05

Table S36. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	33	112
subtype1	23	33
subtype2	0	38
subtype3	10	41

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.703 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	51	94
subtype1	22	34
subtype2	12	26
subtype3	17	34

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.782 (Chi-square test), Q value = 1

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	126	1	2	2	1
subtype1	47	1	1	1	1
subtype2	35	0	0	0	0
subtype3	44	0	1	1	0

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.625 (Chi-square test), Q value = 1

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	77	2	5	12	1	4	10	4	16	2
subtype1	28	1	2	4	0	1	3	3	7	2
subtype2	25	1	1	2	0	0	3	0	3	0
subtype3	24	0	2	6	1	3	4	1	6	0

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0265 (Chi-square test), Q value = 1

Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	16	8	13	17	7	8	4	8	4	15	21	4
subtype1	1	7	0	5	9	2	3	0	5	1	4	8	4
subtype2	0	7	3	3	6	4	0	1	1	0	5	4	0
subtype3	0	2	5	5	2	1	5	3	2	3	6	9	0

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S41. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	48	64	33

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.204 (logrank test), Q value = 1

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	81	44	1.0 - 98.8 (12.2)
subtype1	25	16	1.7 - 84.7 (13.7)
subtype2	35	14	1.0 - 98.8 (12.0)
subtype3	21	14	1.1 - 53.8 (10.8)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0108 (ANOVA), Q value = 0.55

Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	145	56.0 (16.1)
subtype1	48	50.9 (15.5)
subtype2	64	56.9 (15.4)
subtype3	33	61.5 (16.5)

Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.000359 (Fisher's exact test), Q value = 0.02

Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	33	112
subtype1	16	32
subtype2	5	59
subtype3	12	21

Figure S38. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.551 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	51	94
subtype1	17	31
subtype2	20	44
subtype3	14	19

Figure S39. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.532 (Chi-square test), Q value = 1

Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	126	1	2	2	1
subtype1	39	0	1	1	1
subtype2	59	0	1	1	0
subtype3	28	1	0	0	0

Figure S40. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.86 (Chi-square test), Q value = 1

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	77	2	5	12	1	4	10	4	16	2
subtype1	27	0	1	4	0	1	2	2	5	0
subtype2	35	1	3	6	1	2	4	0	7	2
subtype3	15	1	1	2	0	1	4	2	4	0

Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.052 (Chi-square test), Q value = 1

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	16	8	13	17	7	8	4	8	4	15	21	4
subtype1	1	7	0	6	8	3	1	0	3	1	2	7	3
subtype2	0	8	7	5	7	3	2	1	2	2	9	8	0
subtype3	0	1	1	2	2	1	5	3	3	1	4	6	1

Figure S42. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S49. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	56	29	59

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.173 (logrank test), Q value = 1

Table S50. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	44	1.0 - 98.8 (12.2)
subtype1	27	16	1.1 - 53.8 (8.9)
subtype2	17	8	5.4 - 84.7 (17.6)
subtype3	36	20	1.0 - 98.8 (11.9)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0885 (ANOVA), Q value = 1

Table S51. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	144	55.9 (15.8)
subtype1	56	58.9 (16.0)
subtype2	29	57.0 (16.5)
subtype3	59	52.5 (14.8)

Figure S44. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.00304 (Fisher's exact test), Q value = 0.16

Table S52. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	34	110
subtype1	6	50
subtype2	6	23
subtype3	22	37

Figure S45. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.585 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	94
subtype1	20	36
subtype2	12	17
subtype3	18	41

Figure S46. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.263 (Chi-square test), Q value = 1

Table S54. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	125	1	2	2	1
subtype1	50	0	1	1	0
subtype2	21	0	1	1	1
subtype3	54	1	0	0	0

Figure S47. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.809 (Chi-square test), Q value = 1

Table S55. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	75	2	6	12	1	4	11	4	15	2
subtype1	31	0	2	5	0	2	5	1	6	0
subtype2	12	0	1	3	0	2	1	1	4	1
subtype3	32	2	3	4	1	0	5	2	5	1

Figure S48. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0444 (Chi-square test), Q value = 1

Table S56. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	16	8	13	16	6	8	4	8	5	16	20	4
subtype1	0	4	5	4	9	3	5	0	0	2	7	9	1
subtype2	1	2	1	0	4	1	0	2	1	1	5	3	2
subtype3	0	10	2	9	3	2	3	2	7	2	4	8	1

Figure S49. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S57. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	32	18	94

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.283 (logrank test), Q value = 1

Table S58. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	80	44	1.0 - 98.8 (12.2)
subtype1	18	11	3.5 - 78.4 (14.3)
subtype2	11	4	5.4 - 84.7 (19.4)
subtype3	51	29	1.0 - 98.8 (10.3)

Figure S50. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.964 (ANOVA), Q value = 1

Table S59. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	144	55.9 (15.8)
subtype1	32	55.3 (12.6)
subtype2	18	56.6 (16.6)
subtype3	94	56.0 (16.7)

Figure S51. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

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

P value = 0.373 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)	REGIONAL LYMPH NODE
ALL	34	110
subtype1	10	22
subtype2	5	13
subtype3	19	75

Figure S52. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S61. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	94
subtype1	11	21
subtype2	6	12
subtype3	33	61

Figure S53. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.274 (Chi-square test), Q value = 1

Table S62. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	125	1	2	2	1
subtype1	28	0	0	0	1
subtype2	14	0	1	1	0
subtype3	83	1	1	1	0

Figure S54. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.548 (Chi-square test), Q value = 1

Table S63. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	75	2	6	12	1	4	11	4	15	2
subtype1	16	1	1	3	0	0	1	1	5	1
subtype2	6	0	1	2	0	2	2	0	2	1
subtype3	53	1	4	7	1	2	8	3	8	0

Figure S55. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.296 (Chi-square test), Q value = 1

Table S64. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients	I OR II NOS	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	1	16	8	13	16	6	8	4	8	5	16	20	4
subtype1	0	7	1	2	2	1	2	1	3	1	1	6	1
subtype2	0	0	1	0	3	1	0	1	0	1	6	1	1
subtype3	1	9	6	11	11	4	6	2	5	3	9	13	2

Figure S56. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = SKCM-Regional_Metastatic.mergedcluster.txt
Clinical data file = SKCM-Regional_Metastatic.clin.merged.picked.txt
Number of patients = 152
Number of clustering approaches = 8
Number of selected clinical features = 8
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

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

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] 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)

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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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