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

Skin Cutaneous Melanoma: Correlation between molecular cancer subtypes and selected clinical features
(All_Primary 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 7 clinical features across 31 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 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 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 2 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
2 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.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	GENDER	LYMPH NODE METASTASIS	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
Statistical Tests	logrank test	ANOVA	Chi-square test	Fisher's exact test	Chi-square test	ANOVA	Chi-square test
Copy Number Ratio CNMF subtypes	0.646 (1.00)	0.589 (1.00)	0.23 (1.00)	0.278 (1.00)	0.595 (1.00)		0.602 (1.00)
METHLYATION CNMF	0.365 (1.00)	0.839 (1.00)	0.203 (1.00)	0.115 (1.00)	0.576 (1.00)		0.644 (1.00)
RPPA CNMF subtypes		0.841 (1.00)	0.679 (1.00)	1 (1.00)	0.109 (1.00)		0.443 (1.00)
RPPA cHierClus subtypes		0.841 (1.00)	0.679 (1.00)	1 (1.00)	0.109 (1.00)		0.443 (1.00)
RNAseq CNMF subtypes	0.918 (1.00)	0.303 (1.00)	0.468 (1.00)	0.231 (1.00)	0.517 (1.00)		0.325 (1.00)
RNAseq cHierClus subtypes	0.918 (1.00)	0.215 (1.00)	0.492 (1.00)	0.399 (1.00)	0.366 (1.00)		0.212 (1.00)
MIRSEQ CNMF	0.302 (1.00)	0.776 (1.00)	0.132 (1.00)	1 (1.00)	0.485 (1.00)		0.586 (1.00)
MIRSEQ CHIERARCHICAL	0.336 (1.00)	0.201 (1.00)	0.617 (1.00)	1 (1.00)	0.708 (1.00)		0.523 (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
Number of samples	10	8	11

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

P value = 0.646 (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	22	5	0.0 - 27.0 (1.2)
subtype1	6	1	0.1 - 12.1 (0.9)
subtype2	8	1	0.0 - 24.0 (0.5)
subtype3	8	3	0.3 - 27.0 (7.8)

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 = 0.589 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	26	61.1 (15.3)
subtype1	8	61.9 (14.1)
subtype2	8	56.5 (10.9)
subtype3	10	64.1 (19.4)

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 = 0.23 (Chi-square test), Q value = 1

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

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	25	2
subtype1	2	7	1
subtype2	0	7	1
subtype3	0	11	0

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 = 0.278 (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	9	20
subtype1	5	5
subtype2	2	6
subtype3	2	9

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 'LYMPH.NODE.METASTASIS'

P value = 0.595 (Chi-square test), Q value = 1

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

nPatients	N0	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	15	1	1	1	3	1	3	3
subtype1	6	0	0	1	1	0	0	1
subtype2	4	1	1	0	1	0	1	0
subtype3	5	0	0	0	1	1	2	2

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

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

P value = 0.602 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	1	1	1	15	1	3	4	1
subtype1	1	0	1	5	0	1	0	0
subtype2	0	0	0	4	1	1	2	0
subtype3	0	1	0	6	0	1	2	1

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4	5
Number of samples	4	7	11	5	4

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.365 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	24	5	0.0 - 27.0 (1.2)
subtype1	4	0	0.1 - 3.9 (0.9)
subtype2	7	1	0.0 - 24.0 (7.8)
subtype3	6	3	0.5 - 27.0 (3.6)
subtype4	4	0	0.1 - 1.3 (0.3)
subtype5	3	1	0.4 - 12.1 (0.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.839 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	28	59.4 (16.0)
subtype1	4	64.5 (11.1)
subtype2	7	63.9 (15.8)
subtype3	8	56.9 (21.2)
subtype4	5	56.0 (14.0)
subtype5	4	56.0 (15.1)

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

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

P value = 0.203 (Chi-square test), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	27	2
subtype1	0	4	0
subtype2	0	5	2
subtype3	2	9	0
subtype4	0	5	0
subtype5	0	4	0

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.115 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	10	21
subtype1	2	2
subtype2	1	6
subtype3	4	7
subtype4	0	5
subtype5	3	1

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

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

P value = 0.576 (Chi-square test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	16	1	1	1	1	3	1	3	3
subtype1	2	0	0	0	0	1	0	1	0
subtype2	5	0	1	0	0	1	0	0	0
subtype3	3	0	0	0	1	1	1	2	2
subtype4	4	0	0	1	0	0	0	0	0
subtype5	2	1	0	0	0	0	0	0	1

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.644 (Chi-square test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	1	1	1	16	1	4	4	1
subtype1	0	0	0	2	0	1	1	0
subtype2	0	0	0	5	1	0	0	0
subtype3	0	1	0	4	0	2	2	1
subtype4	1	0	0	3	0	0	1	0
subtype5	0	0	1	2	0	1	0	0

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S15. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	6	2	4

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.841 (t-test), Q value = 1

Table S16. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	8	61.5 (11.8)
subtype1	5	62.2 (13.4)
subtype3	3	60.3 (11.2)

Figure S13. 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 = 0.679 (Chi-square test), Q value = 1

Table S17. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	7	1
subtype1	1	4	1
subtype3	1	3	0

Figure S14. 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 = 1 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	2	8
subtype1	1	5
subtype3	1	3

Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

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

P value = 0.109 (Chi-square test), Q value = 1

Table S19. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1A	N2A	N2B	N3	NX
ALL	2	1	1	3	1	1
subtype1	0	1	1	3	0	1
subtype3	2	0	0	0	1	0

Figure S16. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.443 (Chi-square test), Q value = 1

Table S20. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC
ALL	1	2	1	2	2
subtype1	0	1	1	2	1
subtype3	1	1	0	0	1

Figure S17. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S21. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	4	2	6

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.841 (t-test), Q value = 1

Table S22. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	8	61.5 (11.8)
subtype1	3	60.3 (11.2)
subtype3	5	62.2 (13.4)

Figure S18. 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 = 0.679 (Chi-square test), Q value = 1

Table S23. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	7	1
subtype1	1	3	0
subtype3	1	4	1

Figure S19. 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 = 1 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	2	8
subtype1	1	3
subtype3	1	5

Figure S20. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

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

P value = 0.109 (Chi-square test), Q value = 1

Table S25. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1A	N2A	N2B	N3	NX
ALL	2	1	1	3	1	1
subtype1	2	0	0	0	1	0
subtype3	0	1	1	3	0	1

Figure S21. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.443 (Chi-square test), Q value = 1

Table S26. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC
ALL	1	2	1	2	2
subtype1	1	1	0	0	1
subtype3	0	1	1	2	1

Figure S22. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S27. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2
Number of samples	16	11

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.918 (logrank test), Q value = 1

Table S28. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	20	4	0.0 - 27.0 (0.9)
subtype1	12	3	0.1 - 27.0 (2.5)
subtype2	8	1	0.0 - 24.0 (0.5)

Figure S23. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.303 (t-test), Q value = 1

Table S29. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	24	60.7 (15.9)
subtype1	13	63.8 (17.8)
subtype2	11	57.1 (13.1)

Figure S24. 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 = 0.468 (Chi-square test), Q value = 1

Table S30. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	23	2
subtype1	2	13	1
subtype2	0	10	1

Figure S25. 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 = 0.231 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	9	18
subtype1	7	9
subtype2	2	9

Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

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

P value = 0.517 (Chi-square test), Q value = 1

Table S32. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	14	1	1	1	3	1	2	3
subtype1	7	0	1	1	2	1	2	1
subtype2	7	1	0	0	1	0	0	2

Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.325 (Chi-square test), Q value = 1

Table S33. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	1	1	1	14	1	3	3	1
subtype1	0	1	0	7	0	2	3	1
subtype2	1	0	1	7	1	1	0	0

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S34. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	10	1	16

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.918 (logrank test), Q value = 1

Table S35. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	19	4	0.0 - 27.0 (0.5)
subtype1	7	1	0.0 - 24.0 (0.4)
subtype3	12	3	0.1 - 27.0 (2.5)

Figure S29. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.215 (t-test), Q value = 1

Table S36. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	23	60.2 (16.0)
subtype1	10	55.6 (12.8)
subtype3	13	63.8 (17.8)

Figure S30. 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 = 0.492 (Chi-square test), Q value = 1

Table S37. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	22	2
subtype1	0	9	1
subtype3	2	13	1

Figure S31. 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 = 0.399 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	9	17
subtype1	2	8
subtype3	7	9

Figure S32. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

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

P value = 0.366 (Chi-square test), Q value = 1

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

nPatients	N0	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	14	1	1	1	2	1	2	3
subtype1	7	1	0	0	0	0	0	2
subtype3	7	0	1	1	2	1	2	1

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.212 (Chi-square test), Q value = 1

Table S40. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	1	1	1	14	1	2	3	1
subtype1	1	0	1	7	1	0	0	0
subtype3	0	1	0	7	0	2	3	1

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S41. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2
Number of samples	15	13

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.302 (logrank test), Q value = 1

Table S42. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	21	4	0.0 - 27.0 (1.2)
subtype1	13	2	0.0 - 24.0 (0.5)
subtype2	8	2	0.2 - 27.0 (7.0)

Figure S35. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.776 (t-test), Q value = 1

Table S43. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	60.7 (15.5)
subtype1	15	61.5 (15.8)
subtype2	10	59.6 (15.9)

Figure S36. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.132 (Chi-square test), Q value = 1

Table S44. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	24	2
subtype1	0	13	2
subtype2	2	11	0

Figure S37. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'GENDER'

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

Table S45. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	9	19
subtype1	5	10
subtype2	4	9

Figure S38. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

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

P value = 0.485 (Chi-square test), Q value = 1

Table S46. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	15	1	1	1	3	1	2	3
subtype1	8	1	1	0	2	1	0	2
subtype2	7	0	0	1	1	0	2	1

Figure S39. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.586 (Chi-square test), Q value = 1

Table S47. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	1	1	1	15	1	3	3	1
subtype1	0	1	1	8	1	2	1	0
subtype2	1	0	0	7	0	1	2	1

Figure S40. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S48. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	9	6	13

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.336 (logrank test), Q value = 1

Table S49. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	21	4	0.0 - 27.0 (1.2)
subtype1	6	2	0.0 - 17.2 (6.3)
subtype2	5	1	0.2 - 27.0 (3.9)
subtype3	10	1	0.1 - 24.0 (0.9)

Figure S41. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.201 (ANOVA), Q value = 1

Table S50. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	25	60.7 (15.5)
subtype1	7	53.1 (18.6)
subtype2	5	69.4 (9.1)
subtype3	13	61.5 (14.7)

Figure S42. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

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

P value = 0.617 (Chi-square test), Q value = 1

Table S51. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	DISTANT METASTASIS	PRIMARY TUMOR	REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS)
ALL	2	24	2
subtype1	1	7	1
subtype2	1	5	0
subtype3	0	12	1

Figure S43. 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 S52. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	9	19
subtype1	3	6
subtype2	2	4
subtype3	4	9

Figure S44. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

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

P value = 0.708 (Chi-square test), Q value = 1

Table S53. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	15	1	1	1	3	1	2	3
subtype1	5	0	0	1	0	1	1	1
subtype2	3	0	0	0	1	0	1	0
subtype3	7	1	1	0	2	0	0	2

Figure S45. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.523 (Chi-square test), Q value = 1

Table S54. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	1	1	1	15	1	3	3	1
subtype1	0	0	0	6	0	2	0	1
subtype2	0	0	0	3	0	0	2	0
subtype3	1	1	1	6	1	1	1	0

Figure S46. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = SKCM-All_Primary.mergedcluster.txt
Clinical data file = SKCM-All_Primary.clin.merged.picked.txt
Number of patients = 31
Number of clustering approaches = 8
Number of selected clinical features = 7
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] 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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] 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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