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_Samples 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 9 clinical features across 211 patients, 6 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 to Death'.
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 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'PRIMARY.SITE.OF.DISEASE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'PRIMARY.SITE.OF.DISEASE', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
2 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 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 6 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
Time to Death	logrank test	0.134 (1.00)	0.00167 (0.102)	0.0777 (1.00)	0.191 (1.00)	0.0541 (1.00)	0.00198 (0.119)	0.369 (1.00)	0.363 (1.00)
AGE	t-test	0.403 (1.00)	0.151 (1.00)	0.221 (1.00)	0.855 (1.00)	0.0861 (1.00)	0.00232 (0.137)	0.0691 (1.00)	0.435 (1.00)
PRIMARY SITE OF DISEASE	Chi-square test	0.656 (1.00)	0.479 (1.00)	0.0322 (1.00)	0.11 (1.00)	2.39e-11 (1.51e-09)	1.37e-11 (8.77e-10)	0.0108 (0.615)	0.0374 (1.00)
GENDER	Fisher's exact test	0.858 (1.00)	0.18 (1.00)	0.498 (1.00)	0.861 (1.00)	0.457 (1.00)	0.433 (1.00)	0.12 (1.00)	0.358 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.0595 (1.00)	0.632 (1.00)	0.237 (1.00)	0.794 (1.00)	0.3 (1.00)	1 (1.00)	1 (1.00)	0.295 (1.00)
DISTANT METASTASIS	Chi-square test	0.603 (1.00)	0.397 (1.00)	0.518 (1.00)	0.498 (1.00)	0.268 (1.00)	0.44 (1.00)	0.624 (1.00)	0.966 (1.00)
LYMPH NODE METASTASIS	Chi-square test	0.253 (1.00)	0.575 (1.00)	0.0975 (1.00)	0.294 (1.00)	0.457 (1.00)	0.748 (1.00)	0.79 (1.00)	0.396 (1.00)
TUMOR STAGECODE	t-test
NEOPLASM DISEASESTAGE	Chi-square test	0.704 (1.00)	0.383 (1.00)	0.211 (1.00)	0.0732 (1.00)	0.00776 (0.45)	0.000426 (0.0264)	0.195 (1.00)	0.337 (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	43	65	40	54

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

P value = 0.134 (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	114	55	0.1 - 98.8 (10.5)
subtype1	26	12	0.3 - 72.2 (12.5)
subtype2	36	18	0.1 - 51.1 (7.5)
subtype3	19	8	1.0 - 98.8 (15.3)
subtype4	33	17	0.2 - 48.8 (9.3)

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 = 0.403 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	198	56.8 (16.1)
subtype1	40	53.2 (17.3)
subtype2	65	58.0 (17.0)
subtype3	40	58.7 (14.0)
subtype4	53	56.4 (15.5)

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 = 0.656 (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)	REGIONAL LYMPH NODE
ALL	27	26	37	112
subtype1	6	8	8	21
subtype2	11	8	14	32
subtype3	4	5	4	27
subtype4	6	5	11	32

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 = 0.858 (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	76	126
subtype1	17	26
subtype2	24	41
subtype3	13	27
subtype4	22	32

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 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0595 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	199
subtype1	1	42
subtype2	0	65
subtype3	2	38
subtype4	0	54

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

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

P value = 0.603 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	M1C
ALL	176	2	2	2	3
subtype1	34	0	0	1	2
subtype2	57	1	1	0	0
subtype3	38	0	0	0	0
subtype4	47	1	1	1	1

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

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

P value = 0.253 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	110	2	8	17	1	5	14	6	19	5
subtype1	24	0	0	2	0	1	4	0	6	1
subtype2	36	0	5	8	0	1	3	2	5	0
subtype3	23	2	1	2	1	0	4	2	2	1
subtype4	27	0	2	5	0	3	3	2	6	3

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.704 (Chi-square test), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: '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	4	17	10	15	20	9	10	23	9	7	21	27	7
subtype1	1	2	2	2	5	3	3	6	2	0	2	6	3
subtype2	2	3	3	5	8	3	3	7	3	4	5	9	2
subtype3	0	8	1	4	2	3	2	3	3	0	6	5	0
subtype4	1	4	4	4	5	0	2	7	1	3	8	7	2

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S10. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	56	78	77

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00167 (logrank test), Q value = 0.1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	123	59	0.1 - 98.8 (10.1)
subtype1	34	19	0.1 - 45.7 (6.8)
subtype2	45	19	0.1 - 53.8 (8.1)
subtype3	44	21	0.5 - 98.8 (15.3)

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.151 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	207	56.5 (16.0)
subtype1	56	56.5 (16.6)
subtype2	77	59.0 (16.2)
subtype3	74	53.9 (15.1)

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

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

P value = 0.479 (Chi-square test), Q value = 1

Table S13. 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)	REGIONAL LYMPH NODE
ALL	29	28	38	116
subtype1	11	8	11	26
subtype2	11	10	16	41
subtype3	7	10	11	49

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.18 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	131
subtype1	23	33
subtype2	34	44
subtype3	23	54

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.632 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	208
subtype1	0	56
subtype2	1	77
subtype3	2	75

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.397 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	185	2	2	2	3
subtype1	49	1	1	0	0
subtype2	69	1	0	1	0
subtype3	67	0	1	1	3

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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

P value = 0.575 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	114	3	9	17	1	5	15	6	21	5
subtype1	32	1	4	6	0	0	4	1	3	1
subtype2	45	0	3	2	1	3	5	3	9	1
subtype3	37	2	2	9	0	2	6	2	9	3

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.383 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: '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	4	18	10	16	20	10	10	24	10	7	23	29	7
subtype1	1	3	1	4	6	3	5	8	3	2	6	8	2
subtype2	3	4	6	6	5	4	4	12	2	4	7	10	1
subtype3	0	11	3	6	9	3	1	4	5	1	10	11	4

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S19. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	57	31	33

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0777 (logrank test), Q value = 1

Table S20. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	61	37	0.4 - 98.8 (13.0)
subtype1	22	11	1.1 - 98.8 (18.6)
subtype2	16	12	0.4 - 78.4 (9.9)
subtype3	23	14	0.5 - 84.7 (15.3)

Figure S17. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.221 (ANOVA), Q value = 1

Table S21. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	119	56.4 (16.4)
subtype1	56	56.6 (16.4)
subtype2	31	59.9 (16.1)
subtype3	32	52.7 (16.4)

Figure S18. 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 = 0.0322 (Chi-square test), Q value = 1

Table S22. 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)	REGIONAL LYMPH NODE
ALL	15	9	17	80
subtype1	5	4	3	45
subtype2	5	1	5	20
subtype3	5	4	9	15

Figure S19. 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 = 0.498 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	46	75
subtype1	23	34
subtype2	9	22
subtype3	14	19

Figure S20. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.237 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	118
subtype1	1	56
subtype2	2	29
subtype3	0	33

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

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

Table S25. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1B	M1C
ALL	99	1	2	3
subtype1	44	1	0	1
subtype2	27	0	1	0
subtype3	28	0	1	2

Figure S22. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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

P value = 0.0975 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	59	2	4	13	3	8	4	11	3
subtype1	34	1	1	3	2	3	1	1	1
subtype2	11	0	0	6	1	4	2	4	1
subtype3	14	1	3	4	0	1	1	6	1

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.211 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: '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	3	11	8	9	10	5	5	6	6	3	13	15	5
subtype1	2	7	4	5	6	4	2	3	3	1	5	1	2
subtype2	1	1	3	0	3	0	2	1	1	0	4	8	0
subtype3	0	3	1	4	1	1	1	2	2	2	4	6	3

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S28. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	36	55	30

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.191 (logrank test), Q value = 1

Table S29. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	61	37	0.4 - 98.8 (13.0)
subtype1	16	7	1.4 - 98.8 (19.3)
subtype2	25	17	0.4 - 78.4 (11.4)
subtype3	20	13	0.5 - 84.7 (14.5)

Figure S25. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.855 (ANOVA), Q value = 1

Table S30. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	119	56.4 (16.4)
subtype1	36	56.7 (15.1)
subtype2	54	57.0 (17.0)
subtype3	29	54.9 (17.3)

Figure S26. 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 = 0.11 (Chi-square test), Q value = 1

Table S31. 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)	REGIONAL LYMPH NODE
ALL	15	9	17	80
subtype1	2	2	4	28
subtype2	7	3	6	39
subtype3	6	4	7	13

Figure S27. 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 = 0.861 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	46	75
subtype1	14	22
subtype2	22	33
subtype3	10	20

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

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.794 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	118
subtype1	1	35
subtype2	2	53
subtype3	0	30

Figure S29. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.498 (Chi-square test), Q value = 1

Table S34. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1B	M1C
ALL	99	1	2	3
subtype1	26	0	1	1
subtype2	47	1	1	0
subtype3	26	0	0	2

Figure S30. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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

P value = 0.294 (Chi-square test), Q value = 1

Table S35. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2A	N2B	N2C	N3	NX
ALL	59	2	4	13	3	8	4	11	3
subtype1	21	0	0	1	0	3	1	2	1
subtype2	27	1	1	6	3	3	2	5	2
subtype3	11	1	3	6	0	2	1	4	0

Figure S31. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0732 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: '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	3	11	8	9	10	5	5	6	6	3	13	15	5
subtype1	2	6	2	2	1	4	1	2	0	0	3	2	2
subtype2	1	3	5	4	9	0	3	2	4	1	5	6	1
subtype3	0	2	1	3	0	1	1	2	2	2	5	7	2

Figure S32. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S37. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	32	56	60	50

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0541 (logrank test), Q value = 1

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	110	54	0.1 - 98.8 (10.5)
subtype1	22	9	0.1 - 45.7 (1.9)
subtype2	31	17	0.1 - 53.8 (10.8)
subtype3	30	18	0.4 - 70.2 (12.6)
subtype4	27	10	1.0 - 98.8 (21.1)

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0861 (ANOVA), Q value = 1

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	194	56.6 (16.2)
subtype1	32	57.9 (17.6)
subtype2	54	60.0 (15.4)
subtype3	59	52.4 (16.3)
subtype4	49	57.2 (15.5)

Figure S34. 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 = 2.39e-11 (Chi-square test), Q value = 1.5e-09

Table S40. 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)	REGIONAL LYMPH NODE
ALL	27	24	35	112
subtype1	6	15	6	5
subtype2	8	6	6	36
subtype3	9	2	19	30
subtype4	4	1	4	41

Figure S35. 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 = 0.457 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	123
subtype1	10	22
subtype2	22	34
subtype3	27	33
subtype4	16	34

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.3 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	195
subtype1	0	32
subtype2	1	55
subtype3	0	60
subtype4	2	48

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.268 (Chi-square test), Q value = 1

Table S43. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	172	2	2	2	3
subtype1	27	2	0	0	1
subtype2	49	0	1	1	0
subtype3	51	0	1	1	1
subtype4	45	0	0	0	1

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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

P value = 0.457 (Chi-square test), Q value = 1

Table S44. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	109	2	7	17	1	5	13	6	18	5
subtype1	17	1	1	2	0	0	2	2	2	3
subtype2	32	0	2	6	0	3	4	0	6	0
subtype3	30	0	3	5	0	1	3	4	6	2
subtype4	30	1	1	4	1	1	4	0	4	0

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00776 (Chi-square test), Q value = 0.45

Table S45. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: '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	4	17	10	15	20	9	10	22	9	6	20	26	7
subtype1	0	1	0	1	3	2	1	11	1	1	3	3	3
subtype2	2	2	3	5	4	1	6	8	1	3	6	8	0
subtype3	2	7	1	5	7	2	3	2	4	2	5	9	3
subtype4	0	7	6	4	6	4	0	1	3	0	6	6	1

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S46. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	59	77	62

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00198 (logrank test), Q value = 0.12

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	110	54	0.1 - 98.8 (10.5)
subtype1	38	20	0.1 - 53.8 (4.6)
subtype2	41	16	1.0 - 98.8 (14.7)
subtype3	31	18	0.4 - 70.2 (13.3)

Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00232 (ANOVA), Q value = 0.14

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	194	56.6 (16.2)
subtype1	58	62.6 (15.1)
subtype2	75	55.2 (16.5)
subtype3	61	52.8 (15.6)

Figure S42. 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 = 1.37e-11 (Chi-square test), Q value = 8.8e-10

Table S49. 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)	REGIONAL LYMPH NODE
ALL	27	24	35	112
subtype1	14	17	11	17
subtype2	5	5	3	64
subtype3	8	2	21	31

Figure S43. 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 = 0.433 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	123
subtype1	25	34
subtype2	25	52
subtype3	25	37

Figure S44. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S51. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	195
subtype1	1	58
subtype2	1	76
subtype3	1	61

Figure S45. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.44 (Chi-square test), Q value = 1

Table S52. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	172	2	2	2	3
subtype1	53	2	0	0	0
subtype2	67	0	1	1	2
subtype3	52	0	1	1	1

Figure S46. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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

P value = 0.748 (Chi-square test), Q value = 1

Table S53. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	109	2	7	17	1	5	13	6	18	5
subtype1	35	0	1	5	0	0	5	2	5	3
subtype2	43	1	3	7	1	3	5	0	8	1
subtype3	31	1	3	5	0	2	3	4	5	1

Figure S47. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000426 (Chi-square test), Q value = 0.026

Table S54. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: '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	4	17	10	15	20	9	10	22	9	6	20	26	7
subtype1	1	1	1	2	5	4	6	17	0	1	6	9	2
subtype2	1	7	8	7	8	4	1	4	4	2	10	8	2
subtype3	2	9	1	6	7	1	3	1	5	3	4	9	3

Figure S48. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S55. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	87	57	53

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.369 (logrank test), Q value = 1

Table S56. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	109	53	0.1 - 98.8 (11.1)
subtype1	46	21	0.1 - 53.8 (8.8)
subtype2	33	19	0.3 - 98.8 (10.0)
subtype3	30	13	0.1 - 84.7 (14.8)

Figure S49. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0691 (ANOVA), Q value = 1

Table S57. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	193	56.7 (16.0)
subtype1	85	58.4 (16.0)
subtype2	55	52.5 (16.8)
subtype3	53	58.2 (14.4)

Figure S50. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.0108 (Chi-square test), Q value = 0.62

Table S58. 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)	REGIONAL LYMPH NODE
ALL	26	25	36	110
subtype1	12	16	10	49
subtype2	9	7	17	24
subtype3	5	2	9	37

Figure S51. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.12 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	73	124
subtype1	32	55
subtype2	16	41
subtype3	25	28

Figure S52. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S60. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	194
subtype1	1	86
subtype2	1	56
subtype3	1	52

Figure S53. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.624 (Chi-square test), Q value = 1

Table S61. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	171	2	2	2	3
subtype1	78	1	1	1	0
subtype2	52	0	0	0	2
subtype3	41	1	1	1	1

Figure S54. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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

P value = 0.79 (Chi-square test), Q value = 1

Table S62. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	107	2	8	17	1	5	14	6	17	5
subtype1	50	1	2	9	0	3	7	2	7	1
subtype2	32	1	4	4	1	0	5	2	3	2
subtype3	25	0	2	4	0	2	2	2	7	2

Figure S55. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.195 (Chi-square test), Q value = 1

Table S63. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: '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	4	17	10	15	19	8	10	22	9	7	21	25	7
subtype1	1	4	5	5	11	4	7	13	2	3	10	11	2
subtype2	1	8	1	8	3	3	2	6	6	3	3	7	2
subtype3	2	5	4	2	5	1	1	3	1	1	8	7	3

Figure S56. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S64. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2
Number of samples	126	71

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.363 (logrank test), Q value = 1

Table S65. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	109	53	0.1 - 98.8 (11.1)
subtype1	66	31	0.1 - 98.8 (9.6)
subtype2	43	22	0.2 - 84.7 (13.0)

Figure S57. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.435 (t-test), Q value = 1

Table S66. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	193	56.7 (16.0)
subtype1	123	57.3 (16.5)
subtype2	70	55.5 (15.0)

Figure S58. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

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

P value = 0.0374 (Chi-square test), Q value = 1

Table S67. 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)	REGIONAL LYMPH NODE
ALL	26	25	36	110
subtype1	17	22	19	68
subtype2	9	3	17	42

Figure S59. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.358 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	73	124
subtype1	50	76
subtype2	23	48

Figure S60. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.295 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	194
subtype1	1	125
subtype2	2	69

Figure S61. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.966 (Chi-square test), Q value = 1

Table S70. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	M1C
ALL	171	2	2	2	3
subtype1	111	1	1	1	2
subtype2	60	1	1	1	1

Figure S62. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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

P value = 0.396 (Chi-square test), Q value = 1

Table S71. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	N2C	N3	NX
ALL	107	2	8	17	1	5	14	6	17	5
subtype1	76	1	5	10	0	3	10	2	8	3
subtype2	31	1	3	7	1	2	4	4	9	2

Figure S63. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.337 (Chi-square test), Q value = 1

Table S72. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: '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	4	17	10	15	19	8	10	22	9	7	21	25	7
subtype1	2	9	8	11	14	5	8	18	3	4	13	13	4
subtype2	2	8	2	4	5	3	2	4	6	3	8	12	3

Figure S64. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = SKCM-All_Samples.mergedcluster.txt
Clinical data file = SKCM-All_Samples.clin.merged.picked.txt
Number of patients = 211
Number of clustering approaches = 8
Number of selected clinical features = 9
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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