Skin Cutaneous Melanoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 5 different clustering approaches and 3 clinical features across 137 patients, no significant finding detected with P value < 0.05.

4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 5 different clustering approaches and 3 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, no significant finding detected.


Clinical Features	Time to Death	AGE	GENDER
Statistical Tests	logrank test	ANOVA	Fisher's exact test
METHLYATION CNMF	0.413	0.313	0.0695
RNAseq CNMF subtypes	0.14	0.786	0.374
RNAseq cHierClus subtypes	0.644	0.189	0.762
MIRseq CNMF subtypes	0.167	0.526	0.917
MIRseq cHierClus subtypes	0.261	0.843	0.923

Clustering Approach #1: 'METHLYATION CNMF'

Table S1. Get Full Table Description of clustering approach #1: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	15	37	46	38

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.413 (logrank test)

Table S2. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	17	11	5.6 - 131.1 (44.0)
subtype1	4	3	12.6 - 131.1 (52.0)
subtype2	3	2	62.8 - 117.9 (80.3)
subtype3	7	4	5.6 - 84.7 (27.0)
subtype4	3	2	26.4 - 120.5 (32.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.313 (ANOVA)

Table S3. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	18	56.7 (16.5)
subtype1	4	47.8 (16.8)
subtype2	3	69.0 (12.3)
subtype3	8	53.8 (16.2)
subtype4	3	64.0 (17.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0695 (Fisher's exact test)

Table S4. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	45	91
subtype1	3	12
subtype2	15	22
subtype3	10	36
subtype4	17	21

Figure S3. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

Clustering Approach #2: 'RNAseq CNMF subtypes'

Table S5. Get Full Table Description of clustering approach #2: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	43	40	42

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.14 (logrank test)

Table S6. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	15	10	10.1 - 131.1 (44.0)
subtype1	5	5	15.3 - 117.9 (62.8)
subtype2	6	4	10.1 - 131.1 (36.0)
subtype3	4	1	27.0 - 84.7 (62.0)

Figure S4. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.786 (ANOVA)

Table S7. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	16	55.4 (17.0)
subtype1	5	57.4 (26.3)
subtype2	6	51.3 (13.5)
subtype3	5	58.2 (11.3)

Figure S5. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.374 (Fisher's exact test)

Table S8. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	43	82
subtype1	12	31
subtype2	17	23
subtype3	14	28

Figure S6. Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S9. Get Full Table Description of clustering approach #3: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	57	31	37

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.644 (logrank test)

Table S10. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	15	10	10.1 - 131.1 (44.0)
subtype1	6	3	15.3 - 84.7 (54.2)
subtype2	3	2	39.6 - 117.9 (62.8)
subtype3	6	5	10.1 - 131.1 (29.7)

Figure S7. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.189 (ANOVA)

Table S11. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	16	55.4 (17.0)
subtype1	7	51.1 (18.0)
subtype2	3	71.7 (11.5)
subtype3	6	52.2 (15.1)

Figure S8. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.762 (Fisher's exact test)

Table S12. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	43	82
subtype1	21	36
subtype2	9	22
subtype3	13	24

Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S13. Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	56	46	31

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.167 (logrank test)

Table S14. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	10	0.2 - 131.1 (53.4)
subtype1	3	3	62.8 - 117.9 (64.4)
subtype2	8	6	10.1 - 131.1 (29.7)
subtype3	5	1	0.2 - 120.5 (80.0)

Figure S10. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.526 (ANOVA)

Table S15. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	16	55.1 (16.6)
subtype1	3	60.3 (22.5)
subtype2	8	50.1 (17.1)
subtype3	5	59.8 (13.3)

Figure S11. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.917 (Fisher's exact test)

Table S16. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	44	89
subtype1	19	37
subtype2	14	32
subtype3	11	20

Figure S12. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S17. Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	16	29	88

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.261 (logrank test)

Table S18. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	10	0.2 - 131.1 (53.4)
subtype1	2	0	0.2 - 84.7 (42.5)
subtype2	6	4	10.1 - 80.0 (29.7)
subtype3	8	6	15.3 - 131.1 (72.4)

Figure S13. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.843 (ANOVA)

Table S19. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	16	55.1 (16.6)
subtype1	2	56.0 (4.2)
subtype2	6	56.0 (12.6)
subtype3	8	54.1 (21.8)

Figure S14. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.923 (Fisher's exact test)

Table S20. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	44	89
subtype1	6	10
subtype2	9	20
subtype3	29	59

Figure S15. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Methods & Data

Input

Cluster data file = SKCM.mergedcluster.txt
Clinical data file = SKCM.clin.merged.picked.txt
Number of patients = 137
Number of clustering approaches = 5
Number of selected clinical features = 3
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

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)

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