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 8 different clustering approaches and 3 clinical features across 138 patients, no significant finding detected with P value < 0.05.

- 4 subtypes identified in current cancer cohort by 'CN CNMF'. 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 3 subtypes that 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 8 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
CN CNMF | 0.349 | 0.652 | 0.279
METHLYATION CNMF | 0.247 | 0.291 | 0.283
RPPA CNMF subtypes | 0.143 | 0.365 | 0.93
RPPA cHierClus subtypes | 0.165 | 0.585 | 0.313
RNAseq CNMF subtypes | 0.322 | 0.541 | 0.701
RNAseq cHierClus subtypes | 0.507 | 0.316 | 0.192
MIRseq CNMF subtypes | 0.167 | 0.526 | 0.945
MIRseq cHierClus subtypes | 0.261 | 0.843 | 0.925

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 48 | 22 | 42 | 26

'CN CNMF' versus 'Time to Death'

P value = 0.349 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 18 | 11 | 0.2 - 131.1 (41.8)
subtype1 | 8 | 4 | 0.2 - 131.1 (41.8)
subtype2 | 6 | 4 | 5.6 - 120.5 (21.2)
subtype3 | 3 | 3 | 32.5 - 117.9 (64.4)
subtype4 | 1 | 0 | 80.0 - 80.0 (80.0)

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

'CN CNMF' versus 'AGE'

P value = 0.652 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 56.8 (16.0)
subtype1 | 8 | 61.0 (17.5)
subtype2 | 6 | 52.7 (19.0)
subtype3 | 3 | 53.7 (13.7)
subtype4 | 2 | 57.0 (1.4)

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

'CN CNMF' versus 'GENDER'

P value = 0.279 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 46 | 92
subtype1 | 20 | 28
subtype2 | 4 | 18
subtype3 | 13 | 29
subtype4 | 9 | 17

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 34 | 52 | 52

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.247 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 18 | 11 | 0.2 - 131.1 (41.8)
subtype1 | 6 | 3 | 0.2 - 131.1 (52.0)
subtype2 | 4 | 3 | 26.4 - 120.5 (90.3)
subtype3 | 8 | 5 | 5.6 - 84.7 (29.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.291 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 56.8 (16.0)
subtype1 | 6 | 52.3 (14.9)
subtype2 | 4 | 68.0 (17.6)
subtype3 | 9 | 54.8 (15.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.283 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 46 | 92
subtype1 | 13 | 21
subtype2 | 20 | 32
subtype3 | 13 | 39

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

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S9. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 40 | 34 | 25

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.143 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 16 | 11 | 5.6 - 131.1 (41.8)
subtype1 | 8 | 5 | 10.1 - 131.1 (41.8)
subtype2 | 2 | 1 | 62.8 - 80.0 (71.4)
subtype3 | 6 | 5 | 5.6 - 84.7 (23.9)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.365 (ANOVA)

Table S11. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 17 | 56.2 (16.9)
subtype1 | 8 | 58.1 (17.2)
subtype2 | 3 | 65.7 (15.0)
subtype3 | 6 | 49.0 (16.8)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.93 (Fisher's exact test)

Table S12. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 35 | 64
subtype1 | 15 | 25
subtype2 | 11 | 23
subtype3 | 9 | 16

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S13. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 36 | 40 | 23

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.165 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 16 | 11 | 5.6 - 131.1 (41.8)
subtype1 | 3 | 1 | 62.8 - 120.5 (80.0)
subtype2 | 8 | 6 | 10.1 - 131.1 (33.3)
subtype3 | 5 | 4 | 5.6 - 84.7 (32.5)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.585 (ANOVA)

Table S15. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 17 | 56.2 (16.9)
subtype1 | 3 | 62.7 (18.4)
subtype2 | 8 | 58.0 (17.3)
subtype3 | 6 | 50.7 (16.9)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.313 (Fisher's exact test)

Table S16. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 35 | 64
subtype1 | 14 | 22
subtype2 | 16 | 24
subtype3 | 5 | 18

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S17. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 46 | 41 | 47

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.322 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 16 | 10 | 0.2 - 131.1 (41.8)
subtype1 | 7 | 5 | 0.2 - 117.9 (39.6)
subtype2 | 4 | 3 | 12.6 - 131.1 (76.5)
subtype3 | 5 | 2 | 10.1 - 84.7 (44.0)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.541 (ANOVA)

Table S19. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 17 | 55.6 (16.5)
subtype1 | 7 | 59.7 (22.1)
subtype2 | 4 | 47.8 (11.5)
subtype3 | 6 | 56.0 (11.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.701 (Fisher's exact test)

Table S20. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 45 | 89
subtype1 | 14 | 32
subtype2 | 16 | 25
subtype3 | 15 | 32

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S21. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 33 | 68 | 33

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.507 (logrank test)

Table S22. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 16 | 10 | 0.2 - 131.1 (41.8)
subtype1 | 3 | 2 | 0.2 - 117.9 (62.8)
subtype2 | 9 | 5 | 10.1 - 84.7 (39.6)
subtype3 | 4 | 3 | 12.6 - 131.1 (76.5)

Figure S16. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.316 (ANOVA)

Table S23. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 17 | 55.6 (16.5)
subtype1 | 3 | 67.3 (13.6)
subtype2 | 10 | 55.2 (18.0)
subtype3 | 4 | 47.8 (11.5)

Figure S17. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.192 (Fisher's exact test)

Table S24. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 45 | 89
subtype1 | 8 | 25
subtype2 | 22 | 46
subtype3 | 15 | 18

Figure S18. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 57 | 45 | 31

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.167 (logrank test)

Table S26. Clustering Approach #7: '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 S19. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.526 (ANOVA)

Table S27. Clustering Approach #7: '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 S20. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.945 (Fisher's exact test)

Table S28. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 44 | 89
subtype1 | 19 | 38
subtype2 | 14 | 31
subtype3 | 11 | 20

Figure S21. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S29. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 16 | 87 | 30

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.261 (logrank test)

Table S30. Clustering Approach #8: '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 | 8 | 6 | 15.3 - 131.1 (72.4)
subtype3 | 6 | 4 | 10.1 - 80.0 (29.7)

Figure S22. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.843 (ANOVA)

Table S31. Clustering Approach #8: '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 | 8 | 54.1 (21.8)
subtype3 | 6 | 56.0 (12.6)

Figure S23. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.925 (Fisher's exact test)

Table S32. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 44 | 89
subtype1 | 6 | 10
subtype2 | 28 | 59
subtype3 | 10 | 20

Figure S24. Get High-res Image Clustering Approach #8: '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 = 138

- Number of clustering approaches = 8

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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