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.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[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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