Skin Cutaneous Melanoma: Correlation between molecular cancer subtypes and selected clinical features
(metastatic tumor 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 165 patients, one 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.

  • 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 4 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • 3 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, one significant finding detected.

Clinical
Features 		Time
to
Death 		AGE GENDER DISTANT
METASTASIS 		LYMPH
NODE
METASTASIS 		TUMOR
STAGECODE 		NEOPLASM
DISEASESTAGE
Statistical Tests logrank test ANOVA Fisher's exact test Chi-square test Chi-square test ANOVA Chi-square test
Copy Number Ratio CNMF subtypes 0.264
(1.00) 		0.269
(1.00) 		0.734
(1.00) 		0.593
(1.00) 		0.65
(1.00) 		0.342
(1.00)
METHLYATION CNMF 0.128
(1.00) 		0.0552
(1.00) 		0.0675
(1.00) 		0.449
(1.00) 		0.218
(1.00) 		0.623
(1.00)
RPPA CNMF subtypes 0.187
(1.00) 		0.266
(1.00) 		0.271
(1.00) 		0.306
(1.00) 		0.0577
(1.00) 		0.648
(1.00)
RPPA cHierClus subtypes 0.0708
(1.00) 		0.958
(1.00) 		0.736
(1.00) 		0.508
(1.00) 		0.155
(1.00) 		0.0778
(1.00)
RNAseq CNMF subtypes 0.000936
(0.0449) 		0.019
(0.891) 		0.328
(1.00) 		0.493
(1.00) 		0.472
(1.00) 		0.507
(1.00)
RNAseq cHierClus subtypes 0.0254
(1.00) 		0.0313
(1.00) 		0.288
(1.00) 		0.326
(1.00) 		0.725
(1.00) 		0.0863
(1.00)
MIRSEQ CNMF 0.0793
(1.00) 		0.276
(1.00) 		0.887
(1.00) 		0.544
(1.00) 		0.578
(1.00) 		0.205
(1.00)
MIRSEQ CHIERARCHICAL 0.318
(1.00) 		0.425
(1.00) 		0.798
(1.00) 		0.462
(1.00) 		0.285
(1.00) 		0.696
(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 59 41 58
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.264 (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 154 73 0.2 - 346.0 (47.5)
subtype1 59 30 0.2 - 216.9 (47.3)
subtype2 39 22 6.4 - 346.0 (53.5)
subtype3 56 21 4.2 - 314.5 (49.2)

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 = 0.269 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 156 56.2 (16.0)
subtype1 59 57.4 (17.7)
subtype2 39 52.6 (16.1)
subtype3 58 57.4 (13.9)

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 'GENDER'

P value = 0.734 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 62 96
subtype1 23 36
subtype2 18 23
subtype3 21 37

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

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

P value = 0.593 (Chi-square test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 M1A M1B M1C
ALL 134 2 2 1 2
subtype1 50 1 1 0 1
subtype2 32 1 0 1 1
subtype3 52 0 1 0 0

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

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

P value = 0.65 (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 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 87 2 7 13 1 3 10 5 12 2
subtype1 35 0 2 6 0 1 2 4 3 1
subtype2 19 0 2 3 0 1 3 1 5 1
subtype3 33 2 3 4 1 1 5 0 4 0

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 = 0.342 (Chi-square test), Q value = 1

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

nPatients 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 17 9 13 18 8 9 8 7 6 17 18 5
subtype1 4 4 7 7 2 5 3 2 2 6 6 3
subtype2 1 3 3 5 2 0 4 1 1 6 5 2
subtype3 12 2 3 6 4 4 1 4 3 5 7 0

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
Number of samples 46 64 55
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.128 (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 161 77 0.2 - 346.0 (47.5)
subtype1 46 27 0.2 - 204.6 (39.9)
subtype2 62 27 0.9 - 248.6 (49.8)
subtype3 53 23 2.7 - 346.0 (47.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0552 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 163 56.1 (15.8)
subtype1 46 54.6 (16.5)
subtype2 62 59.8 (15.9)
subtype3 55 53.1 (14.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0675 (Fisher's exact test), Q value = 1

Table S11.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 65 100
subtype1 17 29
subtype2 32 32
subtype3 16 39

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

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.449 (Chi-square test), Q value = 1

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

nPatients M0 M1 M1A M1B M1C
ALL 141 2 2 1 2
subtype1 39 1 1 0 0
subtype2 54 1 0 1 0
subtype3 48 0 1 0 2

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

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

P value = 0.218 (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 N2 N2A N2B N2C N3 NX
ALL 90 2 8 13 1 3 11 5 14 2
subtype1 27 0 4 6 0 0 3 1 1 0
subtype2 33 0 3 1 1 2 3 3 9 1
subtype3 30 2 1 6 0 1 5 1 4 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 = 0.623 (Chi-square test), Q value = 1

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

nPatients 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 18 9 14 18 9 9 8 8 6 18 20 5
subtype1 3 1 4 8 2 3 4 3 2 4 6 2
subtype2 4 5 5 5 4 5 4 2 3 7 8 1
subtype3 11 3 5 5 3 1 0 3 1 7 6 2

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 4
Number of samples 26 16 24 34
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.187 (logrank test), Q value = 1

Table S16.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 97 47 2.6 - 346.0 (47.4)
subtype1 26 17 10.1 - 204.6 (47.3)
subtype2 16 3 4.2 - 346.0 (46.6)
subtype3 22 10 2.7 - 248.6 (34.1)
subtype4 33 17 2.6 - 216.9 (47.5)

Figure S13.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.266 (ANOVA), Q value = 1

Table S17.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 98 56.3 (16.7)
subtype1 26 53.5 (16.1)
subtype2 16 61.8 (15.7)
subtype3 23 59.4 (18.0)
subtype4 33 53.8 (16.4)

Figure S14.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.271 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 42 58
subtype1 11 15
subtype2 10 6
subtype3 10 14
subtype4 11 23

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

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.306 (Chi-square test), Q value = 1

Table S19.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 M1B M1C
ALL 80 1 1 2
subtype1 22 0 0 2
subtype2 15 0 0 0
subtype3 20 0 1 0
subtype4 23 1 0 0

Figure S16.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

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

P value = 0.0577 (Chi-square test), Q value = 1

Table S20.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2A N2B N2C N3 NX
ALL 51 2 3 10 1 5 4 7 2
subtype1 12 1 3 3 0 1 2 2 0
subtype2 11 1 0 0 0 2 0 0 1
subtype3 7 0 0 5 1 2 2 3 1
subtype4 21 0 0 2 0 0 0 2 0

Figure S17.  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 = 0.648 (Chi-square test), Q value = 1

Table S21.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients 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 11 8 7 10 5 4 3 4 2 10 11 3
subtype1 2 1 2 2 2 2 0 1 2 4 4 2
subtype2 2 2 2 1 1 1 1 1 0 1 1 0
subtype3 2 2 0 2 0 1 0 1 0 4 5 0
subtype4 5 3 3 5 2 0 2 1 0 1 1 1

Figure S18.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S22.  Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 18 15 32 19 16
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0708 (logrank test), Q value = 1

Table S23.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 97 47 2.6 - 346.0 (47.4)
subtype1 17 11 6.4 - 204.6 (47.5)
subtype2 15 3 4.2 - 346.0 (54.7)
subtype3 31 15 2.6 - 216.9 (53.2)
subtype4 18 11 13.9 - 117.2 (37.8)
subtype5 16 7 2.7 - 248.6 (39.6)

Figure S19.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.958 (ANOVA), Q value = 1

Table S24.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 98 56.3 (16.7)
subtype1 17 54.8 (16.1)
subtype2 15 57.9 (13.3)
subtype3 31 55.3 (16.8)
subtype4 19 56.3 (18.5)
subtype5 16 58.6 (19.2)

Figure S20.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.736 (Chi-square test), Q value = 1

Table S25.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 42 58
subtype1 6 12
subtype2 8 7
subtype3 13 19
subtype4 7 12
subtype5 8 8

Figure S21.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.508 (Chi-square test), Q value = 1

Table S26.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 M1B M1C
ALL 80 1 1 2
subtype1 11 1 0 0
subtype2 12 0 0 0
subtype3 25 0 1 0
subtype4 17 0 0 1
subtype5 15 0 0 1

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

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

P value = 0.155 (Chi-square test), Q value = 1

Table S27.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2A N2B N2C N3 NX
ALL 51 2 3 10 1 5 4 7 2
subtype1 11 0 0 0 0 0 0 2 0
subtype2 9 0 0 1 0 1 0 0 1
subtype3 15 1 0 3 0 1 2 4 0
subtype4 7 0 3 5 0 1 1 1 0
subtype5 9 1 0 1 1 2 1 0 1

Figure S23.  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 = 0.0778 (Chi-square test), Q value = 1

Table S28.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients 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 11 8 7 10 5 4 3 4 2 10 11 3
subtype1 2 0 1 1 3 1 2 0 0 0 1 1
subtype2 4 2 1 0 1 0 1 0 0 2 0 0
subtype3 1 4 3 6 0 1 0 3 0 2 4 0
subtype4 2 1 1 0 1 1 0 0 2 5 4 1
subtype5 2 1 1 3 0 1 0 1 0 1 2 1

Figure S24.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S29.  Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 55 41 55
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000936 (logrank test), Q value = 0.045

Table S30.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 147 72 0.2 - 346.0 (47.5)
subtype1 54 30 6.4 - 346.0 (61.1)
subtype2 40 10 4.2 - 203.0 (54.0)
subtype3 53 32 0.2 - 228.6 (35.9)

Figure S25.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.019 (ANOVA), Q value = 0.89

Table S31.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 149 55.9 (16.1)
subtype1 54 51.2 (16.3)
subtype2 41 57.4 (15.8)
subtype3 54 59.6 (15.2)

Figure S26.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.328 (Fisher's exact test), Q value = 1

Table S32.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 58 93
subtype1 24 31
subtype2 12 29
subtype3 22 33

Figure S27.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.493 (Chi-square test), Q value = 1

Table S33.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 M1A M1B M1C
ALL 127 2 2 1 2
subtype1 46 0 1 0 1
subtype2 37 0 0 0 1
subtype3 44 2 1 1 0

Figure S28.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

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

P value = 0.472 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 84 2 6 12 1 3 9 5 11 2
subtype1 29 1 3 2 0 1 3 4 3 2
subtype2 27 1 1 4 0 0 3 0 2 0
subtype3 28 0 2 6 1 2 3 1 6 0

Figure S29.  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 = 0.507 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients 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 17 9 13 18 7 8 7 7 5 15 17 5
subtype1 8 1 5 8 2 3 1 3 2 5 6 2
subtype2 6 5 2 6 4 1 1 2 0 5 3 1
subtype3 3 3 6 4 1 4 5 2 3 5 8 2

Figure S30.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S36.  Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 39 72 40
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0254 (logrank test), Q value = 1

Table S37.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 147 72 0.2 - 346.0 (47.5)
subtype1 38 23 7.8 - 346.0 (56.8)
subtype2 71 26 2.7 - 248.6 (49.5)
subtype3 38 23 0.2 - 228.6 (34.6)

Figure S31.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0313 (ANOVA), Q value = 1

Table S38.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 149 55.9 (16.1)
subtype1 38 51.1 (15.0)
subtype2 72 55.9 (16.2)
subtype3 39 60.7 (15.8)

Figure S32.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.288 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 58 93
subtype1 17 22
subtype2 23 49
subtype3 18 22

Figure S33.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.326 (Chi-square test), Q value = 1

Table S40.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 M1A M1B M1C
ALL 127 2 2 1 2
subtype1 32 0 0 0 1
subtype2 65 0 1 1 1
subtype3 30 2 1 0 0

Figure S34.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

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

P value = 0.725 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 84 2 6 12 1 3 9 5 11 2
subtype1 19 0 3 2 0 1 1 3 3 1
subtype2 43 2 2 5 1 2 5 2 5 1
subtype3 22 0 1 5 0 0 3 0 3 0

Figure S35.  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 = 0.0863 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients 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 17 9 13 18 7 8 7 7 5 15 17 5
subtype1 6 0 4 5 1 2 1 1 2 4 5 1
subtype2 10 8 6 10 4 1 1 5 2 8 6 1
subtype3 1 1 3 3 2 5 5 1 1 3 6 3

Figure S36.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S43.  Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 33 68 53
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0793 (logrank test), Q value = 1

Table S44.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 72 0.2 - 346.0 (47.5)
subtype1 31 9 0.2 - 248.6 (39.6)
subtype2 67 36 2.6 - 216.9 (41.6)
subtype3 52 27 7.8 - 346.0 (55.7)

Figure S37.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.276 (ANOVA), Q value = 1

Table S45.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 152 55.9 (16.0)
subtype1 32 57.2 (15.8)
subtype2 67 57.5 (16.0)
subtype3 53 53.0 (15.9)

Figure S38.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.887 (Fisher's exact test), Q value = 1

Table S46.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 59 95
subtype1 14 19
subtype2 25 43
subtype3 20 33

Figure S39.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.544 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 M1A M1B M1C
ALL 130 2 2 1 2
subtype1 23 1 1 0 1
subtype2 58 1 1 1 0
subtype3 49 0 0 0 1

Figure S40.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'DISTANT.METASTASIS'

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

P value = 0.578 (Chi-square test), Q value = 1

Table S48.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 84 2 7 13 1 3 10 5 11 2
subtype1 15 0 1 3 0 1 2 1 3 1
subtype2 37 0 2 8 0 2 5 1 6 0
subtype3 32 2 4 2 1 0 3 3 2 1

Figure S41.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.205 (Chi-square test), Q value = 1

Table S49.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients 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 17 9 13 18 7 8 7 7 6 17 17 5
subtype1 2 3 1 4 1 0 2 0 1 5 4 2
subtype2 5 5 5 11 3 5 2 1 2 8 9 2
subtype3 10 1 7 3 3 3 3 6 3 4 4 1

Figure S42.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S50.  Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 19 33 102
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.318 (logrank test), Q value = 1

Table S51.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 72 0.2 - 346.0 (47.5)
subtype1 17 4 0.2 - 203.0 (28.6)
subtype2 33 15 10.1 - 346.0 (61.2)
subtype3 100 53 2.6 - 314.5 (47.3)

Figure S43.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.425 (ANOVA), Q value = 1

Table S52.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 152 55.9 (16.0)
subtype1 18 58.7 (16.4)
subtype2 33 53.0 (13.6)
subtype3 101 56.3 (16.6)

Figure S44.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.798 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 59 95
subtype1 8 11
subtype2 11 22
subtype3 40 62

Figure S45.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.462 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 M1A M1B M1C
ALL 130 2 2 1 2
subtype1 14 1 1 0 0
subtype2 29 0 0 0 1
subtype3 87 1 1 1 1

Figure S46.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'DISTANT.METASTASIS'

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

P value = 0.285 (Chi-square test), Q value = 1

Table S55.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B N2 N2A N2B N2C N3 NX
ALL 84 2 7 13 1 3 10 5 11 2
subtype1 7 0 1 3 0 1 2 0 1 1
subtype2 18 1 2 0 1 0 1 2 4 1
subtype3 59 1 4 10 0 2 7 3 6 0

Figure S47.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.696 (Chi-square test), Q value = 1

Table S56.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients 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 17 9 13 18 7 8 7 7 6 17 17 5
subtype1 0 1 0 3 1 0 1 0 1 5 2 1
subtype2 7 1 3 2 1 2 2 2 2 2 4 1
subtype3 10 7 10 13 5 6 4 5 3 10 11 3

Figure S48.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Methods & Data
Input

  • Cluster data file = SKCM-TM.mergedcluster.txt

  • Clinical data file = SKCM-TM.clin.merged.picked.txt

  • Number of patients = 165

  • 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

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

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

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)
[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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] 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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