Skin Cutaneous Melanoma: Correlation between molecular cancer subtypes and selected clinical features
(Regional_LN 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 116 patients, no significant finding 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.

  • 4 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 5 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 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 4 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, no 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 0.839
(1.00) 		0.0195
(0.899) 		0.566
(1.00) 		0.555
(1.00) 		0.864
(1.00) 		0.27
(1.00)
METHLYATION CNMF 0.632
(1.00) 		0.0625
(1.00) 		0.384
(1.00) 		0.694
(1.00) 		0.719
(1.00) 		0.357
(1.00)
RPPA CNMF subtypes 0.0892
(1.00) 		0.104
(1.00) 		0.212
(1.00) 		0.818
(1.00) 		0.424
(1.00) 		0.512
(1.00)
RPPA cHierClus subtypes 0.185
(1.00) 		0.856
(1.00) 		0.0741
(1.00) 		0.489
(1.00) 		0.177
(1.00) 		0.0129
(0.606)
RNAseq CNMF subtypes 0.0791
(1.00) 		0.387
(1.00) 		1
(1.00) 		0.702
(1.00) 		0.432
(1.00) 		0.113
(1.00)
RNAseq cHierClus subtypes 0.102
(1.00) 		0.0724
(1.00) 		0.671
(1.00) 		0.558
(1.00) 		0.531
(1.00) 		0.102
(1.00)
MIRSEQ CNMF 0.426
(1.00) 		0.0367
(1.00) 		0.496
(1.00) 		0.29
(1.00) 		0.854
(1.00) 		0.00574
(0.275)
MIRSEQ CHIERARCHICAL 0.485
(1.00) 		0.305
(1.00) 		0.158
(1.00) 		0.526
(1.00) 		0.296
(1.00) 		0.0351
(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 30 18 38 26
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.839 (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 110 55 2.6 - 346.0 (47.3)
subtype1 30 14 2.6 - 204.6 (47.4)
subtype2 18 11 7.8 - 346.0 (88.5)
subtype3 38 19 4.2 - 182.0 (43.2)
subtype4 24 11 2.7 - 248.6 (31.9)

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 = 0.0195 (ANOVA), Q value = 0.9

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

nPatients Mean (Std.Dev)
ALL 112 55.4 (16.4)
subtype1 30 61.3 (17.2)
subtype2 18 46.1 (16.0)
subtype3 38 55.6 (16.0)
subtype4 26 54.9 (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 = 0.566 (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 35 77
subtype1 11 19
subtype2 4 14
subtype3 10 28
subtype4 10 16

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 = 0.555 (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 M1B M1C
ALL 98 1 2 1
subtype1 25 0 0 1
subtype2 16 0 0 0
subtype3 35 0 1 0
subtype4 22 1 1 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 = 0.864 (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 61 1 3 10 1 3 8 1 13 2
subtype1 17 0 1 2 0 0 2 1 3 1
subtype2 8 0 1 3 0 0 1 0 2 1
subtype3 23 1 1 2 0 1 3 0 5 0
subtype4 13 0 0 3 1 2 2 0 3 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 = 0.27 (Chi-square test), Q value = 1

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: '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 1 15 7 11 13 5 5 2 4 3 10 18 3
subtype1 0 1 2 3 3 1 4 2 0 1 3 4 1
subtype2 1 1 1 2 2 1 0 0 0 1 2 4 0
subtype3 0 10 2 3 3 3 1 0 1 1 3 7 1
subtype4 0 3 2 3 5 0 0 0 3 0 2 3 1

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 4
Number of samples 20 40 27 29
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.632 (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 114 56 0.9 - 346.0 (47.0)
subtype1 20 9 7.8 - 346.0 (50.7)
subtype2 38 19 3.2 - 182.0 (45.4)
subtype3 27 16 6.4 - 248.6 (47.5)
subtype4 29 12 0.9 - 203.0 (43.2)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0625 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 116 55.1 (16.2)
subtype1 20 51.4 (16.8)
subtype2 40 53.2 (14.3)
subtype3 27 53.4 (17.4)
subtype4 29 62.1 (16.0)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.384 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 37 79
subtype1 4 16
subtype2 11 29
subtype3 11 16
subtype4 11 18

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

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.694 (Chi-square test), Q value = 1

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

nPatients M0 M1 M1B M1C
ALL 102 1 2 1
subtype1 17 0 0 0
subtype2 35 0 1 1
subtype3 25 1 0 0
subtype4 25 0 1 0

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 = 0.719 (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 61 1 4 10 1 3 9 1 15 2
subtype1 10 0 1 2 0 0 1 0 3 1
subtype2 22 1 1 4 0 0 3 1 5 0
subtype3 15 0 2 2 0 1 4 0 1 1
subtype4 14 0 0 2 1 2 1 0 6 0

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 = 0.357 (Chi-square test), Q value = 1

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: '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 1 15 7 11 13 5 5 2 5 3 11 20 3
subtype1 1 1 0 2 2 1 2 1 0 1 2 4 0
subtype2 0 10 1 4 3 3 0 0 2 0 5 6 2
subtype3 0 2 2 3 6 0 1 0 2 1 3 3 1
subtype4 0 2 4 2 2 1 2 1 1 1 1 7 0

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 5
Number of samples 12 24 15 14 15
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0892 (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 79 38 2.6 - 346.0 (47.5)
subtype1 12 6 14.5 - 117.2 (54.7)
subtype2 24 14 6.4 - 248.6 (58.1)
subtype3 15 4 4.2 - 346.0 (54.7)
subtype4 14 10 2.6 - 129.2 (35.6)
subtype5 14 4 2.7 - 182.0 (34.1)

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 = 0.104 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 80 56.5 (15.9)
subtype1 12 52.8 (16.9)
subtype2 24 52.9 (17.3)
subtype3 15 62.2 (13.3)
subtype4 14 63.9 (10.1)
subtype5 15 52.8 (17.4)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.212 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 29 51
subtype1 3 9
subtype2 8 16
subtype3 9 6
subtype4 3 11
subtype5 6 9

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 = 0.818 (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 66 1 2 1
subtype1 11 0 0 0
subtype2 17 1 1 1
subtype3 14 0 0 0
subtype4 11 0 0 0
subtype5 13 0 1 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 = 0.424 (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 45 1 1 7 2 4 1 8 2
subtype1 6 0 1 2 0 0 0 2 0
subtype2 16 0 0 1 0 0 1 2 0
subtype3 10 1 0 0 0 2 0 0 1
subtype4 7 0 0 2 1 0 0 2 0
subtype5 6 0 0 2 1 2 0 2 1

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 = 0.512 (Chi-square test), Q value = 1

Table S21.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: '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 1 11 6 6 8 5 4 2 2 1 6 10 3
subtype1 0 2 0 2 1 1 0 0 0 1 1 3 0
subtype2 1 4 1 1 4 3 1 0 1 0 0 0 3
subtype3 0 2 2 1 2 1 0 1 1 0 1 1 0
subtype4 0 2 1 0 1 0 2 1 0 0 2 3 0
subtype5 0 1 2 2 0 0 1 0 0 0 2 3 0

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
Number of samples 12 22 34 12
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.185 (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 79 38 2.6 - 346.0 (47.5)
subtype1 12 2 18.6 - 346.0 (57.6)
subtype2 22 11 4.2 - 204.6 (41.8)
subtype3 34 19 2.6 - 248.6 (45.3)
subtype4 11 6 14.5 - 117.2 (47.8)

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 = 0.856 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 80 56.5 (15.9)
subtype1 12 56.9 (14.7)
subtype2 22 58.1 (14.3)
subtype3 34 56.6 (17.4)
subtype4 12 53.1 (17.0)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0741 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 29 51
subtype1 8 4
subtype2 5 17
subtype3 13 21
subtype4 3 9

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 = 0.489 (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 66 1 2 1
subtype1 12 0 0 0
subtype2 13 1 1 1
subtype3 30 0 1 0
subtype4 11 0 0 0

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 = 0.177 (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 45 1 1 7 2 4 1 8 2
subtype1 8 1 0 0 0 2 0 0 1
subtype2 13 0 0 1 0 0 1 2 0
subtype3 19 0 0 3 2 2 0 4 1
subtype4 5 0 1 3 0 0 0 2 0

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 = 0.0129 (Chi-square test), Q value = 0.61

Table S28.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: '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 1 11 6 6 8 5 4 2 2 1 6 10 3
subtype1 0 2 2 1 1 0 0 1 1 0 1 1 0
subtype2 1 5 0 0 0 4 1 1 0 0 1 0 3
subtype3 0 2 4 3 7 0 3 0 1 0 2 6 0
subtype4 0 2 0 2 0 1 0 0 0 1 2 3 0

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 42 27 43
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0791 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 110 55 2.6 - 346.0 (47.3)
subtype1 42 22 6.4 - 346.0 (61.3)
subtype2 26 12 4.2 - 121.2 (38.9)
subtype3 42 21 2.6 - 203.0 (34.1)

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 = 0.387 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 112 55.4 (16.4)
subtype1 42 52.7 (17.5)
subtype2 27 57.2 (14.1)
subtype3 43 57.0 (16.7)

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

nPatients FEMALE MALE
ALL 35 77
subtype1 13 29
subtype2 8 19
subtype3 14 29

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 = 0.702 (Chi-square test), Q value = 1

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

nPatients M0 M1 M1B M1C
ALL 98 1 2 1
subtype1 37 1 1 1
subtype2 24 0 0 0
subtype3 37 0 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 = 0.432 (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 61 1 3 10 1 3 8 1 13 2
subtype1 26 0 1 3 0 0 2 1 5 2
subtype2 15 1 0 2 0 0 3 0 3 0
subtype3 20 0 2 5 1 3 3 0 5 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 = 0.113 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: '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 1 15 7 11 13 5 5 2 4 3 10 18 3
subtype1 1 7 1 4 7 3 2 1 1 0 2 6 3
subtype2 0 6 0 3 3 2 0 0 1 0 4 4 0
subtype3 0 2 6 4 3 0 3 1 2 3 4 8 0

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 4
Number of samples 18 29 34 31
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.102 (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 110 55 2.6 - 346.0 (47.3)
subtype1 17 6 3.2 - 182.0 (47.3)
subtype2 28 13 4.2 - 216.9 (38.9)
subtype3 34 20 6.4 - 346.0 (79.7)
subtype4 31 16 2.6 - 121.2 (32.2)

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 = 0.0724 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 112 55.4 (16.4)
subtype1 18 50.9 (15.8)
subtype2 29 57.5 (14.4)
subtype3 34 51.4 (17.0)
subtype4 31 60.6 (16.8)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.671 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 35 77
subtype1 4 14
subtype2 8 21
subtype3 13 21
subtype4 10 21

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 = 0.558 (Chi-square test), Q value = 1

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

nPatients M0 M1 M1B M1C
ALL 98 1 2 1
subtype1 17 0 1 0
subtype2 27 0 0 0
subtype3 29 0 1 1
subtype4 25 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 = 0.531 (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 61 1 3 10 1 3 8 1 13 2
subtype1 10 0 2 2 0 1 2 0 1 0
subtype2 18 0 0 3 0 0 3 0 3 0
subtype3 19 1 0 3 0 0 1 1 4 2
subtype4 14 0 1 2 1 2 2 0 5 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 = 0.102 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: '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 1 15 7 11 13 5 5 2 4 3 10 18 3
subtype1 0 1 2 2 3 0 1 0 1 1 5 0 0
subtype2 0 6 1 3 4 2 0 1 0 0 4 5 0
subtype3 1 6 0 3 6 2 1 0 2 0 0 6 2
subtype4 0 2 4 3 0 1 3 1 1 2 1 7 1

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 42 23 45
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.426 (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 108 55 2.6 - 346.0 (47.3)
subtype1 42 21 2.6 - 216.9 (34.9)
subtype2 22 9 6.4 - 248.6 (47.4)
subtype3 44 25 4.2 - 346.0 (55.3)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0367 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 110 55.5 (16.2)
subtype1 42 58.7 (16.5)
subtype2 23 58.8 (16.4)
subtype3 45 50.7 (15.0)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.496 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 34 76
subtype1 15 27
subtype2 8 15
subtype3 11 34

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

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.29 (Chi-square test), Q value = 1

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

nPatients M0 M1 M1B M1C
ALL 96 1 2 1
subtype1 39 0 1 0
subtype2 18 0 1 1
subtype3 39 1 0 0

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 = 0.854 (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 60 1 3 10 1 3 8 1 12 2
subtype1 24 0 1 4 0 1 5 0 5 0
subtype2 11 0 1 3 0 1 1 1 2 1
subtype3 25 1 1 3 1 1 2 0 5 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 = 0.00574 (Chi-square test), Q value = 0.28

Table S49.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: '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 1 15 7 11 12 5 5 2 4 3 10 17 3
subtype1 0 4 5 4 7 1 3 0 0 0 6 8 0
subtype2 1 2 1 0 3 1 0 2 1 1 4 1 2
subtype3 0 9 1 7 2 3 2 0 3 2 0 8 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 11 25 74
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.485 (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 108 55 2.6 - 346.0 (47.3)
subtype1 10 3 6.4 - 203.0 (39.2)
subtype2 25 14 11.9 - 346.0 (65.5)
subtype3 73 38 2.6 - 248.6 (43.2)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.305 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 110 55.5 (16.2)
subtype1 11 62.3 (15.1)
subtype2 25 53.4 (13.4)
subtype3 74 55.1 (17.1)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.158 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 34 76
subtype1 4 7
subtype2 4 21
subtype3 26 48

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

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.526 (Chi-square test), Q value = 1

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

nPatients M0 M1 M1B M1C
ALL 96 1 2 1
subtype1 11 0 0 0
subtype2 20 0 1 1
subtype3 65 1 1 0

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 = 0.296 (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 60 1 3 10 1 3 8 1 12 2
subtype1 5 0 1 2 0 1 1 0 0 1
subtype2 13 0 0 2 0 0 0 1 5 1
subtype3 42 1 2 6 1 2 7 0 7 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 = 0.0351 (Chi-square test), Q value = 1

Table S56.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: '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 1 15 7 11 12 5 5 2 4 3 10 17 3
subtype1 0 0 1 0 2 1 0 1 0 1 4 0 0
subtype2 1 7 1 1 1 1 1 0 1 0 0 5 2
subtype3 0 8 5 10 9 3 4 1 3 2 6 12 1

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

Methods & Data
Input

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

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

  • Number of patients = 116

  • 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle