Liver Hepatocellular Carcinoma: 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 3 different clustering approaches and 3 clinical features across 59 patients, no significant finding detected with P value < 0.05.

  • 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes 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 3 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 0.272 0.68 0.45
MIRseq CNMF subtypes 0.755 0.199 0.419
MIRseq cHierClus subtypes 0.504 0.465 0.374
Clustering Approach #1: 'CN CNMF'

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

Cluster Labels 1 2 3
Number of samples 19 24 15
'CN CNMF' versus 'Time to Death'

P value = 0.272 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 53 26 0.1 - 90.7 (13.6)
subtype1 16 7 0.5 - 69.6 (22.5)
subtype2 22 11 0.1 - 83.6 (9.5)
subtype3 15 8 0.6 - 90.7 (8.3)

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

'CN CNMF' versus 'AGE'

P value = 0.68 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 54 61.8 (14.1)
subtype1 16 62.1 (16.3)
subtype2 24 60.1 (14.2)
subtype3 14 64.3 (11.7)

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

'CN CNMF' versus 'GENDER'

P value = 0.45 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 21 37
subtype1 9 10
subtype2 8 16
subtype3 4 11

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

Clustering Approach #2: 'MIRseq CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 9 16 20
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.755 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 39 21 0.1 - 83.6 (14.9)
subtype1 7 5 1.1 - 83.6 (11.6)
subtype2 13 8 0.1 - 69.6 (25.3)
subtype3 19 8 0.5 - 53.3 (19.8)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.199 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 42 60.0 (15.9)
subtype1 7 54.9 (19.3)
subtype2 15 56.3 (17.0)
subtype3 20 64.7 (13.0)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.419 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 18 27
subtype1 2 7
subtype2 8 8
subtype3 8 12

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

Clustering Approach #3: 'MIRseq cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 8 29 8
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.504 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 39 21 0.1 - 83.6 (14.9)
subtype1 6 4 1.1 - 83.6 (12.6)
subtype2 26 14 0.1 - 69.6 (17.3)
subtype3 7 3 2.6 - 37.6 (21.4)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.465 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 42 60.0 (15.9)
subtype1 6 58.2 (18.9)
subtype2 28 58.6 (15.9)
subtype3 8 66.4 (13.6)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.374 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 18 27
subtype1 2 6
subtype2 14 15
subtype3 2 6

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

Methods & Data
Input
  • Cluster data file = LIHC.mergedcluster.txt

  • Clinical data file = LIHC.clin.merged.picked.txt

  • Number of patients = 59

  • Number of clustering approaches = 3

  • 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)