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 2 different clustering approaches and 2 clinical features across 45 patients, no significant finding detected with P value < 0.05.

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 2 different clustering approaches and 2 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, no significant finding detected.


Clinical Features	VITALSTATUS	GENDER
Statistical Tests	Fisher's exact test	Fisher's exact test
MIRseq CNMF subtypes	0.793	0.419
MIRseq cHierClus subtypes	0.912	0.374

Clustering Approach #1: 'MIRseq CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	9	16	20

'MIRseq CNMF subtypes' versus 'VITALSTATUS'

P value = 0.793 (Fisher's exact test)

Table S2. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'VITALSTATUS'

nPatients	Class0	Class1
ALL	24	21
subtype1	4	5
subtype2	8	8
subtype3	12	8

Figure S1. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'VITALSTATUS'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.419 (Fisher's exact test)

Table S3. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'GENDER'

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

Figure S2. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'GENDER'

Clustering Approach #2: 'MIRseq cHierClus subtypes'

Table S4. Get Full Table Description of clustering approach #2: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	8	29	8

'MIRseq cHierClus subtypes' versus 'VITALSTATUS'

P value = 0.912 (Fisher's exact test)

Table S5. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'VITALSTATUS'

nPatients	Class0	Class1
ALL	24	21
subtype1	4	4
subtype2	15	14
subtype3	5	3

Figure S3. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'VITALSTATUS'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.374 (Fisher's exact test)

Table S6. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

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

Figure S4. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

Methods & Data

Input

Cluster data file = LIHC.mergedcluster.txt
Clinical data file = LIHC.clin.merged.picked.txt
Number of patients = 45
Number of clustering approaches = 2
Number of selected clinical features = 2
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)

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)

[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] 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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