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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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] 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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