Liver Hepatocellular Carcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Liver Hepatocellular Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid 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 4 different clustering approaches and 3 clinical features across 62 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'CN CNMF'. 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 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 4 subtypes that correlate to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 4 different clustering approaches and 3 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
Statistical Tests	logrank test	ANOVA	Fisher's exact test
CN CNMF	0.751 (1.00)	0.871 (1.00)	0.495 (1.00)
METHLYATION CNMF	0.199 (1.00)	0.154 (1.00)	0.469 (1.00)
MIRseq CNMF subtypes	0.943 (1.00)	0.242 (1.00)	0.158 (1.00)
MIRseq cHierClus subtypes	0.706 (1.00)	0.0122 (0.146)	0.262 (1.00)

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	15	22	24

'CN CNMF' versus 'Time to Death'

P value = 0.751 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	55	26	0.1 - 90.7 (12.8)
subtype1	14	7	0.1 - 90.7 (7.8)
subtype2	19	8	0.4 - 69.6 (14.3)
subtype3	22	11	0.3 - 83.6 (11.3)

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

'CN CNMF' versus 'AGE'

P value = 0.871 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	56	61.8 (14.0)
subtype1	14	60.4 (16.0)
subtype2	19	61.5 (15.0)
subtype3	23	62.9 (12.3)

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

'CN CNMF' versus 'GENDER'

P value = 0.495 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	22	39
subtype1	4	11
subtype2	10	12
subtype3	8	16

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	19	15	27

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.199 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	54	25	0.1 - 90.7 (12.2)
subtype1	15	8	0.4 - 90.7 (19.8)
subtype2	13	7	0.1 - 53.3 (7.1)
subtype3	26	10	0.3 - 83.6 (8.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.154 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	56	60.8 (14.7)
subtype1	15	57.7 (18.8)
subtype2	15	56.9 (15.8)
subtype3	26	64.9 (10.2)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.469 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	23	38
subtype1	9	10
subtype2	6	9
subtype3	8	19

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

Clustering Approach #3: 'MIRseq CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	22	12	27

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.943 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	54	25	0.1 - 83.6 (12.2)
subtype1	19	11	0.1 - 69.6 (14.4)
subtype2	9	4	1.1 - 83.6 (5.9)
subtype3	26	10	0.3 - 53.3 (11.0)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.242 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	56	61.0 (14.8)
subtype1	21	57.2 (14.9)
subtype2	9	59.7 (19.4)
subtype3	26	64.5 (12.7)

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

'MIRseq CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	23	38
subtype1	11	11
subtype2	2	10
subtype3	10	17

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

Clustering Approach #4: 'MIRseq cHierClus subtypes'

Table S13. Get Full Table Description of clustering approach #4: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	8	9	20	24

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.706 (logrank test), Q value = 1

Table S14. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	54	25	0.1 - 83.6 (12.2)
subtype1	8	2	0.3 - 53.3 (2.3)
subtype2	7	4	1.1 - 83.6 (11.6)
subtype3	17	11	0.1 - 69.6 (14.9)
subtype4	22	8	2.6 - 51.2 (14.0)

Figure S10. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0122 (ANOVA), Q value = 0.15

Table S15. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	56	61.0 (14.8)
subtype1	8	64.2 (6.5)
subtype2	7	60.7 (18.5)
subtype3	19	52.6 (16.8)
subtype4	22	67.2 (10.7)

Figure S11. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.262 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	23	38
subtype1	1	7
subtype2	2	7
subtype3	9	11
subtype4	11	13

Figure S12. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Methods & Data

Input

Cluster data file = LIHC-TP.mergedcluster.txt
Clinical data file = LIHC-TP.clin.merged.picked.txt
Number of patients = 62
Number of clustering approaches = 4
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

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)

[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)

[6] 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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