Correlation between molecular cancer subtypes and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Liver Hepatocellular Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RN35TD

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 6 different clustering approaches and 4 clinical features across 73 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. 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 mRNA expression data identified 7 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 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.
2 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 6 different clustering approaches and 4 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	COMPLETENESS OF RESECTION
Statistical Tests	logrank test	t-test	Fisher's exact test	Chi-square test
Copy Number Ratio CNMF subtypes	0.434 (1.00)	0.943 (1.00)	0.24 (1.00)	0.505 (1.00)
METHLYATION CNMF	0.729 (1.00)	0.0508 (1.00)	0.521 (1.00)	0.557 (1.00)
RNAseq CNMF subtypes	0.862 (1.00)	0.141 (1.00)	0.0734 (1.00)	0.374 (1.00)
RNAseq cHierClus subtypes	0.838 (1.00)	0.0219 (0.526)	0.0366 (0.843)	0.411 (1.00)
MIRSEQ CNMF	0.287 (1.00)	0.0533 (1.00)	0.207 (1.00)	0.534 (1.00)
MIRSEQ CHIERARCHICAL	0.974 (1.00)	0.75 (1.00)	0.246 (1.00)	0.221 (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
Number of samples	18	26	28

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.434 (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	66	26	0.1 - 90.7 (13.2)
subtype1	17	7	0.1 - 90.7 (7.3)
subtype2	23	8	0.1 - 79.4 (19.8)
subtype3	26	11	0.1 - 83.6 (11.3)

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 = 0.943 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	70	60.7 (14.4)
subtype1	17	59.7 (15.3)
subtype2	26	61.3 (14.1)
subtype3	27	60.7 (14.6)

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 = 0.24 (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	25	47
subtype1	4	14
subtype2	12	14
subtype3	9	19

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 'COMPLETENESS.OF.RESECTION'

P value = 0.505 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	50	8	1	8
subtype1	12	3	1	1
subtype2	18	2	0	2
subtype3	20	3	0	5

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #2: 'METHLYATION CNMF'

Table S6. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	20	21	31

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.729 (logrank test), Q value = 1

Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	65	25	0.1 - 90.7 (12.8)
subtype1	16	8	0.4 - 90.7 (22.5)
subtype2	19	7	0.1 - 66.3 (6.3)
subtype3	30	10	0.1 - 83.6 (8.3)

Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0508 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	70	59.9 (14.9)
subtype1	19	58.5 (17.0)
subtype2	21	54.4 (16.2)
subtype3	30	64.5 (11.1)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.521 (Fisher's exact test), Q value = 1

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	26	46
subtype1	9	11
subtype2	8	13
subtype3	9	22

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.557 (Chi-square test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	49	8	1	9
subtype1	13	3	0	1
subtype2	15	1	1	4
subtype3	21	4	0	4

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S11. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7	8	9
Number of samples	16	7	6	11	11	3	4	1	1

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.862 (logrank test), Q value = 1

Table S12. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	54	23	0.1 - 83.6 (14.3)
subtype1	13	7	3.0 - 49.0 (19.8)
subtype2	7	3	0.1 - 55.2 (10.1)
subtype3	5	2	6.0 - 46.8 (14.3)
subtype4	11	7	0.3 - 83.6 (23.3)
subtype5	11	4	0.6 - 79.4 (6.7)
subtype6	3	0	1.2 - 13.8 (8.3)
subtype7	4	0	0.1 - 34.1 (15.6)

Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.141 (ANOVA), Q value = 1

Table S13. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	58	60.1 (14.5)
subtype1	16	57.0 (16.7)
subtype2	7	54.7 (13.7)
subtype3	6	61.8 (12.5)
subtype4	11	67.9 (8.5)
subtype5	11	61.5 (14.0)
subtype6	3	69.0 (5.6)
subtype7	4	47.0 (19.6)

Figure S10. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0734 (Chi-square test), Q value = 1

Table S14. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	22	36
subtype1	8	8
subtype2	4	3
subtype3	1	5
subtype4	5	6
subtype5	0	11
subtype6	2	1
subtype7	2	2

Figure S11. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.374 (Chi-square test), Q value = 1

Table S15. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	41	7	1	6
subtype1	8	4	0	3
subtype2	6	0	1	0
subtype3	5	0	0	0
subtype4	8	1	0	2
subtype5	9	1	0	0
subtype6	2	1	0	0
subtype7	3	0	0	1

Figure S12. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S16. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2
Number of samples	26	34

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.838 (logrank test), Q value = 1

Table S17. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	55	23	0.1 - 83.6 (14.3)
subtype1	23	8	0.1 - 55.2 (14.9)
subtype2	32	15	0.3 - 83.6 (14.0)

Figure S13. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0219 (t-test), Q value = 0.53

Table S18. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	59	59.7 (14.7)
subtype1	26	54.5 (16.7)
subtype2	33	63.7 (11.6)

Figure S14. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0366 (Fisher's exact test), Q value = 0.84

Table S19. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	23	37
subtype1	14	12
subtype2	9	25

Figure S15. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.411 (Chi-square test), Q value = 1

Table S20. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	43	7	1	6
subtype1	17	4	0	4
subtype2	26	3	1	2

Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S21. Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	28	14	29

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.287 (logrank test), Q value = 1

Table S22. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	64	25	0.1 - 83.6 (13.2)
subtype1	25	11	0.1 - 69.6 (14.3)
subtype2	11	6	1.1 - 83.6 (8.3)
subtype3	28	8	0.3 - 79.4 (16.8)

Figure S17. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0533 (ANOVA), Q value = 1

Table S23. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	69	60.3 (14.9)
subtype1	28	55.4 (15.2)
subtype2	12	60.8 (17.6)
subtype3	29	64.9 (12.1)

Figure S18. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.207 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	46
subtype1	11	17
subtype2	2	12
subtype3	12	17

Figure S19. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.534 (Chi-square test), Q value = 1

Table S25. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	48	8	1	9
subtype1	20	3	1	4
subtype2	6	3	0	2
subtype3	22	2	0	3

Figure S20. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S26. Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2
Number of samples	8	63

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.974 (logrank test), Q value = 1

Table S27. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	64	25	0.1 - 83.6 (13.2)
subtype1	6	3	1.1 - 83.6 (7.4)
subtype2	58	22	0.1 - 79.4 (14.0)

Figure S21. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.75 (t-test), Q value = 1

Table S28. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	69	60.3 (14.9)
subtype1	8	58.5 (16.9)
subtype2	61	60.6 (14.7)

Figure S22. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.246 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	46
subtype1	1	7
subtype2	24	39

Figure S23. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.221 (Chi-square test), Q value = 1

Table S30. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	48	8	1	9
subtype1	3	2	0	0
subtype2	45	6	1	9

Figure S24. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = LIHC-TP.mergedcluster.txt
Clinical data file = LIHC-TP.clin.merged.picked.txt
Number of patients = 73
Number of clustering approaches = 6
Number of selected clinical features = 4
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] 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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