Correlation between aggregated molecular cancer subtypes and selected clinical features

Sarcoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18W3BPR

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 8 different clustering approaches and 3 clinical features across 66 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'GENDER'.
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 5 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.
4 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.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. 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 8 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	t-test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.262 (1.00)	0.0566 (1.00)	0.00578 (0.139)
METHLYATION CNMF	0.105 (1.00)	0.154 (1.00)	0.2 (1.00)
RNAseq CNMF subtypes	0.802 (1.00)	0.207 (1.00)	0.011 (0.252)
RNAseq cHierClus subtypes	0.467 (1.00)	0.199 (1.00)	0.135 (1.00)
MIRSEQ CNMF	0.0619 (1.00)	0.182 (1.00)	0.208 (1.00)
MIRSEQ CHIERARCHICAL	0.734 (1.00)	0.34 (1.00)	0.205 (1.00)
MIRseq Mature CNMF subtypes	0.591 (1.00)	0.226 (1.00)	0.0488 (1.00)
MIRseq Mature cHierClus subtypes	0.894 (1.00)	0.5 (1.00)	0.0828 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	18	19	8	12

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

P value = 0.262 (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	57	18	0.1 - 143.4 (18.5)
subtype1	18	4	0.1 - 143.4 (13.4)
subtype2	19	6	2.0 - 108.1 (23.1)
subtype3	8	4	1.1 - 42.8 (12.2)
subtype4	12	4	0.1 - 116.9 (30.0)

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 = 0.0566 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	57	63.4 (11.5)
subtype1	18	68.9 (8.8)
subtype2	19	60.9 (11.0)
subtype3	8	64.2 (6.4)
subtype4	12	58.2 (15.7)

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 = 0.00578 (Fisher's exact test), Q value = 0.14

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	28	29
subtype1	13	5
subtype2	4	15
subtype3	6	2
subtype4	5	7

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	22	19	16

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.105 (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	57	18	0.1 - 143.4 (18.5)
subtype1	22	7	3.2 - 143.4 (27.5)
subtype2	19	8	0.1 - 108.1 (9.7)
subtype3	16	3	0.1 - 116.9 (22.0)

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	57	63.4 (11.5)
subtype1	22	64.4 (12.5)
subtype2	19	66.1 (9.3)
subtype3	16	58.8 (11.9)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.2 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	28	29
subtype1	9	13
subtype2	8	11
subtype3	11	5

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S9. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	11	9	13	1	8	8

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.802 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	49	16	0.1 - 143.4 (17.8)
subtype1	11	5	1.1 - 143.4 (14.0)
subtype2	9	2	0.1 - 108.1 (8.6)
subtype3	13	5	3.2 - 70.5 (24.5)
subtype5	8	2	0.1 - 75.5 (18.9)
subtype6	8	2	4.1 - 116.9 (22.0)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.207 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	49	63.1 (11.9)
subtype1	11	63.8 (13.9)
subtype2	9	64.3 (11.4)
subtype3	13	65.8 (10.0)
subtype5	8	65.6 (10.8)
subtype6	8	54.0 (11.6)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.011 (Chi-square test), Q value = 0.25

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

nPatients	FEMALE	MALE
ALL	26	23
subtype1	7	4
subtype2	5	4
subtype3	3	10
subtype5	3	5
subtype6	8	0

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S13. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	2	3
Number of samples	16	34

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.467 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	50	16	0.1 - 143.4 (18.1)
subtype2	16	4	0.1 - 116.9 (22.0)
subtype3	34	12	0.1 - 143.4 (17.4)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.199 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	50	63.1 (11.8)
subtype2	16	59.8 (12.4)
subtype3	34	64.6 (11.3)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.135 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	26	24
subtype2	11	5
subtype3	15	19

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

Clustering Approach #5: 'MIRSEQ CNMF'

Table S17. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	6	30	12	18

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0619 (logrank test), Q value = 1

Table S18. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	66	21	0.1 - 143.4 (18.9)
subtype1	6	3	0.1 - 14.0 (8.0)
subtype2	30	10	0.5 - 143.4 (19.7)
subtype3	12	4	4.5 - 70.5 (23.6)
subtype4	18	4	0.1 - 116.9 (22.8)

Figure S13. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.182 (ANOVA), Q value = 1

Table S19. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	62.6 (12.1)
subtype1	6	71.5 (13.6)
subtype2	30	63.1 (10.5)
subtype3	12	62.2 (11.0)
subtype4	18	59.1 (14.0)

Figure S14. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.208 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	33
subtype1	4	2
subtype2	13	17
subtype3	4	8
subtype4	12	6

Figure S15. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S21. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	2	27	37

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.734 (logrank test), Q value = 1

Table S22. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	64	21	0.1 - 143.4 (19.7)
subtype2	27	9	0.1 - 116.9 (15.0)
subtype3	37	12	0.1 - 143.4 (20.0)

Figure S16. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.34 (t-test), Q value = 1

Table S23. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	62.6 (12.3)
subtype2	27	60.8 (14.2)
subtype3	37	63.9 (10.7)

Figure S17. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.205 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	31	33
subtype2	16	11
subtype3	15	22

Figure S18. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S25. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	13	30	23

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.591 (logrank test), Q value = 1

Table S26. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	66	21	0.1 - 143.4 (18.9)
subtype1	13	5	3.2 - 70.5 (14.0)
subtype2	30	10	0.1 - 143.4 (19.7)
subtype3	23	6	0.1 - 116.9 (22.6)

Figure S19. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.226 (ANOVA), Q value = 1

Table S27. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	62.6 (12.1)
subtype1	13	66.1 (13.4)
subtype2	30	63.6 (10.7)
subtype3	23	59.3 (12.8)

Figure S20. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0488 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	33
subtype1	4	9
subtype2	13	17
subtype3	16	7

Figure S21. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S29. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	2	3
Number of samples	36	30

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.894 (logrank test), Q value = 1

Table S30. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	66	21	0.1 - 143.4 (18.9)
subtype2	36	12	0.1 - 143.4 (19.7)
subtype3	30	9	0.1 - 116.9 (15.0)

Figure S22. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.5 (t-test), Q value = 1

Table S31. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	62.6 (12.1)
subtype2	36	63.6 (10.6)
subtype3	30	61.5 (13.8)

Figure S23. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0828 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	33
subtype2	14	22
subtype3	19	11

Figure S24. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Methods & Data

Input

Cluster data file = SARC-TP.mergedcluster.txt
Clinical data file = SARC-TP.clin.merged.picked.txt
Number of patients = 66
Number of clustering approaches = 8
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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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