Correlation between aggregated molecular cancer subtypes and selected clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1TD9W4M

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 61 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

5 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 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 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.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'GENDER'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 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, 2 significant findings detected.


Clinical Features	AGE	GENDER	RACE
Statistical Tests	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.603 (1.00)	0.00732 (0.176)	0.89 (1.00)
METHLYATION CNMF	0.673 (1.00)	0.129 (1.00)	0.388 (1.00)
RNAseq CNMF subtypes	0.546 (1.00)	0.0382 (0.802)	0.214 (1.00)
RNAseq cHierClus subtypes	0.544 (1.00)	0.0619 (1.00)	0.445 (1.00)
MIRSEQ CNMF	0.616 (1.00)	0.145 (1.00)	0.489 (1.00)
MIRSEQ CHIERARCHICAL	0.513 (1.00)	0.0107 (0.245)	0.543 (1.00)
MIRseq Mature CNMF subtypes	0.239 (1.00)	0.204 (1.00)	0.224 (1.00)
MIRseq Mature cHierClus subtypes	0.338 (1.00)	0.0186 (0.409)	0.409 (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	5
Number of samples	4	7	21	18	7

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.603 (Kruskal-Wallis (anova)), Q value = 1

Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	57	49.2 (13.8)
subtype1	4	43.8 (18.1)
subtype2	7	52.9 (12.5)
subtype3	21	52.0 (14.5)
subtype4	18	47.4 (14.4)
subtype5	7	45.0 (9.4)

Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00732 (Fisher's exact test), Q value = 0.18

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

nPatients	FEMALE	MALE
ALL	37	20
subtype1	3	1
subtype2	2	5
subtype3	19	2
subtype4	9	9
subtype5	4	3

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.89 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	2	7	45
subtype1	0	0	0	3
subtype2	0	0	1	6
subtype3	1	1	3	15
subtype4	0	0	3	15
subtype5	0	1	0	6

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	14	28	19

'METHLYATION CNMF' versus 'AGE'

P value = 0.673 (Kruskal-Wallis (anova)), Q value = 1

Table S6. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	61	49.3 (13.8)
subtype1	14	46.2 (13.7)
subtype2	28	51.2 (14.0)
subtype3	19	48.8 (13.8)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.129 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	40	21
subtype1	7	7
subtype2	22	6
subtype3	11	8

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.388 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
subtype1	0	1	1	11
subtype2	1	0	5	21
subtype3	0	2	1	16

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	13	21	18	9

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.546 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	61	49.3 (13.8)
subtype1	13	48.3 (14.2)
subtype2	21	50.7 (12.8)
subtype3	18	46.0 (13.4)
subtype4	9	54.2 (16.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0382 (Fisher's exact test), Q value = 0.8

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

nPatients	FEMALE	MALE
ALL	40	21
subtype1	10	3
subtype2	15	6
subtype3	7	11
subtype4	8	1

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.214 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
subtype1	0	1	2	8
subtype2	1	0	3	17
subtype3	0	1	0	17
subtype4	0	1	2	6

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	14	25	13	9

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.544 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	61	49.3 (13.8)
subtype1	14	50.1 (11.7)
subtype2	25	50.4 (13.5)
subtype3	13	43.8 (14.6)
subtype4	9	53.1 (16.2)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0619 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	40	21
subtype1	8	6
subtype2	20	5
subtype3	5	8
subtype4	7	2

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.445 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
subtype1	0	1	1	12
subtype2	1	0	5	18
subtype3	0	1	0	11
subtype4	0	1	1	7

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	25	20	16

'MIRSEQ CNMF' versus 'AGE'

P value = 0.616 (Kruskal-Wallis (anova)), Q value = 1

Table S18. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	61	49.3 (13.8)
subtype1	25	46.9 (13.7)
subtype2	20	50.1 (13.6)
subtype3	16	52.1 (14.3)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.145 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	40	21
subtype1	13	12
subtype2	16	4
subtype3	11	5

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.489 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
subtype1	1	2	1	20
subtype2	0	0	3	16
subtype3	0	1	3	12

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

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels	1	2	3	4
Number of samples	23	22	6	10

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.513 (Kruskal-Wallis (anova)), Q value = 1

Table S22. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	61	49.3 (13.8)
subtype1	23	46.3 (12.7)
subtype2	22	50.3 (14.4)
subtype3	6	48.3 (12.2)
subtype4	10	54.6 (15.6)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0107 (Fisher's exact test), Q value = 0.25

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

nPatients	FEMALE	MALE
ALL	40	21
subtype1	10	13
subtype2	19	3
subtype3	3	3
subtype4	8	2

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.543 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
subtype1	0	2	1	19
subtype2	1	0	3	17
subtype3	0	0	1	5
subtype4	0	1	2	7

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

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	14	24	23

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.239 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	61	49.3 (13.8)
subtype1	14	43.3 (15.8)
subtype2	24	52.0 (13.3)
subtype3	23	50.2 (12.3)

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.204 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	40	21
subtype1	8	6
subtype2	19	5
subtype3	13	10

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.224 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
subtype1	0	1	0	12
subtype2	0	0	5	18
subtype3	1	2	2	18

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

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	24	21	16

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.338 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	61	49.3 (13.8)
subtype1	24	45.6 (12.8)
subtype2	21	51.3 (14.1)
subtype3	16	52.2 (14.3)

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0186 (Fisher's exact test), Q value = 0.41

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

nPatients	FEMALE	MALE
ALL	40	21
subtype1	11	13
subtype2	18	3
subtype3	11	5

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.409 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	7	48
subtype1	0	2	1	20
subtype2	1	0	3	16
subtype3	0	1	3	12

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

Methods & Data

Input

Cluster data file = PCPG-TP.mergedcluster.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 61
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)

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

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] 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)

[4] 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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