Correlation between aggregated molecular cancer subtypes and selected clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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/C1GB22QV

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 2 clinical features across 10 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

2 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
2 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 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 4 different clustering approaches and 2 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	AGE	GENDER
Statistical Tests	ANOVA	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.521 (1.00)	1 (1.00)
MIRSEQ CNMF	0.648 (1.00)	1 (1.00)
MIRSEQ CHIERARCHICAL	0.906 (1.00)	0.429 (1.00)
MIRseq Mature cHierClus subtypes	0.747 (1.00)	0.7 (1.00)

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

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

Cluster Labels	3	4
Number of samples	4	5

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.521 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	9	46.8 (11.0)
subtype3	4	49.5 (8.1)
subtype4	5	44.6 (13.4)

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

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

nPatients	FEMALE	MALE
ALL	6	3
subtype3	3	1
subtype4	3	2

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

Clustering Approach #2: 'MIRSEQ CNMF'

Table S4. Description of clustering approach #2: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	3	5	2

'MIRSEQ CNMF' versus 'AGE'

P value = 0.648 (t-test), Q value = 1

Table S5. Clustering Approach #2: 'MIRSEQ CNMF' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	8	47.1 (11.5)
subtype1	3	43.7 (17.6)
subtype2	5	49.2 (7.9)

Figure S3. Get High-res Image Clustering Approach #2: 'MIRSEQ CNMF' versus Clinical Feature #1: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

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

Table S6. Clustering Approach #2: 'MIRSEQ CNMF' versus Clinical Feature #2: 'GENDER'

nPatients	FEMALE	MALE
ALL	6	2
subtype1	2	1
subtype2	4	1

Figure S4. Get High-res Image Clustering Approach #2: 'MIRSEQ CNMF' versus Clinical Feature #2: 'GENDER'

Clustering Approach #3: 'MIRSEQ CHIERARCHICAL'

Table S7. Description of clustering approach #3: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	1	3	4	2

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.906 (t-test), Q value = 1

Table S8. Clustering Approach #3: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	7	50.1 (8.6)
subtype2	3	49.7 (7.1)
subtype3	4	50.5 (10.7)

Figure S5. Get High-res Image Clustering Approach #3: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.429 (Fisher's exact test), Q value = 1

Table S9. Clustering Approach #3: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'GENDER'

nPatients	FEMALE	MALE
ALL	5	2
subtype2	3	0
subtype3	2	2

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

Clustering Approach #4: 'MIRseq Mature cHierClus subtypes'

Table S10. Description of clustering approach #4: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	4	3	3

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.747 (ANOVA), Q value = 1

Table S11. Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	10	48.3 (11.4)
subtype1	4	50.8 (11.0)
subtype2	3	49.7 (7.1)
subtype3	3	43.7 (17.6)

Figure S7. Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.7 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

nPatients	FEMALE	MALE
ALL	7	3
subtype1	2	2
subtype2	3	0
subtype3	2	1

Figure S8. Get High-res Image Clustering Approach #4: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

Methods & Data

Input

Cluster data file = PCPG-TP.mergedcluster.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 10
Number of clustering approaches = 4
Number of selected clinical features = 2
Exclude small clusters that include fewer than K patients, K = 3

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

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

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

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

[2] 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)

[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[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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