Correlation between aggregated molecular cancer subtypes and selected clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1M9075W
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/C1M9075W
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 3 different clustering approaches and 3 clinical features across 21 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

- 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 2 subtypes that correlate to 'Time to Death'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that correlate to 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 3 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 | Time to Death | AGE | GENDER
Statistical Tests | logrank test | t-test | Fisher's exact test
METHLYATION CNMF | 0.585 (1.00) | 0.928 (1.00) | 0.827 (1.00)
RNAseq CNMF subtypes | 0.0302 (0.241) | 0.0691 (0.468) | 0.0669 (0.468)
RNAseq cHierClus subtypes | 0.00815 (0.0734) | 0.359 (1.00) | 0.336 (1.00)

Clustering Approach #1: 'METHLYATION CNMF'

Table S1. Description of clustering approach #1: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 3 | 8 | 10

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.585 (logrank test), Q value = 1

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 21 | 4 | 2.0 - 211.2 (31.7)
subtype1 | 3 | 0 | 22.3 - 127.3 (27.4)
subtype2 | 8 | 1 | 24.7 - 196.6 (44.3)
subtype3 | 10 | 3 | 2.0 - 211.2 (31.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.928 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 54.0 (12.4)
subtype1 | 3 | 52.7 (9.5)
subtype2 | 8 | 53.0 (11.4)
subtype3 | 10 | 55.1 (14.8)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.827 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 13 | 8
subtype1 | 2 | 1
subtype2 | 4 | 4
subtype3 | 7 | 3

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

Clustering Approach #2: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 12 | 9

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0302 (logrank test), Q value = 0.24

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 21 | 4 | 2.0 - 211.2 (31.7)
subtype1 | 12 | 2 | 2.0 - 211.2 (46.8)
subtype2 | 9 | 2 | 4.1 - 74.1 (24.7)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0691 (t-test), Q value = 0.47

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 54.0 (12.4)
subtype1 | 12 | 58.4 (10.0)
subtype2 | 9 | 48.0 (13.3)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0669 (Fisher's exact test), Q value = 0.47

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 13 | 8
subtype1 | 5 | 7
subtype2 | 8 | 1

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

Clustering Approach #3: 'RNAseq cHierClus subtypes'

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

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 6 | 15

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00815 (logrank test), Q value = 0.073

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 21 | 4 | 2.0 - 211.2 (31.7)
subtype1 | 6 | 2 | 4.1 - 74.1 (24.8)
subtype2 | 15 | 2 | 2.0 - 211.2 (35.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.359 (t-test), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 21 | 54.0 (12.4)
subtype1 | 6 | 49.7 (13.1)
subtype2 | 15 | 55.7 (12.2)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.336 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 13 | 8
subtype1 | 5 | 1
subtype2 | 8 | 7

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

Methods & Data
Input

- Cluster data file = DLBC-TP.mergedcluster.txt

- Clinical data file = DLBC-TP.merged_data.txt

- Number of patients = 21

- Number of clustering approaches = 3

- 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

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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