Thyroid Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

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 2 different clustering approaches and 3 clinical features across 44 patients, 3 significant findings detected with P value < 0.05.

CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'GENDER' and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 2 different clustering approaches and 3 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 3 significant findings detected.


Clinical Features	Statistical Tests	MIRseq CNMF subtypes	MIRseq cHierClus subtypes
AGE	ANOVA	0.619	0.11
GENDER	Fisher's exact test	0.0402	0.712
HISTOLOGICAL TYPE	Chi-square test	4.15e-06	1.21e-05

Clustering Approach #1: 'MIRseq CNMF subtypes'

Table S1. Get Full Table Description of clustering approach #1: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	18	18	8

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.619 (ANOVA)

Table S2. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	44	50.2 (16.1)
subtype1	18	51.0 (16.6)
subtype2	18	47.7 (15.5)
subtype3	8	54.2 (17.5)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0402 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	18	26
subtype1	8	10
subtype2	4	14
subtype3	6	2

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 4.15e-06 (Chi-square test)

Table S4. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	3	14	20	7
subtype1	3	0	14	1
subtype2	0	13	3	2
subtype3	0	1	3	4

Figure S3. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

Clustering Approach #2: 'MIRseq cHierClus subtypes'

Table S5. Get Full Table Description of clustering approach #2: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	12	8	24

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.11 (ANOVA)

Table S6. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	44	50.2 (16.1)
subtype1	12	57.8 (15.3)
subtype2	8	43.0 (17.0)
subtype3	24	48.9 (15.4)

Figure S4. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.712 (Fisher's exact test)

Table S7. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	26
subtype1	6	6
subtype2	3	5
subtype3	9	15

Figure S5. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.21e-05 (Chi-square test)

Table S8. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	3	14	20	7
subtype1	3	0	9	0
subtype2	0	0	7	1
subtype3	0	14	4	6

Figure S6. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

Methods & Data

Input

Cluster data file = THCA.mergedcluster.txt
Clinical data file = THCA.clin.merged.picked.txt
Number of patients = 44
Number of clustering approaches = 2
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)

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

Download Results

This is an experimental feature. Location of data archives could not be determined.

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

[4] 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)

[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)