Thyroid Adenocarcinoma: Correlation between copy number variations of arm-level result and molecular subtypes<BR>(follicular cohort)

Thyroid Adenocarcinoma: Correlation between copy number variations of arm-level result and molecular subtypes
(follicular cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and subtypes.

Summary

Testing the association between copy number variation 14 arm-level results and 6 molecular subtypes across 75 patients, 5 significant findings detected with Q value < 0.25.

7q gain cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.
12p gain cnv correlated to 'MRNASEQ_CNMF'.
12q gain cnv correlated to 'MRNASEQ_CNMF'.
22q loss cnv correlated to 'CN_CNMF'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 14 arm-level results and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 5 significant findings detected.


		Molecular subtypes	CN CNMF	METHLYATION CNMF	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nCNV (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
7q gain	5 (7%)	70	0.00308 (0.246)	0.000634 (0.0526)	0.0315 (1.00)	0.031 (1.00)	0.126 (1.00)	0.00737 (0.56)
12p gain	4 (5%)	71	0.0167 (1.00)	0.0051 (0.403)	0.00279 (0.229)	0.031 (1.00)	0.367 (1.00)	0.244 (1.00)
12q gain	4 (5%)	71	0.0167 (1.00)	0.0051 (0.403)	0.00279 (0.229)	0.031 (1.00)	0.367 (1.00)	0.244 (1.00)
22q loss	20 (27%)	55	1.03e-11 (8.63e-10)	0.011 (0.767)	0.0377 (1.00)	0.0128 (0.883)	0.636 (1.00)	0.742 (1.00)
5p gain	3 (4%)	72	0.0632 (1.00)	0.0267 (1.00)	0.0148 (1.00)	0.102 (1.00)	0.613 (1.00)	0.175 (1.00)
5q gain	3 (4%)	72	0.0632 (1.00)	0.0267 (1.00)	0.0148 (1.00)	0.102 (1.00)	0.613 (1.00)	0.175 (1.00)
7p gain	4 (5%)	71	0.0103 (0.729)	0.0051 (0.403)	0.0315 (1.00)	0.031 (1.00)	0.285 (1.00)	0.0477 (1.00)
17p gain	3 (4%)	72	0.171 (1.00)	0.456 (1.00)	0.447 (1.00)	0.768 (1.00)	0.613 (1.00)	0.175 (1.00)
17q gain	3 (4%)	72	0.171 (1.00)	0.456 (1.00)	0.447 (1.00)	0.768 (1.00)	0.613 (1.00)	0.175 (1.00)
2p loss	3 (4%)	72	0.00871 (0.653)	0.0267 (1.00)	0.113 (1.00)	0.102 (1.00)	0.346 (1.00)	0.175 (1.00)
2q loss	3 (4%)	72	0.00871 (0.653)	0.0267 (1.00)	0.113 (1.00)	0.102 (1.00)	0.346 (1.00)	0.175 (1.00)
3q loss	3 (4%)	72	0.00871 (0.653)	0.0267 (1.00)	0.113 (1.00)	0.102 (1.00)	0.346 (1.00)	0.175 (1.00)
11q loss	3 (4%)	72	0.00871 (0.653)	0.456 (1.00)	0.113 (1.00)	0.768 (1.00)	0.613 (1.00)	0.596 (1.00)
13q loss	4 (5%)	71	0.0636 (1.00)	0.0307 (1.00)	0.682 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)

'7q gain mutation analysis' versus 'CN_CNMF'

P value = 0.00308 (Fisher's exact test), Q value = 0.25

Table S1. Gene #4: '7q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	48	8	19
7Q GAIN MUTATED	0	2	3
7Q GAIN WILD-TYPE	48	6	16

Figure S1. Get High-res Image Gene #4: '7q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'7q gain mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000634 (Fisher's exact test), Q value = 0.053

Table S2. Gene #4: '7q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	17	29	21	8
7Q GAIN MUTATED	5	0	0	0
7Q GAIN WILD-TYPE	12	29	21	8

Figure S2. Get High-res Image Gene #4: '7q gain mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'12p gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.00279 (Fisher's exact test), Q value = 0.23

Table S3. Gene #5: '12p gain mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	17	25	18	11
12P GAIN MUTATED	4	0	0	0
12P GAIN WILD-TYPE	13	25	18	11

Figure S3. Get High-res Image Gene #5: '12p gain mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'12q gain mutation analysis' versus 'MRNASEQ_CNMF'

P value = 0.00279 (Fisher's exact test), Q value = 0.23

Table S4. Gene #6: '12q gain mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3	CLUS_4
ALL	17	25	18	11
12Q GAIN MUTATED	4	0	0	0
12Q GAIN WILD-TYPE	13	25	18	11

Figure S4. Get High-res Image Gene #6: '12q gain mutation analysis' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'22q loss mutation analysis' versus 'CN_CNMF'

P value = 1.03e-11 (Fisher's exact test), Q value = 8.6e-10

Table S5. Gene #14: '22q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients	CLUS_1	CLUS_2	CLUS_3
ALL	48	8	19
22Q LOSS MUTATED	3	0	17
22Q LOSS WILD-TYPE	45	8	2

Figure S5. Get High-res Image Gene #14: '22q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Molecular subtypes file = THCA-follicular.transferedmergedcluster.txt
Number of patients = 75
Number of significantly arm-level cnvs = 14
Number of molecular subtypes = 6
Exclude genes that fewer than K tumors have mutations, K = 3

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

[1] 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)

[2] 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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