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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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