Liver Hepatocellular Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features
(primary solid tumor 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 selected clinical features.

Summary

Testing the association between copy number variation 61 arm-level results and 3 clinical features across 61 patients, 2 significant findings detected with Q value < 0.25.

- 18p loss cnv correlated to 'AGE'.

- 18q loss cnv correlated to 'AGE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 61 arm-level results and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test
18p loss | 7 (11%) | 54 | 0.0213 (1.00) | 0.000571 (0.101) | 0.695 (1.00)
18q loss | 9 (15%) | 52 | 0.2 (1.00) | 0.00109 (0.191) | 0.71 (1.00)
1p gain | 8 (13%) | 53 | 0.807 (1.00) | 0.237 (1.00) | 1 (1.00)
1q gain | 33 (54%) | 28 | 0.71 (1.00) | 0.73 (1.00) | 0.423 (1.00)
2p gain | 8 (13%) | 53 | 0.738 (1.00) | 0.115 (1.00) | 0.124 (1.00)
2q gain | 7 (11%) | 54 | 0.867 (1.00) | 0.227 (1.00) | 0.087 (1.00)
4p gain | 4 (7%) | 57 | 0.931 (1.00) | 0.455 (1.00) | 0.129 (1.00)
5p gain | 18 (30%) | 43 | 0.304 (1.00) | 0.227 (1.00) | 1 (1.00)
5q gain | 13 (21%) | 48 | 0.197 (1.00) | 0.201 (1.00) | 1 (1.00)
6p gain | 12 (20%) | 49 | 0.123 (1.00) | 0.79 (1.00) | 0.509 (1.00)
6q gain | 8 (13%) | 53 | 0.00574 (1.00) | 0.506 (1.00) | 0.699 (1.00)
7p gain | 17 (28%) | 44 | 0.816 (1.00) | 0.615 (1.00) | 0.373 (1.00)
7q gain | 18 (30%) | 43 | 0.828 (1.00) | 0.601 (1.00) | 0.397 (1.00)
8p gain | 11 (18%) | 50 | 0.112 (1.00) | 0.577 (1.00) | 0.299 (1.00)
8q gain | 31 (51%) | 30 | 0.271 (1.00) | 0.594 (1.00) | 0.6 (1.00)
9p gain | 3 (5%) | 58 |  | 0.822 (1.00) | 0.293 (1.00)
9q gain | 3 (5%) | 58 |  | 0.822 (1.00) | 0.293 (1.00)
10p gain | 5 (8%) | 56 | 0.166 (1.00) | 0.39 (1.00) | 0.341 (1.00)
12q gain | 3 (5%) | 58 |  | 0.894 (1.00) | 0.293 (1.00)
15q gain | 5 (8%) | 56 | 0.814 (1.00) | 0.328 (1.00) | 0.341 (1.00)
16p gain | 3 (5%) | 58 | 0.0077 (1.00) | 0.149 (1.00) | 1 (1.00)
17p gain | 3 (5%) | 58 | 0.467 (1.00) | 0.711 (1.00) | 0.293 (1.00)
17q gain | 16 (26%) | 45 | 0.0844 (1.00) | 0.876 (1.00) | 0.548 (1.00)
18q gain | 3 (5%) | 58 | 0.937 (1.00) | 0.619 (1.00) | 1 (1.00)
19p gain | 5 (8%) | 56 | 0.759 (1.00) | 0.62 (1.00) | 0.0524 (1.00)
19q gain | 7 (11%) | 54 | 0.795 (1.00) | 0.822 (1.00) | 0.087 (1.00)
20p gain | 12 (20%) | 49 | 0.238 (1.00) | 0.0865 (1.00) | 0.322 (1.00)
20q gain | 13 (21%) | 48 | 0.361 (1.00) | 0.059 (1.00) | 0.517 (1.00)
21q gain | 4 (7%) | 57 | 0.696 (1.00) | 0.613 (1.00) | 0.129 (1.00)
22q gain | 6 (10%) | 55 | 0.16 (1.00) | 0.068 (1.00) | 0.658 (1.00)
Xq gain | 4 (7%) | 57 | 0.228 (1.00) | 0.223 (1.00) | 1 (1.00)
1p loss | 11 (18%) | 50 | 0.689 (1.00) | 0.511 (1.00) | 0.504 (1.00)
1q loss | 3 (5%) | 58 | 0.862 (1.00) | 0.134 (1.00) | 1 (1.00)
2p loss | 3 (5%) | 58 |  |  | 0.293 (1.00)
2q loss | 4 (7%) | 57 |  | 0.0102 (1.00) | 0.615 (1.00)
3p loss | 5 (8%) | 56 | 0.721 (1.00) | 0.763 (1.00) | 0.341 (1.00)
3q loss | 3 (5%) | 58 | 0.628 (1.00) | 0.237 (1.00) | 0.293 (1.00)
4p loss | 9 (15%) | 52 | 0.508 (1.00) | 0.864 (1.00) | 1 (1.00)
4q loss | 15 (25%) | 46 | 0.515 (1.00) | 0.779 (1.00) | 1 (1.00)
5q loss | 4 (7%) | 57 | 0.00925 (1.00) | 0.953 (1.00) | 1 (1.00)
6q loss | 10 (16%) | 51 | 0.258 (1.00) | 0.944 (1.00) | 0.473 (1.00)
7p loss | 5 (8%) | 56 | 0.489 (1.00) | 0.661 (1.00) | 0.0524 (1.00)
7q loss | 7 (11%) | 54 | 0.935 (1.00) | 0.429 (1.00) | 0.24 (1.00)
8p loss | 28 (46%) | 33 | 0.439 (1.00) | 0.262 (1.00) | 0.423 (1.00)
8q loss | 5 (8%) | 56 | 0.582 (1.00) | 0.855 (1.00) | 0.341 (1.00)
9p loss | 15 (25%) | 46 | 0.77 (1.00) | 0.266 (1.00) | 1 (1.00)
9q loss | 13 (21%) | 48 | 0.611 (1.00) | 0.27 (1.00) | 0.753 (1.00)
10p loss | 3 (5%) | 58 | 0.7 (1.00) | 0.0302 (1.00) | 0.547 (1.00)
10q loss | 12 (20%) | 49 | 0.514 (1.00) | 0.588 (1.00) | 0.742 (1.00)
11p loss | 5 (8%) | 56 | 0.691 (1.00) | 0.279 (1.00) | 0.341 (1.00)
11q loss | 8 (13%) | 53 | 0.244 (1.00) | 0.375 (1.00) | 1 (1.00)
12p loss | 3 (5%) | 58 | 0.489 (1.00) | 0.509 (1.00) | 1 (1.00)
13q loss | 22 (36%) | 39 | 0.212 (1.00) | 0.162 (1.00) | 1 (1.00)
14q loss | 21 (34%) | 40 | 0.64 (1.00) | 0.549 (1.00) | 1 (1.00)
15q loss | 7 (11%) | 54 | 0.924 (1.00) | 0.363 (1.00) | 1 (1.00)
16p loss | 11 (18%) | 50 | 0.39 (1.00) | 0.89 (1.00) | 1 (1.00)
16q loss | 19 (31%) | 42 | 0.902 (1.00) | 0.743 (1.00) | 0.571 (1.00)
17p loss | 25 (41%) | 36 | 0.245 (1.00) | 0.372 (1.00) | 0.787 (1.00)
19p loss | 4 (7%) | 57 | 0.23 (1.00) | 0.474 (1.00) | 0.287 (1.00)
21q loss | 9 (15%) | 52 | 0.0736 (1.00) | 0.76 (1.00) | 0.0203 (1.00)
22q loss | 9 (15%) | 52 | 0.624 (1.00) | 0.696 (1.00) | 0.0596 (1.00)

'18p loss mutation analysis' versus 'AGE'

P value = 0.000571 (t-test), Q value = 0.1

Table S1. Gene #57: '18p loss mutation analysis' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 56 | 61.8 (14.0)
18P LOSS MUTATED | 7 | 73.6 (6.4)
18P LOSS WILD-TYPE | 49 | 60.1 (14.0)

Figure S1. Get High-res Image Gene #57: '18p loss mutation analysis' versus Clinical Feature #2: 'AGE'

'18q loss mutation analysis' versus 'AGE'

P value = 0.00109 (t-test), Q value = 0.19

Table S2. Gene #58: '18q loss mutation analysis' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 56 | 61.8 (14.0)
18Q LOSS MUTATED | 9 | 71.6 (6.9)
18Q LOSS WILD-TYPE | 47 | 60.0 (14.3)

Figure S2. Get High-res Image Gene #58: '18q loss mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

- Mutation data file = broad_values_by_arm.mutsig.cluster.txt

- Clinical data file = LIHC-TP.clin.merged.picked.txt

- Number of patients = 61

- Number of significantly arm-level cnvs = 61

- Number of selected clinical features = 3

- Exclude genes that fewer than K tumors have mutations, K = 3

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

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 tumors with and without gene mutations using 't.test' function in R

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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[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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