Cervical Squamous Cell Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features
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 35 arm-level results and 6 clinical features across 26 patients, one significant finding detected with Q value < 0.25.

- 15q gain cnv correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 35 arm-level results and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

|  | Clinical Features | Time to Death | AGE | RADIATIONS RADIATION REGIMENINDICATION | NUMBERPACKYEARSSMOKED | STOPPEDSMOKINGYEAR | TOBACCOSMOKINGHISTORYINDICATOR
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | t-test | t-test
15q gain | 3 (12%) | 23 | 1 (1.00) | 0.818 (1.00) | 1 (1.00) |  |  | 0.000729 (0.101)
1p gain | 5 (19%) | 21 | 0.617 (1.00) | 0.0415 (1.00) | 1 (1.00) |  |  | 0.94 (1.00)
1q gain | 10 (38%) | 16 | 0.414 (1.00) | 0.00733 (1.00) | 0.625 (1.00) |  |  | 0.869 (1.00)
3p gain | 3 (12%) | 23 | 1 (1.00) | 0.742 (1.00) | 0.408 (1.00) |  |  | 0.594 (1.00)
3q gain | 15 (58%) | 11 | 1 (1.00) | 0.426 (1.00) | 0.614 (1.00) |  |  | 0.781 (1.00)
5p gain | 8 (31%) | 18 | 0.617 (1.00) | 0.438 (1.00) | 0.563 (1.00) |  |  | 0.952 (1.00)
6p gain | 3 (12%) | 23 | 1 (1.00) | 0.745 (1.00) | 1 (1.00) |  |  | 0.227 (1.00)
8p gain | 3 (12%) | 23 | 1 (1.00) | 0.561 (1.00) | 0.408 (1.00) |  |  | 0.792 (1.00)
8q gain | 5 (19%) | 21 | 0.617 (1.00) | 0.633 (1.00) | 1 (1.00) |  |  | 0.916 (1.00)
10p gain | 3 (12%) | 23 | 0.0455 (1.00) | 0.332 (1.00) | 1 (1.00) |  |  | 0.291 (1.00)
12p gain | 5 (19%) | 21 | 1 (1.00) | 0.485 (1.00) | 1 (1.00) |  |  | 0.639 (1.00)
12q gain | 3 (12%) | 23 | 1 (1.00) | 0.723 (1.00) | 0.408 (1.00) |  |  | 0.792 (1.00)
16p gain | 4 (15%) | 22 | 0.617 (1.00) | 0.944 (1.00) | 0.511 (1.00) |  |  | 0.792 (1.00)
16q gain | 3 (12%) | 23 | 0.617 (1.00) | 0.165 (1.00) | 0.408 (1.00) |  |  |
18p gain | 3 (12%) | 23 | 0.617 (1.00) | 0.336 (1.00) | 0.0523 (1.00) |  |  | 0.399 (1.00)
20p gain | 8 (31%) | 18 | 1 (1.00) | 0.818 (1.00) | 0.563 (1.00) |  |  | 0.624 (1.00)
20q gain | 9 (35%) | 17 | 1 (1.00) | 0.923 (1.00) | 1 (1.00) |  |  | 0.55 (1.00)
22q gain | 4 (15%) | 22 | 0.617 (1.00) | 0.275 (1.00) | 0.511 (1.00) |  |  | 0.238 (1.00)
3p loss | 8 (31%) | 18 | 0.414 (1.00) | 0.978 (1.00) | 1 (1.00) |  |  | 0.779 (1.00)
4p loss | 10 (38%) | 16 | 0.414 (1.00) | 0.666 (1.00) | 0.625 (1.00) |  |  | 0.367 (1.00)
4q loss | 3 (12%) | 23 | 0.617 (1.00) | 0.43 (1.00) | 0.408 (1.00) |  |  | 0.657 (1.00)
5q loss | 9 (35%) | 17 | 0.414 (1.00) | 0.157 (1.00) | 0.263 (1.00) |  |  | 0.88 (1.00)
8p loss | 7 (27%) | 19 | 0.414 (1.00) | 0.257 (1.00) | 1 (1.00) |  |  | 0.692 (1.00)
10p loss | 5 (19%) | 21 | 0.617 (1.00) | 0.563 (1.00) | 0.155 (1.00) |  |  | 0.246 (1.00)
10q loss | 5 (19%) | 21 | 0.0455 (1.00) | 0.582 (1.00) | 0.155 (1.00) |  |  | 0.0824 (1.00)
11p loss | 5 (19%) | 21 | 0.414 (1.00) | 0.229 (1.00) | 0.155 (1.00) |  |  | 0.15 (1.00)
11q loss | 5 (19%) | 21 | 0.414 (1.00) | 0.933 (1.00) | 0.155 (1.00) |  |  | 0.323 (1.00)
12p loss | 4 (15%) | 22 | 0.0455 (1.00) | 0.077 (1.00) | 1 (1.00) |  |  | 0.436 (1.00)
13q loss | 8 (31%) | 18 | 0.414 (1.00) | 0.907 (1.00) | 0.0721 (1.00) |  |  | 0.673 (1.00)
17p loss | 7 (27%) | 19 | 0.414 (1.00) | 0.451 (1.00) | 0.287 (1.00) |  |  | 0.732 (1.00)
17q loss | 3 (12%) | 23 | 0.221 (1.00) | 0.383 (1.00) | 0.408 (1.00) |  |  | 0.874 (1.00)
18p loss | 3 (12%) | 23 | 0.617 (1.00) | 0.726 (1.00) | 0.408 (1.00) |  |  | 0.227 (1.00)
18q loss | 4 (15%) | 22 | 0.414 (1.00) | 0.491 (1.00) | 0.511 (1.00) |  |  | 0.075 (1.00)
20p loss | 3 (12%) | 23 | 0.617 (1.00) | 0.332 (1.00) | 1 (1.00) |  |  |
21q loss | 6 (23%) | 20 | 0.617 (1.00) | 0.92 (1.00) | 0.542 (1.00) |  |  | 0.0689 (1.00)

'15q gain mutation analysis' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000729 (t-test), Q value = 0.1

Table S1. Gene #12: '15q gain mutation analysis' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 25 | 1.8 (1.1)
15Q GAIN MUTATED | 3 | 1.0 (0.0)
15Q GAIN WILD-TYPE | 22 | 2.0 (1.1)

Figure S1. Get High-res Image Gene #12: '15q gain mutation analysis' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

Methods & Data
Input

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

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

- Number of patients = 26

- Number of significantly arm-level cnvs = 35

- Number of selected clinical features = 6

- 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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