Correlation between copy number variations of arm-level result and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C12R3R3V
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12R3R3V
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 32 arm-level events and 5 clinical features across 191 patients, 8 significant findings detected with Q value < 0.25.

- 10q gain cnv correlated to 'Time to Death'.

- 3p loss cnv correlated to 'Time to Death'.

- 3q loss cnv correlated to 'Time to Death'.

- 5q loss cnv correlated to 'Time to Death'.

- 12p loss cnv correlated to 'Time to Death'.

- 18p loss cnv correlated to 'Time to Death'.

- 18q loss cnv correlated to 'Time to Death' and 'YEARS_TO_BIRTH'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 32 arm-level events and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.

|  | Clinical Features | Time to Death | YEARS TO BIRTH | GENDER | RACE | ETHNICITY
| nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test
18q loss | 4 (2%) | 187 | 0.00664 (0.177) | 0.0121 (0.241) | 0.127 (0.521) | 1 (1.00) | 1 (1.00)
10q gain | 3 (2%) | 188 | 0.00577 (0.177) | 0.654 (1.00) | 0.252 (0.823) | 0.233 (0.809) | 1 (1.00)
3p loss | 3 (2%) | 188 | 0.000763 (0.061) | 0.0208 (0.3) | 0.252 (0.823) | 1 (1.00) | 1 (1.00)
3q loss | 3 (2%) | 188 | 0.000763 (0.061) | 0.0208 (0.3) | 0.252 (0.823) | 1 (1.00) | 1 (1.00)
5q loss | 6 (3%) | 185 | 0.00532 (0.177) | 0.0519 (0.463) | 0.0325 (0.346) | 0.415 (1.00) | 1 (1.00)
12p loss | 4 (2%) | 187 | 0.00166 (0.0886) | 0.869 (1.00) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
18p loss | 5 (3%) | 186 | 0.011 (0.241) | 0.367 (1.00) | 0.0642 (0.463) | 0.361 (1.00) | 1 (1.00)
1p gain | 3 (2%) | 188 |  | 0.115 (0.513) | 0.592 (1.00) | 1 (1.00) | 1 (1.00)
4p gain | 4 (2%) | 187 | 0.506 (1.00) | 0.328 (0.972) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
4q gain | 4 (2%) | 187 | 0.506 (1.00) | 0.328 (0.972) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
8p gain | 22 (12%) | 169 | 0.412 (1.00) | 0.112 (0.513) | 0.0734 (0.463) | 1 (1.00) | 0.313 (0.972)
8q gain | 23 (12%) | 168 | 0.464 (1.00) | 0.167 (0.621) | 0.0723 (0.463) | 1 (1.00) | 0.325 (0.972)
11p gain | 5 (3%) | 186 | 0.792 (1.00) | 0.123 (0.519) | 1 (1.00) | 1 (1.00) | 0.0781 (0.463)
11q gain | 7 (4%) | 184 | 0.37 (1.00) | 0.047 (0.463) | 1 (1.00) | 1 (1.00) | 0.108 (0.513)
13q gain | 6 (3%) | 185 | 0.582 (1.00) | 0.12 (0.519) | 0.69 (1.00) | 1 (1.00) | 1 (1.00)
17q gain | 3 (2%) | 188 | 0.856 (1.00) | 0.113 (0.513) | 1 (1.00) | 1 (1.00) | 1 (1.00)
19p gain | 5 (3%) | 186 | 0.91 (1.00) | 0.715 (1.00) | 0.378 (1.00) | 1 (1.00) | 1 (1.00)
19q gain | 5 (3%) | 186 | 0.91 (1.00) | 0.715 (1.00) | 0.378 (1.00) | 1 (1.00) | 1 (1.00)
21q gain | 8 (4%) | 183 | 0.0661 (0.463) | 0.401 (1.00) | 0.0732 (0.463) | 0.185 (0.672) | 1 (1.00)
22q gain | 9 (5%) | 182 | 0.564 (1.00) | 0.0629 (0.463) | 0.513 (1.00) | 1 (1.00) | 1 (1.00)
xp gain | 3 (2%) | 188 | 0.452 (1.00) | 0.108 (0.513) | 1 (1.00) | 1 (1.00) | 1 (1.00)
xq gain | 3 (2%) | 188 | 0.452 (1.00) | 0.108 (0.513) | 1 (1.00) | 1 (1.00) | 1 (1.00)
7p loss | 17 (9%) | 174 | 0.0916 (0.505) | 0.458 (1.00) | 0.802 (1.00) | 1 (1.00) | 1 (1.00)
7q loss | 20 (10%) | 171 | 0.0225 (0.3) | 0.513 (1.00) | 1 (1.00) | 0.721 (1.00) | 1 (1.00)
15q loss | 4 (2%) | 187 | 0.8 (1.00) | 0.677 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
16q loss | 4 (2%) | 187 | 0.0189 (0.3) | 0.388 (1.00) | 0.627 (1.00) | 1 (1.00) | 1 (1.00)
17p loss | 13 (7%) | 178 | 0.112 (0.513) | 0.765 (1.00) | 0.147 (0.574) | 0.654 (1.00) | 1 (1.00)
17q loss | 7 (4%) | 184 | 0.274 (0.878) | 0.196 (0.697) | 0.458 (1.00) | 1 (1.00) | 1 (1.00)
19p loss | 3 (2%) | 188 |  | 0.151 (0.574) | 0.592 (1.00) | 1 (1.00) | 1 (1.00)
19q loss | 3 (2%) | 188 |  | 0.151 (0.574) | 0.592 (1.00) | 1 (1.00) | 1 (1.00)
xp loss | 5 (3%) | 186 | 0.081 (0.463) | 0.0283 (0.323) | 0.0642 (0.463) | 1 (1.00) | 1 (1.00)
xq loss | 5 (3%) | 186 | 0.081 (0.463) | 0.0283 (0.323) | 0.0642 (0.463) | 1 (1.00) | 1 (1.00)

'10q gain' versus 'Time to Death'

P value = 0.00577 (logrank test), Q value = 0.18

Table S1. Gene #6: '10q gain' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 116 | 0.0 - 94.1 (11.0)
10Q GAIN MUTATED | 3 | 2 | 0.0 - 2.0 (1.9)
10Q GAIN WILD-TYPE | 175 | 114 | 0.0 - 94.1 (12.0)

Figure S1. Get High-res Image Gene #6: '10q gain' versus Clinical Feature #1: 'Time to Death'

'3p loss' versus 'Time to Death'

P value = 0.000763 (logrank test), Q value = 0.061

Table S2. Gene #17: '3p loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 116 | 0.0 - 94.1 (11.0)
3P LOSS MUTATED | 3 | 3 | 0.0 - 7.0 (1.0)
3P LOSS WILD-TYPE | 175 | 113 | 0.0 - 94.1 (12.0)

Figure S2. Get High-res Image Gene #17: '3p loss' versus Clinical Feature #1: 'Time to Death'

'3q loss' versus 'Time to Death'

P value = 0.000763 (logrank test), Q value = 0.061

Table S3. Gene #18: '3q loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 116 | 0.0 - 94.1 (11.0)
3Q LOSS MUTATED | 3 | 3 | 0.0 - 7.0 (1.0)
3Q LOSS WILD-TYPE | 175 | 113 | 0.0 - 94.1 (12.0)

Figure S3. Get High-res Image Gene #18: '3q loss' versus Clinical Feature #1: 'Time to Death'

'5q loss' versus 'Time to Death'

P value = 0.00532 (logrank test), Q value = 0.18

Table S4. Gene #19: '5q loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 116 | 0.0 - 94.1 (11.0)
5Q LOSS MUTATED | 6 | 6 | 1.0 - 12.0 (7.0)
5Q LOSS WILD-TYPE | 172 | 110 | 0.0 - 94.1 (12.0)

Figure S4. Get High-res Image Gene #19: '5q loss' versus Clinical Feature #1: 'Time to Death'

'12p loss' versus 'Time to Death'

P value = 0.00166 (logrank test), Q value = 0.089

Table S5. Gene #22: '12p loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 116 | 0.0 - 94.1 (11.0)
12P LOSS MUTATED | 3 | 3 | 0.0 - 7.0 (4.0)
12P LOSS WILD-TYPE | 175 | 113 | 0.0 - 94.1 (12.0)

Figure S5. Get High-res Image Gene #22: '12p loss' versus Clinical Feature #1: 'Time to Death'

'18p loss' versus 'Time to Death'

P value = 0.011 (logrank test), Q value = 0.24

Table S6. Gene #27: '18p loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 116 | 0.0 - 94.1 (11.0)
18P LOSS MUTATED | 5 | 5 | 1.0 - 12.0 (7.0)
18P LOSS WILD-TYPE | 173 | 111 | 0.0 - 94.1 (12.0)

Figure S6. Get High-res Image Gene #27: '18p loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'Time to Death'

P value = 0.00664 (logrank test), Q value = 0.18

Table S7. Gene #28: '18q loss' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 178 | 116 | 0.0 - 94.1 (11.0)
18Q LOSS MUTATED | 4 | 4 | 1.0 - 10.0 (4.5)
18Q LOSS WILD-TYPE | 174 | 112 | 0.0 - 94.1 (12.0)

Figure S7. Get High-res Image Gene #28: '18q loss' versus Clinical Feature #1: 'Time to Death'

'18q loss' versus 'YEARS_TO_BIRTH'

P value = 0.0121 (Wilcoxon-test), Q value = 0.24

Table S8. Gene #28: '18q loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 191 | 55.2 (16.1)
18Q LOSS MUTATED | 4 | 72.8 (5.4)
18Q LOSS WILD-TYPE | 187 | 54.9 (16.0)

Figure S8. Get High-res Image Gene #28: '18q loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

Methods & Data
Input

- Copy number data file = broad_values_by_arm.txt from GISTIC pipeline

- Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/LAML-TB/22529564/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/22506504/LAML-TB.merged_data.txt

- Number of patients = 191

- Number of significantly arm-level cnvs = 32

- Number of selected clinical features = 5

- Exclude regions 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

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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- 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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[3] 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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