A Genomic Random Interval Model for Statistical Analysis of Genomic Lesion Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2013 Jul 12

PMID 23842812

Citations 13

Authors

Stan Pounds

Cheng Cheng

Shaoyu Li

Zhifa Liu

Jinghui Zhang

Charles Mullighan

Affiliations

Soon will be listed here.

Abstract

Motivation: Tumors exhibit numerous genomic lesions such as copy number variations, structural variations and sequence variations. It is difficult to determine whether a specific constellation of lesions observed across a cohort of multiple tumors provides statistically significant evidence that the lesions target a set of genes that may be located across different chromosomes but yet are all involved in a single specific biological process or function.

Results: We introduce the genomic random interval (GRIN) statistical model and analysis method that evaluates the statistical significance of the abundance of genomic lesions that overlap a specific locus or a pre-defined set of biologically related loci. The GRIN model retains certain biologically important properties of genomic lesions that are ignored by other methods. In a simulation study and two example analyses of leukemia genomic lesion data, GRIN more effectively identified important loci as significant than did three methods based on a permutation-of-markers model. GRIN also identified biologically relevant pathways with a significant abundance of lesions in both examples.

Availability: An R package will be freely available at CRAN and www.stjuderesearch.org/site/depts/biostats/software.

Citing Articles

Patterns and variations of copy number alterations in acute myeloid leukemia: insights from the LeukAtlas database.

Su Y, Han Z, Ji Y, Liu A, Zou D, Yan L Leukemia. 2025; .

PMID: 39894867 DOI: 10.1038/s41375-025-02514-9.

A new genomic framework to categorize pediatric acute myeloid leukemia.

Umeda M, Ma J, Westover T, Ni Y, Song G, Maciaszek J Nat Genet. 2024; 56(2):281-293.

PMID: 38212634 PMC: 10864188. DOI: 10.1038/s41588-023-01640-3.

Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics.

Cao X, H Elsayed A, Pounds S Methods Mol Biol. 2023; 2629:349-373.

PMID: 36929085 DOI: 10.1007/978-1-0716-2986-4_16.

The genomic landscape of pediatric acute lymphoblastic leukemia.

Brady S, Roberts K, Gu Z, Shi L, Pounds S, Pei D Nat Genet. 2022; 54(9):1376-1389.

PMID: 36050548 PMC: 9700506. DOI: 10.1038/s41588-022-01159-z.

Integrated Genomic Analysis Identifies UBTF Tandem Duplications as a Recurrent Lesion in Pediatric Acute Myeloid Leukemia.

Umeda M, Ma J, Huang B, Hagiwara K, Westover T, Abdelhamed S Blood Cancer Discov. 2022; 3(3):194-207.

PMID: 35176137 PMC: 9780084. DOI: 10.1158/2643-3230.BCD-21-0160.

References

Wang J, Mullighan C, Easton J, Roberts S, Heatley S, Ma J . CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nat Methods. 2011; 8(8):652-4. PMC: 3527068. DOI: 10.1038/nmeth.1628. View

Mermel C, Schumacher S, Hill B, Meyerson M, Beroukhim R, Getz G . GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011; 12(4):R41. PMC: 3218867. DOI: 10.1186/gb-2011-12-4-r41. View

Sanchez-Garcia F, Akavia U, Mozes E, Peer D . JISTIC: identification of significant targets in cancer. BMC Bioinformatics. 2010; 11:189. PMC: 2873534. DOI: 10.1186/1471-2105-11-189. View

Pounds S, Cheng C . Robust estimation of the false discovery rate. Bioinformatics. 2006; 22(16):1979-87. DOI: 10.1093/bioinformatics/btl328. View

Fontanillo C, Aibar S, Sanchez-Santos J, De Las Rivas J . Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer. BMC Genomics. 2012; 13 Suppl 5:S5. PMC: 3476997. DOI: 10.1186/1471-2164-13-S5-S5. View

Coustan-Smith E, Mullighan C, Onciu M, Behm F, Raimondi S, Pei D . Early T-cell precursor leukaemia: a subtype of very high-risk acute lymphoblastic leukaemia. Lancet Oncol. 2009; 10(2):147-56. PMC: 2840241. DOI: 10.1016/S1470-2045(08)70314-0. View

Witten D, Tibshirani R . Extensions of sparse canonical correlation analysis with applications to genomic data. Stat Appl Genet Mol Biol. 2009; 8:Article28. PMC: 2861323. DOI: 10.2202/1544-6115.1470. View

Holmfeldt L, Wei L, Diaz-Flores E, Walsh M, Zhang J, Ding L . The genomic landscape of hypodiploid acute lymphoblastic leukemia. Nat Genet. 2013; 45(3):242-52. PMC: 3919793. DOI: 10.1038/ng.2532. View

Yuan X, Yu G, Hou X, Shih I, Clarke R, Zhang J . Genome-wide identification of significant aberrations in cancer genome. BMC Genomics. 2012; 13:342. PMC: 3428679. DOI: 10.1186/1471-2164-13-342. View

10.

Zhang J, Ding L, Holmfeldt L, Wu G, Heatley S, Payne-Turner D . The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature. 2012; 481(7380):157-63. PMC: 3267575. DOI: 10.1038/nature10725. View

11.

Yuan X, Zhang J, Yang L, Zhang S, Chen B, Geng Y . TAGCNA: a method to identify significant consensus events of copy number alterations in cancer. PLoS One. 2012; 7(7):e41082. PMC: 3399811. DOI: 10.1371/journal.pone.0041082. View

12.

Mullighan C, Su X, Zhang J, Radtke I, Phillips L, Miller C . Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. N Engl J Med. 2009; 360(5):470-80. PMC: 2674612. DOI: 10.1056/NEJMoa0808253. View

13.

Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D . Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci U S A. 2007; 104(50):20007-12. PMC: 2148413. DOI: 10.1073/pnas.0710052104. View

14.

Witten D, Tibshirani R, Hastie T . A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics. 2009; 10(3):515-34. PMC: 2697346. DOI: 10.1093/biostatistics/kxp008. View