Preferential Allele Expression Analysis Identifies Shared Germline and Somatic Driver Genes in Advanced Ovarian Cancer

Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore d...

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Published inPLoS genetics Vol. 12; no. 1; p. e1005755
Main Authors Halabi, Najeeb M, Martinez, Alejandra, Al-Farsi, Halema, Mery, Eliane, Puydenus, Laurence, Pujol, Pascal, Khalak, Hanif G, McLurcan, Cameron, Ferron, Gwenael, Querleu, Denis, Al-Azwani, Iman, Al-Dous, Eman, Mohamoud, Yasmin A, Malek, Joel A, Rafii, Arash
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.01.2016
Public Library of Science (PLoS)
Subjects
Alleles
Allelic Imbalance
Allelic Imbalance - genetics
Allelomorphism
Bias
Biochemistry, Molecular Biology
Biology
Cancer
Cell adhesion & migration
Female
Gene expression
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Genetic aspects
Genomes
Genomics
Germ Cells
Gynecology and obstetrics
Health aspects
High-Throughput Nucleotide Sequencing
Human health and pathology
Humans
Life Sciences
Metastasis
Mutation
Neoplasm Proteins
Neoplasm Proteins - biosynthesis
Neoplasm Proteins - genetics
Observations
Ovarian cancer
Ovarian Neoplasms
Ovarian Neoplasms - genetics
Ovarian Neoplasms - pathology
Patients
Studies
Transcriptome
Online AccessGet full text

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Abstract Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore developed a novel approach combining genome and transcriptome sequencing data from the same sample that corrects for sample heterogeneity and identifies significant preferentially expressed alleles. We applied this analysis to epithelial ovarian cancer samples consisting of matched primary ovary and peritoneum and lymph node metastasis. We find that preferentially expressed variant alleles include germline and somatic variants, are shared at a relatively high frequency between patients, and are in gene networks known to be involved in cancer processes. Analysis at a patient level identifies patient-specific preferentially expressed alleles in genes that are targets for known drugs. Analysis at a site level identifies patterns of site specific preferential allele expression with similar pathways being impacted in the primary and metastasis sites. We conclude that genes with preferentially expressed variant alleles can act as cancer drivers and that targeting those genes could lead to new therapeutic strategies.
AbstractList Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore developed a novel approach combining genome and transcriptome sequencing data from the same sample that corrects for sample heterogeneity and identifies significant preferentially expressed alleles. We applied this analysis to epithelial ovarian cancer samples consisting of matched primary ovary and peritoneum and lymph node metastasis. We find that preferentially expressed variant alleles include germline and somatic variants, are shared at a relatively high frequency between patients, and are in gene networks known to be involved in cancer processes. Analysis at a patient level identifies patient-specific preferentially expressed alleles in genes that are targets for known drugs. Analysis at a site level identifies patterns of site specific preferential allele expression with similar pathways being impacted in the primary and metastasis sites. We conclude that genes with preferentially expressed variant alleles can act as cancer drivers and that targeting those genes could lead to new therapeutic strategies.
Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore developed a novel approach combining genome and transcriptome sequencing data from the same sample that corrects for sample heterogeneity and identifies significant preferentially expressed alleles. We applied this analysis to epithelial ovarian cancer samples consisting of matched primary ovary and peritoneum and lymph node metastasis. We find that preferentially expressed variant alleles include germline and somatic variants, are shared at a relatively high frequency between patients, and are in gene networks known to be involved in cancer processes. Analysis at a patient level identifies patient-specific preferentially expressed alleles in genes that are targets for known drugs. Analysis at a site level identifies patterns of site specific preferential allele expression with similar pathways being impacted in the primary and metastasis sites. We conclude that genes with preferentially expressed variant alleles can act as cancer drivers and that targeting those genes could lead to new therapeutic strategies. Identifying genes that contribute to cancer biology is complicated partly because cancers can have dozens of somatic mutations and thousands of germline variants. Somatic mutations are gene variants that arise after conception in an organism while germline variants are gene variants present at conception in an organism. Most methods to identify cancer drivers have focused on determining somatic mutations. In this study we attempt to identify, from a tumor sample, important germline and somatic variants by determining if a variant is expressed (made into RNA) more than expected from the amount of the variant in the genome. The preferred expression of a variant could benefit cancer cells. When applying our analysis to ovarian cancer samples we found that despite the apparent heterogeneity, different patients frequently share the same genes with preferentially expressed variants. These genes in many cases are known to affect cancer processes such as DNA repair, cell adhesion and cell signaling and are targetable with known drugs. We therefore conclude that our analysis can identify germline and somatic gene variants that contribute to cancer biology and can potentially guide individualized therapies.
  Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore developed a novel approach combining genome and transcriptome sequencing data from the same sample that corrects for sample heterogeneity and identifies significant preferentially expressed alleles. We applied this analysis to epithelial ovarian cancer samples consisting of matched primary ovary and peritoneum and lymph node metastasis. We find that preferentially expressed variant alleles include germline and somatic variants, are shared at a relatively high frequency between patients, and are in gene networks known to be involved in cancer processes. Analysis at a patient level identifies patient-specific preferentially expressed alleles in genes that are targets for known drugs. Analysis at a site level identifies patterns of site specific preferential allele expression with similar pathways being impacted in the primary and metastasis sites. We conclude that genes with preferentially expressed variant alleles can act as cancer drivers and that targeting those genes could lead to new therapeutic strategies.
Audience Academic
Author Querleu, Denis
Al-Azwani, Iman
Al-Farsi, Halema
McLurcan, Cameron
Malek, Joel A
Rafii, Arash
Khalak, Hanif G
Puydenus, Laurence
Al-Dous, Eman
Martinez, Alejandra
Halabi, Najeeb M
Pujol, Pascal
Mery, Eliane
Mohamoud, Yasmin A
Ferron, Gwenael
AuthorAffiliation 4 Oncogenetics, Centre Hospitalier Regional Universitaire de Montpellier, Montpellier, France
University of Washington, UNITED STATES
6 Biosciences Department, University of Birmingham, Birmingham, United Kingdom
1 Department of Genetic Medicine, Weill-Cornell Medical College, New York, United States of America
2 Surgery Department, Institute Claudius Regaud, Toulouse, France
7 Genomics Core, Weill-Cornell Medical in Qatar, Doha, Qatar
8 Stem Cells and Microenvironment Laboratory, Weill-Cornell Medical College in Qatar, Doha, Qatar
5 Advanced Computing, Weill-Cornell Medical College in Qatar, Doha, Qatar
3 Pathology Department, Institute Claudius Regaud, Toulouse, France
AuthorAffiliation_xml – name: 7 Genomics Core, Weill-Cornell Medical in Qatar, Doha, Qatar
– name: 3 Pathology Department, Institute Claudius Regaud, Toulouse, France
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– name: 1 Department of Genetic Medicine, Weill-Cornell Medical College, New York, United States of America
– name: 5 Advanced Computing, Weill-Cornell Medical College in Qatar, Doha, Qatar
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ContentType Journal Article
Copyright COPYRIGHT 2016 Public Library of Science
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2016 Halabi et al 2016 Halabi et al
2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Halabi NM, Martinez A, Al-Farsi H, Mery E, Puydenus L, Pujol P, et al. (2016) Preferential Allele Expression Analysis Identifies Shared Germline and Somatic Driver Genes in Advanced Ovarian Cancer. PLoS Genet 12(1): e1005755. doi:10.1371/journal.pgen.1005755
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– notice: 2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Halabi NM, Martinez A, Al-Farsi H, Mery E, Puydenus L, Pujol P, et al. (2016) Preferential Allele Expression Analysis Identifies Shared Germline and Somatic Driver Genes in Advanced Ovarian Cancer. PLoS Genet 12(1): e1005755. doi:10.1371/journal.pgen.1005755
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PMCID: PMC4703369
Conceived and designed the experiments: NMH DQ AR AM. Performed the experiments: AM EM LP GF DQ JAM AR HAF PP IAA EAD YAM. Analyzed the data: NMH AM HGK CM JAM AR. Contributed reagents/materials/analysis tools: JAM. Wrote the paper: NMH AM AR HAF.
The authors have declared that no competing interests exist.
ORCID 0000-0002-7633-3536
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SSID ssj0035897
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Snippet Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of...
  Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of...
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StartPage e1005755
SubjectTerms Alleles
Allelic Imbalance
Allelic Imbalance - genetics
Allelomorphism
Bias
Biochemistry, Molecular Biology
Biology
Cancer
Cell adhesion & migration
Female
Gene expression
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Genetic aspects
Genomes
Genomics
Germ Cells
Gynecology and obstetrics
Health aspects
High-Throughput Nucleotide Sequencing
Human health and pathology
Humans
Life Sciences
Metastasis
Mutation
Neoplasm Proteins
Neoplasm Proteins - biosynthesis
Neoplasm Proteins - genetics
Observations
Ovarian cancer
Ovarian Neoplasms
Ovarian Neoplasms - genetics
Ovarian Neoplasms - pathology
Patients
Studies
Transcriptome
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Title Preferential Allele Expression Analysis Identifies Shared Germline and Somatic Driver Genes in Advanced Ovarian Cancer
URI https://www.ncbi.nlm.nih.gov/pubmed/26735499
https://search.proquest.com/docview/1760855525
https://hal.umontpellier.fr/hal-03127097
https://pubmed.ncbi.nlm.nih.gov/PMC4703369
https://doaj.org/article/cc8c5574e1de4071ae181c6134d40e89
http://dx.doi.org/10.1371/journal.pgen.1005755
Volume 12
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