Preferential Allele Expression Analysis Identifies Shared Germline and Somatic Driver Genes in Advanced Ovarian Cancer
| Autor: | Iman K. Al-Azwani, Eman K. Al-Dous, Eliane Mery, Alejandra Martinez, Joel A. Malek, Hanif Khalak, Yasmin A. Mohamoud, Denis Querleu, Cameron McLurcan, Laurence Puydenus, Pascal Pujol, Najeeb Halabi, Gwenael Ferron, Halema Alfarsi, Arash Rafii |
|---|---|
| Přispěvatelé: | Weill Medical College of Cornell University [New York], Institut Claudius Regaud, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Weill Cornell Medical College in Qatar (WCMC-Q), Weill Cornell Medicine [Qatar], University of Birmingham [Birmingham], Développement embryonnaire précoce humain et pluripotence EmbryoPluripotency (UMR 1203), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-CHU Montpellier, Herrada, Anthony |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
MESH: Neoplasm Proteins
0301 basic medicine Cancer Research MESH: Mutation lcsh:QH426-470 medicine.medical_treatment [SDV.CAN]Life Sciences [q-bio]/Cancer [SDV.MHEP.GEO]Life Sciences [q-bio]/Human health and pathology/Gynecology and obstetrics Biology Germline Targeted therapy Metastasis Transcriptome 03 medical and health sciences Germline mutation [SDV.CAN] Life Sciences [q-bio]/Cancer [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN] Genetics medicine Allele MESH: High-Throughput Nucleotide Sequencing Molecular Biology Gene Genetics (clinical) Ecology Evolution Behavior and Systematics MESH: Gene Regulatory Networks MESH: Humans MESH: Alleles MESH: Transcriptome MESH: Allelic Imbalance Cancer MESH: Gene Expression Regulation Neoplastic medicine.disease 3. Good health lcsh:Genetics [SDV.MHEP.GEO] Life Sciences [q-bio]/Human health and pathology/Gynecology and obstetrics MESH: Ovarian Neoplasms MESH: Germ Cells 030104 developmental biology [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN] MESH: Female Research Article |
| Zdroj: | PLoS Genetics PLoS Genetics, Public Library of Science, 2016, 12 (1), pp.e1005755. ⟨10.1371/journal.pgen.1005755⟩ PLoS Genetics, Vol 12, Iss 1, p e1005755 (2016) |
| ISSN: | 1553-7404 1553-7390 |
| DOI: | 10.1371/journal.pgen.1005755⟩ |
| Popis: | 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. Author Summary 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. |
| Databáze: | OpenAIRE |
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