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Clinical Utility of Single Nucleotide Polymorphism (SNP) Microarrays in Pediatric Cancer and Non-Malignant Hematologic Disorders
420 Clinical Utility of Single Nucleotide Polymorphism (SNP) Microarrays in Pediatric Cancer and Non-Malignant Hematologic Disorders Xin-Yan Lu, Yi-Jue Zhao, Sivashankarappa Gurusiddappa, Ching C. Lau, Jason M. Shohet, Pulivarthi H. Rao, Karen R. Rabin, Sharon E. Plon Texas Children’s Cancer and Hematology Centers, Department of Pediatrics, Baylor College of Medicine, Houston, TX
Array-based technology has been showing great impact in clinical cancer cytogenetics. This study was to test the feasibility of single nucleotide polymorphism (SNP) microarrays in the clinical diagnosis of pediatric cancers. A total of 74 cases, including 39 acute leukemias (27 B-cell precursor acute lymphoblastic leukemia [BCPALL], 6 T-cell lymphoblastic leukemia, 5 acute myeloid leukemia, and 1 mix-phenotypic acute leukemia), 15 solid tumors, 5 myelodysplastic disorders, 6 non-malignant hematologic disorders, 5 lymphomas, 1 chronic myeloid leukemia cell line, as well as 3 normal remission marrows were tested using Illumina Cyto 12 and/ or 1M quad SNP arrays. Overall the SNP array data showed good concordance with cytogenetic/FISH data. SNP arrays revealed most cytogenetically unidentifiable abnormalities including copy neutral loss of heterozygosity (CN-LOH). In addition, SNP arrays provided prognostic information in 10 acute leukemia cases previously reported with normal cytogenetics and/or FISH and detected deletion of cancer genes (PAX5, MLL, PTEN and EVI1, etc.) at the exon level. Importantly, in 1 case with thrombocytopenia and a known constitutional RUNX1 gene deletion, SNP array revealed a low level (w20%) of mosaic CN-LOH for chromosome 21; and in 2 BCP-ALL cases reported as hyperdiploid by chromosome /FISH analyses, SNP array detected CN-LOH in all disomic chromosomes, indicating that the hyperdiploidy in fact resulted from doubling of a hypodiploid clone. Finally, SNP array detected 3 novel amplifications at 2p25.2 in a neuroblastoma case originally reported with MYCN amplification and amplicons at 2p25.1wp24.3 encompassing 16 genes in a high-grade glioma originally reported with double minute chromosomes. In conclusion, SNP arrays could not only confirm most karyotypic/FISH data but could also detect additional genomic aberrations, some with significant clinical implications. This pilot study shows that SNP array has great potential as a diagnostic tool in pediatric cancer cytogenetics and can be integrated into routine clinical cancer cytogenetic testing. Conflict of Interest: None.
Atlas of Cytogenomics in Oncology and Hematology: a Platform-Neutral Clinical Cancer Genomics Database Bixia Xiang a, Annette Leon b, Marilyn M. Li c, Anwar M. Iqbal d, Peining Li e, Shibo Li f, Peter R. Papenhausen g, Stuart Schwartz g, Xiao-Xiang Zhang g, Katherine B. Geiersbach h, Sarah South h, Guangyu Gu h, Jacqueline R. Batanian i, Xinyan Lu j, Daynna J. Wolff k, Iya Znoyko k, Rajyalakshmi Luthra l, Su S. Chen l, Keyur P. Patel l, Rachel L. Sargent l, Rizwan C. Naeem m, Lina Shao n, Renu Bajaj o, Stephen C. Peiper o, Zi-Xuan (Zoe) Wang o, Teresa Smolarek p, Lauren S. Jenkins q, Xu Li q, Feng Li q, Sainan Wei r, Jennelle C. Hodge s, Joyce L. Murata-Collins a, Zhiwei Che t, Ausaf Ahmad d, Ming Qi u, Stephen J. Forman a, Gail H. Vance v, Robert G. Best w a Cytogenetics Laboratory, City of Hope National Medical Center; b GenPath Diagnostics, BioReference Laboratories; c Department of Molecular and Human Genetics, Baylor College of Medicine;
Abstracts d
Department of Pathology and Laboratory Medicine, University of Rochester Medical Center; e Molecular Cytogenetics Laboratory, Department of Genetics, Yale School of Medicine; f Oklahoma University Health Sciences Center; g Laboratory Corporation of America; h Department of Pathology, ARUP Laboratories, University of Utah; i Department of Pediatrics and Pathology, Cardinal Glennon Children’s Hospital, Saint Louis University School of Medicine; j Texas Children’s Hospital, Baylor College of Medicine; k Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Molecular Diagnostic Laboratory, Department of Hematopathology; l The University of Texas M.D. Anderson Cancer Center; m Albert Einstein College of Medicine and Montefiore Medical Center; n Department of Pathology, University of Michigan; o Department of Anatomy, Pathology and Cell Biology, Thomas Jefferson University Hospital; p Cincinnati Children’s Hospital Medical Center; q Cytogenetics Laboratory, Kaiser Permanente San Jose Medical Center; r Department of Pediatrics and Human Development, Michigan State University; s Department of Laboratory Medicine and Pathology, Mayo Clinic; t Department of Application Science, BioDiscovery, Inc; u Center for Genetic and Genomic Medicine, Zhejiang University School of Medicine, James Watson Institute of Genome Sciences, P.R. China; v Department of Medical and Molecular Genetics, Indiana University School of Medicine; w University of South Carolina School of Medicine
Clinical interpretation of complex somatic genomic data remains the largest obstacle for the widespread application of array technologies for personalized medicine approaches in hematology and oncology specimens. To address this challenge, a database, tentatively named the Atlas of Cytogenomics in Oncology and Hematology, has been designed with 2 major objectives: 1) cataloging copy number alterations (CNA) and loss of heterozygosity (LOH) incidence maps to build strong empirical evidence to facilitate interpretation of clinical cytogenomic findings, and 2) generation of new knowledge by using machine learning algorithms. The database already contains over 2000 cancer array cases from 22 clinical laboratories. Raw genomic data and associated clinical information are accepted from any array platform and currently includes Agilent, Affymetrix, Illumina, and NimbleGen. Cases span the common clinical indication categories including: chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), acute lymphocytic leukemia (ALL), myeloproliferative neoplasms (MPN), plasma cell myeloma (PCM), and some solid tumors. For each disease group, the database provides: 1) Incidence maps for CNA and LOH, 2) Hyperlinks to relevant cytogenetics resources, and 3) Cytogenomic literature collection, summary, and an interface for user interaction. This database is dynamic: acquiring continuous data contribution from clinical laboratories, being scalable to whole genome sequence (WGS) data, and accommodating increasingly complex cancer indications stratification. While the initial purpose is as a catalog of CNA, LOH, and genotype-phenotype correlations from clinical empirical data, the machine learning algorithms will identify new insights into complex genomic patterns toward the goal of developing personalized evidence-based therapeutic interventions. Conflict of Interest: None.
Ascertainment of Recurrent Translocations by Chromosomal Microarray Analysis Guangyu Gu a,b, Maria Sederberg b, Christian Paxton b, Leslie Rowe b, Katherine Geiersbach a,b, Sarah South a,b a Division of Medical Genetics, University of Utah, Salt Lake City, UT; b ARUP Laboratories, Salt Lake City, UT
Array-based technology has been showing great impact in clinical cancer cytogenetics. This study was to test the feasibility of single nucleotide polymorphism (SNP) microarrays in the clinical diagnosis of pediatric cancers. A total of 74 cases, including 39 acute leukemias (27 B-cell precursor acute lymphoblastic leukemia [BCPALL], 6 T-cell lymphoblastic leukemia, 5 acute myeloid leukemia, and 1 mix-phenotypic acute leukemia), 15 solid tumors, 5 myelodysplastic disorders, 6 non-malignant hematologic disorders, 5 lymphomas, 1 chronic myeloid leukemia cell line, as well as 3 normal remission marrows were tested using Illumina Cyto 12 and/ or 1M quad SNP arrays. Overall the SNP array data showed good concordance with cytogenetic/FISH data. SNP arrays revealed most cytogenetically unidentifiable abnormalities including copy neutral loss of heterozygosity (CN-LOH). In addition, SNP arrays provided prognostic information in 10 acute leukemia cases previously reported with normal cytogenetics and/or FISH and detected deletion of cancer genes (PAX5, MLL, PTEN and EVI1, etc.) at the exon level. Importantly, in 1 case with thrombocytopenia and a known constitutional RUNX1 gene deletion, SNP array revealed a low level (w20%) of mosaic CN-LOH for chromosome 21; and in 2 BCP-ALL cases reported as hyperdiploid by chromosome /FISH analyses, SNP array detected CN-LOH in all disomic chromosomes, indicating that the hyperdiploidy in fact resulted from doubling of a hypodiploid clone. Finally, SNP array detected 3 novel amplifications at 2p25.2 in a neuroblastoma case originally reported with MYCN amplification and amplicons at 2p25.1wp24.3 encompassing 16 genes in a high-grade glioma originally reported with double minute chromosomes. In conclusion, SNP arrays could not only confirm most karyotypic/FISH data but could also detect additional genomic aberrations, some with significant clinical implications. This pilot study shows that SNP array has great potential as a diagnostic tool in pediatric cancer cytogenetics and can be integrated into routine clinical cancer cytogenetic testing. Conflict of Interest: None.
Atlas of Cytogenomics in Oncology and Hematology: a Platform-Neutral Clinical Cancer Genomics Database Bixia Xiang a, Annette Leon b, Marilyn M. Li c, Anwar M. Iqbal d, Peining Li e, Shibo Li f, Peter R. Papenhausen g, Stuart Schwartz g, Xiao-Xiang Zhang g, Katherine B. Geiersbach h, Sarah South h, Guangyu Gu h, Jacqueline R. Batanian i, Xinyan Lu j, Daynna J. Wolff k, Iya Znoyko k, Rajyalakshmi Luthra l, Su S. Chen l, Keyur P. Patel l, Rachel L. Sargent l, Rizwan C. Naeem m, Lina Shao n, Renu Bajaj o, Stephen C. Peiper o, Zi-Xuan (Zoe) Wang o, Teresa Smolarek p, Lauren S. Jenkins q, Xu Li q, Feng Li q, Sainan Wei r, Jennelle C. Hodge s, Joyce L. Murata-Collins a, Zhiwei Che t, Ausaf Ahmad d, Ming Qi u, Stephen J. Forman a, Gail H. Vance v, Robert G. Best w a Cytogenetics Laboratory, City of Hope National Medical Center; b GenPath Diagnostics, BioReference Laboratories; c Department of Molecular and Human Genetics, Baylor College of Medicine;
Abstracts d
Department of Pathology and Laboratory Medicine, University of Rochester Medical Center; e Molecular Cytogenetics Laboratory, Department of Genetics, Yale School of Medicine; f Oklahoma University Health Sciences Center; g Laboratory Corporation of America; h Department of Pathology, ARUP Laboratories, University of Utah; i Department of Pediatrics and Pathology, Cardinal Glennon Children’s Hospital, Saint Louis University School of Medicine; j Texas Children’s Hospital, Baylor College of Medicine; k Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Molecular Diagnostic Laboratory, Department of Hematopathology; l The University of Texas M.D. Anderson Cancer Center; m Albert Einstein College of Medicine and Montefiore Medical Center; n Department of Pathology, University of Michigan; o Department of Anatomy, Pathology and Cell Biology, Thomas Jefferson University Hospital; p Cincinnati Children’s Hospital Medical Center; q Cytogenetics Laboratory, Kaiser Permanente San Jose Medical Center; r Department of Pediatrics and Human Development, Michigan State University; s Department of Laboratory Medicine and Pathology, Mayo Clinic; t Department of Application Science, BioDiscovery, Inc; u Center for Genetic and Genomic Medicine, Zhejiang University School of Medicine, James Watson Institute of Genome Sciences, P.R. China; v Department of Medical and Molecular Genetics, Indiana University School of Medicine; w University of South Carolina School of Medicine
Clinical interpretation of complex somatic genomic data remains the largest obstacle for the widespread application of array technologies for personalized medicine approaches in hematology and oncology specimens. To address this challenge, a database, tentatively named the Atlas of Cytogenomics in Oncology and Hematology, has been designed with 2 major objectives: 1) cataloging copy number alterations (CNA) and loss of heterozygosity (LOH) incidence maps to build strong empirical evidence to facilitate interpretation of clinical cytogenomic findings, and 2) generation of new knowledge by using machine learning algorithms. The database already contains over 2000 cancer array cases from 22 clinical laboratories. Raw genomic data and associated clinical information are accepted from any array platform and currently includes Agilent, Affymetrix, Illumina, and NimbleGen. Cases span the common clinical indication categories including: chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), acute lymphocytic leukemia (ALL), myeloproliferative neoplasms (MPN), plasma cell myeloma (PCM), and some solid tumors. For each disease group, the database provides: 1) Incidence maps for CNA and LOH, 2) Hyperlinks to relevant cytogenetics resources, and 3) Cytogenomic literature collection, summary, and an interface for user interaction. This database is dynamic: acquiring continuous data contribution from clinical laboratories, being scalable to whole genome sequence (WGS) data, and accommodating increasingly complex cancer indications stratification. While the initial purpose is as a catalog of CNA, LOH, and genotype-phenotype correlations from clinical empirical data, the machine learning algorithms will identify new insights into complex genomic patterns toward the goal of developing personalized evidence-based therapeutic interventions. Conflict of Interest: None.
Ascertainment of Recurrent Translocations by Chromosomal Microarray Analysis Guangyu Gu a,b, Maria Sederberg b, Christian Paxton b, Leslie Rowe b, Katherine Geiersbach a,b, Sarah South a,b a Division of Medical Genetics, University of Utah, Salt Lake City, UT; b ARUP Laboratories, Salt Lake City, UT