The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases
Abstract
Abstract The increasing availability of genomic and transcriptomic sequencing has uncovered diverse genomic alterations and distinct gene expression profiles driving hematologic diseases, yet a data integration and sharing platform dedicated to hematology remains lacking. We developed the American Society of Hematology (ASH) HematOmics Program (ASHOP; ashop.hematology.org), a resource for exploring somatic alterations and gene fusions, transcriptomic results, and clinical data from 5960 patients spanning B-cell precursor and T-cell acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndromes, and chronic lymphocytic leukemia. Users can explore genomic alteration landscapes and comutation patterns via lollipop and matrix plots and analyze significantly altered genes in user-defined subcohorts. Transcriptomes can be explored through interactive uniform manifold approximation and projections, clustering, differential expression, and pathway enrichment. Genomic, transcriptomic features, and clinical outcomes can be correlated in a user-driven manner or combined to precisely define study cohorts. We illustrate the following 4 use cases of ASHOP: (1) stratification of DUX4-rearranged B-cell leukemias into Early/Multipotent and Committed subgroups with distinct outcomes, (2) characterization of HOXA/HOXB expression patterns in acute myeloid leukemias, (3) correlating mutational burden with mismatch repair deficiency and mutational signatures, and (4) investigation of TP53 alteration landscape. ASHOP is an open-access resource to inform genomic and transcriptomic data interpretation for hematologic malignancies and will expand to support additional diseases and data modalities from the ASH community.
Article Details
Authors (23)
Congyu Lu
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Gavriel Y. Matt
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Robin Paul
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Jian Wang
Edgar Sioson
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Karishma Gangwani
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Aleksandar Acić
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Andrew Willems
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Airen Zaldívar Peraza
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Colleen Reilly
1Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN
Petri Pölönen
Qingsong Gao
2Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN
Qian Li
Stanley B. Pounds
3Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
Andy G. X. Zeng
Sihan Li
Division of Ribonucleic Acid (RNA) and Gene Regulation, Institute of Medical Science, The University of Tokyo
Niroshan Nadarajah
7Munich Leukemia Laboratory, Munich, Germany
Samuel W. Brady
Ilaria Iacobucci
2Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN
Torsten Haferlach
7Munich Leukemia Laboratory, Munich, Germany
Miquella C. Rose
8American Society of Hematology, Washington, DC
Charles G. Mullighan
Xin Zhou