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Blood Cancer Discovery
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Genomic Characterization of HIV-Associated Plasmablastic Lymphoma Identifies Pervasive Mutations in the JAK–STAT Pathway

Zhaoqi Liu, Ioan Filip, Karen Gomez, Dewaldt Engelbrecht, Shabnum Meer, Pooja N. Lalloo, Pareen Patel, Yvonne Perner, Junfei Zhao, Jiguang Wang, Laura Pasqualucci, Raul Rabadan and Pascale Willem
Zhaoqi Liu
1Program for Mathematical Genomics, Columbia University, New York, New York.
2Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, New York.
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Ioan Filip
1Program for Mathematical Genomics, Columbia University, New York, New York.
2Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, New York.
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Karen Gomez
1Program for Mathematical Genomics, Columbia University, New York, New York.
2Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, New York.
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Dewaldt Engelbrecht
3Department of Haematology and Molecular Medicine, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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Shabnum Meer
4Department of Oral Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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Pooja N. Lalloo
3Department of Haematology and Molecular Medicine, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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Pareen Patel
3Department of Haematology and Molecular Medicine, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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Yvonne Perner
5Department of Anatomical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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Junfei Zhao
1Program for Mathematical Genomics, Columbia University, New York, New York.
2Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, New York.
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Jiguang Wang
6Division of Life Science, Department of Chemical and Biological Engineering, Center for Systems Biology and Human Health and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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  • ORCID record for Jiguang Wang
Laura Pasqualucci
7Institute for Cancer Genetics.
8Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York.
9Department of Pathology and Cell Biology, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York.
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  • For correspondence: lp171@cumc.columbia.edu rr2579@cumc.columbia.edu pascale.willem@nhls.ac.za
Raul Rabadan
1Program for Mathematical Genomics, Columbia University, New York, New York.
2Departments of Systems Biology and Biomedical Informatics, Columbia University, New York, New York.
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  • For correspondence: lp171@cumc.columbia.edu rr2579@cumc.columbia.edu pascale.willem@nhls.ac.za
Pascale Willem
3Department of Haematology and Molecular Medicine, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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  • For correspondence: lp171@cumc.columbia.edu rr2579@cumc.columbia.edu pascale.willem@nhls.ac.za
DOI: 10.1158/2643-3230.BCD-20-0051 Published July 2020
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    Figure 1.

    The landscape of putative driver gene mutations in PBL. A, Sample information, MYC translocation, and somatic mutation information are shown for 110 cases of PBL samples. The heatmap represents individual mutations in each sample, color-coded by type of mutation. B–E, Individual gene mutation maps for frequently mutated genes, showing mutation subtype, position, and evidence of mutational hotspots, based on COSMIC database information. Y-axis counts at the bottom of the maps reflect the number of identified mutations in the COSMIC database. F, Sanger validation of single nucleotide variants (SNV) in STAT3 mutants.

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    Figure 2.

    Recurrent copy number changes in PBL. A, GISTIC 2.0 results showing recurrent copy number changes in PBL samples. The green line indicates q-value = 1.0 × 10−6. B, A zoomed-in view of 11p13 on 17 cases of PBL, which shows consistent focal copy number gains of CD44. The figure was generated by the IGV browser using CNV segment files from SNP-FASST2 algorithm. C, Scatter plot representations of genes located in regions with recurrent copy number gains in PBL (q-value <1.0 × 10−6, GISTIC 2.0). The horizontal axis indicates the −log10(q-value) from the GISTIC report, and the vertical axis is the median gene expression level (normalized RPKM value) from PBL RNA-seq data (n = 20). D, IHC showing strong CD44 protein expression on tumor cells membrane in a representative sample (A10), which has a focal CD44 copy number gain.

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    Figure 3.

    Comparative analysis of PBL and other B-cell malignancies. A, Unsupervised clustering based on frequencies of the most recurrently mutated genes from PBL, multiple myeloma, CLL, and two main subtypes of DLBCL (Methods). B, Hierarchical clustering of mRNA expression profiles across plasmablastic lymphoma, multiple myeloma, CLL, DLBCL and normal B cells, including centroblast (CB), naïve B (NB), and memory B cells (MB). Note that the JAK–STAT signaling pathway does not appear in this figure because only the top 1,000 most aberrantly expressed genes were selected for this analysis. Functional enrichment analysis was performed using g:profiler.

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    Figure 4.

    Activation of JAK–STAT pathway in PBL. A, Preranked gene set enrichment analysis indicating the significant positive enrichment of KEGG JAK–STAT signaling pathway in PBL. The preranked gene list was generated on the basis of the median expression level on PBL samples. B, Enrichment comparisons of JAK–STAT pathway between PBL and other B-cell malignancies by performing single-sample GSEA (ssGSEA). Pairwise P values were derived from t test. C and D, IHC plots showing pSTAT3 protein expression in >75% of tumor cells, confirming STAT3 activation in STAT3 mutated cases. Results using anti-pSTAT3 antibody are photographed at ×100 and ×400 magnification.

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    Figure 5.

    EBV transcription programs in PBL. Heatmap illustrating the full genome expression of EBV in the RNA-seq data of PBL samples (virus gene counts are normalized per million host reads). 16/17 EBV-positive samples show significantly higher expression of lytic genes such as BALF4 (encoding the envelope glycoprotein B) and BALF5 (encoding the DNA polymerase catalytic subunit) compared with the expression of canonical latency programs.

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Blood Cancer Discovery: 1 (1)
July 2020
Volume 1, Issue 1
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Genomic Characterization of HIV-Associated Plasmablastic Lymphoma Identifies Pervasive Mutations in the JAK–STAT Pathway
Zhaoqi Liu, Ioan Filip, Karen Gomez, Dewaldt Engelbrecht, Shabnum Meer, Pooja N. Lalloo, Pareen Patel, Yvonne Perner, Junfei Zhao, Jiguang Wang, Laura Pasqualucci, Raul Rabadan and Pascale Willem
Blood Cancer Discov July 1 2020 (1) (1) 112-125; DOI: 10.1158/2643-3230.BCD-20-0051

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Genomic Characterization of HIV-Associated Plasmablastic Lymphoma Identifies Pervasive Mutations in the JAK–STAT Pathway
Zhaoqi Liu, Ioan Filip, Karen Gomez, Dewaldt Engelbrecht, Shabnum Meer, Pooja N. Lalloo, Pareen Patel, Yvonne Perner, Junfei Zhao, Jiguang Wang, Laura Pasqualucci, Raul Rabadan and Pascale Willem
Blood Cancer Discov July 1 2020 (1) (1) 112-125; DOI: 10.1158/2643-3230.BCD-20-0051
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