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Identification of apilimod as a first-in-class PIKfyve kinase inhibitor for treatment of B-cell non-Hodgkin lymphoma.

ABSTRACT: We identified apilimod as an antiproliferative compound by high-throughput screening of clinical-stage drugs. Apilimod exhibits exquisite specificity for phosphatidylinositol-3-phosphate 5-kinase (PIKfyve) lipid kinase and has selective cytotoxic activity in B-cell non-Hodgkin lymphoma (B-NHL) compared with normal cells. Apilimod displays nanomolar activity in vitro, and in vivo studies demonstrate single-agent efficacy as well as synergy with approved B-NHL drugs. Using biochemical and knockdown approaches, and discovery of a kinase domain mutation conferring resistance, we demonstrate that apilimod-mediated cytotoxicity is driven by PIKfyve inhibition. Furthermore, a critical role for lysosome dysfunction as a major factor contributing to apilimod's cytotoxicity is supported by a genome-wide CRISPR screen. In the screen, TFEB (master transcriptional regulator of lysosomal biogenesis) and endosomal/lysosomal genes CLCN7, OSTM1, and SNX10 were identified as important determinants of apilimod sensitivity. These findings thus suggest that disruption of lysosomal homeostasis with apilimod represents a novel approach to treat B-NHL.

SUBMITTER: Gayle S

PROVIDER: S-EPMC5766845 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Despite decades of intensive research, NHL (non-Hodgkin lymphoma) still remains poorly understood and is largely incurable. Recent molecular studies suggest that genomic variants measured with SNPs (single nucleotide polymorphisms) in genes may have additional predictive power for NHL prognosis beyond clinical risk factors. We analyzed a genetic association study. The prognostic cohort consisted of 346 patients, among whom 138 had DLBCL (diffuse large B-cell lymphoma) and 101 had FL ( follicular lymphoma). For DLBCL, we analyzed 1229 SNPs which represented 122 KEGG pathways. For FL, we analyzed 1228 SNPs which represented 122 KEGG pathways. Unlike in existing studies, we targeted at identifying pathways with significant additional predictive power beyond clinical factors. In addition, we accounted for the joint effects of multiple SNPs within pathways, whereas some existing studies drew pathway-level conclusions based on separate analysis of individual SNPs. For DLBCL, we identified four pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 2.535, 2.220, 2.094, 2.453, and 2.512, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 0.552. For FL, we identified two pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 4.320 and 3.532, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 1.212. For NHL overall, we identified three pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 5.722, 5.314, and 5.441, respective. As a comparison, the clinical factors had a median of the prediction logrank statistics around 4.411. The identified pathways have sound biological bases. In addition, they are different from those identified using existing approaches. They may provide further insights into the biological mechanisms underlying the prognosis of NHL.

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Identification of apilimod as a first-in-class PIKfyve kinase inhibitor for treatment of B-cell non-Hodgkin lymphoma.

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