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1.
Cancers (Basel) ; 16(13)2024 Jul 02.
Article in English | MEDLINE | ID: mdl-39001501

ABSTRACT

The recurrence of diffuse large B-cell lymphoma (DLBCL) has been observed in 40% of cases. The standard of care for refractory/relapsed DLBCL (RR-DLBCL) is platinum-based treatment prior to autologous stem cell transplantation; however, the prognosis for RR-DLBCL patients remains poor. Thus, to identify genes affecting the cisplatin response in DLBCL, cisplatin-based whole-genome CRISPR-Cas9 knockout screens were performed in this study. We discovered DNA damage response (DDR) pathways as enriched among identified sensitizing CRISPR-mediated gene knockouts. In line, the knockout of the nucleotide excision repair genes XPA and ERCC6 sensitized DLBCL cells to platinum drugs irrespective of proliferation rate, thus documenting DDR as essential for cisplatin sensitivity in DLBCL. Functional analysis revealed that the loss of XPA and ERCC6 increased DNA damage levels and altered cell cycle distribution. Interestingly, we also identified BTK, which is involved in B-cell receptor signaling, to affect cisplatin response. The knockout of BTK increased cisplatin sensitivity in DLBCL cells, and combinatory drug screens revealed a synergistic effect of the BTK inhibitor, ibrutinib, with platinum drugs at low concentrations. Applying local and external DLBCL cohorts, we addressed the clinical relevance of the genes identified in the CRISPR screens. BTK was among the most frequently mutated genes with a frequency of 3-5%, and XPA and ERCC6 were also mutated, albeit at lower frequencies. Furthermore, 27-54% of diagnostic DLBCL samples had mutations in pathways that can sensitize cells to cisplatin. In conclusion, this study shows that XPA and ERCC6, in addition to BTK, are essential for the response to platinum-based drugs in DLBCL.

2.
Cancer Chemother Pharmacol ; 89(4): 431-440, 2022 04.
Article in English | MEDLINE | ID: mdl-35190872

ABSTRACT

PURPOSE: Platinum-containing therapy is standard treatment for relapsed Diffuse Large B-Cell Lymphoma (DLBCL). However, the efficacy of treatment is limited by drug resistance leading to relapse. Cisplatin resistance has been linked to impairments of the DNA damage response, and several DNA repair proteins have been identified as clients of the molecular chaperone Hsp90. Here, we investigated the combinatory treatment of cisplatin and the Hsp90 inhibitor, 17AAG, in DLBCL cells to evaluate if inhibition of Hsp90 could sensitize DLBCL cells to cisplatin treatment. METHODS: Cell viability was assessed for cisplatin and 17AAG as monotherapies and for 25 different combinations in 7 DLBCL cell lines, where the Bliss Independence Model and the Combination Index were applied to assess their interaction. Induction of apoptosis and DNA damage response were evaluated by measuring Annexin V and γH2AX levels after 48 h of exposure. RESULTS: 17AAG synergized with cisplatin in DLBCL cells as detected in both interaction assessment models, resulting in a lower viability after 48 h for the combination-treated cells compared to both vehicle and single drug-treated cells. The combination also induced a stronger apoptotic response and an increase in DNA damage in 17AAG, cisplatin- and combination-treated cells compared to vehicle-treated cells, with the effect of the combination generally being higher than compared to both single drugs. CONCLUSION: This study demonstrates that 17AAG sensitizes DLBCL cells to cisplatin treatment. This effect is correlated with increased apoptotic and DNA damage response, potentially mediated by downregulation of Hsp90 clients in DNA repair pathways. Thus, cisplatin resistance could plausibly be overcome by combining the treatment with an Hsp90 inhibiting drug.


Subject(s)
Antineoplastic Agents , Lymphoma, Large B-Cell, Diffuse , Antineoplastic Agents/pharmacology , Antineoplastic Agents/therapeutic use , Apoptosis , Benzoquinones/pharmacology , Cell Line, Tumor , Cisplatin/pharmacology , Cisplatin/therapeutic use , HSP90 Heat-Shock Proteins , Humans , Lactams, Macrocyclic/pharmacology , Lymphoma, Large B-Cell, Diffuse/drug therapy , Lymphoma, Large B-Cell, Diffuse/pathology , Neoplasm Recurrence, Local/drug therapy
3.
Cancer Drug Resist ; 4(1): 208-222, 2021.
Article in English | MEDLINE | ID: mdl-35582014

ABSTRACT

Aim: Bendamustine is primarily used for treatment of indolent lymphomas but has shown efficacy in some patients with diffuse large B-cell lymphoma (DLBCL) and multiple myeloma (MM). Molecular-based patient stratification for identification of resistant patients, who will benefit from alternative treatments, is important. The aim of this study was to develop a resistance gene signature (REGS) from bendamustine dose-response assays in cultures of DLBCL and MM cell lines, enabling prediction of bendamustine response in DLBCL and MM patients. Methods: Bendamustine response was determined in 14 DLBCL and 11 MM cell lines. Using baseline gene expression profiles and degree of growth inhibition after bendamustine exposure, a bendamustine REGS was developed and examined for the risk stratification potential in DLBCL (n = 971) and MM (n = 1,126) patients divided into prognostic subtypes. Results: Bendamustine resistance significantly correlated with resistance to cyclophosphamide in DLBCL and melphalan in MM cell lines. The bendamustine REGS showed significantly lower bendamustine resistance probabilities in DLBCL patients with GCB subtype tumors and in tumors of the differentiation dependent centrocyte and plasmablast subtypes. In MM patients, pre-BII classified tumors displayed high bendamustine resistance probabilities and the plasma cell subtype had lower bendamustine resistance probability than memory cells. Furthermore, tumors belonging to the 4p14, MAF, and D2 TC subclasses consistently displayed high bendamustine resistance probabilities. Conclusion: Significant differences in predicted response to bendamustine were found in molecular subtypes of DLBCL and MM, encouraging validation in prospective bendamustine-treated cohorts with available gene expression profiles and follow-up data.

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