Acquired drug resistance is the greatest obstacle to the successful treatment of multiple myeloma (MM). Despite recent advanced treatment options such as liposomal formulations, proteasome inhibitors, immunomodulatory drugs, myeloma-targeted antibodies, and histone deacetylase inhibitors, MM is still considered an incurable disease. We investigated whether the clinical exportin 1 (XPO1) inhibitor selinexor (KPT-330), when combined with pegylated liposomal doxorubicin (PLD) or doxorubicin hydrochloride, could overcome acquired drug resistance in multidrug-resistant human MM xenograft tumors, four different multidrug-resistant MM cell lines, or ex vivo MM biopsies from relapsed/refractory patients. Mechanistic studies were performed to assess co-localization of topoisomerase II alpha (TOP2A), DNA damage, and siRNA knockdown of drug targets. Selinexor was found to restore sensitivity of multidrug-resistant 8226B25, 8226Dox6, 8226Dox40, and U266PSR human MM cells to doxorubicin to levels found in parental myeloma cell lines. NOD/SCID-γ mice challenged with drug-resistant or parental U266 human MM and treated with selinexor/PLD had significantly decreased tumor growth and increased survival with minimal toxicity. Selinexor/doxorubicin treatment selectively induced apoptosis in CD138/light-chain-positive MM cells without affecting non-myeloma cells in ex vivo-treated bone marrow aspirates from newly diagnosed or relapsed/refractory MM patients. Selinexor inhibited XPO1-TOP2A protein complexes (proximity ligation assay), preventing nuclear export of TOP2A in both parental and multidrug-resistant MM cell lines. Selinexor/doxorubicin treatment significantly increased DNA damage (comet assay/γ-H2AX) in both parental and drug-resistant MM cells. TOP2A knockdown reversed both the anti-tumor effect and significantly reduced DNA damage induced by selinexor/doxorubicin treatment. The combination of an XPO1 inhibitor and liposomal doxorubicin was highly effective against acquired drug resistance in in vitro MM models, in in vivo xenograft studies, and in ex vivo samples obtained from patients with relapsed/refractory myeloma. This drug combination synergistically induced TOP2A-mediated DNA damage and subsequent apoptosis. In addition, based on our preclinical data, we have initiated a phase I/II study with the XPO1 inhibitor selinexor and PLD (ClinicalTrials.gov NCT02186834). Initial results from both preclinical and clinical trials have shown significant promise for this drug combination for the treatment of MM.

The clinical utility of histone/protein deacetylase (HDAC) inhibitors in combinatorial regimens with proteasome inhibitors for patients with relapsed and refractory multiple myeloma (MM) is often limited by excessive toxicity due to HDAC inhibitor promiscuity with multiple HDACs. Therefore, more selective inhibition minimizing off-target toxicity may increase the clinical effectiveness of HDAC inhibitors. We demonstrated that plasma cell development and survival are dependent upon HDAC11, suggesting this enzyme is a promising therapeutic target in MM. Mice lacking HDAC11 exhibited markedly decreased plasma cell numbers. Accordingly, in vitro plasma cell differentiation was arrested in B cells lacking functional HDAC11. Mechanistically, we showed that HDAC11 is involved in the deacetylation of IRF4 at lysine ¹⁰³ . Further, targeting HDAC11 led to IRF4 hyperacetylation, resulting in impaired IRF4 nuclear localization and target promoter binding. Importantly, transient HDAC11 knockdown or treatment with elevenostat, an HDAC11-selective inhibitor, induced cell death in MM cell lines. Elevenostat produced similar anti-MM activity in vivo, improving survival among mice inoculated with 5TGM1 MM cells. Elevenostat demonstrated nanomolar ex vivo activity in 34 MM patient specimens and synergistic activity when combined with bortezomib. Collectively, our data indicated that HDAC11 regulates an essential pathway in plasma cell biology establishing its potential as an emerging theraputic vulnerability in MM.

Multiple myeloma is a genetically complex hematologic neoplasia in which malignant plasma cells constantly operate at the maximum limit of their unfolded protein response (UPR) due to a high secretory burden of immunoglobulins and cytokines. The endoplasmic reticulum (ER) resident protein disulfide isomerase, PDIA1 is indispensable for maintaining structural integrity of cysteine-rich antibodies and cytokines that require accurate intramolecular disulfide bond arrangement. PDIA1 expression analysis from RNA-seq of multiple myeloma patients demonstrated an inverse relationship with survival in relapsed or refractory disease, supporting its critical role in myeloma persistence. Using a structure-guided medicinal chemistry approach, we developed a potent, orally bioavailable small molecule PDIA1 inhibitor CCF642-34. The inhibition of PDIA1 overwhelms the UPR in myeloma cells, resulting in their apoptotic cell death at doses that do not affect the normal CD34⁺ hematopoietic stem and progenitor cells. Bortezomib resistance leads to increased PDIA1 expression and thus CCF642-34 sensitivity, suggesting that proteasome inhibitor resistance leads to PDIA1 dependence for proteostasis and survival. CCF642-34 induces acute unresolvable UPR in myeloma cells, and oral treatment increased survival of mice in the syngeneic 5TGM1 model of myeloma. Results support development of CCF642-34 to selectively target the plasma cell program and overcome the treatment-refractory state in myeloma.

Time-lapse microscopy is a powerful technique that generates large volumes of image-based information to quantify the behaviors of cell populations. This method has been applied to cancer studies to estimate the drug response for precision medicine and has great potential to address inter-patient (or intertumoral) heterogeneity. A couple of algorithms exist to analyze time-lapse microscopy images; however, most deal with very high-resolution images involving few cells (typically cell lines). There are currently no advanced and efficient computational frameworks available to process large-scale time-lapse microscopy imaging data to estimate patient-specific response to therapy based on a large population of primary cells. In this paper, we propose a robust and user-friendly pipeline to preprocess the images and track the behaviors of thousands of cancer cells simultaneously for a better drug response prediction of cancer patients. Availability and Implementation: Source code is available at: https://github.com/CompbioLabUCF/CellTrack ACM Reference Format: Qibing Jiang, Praneeth Sudalagunta, Mark B. Meads, Khandakar Tanvir Ahmed, Tara Rutkowski, Ken Shain, Ariosto S. Silva, and Wei Zhang. 2020. An Advanced Framework for Time-lapse Microscopy Image Analysis. In Proceedings of BioKDD: 19th International Workshop on Data Mining In Bioinformatics (BioKDD). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn