Department of Drug Discovery and Development, Harrison College of Pharmacy, Auburn University, Auburn, AL, USA.
Center for Pharmacogenomics and Single-Cell Omics initiative (AUPharmGx), Harrison College of Pharmacy, Auburn University, Auburn, AL, USA.
Blood Cancer J. 2022 Mar 9;12(3):39. doi: 10.1038/s41408-022-00636-2.
Multiple myeloma, the second-most common hematopoietic malignancy in the United States, still remains an incurable disease with dose-limiting toxicities and resistance to primary drugs like proteasome inhibitors (PIs) and Immunomodulatory drugs (IMiDs).We have created a computational pipeline that uses pharmacogenomics data-driven optimization-regularization/greedy algorithm to predict novel drugs ("secDrugs") against drug-resistant myeloma. Next, we used single-cell RNA sequencing (scRNAseq) as a screening tool to predict top combination candidates based on the enrichment of target genes. For in vitro validation of secDrugs, we used a panel of human myeloma cell lines representing drug-sensitive, innate/refractory, and acquired/relapsed PI- and IMiD resistance. Next, we performed single-cell proteomics (CyTOF or Cytometry time of flight) in patient-derived bone marrow cells (ex vivo), genome-wide transcriptome analysis (bulk RNA sequencing), and functional assays like CRISPR-based gene editing to explore molecular pathways underlying secDrug efficacy and drug synergy. Finally, we developed a universally applicable R-software package for predicting novel secondary therapies in chemotherapy-resistant cancers that outputs a list of the top drug combination candidates with rank and confidence scores.Thus, using 17AAG (HSP90 inhibitor) + FK866 (NAMPT inhibitor) as proof of principle secDrugs, we established a novel pipeline to introduce several new therapeutic options for the management of PI and IMiD-resistant myeloma.
在美国，多发性骨髓瘤是第二大常见的血液系统恶性肿瘤，但仍是一种无法治愈的疾病，存在剂量限制毒性和对蛋白酶体抑制剂（PI）和免疫调节药物（IMiD）等主要药物的耐药性。我们创建了一个计算管道，该管道使用基于药物基因组学数据的优化正则化/贪婪算法来预测针对耐药性骨髓瘤的新型药物（“secDrugs”）。接下来，我们使用单细胞 RNA 测序（scRNAseq）作为筛选工具，根据靶基因的富集来预测最佳组合候选物。为了对 secDrugs 进行体外验证，我们使用了一组代表药物敏感、先天/难治性和获得性/复发性 PI 和 IMiD 耐药性的人骨髓瘤细胞系。接下来，我们在患者来源的骨髓细胞（体外）中进行单细胞蛋白质组学（CyTOF 或 Cytometry time of flight）、全基因组转录组分析（批量 RNA 测序）以及基于 CRISPR 的基因编辑等功能测定，以探索 secDrug 疗效和药物协同作用的分子途径。最后，我们开发了一种通用的 R 软件包，用于预测化疗耐药性癌症中的新型二线治疗方法，该软件包输出了一份具有排名和置信度评分的最佳药物组合候选物列表。因此，我们使用 17AAG（HSP90 抑制剂）+ FK866（NAMPT 抑制剂）作为 secDrugs 的原理验证，为管理 PI 和 IMiD 耐药性骨髓瘤建立了一种新的治疗方案。
