Integrating Pharmacogenomics Data-Driven Computational Drug Prediction with Single-Cell RNAseq to Demonstrate the Efficacy of a NAMPT Inhibitor against Aggressive, Taxane-Resistant, and Stem-like Cells in Lethal Prostate Cancer.

Simple Summary: Prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer deaths in US men. Resistance to standard medical castration and secondary taxane-based chemotherapy, the presence of cancer stem-like cells representing epithelial to mesenchymal transdiffer...

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Publicado en:Cancers Vol. 14; no. 23; pp. 6009 - 6048
Autores principales: Mazumder, Suman, Mitra Ghosh, Taraswi, Mukherjee, Ujjal K., Chakravarti, Sayak, Amiri, Farshad, Waliagha, Razan S., Hemmati, Farnaz, Mistriotis, Panagiotis, Ahmed, Salsabil, Elhussin, Isra, Salam, Ahmad-Bin, Dean-Colomb, Windy, Yates, Clayton, Arnold, Robert D., Mitra, Amit K.
Formato: equations & formulas pictorial research tables/charts Journal Article
Publicado: MDPI Dec2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Dec2022
      vid: 14
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      pub: MDPI
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        10.3390/cancers14236009
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        atl: Integrating Pharmacogenomics Data-Driven Computational Drug Prediction with Single-Cell RNAseq to Demonstrate the Efficacy of a NAMPT Inhibitor against Aggressive, Taxane-Resistant, and Stem-like Cells in Lethal Prostate Cancer.
      aug:
        au:
          Mazumder, Suman
          Mitra Ghosh, Taraswi
          Mukherjee, Ujjal K.
          Chakravarti, Sayak
          Amiri, Farshad
          Waliagha, Razan S.
          Hemmati, Farnaz
          Mistriotis, Panagiotis
          Ahmed, Salsabil
          Elhussin, Isra
          Salam, Ahmad-Bin
          Dean-Colomb, Windy
          Yates, Clayton
          Arnold, Robert D.
          Mitra, Amit K.
        affil: Department of Drug Discovery and Development, Harrison College of Pharmacy, Auburn University, Auburn, AL 36849, USA
      sug:
        subj:
          Prostatic Neoplasms Drug Therapy
          Drug Resistance, Neoplasm
          Transferases Antagonists and Inhibitors
          Drug Efficacy Evaluation
          Pharmacogenetics
          RNA
          Sequence Analysis
          Prediction Models Evaluation
          Human
          Male
          Prostatic Neoplasms Prognosis
          Epithelial-Mesenchymal Transition
          Stem Cells
          Signal Transduction
          Cell Movement
          Neoplasm Metastasis
          Cell Migration Assays
          Neoplasm Invasiveness
          Homeostasis
          Autophagy
          Drug Synergism
          Male
      ab: Simple Summary: Prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer deaths in US men. Resistance to standard medical castration and secondary taxane-based chemotherapy, the presence of cancer stem-like cells representing epithelial to mesenchymal transdifferentiation (EMT), and neuroendocrine (NEPC) subtypes are serious causes of concern for prostate cancer (PCa) treatment. Drug development against these advanced/lethal variants of PCa is, therefore, a significant unmet challenge. We have designed a novel computational prediction algorithm called "secDrug" that identified novel secondary drugs for the management of advanced-stage cancers. Using FK866 (a nicotinamide phosphoribosyltransferase/NAMPT inhibitor) as a proof-of-concept secDrug, we established a novel, universally applicable, preclinical drug development pipeline that incorporates bulk-tumor and single-cell RNA sequencing, microfluidics, as well as in vitro (cell line models representing clinically advanced PCa), and patient-based validation to introduce secondary drug choices to potentially circumvent subclonal aggressiveness, drug resistance, and stemness for the management of lethal subtypes of PCa. Metastatic prostate cancer/PCa is the second leading cause of cancer deaths in US men. Most early-stage PCa are dependent on overexpression of the androgen receptor (AR) and, therefore, androgen deprivation therapies/ADT-sensitive. However, eventual resistance to standard medical castration (AR-inhibitors) and secondary chemotherapies (taxanes) is nearly universal. Further, the presence of cancer stem-like cells (EMT/epithelial-to-mesenchymal transdifferentiation) and neuroendocrine PCa (NEPC) subtypes significantly contribute to aggressive/lethal/advanced variants of PCa (AVPC). In this study, we introduced a pharmacogenomics data-driven optimization-regularization-based computational prediction algorithm ("secDrugs") to predict novel drugs against lethal PCa. Integrating secDrug with single-cell RNA-sequencing/scRNAseq as a 'Double-Hit' drug screening tool, we demonstrated that single-cells representing drug-resistant and stem-cell-like cells showed high expression of the NAMPT pathway genes, indicating potential efficacy of the secDrug FK866 which targets NAMPT. Next, using several cell-based assays, we showed substantial impact of FK866 on clinically advanced PCa as a single agent and in combination with taxanes or AR-inhibitors. Bulk-RNAseq and scRNAseq revealed that, in addition to NAMPT inhibition, FK866 regulates tumor metastasis, cell migration, invasion, DNA repair machinery, redox homeostasis, autophagy, as well as cancer stemness–related genes, HES1 and CD44. Further, we combined a microfluidic chip-based cell migration assay with a traditional cell migration/'scratch' assay and demonstrated that FK866 reduces cancer cell invasion and motility, indicating abrogation of metastasis. Finally, using PCa patient datasets, we showed that FK866 is potentially capable of reversing the expression of several genes associated with biochemical recurrence, including IFITM3 and LTB4R. Thus, using FK866 as a proof-of-concept candidate for drug repurposing, we introduced a novel, universally applicable preclinical drug development pipeline to circumvent subclonal aggressiveness, drug resistance, and stemness in lethal PCa.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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