Identification of Small Molecule Enhancers of Immunotherapy for Melanoma

Dextras, Christopher; Dashnyam, Myagmarjav; Griner, Lesley A. Mathews; Sundaresan, Janani; Chim, Bryan; Yu, Zhiya; Vodnala, Suman K.; Lee, Chyi‐Chia Richard; Hu, Xin; Southall, Noel; Marugán, Juan; Jadhav, Ajit; Restifo, Nicholas P.; Acquavella, Nicolas; Ferrer, Marc; Singh, Anju

doi:10.1038/s41598-020-62369-1

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…For example, PHA-793887 is a potent inhibitor of multiple cyclin-dependent kinases such as CDK2, CDK5 and CDK7, and has been shown to possess the ability to affect the differentiation of melanoma cells. [ 57 , 58 ]. This drug is currently in a clinical trial phase [ 59 ].…”

Section: Resultsmentioning

confidence: 99%

Computationally repurposing drugs for breast cancer subtypes using a network-based approach

et al. 2022

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Abstract‘De novo’ drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called ‘in silico’ drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

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Section: Resultsmentioning

confidence: 99%

Computationally repurposing drugs for breast cancer subtypes using a network-based approach

et al. 2022

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“…Compared with the properties of PC3 cells, LNCaP cells might represent a striking feature of early androgen-dependent PRCA, as the significantly enriched drug radicicol potentiates radiation-induced cell killing in a hormone-sensitive PRCA cell line through degradation of the androgen receptor [33]. PHA-793887 is an inhibitor of multiple cyclindependent kinases (CDKs), with activity against CDK2, CDK1, and CDK4, and has been validated to enhance immunotherapy against melanoma [34]. However, few studies have investigated the effects of these four drugs on PRCA, and thus, further study is necessary.…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers for immunotherapy response in prostate cancer and potential drugs to alleviate immunosuppression

Zhang

Ding

Peng

et al. 2022

Aging

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Background: Immunotherapy has a significant effect on the treatment of many tumor types. However, prostate cancers generally fail to show significant responses to immunotherapy owing to their immunosuppressive microenvironments. To sustain progress towards more effective immunotherapy for prostate cancer, comprehensive analyses of the genetic characteristics of the immune microenvironment and novel therapeutic strategies are required. Methods: The transcriptome profiles of patients with prostate cancer were obtained from GEO and processed with the TIDE algorithm to predict their responses to immunotherapy. Next, the significant differentially expressed genes (DEGs) between the responder and non-responder groups were identified and used to compute the co-expression modules by WGCNA. Then, co-expression networks were constructed and survival analysis was applied to hub genes. Finally, drug candidates to alleviate immunosuppression were filtered in prostate cancer using GSEA based on hub genes. Results: In total, we identified 2758 significant DEGs and constructed 16 co-expression modules, seven of which were significantly correlated with the immune response score. In total, 133 hub genes were identified, of which 13 were significantly associated with prostate cancer prognosis. Co-expression networks of hub genes were constructed with KMT2B at the center. Finally, six candidate drugs for prostate cancer immunotherapy were identified in PC3 and LNCaP cell lines. Conclusions: We obtained datasets from multiple platforms, performed integrated bioinformatic analysis to identify 133 hub genes and 13 biomarkers of an immunotherapy response, and six candidate drugs were filtered to inhibit the immunosuppressive tumor microenvironment, to ultimately improve patient responses to immunotherapy in prostate cancer.

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“…For the HTS viability assay, the T cells were purified, polarized, plated and treated with compounds as described above for HTS β-gal assay except Columns 1 and 2 had no cells and served as basal control. After 48 h of incubation, 3 μL of CellTiter-Glo reagent (Promega) was added using a Bioraptor Flying Reagent Dispenser (Aurora Discovery-BD) 50 . The plates were incubated for 15 min at room temperature and the luminescence signal was captured using a ViewLux (Perkin Elmer) containing a luminescent filter.…”

Section: Methodsmentioning

confidence: 99%

Identification of Small Molecule Inhibitors of a Mir155 Transcriptional Reporter in Th17 Cells

Singh

Dashynam

Chim

et al. 2021

Sci Rep

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MicroRNA miR-155 is an important regulatory molecule in the immune system and is highly expressed and functional in Th17 cells, a subset of CD4+ T helper cells which are key players in autoimmune diseases. Small molecules that can modulate miR-155 may potentially provide new therapeutic avenues to inhibit Th17 cell-mediated autoimmune diseases. Here, we present a novel high-throughput screening assay using primary T cells from genetically engineered Mir155 reporter mice, and its use to screen libraries of small molecules to identify novel modulators of Th17 cell function. We have discovered a chemical series of (E)-1-(phenylsulfonyl)-2-styryl-1H-benzo[d] imidazoles as novel down-regulators of Mir155 reporter and cytokine expression in Th17 cells. In addition, we found that FDA approved antiparasitic agents belonging to the ‘azole’ family also down-regulate Mir155 reporter and cytokine expression in Th17 cells, and thus could potentially be repurposed to treat Th17-driven immunopathologies.

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Identification of Small Molecule Enhancers of Immunotherapy for Melanoma

Abstract: The delta-delta ct was then calculated to compare effects of treatment on gene expression in addition to the melt curve analysis to ensure single product formation. Statistics. Data were analyzed using a 2-tailed Student's t-test and p < 0.05 was considered statistically significant.

Cited by 7 publications

References 34 publications

Computationally repurposing drugs for breast cancer subtypes using a network-based approach

Computationally repurposing drugs for breast cancer subtypes using a network-based approach

Identification of biomarkers for immunotherapy response in prostate cancer and potential drugs to alleviate immunosuppression

Identification of Small Molecule Inhibitors of a Mir155 Transcriptional Reporter in Th17 Cells

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