Loss of NF1 in Melanoma Confers Sensitivity to SYK Kinase Inhibition

Abecunas, Cara; Whitehead, Christopher; Ziemke, Elizabeth K.; Baumann, Douglas G.; Frankowski-McGregor, Christy L.; Sebolt–Leopold, Judith S.; Fallahi-Sichani, Mohammad

doi:10.1158/0008-5472.can-22-0883

Cited by 6 publications

(3 citation statements)

References 50 publications

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“…Finally, for melanomas with acquired resistance to RAF inhibitors, DMEA identified CHK inhibitors and SYK inhibitors as potentially beneficial. In fact, both CHK1 and SYK kinases have been identified as drug targets for melanomas resistant to RAF inhibitors [50, 51]. Collectively, these results support that DMEA can even identify drug mechanisms beneficial for combination treatments and drug-resistant cancers.…”

Section: Discussionmentioning

confidence: 77%

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

Garana

Jeon

Joly

et al. 2022

Preprint

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Motivation: Targeted therapeutics have the potential for efficacy against tumors with minimal effects on normal tissues. However, predicting effective drugs from molecular signatures remains a challenge. Here, we present Drug Mechanism Enrichment Analysis (DMEA), a method that uses a transcriptomic signature to predict drug mechanism(s) of action to which a tumor cell may be sensitive or resistant. The method derives its power by aggregating data from many drugs with a shared mechanism of action. Results: We first tested the sensitivity of DMEA using synthetic data. We next validated that DMEA recapitulated known sensitivities to HMGCR, EGFR, and RAF inhibitors while also identifying drug mechanisms for resistant cancers. Finally, we predicted tissue-dependent drug sensitivity for tumors with high and low expression of the cystine/glutamate antiporter xCT. Collectively, DMEA is a novel bioinformatic tool that uses molecular signatures to predict targeted therapeutics sharing a common mechanism of action. Availability and implementation: DMEA is freely available to download as an R package at: https://github.com/BelindaBGarana/DMEA.

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Section: Discussionmentioning

confidence: 77%

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

Garana

Jeon

Joly

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, for melanomas with acquired resistance to RAF inhibitors, DMEA identified CHK inhibitors and SYK inhibitors as potentially beneficial. In fact, both CHK1 and SYK kinases have been identified as drug targets for melanomas resistant to RAF inhibitors [ 58 , 59 ]. Collectively, these results support that DMEA can even identify drug mechanisms beneficial for combination treatments and drug-resistant cancers.…”

Section: Discussionmentioning

confidence: 99%

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

et al. 2023

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Background There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. Results First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. Conclusions DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA.

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“…The inhibitory effects of SYK and its relationship with mitochondrial function may help assess patients’ response to treatment. If SYK inhibitors can effectively suppress the growth and survival of melanoma cells, SYK activity levels may serve as a biomarker for predicting treatment outcomes ( Figure 1 ) ( 68 ). Zhicheng Zhou et al.…”

Section: Discovery Of Predictive Biomarkersmentioning

confidence: 99%

Advancing immunotherapy for melanoma: the critical role of single-cell analysis in identifying predictive biomarkers

He,

Lu,

Feng

et al. 2024

Front. Immunol.

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Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes.

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Loss of NF1 in Melanoma Confers Sensitivity to SYK Kinase Inhibition

Cited by 6 publications

References 50 publications

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

Advancing immunotherapy for melanoma: the critical role of single-cell analysis in identifying predictive biomarkers

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