Targeting prostate tumor low-molecular weight tyrosine phosphatase for oxidation-sensitizing therapy.

Protein tyrosine phosphatases (PTPs) play major roles in cancer and are emerging as therapeutic targets. Recent reports suggest low-molecular weight PTP (LMPTP)-encoded by the ACP1 gene-is overexpressed in prostate tumors. We found ACP1 up-regulated in human prostate tumors and ACP1 expression inversely correlated with overall survival. Using CRISPR-Cas9-generated LMPTP knockout C4-2B and MyC-CaP cells, we identified LMPTP as a critical promoter of prostate cancer (PCa) growth and bone metastasis. Through metabolomics, we found that LMPTP promotes PCa cell glutathione synthesis by dephosphorylating glutathione synthetase on inhibitory Tyr270. PCa cells lacking LMPTP showed reduced glutathione, enhanced activation of eukaryotic initiation factor 2-mediated stress response, and enhanced reactive oxygen species after exposure to taxane drugs. LMPTP inhibition slowed primary and bone metastatic prostate tumor growth in mice. These findings reveal a role for LMPTP as a critical promoter of PCa growth and metastasis and validate LMPTP inhibition as a therapeutic strategy for treating PCa through sensitization to oxidative stress.

Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry.

Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening, integrating isotope-enriched metabolomics data and computational data mining, on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

Predicting and Quantifying Antagonistic Effects of Natural Compounds Given with Chemotherapeutic Agents: Applications for High-Throughput Screening.

Natural products have been used for centuries to treat various human ailments. In recent decades, multi-drug combinations that utilize natural products to synergistically enhance the therapeutic effects of cancer drugs have been identified and have shown success in improving treatment outcomes. While drug synergy research is a burgeoning field, there are disagreements on the definitions and mathematical parameters that prevent the standardization and proper usage of the terms synergy, antagonism, and additivity. This contributes to the relatively small amount of data on the antagonistic effects of natural products on cancer drugs that can diminish their therapeutic efficacy and prevent cancer regression. The ability of natural products to potentially degrade or reverse the molecular activity of cancer therapeutics represents an important but highly under-emphasized area of research that is often overlooked in both pre-clinical and clinical studies. This review aims to evaluate the body of work surrounding the antagonistic interactions between natural products and cancer therapeutics and highlight applications for high-throughput screening (HTS) and deep learning techniques for the identification of natural products that antagonize cancer drug efficacy.

Stable Isotope Tracing Metabolomics to Investigate the Metabolic Activity of Bioactive Compounds for Cancer Prevention and Treatment.

A major hallmark of cancer is the metabolic reprogramming of cancer cells to fuel tumor growth and proliferation. Various plant-derived bioactive compounds efficiently target the metabolic vulnerabilities of cancer cells and exhibit potential as emerging therapeutic agents. Due to their safety and common use as dietary components, they are also ideal for cancer prevention. However, to render their use as efficient as possible, the mechanism of action of these phytochemicals needs to be well characterized. Stable isotope tracing is an essential technology to study the molecular mechanisms by which nutraceuticals modulate and target cancer metabolism. The use of positionally labeled tracers as exogenous nutrients and the monitoring of their downstream metabolites labeling patterns enable the analysis of the specific metabolic pathway activity, via the relative production and consumption of the labeled metabolites. Although stable isotope tracing metabolomics is a powerful tool to investigate the molecular activity of bioactive compounds as well as to design synergistic nutraceutical combinations, this methodology is still underutilized. This review aims to investigate the research efforts and potentials surrounding the use of stable isotope tracing metabolomics to examine the metabolic alterations mediated by bioactive compounds in cancer.