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Drug Discovery Using Evolutionary Similarities in Chemical Binding to Inhibit Patient-Derived Hepatocellular Carcinoma

ABSTRACT: Drug resistance causes therapeutic failure in refractory cancer. Cancer drug resistance stems from various factors, such as patient heterogeneity and genetic alterations in somatic cancer cells, including those from identical tissues. Generally, resistance is intrinsic for cancers; however, cancer resistance becomes common owing to an increased drug treatment. Unfortunately, overcoming this issue is not yet possible. The present study aimed to evaluate a clinical approach using candidate compounds 19 and 23, which are sarcoplasmic/endoplasmic reticulum calcium ATPase (SERCA) inhibitors, discovered using the evolutionary chemical binding similarity method. mRNA sequencing indicated SERCA as the dominant marker of patient-derived anti-cancer drug-resistant hepatocellular carcinoma (HCC), but not of patient-derived anti-cancer drug-sensitive HCC. Candidate compounds 19 and 23 led to significant tumor shrinkage in a tumor xenograft model of anti-cancer drug-resistant patient-derived HCC cells. Our results might be clinically significant for the development of novel combinatorial strategies that selectively and efficiently target highly malignant cells such as drug-resistant and cancer stem-like cells.

SUBMITTER: Lim J

PROVIDER: S-EPMC9322808 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHepatocellular carcinoma (HCC) remains difficult to treat due to limited effective treatment options. While the proteasome inhibitor bortezomib has shown promising preclinical activity in HCC, clinical trials of bortezomib showed no advantage over the standard-of-care treatment sorafenib, highlighting the need for more clinically relevant therapeutic strategies. Here, we propose that rational drug combination design and validation in patient-derived HCC avatar models such as patient-derived xenografts (PDXs) and organoids can improve proteasome inhibitor-based therapeutic efficacy and clinical potential.MethodsHCC PDXs and the corresponding PDX-derived organoids (PDXOs) were generated from primary patient samples for drug screening and efficacy studies. To identify effective proteasome inhibitor-based drug combinations, we applied a hybrid experimental-computational approach, Quadratic Phenotypic Optimization Platform (QPOP) on a pool of nine drugs comprising proteasome inhibitors, kinase inhibitors and chemotherapy agents. QPOP utilizes small experimental drug response datasets to accurately identify globally optimal drug combinations.ResultsPreliminary drug screening highlighted the increased susceptibility of HCC PDXOs towards proteasome inhibitors. Through QPOP, the combination of second-generation proteasome inhibitor ixazomib (Ixa) and CDK inhibitor dinaciclib (Dina) was identified to be effective against HCC. In vitro and in vivo studies demonstrated the synergistic pro-apoptotic and anti-proliferative activity of Ixa + Dina against HCC PDXs and PDXOs. Furthermore, Ixa + Dina outperformed sorafenib in mitigating tumor formation in mice. Mechanistically, increased activation of JNK signaling mediates the combined anti-tumor effects of Ixa + Dina in HCC tumor cells.ConclusionsRational drug combination design in patient-derived avatars highlights the therapeutic potential of proteasome and CDK inhibitors and represents a feasible approach towards developing more clinically relevant treatment strategies for HCC.

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Drug Discovery Using Evolutionary Similarities in Chemical Binding to Inhibit Patient-Derived Hepatocellular Carcinoma

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