Targeting Jak/Stat pathway as a therapeutic strategy against SP/CD44+ tumorigenic cells in Akt/ß-catenin-driven hepatocellular carcinoma.

BACKGROUND & AIMS: Hepatic resection and liver transplantation with adjuvant chemo- and radiotherapy are the mainstay of hepatocellular carcinoma (HCC) treatment, but the 5-year survival rate remains poor because of frequent recurrence and intrahepatic metastasis. Only sorafenib and lenvatinib are currently approved for the first-line treatment of advanced, unresected HCC, but they yield modest survival benefits. Thus, there is a need to identify new therapeutic targets to improve current HCC treatment modalities. METHODS: The HCC tumor model was generated by hydrodynamic transfection of AKT1 and ß-catenin (CTNNB1) oncogenes. Cancer cells with stemness properties were characterized following isolation using side population (SP) and CD44 surface markers by flow cytometry. The effect of Jak/Stat inhibitors was analyzed in vitro by using tumorsphere culture and in vivo using an allograft mouse model. RESULTS: Co-activation of both Wnt/ß-catenin and Akt/mTOR pathways was found in 14.4% of our HCC patient cohort. More importantly, these patients showed poorer survival than those with either Wnt/ß-catenin or Akt/mTOR pathway activation alone, demonstrating the clinical relevance of our study. In addition, we observed that Akt/ß-catenin tumors contained a subpopulation of cells with stem/progenitor-like characteristics identified through SP analysis and expression of the cancer stem cell-like marker CD44, which may contribute to tumor self-renewal and drug resistance. Consequently, we identified small molecule inhibitors of the Jak/Stat pathway that demonstrated efficacy in mitigating tumor proliferation and formation in Akt/ß-catenin-driven HCC. CONCLUSIONS: In conclusion, we have shown that Akt/ß-catenin tumors contain a subpopulation of tumor-initiating cells with stem/progenitor-like characteristics which can be effectively targeted with inhibitors of the Jak/Stat pathway, demonstrating that inhibition of the Jak/Stat pathway could be an alternative method to overcome drug resistance and effectively treat Akt/ß-catenin-driven HCC tumors. LAY SUMMARY: The prognosis for patients with hepatocellular carcinoma is poor, partly because of the lack of effective treatment options for those with more advanced disease. In this study, we identified a subpopulation of cancer cells with stem cell-like properties that were critical for tumor maintenance and growth in a mouse model of hepatocellular carcinoma. Through further experiments, we demonstrated that the Jak/Stat pathway is a promising therapeutic target in hepatocellular carcinoma.

A functional personalised oncology approach against metastatic colorectal cancer in matched patient derived organoids.

Globally, colorectal cancer (CRC) is the third most frequently occurring cancer. Progression on to an advanced metastatic malignancy (metCRC) is often indicative of poor prognosis, as the 5-year survival rates of patients decline rapidly. Despite the availability of many systemic therapies for the management of metCRC, the long-term efficacies of these regimens are often hindered by the emergence of treatment resistance due to intratumoral and intertumoral heterogeneity. Furthermore, not all systemic therapies have associated biomarkers that can accurately predict patient responses. Hence, a functional personalised oncology (FPO) approach can enable the identification of patient-specific combinatorial vulnerabilities and synergistic combinations as effective treatment strategies. To this end, we established a panel of CRC patient-derived organoids (PDOs) as clinically relevant biological systems, of which three pairs of matched metCRC PDOs were derived from the primary sites (ptCRC) and metastatic lesions (mCRC). Histological and genomic characterisation of these PDOs demonstrated the preservation of histopathological and genetic features found in the parental tumours. Subsequent application of the phenotypic-analytical drug combination interrogation platform, Quadratic Phenotypic Optimisation Platform, in these pairs of PDOs identified patient-specific drug sensitivity profiles to epigenetic-based combination therapies. Most notably, matched PDOs from one patient exhibited differential sensitivity patterns to the rationally designed drug combinations despite being genetically similar. These findings collectively highlight the limitations of current genomic-driven precision medicine in guiding treatment strategies for metCRC patients. Instead, it suggests that epigenomic profiling and application of FPO could complement the identification of novel combinatorial vulnerabilities to target synchronous ptCRC and mCRC.

Splice-switch oligonucleotide-based combinatorial platform prioritizes synthetic lethal targets CHK1 and BRD4 against MYC-driven hepatocellular carcinoma.

Deregulation of MYC is among the most frequent oncogenic drivers in hepatocellular carcinoma (HCC). Unfortunately, the clinical success of MYC-targeted therapies is limited. Synthetic lethality offers an alternative therapeutic strategy by leveraging on vulnerabilities in tumors with MYC deregulation. While several synthetic lethal targets of MYC have been identified in HCC, the need to prioritize targets with the greatest therapeutic potential has been unmet. Here, we demonstrate that by pairing splice-switch oligonucleotide (SSO) technologies with our phenotypic-analytical hybrid multidrug interrogation platform, quadratic phenotypic optimization platform (QPOP), we can disrupt the functional expression of these targets in specific combinatorial tests to rapidly determine target-target interactions and rank synthetic lethality targets. Our SSO-QPOP analyses revealed that simultaneous attenuation of CHK1 and BRD4 function is an effective combination specific in MYC-deregulated HCC, successfully suppressing HCC progression in vitro. Pharmacological inhibitors of CHK1 and BRD4 further demonstrated its translational value by exhibiting synergistic interactions in patient-derived xenograft organoid models of HCC harboring high levels of MYC deregulation. Collectively, our work demonstrates the capacity of SSO-QPOP as a target prioritization tool in the drug development pipeline, as well as the therapeutic potential of CHK1 and BRD4 in MYC-driven HCC.

Histone-lysine N-methyltransferase EHMT2 (G9a) inhibition mitigates tumorigenicity in Myc-driven liver cancer.

Hepatocellular carcinoma (HCC) is the third deadliest and sixth most common cancer in the world. Histone-lysine N-methyltransferase EHMT2 (also known as G9a) is a histone methyltransferase frequently overexpressed in many cancer types, including HCC. We showed that Myc-driven liver tumours have a unique H3K9 methylation pattern with corresponding G9a overexpression. This phenomenon of increased G9a was further observed in our c-Myc-positive HCC patient-derived xenografts. More importantly, we showed that HCC patients with higher c-Myc and G9a expression levels portend a poorer survival with lower median survival months. We demonstrated that c-Myc interacts with G9a in HCC and cooperates to regulate c-Myc-dependent gene repression. In addition, G9a stabilises c-Myc to promote cancer development, contributing to the growth and invasive capacity in HCC. Furthermore, combination therapy between G9a and synthetic-lethal target of c-Myc, CDK9, demonstrates strong efficacy in patient-derived avatars of Myc-driven HCC. Our work suggests that targeting G9a could prove to be a potential therapeutic avenue for Myc-driven liver cancer. This will increase our understanding of the underlying epigenetic mechanisms of aggressive tumour initiation and lead to improved therapeutic and diagnostic options for Myc-driven hepatic tumours.

Artificial Intelligence Effecting a Paradigm Shift in Drug Development.

The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.

Frequent upregulation of G9a promotes RelB-dependent proliferation and survival in multiple myeloma.

BACKGROUND: Multiple myeloma is an incurable hematological malignancy characterized by a heterogeneous genetic and epigenetic landscape. Although a number of genetic aberrations associated with myeloma pathogenesis, progression and prognosis have been well characterized, the role of many epigenetic aberrations in multiple myeloma remain elusive. G9a, a histone methyltransferase, has been found to promote disease progression, proliferation and metastasis via diverse mechanisms in several cancers. A role for G9a in multiple myeloma, however, has not been previously explored. METHODS: Expression levels of G9a/EHMT2 of multiple myeloma cell lines and control cells Peripheral Blood Mononuclear Cells (PBMCs) were analyzed. Correlation of G9a expression and overall survival of multiple myeloma patients were analyzed using patient sample database. To further study the function of G9a in multiple myeloma, G9a depleted multiple myeloma cells were built by lentiviral transduction, of which proliferation, colony formation assays as well as tumorigenesis were measured. RNA-seq of G9a depleted multiple myeloma with controls were performed to explore the downstream mechanism of G9a regulation in multiple myeloma. RESULTS: G9a is upregulated in a range of multiple myeloma cell lines. G9a expression portends poorer survival outcomes in a cohort of multiple myeloma patients. Depletion of G9a inhibited proliferation and tumorigenesis in multiple myeloma. RelB was significantly downregulated by G9a depletion or small molecule inhibition of G9a/GLP inhibitor UNC0642, inducing transcription of proapoptotic genes Bim and BMF. Rescuing RelB eliminated the inhibition in proliferation and tumorigenesis by G9a depletion. CONCLUSIONS: In this study, we demonstrated that G9a is upregulated in most multiple myeloma cell lines. Furthermore, G9a loss-of-function analysis provided evidence that G9a contributes to multiple myeloma cell survival and proliferation. This study found that G9a interacts with NF-κB pathway as a key regulator of RelB in multiple myeloma and regulates RelB-dependent multiple myeloma survival. G9a therefore is a promising therapeutic target for multiple myeloma.

Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine.

Artificial intelligence holds great promise in transforming how drugs are designed and patients are treated. In a study recently published in Science Translational Medicine, a unique artificial intelligence platform makes efficient use of small experimental datasets to design new drug combinations as well as identify the best drug combinations for specific patient samples. This quadratic phenotypic optimization platform (QPOP) does not rely on previous assumptions of molecular mechanisms of disease, but rather uses system-specific experimental data to determine the best drug combinations for a specific disease model or a patient sample. In this commentary, we explore how QPOP was applied toward multiple myeloma in the study. We also discuss how this study demonstrates the potential for applications of QPOP toward improving therapeutic regimen design and personalized medicine.

Datasets describing the growth and molecular features of hepatocellular carcinoma patient-derived xenograft cells grown in a three-dimensional macroporous hydrogel.

This data article presents datasets associated with the research article entitled "Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer" (Fong et al., 2018) [1]. A three-dimensional macroporous sponge system was used to generate in vitro counterparts to various hepatocellular carcinoma patient-derived xenograft (HCC-PDX) lines. This article describes the viability, proliferative capacity and molecular features (genomic and transcriptomic profiles) of the cultured HCC-PDX cells. The sequencing datasets are made publicly available to enable critical or further analyzes.

Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP).

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.