ObjectiveAs the current therapeutic strategies for human hepatocellular carcinoma (HCC) have been proven to have limited effectiveness, immunotherapy becomes a compelling way to tackle the disease. We aim to provide humanised mouse (humice) models for the understanding of the interaction between human cancer and immune system, particularly for human-specific drug testing.DesignPatient-derived xenograft tumours are established with type I human leucocyte antigen matched human immune system in NOD-scid Il2rg−/− (NSG) mice. The longitudinal changes of the tumour and immune responses as well as the efficacy of immune checkpoint inhibitors are investigated.ResultsSimilar to the clinical outcomes, the human immune system in our model is educated by the tumour and exhibits exhaustion phenotypes such as a significant declination of leucocyte numbers, upregulation of exhaustion markers and decreased the production of human proinflammatory cytokines. Notably, cytotoxic immune cells decreased more rapidly compared with other cell types. Tumour infiltrated T cells have much higher expression of exhaustion markers and lower cytokine production compared with peripheral T cells. In addition, tumour-associated macrophages and myeloid-derived suppressor cells are found to be highly enriched in the tumour microenvironment. Interestingly, the tumour also changes gene expression profiles in response to immune responses by upregulating immune checkpoint ligands. Most importantly, in contrast to the NSG model, our model demonstrates both therapeutic and side effects of immune checkpoint inhibitors pembrolizumab and ipilimumab.ConclusionsOur work provides a model for immune-oncology study and a useful parallel-to-human platform for anti-HCC drug testing, especially immunotherapy.

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Nanomaterials have the potential to improve how patients are clinically treated and diagnosed. While there are a number of nanomaterials that can be used toward improved drug delivery and imaging, how these nanomaterials confer an advantage over other nanomaterials, as well as current clinical approaches is often application or disease specific. How the unique properties of carbon nanomaterials, such as nanodiamonds, carbon nanotubes, carbon nanofibers, graphene, and graphene oxides, make them promising nanomaterials for a wide range of clinical applications are discussed herein, including treating chemoresistant cancer, enhancing magnetic resonance imaging, and improving tissue regeneration and stem cell banking, among others. Additionally, the strategies for further improving drug delivery and imaging by carbon nanomaterials are reviewed, such as inducing endothelial leakiness as well as applying artificial intelligence toward designing optimal nanoparticle-based drug combination delivery. While the clinical application of carbon nanomaterials is still an emerging field of research, there is substantial preclinical evidence of the translational potential of carbon nanomaterials. Early clinically trial studies are highlighted, further supporting the use of carbon nanomaterials in clinical applications for both drug delivery and imaging.

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.

In recent years, large scale genomics and genome-wide studies using comprehensive genomic tools have reshaped our understanding of cancer evolution and heterogeneity. Hepatocellular carcinoma, being one of the most deadly cancers in the world has been well established as a disease of the genome that harbours a multitude of genetic and epigenetic aberrations during the process of liver carcinogenesis. As such, in depth understanding of the cancer epigenetics in cancer specimens and biopsy can be useful in clinical settings for molecular subclassification, prognosis, and prediction of therapeutic responses. In this review, we present a concise discussion on recent progress in the field of liver cancer epigenetics and some of the current works that contribute to the progress of liver cancer therapeutics.

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In 2019/2020, the emergence of coronavirus disease 2019 (COVID-19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID-19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi-drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose-finding to achieve drug synergy. This approach is widely-used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI-based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top-ranked combination being optimally and sub-optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism-agnostic, and potentially applicable to the systematic N-of-1 and population-wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID-19 and future pandemics.