An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma.

Although combination therapy is the standard of care for relapsed/refractory non-Hodgkin's lymphoma (RR-NHL), combination treatment chosen for an individual patient is empirical, and response rates remain poor in individuals with chemotherapy-resistant disease. Here, we evaluate an experimental-analytic method, quadratic phenotypic optimization platform (QPOP), for prediction of patient-specific drug combination efficacy from a limited quantity of biopsied tumor samples. In this prospective study, we enrolled 71 patients with RR-NHL (39 B cell NHL and 32 NK/T cell NHL) with a median of two prior lines of treatment, at two academic hospitals in Singapore from November 2017 to August 2021. Fresh biopsies underwent ex vivo testing using a panel of 12 drugs with known efficacy against NHL to identify effective single and combination treatments. Individualized QPOP reports were generated for 67 of 75 patient samples, with a median turnaround time of 6 days from sample collection to report generation. Doublet drug combinations containing copanlisib or romidepsin were most effective against B cell NHL and NK/T cell NHL samples, respectively. Off-label QPOP-guided therapy offered at physician discretion in the absence of standard options (n = 17) resulted in five complete responses. Among patients with more than two prior lines of therapy, the rates of progressive disease were lower with QPOP-guided treatments than with conventional chemotherapy. Overall, this study shows that the identification of patient-specific drug combinations through ex vivo analysis was achievable for RR-NHL in a clinically applicable time frame. These data provide the basis for a prospective clinical trial evaluating ex vivo-guided combination therapy in RR-NHL.

Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide, often manifesting at the advanced stage when cure is no longer possible. The discrepancy between preclinical findings and clinical outcome in HCC is well-recognized. So far, sorafenib is the only targeted therapy approved as first-line therapy for patients with advanced HCC. There is an urgent need for improved preclinical models for the development of HCC-targeted therapies. Patient-derived xenograft (PDX) tumor models have been shown to closely recapitulate human tumor biology and predict patient drug response. However, the use of PDX models is currently limited by high costs and low throughput. In this study, we engineered in vitro conditions conducive for the culture of HCC-PDX organoids derived from a panel of 14 different HCC-PDX lines through the use of a three-dimensional macroporous cellulosic sponge system. To validate the in vitro HCC-PDX models, both in vivo and in vitro HCC-PDX models were subjected to whole exome sequencing and RNA-sequencing. Correlative studies indicate strong concordance in genomic and transcriptomic profiles as well as intra-tumoral heterogeneity between each matched in vitro-in vivo HCC-PDX pairs. Furthermore, we demonstrate the feasibility of using these in vitro HCC-PDX models for drug testing, paving the way for more efficient preclinical studies in HCC drug development.

Identification and Optimization of Combinatorial Glucose Metabolism Inhibitors in Hepatocellular Carcinomas.

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. The expression of glucose transporter isoform 1, a key factor in transporting glucose into cancer cells, is overexpressed in several human cancers, including HCC. In addition, this has been shown to correlate with a higher proliferation index and more advanced stages in HCC, suggesting that inhibition of glucose metabolism is a promising therapeutic strategy. Our study used high-content screening (HCS) for compounds that target glucose metabolism and effect cell death in HCC cells. Specifically, we showed that a fluorescent 2-deoxyglucose analog, 2-[N-(7-nitrobenz-2- oxa-1,3-diazol-4-yl)amino]-2-deoxyglucose, and CellTrace Calcein Red-Orange AM can be used reliably as readouts for glucose uptake and proliferative index, respectively, to identify drug candidates that simultaneously reduce glucose uptake and induce cell death in HCC cells. Thus, fluorescent glucose uptake bioprobes can be implemented in HCS assays to identify previously unknown regulators of glucose metabolism in HCC. In addition, our study also employs the use of feedback system control (FSC.II), a platform that optimizes the combinations of drugs identified through HCS. The coordinated use of HCS and FSC.II can improve the development of drug combinations and uncover previously unidentified signaling pathways that govern HCC as well as other cancers.