Pancreatic Cancer Patient-derived Organoids Can Predict Response to Neoadjuvant Chemotherapy.

OBJECTIVE: To evaluate if patient-derived organoids (PDOs) may predict response to neoadjuvant (NAT) chemotherapy in patients with pancreatic adenocarcinoma. BACKGROUND: PDOs have been explored as a biomarker of therapy response and for personalized therapeutics in patients with pancreatic cancer. METHODS: During 2017-2021, patients were enrolled into an IRB-approved protocol and PDO cultures were established. PDOs of interest were analyzed through a translational pipeline incorporating molecular profiling and drug sensitivity testing. RESULTS: One hundred thirty-six samples, including both surgical resections and fine needle aspiration/biopsy from 117 patients with pancreatic cancer were collected. This biobank included diversity in stage, sex, age, and race, with minority populations representing 1/3 of collected cases (16% Black, 9% Asian, 7% Hispanic/Latino). Among surgical specimens, PDO generation was successful in 71% (15 of 21) of patients who had received NAT prior to sample collection and in 76% (39 of 51) of patients who were untreated with chemotherapy or radiation at the time of collection. Pathological response to NAT correlated with PDO chemotherapy response, particularly oxaliplatin. We demonstrated the feasibility of a rapid PDO drug screen and generated data within 7 days of tissue resection. CONCLUSION: Herein we report a large single-institution organoid biobank, including ethnic minority samples. The ability to establish PDOs from chemotherapy-naive and post-NAT tissue enables longitudinal PDO generation to assess dynamic chemotherapy sensitivity profiling. PDOs can be rapidly screened and further development of rapid screening may aid in the initial stratification of patients to the most active NAT regimen.

Remodeling of Intrahepatic Ducts in a Model of Caroli Syndrome: Is Scar Carcinoma a Consequence of Laplace's Law?

Caroli syndrome, characterized by saccular dilatation of intrahepatic ducts and congenital hepatic fibrosis, is without therapy in part due to its ultra-rare prevalence and the apparent lack of availability of a suitable experimental model. While the PCK rat has long been used as a model of fibropolycystic kidney disease, hepatobiliary biophysics in this animal model is incompletely characterized. Compared to age-matched, wild-type controls, the PCK rat demonstrated severe hepatomegaly and large saccular dilated intrahepatic ducts. Nevertheless, hepatic density was greater in the PCK rat, likely due to severe duct wall sclerosis accompanied by scarring across the hepatic parenchyma. Extracellular matrix accumulation appeared proportional to duct cross-sectional area and liver volume and appeared compensatory in nature. The PCK rat livers exhibited both cholangiocarcinoma and hepatocellular carcinoma coincident with areas of increased extracellular matrix deposition. Together, these data suggest that the PCK rat model mimics at least in part the spectrum of hepatobiliary pathology observed in Caroli syndrome and highlights the attendant risk associated with this disease.

Deep Learning in Drug Discovery and Medicine; Scratching the Surface.

The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy. Indeed, precision medicine-based therapies that first found their place in oncology are rapidly finding uses in autoimmune, renal and other diseases. More recently a new renaissance that is shaping everyday life is making its way into healthcare. Drug discovery and medicine that started with Ayurveda in India are now benefiting from an altogether different artificial intelligence (AI)-one which is automating the invention of new chemical entities and the mining of large databases in health-privacy-protected vaults. Indeed, disciplines as diverse as language, neurophysiology, chemistry, toxicology, biostatistics, medicine and computing have come together to harness algorithms based on transfer learning and recurrent neural networks to design novel drug candidates, a priori inform on their safety, metabolism and clearance, and engineer their delivery but only on demand, all the while cataloging and comparing omics signatures across traditionally classified diseases to enable basket treatment strategies. This review highlights inroads made and being made in directed-drug design and molecular therapy.