Patient-derived Organoid Pharmacotyping As A Predictive Tool for Therapeutic Selection in Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: We integrate a new approach to chemosensitivity data for clinically-relevant regimen matching, and demonstrate the relationship with clinical outcomes in a large PDO biobank. SUMMARY BACKGROUND DATA: Pancreatic ductal adenocarcinoma (PDAC) usually recurs following potentially curative resection. Prior studies related patient-derived organoid (PDO) chemosensitivity with clinical responses. METHODS: PDOs were established from pre-treatment biopsies in a multi-institution clinical trial (n=21) and clinical specimens at a high-volume pancreatectomy center (n=74, of which 48 were pre-treated). PDO in vitro chemosensitivities to standard-of-care chemotherapeutics (pharmacotypes) were matched to potential clinically-relevant regimens by a weighted nearest-neighbors analysis. Clinical outcomes were then compared for patients who had well-matched versus poorly-matched treatment according to this metric. RESULTS: Our function matched 91% of PDOs to a standard-of-care regimen (9% pan-resistant). PDOs poorly-matched to the neoadjuvant regimen received would have matched to an alternative in 34% of cases. Patients receiving neoadjuvant chemotherapy well-matched to their pharmacotype experienced improved CA 19-9 response (60% decreased to normal when well-matched, 29% when poorly-matched, P<0.05) and lymph node down-staging (33% N0 after poorly-matched, 69% after well-matched, P<0.05). Patients receiving both well-matched neoadjuvant and adjuvant chemotherapy experienced improved recurrence-free- and overall survival (median RFS 8.5 mo poorly-matched, 15.9 mo well-matched, P<0.05; median OS 19.5 vs. 30.3 mo, P<0.05). CONCLUSION: In vitro PDO pharmacotyping can inform PDAC therapy selection. We demonstrate improved outcomes including survival for patients treated with regimens well-matched to their PDO chemosensitivities. A subsequent prospective study using PDO pharmacotype matching could improve oncologic outcomes and improve quality of life by avoiding therapies not expected to be effective.

Patient-derived Organoid Pharmacotyping is a Clinically Tractable Strategy for Precision Medicine in Pancreatic Cancer.

OBJECTIVE: PDAC patients who undergo surgical resection and receive effective chemotherapy have the best chance of long-term survival. Unfortunately, we lack predictive biomarkers to guide optimal systemic treatment. Ex-vivo generation of PDO for pharmacotyping may serve as predictive biomarkers in PDAC. The goal of the current study was to demonstrate the clinical feasibility of a PDO-guided precision medicine framework of care. METHODS: PDO cultures were established from surgical specimens and endoscopic biopsies, expanded in Matrigel, and used for high-throughput drug testing (pharmacotyping). Efficacy of standard-of-care chemotherapeutics was assessed by measuring cell viability after drug exposure. RESULTS: A framework for rapid pharmacotyping of PDOs was established across a multi-institutional consortium of academic medical centers. Specimens obtained remotely and shipped to a central biorepository maintain viability and allowed generation of PDOs with 77% success. Early cultures maintain the clonal heterogeneity seen in PDAC with similar phenotypes (cystic-solid). Late cultures exhibit a dominant clone with a pharmacotyping profile similar to early passages. The biomass required for accurate pharmacotyping can be minimized by leveraging a high-throughput technology. Twenty-nine cultures were pharmacotyped to derive a population distribution of chemotherapeutic sensitivity at our center. Pharmacotyping rapidly-expanded PDOs was completed in a median of 48 (range 18-102) days. CONCLUSIONS: Rapid development of PDOs from patients undergoing surgery for PDAC is eminently feasible within the perioperative recovery period, enabling the potential for pharmacotyping to guide postoperative adjuvant chemotherapeutic selection. Studies validating PDOs as a promising predictive biomarker are ongoing.

SMAD4 is a potential prognostic marker in human breast carcinomas.

SMAD4 is a downstream mediator of transforming growth factor beta. While its tumor suppressor function has been investigated as a prognostic biomarker in several human malignancies, its role as a prognostic marker in breast carcinoma is still undefined. We investigated SMAD4 expression in breast carcinoma samples of different histologic grades to evaluate the association between SMAD4 and outcome in breast cancer. We also investigated the role of SMAD4 expression status in MDA-MB-468 breast cancer cells in responding to TGF-ß stimulation. SMAD4 expression was assessed in 53 breast ductal carcinoma samples and in the surrounding normal tissue from 50 of the samples using immunohistochemistry, Western blot, and real-time PCR. TGF-ß-SMAD and non-SMAD signaling was assessed by Western blot in MDA-MB-468 cells with and without SMAD4 restoration. SMAD4 expression was reduced in ductal breast carcinoma as compared to surrounding uninvolved ductal breast epithelia (p < 0.05). SMAD4 expression levels decreased from Grade 1 to Grade 3 ductal breast carcinoma as assessed by immunohistochemistry (p < 0.05). Results were recapitulated by tissue array. In addition, immunohistochemistry results were further confirmed at the protein and mRNA level. We then found that non-SMAD MEK/MAPK signaling was significantly different between SMAD4 expressing MDA-MB-468 cells and SMAD4-null MDA-MB-468 cells. This is the first study indicating that SMAD4 plays a key role in shifting MAPK signaling. Further, we have demonstrated that SMAD4 has a potential role in the development of breast carcinoma and SMAD4 was a potential prognostic marker of breast carcinoma. Our findings further support the role of SMAD4 in breast carcinoma development. In addition, we observed an inverse relationship between SMAD4 levels and breast carcinoma histological grade. Our finding indicated that SMAD4 expression level in breast cancer cells played a role in responding non-SMAD signaling but not the canonic SMAD signaling. Further mechanistic studies are necessary to establish the role of SMAD4 in breast carcinoma prognosis and potential specific targeting.

Fibroblasts in the Aged Pancreas Drive Pancreatic Cancer Progression.

Pancreatic cancer is more prevalent in older individuals and often carries a poorer prognosis for them. The relationship between the microenvironment and pancreatic cancer is multifactorial, and age-related changes in nonmalignant cells in the tumor microenvironment may play a key role in promoting cancer aggressiveness. Because fibroblasts have profound impacts on pancreatic cancer progression, we investigated whether age-related changes in pancreatic fibroblasts influence cancer growth and metastasis. Proteomics analysis revealed that aged fibroblasts secrete different factors than young fibroblasts, including increased growth/differentiation factor 15 (GDF-15). Treating young mice with GDF-15 enhanced tumor growth, whereas aged GDF-15 knockout mice showed reduced tumor growth. GDF-15 activated AKT, rendering tumors sensitive to AKT inhibition in an aged but not young microenvironment. These data provide evidence for how aging alters pancreatic fibroblasts and promotes tumor progression, providing potential therapeutic targets and avenues for studying pancreatic cancer while accounting for the effects of aging. SIGNIFICANCE: Aged pancreatic fibroblasts secrete GDF-15 and activate AKT signaling to promote pancreatic cancer growth, highlighting the critical role of aging-mediated changes in the pancreatic cancer microenvironment in driving tumor progression. See related commentary by Isaacson et al., p. 1185.

PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid coculture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing data indicated that CAF density is associated with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF cocultures provided in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in cocultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGFA) interactions with neuropilin-1 mediating CAF-epithelial cell cross-talk. Together, this study introduces transfer learning from human single-cell data to organoid coculture analyses for experimental validation of discoveries of cell-cell cross-talk and identifies fibroblast-mediated regulation of EMT and inflammation. SIGNIFICANCE: Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.

Mesothelin CAR T Cells Secreting Anti-FAP/Anti-CD3 Molecules Efficiently Target Pancreatic Adenocarcinoma and its Stroma.

PURPOSE: Targeting solid tumors with chimeric antigen receptor (CAR) T cells remains challenging due to heterogenous target antigen expression, antigen escape, and the immunosuppressive tumor microenvironment (TME). Pancreatic cancer is characterized by a thick stroma generated by cancer-associated fibroblasts (CAF), which may contribute to the limited efficacy of mesothelin-directed CAR T cells in early-phase clinical trials. To provide a more favorable TME for CAR T cells to target pancreatic ductal adenocarcinoma (PDAC), we generated T cells with an antimesothelin CAR and a secreted T-cell-engaging molecule (TEAM) that targets CAF through fibroblast activation protein (FAP) and engages T cells through CD3 (termed mesoFAP CAR-TEAM cells). EXPERIMENTAL DESIGN: Using a suite of in vitro, in vivo, and ex vivo patient-derived models containing cancer cells and CAF, we examined the ability of mesoFAP CAR-TEAM cells to target PDAC cells and CAF within the TME. We developed and used patient-derived ex vivo models, including patient-derived organoids with patient-matched CAF and patient-derived organotypic tumor spheroids. RESULTS: We demonstrated specific and significant binding of the TEAM to its respective antigens (CD3 and FAP) when released from mesothelin-targeting CAR T cells, leading to T-cell activation and cytotoxicity of the target cell. MesoFAP CAR-TEAM cells were superior in eliminating PDAC and CAF compared with T cells engineered to target either antigen alone in our ex vivo patient-derived models and in mouse models of PDAC with primary or metastatic liver tumors. CONCLUSIONS: CAR-TEAM cells enable modification of tumor stroma, leading to increased elimination of PDAC tumors. This approach represents a promising treatment option for pancreatic cancer.

Targeted treatment of cancer with radiofrequency electromagnetic fields amplitude-modulated at tumor-specific frequencies.

In the past century, there have been many attempts to treat cancer with low levels of electric and magnetic fields. We have developed noninvasive biofeedback examination devices and techniques and discovered that patients with the same tumor type exhibit biofeedback responses to the same, precise frequencies. Intrabuccal administration of 27.12 MHz radiofrequency (RF) electromagnetic fields (EMF), which are amplitude-modulated at tumor-specific frequencies, results in long-term objective responses in patients with cancer and is not associated with any significant adverse effects. Intrabuccal administration allows for therapeutic delivery of very low and safe levels of EMF throughout the body as exemplified by responses observed in the femur, liver, adrenal glands, and lungs. In vitro studies have demonstrated that tumor-specific frequencies identified in patients with various forms of cancer are capable of blocking the growth of tumor cells in a tissue- and tumor-specific fashion. Current experimental evidence suggests that tumor-specific modulation frequencies regulate the expression of genes involved in migration and invasion and disrupt the mitotic spindle. This novel targeted treatment approach is emerging as an appealing therapeutic option for patients with advanced cancer given its excellent tolerability. Dissection of the molecular mechanisms accounting for the anti-cancer effects of tumor-specific modulation frequencies is likely to lead to the discovery of novel pathways in cancer.

Morphology-guided transcriptomic analysis of human pancreatic cancer organoids reveals microenvironmental signals that enhance invasion.

Pancreatic ductal adenocarcinoma (PDAC) frequently presents with metastasis, but the molecular programs in human PDAC cells that drive invasion are not well understood. Using an experimental pipeline enabling PDAC organoid isolation and collection based on invasive phenotype, we assessed the transcriptomic programs associated with invasion in our organoid model. We identified differentially expressed genes in invasive organoids compared with matched noninvasive organoids from the same patients, and we confirmed that the encoded proteins were enhanced in organoid invasive protrusions. We identified 3 distinct transcriptomic groups in invasive organoids, 2 of which correlated directly with the morphological invasion patterns and were characterized by distinct upregulated pathways. Leveraging publicly available single-cell RNA-sequencing data, we mapped our transcriptomic groups onto human PDAC tissue samples, highlighting differences in the tumor microenvironment between transcriptomic groups and suggesting that non-neoplastic cells in the tumor microenvironment can modulate tumor cell invasion. To further address this possibility, we performed computational ligand-receptor analysis and validated the impact of multiple ligands (TGF-ß1, IL-6, CXCL12, MMP9) on invasion and gene expression in an independent cohort of fresh human PDAC organoids. Our results identify molecular programs driving morphologically defined invasion patterns and highlight the tumor microenvironment as a potential modulator of these programs.

Cancer-cell-derived sialylated IgG as a novel biomarker for predicting poor pathological response to neoadjuvant therapy and prognosis in pancreatic cancer.

BACKGROUND: Neoadjuvant therapy (NAT) is increasingly applied in pancreatic ductal adenocarcinoma (PDAC); however, accurate prediction of therapeutic response to NAT remains a pressing clinical challenge. Cancer-cell-derived sialylated immunoglobulin G (SIA-IgG) was previously identified as a prognostic biomarker in PDAC. This study aims to explore whether SIA-IgG expression in treatment-naïve fine needle aspirate (FNA) biopsy specimens could predict the pathological response (PR) to NAT for PDAC. METHODS: Endoscopic ultrasonography-guided FNA biopsy specimens prior to NAT were prospectively obtained from 72 patients with PDAC at the Johns Hopkins Hospital. SIA-IgG expression of PDAC specimens was assessed by immunohistochemistry. Associations between SIA-IgG expression and PR, as well as patient prognosis, were analyzed. A second cohort enrolling surgically resected primary tumor specimens from 79 patients with PDAC was used to validate the prognostic value of SIA-IgG expression. RESULTS: SIA-IgG was expressed in 58.3% of treatment-naïve FNA biopsies. Positive SIA-IgG expression at diagnosis was associated with unfavorable PR and can serve as an independent predictor of PR. The sensitivity and specificity of SIA-IgG expression in FNA specimens in predicting an unfavorable PR were 63.9% and 80.6%, respectively. Both positive SIA-IgG expression in treatment-naïve FNA specimens and high SIA-IgG expression in surgically resected primary tumor specimens were significantly associated with shorter survival. CONCLUSIONS: Assessment of SIA-IgG on FNA specimens prior to NAT may help predict PR for PDAC. Additionally, SIA-IgG expression in treatment-naïve FNA specimens and surgically resected primary tumor specimens were predictive of the prognosis for PDAC.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

Precision Medicine in Pancreatic Cancer: Patient-Derived Organoid Pharmacotyping Is a Predictive Biomarker of Clinical Treatment Response.

PURPOSE: Patient-derived organoids (PDO) are a promising technology to support precision medicine initiatives for patients with pancreatic ductal adenocarcinoma (PDAC). PDOs may improve clinical next-generation sequencing (NGS) and enable rapid ex vivo chemotherapeutic screening (pharmacotyping). EXPERIMENTAL DESIGN: PDOs were derived from tissues obtained during surgical resection and endoscopic biopsies and studied with NGS and pharmacotyping. PDO-specific pharmacotype is assessed prospectively as a predictive biomarker of clinical therapeutic response by leveraging data from a randomized controlled clinical trial. RESULTS: Clinical sequencing pipelines often fail to detect PDAC-associated somatic mutations in surgical specimens that demonstrate a good pathologic response to previously administered chemotherapy. Sequencing the PDOs derived from these surgical specimens, after biomass expansion, improves the detection of somatic mutations and enables quantification of copy number variants. The detection of clinically relevant mutations and structural variants is improved following PDO biomass expansion. On clinical trial, PDOs were derived from biopsies of treatment-naïve patients prior to treatment with FOLFIRINOX (FFX). Ex vivo PDO pharmacotyping with FFX components predicted clinical therapeutic response in these patients with borderline resectable or locally advanced PDAC treated in a neoadjuvant or induction paradigm. PDO pharmacotypes suggesting sensitivity to FFX components were associated with longitudinal declines of tumor marker, carbohydrate-antigen 19-9 (CA-19-9), and favorable RECIST imaging response. CONCLUSIONS: PDOs established from tissues obtained from patients previously receiving cytotoxic chemotherapies can be accomplished in a clinically certified laboratory. Sequencing PDOs following biomass expansion improves clinical sequencing quality. High in vitro sensitivity to standard-of-care chemotherapeutics predicts good clinical response to systemic chemotherapy in PDAC. See related commentary by Zhang et al., p. 3176.

Multi-omic analyses of changes in the tumor microenvironment of pancreatic adenocarcinoma following neoadjuvant treatment with anti-PD-1 therapy.

Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy necessitates optimization and maintenance of activated effector T cells (Teff). We prospectively collected and applied multi-omic analyses to paired pre- and post-treatment PDAC specimens collected in a platform neoadjuvant study of granulocyte-macrophage colony-stimulating factor-secreting allogeneic PDAC vaccine (GVAX) vaccine ± nivolumab (anti-programmed cell death protein 1 [PD-1]) to uncover sensitivity and resistance mechanisms. We show that GVAX-induced tertiary lymphoid aggregates become immune-regulatory sites in response to GVAX + nivolumab. Higher densities of tumor-associated neutrophils (TANs) following GVAX + nivolumab portend poorer overall survival (OS). Increased T cells expressing CD137 associated with cytotoxic Teff signatures and correlated with increased OS. Bulk and single-cell RNA sequencing found that nivolumab alters CD4+ T cell chemotaxis signaling in association with CD11b+ neutrophil degranulation, and CD8+ T cell expression of CD137 was required for optimal T cell activation. These findings provide insights into PD-1-regulated immune pathways in PDAC that should inform more effective therapeutic combinations that include TAN regulators and T cell activators.

Implantation of a neoantigen-targeted hydrogel vaccine prevents recurrence of pancreatic adenocarcinoma after incomplete resection.

Tumor involvement of major vascular structures limits surgical options in pancreatic adenocarcinoma (PDAC), which in turn limits opportunities for cure. Despite advances in locoregional approaches, there is currently no role for incomplete resection. This study evaluated a gelatinized neoantigen-targeted vaccine applied to a grossly positive resection margin in preventing local recurrence. Incomplete surgical resection was performed in mice bearing syngeneic flank Panc02 tumors, leaving a 1 mm rim adherent to the muscle bed. A previously validated vaccine consisting of neoantigen peptides, a stimulator of interferon genes (STING) agonist and AddaVaxTM (termed PancVax) was embedded in a hyaluronic acid hydrogel and applied to the tumor bed. Tumor remnants, regional lymph nodes, and spleens were analyzed using histology, flow cytometry, gene expression profiling, and ELISPOT assays. The immune microenvironment at the tumor margin after surgery alone was characterized by a transient influx of myeloid-derived suppressor cells (MDSCs), prolonged neutrophil influx, and near complete loss of cytotoxic T cells. Application of PancVax gel was associated with enhanced T cell activation in the draining lymph node and expansion of neoantigen-specific T cells in the spleen. Mice implanted with PancVax gel demonstrated no evidence of residual tumor at two weeks postoperatively and healed incisions at two months postoperatively without local recurrence. In summary, application of PancVax gel at a grossly positive tumor margin led to systemic expansion of neoantigen-specific T cells and effectively prevented local recurrence. These findings support further work into locoregional adjuncts to immune modulation in PDAC.

Modeling human pancreatic ductal adenocarcinoma for translational research: current options, challenges, and prospective directions.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with one of the lowest survival rates. Early detection, an improved understanding of tumor biology, and novel therapeutic discoveries are needed in order to improve overall patient survival. Scientific progress towards meeting these goals relies upon accurate modeling of the human disease. From two-dimensional (2D) cell lines to the advanced modeling available today, we aim to characterize the critical tools in efforts to further understand PDAC biology. The National Center for Biotechnology Information's PubMed and the Elsevier's SCOPUS were used to perform a comprehensive literature review evaluating preclinical human-derived PDAC models. Keywords included pancreatic cancer, PDAC, preclinical models, KRAS mutations, xenograft, co-culturing fibroblasts, co-culturing lymphocytes and PDAC immunotherapy Initial search was limited to articles about PDAC and was then expanded to include other gastrointestinal malignancies where information may complement our effort. A supervised review of the key literature's references was utilized to augment the capture of relevant data. The discovery and refinement of techniques enabling immortalized 2D cell culture provided the cornerstone for modern cancer biology research. Cell lines have been widely used to represent PDAC in vitro but are limited in capacity to model three-dimensional (3D) tumor attributes and interactions within the tumor microenvironment. Xenografts are an alternative method to model PDAC with improved capacity to understand certain aspects of 3D tumor biology in vivo while limited by the use of immunodeficient mice. Advances of in vitro modeling techniques have led to 3D organoid models for PDAC biology. Co-culturing models in the 3D environment have been proposed as an efficient modeling system for improving upon the limitations encountered in the standard 2D and xenograft tumor models. The integrated network of cells and stroma that comprise PDAC in vivo need to be accurately depicted ex vivo to continue to make progress in this disease. Recapitulating the complex tumor microenvironment in a preclinical model of human disease is an outstanding and urgent need in PDAC. Definitive characterization of available human models for PDAC serves to further the core mission of pancreatic cancer translational research.

Inhibition of miR-21 Regulates Mutant KRAS Effector Pathways and Intercepts Pancreatic Ductal Adenocarcinoma Development.

Almost all pancreatic ductal adenocarcinomas (PDA) develop following KRAS activation, which triggers epithelial transformation and recruitment of desmoplastic stroma through additional transcriptional and epigenetic regulation, but only a few of these regulatory mechanisms have been described. We profiled dysregulated miRNAs starting with the earliest premalignant pancreatic intraepithelial neoplasias (PanIN) in genetically engineered mutated KRAS and P53 (KPC) mice programmed to recapitulate human PDA tumorigenesis. We identified miR-21 and miR-224 as cell-specific and compartment-specific regulators in PanINs and PDA. miR-21 is overexpressed in tumor epithelial cells of premalignant ducts, while miR-224 is overexpressed in cancer-associated fibroblasts in PDA stroma. Inhibition of miR-21 reverted protumorigenic functionalities to baseline levels. Overexpression of miR-224 induced activated phenotypes in normal fibroblasts. In vivo miR-21 inhibition improved survival in established PDA. Importantly, early systemic miR-21 inhibition completely intercepted premalignant progression. Finally, an evaluation of miR-21 expression in the PDA cohort of The Cancer Genome Atlas identified a correlation between tumor epithelial cell content and miR-21 expression in human tumors providing further rationale for conducting human studies. Thus, miR-21 may be useful for early PanIN detection, and for intercepting developing premalignant pancreatic lesions and other KRAS-driven premalignancies.

Immune-Related Adverse Events Requiring Hospitalization: Spectrum of Toxicity, Treatment, and Outcomes.

PURPOSE: Immune checkpoint inhibitors (ICIs) cause immune-related adverse events (irAEs). The proportion of patients who are hospitalized for irAEs and their spectrum, management, and outcomes are not well described. METHODS: We report the proportion of hospitalized patients in an academic center who were treated with ICIs from May to December 2017. Patient characteristics, toxicities, management, and outcomes for confirmed irAE admissions are reported. Associations between patient features and irAE hospitalizations are examined. RESULTS: Twenty-three percent (n = 100) of 443 patients who were admitted to an academic oncology center over 6 months had ever received ICIs. Of these patients, 41% were admitted for suspected irAEs and 23% were confirmed irAEs. IrAEs accounted for 5% of all oncology hospitalizations (n = 23). Ninety-one percent of patients with confirmed irAEs prompted a medicine subspecialist consultation, most commonly gastroenterology (22%). Fifteen patients (65%) had their irAEs improve/resolve, seven (30%) had worsening irAEs, and three (13%) died of their irAEs. The majority of patients (n = 20; 87%) discontinued ICIs after discharge. Among ICI-treated patients who required admission, an increased likelihood of irAE-related hospitalization was associated with patient age older than 65 years (odds ratio, 5.4; 95% CI, 1.6 to 17.8) and receipt of combination immunotherapy (OR, 6.8; 95% CI, 2.0 to 23.2). CONCLUSION: A notable proportion of ICI-treated patients are hospitalized for irAEs, and these patients have a high demand for multidisciplinary management. Older age and combination ICI treatment were associated with an increased risk of irAE-related hospitalization. Whereas these data are from an academic center and include patients in clinical trials, with expanding use of ICIs, these data have important implications for inpatient service planning and risk stratification.

Tumour-specific amplitude-modulated radiofrequency electromagnetic fields induce differentiation of hepatocellular carcinoma via targeting Cav3.2 T-type voltage-gated calcium channels and Ca2+ influx.

BACKGROUND: Administration of amplitude modulated 27·12 MHz radiofrequency electromagnetic fields (AM RF EMF) by means of a spoon-shaped applicator placed on the patient's tongue is a newly approved treatment for advanced hepatocellular carcinoma (HCC). The mechanism of action of tumour-specific AM RF EMF is largely unknown. METHODS: Whole body and organ-specific human dosimetry analyses were performed. Mice carrying human HCC xenografts were exposed to AM RF EMF using a small animal AM RF EMF exposure system replicating human dosimetry and exposure time. We performed histological analysis of tumours following exposure to AM RF EMF. Using an agnostic genomic approach, we characterized the mechanism of action of AM RF EMF. FINDINGS: Intrabuccal administration results in systemic delivery of athermal AM RF EMF from head to toe at levels lower than those generated by cell phones held close to the body. Tumour shrinkage results from differentiation of HCC cells into quiescent cells with spindle morphology. AM RF EMF targeted antiproliferative effects and cancer stem cell inhibiting effects are mediated by Ca2+ influx through Cav3·2 T-type voltage-gated calcium channels (CACNA1H) resulting in increased intracellular calcium concentration within HCC cells only. INTERPRETATION: Intrabuccally-administered AM RF EMF is a systemic therapy that selectively block the growth of HCC cells. AM RF EMF pronounced inhibitory effects on cancer stem cells may explain the exceptionally long responses observed in several patients with advanced HCC. FUND: Research reported in this publication was supported by the National Cancer Institute's Cancer Centre Support Grant award number P30CA012197 issued to the Wake Forest Baptist Comprehensive Cancer Centre (BP) and by funds from the Charles L. Spurr Professorship Fund (BP). DWG is supported by R01 AA016852 and P50 AA026117.