Hematogenous dissemination is thought to be a late event in cancer progression. We showed recently that pancreas cells can be detected in the bloodstream before tumor formation, in a genetic model of pancreatic ductal adenocarcinoma (PDAC). To confirm these findings in humans, we used microfluidic geometrically enhanced immunocapture to detect circulating pancreas epithelial cells (CECs) in patient blood samples. We captured >3 CECs/ml in 7 of 21 (33%) of patients with cystic lesions and no clinical diagnosis of cancer (Sendai criteria negative), 8 of 11 (73%) with PDAC, and in 0 of 19 patients without cysts or cancer (controls). These findings indicate that cancer cells are present in the circulation of patients before tumors develop, which might be used in risk assessment.

We have developed and optimized a microfluidic device platform for the capture and analysis of circulating pancreatic cells (CPCs) and pancreatic circulating tumor cells (CTCs). Our platform uses parallel anti-EpCAM and cancer-specific mucin 1 (MUC1) immunocapture in a silicon microdevice. Using a combination of anti-EpCAM and anti-MUC1 capture in a single device, we are able to achieve efficient capture while extending immunocapture beyond single marker recognition. We also have detected a known oncogenic KRAS mutation in cells spiked in whole blood using immunocapture, RNA extraction, RT-PCR and Sanger sequencing. To allow for downstream single-cell genetic analysis, intact nuclei were released from captured cells by using targeted membrane lysis. We have developed a staining protocol for clinical samples, including standard CTC markers; DAPI, cytokeratin (CK) and CD45, and a novel marker of carcinogenesis in CPCs, mucin 4 (MUC4). We have also demonstrated a semi-automated approach to image analysis and CPC identification, suitable for clinical hypothesis generation. Initial results from immunocapture of a clinical pancreatic cancer patient sample show that parallel capture may capture more of the heterogeneity of the CPC population. With this platform, we aim to develop a diagnostic biomarker for early pancreatic carcinogenesis and patient risk stratification.

Advances in rare cell capture technology have made possible the interrogation of circulating tumor cells (CTCs) captured from whole patient blood. However, locating captured cells in the device by manual counting bottlenecks data processing by being tedious (hours per sample) and compromises the results by being inconsistent and prone to user bias. Some recent work has been done to automate the cell location and classification process to address these problems, employing image processing and machine learning (ML) algorithms to locate and classify cells in fluorescent microscope images. However, the type of machine learning method used is a part of the design space that has not been thoroughly explored. Thus, we have trained four ML algorithms on three different datasets. The trained ML algorithms locate and classify thousands of possible cells in a few minutes rather than a few hours, representing an order of magnitude increase in processing speed. Furthermore, some algorithms have a significantly (P < 0.05) higher area under the receiver operating characteristic curve than do other algorithms. Additionally, significant (P < 0.05) losses to performance occur when training on cell lines and testing on CTCs (and vice versa), indicating the need to train on a system that is representative of future unlabeled data. Optimal algorithm selection depends on the peculiarities of the individual dataset, indicating the need of a careful comparison and optimization of algorithms for individual image classification tasks. V C 2016 International Society for Advancement of Cytometry

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We used automated electrorotation to measure the cytoplasmic permittivity, cytoplasmic conductivity, and specific membrane capacitance of pancreatic cancer cells under environmental perturbation to evaluate the effects of serum starvation, epithelial-to-mesenchymal transition, and evolution of chemotherapy resistance which may be associated with the development and dissemination of cancer. First, we compared gemcitabine-resistant BxPC3 subclones with gemcitabine-naive parental cells. Second, we serum-starved BxPC3 and PANC-1 cells and compared them to untreated counterparts. Third, we induced the epithelial-to-mesenchymal transition in PANC-1 cells and compared them to untreated PANC-1 cells. We also measured the electrorotation spectra of white blood cells isolated from a healthy donor. The properties from fit electrorotation spectra were used to compute dielectrophoresis (DEP) spectra and crossover frequencies. For all three experiments, the median crossover frequency for both treated and untreated pancreatic cancer cells remained significantly lower than the median crossover frequency for white blood cells. The robustness of the crossover frequency to these treatments indicates that DEP is a promising technique for enhancing capture of circulating cancer cells. Published by AIP Publishing. [http://dx

Intraductal Papillary Mucinous Neoplasms (IPMNs) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbor a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss of NKX6-2 expression is a consistent feature of IPMN progression, while re-expression of NKX6-2 in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

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Conventional genetically engineered mouse models (GEMMs) are time consuming, laborious and offer limited spatio-temporal control. Here, we describe the development of a streamlined platform for in vivo gene activation using CRISPR activation (CRISPRa) technology. Unlike conventional GEMMs, our model system allows for flexible, sustained and timed activation of one or more target genes using single or pooled lentiviral guides. Using Myc and Yap1 as model oncogenes, we demonstrate gene activation in primary pancreatic organoid cultures in vitro and enhanced tumorigenic potential in Myc-activated organoids when transplanted orthotopically. By implementing our model as an autochthonous lung cancer model, we show that transduction-mediated Myc activation leads to accelerated tumor progression and significantly reduced overall survival relative to non-targeted tumor controls. Furthermore, we found that Myc-activation led to the acquisition of an immune suppressive cold tumor microenvironment. Through cross-species validation of our results using publicly available RNA/DNA-seq data sets, we were able to link MYC to a previously described, immunosuppressive transcriptomic subtype in patient tumors, thus identifying a patient cohort that may benefit from combined MYC/immune-targeted therapies. Overall, our work demonstrates how CRISPRa can be used for rapid functional validation of putative oncogenes and may allow for the identification and evaluation of potential metastatic and oncogenic drivers through competitive screening.