One of the major challenges in using pancreatic cancer patient–derived organoids (PDOs) in precision oncology is the time from biopsy to functional characterization. This is particularly true for endoscopic ultrasound-guided fine-needle aspiration biopsies, typically resulting in specimens with limited tumor cell yield. Here, we tested conditioned media of individual PDOs for cell-free DNA to detect driver mutations already early on during the expansion process to accelerate the genetic characterization of PDOs as well as subsequent functional testing. Importantly, genetic alterations detected in the PDO supernatant, collected as early as 72 hours after biopsy, recapitulate the mutational profile of the primary tumor, indicating suitability of this approach to subject PDOs to drug testing in a reduced time frame. In addition, we demonstrated that this workflow was practicable, even in patients for whom the amount of tumor material was not sufficient for molecular characterization by established means. Together, our findings demonstrate that generating PDOs from very limited biopsy material permits molecular profiling and drug testing. With our approach, this can be achieved in a rapid and feasible fashion with broad implications in clinical practice.

To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.

Purpose: PASylation® offers the ability to systematically tune and optimize the pharmacokinetics of protein tracers for molecular imaging. Here we report the first clinical translation of a PASylated Fab fragment (⁸⁹Zr∙Df-HER2-Fab-PAS₂₀₀) for the molecular imaging of tumor-related HER2 expression. Methods: A patient with HER2-positive metastatic breast cancer received 37 MBq of ⁸⁹Zr∙Df-HER2-Fab-PAS₂₀₀ at a total mass dose of 70 μg. PET/CT was carried out 6, 24, and 45 h after injection, followed by image analysis of biodistribution, normal organ uptake, and lesion targeting. Results: Images show a biodistribution typical for protein tracers, characterized by a prominent blood pool 6 h p.i., which decreased over time. Lesions were detectable as early as 24 h p.i. ⁸⁹Zr∙Df-HER2-Fab-PAS₂₀₀ was tolerated well. Conclusion: This study demonstrates that a PASylated Fab tracer shows appropriate blood clearance to allow sensitive visualization of small tumor lesions in a clinical setting.

Background: Previously, we used inhibitors blocking BET bromodomain binding proteins (BRDs) in Ewing sarcoma (EwS) and observed that long term treatment resulted in the development of resistance. Here, we analyze the possible interaction of BRD4 with cyclin-dependent kinase (CDK) 9. Methods: Co-immunoprecipitation experiments (CoIP) to characterize BRD4 interaction and functional consequences of inhibiting transcriptional elongation were assessed using drugs targeting of BRD4 or CDK9, either alone or in combination. Results: CoIP revealed an interaction of BRD4 with EWS-FLI1 and CDK9 in EwS. Treatment of EwS cells with CDKI-73, a specific CDK9 inhibitor (CDK9i), induced a rapid downregulation of EWS-FLI1 expression and block of contact-dependent growth. CDKI-73 induced apoptosis in EwS, as depicted by cleavage of Caspase 7 (CASP7), PARP and increased CASP3 activity, similar to JQ1. Microarray analysis following CDKI-73 treatment uncovered a transcriptional program that was only partially comparable to BRD inhibition. Strikingly, combined treatment of EwS with BRD- and CDK9-inhibitors re-sensitized cells, and was overall more effective than individual drugs not only in vitro but also in a preclinical mouse model in vivo. Conclusion: Treatment with BRD inhibitors in combination with CDK9i offers a new treatment option that significantly blocks the pathognomonic EWS-ETS transcriptional program and malignant phenotype of EwS.

Background: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. Methods: Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. Results: A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. Conclusion: In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.