Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer.

Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles. The results indicated varying sensitivity across architectures, with ConvNeXt demonstrating the strongest performance. Notably, newer architectures achieved superior performance, even though with challenges in differentiating closely related Gleason grades. The ConvNeXt model was capable of learning a balance between complexity and generalizability. Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.

Proteomic and transcriptomic characterisation of FIA10, a novel murine leukemic cell line that metastasizes into the brain.

Brain metastasis leads to increased mortality and is a major site of relapse for several cancers, yet the molecular mechanisms of brain metastasis are not well understood. In this study, we established and characterized a new leukemic cell line, FIA10, that metastasizes into the central nervous system (CNS) following injection into the tail vein of syngeneic mice. Mice injected with FIA10 cells developed neurological symptoms such as loss of balance, tremor, ataxic gait and seizures, leading to death within 3 months. Histopathology coupled with PCR analysis clearly showed infiltration of leukemic FIA10 cells into the brain parenchyma of diseased mice, with little involvement of bone marrow, peripheral blood and other organs. To define pathways that contribute to CNS metastasis, global transcriptome and proteome analysis was performed on FIA10 cells and compared with that of the parental stem cell line FDCP-Mix and the related FIA18 cells, which give rise to myeloid leukemia without CNS involvement. 188 expressed genes (RNA level) and 189 proteins were upregulated (log2 ratio FIA10/FIA18 ≥ 1) and 120 mRNAs and 177 proteins were downregulated (log2 ratio FIA10/FIA18 ≤ 1) in FIA10 cells compared with FIA18 cells. Major upregulated pathways in FIA10 cells revealed by biofunctional analyses involved immune response components, adhesion molecules and enzymes implicated in extracellular matrix remodeling, opening and crossing the blood-brain barrier (BBB), molecules supporting migration within the brain parenchyma, alterations in metabolism necessary for growth within the brain microenvironment, and regulators for these functions. Downregulated RNA and protein included several tumor suppressors and DNA repair enzymes. In line with the function of FIA10 cells to specifically infiltrate the brain, FIA10 cells have acquired a phenotype that permits crossing the BBB and adapting to the brain microenvironment thereby escaping immune surveillance. These data and our model system FIA10 will be valuable resources to study the occurrence of brain metastases and may help in the development of potential therapies against brain invasion.

Individualized targeted treatment in a case of a rare TFG::ROS1 fusion positive inflammatory myofibroblastic tumor (IMT).

BACKGROUND: Inflammatory myofibroblastic tumor (IMTs) are rare mesenchymal neoplasms with slow growth. Resection is considered as therapeutic standard, with chemotherapy being insufficiently effective in advanced disease. ALK translocations are present in 50% of cases, ROS1 fusions (YWHAE::ROS1, TFG::ROS1) are extremely rare. Here, we present a case with TFG::ROS1 fusion and highlight the significance of molecular tumor boards (MTBs) in clinical precision oncology for post-last-line therapy. CASE PRESENTATION: A 32-year-old woman presented with IMT diagnosed at age 27 for biopsy and treatment evaluation. Previous treatments included multiple resections and systemic therapy with vinblastine, cyclophosphamide, and methotrexate. A computed tomography scan showed extensive tumor infiltration of the psoas muscles and the posterior abdomen. Next generation sequencing revealed an actionable ROS1 fusion (TFG::ROS1) with breakpoints at exon 4/35 including the kinase domain and activating the RAS-pathway. TFG, the Trk-fused gene, exerts functions such as intracellular trafficking and exhibits high sequence homology between species. Based on single reports about efficacy of ROS1-targeting in ROS1 translocation positive IMTs the patient was started on crizotinib, an ATP-competitive small molecule c-MET, ALK and ROS1-inhibitor. With a follow-up of more than 9 months, the patient continues to show a profound response with major tumor regression, improved quality of life and no evidence for severe adverse events. CONCLUSION: This case underscores the importance of the availability of modern molecular diagnostics and interdisciplinarity in precision oncology to identify rare, disease-defining genotypes that make an otherwise difficult-to-treat disease targetable.

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.

BACKGROUND: The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way. RECENT FINDINGS: Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker. CONCLUSIONS: The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

Identification of Disparities in Personalized Cancer Care-A Joint Approach of the German WERA Consortium.

(1) Background: molecular tumor boards (MTBs) are crucial instruments for discussing and allocating targeted therapies to suitable cancer patients based on genetic findings. Currently, limited evidence is available regarding the regional impact and the outreach component of MTBs; (2) Methods: we analyzed MTB patient data from four neighboring Bavarian tertiary care oncology centers in Würzburg, Erlangen, Regensburg, and Augsburg, together constituting the WERA Alliance. Absolute patient numbers and regional distribution across the WERA-wide catchment area were weighted with local population densities; (3) Results: the highest MTB patient numbers were found close to the four cancer centers. However, peaks in absolute patient numbers were also detected in more distant and rural areas. Moreover, weighting absolute numbers with local population density allowed for identifying so-called white spots-regions within our catchment that were relatively underrepresented in WERA MTBs; (4) Conclusions: investigating patient data from four neighboring cancer centers, we comprehensively assessed the regional impact of our MTBs. The results confirmed the success of existing collaborative structures with our regional partners. Additionally, our results help identifying potential white spots in providing precision oncology and help establishing a joint WERA-wide outreach strategy.

[Standardized diagnosis of pancreatic head carcinoma]. / Standardisierte Befundung des Pankreaskopfkarzinoms.

Most pancreatic ductal adenocarcinomas are localized in the pancreatic head. Due to the complex anatomic relationships with the surrounding organs and vascular structures in the retroperitoneal space and to the presence of numerous transection margins and dissection planes, pancreatic head resections belong to the most complex specimens concerning grossing and sampling for histopathologic analysis.Here we discuss current guidelines for standardized grossing and reporting of pancreatic cancer, with special reference to the assessment of the resection margin status. The importance of standardized reporting for the sake of completeness, comprehensibility, comparability, and quality control as well as for the integration of pathology reports in interdisciplinary digital workflows and artificial intelligence applications will be emphasized.

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.

BACKGROUND: Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status from routine histology slides. METHODS: In this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Switzerland, Japan, Italy, Germany, the UK and the USA). We trained a deep learning-based classifier to detect microsatellite instability and EBV positivity from digitised, haematoxylin and eosin stained resection slides without annotating tumour containing regions. The performance of the classifier was assessed by within-cohort cross-validation in all ten cohorts and by external validation, for which we split the cohorts into a five-cohort training dataset and a five-cohort test dataset. We measured the area under the receiver operating curve (AUROC) for detection of microsatellite instability and EBV status. Microsatellite instability and EBV status were determined to be detectable if the lower bound of the 95% CI for the AUROC was above 0·5. FINDINGS: Across the ten cohorts, our analysis included 2823 patients with known microsatellite instability status and 2685 patients with known EBV status. In the within-cohort cross-validation, the deep learning-based classifier could detect microsatellite instability status in nine of ten cohorts, with AUROCs ranging from 0·597 (95% CI 0·522-0·737) to 0·836 (0·795-0·880) and EBV status in five of eight cohorts, with AUROCs ranging from 0·819 (0·752-0·841) to 0·897 (0·513-0·966). Training a classifier on the pooled training dataset and testing it on the five remaining cohorts resulted in high classification performance with AUROCs ranging from 0·723 (95% CI 0·676-0·794) to 0·863 (0·747-0·969) for detection of microsatellite instability and from 0·672 (0·403-0·989) to 0·859 (0·823-0·919) for detection of EBV status. INTERPRETATION: Classifiers became increasingly robust when trained on pooled cohorts. After prospective validation, this deep learning-based tissue classification system could be used as an inexpensive predictive biomarker for immunotherapy in gastric cancer. FUNDING: German Cancer Aid and German Federal Ministry of Health.

Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images.

In this study, we developed the Binary ImaGe Colon Metastasis classifier (BIg-CoMet), a semi-guided approach for the stratification of colon cancer patients into two risk groups for the occurrence of distant metastasis, using an InceptionResNetV2-based deep learning model trained on binary images. We enrolled 291 colon cancer patients with pT3 and pT4 adenocarcinomas and converted one cytokeratin-stained representative tumor section per case into a binary image. Image augmentation and dropout layers were incorporated to avoid overfitting. In a validation collective (n = 128), BIg-CoMet was able to discriminate well between patients with and without metastasis (AUC: 0.842, 95% CI: 0.774-0.911). Further, the Kaplan-Meier curves of the metastasis-free survival showed a highly significant worse clinical course for the high-risk group (log-rank test: p < 0.001), and we demonstrated superiority over other established risk factors. A multivariable Cox regression analysis adjusted for confounders supported the use of risk groups as a prognostic factor for the occurrence of metastasis (hazard ratio (HR): 5.4, 95% CI: 2.5-11.7, p < 0.001). BIg-CoMet achieved good performance for both UICC subgroups, especially for UICC III (n = 53), with a positive predictive value of 80%. Our study demonstrates the ability to stratify colon cancer patients via a semi-guided process on images that primarily reflect tumor architecture.

Number of pathologists in Germany: comparison with European countries, USA, and Canada.

The rapid development of pathology is in contrast to a shortage of qualified staff. The aims of the present study are to compile basic information on the numbers of German physicians in pathology and to compare it with the situation in Europe and overseas. In addition, model calculations will shed light on the effects of part-time working models. Various publicly accessible databases (EuroStat) as well as publications of medical associations and professional associations of European countries and the USA/Canada were examined. In addition, a survey was carried out among the institutes of German universities. Figures from 24 European countries and the USA/Canada were evaluated. With one pathologist per 47,989 inhabitants, the density of pathologists in Germany in relation to the population is the second-lowest in Europe (average: 32,018). Moreover, the proportion of pathologists among the physicians working in Germany is the lowest in Europe and at the same time lower than in the USA and Canada (Germany: 1:200, USA: 1:70, Canada: 1:49). The ratio of pathologists to medical specialists is shifted in the same direction. The survey among university pathologists revealed a relevant increase in the workload over the last 10 years. The majority of institutes can manage this workload only with considerable difficulties. With a ratio between specialists and residents of 1:1, the university institutes show a high commitment in the area of training. The results of this study indicate a shortage of pathologists in Germany that could lead to a bottleneck in large parts of the health system.

Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer-specific death from digital images of immunohistochemically (IHC) stained tissue microarrays (TMAs). Specifically, we evaluated a cohort of 248 gastric cancer patients using convolutional neural networks (CNNs) in an end-to-end weakly supervised scheme independent of subjective pathologist input. To account for the time-to-event characteristic of the outcome data, we developed new survival models to guide the network training. In addition to the standard H&E staining, we investigated the prognostic value of a panel of immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki67). Our CNN-derived risk scores provided additional prognostic value when compared to the gold standard prognostic tool TNM stage. The CNN-derived risk scores were also shown to be superior when systematically compared to cell density measurements or a CNN score derived from binary 5-year survival classification, which ignores time-to-event. To better understand the underlying biological mechanisms, we qualitatively investigated risk heat maps for each marker which visualised the network output. We identified patterns of biological interest that were related to low risk of cancer-specific death such as the presence of B-cell predominated clusters and Ki67 positive sub-regions and showed that the corresponding risk scores had prognostic value in multivariate Cox regression analyses (Ki67&CD20 risks: hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.15-1.89, p = 0.002; CD20&CD68 risks: HR = 1.33, 95% CI = 1.07-1.67, p = 0.009). Our study demonstrates the potential additional value that deep learning in combination with a panel of IHC markers can bring to the field of precision oncology.

Software-assisted decision support in digital histopathology.

Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the daily task for pathologists is changing drastically and will become increasingly demanding in order to take advantage of the development of modern computer technologies. The role of pathologist has rapidly evolved from exclusively describing the morphology and phenomenology of a disease, to becoming a gatekeeper for novel and most effective treatment options. This is possible based on the retrieval and management of a wide range of complex information from tissue or a group of cells and associated meta-data. Intelligent and self-learning software solutions can support and guide pathologists to score clinically relevant decisions based on the accurate and robust quantification of multiple target molecules or surrogate biomarker as companion or complimentary diagnostics along with relevant spatial relationships and contextual information from digital H&E and multiplexed images. With the availability of multiplex staining techniques on a single slide, high-resolution image analysis tools, and high-end computer hardware, machine and deep learning solutions now offer diagnostic rulesets and algorithms that still require clinical validation in well-designed studies. Before entering the clinical practice, the 'human factor' pathologist needs to develop trust in the output coming from the 'digital black box of computational pathology', including image analysis solutions and artificial intelligence algorithms to support critical clinical decisions which otherwise would not be available. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Specific immune cell and lymphatic vessel signatures identified by image analysis in renal cancer.

Anti-angiogenic therapy and immune checkpoint inhibition are novel treatment strategies for patients with renal cell carcinoma. Various components and structures of the tumor microenvironment are potential predictive biomarkers and also attractive treatment targets. Macrophages, tumor infiltrating lymphocytes, vascular and lymphatic vessels represent an important part of the tumor immune environment, but their functional phenotypes and relevance for clinical outcome are yet ill defined. We applied Tissue Phenomics methods including image analysis for the standardized quantification of specific components and structures within the tumor microenvironment to profile tissue sections from 56 clear cell renal cell carcinoma patients. A characteristic composition and unique spatial relationship of CD68+ macrophages and tumor infiltrating lymphocytes correlated with overall survival. An inverse relationship was found between vascular (CD34) and lymphatic vessel (LYVE1) density. In addition, outcome was significantly better in patients with high blood vessel density in the tumors, whereas increased lymphatic vessel density in the tumors was associated with worse outcome. The Tissue Phenomics imaging analysis approach allowed visualization and simultaneous quantification of immune environment components, adding novel contextual information, and biological insights with potential applications in treatment response prediction.

New insights into the inflamed tumor immune microenvironment of gastric cancer with lymphoid stroma: from morphology and digital analysis to gene expression.

BACKGROUND: Gastric cancer with lymphoid stroma (GCLS) is characterized by prominent stromal infiltration of T-lymphocytes. The aim of this study was to investigate GCLS biology through analysis of clinicopathological features, EBV infection, microsatellite instability (MSI), immune gene-expression profiling and PD-L1 status in neoplastic cells and tumor immune microenvironment. METHODS: Twenty-four GCLSs were analyzed by RNA in situ hybridization for EBV (EBER), PCR/fragment analysis for MSI, immunohistochemistry (PD-L1, cytokeratin, CD3, CD8), co-immunofluorescence (CK/PD-L1, CD68/PD-L1), NanoString gene-expression assay for immune-related genes and PD-L1 copy number alterations. CD3+ and CD8+ T-cell densities were calculated by digital analysis. Fifty-four non-GCLSs were used as control group. RESULTS: GCLSs displayed distinctive clinicopathological features, such as lower pTNM stage (p = 0.02) and better overall survival (p = 0.01). EBV+ or MSI-high phenotype was found in 66.7 and 16.7% cases, respectively. GCLSs harbored a cytotoxic T-cell-inflamed profile, particularly at the invasive front of tumors (p < 0.01) and in EBV+ cases (p = 0.01). EBV+ GCLSs, when compared to EBV- GCLSs, showed higher mRNA expression of genes related to Th1/cytotoxic and immunosuppressive biomarkers. PD-L1 protein expression, observed in neoplastic and immune stromal cells (33.3 and 91.7%, respectively), and PD-L1 amplification (18.8%) were restricted to EBV+/MSI-high tumors and correlated with high values of PD-L1 mRNA expression. CONCLUSIONS: This study shows that GCLS has a distinctive clinico-pathological and molecular profile. Furthermore, through an in-depth study of tumor immune microenvironment-by digital analysis and mRNA expression profiling-it highlights the role of EBV infection in promoting an inflamed tumor microenvironment, with putative therapeutic implications.

Synthesis of next generation dual-responsive cross-linked nanoparticles and their application to anti-cancer drug delivery.

Rare earth metal-mediated group transfer polymerisation enables the synthesis of previously inaccessible block copolymers of 2-vinylpyridine, diethyl vinylphosphonate and the new diallyl vinylphosphonate monomer. This precision polymerisation and the selective cross-linking of allyl side groups via thiol-ene click chemistry leads to the formation of well-defined dual-responsive nanoparticles. We demonstrate that these next generation nanocarriers are pH- and temperature-responsive and are capable of efficiently delivering doxorubicin into the nucleus of cancer cells. High anti-cancer activity could be demonstrated via cytotoxicity tests on breast cancer (MCF-7) and cervical cancer (HeLa) cells. These results validate this modular synthesis route as an ideal platform for the development of sophisticated nanocarriers for future drug delivery applications.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

Fluorescent Polyvinylphosphonate Bioconjugates for Selective Cellular Delivery.

To date, many poly(ethylene glycol) (PEG) and poly(N-isopropylacrylamide) (PNIPAAm) biomolecule conjugates have been described, but they often show long response times, are not bio-inert, or lose function in biological fluids. Herein, we present a modular synthetic approach to generate polyvinylphosphonate biomolecule conjugates. These conjugates exhibit a sharp phase transition temperature even under physiological conditions where few other examples with this property have been described to date. Furthermore, it was feasible to add biological functions to the polymers via the conjugation step. The polyvinylphosphonate cholesterol constructs are attached to the cellular membrane and the folic acid anchored polymers are shuttled into the cells. This is an exceptional finding through a straightforward synthetic approach.

Treatment of advanced gastrointestinal tumors with genetically modified autologous mesenchymal stromal cells (TREAT-ME1): study protocol of a phase I/II clinical trial.

BACKGROUND: Adenocarcinoma originating from the digestive system is a major contributor to cancer-related deaths worldwide. Tumor recurrence, advanced local growth and metastasis are key factors that frequently prevent these tumors from curative surgical treatment. Preclinical research has demonstrated that the dependency of these tumors on supporting mesenchymal stroma results in susceptibility to cell-based therapies targeting this stroma. METHODS/DESIGN: TREAT-ME1 is a prospective, uncontrolled, single-arm phase I/II study assessing the safety and efficacy of genetically modified autologous mesenchymal stromal cells (MSC) as delivery vehicles for a cell-based gene therapy for advanced, recurrent or metastatic gastrointestinal or hepatopancreatobiliary adenocarcinoma. Autologous bone marrow will be drawn from each eligible patient after consent for bone marrow donation has been obtained (under a separate EC-approved protocol). In the following ~10 weeks the investigational medicinal product (IMP) is developed for each patient. To this end, the patient's MSCs are stably transfected with a gamma-retroviral, replication-incompetent and self-inactivating (SIN) vector system containing a therapeutic promoter - gene construct that allows for tumor-specific expression of the therapeutic gene. After release of the IMP the patients are enrolled after given informed consent for participation in the TREAT-ME 1 trial. In the phase I part of the study, the safety of the IMP is tested in six patients by three treatment cycles consisting of re-transfusion of MSCs at different concentrations followed by administration of the prodrug Ganciclovir. In the phase II part of the study, sixteen patients will be enrolled receiving IMP treatment. A subgroup of patients that qualifies for surgery will be treated preoperatively with the IMP to verify homing of the MSCs to tumors as to be confirmed in the surgical specimen. DISCUSSION: The TREAT-ME1 clinical study involves a highly innovative therapeutic strategy combining cell and gene therapy and is conducted at a high level of pharmaceutical quality ensuring patient safety. This patient-tailored approach represents the first clinical study worldwide utilizing genetically engineered MSCs in humans. TRIAL REGISTRATION: EU Clinical Trials Register/European Union Drug Regulating Authorities Clinical Trials Database number: 2012-003741-15.

Quantification of in vitro mesenchymal stem cell invasion into tumor spheroids using selective plane illumination microscopy.

Mesenchymal stem cell (MSC) homing and integration into tumors are under evaluation for clinical application. This approach requires the identification of conditions for optimal tumor invasion. We describe a tool for the in vitro comparison of parameters influencing invasion. Human MSC added to experimental tumor spheroids variably migrates toward the center of the structure. To determine MSC distribution inside the three-dimensional specimen, spatial analysis was performed using selective plane illumination microscopy. A standardized method to quantify and compare the invasion potential of variably treated MSC into experimental tumor environments allows efficient screening for optimizing conditions.

Modulation of mesenchymal stromal cell characteristics by microcarrier culture in bioreactors.

Mesenchymal stromal cells (MSCs) are promising candidates for cell therapy. Their therapeutic use requires extensive expansion to obtain a sufficiently high number of cells for clinical applications. State-of-the-art expansion systems, that is, primarily culture flask-based systems, are limited regarding scale-up, automation, and reproducibility. To overcome this bottleneck, microcarrier (MC)-based expansion processes have been developed. For the first time, MSCs from the perinatal sources umbilical cord (UC) and amniotic membrane (AM) were expanded on MCs. This study focuses on the comparison of flask- and Cytodex 1 MC-expanded MSCs by evaluating the influence of the expansion process on biological MSC characteristics. Furthermore, we tested the hypothesis to obtain more homogeneous MSC preparations by expanding cells on MCs in controlled large-scale bioreactors. MSCs were extensively characterized determining morphology, cell growth, surface marker expression, and functional properties such as differentiation capacity, secretion of paracrine factors, and gene expression. Based on their gene expression profile MSCs from different donors and sources clearly clustered in distinct groups solely depending on the expansion process-MC or flask culture. MC- and flask-expanded MSCs significantly differed from each other regarding surface markers and both paracrine factors and gene expression profiles. Furthermore, based on gene expression analysis, MC cultivation of MSCs in controlled bioreactor systems resulted in less heterogeneity between cells from different donors. In conclusion, MC-based MSC expansion in controlled bioreactors has the potential to reliably produce MSCs with altered characteristics and functions as compared to flask-expanded MSCs. These findings may be useful for the generation of MSCs with tailored properties for clinical applications.

Bone marrow-derived mesenchymal stem cells migrate to healthy and damaged salivary glands following stem cell infusion.

Xerostomia is a severe side effect of radiation therapy in head and neck cancer patients. To date, no satisfactory treatment option has been established. Because mesenchymal stem cells (MSCs) have been identified as a potential treatment modality, we aimed to evaluate stem cell distribution following intravenous and intraglandular injections using a surgical model of salivary gland damage and to analyse the effects of MSC injections on the recruitment of immune cells. The submandibular gland ducts of rats were surgically ligated. Syngeneic adult MSCs were isolated, immortalised by simian virus 40 (SV40) large T antigen and characterized by flow cytometry. MSCs were injected intravenously and intraglandularly. After 1, 3 and 7 days, the organs of interest were analysed for stem cell recruitment. Inflammation was analysed by immunohistochemical staining. We were able to demonstrate that, after intravenous injection, MSCs were recruited to normal and damaged submandibular glands on days 1, 3 and 7. Unexpectedly, stem cells were recruited to ligated and non-ligated glands in a comparable manner. After intraglandular injection of MSCs into ligated glands, the presence of MSCs, leucocytes and macrophages was enhanced, compared to intravenous injection of stem cells. Our data suggest that injected MSCs were retained within the inflamed glands, could become activated and subsequently recruited leucocytes to the sites of tissue damage.