Quality assessment of cellular therapies: the emerging role of molecular assays

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP <71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated, and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (CFU)/mL using metagenomic classification. Machine learning can be coupled with long-read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP <71> organisms to 10 CFU/mL. IMPORTANCE This research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long-read nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.

Role of flow cytometry in evaluation of the cellular therapy products used in haematopoietic stem cell transplantation

Cellular therapy nowadays includes various products from haematopoietic stem cells (HSC) collected from bone marrow, peripheral blood, and umbilical cord blood to more complex adoptive immune therapy for the treatment of malignant diseases, and gene therapy for inherited immune deficiencies. Broader utilization of cellular therapy requires extensive quality testing of these products that should fulfil the same requirements regarding composition, purity, and potency nevertheless they are manufactured in various centres. Technical improvements of the flow cytometers accompanied by the increased number of available reagents and fluorochromes used to conjugate monoclonal antibodies, enable detailed and precise insight into the function of the immune system and other areas of cell biology, and allows cell evaluation based on size, shape, and morphology or assessment of cell surface markers, as well as cell purity and viability, which greatly contributes to the development and progress of the cell therapy. The aim of this paper is to give an overview of the current use and challenges of flow cytometry analysis in quality assessment of cellular therapy products, with regard to basic principles of determining HSC and leukocyte subpopulation, assessment of cells viability and quality of thawed cryopreserved HSC as well as the importance of validation and quality control of flow cytometry methods according to good laboratory practice.

Multivariate data analysis in cell gene therapy manufacturing

The emergence of cell gene therapy (CGT) as a safe and efficacious treatment for numerous severe inherited and acquired human diseases has led to growing interest and investment in new CGT products. The most successful of these have been autologous viral vector-based treatments. The development of viral vector manufacturing processes and ex vivo patient cell processing capabilities is a pressing issue in the advancement of autologous viral vector-based CGT treatments. In viral vector production, scale-up is a critical task due to the limited scalability of traditional laboratory systems and the demand for high volumes of viral vector manufactured in accordance with current good manufacturing practice. Ex vivo cell processing methods require optimisation and automation before they can be scaled out, and several other manufacturing challenges are prevalent such as high levels of raw material and process variability, difficulty characterising complex materials, and a lack of knowledge of critical process parameters and their effect on critical quality attributes of the viral vector and cell drug products. Multivariate data analysis (MVDA) has been leveraged successfully in a variety of applications in the chemical and biochemical industries, including for tasks such as bioprocess monitoring, identification of critical process parameters and assessment of process variability and comparability during process development, scale-up and technology transfer. Henceforth, MVDA is reviewed here as a suitable tool for tackling some of the challenges faced in the development of CGT manufacturing processes. A summary of some key CGT manufacturing challenges is provided along with a review of MVDA applications to mammalian and microbial processes, and an exploration of the potential benefits, requirements and pre-requisites of MVDA applications in the development of CGT manufacturing processes.

Expression of CD14, IL10, and Tolerogenic Signature in Dendritic Cells Inversely Correlate with Clinical and Immunologic Response to TARP Vaccination in Prostate Cancer Patients

Purpose: Despite the vast number of clinical trials conducted so far, dendritic cell (DC)-based cancer vaccines have mostly shown unsatisfactory results. Factors and manufacturing procedures essential for these therapeutics to induce effective antitumor immune responses have yet to be fully characterized. We here aimed to identify DC markers correlating with clinical and immunologic response in a prostate carcinoma vaccination regimen.Experimental Design: We performed an extensive characterization of DCs used to vaccinate 18 patients with prostate carcinoma enrolled in a pilot trial of T-cell receptor gamma alternate reading frame protein (TARP) peptide vaccination (NCT00908258). Peptide-pulsed DC preparations (114) manufactured were analyzed by gene expression profiling, cell surface marker expression and cytokine release secretion, and correlated with clinical and immunologic responses.Results: DCs showing lower expression of tolerogenic gene signature induced strong antigen-specific immune response and slowing in PSA velocity, a surrogate for clinical response. These DCs were also characterized by lower surface expression of CD14, secretion of IL10 and MCP-1, and greater secretion of MDC. When combined, these four factors were able to remarkably discriminate DCs that were sufficiently potent to induce strong immunologic response.Conclusions: DC factors essential for the activation of immune responses associated with TARP vaccination in prostate cancer patients were identified. This study highlights the importance of in-depth characterization of DC vaccines and other cellular therapies, to understand the critical factors that hinder potency and potential efficacy in patients. Clin Cancer Res; 23(13); 3352-64. ©2017 AACR.

Quality controls in cellular immunotherapies: rapid assessment of clinical grade dendritic cells by gene expression profiling

Cell-based immunotherapies are among the most promising approaches for developing effective and targeted immune response. However, their clinical usefulness and the evaluation of their efficacy rely heavily on complex quality control assessment. Therefore, rapid systematic methods are urgently needed for the in-depth characterization of relevant factors affecting newly developed cell product consistency and the identification of reliable markers for quality control. Using dendritic cells (DCs) as a model, we present a strategy to comprehensively characterize manufactured cellular products in order to define factors affecting their variability, quality and function. After generating clinical grade human monocyte-derived mature DCs (mDCs), we tested by gene expression profiling the degrees of product consistency related to the manufacturing process and variability due to intra- and interdonor factors, and how each factor affects single gene variation. Then, by calculating for each gene an index of variation we selected candidate markers for identity testing, and defined a set of genes that may be useful comparability and potency markers. Subsequently, we confirmed the observed gene index of variation in a larger clinical data set. In conclusion, using high-throughput technology we developed a method for the characterization of cellular therapies and the discovery of novel candidate quality assurance markers.

Classification of dendritic cell phenotypes from gene expression data

Background

The selection of relevant genes for sample classification is a common task in many gene expression studies. Although a number of tools have been developed to identify optimal gene expression signatures, they often generate gene lists that are too long to be exploited clinically. Consequently, researchers in the field try to identify the smallest set of genes that provide good sample classification. We investigated the genome-wide expression of the inflammatory phenotype in dendritic cells. Dendritic cells are a complex group of cells that play a critical role in vertebrate immunity. Therefore, the prediction of the inflammatory phenotype in these cells may help with the selection of immune-modulating compounds.

Results

A data mining protocol was applied to microarray data for murine cell lines treated with various inflammatory stimuli. The learning and validation data sets consisted of 155 and 49 samples, respectively. The data mining protocol reduced the number of probe sets from 5,802 to 10, then from 10 to 6 and finally from 6 to 3. The performances of a set of supervised classification models were compared. The best accuracy, when using the six following genes --Il12b, Cd40, Socs3, Irgm1, Plin2 and Lgals3bp-- was obtained by Tree Augmented Naïve Bayes and Nearest Neighbour (91.8%). Using the smallest set of three genes --Il12b, Cd40 and Socs3-- the performance remained satisfactory and the best accuracy was with Support Vector Machine (95.9%). These data mining models, using data for the genes Il12b, Cd40 and Socs3, were validated with a human data set consisting of 27 samples. Support Vector Machines (71.4%) and Nearest Neighbour (92.6%) gave the worst performances, but the remaining models correctly classified all the 27 samples.

Conclusions

The genes selected by the data mining protocol proposed were shown to be informative for discriminating between inflammatory and steady-state phenotypes in dendritic cells. The robustness of the data mining protocol was confirmed by the accuracy for a human data set, when using only the following three genes: Il12b, Cd40 and Socs3. In summary, we analysed the longitudinal pattern of expression in dendritic cells stimulated with activating agents with the aim of identifying signatures that would predict or explain the dentritic cell response to an inflammatory agent.