High-throughput monitoring of integration site clonality in preclinical and clinical gene therapy studies

Clonal tracking in cancer and metastasis

The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.

Tagmentation-based analysis reveals the clonal behavior of CAR-T cells in association with lentivector integration sites

Integration site (IS) analysis is essential in ensuring safety and efficacy of gene therapies when integrating vectors are used. Although clinical trials of gene therapy are rapidly increasing, current methods have limited use in clinical settings because of their lengthy protocols. Here, we describe a novel genome-wide IS analysis method, "detection of the integration sites in a time-efficient manner, quantifying clonal size using tagmentation sequencing" (DIStinct-seq). In DIStinct-seq, a bead-linked Tn5 transposome is used, allowing the sequencing library to be prepared within a single day. We validated the quantification performance of DIStinct-seq for measuring clonal size with clones of known IS. Using ex vivo chimeric antigen receptor (CAR)-T cells, we revealed the characteristics of lentiviral IS. We then applied it to CAR-T cells collected at various times from tumor-engrafted mice, detecting 1,034-6,233 IS. Notably, we observed that the highly expanded clones had a higher integration frequency in the transcription units and vice versa in genomic safe harbors (GSH). Also, in GSH, persistent clones had more frequent IS. Together with these findings, the new IS analysis method will help to improve the safety and efficacy of gene therapies.

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Genome-wide integration site detection using Cas9 enriched amplification-free long-range sequencing

The gene and cell therapy fields are advancing rapidly, with a potential to treat and cure a wide range of diseases, and lentivirus-based gene transfer agents are the vector of choice for many investigators. Early cases of insertional mutagenesis caused by gammaretroviral vectors highlighted that integration site (IS) analysis was a major safety and quality control checkpoint for lentiviral applications. The methods established to detect lentiviral integrations using next-generation sequencing (NGS) are limited by short read length, inadvertent PCR bias, low yield, or lengthy protocols. Here, we describe a new method to sequence IS using Amplification-free Integration Site sequencing (AFIS-Seq). AFIS-Seq is based on amplification-free, Cas9-mediated enrichment of high-molecular-weight chromosomal DNA suitable for long-range Nanopore MinION sequencing. This accessible and low-cost approach generates long reads enabling IS mapping with high certainty within a single day. We demonstrate proof-of-concept by mapping IS of lentiviral vectors in a variety of cell models and report up to 1600-fold enrichment of the signal. This method can be further extended to sequencing of Cas9-mediated integration of genes and to in vivo analysis of IS. AFIS-Seq uses long-read sequencing to facilitate safety evaluation of preclinical lentiviral vector gene therapies by providing IS analysis with improved confidence.

Multiplexed clonality verification of cell lines for protein biologic production

During the development of cell lines for therapeutic protein production, a vector harboring a product transgene is integrated into the genome. To ensure production stability and consistent product quality, single-cell cloning is then performed. Since cells derived from the same parental clone have the same transgene integration locus, the identity of the integration site can also be used to verify the clonality of a production cell line. In this study, we present a high-throughput pipeline for clonality verification through integration site analysis. Sequence capture of genomic fragments that contain both vector and host cell genome sequences was used followed by next-generation sequencing to sequence the relevant vector-genome junctions. A Python algorithm was then developed for integration site identification and validated using a cell line with known integration sites. Using this system, we identified the integration sites of the host vector for 31 clonal cell lines from five independent vector integration events while using one set of probes against common features of the host vector for transgene integration. Cell lines from the same lineage had common integration sites, and they were distinct from unrelated cell lines. The integration sites obtained for each clone as part of the analysis may also be used for clone selection, as the sites can have a profound effect on the transgene’s transcript level and the stability of the resulting cell line. This method thus provides a rapid system for integration site identification and clonality verification.

Model Based Analysis of Clonal Developments Allows for Early Detection of Monoclonal Conversion and Leukemia

The availability of several methods to unambiguously mark individual cells has strongly fostered the understanding of clonal developments in hematopoiesis and other stem cell driven regenerative tissues. While cellular barcoding is the method of choice for experimental studies, patients that underwent gene therapy carry a unique insertional mark within the transplanted cells originating from the integration of the retroviral vector. Close monitoring of such patients allows accessing their clonal dynamics, however, the early detection of events that predict monoclonal conversion and potentially the onset of leukemia are beneficial for treatment. We developed a simple mathematical model of a self-stabilizing hematopoietic stem cell population to generate a wide range of possible clonal developments, reproducing typical, experimentally and clinically observed scenarios. We use the resulting model scenarios to suggest and test a set of statistical measures that should allow for an interpretation and classification of relevant clonal dynamics. Apart from the assessment of several established diversity indices we suggest a measure that quantifies the extension to which the increase in the size of one clone is attributed to the total loss in the size of all other clones. By evaluating the change in relative clone sizes between consecutive measurements, the suggested measure, referred to as maximum relative clonal expansion (mRCE), proves to be highly sensitive in the detection of rapidly expanding cell clones prior to their dominant manifestation. This predictive potential places the mRCE as a suitable means for the early recognition of leukemogenesis especially in gene therapy patients that are closely monitored. Our model based approach illustrates how simulation studies can actively support the design and evaluation of preclinical strategies for the analysis and risk evaluation of clonal developments.