γ-TRIS: a graph-algorithm for comprehensive identification of vector genomic insertion sites

InCliniGene enables high-throughput and comprehensive in vivo clonal tracking toward clinical genomics data integration

High-throughput clonal tracking in patients under hematopoietic stem cell gene therapy with integrating vector is instrumental in assessing bio-safety and efficacy. Monitoring the fate of millions of transplanted clones and their progeny across differentiation and proliferation over time leverages the identification of the vector integration sites, used as surrogates of clonal identity. Although γ-tracking retroviral insertion sites (γ-TRIS) is the state-of-the-art algorithm for clonal identification, the computational drawbacks in the tracking algorithm, based on a combinatorial all-versus-all strategy, limit its use in clinical studies with several thousands of samples per patient. We developed the first clonal tracking graph database, InCliniGene (https://github.com/calabrialab/InCliniGene), that imports the output files of γ-TRIS and generates the graph of clones (nodes) connected by arches if two nodes share common genomic features as defined by the γ-TRIS rules. Embedding both clonal data and their connections in the graph, InCliniGene can track all clones longitudinally over samples through data queries that fully explore the graph. This approach resulted in being highly accurate and scalable. We validated InCliniGene using an in vitro dataset, specifically designed to mimic clinical cases, and tested the accuracy and precision. InCliniGene allows extensive use of γ-TRIS in large gene therapy clinical applications and naturally realizes the full data integration of molecular and genomics data, clinical and treatment measurements and genomic annotations. Further extensions of InCliniGene with data federation and with application programming interface will support data mining toward precision, personalized and predictive medicine in gene therapy. Database URL:  https://github.com/calabrialab/InCliniGene.

IS-Seq: a bioinformatics pipeline for integration sites analysis with comprehensive abundance quantification methods

BACKGROUND

Integration site (IS) analysis is a fundamental analytical platform for evaluating the safety and efficacy of viral vector based preclinical and clinical Gene Therapy (GT). A handful of groups have developed standardized bioinformatics pipelines to process IS sequencing data, to generate reports, and/or to perform comparative studies across different GT trials. Keeping up with the technological advances in the field of IS analysis, different computational pipelines have been published over the past decade. These pipelines focus on identifying IS from single-read sequencing or paired-end sequencing data either using read-based or using sonication fragment-based methods, but there is a lack of a bioinformatics tool that automatically includes unique molecular identifiers (UMI) for IS abundance estimations and allows comparing multiple quantification methods in one integrated pipeline.

RESULTS

Here we present IS-Seq a bioinformatics pipeline that can process data from paired-end sequencing of both old restriction sites-based IS collection methods and new sonication-based IS retrieval systems while allowing the selection of different abundance estimation methods, including read-based, Fragment-based and UMI-based systems.

CONCLUSIONS

We validated the performance of IS-Seq by testing it against the most popular  analytical workflow available in the literature (INSPIIRED) and using different scenarios. Lastly, by performing extensive simulation studies and a comprehensive wet-lab assessment of our IS-Seq pipeline we could show that in clinically relevant scenarios, UMI quantification provides better accuracy than the currently most widely used sonication fragment counts as a method for IS abundance estimation.

Longitudinal single-cell profiling of chemotherapy response in acute myeloid leukemia

Acute myeloid leukemia may be characterized by a fraction of leukemia stem cells (LSCs) that sustain disease propagation eventually leading to relapse. Yet, the contribution of LSCs to early therapy resistance and AML regeneration remains controversial. We prospectively identify LSCs in AML patients and xenografts by single-cell RNA sequencing coupled with functional validation by a microRNA-126 reporter enriching for LSCs. Through nucleophosmin 1 (NPM1) mutation calling or chromosomal monosomy detection in single-cell transcriptomes, we discriminate LSCs from regenerating hematopoiesis, and assess their longitudinal response to chemotherapy. Chemotherapy induced a generalized inflammatory and senescence-associated response. Moreover, we observe heterogeneity within progenitor AML cells, some of which proliferate and differentiate with expression of oxidative-phosphorylation (OxPhos) signatures, while others are OxPhos (low) miR-126 (high) and display enforced stemness and quiescence features. miR-126 (high) LSCs are enriched at diagnosis in chemotherapy-refractory AML and at relapse, and their transcriptional signature robustly stratifies patients for survival in large AML cohorts.

AAV integration in human hepatocytes

Recombinant adeno-associated viral (rAAV) vectors are considered promising tools for gene therapy directed at the liver. Whereas rAAV is thought to be an episomal vector, its single-stranded DNA genome is prone to intra- and inter-molecular recombination leading to rearrangements and integration into the host cell genome. Here, we ascertained the integration frequency of rAAV in human hepatocytes transduced either ex vivo or in vivo and subsequently expanded in a mouse model of xenogeneic liver regeneration. Chromosomal rAAV integration events and vector integrity were determined using the capture-PacBio sequencing approach, a long-read next-generation sequencing method that has not previously been used for this purpose. Chromosomal integrations were found at a surprisingly high frequency of 1%-3% both in vitro and in vivo. Importantly, most of the inserted rAAV sequences were heavily rearranged and were accompanied by deletions of the host genomic sequence at the integration site.

VSeq-Toolkit: Comprehensive Computational Analysis of Viral Vectors in Gene Therapy

Viral vector characterization and analysis are important components for the development of safe gene therapeutic products, elucidating the potential genotoxic and immunogenic effects of vectors and establishing their safety profiles. Here, we present VSeq-Toolkit, which offers varying analysis modes for viral gene therapy data. The first mode determines the undesirable known contaminants and their frequency in viral preparations or other sequencing data. The second mode is designed for the analysis of intra-vector fusion breakpoints and the third mode for unraveling the viral-host fusion events distribution. Analysis modes of our toolkit can be executed independently or together and allow the analysis of multiple viral vectors concurrently. It has been designed and evaluated for the analysis of short read high-throughput sequencing data, including whole-genome or targeted sequencing. VSeq-Toolkit is developed in Perl and Bash programming languages and is available at https://github.com/CompMeth/VSeq-Toolkit.