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Lassen, U.

Publications and source records attributed to Lassen, U..

3 recordsLinked to original sources

Identification and characterization of neoantigen-reactive CD8+ T cells following checkpoint blockade therapy in a pan-cancer setting

BackgroundImmune checkpoint blockade (ICB) has been approved as first-line or second-line therapies for an expanding list of malignancies. T cells recognizing mutation-derived neoantigens are hypothesized to play a major role in tumor elimination. However, the dynamics and characteristics of such neoantigen-reactive T cells (NARTs) in the context of ICB are still limitedly understood. MethodsTo explore this, tumor biopsies and peripheral blood were obtained pre- and post-treatment from 20 patients with solid metastatic tumors, in a Phase I basket trial. From whole-exome sequencing and RNA-seq data, patient-specific libraries of neopeptides were predicted and screened with DNA barcode-labeled MHC multimers for CD8+ T cell reactivity, in conjunction with the evaluation of T cell phenotype. ResultsWe were able to detect NARTs in the peripheral blood and tumor biopsies for the majority of the patients; however, we did not observe any significant difference between the disease control and progressive disease patient groups, in terms of the breadth and magnitude of the detected NARTs. We also observed that the hydrophobicity of the peptide played a role in defining neopeptides resulting in NARTs response. A trend towards a treatment-induced phenotype signature was observed in the NARTs post-treatment, with the appearance of Ki67+ CD27+ PD-1+ subsets in the PBMCs and CD39+ Ki67+ TCF-1+ subsets in the TILs. Finally, the estimation of T cells from RNAseq was increasing post versus pre-treatment for disease control patients. ConclusionOur data demonstrates the possibility of monitoring the characteristics of NARTs from tumor biopsies and peripheral blood, and that such characteristics could potentially be incorporated with other immune predictors to understand further the complexity governing clinical success for ICB therapy.

immunology↗

The Minimal Dataset for Cancer of the 1+Million Genomes Initiative

For a real impact on healthcare, precision cancer medicine requires accessibility and interoperability of clinical and genomic data across centres and countries. Due to the heterogeneous digitization in Europe and worldwide, the definition of models for standardised data collection and usability becomes mandatory if countries want to work together on this mission. The European Union 1+Million Genomes (1+MG) initiative, supported by the Horizon 2020 Beyond 1 Million Genomes project, aims at outlining data models, guidance, best practices, and technical infrastructures for transnational access to sequenced genomes, including cancer genomes. Within the framework of the cancer-focused Working Group 9, we developed the 1+MG-Minimal Dataset for Cancer (1+MG-MDC)-a data model encompassing 140 items and organized in eight conceptual domains for the collection of cancer-related clinical information and genomics metadata. The 1+MG-MDC, which results from a multidisciplinary effort, leverages pre-existing models and emphasizes the annotation and traceability of multiple aspects relevant to the complex longitudinal path of the cancer disease and its treatment. We strived to make the 1+MG-MDC easy to adopt, yet comprehensive, addressing the needs of both clinicians and researchers. We will periodically revise and update it to ensure it remains fit for purpose. We propose the 1+MG-MDC as a model to create homogeneous databases, which would, in turn, guide discussions on clinical and genomic features with prognostic or therapeutic value and foster real-world data research.

cancer biology↗

In depth profiling of the cancer proteome from the flowthrough of standard RNA-preparation kits for precision oncology

Cancer is a highly heterogeneous disease, even within the same patient. Biopsies taken from different regions of a tumor may stand in stark molecular contrast to each other. Therefore, the ability to generate meaningful data from multiple platforms using the same biopsy is crucial for translating multi-omics characterizations into the clinic. However, it is generally a cumbersome and lengthy procedure to generate DNA, RNA and protein material from the same biopsy. The Qiagen AllPrep kit is an accessible, straightforward, and widely used kit in clinics worldwide to process biopsies and generate genomic and transcriptomic data from tumors. We aimed to determine if high-quality proteomics data could also be obtained from the remaining material. Here, we investigated procedures for generating deep and quantitatively accurate proteomic information in high throughput from Qiagen AllPrep flowthroughs. With a number of refinements, we obtain in excess of 10,000 quantified proteins, from 60 samples per day, achieving a substantial coverage of the total proteome. Additionally, we successfully characterize the tumors using phosphoproteomics. Combining a standard kit with in-depth proteomics will be an attractive approach for clinics seeking to implement multi-omics-based precision oncology.

cancer biology↗