Klinikum Rechts Der Isar Der Technischen Universitat Munchen
Development of a TME-based Immunoscore as a prognostic and predictive biomarker for ALCL
Supervisor: Lena ILLERT
Research objectives/Expected results:
This project aims to develop a TME (Tumour Microenvironment)-based biomarker for the prognosis and the therapeutic efficacy of ALCL, that integrates immune context and T-cell fitness in addition to classical tumour cell characteristics.
Currently, there is increasing evidence that the TME has a decisive role in the prognosis and therapy prediction of tumours. In solid entities and especially in colorectal cancer (CRC), the so-called “Immunoscore” has recently been established, which has been shown to be of higher prognostic relevance than TNM classification or tumour differentiation. However, T-cell lymphomas depict a distinct immunological tumour entity, that behaves completely differently from solid tumours regarding immunosurveillance and immunotherapeutic approaches. It has also been shown that immune checkpoint blockade inhibitor treatment of patients can lead to successful but also fatal outcomes in T cell-NHL.
Due to these fundamentally different immunological processes, the application of the Immunoscore established in CRC will not be transferable to T-cell lymphomas and needs to be refined for these entities. Therefore, this project aims to:
- Develop a TME-based, Prognostic and Predictive solid Biomarker-Immunoscore. We will map immune cells in ALCL by spatial mapping, gene expression profiling and proteomic profiling. Therefore, we will combine traditional methods with cutting edge technologies. For proteomic profiling of immune cells, we will use next to Flow cytometry, Cytometry by time-of-flight (CyTOF) based high throughput spectrometry on a single cell level. Spatial biomarker mapping will be performed by multiplexed imaging using the Akoya Codex Imaging system. Gene expression profiling will be done by scRNAseq and NanoString nCounter technology for FFPE samples. Due to the limited human sample number of ALCLs, we will also include samples from ALCL transgenic mice to develop the ALCL Immunoscore. Based on our preliminary work, we will have access to samples from a clinically relevant mouse model, which is clearly immunogenic in mice, a typical feature of human ALCL and a crucial prerequisite for the development of an Immunoscore. A prognostic score is generated from the established biomarkers in correlation with clinical markers and the mice/patient's outcome by bioinformatic modelling;
- Development of a liquid Immunoscore of circulating T-cells to determine the therapeutic response and detection of early relapse in ALCL. We will use immune profiling of peripheral blood from patients/mice with ALCL before and after treatment with chemotherapy and ALK TKI treatment to identify pharmacodynamic changes in circulating exhausted-phenotype T-cells. Based on our preliminary data, we will focus on CD4 T cells and NK cells but also include CD8 T cells. We will use mass cytometry (CyTOF) with high-dimensional visualization and unsupervised clustering in combination with RNAseq to deeply characterize reactive T/ NK cells. We will be able to identify a blood-based predictive Immunoscore which may predict response to chemotherapeutic/targeted treatment;
- Concept-development of a prospective trial for validation of the ALCL-Immunoscore (solid and liquid). We will derive a concept and design a trial to validate the developed solid and fluid Immunoscores. The concept will include protocol development and writing, statistical planning (including case number calculation), selection of different study sites, and definition and organization of the sampling process.
Due to the rarity of the ALCL cases, this study is only possible in cooperation with other countries within the EU, for example in networks like this one.
Year 2: CBMed: Biomarker development applying the DC’s research but receiving assistance from experts at CBMed (4 months)
Year 3: GPOH gemeinnützige GmbH: Ttrial design from an expert team of trialists and statisticians who will assist with the development of a clinical trial plan (4 months)