Analyse von CMV-Reaktivierungen in Patienten mit Hirntumoren oder Hirnmetastasen während und nach einer Radio(chemo)therapie
Das Zytomegalievirus gehört zur Gruppe der Herpesviren und verbleibt nach Infektion ein Leben lang im Körper, jedoch in der Regel ohne irgendwelche Krankheitssymptome auszulösen (latente Infektion). Es kann jedoch durch ein geschwächtes Immunsystem, UV-Strahlung, Stress und andere Faktoren zu einer Reaktivierung kommen. Eine symptomatische CMV Erkrankung von immunsupprimierten Patienten kann beispielsweise durch eine rechtzeitige Frühtherapie mit Virostatikum verhindert werden. Ohne Einschreiten könnte es zu ernsten Erkrankungen der Lunge, Leber, Niere und des Herzens kommen. Demzufolge führt es zu einer weiteren Schwächung der Immunabwehr und einer erhöhten Infektionsgefahr. Dieses Projekt konzentriert sich daher auf eine vorzeitige Detektion des Virus in Glioblastom Patienten die eine erhöhte Gefährdung zur Reaktivierung darstellen. Dazu wurde bereits vor einigen Jahren die GLIO-CMV01 Studie (NCT02600065) initiiert um alle Patienten zu überwachen, die in der Strahlenklinik am Kopf bestrahlt werden. Hier übernimmt das Institut für Virologie die Bestimmung der Viruslast und die Bestimmung der Immunität. Zusätzlich wird die sensible droplet digital PCR verwendet, bei der in der Regel schon geringste Viruspartikel detektiert werden können.
Hallmark Publikation:
Biomarkers for Cancers with Immune Checkpoint Inhibitors
The immune system protects the body from illness by recognizing and killing invaders such as bacteria and viruses. It is tightly controlled by the immune checkpoint response to avoid fighting its own body (autoimmunity) while retaining the potential to fight off invaders. Immune checkpoint involves the initiation, boosting and dampening immune responses in a well-regulated spatio-temporal manner. However, cancer cells evade immune attack by harnessing the immune checkpoint, e.g. by expressing the programmed cell death ligand 1 (PD-L1) protein, which binds to the PD-1 receptor on T cells, to delude T cells into recognizing cancer as “self”. Hence, immune checkpoint inhibitors (ICIs) have been developed to prevent cancers from immune evasion. Due to its high specificity and low toxicity, ICI therapy has become popular in recent years. However, even though 46.3% of US cancer patients were considered eligible for ICI treatment as reported by a cross-sectional study in 2018, treatment response remained below 20%. Therefore, identifying biomarkers from blood or tumor tissues will enable real-time disease monitoring to facilitate the evaluation of ICI therapeutic efficacy. Hence, this study aims to use blood biomarkers as dynamic predictors to monitor treatment outcomes and decide treatment termination. To identify the optimal dynamic predictors, this study analyzes the correlation between blood biomarkers over the course of treatment and treatment outcomes of cancer patients undergoing ICI therapy from shared clinical datasets. The identified predictors will be subject to validation in future prospective studies.
1. Genomic Main Predictor / Independent Variable: Long non-coding RNAs (lncRNAs), RNA Dysregulation, Circular RNAs (circRNAs), gene fusion, alternative splicing, RNA editing, immune cell subsets, gene mutation, copy number variants (CNVs), etc.
2. Peripheral blood Predictor / Independent Variable: ① Blood cell counts, including Tregs counts, myeloid-derived suppressor cells (MDSCs), etc. ② Serum markers, including peripheral blood PD-1/PD-L1, etc. ③ Tumor markers: carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), cancer antigen 199 (CA199), etc.
The goal is to apply these biomarkers in future clinical practice to improve ICI treatment outcomes.
1.To uncover longitudinal peripheral blood biomarkers relevant to assessing the efficacy and safety of ICI therapy to improve treatment outcomes.
2.Identification of dynamic biomarkers correlated to adverse effects (AEs), which includes the prediction of immune-related adverse effects (irAEs), treatment-related adverse effects (TRAEs), and specific AEs of cancer patients undergoing ICI therapy.
Keywords Methods:
Multicolor Flow Cytometry; Bioinformatics; Computational Immuno-Oncology; Neoantigen Discovery; RNA Dysregulation; Blood Cell Count Test
Selected Publications:
Zhou, J.G.; Donaubauer, A.J.; Frey, B.; Becker, I.; Rutzner, S.; Eckstein, M.; Sun, R.; Ma, H.; Schubert, P.; Schweizer, C., et al. Prospective development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with recurrent/metastatic cancer to immune checkpoint inhibitors. Journal for immunotherapy of cancer 2021, 9, doi:10.1136/jitc-2020-001845.
Zhou, J.G.; Liang, B.; Liu, J.G.; Jin, S.H.; He, S.S.; Frey, B.; Gu, N.; Fietkau, R.; Hecht, M.; Ma, H., et al. Identification of 15 lncRNAs Signature for Predicting Survival Benefit of Advanced Melanoma Patients Treated with Anti-PD-1 Monotherapy. Cells 2021, 10, doi:10.3390/cells10050977.
Zhou, J.G.; Donaubauer, A.; Frey, B.; Becker, I.; Rutzner, S.; Sun, R.; Ma, H.; Fietkau, R.; Deutsch, E.; Gaipl, U., et al. P14.16 The Early Landscape of Immune Cell Subsets in Metastatic NSCLC Patients Treated with Immune Checkpoint Inhibitors. Journal of Thoracic Oncology; 2021. https://doi.org/10.1016/j.jtho.2021.01.522
Zhou, J. G. , Ma,H.; Gaipl, U. S.; Frey, B.; Hecht, M.; Fietkau, R. 382 - Longitudinal C-reactive protein (CRP) as an individualized dynamic predictor for metastatic cancer patients treated with immune checkpoint inhibitors: Findings from
the prospective ST-ICI cohort, AACR Annual Meeting 2021.