Tumor Cell Determinants of Oncolytic Immunotherapy
Christine E. Engeland
Contributing CCU members
Sophie C. S. Pernickel, Nicolas Duus, Theresa E. Schäfer, Jessica Albert, Birgit Hoyler, Stefanie Prien
Several oncolytic viruses are currently advancing in clinical development. However, biomarkers to predict which patients will benefit most from these novel therapeutics are currently lacking. We study patient-derived models to identify tumor-intrinsic factors associated with response or resistance to oncolytic virotherapy with a focus on, but not limited to, the measles vaccine strain.
Collaboration partners in clinical and translational research include the group of Dr. Claudia Ball (NCT Dresden), Dr. Priya Chudasama (Precision Sarcoma Research, DKFZ and NCT Heidelberg), Prof. Dr. Stefan Fröhling (Molecular and Cellular Oncology, DKFZ and NCT Heidelberg), and Prof. Dr. Jessica Hassel (Dermato-Oncology, NCT Heidelberg). Bioinformatic experts working on this project are Dr. Mario Huerta (Translational Medical Oncology, NCT Heidelberg), Prof. Dr. Benedikt Brors, Sebastian Uhrig, and Dr. Jennifer Hüllein (Applied Bioinformatics, DKFZ).
Differential sensitivity of individual tumors to oncolytic virotherapy. Spheroid cultures derived from samples from three colorectal cancer patients were treated with oncolytic measles viruses encoding enhanced green fluorescent protein (eGFP) and images were acquired after 48 h. Scale bars: 200 µm. Culture A shows limited, B intermediate and C high sensitivity to virus treatment.
- Mechanisms of measles virus oncolytic immunotherapy. Pidelaserra-Martí G, Engeland CE. Cytokine & Growth Factor Reviews 2020 Jul 3:S1359-6101(20)30175-1.
- Targeted BiTE Expression by an Oncolytic Vector Augments Therapeutic Efficacy Against Solid Tumors. Speck T, Heidbuechel JPW, Veinalde R, Jaeger D, von Kalle C, Ball CR, Ungerechts G, Engeland CE. Clinical Cancer Research 2018; 24: 2128-37.
- CTLA-4 and PD-L1 checkpoint blockade enhances oncolytic measles virus therapy. Engeland CE, Grossardt C, Veinalde R, Bossow S, Lutz D, Kaufmann JK, Shevchenko I, Umansky V, Nettelbeck DM, Weichert W, Jäger D, von Kalle C, Ungerechts G. Molecular Therapy 2014 Nov;22(11):1949-59.
Deutsche Krebshilfe, Mildred Scheel MD Fellowships (to ND and TES)
Systems Immunodiagnostics Platforms for Adaptive Clinical Trials and Personalized Immuno-oncology
Thomas Walle, Guy Ungerechts
Contributing CCU members
Sunanjay Bajaj, Birgit Hoyler, Stefanie Prien
Combination cancer immunotherapies hold promise to dramatically improve overall survival of cancer patients and can readily be encoded in viral vectors. However, the number of possible combinations exceeds the numbers of feasible clinical trials. Even adaptive clinical trials which rapidly modify the trial protocol based on patients' outcomes fail to deliver the speed required to screen enough combination therapies.
We and others have shown that similar immune responses can be observed across multiple types of tumor and combination immunotherapies. Here we will harness these patterns to create a pan-solid tumor, low-cost, multiparametric and interpretable cellular biomarker model in cancer patients across multiple combination cancer immunotherapies in the NCT ANTICIPATE trial.
Schema indicating the suggested treatment strategy based on our immunodiagnostics platforms. Patients undergo combination cancer immunotherapy number 1 (T1) for which a response probability is calculated using a multiparametric biomarker model. If the response probability given T1 (PR) is higher than the average response probability (Pø), patients continue with T1 and the likelihood of response (PT1|R) as assessed by CT scan is incorporated into the model (PR|T1). Otherwise patients switch to therapy T2. This process can be repeated for multiple times.
This biomarker model will allow for fast response assessment and rapid cycling of cancer patients through multiple therapies until an effective therapy is found. For this purpose, we will analyze changes in peripheral blood mononuclear cell (PBMC) phenotype under steady state and under controlled ex vivo perturbation using CITE-seq (10.1038/nmeth.4380) and flow cytometry with rational panels driven by feature selection methods. We will also analyze the serum proteome PBMCs are embedded in by mass spectrometry diligently working together with the laboratory of Prof. Dr. Jeroen Krijgsveld at DKFZ. In collaboration with the laboratory of Dr. Dana Pe'er at the Memorial Sloan Kettering Cancer Center (New York, U.S.A.) we will use these omics data to construct a machine learning classifier to accelerate cancer immunotherapy development in adaptive clinical trials by a manifold and head towards personalized virotherapy immunomodulation.
- Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Goswami S*, Walle T*, Cornish AE*, Basu S*, [...], Sharma P. Nature Medicine. 2020; 26: 39-46. *equal contribution
DKFZ Clinician Scientist Program (Dieter Morszeck Stiftung, to TW)