About this trial
Inhibitors of the programmed cell death protein 1 (PD-1)/PD-L1 immune checkpoint signaling pathway are already approved in the treatment of various tumor entities in relapsed or metastatic stages. Different exploratory trials suggest that the combination of radiotherapy and PD-1/PD-L1 inhibitors is highly effective, especially in oligometastatic stages and if all lesions are treated with ablative radiotherapy. In addition, the role of predictive biomarkers is becoming increasingly important for future therapy algorithms. First data, also from our group, indicate clearly that dynamic changes of immune cells and their activation markers in the peripheral blood (immune matrix) can be used as predictive biomarkers. During the planned STICI-02 trial predictive immune matrix derived from the STICI01 trial (NCT03453892) will be validated in the groups of patient suffering from HNSCC (palliative), NSCLC (separately palliative and adjuvant) and "other solid tumors" (including in particular esophageal carcinomas, urothelial and renal carcinomas, small cell bronchial carcinomas and squamous cell carcinomas of the skin \[depending on the current drug approval\]). Within the framework of scientific accompanying projects, the predictive value of markers in tumor tissue and of pattern radiomics analyses will be analyzed accompanying the immunophenotyping in peripheral blood. The side effects
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients treatable for HNSCC (palliative), NSCLC (separately palliative and adjuvant) or "other solid tumour"
Indication for system therapy with a PD-1/PD-L1 inhibitor according to clinical standards
Patients without or with radiation of one or more metastases
Age at least 18 years
Disqualifiers
Melanoma patients
Fertile patients who refuse effective contraception during study treatment
Persistent drug and/or alcohol abuse
Patients not able or willing to behave according to study protocol
Trial population
The study cohort consist of patients suffering from HNSCC (palliative), NSCLC (separately palliative and adjuvant) and "other solid tumors" (including in particular esophageal carcinomas, urothelial and renal carcinomas, small cell bronchial carcinomas and squamous cell carcinomas of the skin \[depending on the current drug approval\]) which will be treated with ICI (PD-1/PD-L1) and potential radiation of metastases at Department of Radiation Oncology of Universitätsklinikum Erlangen.
Trial design
Cohort
Prospective
Treatments tested in this trial
Conventional Therapy acc. to prevailing clincal approved schemes
Other interventionThe study is observational. The treatment-plan of the underlying disease remains unchanged. The treatment of the patient is according to the prevailing clinical approved schemes at the respective entities. Blood will be drawn from patients at several time points during and after RT and ICI for detailed immunomonitoring of the patients.
Treatment groups
Trial outcomes
Primary outcomes
Overallsurvival
Prospective investigation of the survival of the patients.
Longitudinal Immunophenotype
Longitudonal Immunophenotyping of the patients: Detection of about 30 distinct immune cell (sub)types together with their activation markers during treatement.
Predictors in pattern recognition analyses
Identification of possible predictors in pattern recognition analyses from clinical imaging data sets.
Immune-associated side effects
Detection of immune-associated side effects
Secondary outcomes
Detection of adverse events according to NCI CTAE (v4.0)
The adverse effects of Immunecheckpoint and Radiotherapy is recorded by official questionaire
Progression free survival
survival of the patient without progression of malignant disesase
Treatment response (according to RECIST and iRECIST criteria)
RECIST and iRECIST criteria will used to analyse the response of the patient to the respective treatement
Attempt to establish an immune matrix of responding/non-responding patients
Analysis of clincal, MRI and imunnologic, molecular data to develop an biomarkermatrix with processes of machine learning
Sponsors and contacts
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