Long-Term Observation of Patients With Cancer - an Entity-Independent Registry for Healthcare and Translational Research at the University Medicine Dresden (Cancer-Reg-VT). / Langzeitbeobachtung von Patienten mit Krebserkrankungen ­ ein entitätsunabhängiges Register für Versorgungsforschung und translationale Forschung an der Universitätsmedizin Dresden (Cancer-Reg-VT).

INTRODUCTION: Translational research is important, especially in medicine where decisions affect people's lives. Clinical registries and the studies embedded in them allow the depiction of actual care practice under routine conditions. Translating the findings of health services research back into clinical research through prospective cohort studies has the potential to drive medical innovations faster, more effectively and, above all, in a more targeted manner. These must therefore be a central component of cutting-edge oncological research. OBJECTIVE: The aim of the registry is the establishment of clinical cohorts and the provision of a comprehensive, high-quality data set for oncological diseases. METHODS/DESIGN: The registry will prospectively record all patients treated for cancer at Dresden University Hospital (UKD). In addition to the data from the hospital information systems (ORBIS, TDS, GEPADO, etc.), monitoring of health-related quality of life (HRQOL) is to be carried out at regular intervals at the beginning and during the course of treatment. In addition, individual linkage with data from clinical cancer registries and health insurance companies (including AOK PLUS) is planned for a period of five years before and after inclusion. All these data will be merged in a registry database. The selection of variables and measurement time points is closely based on the guidelines for colorectal carcinoma of the international initiative ICHOM (International Consortium for Health Outcomes Measurement). The study management software (STeVe) separates personal identification characteristics (IDAT) and medical data (MDAT) at an early stage. The independent trust centre of the TU Dresden (Treuhandstelle) ensures that no personal data enter the registry database. It is thereby also ensured that the data owners involved (UKD, biobank, health insurance company, cancer registry, patient) only receive the personal data they need for allocation. The MOSAIC software tools recommended by the TMF (Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V.) are used to manage the pseudonyms. DISCUSSION/CONCLUSION: With the registry, previously missing evidence on the effectiveness, safety and costs of diagnostic and therapeutic measures can be made, taking into account long-term and patient-reported outcomes of routine care. The data potentially allow for the identification of barriers to and facilitators of innovative promising cancer diagnostics and therapies. They also enable generation of scientifically relevant hypotheses in the field of translational and outcomes research.

Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence.

CTLA-4 is an inhibitory immune checkpoint receptor and a negative regulator of anti-tumor T-cell function. This study is aimed for a comparative analysis of CTLA-4+ cells between different tumor entities. To quantify CTLA-4+ cells, 4582 tumor samples from 90 different tumor entities as well as 608 samples of 76 different normal tissue types were analyzed by immunohistochemistry in a tissue microarray format. Two different antibody clones (MSVA-152R and CAL49) were validated and quantified using a deep learning framework for automated exclusion of unspecific immunostaining. Comparing both CTLA-4 antibodies revealed a clone dependent unspecific staining pattern in adrenal cortical adenoma (63%) for MSVA-152R and in pheochromocytoma (67%) as well as hepatocellular carcinoma (36%) for CAL49. After automated exclusion of non-specific staining reaction (3.6%), a strong correlation was observed for the densities of CTLA-4+ lymphocytes obtained by both antibodies (r = 0.87; p < 0.0001). A high CTLA-4+ cell density was linked to low pT category (p < 0.0001), absent lymph node metastases (p = 0.0354), and PD-L1 expression in tumor cells or inflammatory cells (p < 0.0001 each). A high CTLA-4/CD3-ratio was linked to absent lymph node metastases (p = 0.0295) and to PD-L1 positivity on immune cells (p = 0.0026). Marked differences exist in the number of CTLA-4+ lymphocytes between tumors. Analyzing two independent antibodies by a deep learning framework can facilitate automated quantification of immunohistochemically analyzed target proteins such as CTLA-4.

Prevalence and clinical significance of VHL mutations and 3p25 deletions in renal tumor subtypes.

PURPOSE: To evaluate prevalence and clinical impact of VHL mutations and deletions (3p), a cohort of consecutive kidney tumors was analyzed by DNA sequencing and fluorescence in-situ hybridization (FISH). PATIENTS AND METHODS: The study includes 1,805 patients with renal tumors who were surgically treated at the Department of Urology at the University Medical Center Hamburg-Eppendorf between 1994 and 2015. The cohort included 1,176 clear cell, 270 papillary, 101 chromophobe, and 28 clear cell (tubulo) papillary cancers, as well as 149 oncocytomas and 81 less common subtypes. RESULTS: Among 431 successfully analyzed tumors, VHL mutations were found in 59.3% of clear cell, 5.2% of papillary, 3.1% of chromophobe carcinomas and in 7.3% of oncocytomas as well as in the rare kidney tumor types (25%-60%). FISH analysis was successful in 1,403 cases. 3p25 deletion was found in 57.2% of clear cell, 17.6% of papillary, 17.7% of chromophobe carcinomas and in 11.9% of oncocytomas as well as in the rare kidney tumor types (16.7%-50%). No statistically significant associations between VHL mutation/deletion and tumor grade, stage, and clinical outcome was found. Only in the subgroup of papillary cancers, 3p deletion was significantly associated with lymph node and distant metastasis as well as with poor patient outcome (p < 0.05 each). CONCLUSIONS: The presence of a VHL mutation in virtually all renal tumor subtypes suggests that VHL analysis cannot be used to distinguish between renal tumor subtypes. Consequently, anti-VHL treatment strategies should not be limited to patients with clear cell cancer.