A Retrospective Analysis of Biliary Tract Cancer Patients Presented to the Molecular Tumor Board at the Comprehensive Cancer Center Munich.

BACKGROUND AND OBJECTIVE: With the rising importance of precision oncology in biliary tract cancer (BTC), the aim of this retrospective single-center analysis was to describe the clinical and molecular characteristics of patients with BTC who underwent comprehensive genomic profiling (CGP) and were discussed in the CCCMunichLMU molecular tumor board (MTB). PATIENTS AND METHODS: In this single-center observational study, we included BTC patients with intrahepatic cholangiocarcinoma (iCCA), extrahepatic CCA (eCCA), and gallbladder cancer (GB), who had been discussed in the institutional MTB from May 29, 2017, to July 25, 2022. Patients were followed up until 31 January 2023. Data were retrospectively collected by review of medical charts, and MTB recommendation. RESULTS: In total, 153 cases were registered to the MTB with a median follow-up of 15 months. Testing was successful in 81.7% of the patients. CGP detected targetable alterations in 35.3% of our BTC patients (most commonly ARID1A/ERBB2/IDH1/PIK3CA/BRAF-mutations and FGFR2-fusions). Recommendations for molecularly guided therapy were given in 46.4%. Of those, treatment implementation of targeted therapy followed in 19.4%. In patients receiving the recommended treatment, response rate was 57% and median overall survival was 19 months (vs 8 months in the untreated cohort). The progression-free survival ratio of 1.45 suggest a clinical benefit of molecularly guided treatment. CONCLUSIONS: In line with previous work, our series demonstrates feasibility and clinical utility of comprehensive genomic profiling in BTC patients. With the growing number of targeted agents with clinical activity in BTC, CGP should become standard of care in the management of this group of patients.

Knowledge bases and software support for variant interpretation in precision oncology.

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers.

Checkpoint inhibitors (CPI) have significantly changed the therapeutic landscape of oncology. We adopted a non-invasive metabolomic approach to understand immunotherapy response and failure in 28 urological cancer patients. In total, 134 metabolites were quantified in patient sera before the first, second, and third CPI doses. Modeling the association between metabolites and CPI response and patient characteristics revealed that one predictive metabolite class  (n = 9/10) were very long-chain fatty acid-containing lipids (VLCFA-containing lipids). The best predictive performance was achieved through a multivariate model, including age and a centroid of VLCFA-containing lipids prior to first immunotherapy (sensitivity: 0.850, specificity: 0.825, ROC: 0.935). We hypothesize that the association of VLCFA-containing lipids with CPI response is based on enhanced peroxisome signaling in T cells, which results in a switch to fatty acid catabolism. Beyond use as a novel predictive non-invasive biomarker, we envision that nutritional supplementation with VLCFA-containing lipids might serve as an immuno sensitizer.