Biomedical subjects

Anna Saborowski

Publications and source records attributed to Anna Saborowski.

3 recordsLinked to original sources

PubMed2026

Futibatinib after non-covalent FGFR inhibitors in FGFR2-rearranged intrahepatic cholangiocarcinoma: clinical activity and resistance patterns.

PURPOSE: The optimal sequencing of non-covalent and covalent FGFR inhibitors in FGFR2-rearranged intrahepatic cholangiocarcinoma (iCCA) remains undefined. Futibatinib, an irreversible FGFR1-4 inhibitor, may retain activity in the setting of acquired resistance to non-covalent FGFR inhibitors, but data on the patterns of acquired alterations are limited. METHODS: We conducted a retrospective multicenter study across three European centers including patients with advanced FGFR2-rearranged iCCA treated with futibatinib after progression on non-covalent FGFR inhibitors. Clinical outcomes and safety were evaluated. Available genomic profiling at progression was analyzed to characterize resistance mechanisms and their association with outcomes. RESULTS: Sixteen patients were included. Median progression-free survival (mPFS) with prior non-covalent FGFR inhibitors was 10.2 months (95% CI 7.0-15.5), with an objective response rate (ORR) of 60.0%. Among patients with post-progression genomic profiling (n = 11), all harbored FGFR2 resistance mutations, with polyclonal alterations (≥2) in 45.5%. A higher burden of FGFR2 mutations and the presence of co-alterations were associated with shorter mPFS on non-covalent inhibitors. Futibatinib was administered at a median of fourth-line therapy. ORR was 31.3% and disease control rate was 50.0%. Median PFS and overall survival were 4.5 months (95% CI 2.0-9.1) and 9.9 months (95% CI 5.7-not reached), respectively. Notably, outcomes with futibatinib were independent of the number of acquired FGFR2 resistance mutations, and the adverse impact of co-alterations appeared attenuated. Safety was consistent with the known profile. CONCLUSIONS: Futibatinib demonstrates clinically meaningful activity after progression on non-covalent FGFR inhibitors, supporting its use in FGFR2-rearranged iCCA, including in the post-non-covalent inhibitor setting. The distinct resistance patterns provide a biological rationale for the continued efficacy of covalent FGFR inhibition. Prospective studies incorporating longitudinal molecular profiling are needed to optimize treatment sequencing.

Drug resistance↗

PubMed2026

Machine learning for population-level risk prediction of future cholangiocarcinoma.

BACKGROUND: The poor prognosis of cholangiocarcinoma (CCA) is largely driven by rapid, asymptomatic disease progression, which usually results in a late diagnosis in the absence of established screening strategies. An early, cost-effective, and universally applicable risk assessment strategy would therefore be valuable. METHODS: We developed machine learning (ML) models on prospective, multimodal data from 487,495 UK Biobank (UKB) participants, of whom 649 developed CCA during follow-up. Data from England (80%) were utilised for ML development via five-fold cross-validation, and then all models were tested on withheld data from Scotland, Wales, and Newcastle (20%). Iterative ablation studies reduced inputs from >150 features across demographic data, lifestyle, health records, blood parameters, genomics, and metabolomics to models built on five and ten routinely available clinical parameters. These were externally validated in the Penn Medicine Biobank (PMBB; n = 2638; 28 CCA), All of Us Research Program (AOU; n = 330,433; 362 CCA), Japan Medical Data Centre Claims Database (JMDC; n = 8,425,522; 723 CCA) and TriNetX (n = 728,886; 1592 CCA). FINDINGS: We show that ML models integrating biliary-disease associated health records and Gamma glutamyltransferase can stratify risk of future CCA. Evaluation on the UKB test set as well as three independent cohorts revealed robust performance and generalisability across ethnicities. We achieved AUROCs of 0.71 [95% CI: 0.703-0.711], 0.77 [95% CI: 0.764-0.778 ], 0.796 [95% CI: 0.795-0.798] and 0.8 [95% CI: 0.794-0.805] for UKB, PMBB, AOU, and JMDC respectively, with respective AUPRCs of 0.014 [95% CI: 0.009-0.018], 0.042 [95% CI: 0.037-0.048], 0.038 [95% CI: 0.033-0.042] and 0.001 [95% CI: 0.001-0.001]. In AOU, application of the Youden J-optimised threshold yielded a number needed to screen of 79. Separate models for intra- and extrahepatic CCA did not improve performance. In line with the pathophysiology, performance declined for longer intervals between assessment and event. A group-level analysis in the TriNetX cohort revealed hazard ratios of up to 82.5 [95% CI: 26.4-257.96]. We provide extensive interpretability results and release all source codes used to develop the presented models. INTERPRETATION: We provide a comprehensive framework for early CCA risk stratification in the general population, identifying key predictors, and demonstrating the potential of data-driven models in personalised screening for hepatobiliary cancer. FUNDING: German Cancer Aid (grant #70115730), Junior Principal Investigator Fellowship programme of RWTH Aachen Excellence strategy.

Humans↗

PubMed2026

The future of blood-based biomarkers in liver cancer.

Liquid biomarkers hold substantial promise in liver cancer, with potential applications in risk stratification, surveillance and early detection, therapeutic decision-making, and treatment-response monitoring. In parallel with oncologic advances, liquid biopsy has gained increasing attention. However, despite expanded research efforts, prospective clinical validation remains limited. While cell-free DNA-based detection of actionable alterations has entered clinical practice in select contexts, most candidate liquid biomarkers still require rigorous evaluation through translational research embedded in clinical trials and prospective cohort studies. In this review, we summarise the current landscape of blood-based biomarkers across the cancer care continuum for individuals at risk, or diagnosed with hepatocellular carcinoma and biliary tract cancers, and discuss the key challenges and opportunities that lie ahead.

Humans↗