Purpose: In some patients with prostate cancer, bone marrow carcinomatosis develops later in the course of the disease, which has a poor prognosis. These are often heavily pretreated patients in the castration-resistant situation for whom there are no other therapeutic options, because either all available systemic therapies have already been used or the use of one is not possible due to the cytopenias associated with bone marrow carcinomatosis. In our literature search, there are no data on this treatment in the setting available, especially no clinical trial or even randomized data. This case series is to determine the clinical efficacy of metronomic cyclophosphamide in patients with metastatic castration-resistant prostate cancer and bone marrow carcinomatosis, particularly with regard to stabilization of the blood count (thrombocytopenias) and thus the possibility of further (more toxic) lines of therapy. Methods: Retrospective unicenter analysis was performed on eleven patients between 54 and 84 years of age on metronomic cyclophosphamide for bone marrow carcinomatosis in metastatic castration-resistant prostate cancer treated at a Swiss cancer center between 2014 and 2023. Results: Eleven patients received metronomic cyclophosphamide for varying periods of time; the majority had severe cytopenias (especially thrombocytopenias). Partially hematologic stabilization was achieved with administration of further systemic therapies. Conclusion: Our case series demonstrates that the use of metronomic cyclophosphamide allows hematologic stabilization for months, benefiting patients who had already received all available therapies for metastatic castration-resistant prostate cancer. Alternatively, it may act as bridging therapy to allow consecutive administration of more toxic therapies with proven survival benefit.

Belzutifan is a new HIF-α inhibitor mainly used in two different indications: von Hippel–Lindau syndrome-associated renal cell carcinoma, haemangioblastomas and pancreatic neuroendocrine tumours, as well as sporadic advanced pre-treated renal cell carcinoma. Although efficacy has been demonstrated in phase II and III studies, belzutifan is still not approved in many countries. In addition, von Hippel–Lindau syndrome is a rare disease. Therefore, there is virtually no real-world experience data of belzutifan efficacy available. We aim to determine the real-world efficacy and tolerability of belzutifan in patients with von Hippel–Lindau syndrome-associated tumours and in patients with sporadic advanced tyrosine kinase- and immune checkpoint inhibitors pre-treated for renal cell carcinoma. A retrospective analysis of five patients treated with belzutifan between 2023 and 2024 at a Swiss cancer centre was conducted. In this case series, all patients consistently benefitted from belzutifan with response to treatment. This case series provides real-world evidence that belzutifan is an effective and well-tolerated treatment option for patients with von Hippel–Lindau syndrome-associated renal cell carcinoma, haemangioblastomas and sporadic advanced pre-treated renal cell carcinoma.

Background Metastatic castration-resistant prostate cancer (mCRPC) presents a challenge for clinicians in determining the optimal treatment sequence because of the lack of direct head-to-head comparisons, which is further complicated by the now-widespread use of androgen receptor pathway inhibitors (ARPIs) in metastatic hormone-sensitive prostate cancer (mHSPC). Objective This study is a Bayesian network meta-analysis (NMA) intended to provide a comprehensive evaluation and comparison of the efficacy of mCRPC treatments across different treatment lines. Patients and Methods We performed a systematic search of ClinicalTrials.gov, extracted information, assessed the risk of bias, and reconstructed missing outcomes. We performed an NMA to evaluate treatment efficacy for overall survival (OS) and progression-free survival (PFS) in first and subsequent lines. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) NMA guidelines and was registered with PROSPERO (CRD42024499607). Results The NMA included 43 trials with 33,494 patients. ARPI-based therapies, particularly in combination with poly(ADP-ribose) polymerase inhibitors, demonstrated the most significant benefits for OS and PFS in first-line mCRPC treatment, followed by chemotherapy regimens. However, ARPI re-treatment showed limited effectiveness in subsequent lines, leading to weaker OS and PFS benefits. Conclusions This NMA highlights the superiority of ARPI-based therapies and chemotherapies as first-line options for mCRPC while emphasizing the need for treatment class switching after ARPI failure. To refine treatment sequencing and enable precision care, future research should integrate individual participant data to better address patient-level heterogeneity and identify biomarkers for personalized therapy.

Objectives: Precision oncology relies on accurate interpretation of tumour-detected gene variants, to guide personalised treatment decisions. However, interpretation of variants in context requires extensive information that is often buried within unstructured biomedical literature and obscured by inconsistent nomenclature, making manual retrieval labour-intensive and prone to omissions. In this study, our aim was to evaluate the potential of large language models to extract variant-treatment-cancer associations at scale from published abstracts. Design: We developed Variantscape , a large-scale, automated pipeline and open-access web tool. It integrates traditional natural language processing methods with state-of-the-art large language models to extract, standardise, and analyse co-associations between genetic variants, cancer types, and therapeutic interventions from published biomedical abstracts. Results: From over 3.7 million abstracts published between January 2014 and October 2026, 886,195 articles containing gene names were eligible for extraction. Among these, 37,895 (4.3%) mentioned a variant, cancer type, and therapeutic agent together, covering 11,985 unique variants across 197 cancer types and 547 therapeutic agents. LLM verification against the abstract text confirmed at least one association in 35,224 papers, giving a literature-derived graph of 10,571 nodes and 44,644 verified edges. Canonical alterations in well-studied cancers (e.g., BRAF V600E in melanoma) were strongly linked to established treatments, while several rare variants also emerged with high-confidence literature support. Conclusions: By applying large language models to biomedical literature, Variantscape enables scalable, context-aware extraction of trilateral variant-treatment-cancer relationships. This approach supports early evidence synthesis/hypothesis generation, highlights underrecognized or rare associations for MTB preparation, and offers a practical resource for accelerating discovery and supporting precision oncology research and translation. Funding: This study was funded by the School of Medicine at the University of St. Gallen in Switzerland under grant number 2300380. Article Summary: Strengths and limitations of this study: ● Variantscape combines traditional natural language processing with large language models to enable scalable, automated extraction of variant-cancer-treatment relationships from over 3.7 million biomedical abstracts, substantially reducing the manual search burden inherent to existing molecular tumour board workflows. ● The pipeline applies large language model-based inference to standardise inconsistent genetic nomenclature across the literature, improving the comparability and reliability of extracted associations that would otherwise be obscured by terminological variation. ● Variantscape is designed as a continuously updatable, open-access resource, meaning it can incorporate newly published evidence without requiring repeated manual intervention. ● The pipeline relies solely on abstract-level text rather than full-text articles, which may limit the depth and granularity of extracted associations, as key methodological details, variant context, and nuanced clinical findings are frequently reported only within the body of published papers. ● Automated extraction using large language models introduces a risk of misattribution of variant-cancer-treatment relationships, and without systematic manual validation of outputs, the precision of identified associations at scale remains difficult to fully characterise.