Nanopore-based consensus sequencing enables accurate multimodal tumor cell-free DNA profiling.

Shallow genome-wide cell-free DNA (cfDNA) sequencing holds great promise for non-invasive cancer monitoring by providing reliable copy number alteration (CNA) and fragmentomic profiles. Single nucleotide variations (SNVs) are, however, much harder to identify with low sequencing depth due to sequencing errors. Here we present Nanopore Rolling Circle Amplification (RCA)-enhanced Consensus Sequencing (NanoRCS), which leverages RCA and consensus calling based on genome-wide long-read nanopore sequencing to enable simultaneous multimodal tumor fraction estimation through SNVs, CNAs, and fragmentomics.

Response to systemic therapies in patient-derived cell lines from primary and recurrent adult granulosa cell tumors.

In patients with the rare adult-type granulosa cell tumors (aGCT), surgery is the primary treatment for both primary and recurrent disease. In cases of inoperable disease, systematic therapy is administered, but variable response rates and drug resistance complicate predicting the most effective therapy. Drug screen testing on patient-derived cell lines may offer a solution.

Evaluation of the recently established Dutch nationwide Archipelago of Ovarian Cancer Research biobank.

Fundamental and translational research in ovarian cancer aims to enhance understanding of disease mechanisms and improve treatment and survival outcomes. To support this, we established the Dutch multicenter, interdisciplinary Archipelago of Ovarian Cancer Research (AOCR) infrastructure, which includes a nationwide biobank. In this study, we share our experiences in establishing the infrastructure, offer guidance for similar initiatives, and evaluate the AOCR patient cohort.

Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer.

: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by assisting pathologists in efficiently assessing serial sections for metastases, reducing workload and costs while enhancing accuracy.