Optimizing Risk Assessment Through cfDNA in Pancreatic Neuroendocrine Tumors for Clinical Evaluation: a Retrospective Study

Pancreatic neuroendocrine tumors (PanNETs) are rare, heterogeneous neoplasms arising from the endocrine tissue of the pancreas. Non-functioning pancreatic neuroendocrine tumors (NF- PanNETs) have traditionally been considered rare; however, their reported incidence has significantly increased over the past two decades. Current estimates suggest that approximately 1-5% of the general population may harbor undiagnosed NF-PanNETs. Given this prevalence, there is an increasing need for reliable diagnostic tools to distinguish high-risk, aggressive tumors from benign, indolent lesions.

NF-PanNETs exhibit a wide spectrum of biological behavior, ranging from slow-growing, indolent tumors to highly aggressive variants prone to local invasion and distant metastases. However, due to their frequent asymptomatic presentation and the absence of reliable preoperative markers for tumor aggressiveness, clinical management remains challenging. Current diagnostic methods rely on imaging and invasive biopsies. However, this approach has limitations in detecting early-stage disease and assessing tumor dynamics. Indeed, it often leads to overtreatment in low-risk patients through unnecessary surgical interventions, while potentially undertreating those who might benefit from more aggressive therapeutic strategies, such as neoadjuvant therapy.

In recent years, cell-free DNA (cfDNA) has emerged as a promising non-invasive biomarker in the oncology setting, proving to be extremely useful for diagnosis, prognosis and therapeutic purposes.

In the specific context of PanNETs, there is limited research on the value of cfDNA. Despite showing encouraging results, the few existing investigations on this subject, have included only small and heterogeneous cohorts, varying primary tumor sites, stages and differentiation status.

This exploratory study will analyze cfDNA profiles from approximately 30 NF-PanNET patients (10 under active surveillance, 10 with indolent tumors post-surgery, and 10 with aggressive tumors post-surgery) using advanced methylome and nucleosome footprinting technologies. Analyses in the three patient subgroups will be compared with data from a previously characterized cohort of healthy subjects.