Molecular Profiling of Well-Differentiated Neuroendocrine Tumours: The Role of ctDNA in Real-World Practice

SIMPLE SUMMARY: The analysis of circulating tumour DNA (ctDNA) can help to identify genetic alterations present in cancer cells without the need to access tumour tissue, which can be an invasive approach. This study explored the feasibility of analysing ctDNA in patients with advanced well-differentiated neuroendocrine tumours (WdNETs). A total of 45 patients (15 with WdNETs) were included. Although feasible (with a non-evaluable sample rate of 27.8%), mutation-based ctDNA analysis was of limited clinical utility for patients with advanced WdNETs. While patients with WdNETs could still be offered genomic profiling (if available and reimbursed), it is important to manage patients’ expectations regarding the likelihood of the results impacting their treatment. ABSTRACT: Background: The role of tumour genomic profiling in the clinical management of well-differentiated neuroendocrine tumours (WdNETs) is unclear. Circulating tumour DNA (ctDNA) may be a useful surrogate for tumour tissue when the latter is insufficient for analysis. Methods: Patients diagnosed with WdNETs underwent ctDNA genomic profiling (FoundationLiquid(®)); non-WdNETs (paraganglioma, goblet cell or poorly-differentiated neuroendocrine carcinoma) were used for comparison. The aim was to determine the rate of: test failure (primary end-point), “pathological alterations” (PAs) (secondary end-point) and patients for whom ctDNA analysis impacted management (secondary end-point). Results: Forty-five patients were included. A total of 15 patients with WdNETs (18 ctDNA samples) were eligible: 8 females (53.3%), median age 63.2 years (range 23.5–86.8). Primary: small bowel (8; 53.3%), pancreas (5; 33.3%), gastric (1; 6.7%) and unknown primary (1; 6.7%); grade (G)1 (n = 5; 33.3%), G2 (9; 60.0%) and G3 (1; 6.7%); median Ki-67: 5% (range 1–30). A total of 30 patients with non-WdNETs (34 ctDNA samples) were included. Five WdNETs samples (27.78%) failed analysis (vs. 17.65% in non-WdNETs; p-value 0.395). Of the 13 WdNET samples with successful ctDNA analyses, PAs were detected in 6 (46.15%) (vs. 82.14% in non-WdNETs; p-value 0.018). In WdNETs, the PA rate was independent of concomitant administration anti-cancer systemic therapies (2/7; 28.57% vs. 4/6; 66.67%; p-value 0.286) at the time of the ctDNA analysis: four, one and one samples had one, two and three PAs, respectively. These were: CDKN2A mutation (mut) (one sample), CHEK2mut (one), TP53mut (one), FGFR2 amplification (one), IDH2mut (one), CTNNB1mut (one), NF1mut (one) and PALB2mut (one). None were targetable (0%) or impacted clinical management (0%). There was a lower maximum mutant allele frequency (mMAF) in WdNETs (mean 0.33) vs. non-WdNETs (mean 26.99), even though differences did not reach statistical significance (p-value 0.0584). Conclusions: Although feasible, mutation-based ctDNA analysis was of limited clinical utility for patients with advanced WdNETs. The rates of PAs and mMAFs were higher in non-WdNETs. While patients with WdNETs could still be offered genomic profiling (if available and reimbursed), it is important to manage patients’ expectations regarding the likelihood of the results impacting their treatment.