## 4 Discussion
The approach was firstly validated with commercially available cfDNA reference material providing \(EGFR\) hotspot variants at MAFs down to 0.1% and confident levels of sensitivity and specificity achieved (92.5% and 100%, respectively). From an overall view of the sequencing performance of the panel, it emerged that not all amplicons performed similarly. This was likely due to a different PCR amplification efficiency, expected when dealing with multiple genes/target regions panels where DNA library generation occurs in single-tube reactions. According to literature, it is verifiable in both small (as in our case) as well as large NGS panel [31]. Regarding the pre-analytical parameters, in our cohort we found a considerable rate of heterogeneity in levels of cfDNA abundance across the plasma samples (2.6-309 ng/mL of plasma), indicating that it is not possible to predict how much cfDNA will be achieved, neither at an inter- or intra-patient level. However, this issue is well known, and in fact that the shedding of cfDNA into the bloodstream is affected by several individual characteristics, such as tumor stage, metastasis, inflammation, treatment, and comorbidities [1; 32; 18; 3]. LoD evaluation through the samples revealed, not surprisingly, that higher quantities of cfDNA input allowed to reach very low levels of LoD (up to 0.02%), as also similarly reported by others [17], with the advantage to preserve specificity of the NGS workflow. It is interesting to note that at least 0.1% LoD was reached in 37/92 cases (40.22%). In order to achieve such LoD value, the manufacturer recommends to employ at least 20 ng of cfDNA input for NGS library preparation. In our data, we demonstrated that even when \(<\)20 ng of cfDNA were available, it was still possible a 0.1% LoD for almost the half (16/37, 43.20%) of these samples. This further suggests that predetermined cfDNA input for LoD can, therefore, only be a vague reference of what would be expected. Bearing in mind what is reported by manufacturer to reach the main goal of a 0.1% LoD, we showed a fulfillment of the median value of molecular families (MedMolCov \(>\)2500) and the median values of read coverage (MedReadCov \(>\)25,000) in more than half of sequenced samples (60.9%). Not surprisingly, a strong correlation between the cfDNA input and MedMolCov was found, since it is expected that the more cfDNA that is introduced for NGS libraries, the higher will be the number of molecular families sequenced, as already reported [17]. By contrast, no significant association was found between DNA quantity (both cfDNA input and cfDNA abundance) and the _EGFR_ mutational status (\(p>0.15\)). However, this finding is not surprising, indeed it is known as the ctDNA portion can widely vary of total cfDNA, also in NSCLC [33; 34]. Anyways, our data are encouraging since we showed that it is possible to carry out mutations even in plasma samples with low amounts of cfDNA, with the huge advantage of a reduced number of \"non-informative\" blood draws in routine clinical practice. These data together demonstrate, first of all, the high translational strength of the assay that is shown to be reliable and sensitive. Secondly, the automatic bioinformatics pipeline allows us to directly have the output files (BAM and VCF) for each sequenced sample and execute a graphical and statistical overall evaluation through predefined parameters available in TSS. Nevertheless, despite the previous promising results, this cannot still be considered an optimal analysis workflow in all instances; indeed, if on the one hand molecular tagging lowered drastically the rate of false positives, on the other false negatives can still occur. In particular, in our cohort, we reported the variant calling failure of three complex mutations. It is important to note that in these cases the mutations were filtered out by the TVC plugin despite reaching the values of MAF and coverage to enable a call.