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Circulating tumor DNA (ctDNA) has demonstrated great potential as a noninvasive biomarker to assess minimal residual disease (MRD) and profile tumor genotypes in patients with non‐small‐cell lung cancer (NSCLC). However, little is known about its dynamics during and after tumor resection, or its potential for predicting clinical outcomes. Here, we applied a targeted‐capture high‐throughput sequencing approach to profile ctDNA at various disease milestones and assessed its predictive value in patients with early‐stage and locally advanced NSCLC. We prospectively enrolled 33 consecutive patients with stage IA to IIIB NSCLC undergoing curative‐intent tumor resection (median follow‐up: 26.2 months). From 21 patients, we serially collected 96 plasma samples before surgery, during surgery, 1–2 weeks postsurgery, and during follow‐up. Deep next‐generation sequencing using unique molecular identifiers was performed to identify and quantify tumor‐specific mutations in ctDNA. Twelve patients (57%) had detectable mutations in ctDNA before tumor resection. Both ctDNA detection rates and ctDNA concentrations were significantly higher in plasma obtained during surgery compared with presurgical specimens (57% versus 19% ctDNA detection rate, and 12.47 versus 6.64 ng·mL−1, respectively). Four patients (19%) remained ctDNA‐positive at 1–2 weeks after surgery, with all of them (100%) experiencing disease progression at later time points. In contrast, only 4 out of 12 ctDNA‐negative patients (33%) after surgery experienced relapse during follow‐up. Positive ctDNA in early postoperative plasma samples was associated with shorter progression‐free survival (P = 0.013) and overall survival (P = 0.004). Our findings suggest that, in early‐stage and locally advanced NSCLC, intraoperative plasma sampling results in high ctDNA detection rates and that ctDNA positivity early after resection identifies patients at risk for relapse., ctDNA profiling in plasma revealed higher levels and detection rates of ctDNA during tumor resection as compared to pretreatment time points in patients with early‐stage or locally advanced NSCLC. Moreover, patients testing positive for ctDNA immediately after tumor resection had worse clinical outcomes than patients with undetectable ctDNA. Our research highlights the role of ctDNA assessment for guiding treatment in patients with respectable NSCLC.

This prospective trial aimed to investigate whether tumor-specific cKIT and PDGFRA mutations can be detected and quantified in circulating tumor (ct)DNA in patients with active GIST, and whether detection indicates disease activity. We included 25 patients with active disease and cKIT or PDGFRA mutations detected in tissue. Mutant ctDNA was detected in the peripheral blood plasma using allele-specific ligation (L-)PCR and droplet digital (d)PCR. CtDNA harboring tumor-specific cKIT or PDGFRA mutations was detected at least once in 16 out of 25 patients using L-PCR (64%) and in 20 out of 25 patients with dPCR (80%). Using dPCR, the absolute numbers of ctDNA fragments (DNA copies/ml) and the mutant allele frequency (MAF; in percent of wild-type control) strongly correlated with tumor size expressed as RECIST1.1 sum of diameter (SOD) in mm (ρ = 0.3719 and 0.408, respectively, p < 0.0001) and response status (ρ = 0.3939 and 0.392, respectively, p < 0.0001 and p < 0.001). Specificity of dPCR for detection of progression was 79.2% with a sensitivity of 55.2% and dPCR discriminated CR from active disease with a specificity of 96% and s sensitivity of 44.7%. With L-PCR, correlations of MAF with tumor size and response status were less prominent. Serial ctDNA measurement reflected individual disease courses over time. Targeted panel sequencing of four patients detected additional driver mutations in all cases and secondary resistance mutations in two cases. Thus, ctDNA indicates disease activity in patients with GIST and should be incorporated as companion biomarker in future prospective trials.

Summary • Dual targeting of proteins to more than one subcellular localization has been found in animals, in fungi and in plants. In the latter, ambiguous N-terminal targeting signals have been described that result in the protein being located in both mitochondria and plastids. We have developed ambiguous targeting predictor (ATP), a machine-learning implementation that classifies such ambiguous targeting signals. • Ambiguous targeting predictor is based on a support vector machine implementation that makes use of 12 different amino acid features. Prediction results were validated using fluorescent protein fusion. • Both in silico and in vivo evaluations demonstrate that ambiguous targeting predictor is useful for predicting dual targeting to mitochondria and plastids. Proteins that are targeted to both organelles by tandemly arrayed signals (so-called twin targeting) can be predicted by both ambiguous targeting predictor and a combination of single targeting prediction tools. Comparison of ambiguous targeting predictor with previous experimental approaches, as well as in silico approaches, shows good congruence. • Based on the prediction results, land plant genomes are expected to encode, on average, > 400 proteins that are located in mitochondria and plastids. Ambiguous targeting predictor is helpful for functional genome annotation and can be used as a tool to further our understanding about dual protein targeting and its evolution.