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.

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Background and aimsBesides well-defined genetic alterations, the dedifferentiation of mature acinar cells is an important prerequisite for pancreatic carcinogenesis. Acinar-specific genes controlling cell homeostasis are extensively downregulated during cancer development; however, the underlying mechanisms are poorly understood. Now, we devised a novel in vitro strategy to determine genome-wide dynamics in the epigenetic landscape in pancreatic carcinogenesis.DesignWith our in vitro carcinogenic sequence, we performed global gene expression analysis and ChIP sequencing for the histone modifications H3K4me3, H3K27me3 and H2AK119ub. Followed by a comprehensive bioinformatic approach, we captured gene clusters with extensive epigenetic and transcriptional remodelling. Relevance of Ring1b-catalysed H2AK119ub in acinar cell reprogramming was studied in an inducible Ring1b knockout mouse model. CRISPR/Cas9-mediated Ring1b ablation as well as drug-induced Ring1b inhibition were functionally characterised in pancreatic cancer cells.ResultsThe epigenome is vigorously modified during pancreatic carcinogenesis, defining cellular identity. Particularly, regulatory acinar cell transcription factors are epigenetically silenced by the Ring1b-catalysed histone modification H2AK119ub in acinar-to-ductal metaplasia and pancreatic cancer cells. Ring1b knockout mice showed greatly impaired acinar cell dedifferentiation and pancreatic tumour formation due to a retained expression of acinar differentiation genes. Depletion or drug-induced inhibition of Ring1b promoted tumour cell reprogramming towards a less aggressive phenotype.ConclusionsOur data provide substantial evidence that the epigenetic silencing of acinar cell fate genes is a mandatory event in the development and progression of pancreatic cancer. Targeting the epigenetic repressor Ring1b could offer new therapeutic options.

Noninvasive disease monitoring and risk stratification by circulating tumor DNA (ctDNA) profiling has become a potential novel strategy for patient management in B-cell lymphoma. Emerging innovative therapeutic options and an unprecedented growth in our understanding of biological and molecular factors underlying lymphoma heterogeneity have fundamentally increased the need for precision-based tools facilitating personalized and accurate disease profiling and quantification. By capturing the entire mutational landscape of tumors, ctDNA assessment has some decisive advantages over conventional tissue biopsies, which usually target only one single tumor site. Due to its non- or minimal-invasive nature, serial and repeated ctDNA profiling provides a real-time picture of the genetic composition and facilitates quantification of tumor burden any time during the course of the disease. In this review, we present a comprehensive overview of technologies used for ctDNA detection and genotyping in B-cell lymphoma, focusing on pre-analytical and technical requirements, the advantages and limitations of various approaches, and highlight recent advances around improving sensitivity and suppressing technical errors. We broadly review potential applications of ctDNA in clinical practice and for translational research by describing how ctDNA might enhance lymphoma subtype classification, treatment response assessment, outcome prediction, and monitoring of measurable residual disease. We finally discuss how ctDNA could be implemented in prospective clinical trials as a novel surrogate endpoint and be utilized as a decision-making tool to guide lymphoma treatment in the future.

PURPOSE Clinical outcomes of patients with CNS lymphomas (CNSLs) are remarkably heterogeneous, yet identification of patients at high risk for treatment failure is challenging. Furthermore, CNSL diagnosis often remains unconfirmed because of contraindications for invasive stereotactic biopsies. Therefore, improved biomarkers are needed to better stratify patients into risk groups, predict treatment response, and noninvasively identify CNSL. PATIENTS AND METHODS We explored the value of circulating tumor DNA (ctDNA) for early outcome prediction, measurable residual disease monitoring, and surgery-free CNSL identification by applying ultrasensitive targeted next-generation sequencing to a total of 306 tumor, plasma, and CSF specimens from 136 patients with brain cancers, including 92 patients with CNSL. RESULTS Before therapy, ctDNA was detectable in 78% of plasma and 100% of CSF samples. Patients with positive ctDNA in pretreatment plasma had significantly shorter progression-free survival (PFS, P < .0001, log-rank test) and overall survival (OS, P = .0001, log-rank test). In multivariate analyses including established clinical and radiographic risk factors, pretreatment plasma ctDNA concentrations were independently prognostic of clinical outcomes (PFS HR, 1.4; 95% CI, 1.0 to 1.9; P = .03; OS HR, 1.6; 95% CI, 1.1 to 2.2; P = .006). Moreover, measurable residual disease detection by plasma ctDNA monitoring during treatment identified patients with particularly poor prognosis following curative-intent immunochemotherapy (PFS, P = .0002; OS, P = .004, log-rank test). Finally, we developed a proof-of-principle machine learning approach for biopsy-free CNSL identification from ctDNA, showing sensitivities of 59% (CSF) and 25% (plasma) with high positive predictive value. CONCLUSION We demonstrate robust and ultrasensitive detection of ctDNA at various disease milestones in CNSL. Our findings highlight the role of ctDNA as a noninvasive biomarker and its potential value for personalized risk stratification and treatment guidance in patients with CNSL.