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We report a personalized tumor-informed technology, Patient-specific pROgnostic and Potential tHErapeutic marker Tracking (PROPHET) using deep sequencing of 50 patient-specific variants to detect molecular residual disease (MRD) with a limit of detection of 0.004%. PROPHET and state-of-the-art fixed-panel assays were applied to 760 plasma samples from 181 prospectively enrolled early stage non-small cell lung cancer patients. PROPHET shows higher sensitivity of 45% at baseline with circulating tumor DNA (ctDNA). It outperforms fixed-panel assays in prognostic analysis and demonstrates a median lead-time of 299 days to radiologically confirmed recurrence. Personalized non-canonical variants account for 98.2% with prognostic effects similar to canonical variants. The proposed tumor-node-metastasis-blood (TNMB) classification surpasses TNM staging for prognostic prediction at the decision point of adjuvant treatment. PROPHET shows potential to evaluate the effect of adjuvant therapy and serve as an arbiter of the equivocal radiological diagnosis. These findings highlight the potential advantages of personalized cancer techniques in MRD detection., Competing Interests: Declaration of interests Z.Z., C.W., X.L., D.P., F.Q., S.W., X.Z., and S.C. are employment and stock/stock option ownership in Burning Rock Biotech. The remaining authors declare no potential conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Detection of circulating tumor DNA (ctDNA) is a promising method for postoperative surveillance of lung cancer. However, relatively low positive rate in early stage patients restricts its application. Aberrant methylation of ctDNA can be detected in blood samples, and may provide a more sensitive method. This study is designed to systematically evaluate and compare the detection of aberrant methylation and mutations in ctDNA among surgical non-small cell lung cancer (NSCLC) patients, aiming to investigate the feasibility of ctDNA detection as a means of lung cancer surveillance., Methods: This is a prospective observational study. Consecutive surgical NSCLC patients will be recruited. Blood samples will be collected both before and after surgery (during the follow-up period), while matching tumor tissues and tumor-adjacent normal tissues will be collected during surgery. Quantitative analysis of aberrant methylation and mutations of ctDNA will be conducted in combination with a three-year follow-up data., Discussion: This is the first registered prospective study designed to investigate the feasibility of ctDNA methylation detection as a means of postoperative lung cancer surveillance. We will systematically evaluate and compare the quantitative detection of ctDNA mutations and ctDNA methylation in surgical NSCLC patients, combining with the follow-up information. By integrating genetic and epigenetic information of ctDNA, more effective strategies for postoperative surveillance may be defined., Trial Registration: This study (MEDAL, MEthylation based Dynamic Analysis for Lung cancer) was registered on ClinicalTrials.gov on 08/05/2018 (NCT03634826; Pre-results).

Noninvasive genotyping of driver genes and monitoring of tumor dynamics help make better personalized therapeutic decisions. However, neither PCR-based assays nor amplicon-based targeted sequencing can detect fusion genes like anaplastic lymphoma kinase (ALK) rearrangements in blood samples. To investigate the feasibility and performance of capture-based sequencing on ALK fusion detection, we developed a capture-based targeted sequencing panel to detect and quantify rearrangement events, along with other driver mutation variants in plasma. In this perspective study, we screened 364 patients with advanced non-small cell lung cancer (NSCLC) for ALK rearrangements, and collected blood samples from 24 of them with confirmed ALK rearrangements based on their tissue biopsies. ALK rearrangements were successfully detected in 19 of 24 patients at baseline with 79.2% (95% CI 57.9%, 92.9%) sensitivity and 100% (36/36) specificity. Among the 24 patients, we obtained longitudinal blood samples from 7 of them after either chemotherapy and/or Crizotinib treatment for disease monitoring. The by-sample detection rate of ALK rearrangements after treatment drops to 69.2% (9 of 13). In addition to detecting ALK rearrangements, we also detected 3 Crizotinib resistant mutations, ALK L1152R, ALK I1171T and ALK L1196M from patient P4. ctDNA concentration correlates with responses and disease progression, reflecting its ability as a biomarker. Our findings suggest capture-based sequencing can detect and quantify ALK rearrangements as well as other somatic mutations, including mutations mediated drug resistance, in plasma with high sensitivity, paving the way for its application in identifying driver fusion genes and monitoring tumor dynamics in the clinic.

The performance of next generation sequencing in profiling somatic mutations from plasma samples, especially in early‐stage cancer, is limited by its sensitivity due to the extremely low amount of cell‐free DNA (cfDNA). Polymerase‐chain reaction amplification of limited amounts of DNA often results in product redundancy and the amplification of contaminant DNA. Numerous methods have been developed to detect and quantify ultralow frequency mutations. In this study, the potential of tagging each DNA molecule with a unique molecular identifier (UMI) is explored to detect ultralow frequency mutations in early‐stage lung cancer patients. Paired tissue and blood samples from 32 patients with early‐stage (stage IA‐IIB) nonsmall cell lung cancer are sequenced using a panel consisting of 168 cancer‐related genes. Collectively, 93.8% of tissue samples and 59.4% of plasma samples have mutations detected. The concordance rates are acceptable for all patients except for stage IA. Notably, mutations at an alleleic frequency as low as 0.1% are detected in UMI‐tagged cfDNA samples. Moreover, patients with nonadenocarcinoma histology have a significantly higher detection rate than patients with adenocarcinoma (92.9% vs 33.3%, P = 0.002). The study demonstrates the potential of UMI in early detection of lung cancer from plasma samples. [ABSTRACT FROM AUTHOR]