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The identification and quantification of actionable mutations are of critical importance for effective genotype-directed therapies, prognosis and drug response monitoring in patients with non-small-cell lung cancer (NSCLC). Although tumor tissue biopsy remains the gold standard for diagnosis of NSCLC, the analysis of circulating tumor DNA (ctDNA) in plasma, known as liquid biopsy, has recently emerged as an alternative and noninvasive approach for exploring tumor genetic constitution. In this study, we developed a protocol for liquid biopsy using ultra-deep massively parallel sequencing (MPS) with unique molecular identifier tagging and evaluated its performance for the identification and quantification of tumor-derived mutations from plasma of patients with advanced NSCLC. Paired plasma and tumor tissue samples were used to evaluate mutation profiles detected by ultra-deep MPS, which showed 87.5% concordance. Cross-platform comparison with droplet digital PCR demonstrated comparable detection performance (91.4% concordance, Cohen's kappa coefficient of 0.85 with 95% CI = 0.72-0.97) and great reliability in quantification of mutation allele frequency (Intraclass correlation coefficient of 0.96 with 95% CI = 0.90-0.98). Our results highlight the potential application of liquid biopsy using ultra-deep MPS as a routine assay in clinical practice for both detection and quantification of actionable mutation landscape in NSCLC patients.

Breast cancer is the leading cause of cancer death in Vietnamese women, but its mutational landscape and actionable alterations for targeted therapies remain unknown. After treatment, a sensitive biomarker to complement conventional imaging to monitor patients is also lacking. In this prospective multi‐center study, 134 early‐stage breast cancer patients eligible for curative‐intent surgery were recruited. Genomic DNA from tumor tissues and paired white blood cells were sequenced to profile all tumor‐derived mutations in 95 cancer‐associated genes. Our bioinformatic algorithm was then utilized to identify top mutations for individual patients. Serial plasma samples were collected before surgery and at scheduled visits after surgery. Personalized assay tracking the selected mutations were performed to detect circulating tumor DNA (ctDNA) in the plasma. We found that the mutational landscape of the Vietnamese was largely similar to other Asian cohorts, showing higher TP53 mutation frequency than in Caucasians. Alterations in PIK3CA and PI3K signaling were dominant, particularly in our triple‐negative subgroup. Using top‐ranked mutations, we detected ctDNA in pre‐operative plasma in 24.6–43.5% of the hormone‐receptor‐positive groups and 76.9–80.8% of the hormone‐receptor‐negative groups. The detection rate was associated with breast cancer subtypes and clinicopathological features that increased the risk of relapse. Interim analysis after a 15‐month follow‐up revealed post‐operative detection of ctDNA in all three patients that had recurrence, with a lead time of 7–13 months ahead of clinical diagnosis. Our personalized assay is streamlined and affordable with promising clinical utility in residual cancer surveillance. We also generated the first somatic variant dataset for Vietnamese breast cancer women that could lay the foundation for precision cancer medicine in Vietnam.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

BackgroundColorectal cancer (CRC) is the fifth most common cancer with rising prevalence in Vietnam. However, there is no data about the mutational landscape and actionable alterations in the Vietnamese patients. During post-operative surveillance, clinical tools are limited to stratify risk of recurrence and detect residual disease.MethodIn this prospective multi-center study, 103 CRC patients eligible for curative-intent surgery were recruited. Genomic DNA from tumor tissue and paired white blood cells were sequenced to profile all tumor-derived somatic mutations in 95 cancer-associated genes. Our bioinformatic algorithm identified top mutations unique for individual patient, which were then used to monitor the presence of circulating tumor DNA (ctDNA) in serial plasma samples.ResultsThe top mutated genes in our cohort were APC, TP53 and KRAS. 41.7% of the patients harbored KRAS and NRAS mutations predictive of resistance to Cetuximab and Panitumumab respectively; 41.7% had mutations targeted by either approved or experimental drugs. Using a personalized subset of top ranked mutations, we detected ctDNA in 90.5% of the pre-operative plasma samples, whereas carcinoembryonic antigen (CEA) was elevated in only 41.3% of them. Interim analysis after 16-month follow-up revealed post-operative detection of ctDNA in two patients that had recurrence, with the lead time of 4-10.5 months ahead of clinical diagnosis. CEA failed to predict recurrence in both cases.ConclusionOur assay showed promising dual clinical utilities in residual cancer surveillance and actionable mutation profiling for targeted therapies in CRC patients. This could lay foundation to empower precision cancer medicine in Vietnam and other developing countries.

Abstract Targeted therapy with tyrosine kinase inhibitors (TKI) provides survival benefits to a majority of patients with non-small cell lung cancer (NSCLC). However, resistance to TKI almost always develops after treatment. Although genetic and epigenetic alterations have each been shown to drive resistance to TKI in cell line models, clinical evidence for their contribution in the acquisition of resistance remains limited. Here, we employed liquid biopsy for simultaneous analysis of genetic and epigenetic changes in 122 Vietnamese NSCLC patients undergoing TKI therapy and displaying acquired resistance. We detected multiple profiles of resistance mutations in 51 patients (41.8%). Of those, genetic alterations in EGFR, particularly EGFR amplification (n = 6), showed pronounced genome instability and genome-wide hypomethylation. Interestingly, the level of hypomethylation was associated with the duration of response to TKI treatment. We also detected hypermethylation in regulatory regions of Homeobox genes which are known to be involved in tumor differentiation. In contrast, such changes were not observed in cases with MET (n = 4) and HER2 (n = 4) amplification. Thus, our study showed that liquid biopsy could provide important insights into the heterogeneity of TKI resistance mechanisms in NSCLC patients, providing essential information for prediction of resistance and selection of subsequent treatment.