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Background: Biomarker testing has gradually become standard of care in precision oncology to help physicians select optimal treatment for patients. Compared to single-gene or small gene panel testing, comprehensive genomic profiling (CGP) has emerged as a more time- and tissue-efficient method. This study demonstrated in-depth analytical validation of K-4CARE, a CGP assay that integrates circulating tumor DNA (ctDNA) tracking for residual cancer surveillance. Methods: The assay utilized a panel of 473 cancer-relevant genes with a total length of 1.7 Mb. Reference standards were used to evaluate limit of detection (LOD), concordance, sensitivity, specificity and precision of the assay to detect single nucleotide variants (SNVs), small insertion/deletions (Indels), gene amplification and fusion, microsatellite instability (MSI) and tumor mutational burden (TMB). The assay was then benchmarked against orthogonal methods using 155 clinical samples from 10 cancer types. In selected cancers, top tumor-derived somatic mutations, as ranked by our proprietary algorithm, were used to detect ctDNA in the plasma. Results: For detection of somatic SNVs and Indels, gene fusion and amplification, the assay had sensitivity of >99%, 94% and >99% respectively, and specificity of >99%. Detection of germline variants also achieved sensitivity and specificity of >99%. For TMB measurement, the correlation coefficient between whole-exome sequencing and our targeted panel was 97%. MSI analysis when benchmarked against polymerase chain reaction method showed sensitivity of 94% and specificity of >99%. The concordance between our assay and the TruSight Oncology 500 assay for detection of somatic variants, TMB and MSI measurement was 100%, 89%, and 98% respectively. When CGP-informed mutations were used to personalize ctDNA tracking, the detection rate of ctDNA in liquid biopsy was 79%, and clinical utility in cancer surveillance was demonstrated in 2 case studies. Conclusion: K-4CARE™ assay provides comprehensive and reliable genomic information that fulfills all guideline-based biomarker testing for both targeted therapy and immunotherapy. Integration of ctDNA tracking helps clinicians to further monitor treatment response and ultimately provide well-rounded care to cancer patients.
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Introduction: Breast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes. Methods: Here, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects. Results: We identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity. Discussion: Our findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.
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Background: Colorectal 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. Method: In 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. Results: The 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. Conclusion: Our 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.