Background: Circulating tumor DNA (ctDNA) offers a convenient way to monitor tumor progression and treatment response. Because tumor mutational profiles are highly variable from person to person, a fixed content panel may be insufficient to track treatment response in all patients. Methods: We design ctDNA fingerprint panels specific to individual patients which are based on whole exome sequencing and target to high frequency clonal population clusters in patients. We test the fingerprint panels in 313 patients who together have eight tumor types (colorectal, hepatocellular, gastric, breast, pancreatic, and esophageal carcinomas and lung cancer and cholangiocarcinoma) and exposed to multiple treatment methods (surgery, chemotherapy, radiotherapy, targeted-drug therapy, immunotherapy, and combinations of them). We also monitor drugrelated mutations in the patients using a pre-designed panel with eight hotspot genes. Results: 291 (93.0%) designed fingerprint panels harbor less than ten previously known tumor genes. We detected 7475 ctDNA mutations in 238 (76%) patients and 6196 (96.0%) of the mutations are detected in only one test. Both the level of ctDNA content fraction (CCF) and fold change of CCF (between the definitive and proceeding tests) are highly correlated with clinical outcomes (p-values 1.36e-6 for level and 5.64e-10 for fold change, Kruskal-Wallis test). The CCFs of PD patients are an order of magnitude higher than the CCFs of SD and OR patients (median/mean 2.22%/8.96% for SD, 0.18/0.21% for PD, and 0.31/0.54% for OR; pairwise p-values 7.8e-6 for SD ~ PD, 2.7e-4 for OR ~ PD, and 7.0e-3 for SD ~ OR, Wilcoxon rank sum test). The fold change of CCF distinguishes the patient groups even better, which increases for PD, remains stable for SD, and decreases for OR patients (p-values 0.002, ~ 1, and 0.0001 respectively, Wilcoxon signed-rank test). Eleven drug-related mutations are identified from nine out of the 313 patients. Conclusions: The ctDNA fingerprint method improves both specificity and sensitivity of monitoring treatment response across several tumor types. It can identify tumor relapse/recurrence potentially earlier than imaging-based diagnosis. When augmented with tumor hotspot genes, it can track acquired drug-related mutations in patients.
Background: Gastric cancer (GC) is a heterogeneous disease, and is a leading cause of cancer deaths in Eastern Asia. Genomic analysis, such as whole-exome sequencing (WES), can help identify key genetic alterations leading to the malignancy and diversity of GC, and may help identify new drug targets.Methods: We identified genomic alterations in a cohort of 38 GC patients, including 26 metastatic and 12 non-metastatic patients. We analyzed the association between novel gene mutations and copy number variations (CNVs) with tumor metastasis and patient survival.Results: A number of significantly mutated genes in somatic and germline cells were identified. Among them, ATAD3B somatic mutation, a potential biomarker of immunotherapy in stomach cancers, was associated with better patient survival (P=0.0939) and metastasis (P=0.074). POLE germline variation was correlated with shorter overall survival (OS; P=0.0100). Novel CNVs were also identified and can potentially be used as biomarkers. These included 9p24.1 deletion (P=0.0376) and 16p11.2 amplification (P=0.0066), which were both associated with shorter OS. CNVs of several genes including MMP9, PTPN1, and SS18L1 were found to be significantly related to metastasis (P<0.05). Conclusions:We characterized the mutational landscape of 38 GC patients and discovered several potential new predictive markers of survival and metastasis in GC.
Background Treating patients with advanced sarcomas is challenging due to great histologic diversity among its subtypes. Leiomyosarcoma (LMS) and de-differentiated liposarcoma (DDLPS) are two common and aggressive subtypes of soft tissue sarcoma (STS). They differ significantly in histology and clinical behaviors. However, the molecular driving force behind the difference is unclear. Methods We collected 20 LMS and 12 DDLPS samples and performed whole exome sequencing (WES) to obtain their somatic mutation profiles. We also performed RNA-Seq to analyze the transcriptomes of 8 each of the LMS and DDLPS samples and obtained information about differential gene expression, pathway enrichment, immune cell infiltration in tumor microenvironment, and chromosomal rearrangement including gene fusions. Selected gene fusion events from the RNA-seq prediction were checked by RT-PCR in tandem with Sanger sequencing. Results We detected loss of function mutation and deletion of tumor suppressors mostly in LMS, and oncogene amplification mostly in DDLPS. A focal amplification affecting chromosome 12q13–15 region which encodes MDM2, CDK4 and HMGA2 is notable in DDLPS. Mutations in TP53, ATRX, PTEN, and RB1 are identified in LMS but not DDLPS, while mutation of HERC2 is only identified in DDLPS but not LMS. RNA-seq revealed overexpression of MDM2, CDK4 and HMGA2 in DDLPS and down-regulation of TP53 and RB1 in LMS. It also detected more fusion events in DDLPS than LMS (4.5 vs. 1, p = 0.0195), and the ones involving chromosome 12 in DDLPS stand out. RT-PCR and Sanger sequencing verified the majority of the fusion events in DDLPS but only one event in LMS selected to be tested. The tumor microenvironmental signatures are highly correlated with histologic types. DDLPS has more endothelial cells and fibroblasts content than LMS. Conclusions Our analysis revealed different recurrent genetic variations in LMS and DDLPS including simultaneous upregulation of gene expression and gene copy number amplification of MDM2 and CDK4. Up-regulation of tumor related genes is favored in DDLPS, while loss of suppressor function is favored in LMS. DDLPS harbors more frequent fusion events which can generate neoepitopes and potentially targeted by personalized immune treatment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.