Application of personalized plasma ctDNA detection in dynamic monitoring of recurrence after resection of colorectal cancer liver metastases.
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer
PROVIDER: 2696089 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Accurately evaluating minimal residual disease (MRD) could facilitate early intervention and personalized adjuvant therapies. Here, using ultradeep targeted next-generation sequencing (NGS), we evaluate the clinical utility of circulating tumor DNA (ctDNA) for dynamic recurrence risk and adjuvant chemotherapy (ACT) benefit prediction in resected non-small cell lung cancer (NSCLC). Both postsurgical and post-ACT ctDNA positivity are significantly associated with worse recurrence-free survival. In stage II-III patients, the postsurgical ctDNA positive group benefit from ACT, while ctDNA negative patients have a low risk of relapse regardless of whether or not ACT is administered. During disease surveillance, ctDNA positivity precedes radiological recurrence by a median of 88 days. Using joint modeling of longitudinal ctDNA analysis and time-to-recurrence, we accurately predict patients' postsurgical 12-month and 15-month recurrence status. Our findings reveal longitudinal ctDNA analysis as a promising tool to detect MRD in NSCLC, and we show pioneering work of using postsurgical ctDNA status to guide ACT and applying joint modeling to dynamically predict recurrence risk, although the results need to be further confirmed in future studies.
| S-EPMC8605017 | biostudies-literature
Project description: Not available
| S-EPMC9118575 | biostudies-literature
Project description:Surveillance to detect cancer recurrence is an important part of care for cancer survivors. In this paper we discuss the design of optimal strategies for early detection of disease recurrence based on each patient's distinct biomarker trajectory and periodically updated risk estimated in the setting of a prospective cohort study. We adopt a latent class joint model which considers a longitudinal biomarker process and an event process jointly, to address heterogeneity of patients and disease, to discover distinct biomarker trajectory patterns, to classify patients into different risk groups, and to predict the risk of disease recurrence. The model is used to develop a monitoring strategy that dynamically modifies the monitoring intervals according to patients' current risk derived from periodically updated biomarker measurements and other indicators of disease spread. The optimal biomarker assessment time is derived using a utility function. We develop an algorithm to apply the proposed strategy to monitoring of new patients after initial treatment. We illustrate the models and the derivation of the optimal strategy using simulated data from monitoring prostate cancer recurrence over a 5-year period.
| S-EPMC4269482 | biostudies-literature
Project description:BackgroundMethods available at home for capturing facial images to track changes in skin quality and evaluate skincare treatments are limited. In this study, we developed a smartphone camera application (app) for personalized facial aesthetic monitoring.Materials and methodsA face alignment indicators (FAIN) system utilizing facial landmark detection, an artificial intelligence technique, to estimate key facial parts, was implemented into the app to maintain a consistent facial appearance during image capture. The FAIN system is composed of a fixed target indicator and an alignment indicator that dynamically changes its shape according to the user's face position, size, and orientation. Users align their faces to match the alignment indicator with the fixed target indicator, and the image is automatically captured when alignment is achieved.ResultsWe investigated the app's effectiveness in ensuring a consistent facial appearance by analyzing both geometric and colorimetric data. Geometric information from captured faces and colorimetric data from stickers applied to the faces were utilized. The coefficients of variation (CVs) for the L*, a*, and b* values of the stickers were higher compared to those measured by a colorimeter, with CVs of 14.9 times, 8.14 times, and 4.41 times for L*, a*, and b*, respectively. To assess the feasibility of the app for facial aesthetic monitoring, we tracked changes in pseudo-skin color on the cheek of a participant using skin-colored stickers. As a result, we observed the smallest color difference ∆Eab of 1.901, which can be considered as the experimentally validated detection limit using images acquired by the app.ConclusionWhile the current monitoring method is a relative quantification approach, it contributes to evidence-based evaluations of skincare treatments.
| S-EPMC11230921 | biostudies-literature
Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy.
2010-04-14 | GSE17856 | GEO
Project description:The key radiomic features were found to vary after neoadjuvant chemotherapy. Moreover, lncRNAs were discovered to be significantly correlated with radiomics and recurrence-free survival (RFS). The findings indicate that the radiomic signature can be conveniently used for individualized prediction of RFS and that radiomics is associated with lncRNAs in breast cancer.
2023-11-21 | GSE189371 | GEO
Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy. 43 tumor (JT) and 44 non-tumor (JNT) liver tissues surgically resected from patients with HCV-associated hepatocellular carcinoma; 8 non-tumor liver tissues (control samples, JC) surgically resected from HCV- or HBV-free patients with metastatic liver tumor. Inter-batch normalization was carried out using Distance Weighted Discrimination procedure. The supplementary file 'GSE17856_Readme.txt' contains a description of the replicates used for normalization. The 'GSE17856_US14702406_2514850*' files are the raw data files for the replicates.
2010-04-14 | E-GEOD-17856 | biostudies-arrayexpress
Project description:BackgroundRAS assessment is mandatory for therapy decision in metastatic colorectal cancer (mCRC) patients. This determination is based on tumor tissue, however, genotyping of circulating tumor (ct)DNA offers clear advantages as a minimally invasive method that represents tumor heterogeneity. Our study aims to evaluate the use of ctDNA as an alternative for determining baseline RAS status and subsequent monitoring of RAS mutations during therapy as a component of routine clinical practice.Patients and methodsRAS mutational status in plasma was evaluated in mCRC patients by OncoBEAM™ RAS CRC assay. Concordance of results in plasma and tissue was retrospectively evaluated. RAS mutations were also prospectively monitored in longitudinal plasma samples from selected patients.ResultsAnalysis of RAS in tissue and plasma samples from 115 mCRC patients showed a 93% overall agreement. Plasma/tissue RAS discrepancies were mainly explained by spatial and temporal tumor heterogeneity. Analysis of clinico-pathological features showed that the site of metastasis (i.e. peritoneal, lung), the histology of the tumor (i.e. mucinous) and administration of treatment previous to blood collection negatively impacted the detection of RAS in ctDNA. In patients with baseline mutant RAS tumors treated with chemotherapy/antiangiogenic, longitudinal analysis of RAS ctDNA mirrored response to treatment, being an early predictor of response. In patients RAS wt, longitudinal monitoring of RAS ctDNA revealed that OncoBEAM was useful to detect emergence of RAS mutations during anti-EGFR treatment.ConclusionThe high overall agreement in RAS mutational assessment between plasma and tissue supports blood-based testing with OncoBEAM™ as a viable alternative for genotyping RAS of mCRC patients in routine clinical practice. Our study describes practical clinico-pathological specifications to optimize RAS ctDNA determination. Moreover, OncoBEAM™ is useful to monitor RAS in patients undergoing systemic therapy to detect resistance and evaluate the efficacy of particular treatments.
| S-EPMC5834035 | biostudies-literature
Project description:BACKGROUND & AIMS: Sorafenib is the standard systemic therapy for advanced hepatocellular carcinoma (HCC). In early-stage HCC, survival benefits of resection/local ablation are compromised by high recurrence rates (70% at 5-years). In this setting, the phase-3 STORM-trial did not achieve its primary end-point of improving recurrence-free-survival (RFS) comparing sorafenib to placebo as adjuvant treatment. Herein we present results of the biomarker companion study BIOSTORM in which we define predictors of recurrence prevention with sorafenib and prognostic factors with B-level of evidence. METHODS: Tumor tissue from 188 patients randomized to receive sorafenib (83) or placebo (105) in the STORM trial was collected. Analyses included gene-expression profiling, targeted exome sequencing (18 known onco-drivers), immunohistochemistry (pERK, pVEGFR2, Ki67), FISH (VEGFA) and immunome. A gene signature capturing improved RFS in sorafenib-treated patients was generated. All 70 RFS events were recurrences, thus time-to-recurrence equaled RFS. Predictive and prognostic value was assessed using Cox regression models and interaction test. RESULTS: BIOSTORM recapitulates clinical and pathological characteristics of STORM patients. None of the biomarkers tested that are known to be related to angiogenesis and proliferation, or previously proposed gene-signatures, or mutations predicted sorafenib benefit. A newly generated 146-gene signature identifying 30% of patients captured benefit to sorafenib in terms of RFS (p-of-interaction=0.04). These sorafenib responders were significantly enriched in CD4+ T, B, and cytolytic Natural Killer cells, and showed absence of activated macrophages and other components of adaptive immunity. Hepatocytic pERK (HR=2.41; p=0.012) and microvascular invasion (HR=2.09; p=0.017) were independent prognostic factors. CONCLUSIONS: In BIOSTORM, hepatocytic pERK and microvascular invasion predicted poor RFS. No mutation, gene amplification or previously proposed gene-signatures predicted sorafenib benefit. A newly generated multi-gene signature associated with improved RFS on sorafenib warrants further validation. KEYWORDS: sorafenib; tyrosine-kinase inhibitor; trial enrichment; stratification.
2018-08-21 | GSE109211 | GEO
Project description:Non-invasive molecular monitoring of tumour burden and clonal selection in response to targeted therapy.
| PRJEB9309 | ENA