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
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| S-EPMC8605017 | biostudies-literature
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| S-EPMC4269482 | 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: Not available
| 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:Introduction: The I-SPY 1 TRIAL was designed to evaluate complete pathologic response and tumor volume change, measured by magnetic resonance imaging (MRI), stratified by molecular subtypes and link response to 3-year recurrence free survival (RFS).Methods: Eligible patients had T =3 cm and received neoadjuvant chemotherapy with AC plus optional taxane. Serial MRI, biopsy, and blood draws were conducted over the course of treatment, and a database of multiple molecular profiles was assembled.Results: A total of 221 patients were eligible for analysis: median tumor size was 6.0 cm and median age was 49 years. The I-SPY 1 TRIAL patients had tumors with aggressive biology: 45% were estrogen receptor (ER) negative, 31% Her2+; and 91% high risk by the Netherlands Cancer Institute (NKI) 70-gene profile. After a median of 3.9 years, RFS was 77%. HR/HER2 status improved predictability of RFS with the greatest difference between HR+/HER2- and triple-negative disease (hazard ratio 0.39). Wound healing signature activation, p53 mutation, and Risk of Relapse (ROR) score were highly correlated and also significantly improved the accuracy of RFS predictions when added to stage. The rate of pathologic complete response (pCR) varied considerably from a low of 0-2% (NKI low risk, luminal A) to a high of 43-61% (17q Amplified, HER2 enriched, HR-/HER2+). Both pCR and the more refined residual cancer burden (RCB) were significant predictors of RFS for all patients and even more predictive when analyzed within biomarker subsets. Good risk (NKI low, ROR-S low risk, Wound Healing quiescent, p53 wild type) signatures were associated with significantly higher 3-year RFS than poor risk expression signatures (ROR-S high risk, Wound Healing Activated, p53 mutation, NKI high risk).Conclusion: Importantly, in this set of biologically poor prognosis tumors, pCR predicts for better outcome, especially when analyzed within breast cancer subsets. Keywords: reference x sample
2012-01-13 | GSE22226 | GEO
Project description:Non-invasive molecular monitoring of tumour burden and clonal selection in response to targeted therapy.
| PRJEB9309 | ENA
Project description: Not available
| S-EPMC5427723 | biostudies-literature