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Prediction of Radiation-induced Late Intestinal Injury

ABSTRACT: Our team has constructed a prediction model based on the phase separation level of DNA repair factors (MRNIP、NONO、NOP53) to predict radiation-induced late intestinal injury (RLII) and verified the predictive efficacy of the system in retrospective studies. This clinical study intends to further prospectively verify the accuracy of this prediction model in rectal cancer patients. In this study, we plan to enroll 200 patients diagnosed with locally advanced rectal cancer by pathology and MRI, who undergo neoadjuvant chemoradiotherapy (NCRT) and total mesorectal excision (TME). We will follow up the occurrence and progression of radiation-induced intestinal injury within 1 year after TME. Phase separation level of DNA repair factors will be detected in pathological tissue after TME and applied to the prediction model to predict RLII. Based on the clinical diagnosis of RLII, the area under curve (AUC), accuracy, precision, specificity, and sensitivity of this prediction model in predicting RLII will be evaluated. The main outcome hypothesis is that the AUC of RLII predicted by the prediction model based on the phase separation level of DNA repair factors is more than 0.8.

DISEASE(S): Radiation-induced Intestinal Injury,Wounds And Injuries

PROVIDER: 9987 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Chronic injury in kidney transplants remains a major cause of graft loss. The aim of this study was to identify a predictive gene set capable of classifying renal grafts at risk for progressive injury due to fibrosis.The Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicenter study. Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by microarray. Genes were sought which correlated with subsequent 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters. We identified a set of 13 genes that was independently predictive for the development of fi brosis at 1 year (ie, CADI-12 >=2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fi brosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by Affymetrix exon arrays. Genes that were specifically correlated with 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters were identified by correlation analysis. The minimal geneset was further identified to predict the progressors/non progressors and validated by qPCR in an independent cohort and two public datasets. This dataset is part of the TransQST collection.

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Prediction of Radiation-induced Late Intestinal Injury

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