Metabolomics signature as a survival predictor in patients with resectable colorectal liver metastasis.
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ABSTRACT:
SUBMITTER: Gonzalez-Olmedo C
PROVIDER: S-EPMC10797245 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Publications
Metabolomics signature as a survival predictor in patients with resectable colorectal liver metastasis.
González-Olmedo Carmen C García-Verdejo Francisco José FJ Reguera-Teba Antonio A Rosa-Garrido Carmen C López-López José Antonio JA Díaz-Beltrán Leticia L García Patricia Mena PM Luque-Caro Natalia N Gálvez-Montosa Fernando F Ortega-Granados Ana Laura AL Ruiz-Sanjuan María M Cózar-Ibáñez Antonio A Sainz Juan J Marchal Juan Antonio JA Camacho José J Del Palacio José Pérez JP Fernández-Godino Rosario R Díaz Caridad C Sánchez-Rovira Pedro P
Clinical and translational medicine 20240101 1
Similar Datasets
Project description:ObjectiveTo determine whether genomic risk groups identified by somatic mutation testing of colorectal liver metastasis (CRLM) can be used for "molecularly-guided" selection for adjuvant systemic chemotherapy and hepatic artery infusion of FUDR (SYS+HAI-FUDR).BackgroundSeveral genomic biomarkers have been associated with clinical phenotype and survival for patients with resectable CRLM. It is unknown whether prognostication afforded by genomic stratification translates into enhanced patient selection for adjuvant hepatic artery infusion therapy.MethodsConsecutive patients with resected CRLM and available mutational characterization via Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets were reviewed from a prospective institutional database. Patients were stratified into three genomic risk groups based on previously defined alterations in SMAD4, EGFR and the RAS/RAF pathway. The association between SYS+HAI-FUDR and overall survival, relative to adjuvant chemotherapy alone (SYS), was evaluated in each genomic risk group by Cox proportional hazard regression and propensity score matched analyses.ResultsA total of 334 patients (SYS+HAI-FUDR 204; SYS 130) were identified; the rates of RAS/RAF alterations and SMAD4 inactivation were 47.4% and 11.7%, respectively. After a median follow-up of 58 months, adjuvant SYS+HAI-FUDR was independently associated with a reduced risk of death (HR 0.50, 95%CI 0.26-0.98, P = 0.045) in the low-risk genomic group, but not in the moderate-risk (HR 1.07, 95%CI 0.5-2.07, P = 0.749) or high-risk (HR 1.62, 95%CI 0.29-9.12, P = 0.537) cohorts. Following propensity score matching, adjuvant SYS+HAI-FUDR remained associated with significant improvements in long-term survival selectively in the low-risk genomic cohort (5-year actuarial survival: 89% vs. 68%, P = 0.019).ConclusionsGenomic alterations in RAS/RAF, SMAD4, and EGFR may be useful to guide treatment selection in resectable CRLM patients and warrant external validation and integration in future clinical trial design.
| S-EPMC8754193 | biostudies-literature
Project description:BackgroundNo well-performing nomogram has been developed specifically to predict individual-patient cancer-specific survival (CSS) and overall survival (OS) among patients with resectable colorectal liver metastasis (CRLM) who undergo simultaneous resection of primary and hepatic lesions without neoadjuvant chemotherapy (NAC). We aim to investigate the prognosis of patients with resectable CRLM undergoing simultaneous resection of primary and hepatic lesions without NAC.MethodsData of patients with CRLM in the Surveillance, Epidemiology and End Results Program (cohort, n = 225) were collected as the training set, and data of patients with CRLM treated at the National Cancer Center (cohort, n = 180) were collected as the validation set. The prognostic value of the clinicopathological parameters in the training cohort was assessed using Kaplan‒Meier curves and univariate and multivariate Cox proportional hazards models, and OS and CSS nomograms integrated with the prognostic variables were constructed. Calibration analyses, receiver operating characteristic (ROC) curves, and decision curve analyses (DCAs) were then performed to evaluate the performance of the nomograms.ResultsThere was no collinearity among the collected variables. Three factors were associated with OS and CSS: the pretreatment carcinoembryonic antigen (CEA) concentration, pathologic N (pN) stage, and adjuvant chemotherapy (each p < 0.05). OS and CSS nomograms were constructed using these three parameters. The calibration plots revealed favorable agreement between the predicted and observed outcomes. The areas under the ROC curves were approximately 0.7. The DCA plots revealed that both nomograms had satisfactory clinical benefits. The ROC curves and DCAs also confirmed that the nomogram surpassed the tumor, node, and metastasis staging system.ConclusionThe herein-described nomograms containing the pretreatment CEA concentration, pN stage, and adjuvant chemotherapy may be effective models for predicting postoperative survival in patients with CRLM.
| S-EPMC10686155 | biostudies-literature
Project description:Numerous factors affect the prognosis of colorectal liver metastasis (CRLM) patients after hepatic resection. We investigated several factors related to overall survival in patients with CRLM to identify those most likely to benefit from hepatic resection, and produced a rational tumor biology score system. Three hundred CRLM patients treated with preoperative chemotherapy followed by hepatic resection between 2006 and 2016 were enrolled in our study. Clinicopathologic and long-term survival data were collected and assessed. Patient 1-, 3-, and 5-year overall survival rates were 92.7%, 58.3%, and 45.8%, respectively, while 1-, 3-, and 5-year disease-free survival rates were 44.7%, 28.6%, and 24.2%, respectively. Multivariate Cox regression analysis revealed poor preoperative chemotherapy response, Fong clinical risk score > 2, and KRAS mutation to be independent prognostic indicators in CRLM patients. As part of a preoperative staging system in which one point was assigned for each factor, a total score (out of 3) was predictive of long-term survival following surgery. These factors facilitate personalized prognostic assessments in CRLM patients planning for resection.
| S-EPMC5668107 | biostudies-literature
Project description: Not available
| S-EPMC10950626 | biostudies-literature
Project description:Morphological response is considered an improved surrogate to the Response Evaluation Criteria in Solid Tumors (RECIST) model with regard to predicting the prognosis for patients with colorectal liver metastases. However, its use as a decision-making tool for surgical intervention has not been examined. The present study assessed the morphological response in 50 patients who underwent chemotherapy with or without bevacizumab for initially un-resectable colorectal liver metastases. Changes in tumor morphology between heterogeneous with uncertain borders and homogeneous with clear borders were defined as an optimal response (OR). Patients were also assessed as having an incomplete response (IR), and an absence of marked changes was assessed as no response (NR). No significant difference was observed in progression-free survival (PFS) between complete response/partial response (CR/PR) and stable disease/progressive disease (SD/PD), according to RECIST. By contrast, PFS for OR/IR patients was significantly improved compared with that for NR patients (13.2 vs. 8.7 months; P=0.0426). Exclusion of PD enhanced the difference in PFS between OR/IR and NR patients (15.1 vs. 9.3 months; P<0.0001), whereas no difference was observed between CR/PR and SD. The rate of OR and IR in patients treated with bevacizumab was 47.4% (9/19), but only 19.4% (6/31) for patients that were not administered bevacizumab. Comparison of the survival curves between OR/IR and NR patients revealed similar survival rates at 6 months after chemotherapy, but the groups exhibited different survival rates subsequent to this period of time. Patients showing OR/IR within 6 months appeared to be oncologically stable and could be considered as candidates for surgical intervention, including rescue liver resection. Comparing the pathological and morphological features of the tumor with representative optimal response, living tumor cells were revealed to be distributed within the area of vascular reconstruction induced by bevacizumab, resulting in a predictive value for prognosis in the patients treated with bevacizumab. The present findings provided the evidence for physicians to consider patients with previously un-resectable metastatic colorectal cancer as candidates for surgical treatment. Morphological response is a useful decision-making tool for evaluating these patients for rescue liver resection following chemotherapy.
| S-EPMC5529781 | biostudies-other
Project description:ObjectiveThe purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases.MethodsWe retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set.ResultsFor TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone.ConclusionRadiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.
| S-EPMC11389941 | biostudies-literature
Project description: Not available
| S-EPMC10432297 | biostudies-literature
Project description:BackgroundHistopathological assessment has a low potential to predict clinical outcome in patients with the same stage of colorectal cancer. More specific and sensitive biomarkers to determine patients' survival are needed. We aimed to determine gene expression signatures as reliable prognostic marker that could predict survival of colorectal cancer patients with Dukes' B and C.MethodsWe examined microarray gene expression profiles of 78 archived tissues of patients with Dukes' B and C using the Illumina DASL assay. The gene expression data were analyzed using the GeneSpring software and R programming.ResultsThe outliers were detected and replaced with randomly chosen genes from the 90 % confidence interval of the robust mean for each group. We performed three statistical methods (SAM, LIMMA and t-test) to identify significant genes. There were 19 significant common genes identified from microarray data that have been permutated 100 times namely NOTCH2, ITPRIP, FRMD6, GFRA4, OSBPL9, CPXCR1, SORCS2, PDC, C12orf66, SLC38A9, OR10H5, TRIP13, MRPL52, DUSP21, BRCA1, ELTD1, SPG7, LASS6 and DUOX2. This 19-gene signature was able to significantly predict the survival of patients with colorectal cancer compared to the conventional Dukes' classification in both training and test sets (p < 0.05). The performance of this signature was further validated as a significant independent predictor of survival using patient cohorts from Australia (n = 185), USA (n = 114), Denmark (n = 37) and Norway (n = 95) (p < 0.05). Validation using quantitative PCR confirmed similar expression pattern for the six selected genes.ConclusionProfiling of these 19 genes may provide a more accurate method to predict survival of patients with colorectal cancer and assist in identifying patients who require more intensive treatment.
| S-EPMC5016995 | biostudies-literature
Project description:BackgroundAn artificial intelligence method could accelerate the clinical implementation of tumour-stroma ratio (TSR), which has prognostic relevance in colorectal cancer (CRC). We, therefore, developed a deep learning model for the fully automated TSR quantification on routine haematoxylin and eosin (HE) stained whole-slide images (WSI) and further investigated its prognostic validity for patient stratification.MethodsWe trained a convolutional neural network (CNN) model using transfer learning, with its nine-class tissue classification performance evaluated in two independent test sets. Patch-level segmentation on WSI HE slides was performed using the model, with TSR subsequently derived. A discovery (N=499) and validation cohort (N=315) were used to evaluate the prognostic value of TSR for overall survival (OS).FindingsThe CNN-quantified TSR was a prognostic factor, independently of other clinicopathologic characteristics, with stroma-high associated with reduced OS in the discovery (HR 1.72, 95% CI 1.24-2.37, P=0.001) and validation cohort (2.08, 1.26-3.42, 0.004). Integrating TSR into a Cox model with other risk factors showed improved prognostic capability.InterpretationWe developed a deep learning model to quantify TSR based on histologic WSI of CRC and demonstrated its prognostic validity for patient stratification for OS in two independent CRC patient cohorts. This fully automatic approach allows for the objective and standardised application while reducing pathologists' workload. Thus, it can potentially be of significant aid in clinical prognosis prediction and decision-making.FundingNational Key Research and Development Program of China, National Science Fund for Distinguished Young Scholar, and National Science Foundation for Young Scientists of China.
| S-EPMC7648125 | biostudies-literature
Project description: Not available
| S-EPMC7418800 | biostudies-literature