Similar Datasets
Project description:Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I-IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.
| S-EPMC5668084 | biostudies-literature
Project description:Gene expression profiling of 273 pre-treatment endoscopic FFPE oesophageal and oesophago-gastric junctional adenocarcinomas for validation of a DNA Damage Response Deficiency Assay. All patients were treated with neo-adjuvant chemotherapy followed by surgical resection and were collected at four UK centres in the Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) consortium between 2004 and 2013. Biopsies were reviewed for pathological subtype prior to marking for macrodissection and samples containing at least 50% adenocarcinoma tissue by area were taken forward. Total RNA was extracted using the RecoverallTM Total Nucleic Acid Isolation Kit for FFPE (Thermo Fisher Scientific, Waltham, MA) and amplified using the NuGen Ovation FFPE Amplification System v3 (NuGen San Carlos, CA). The amplified product was hybridized to the Almac Diagnostics Xcel™ array (Almac, Craigavon, United Kingdom), a cDNA microarray-based technology optimized for archival FFPE tissue, and analysed using the Affymetrix 7G scanner (Affymetrix, Santa Clara, CA)
2019-01-31 | E-MTAB-6969 | biostudies-arrayexpress
Project description:BackgroundEven after curative resection, pancreatic ductal adenocarcinoma (PDAC) patients suffer a high rate of recurrence. There is an unmet need to predict which patients will experience early recurrence after resection in order to adjust treatment strategies.MethodsData of patients with resectable PDAC undergoing surgical resection between January 2005 and September 2018 were reviewed to stratify for early recurrence defined as occurring within 6 months of resection. Preoperative data including demographics, tumor markers, blood immune-inflammatory factors and clinicopathological data were examined. We employed Elastic Net, a sparse modeling method, to construct models predicting early recurrence using these multiple preoperative factors. As a result, seven preoperative factors were selected: age, duke pancreatic monoclonal antigen type 2 value, neutrophil:lymphocyte ratio, systemic immune-inflammation index, tumor size, lymph node metastasis and is peripancreatic invasion. Repeated 10-fold cross-validations were performed, and area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate the usefulness of the models.ResultsA total of 136 patients was included in the final analysis, of which 35 (34%) experienced early recurrence. Using Elastic Net, we found that 7 of 14 preoperative factors were useful for the predictive model. The mean AUC of all models constructed in the repeated validation was superior to the standard marker CA 19-9 (0.718 vs 0.657), whereas the AUC of the model constructed from the entire patient cohort was 0.767. Decision curve analysis showed that the models had a higher mean net benefit across the majority of the range of reasonable threshold probabilities.ConclusionA model using multiple preoperative factors can improve prediction of early resectable PDAC recurrence.
| S-EPMC8041173 | biostudies-literature
Project description:DNA methylation arrays for 8 Surgical specimens(DNA extracted from FFPE slides) after cetuximab treatment
2020-01-17 | GSE110995 | GEO
Project description:Background and objectivesNeoadjuvant chemotherapy (NAT) for pancreatic adenocarcinoma (PDAC) is increasingly being utilized. However, a significant number of patients will experience early recurrence, possibly negating the benefit of surgery. We aimed to identify factors implicated in early disease recurrence.MethodsA retrospective review of pancreaticoduodenectomies performed between 2005 and 2017 at our institution for PDAC following NAT was performed. A 6-month cut-off was used to stratify patients into early/late recurrence groups. Multivariate analysis was performed to identify predictors of recurrence.ResultsOf 273 patients, 64 (23%) developed early recurrence or died within 90 days of surgery. The median time to recurrence was 4 months (95% confidence interval [CI]: 2.2-4.3) in the early group versus 16 months (95% CI: 13.7-19.9) in the late group. The former had higher baseline and post-NAT Ca19-9 levels than the latter (472 vs. 153 IU/ml, p = 0.001 and 71 vs. 39 IU/ml, p = 0.005, respectively). A higher positive lymph node ratio significantly increased the risk of early recurrence (hazard ratio [HR]: 15.9, p < 0.001) while adjuvant chemotherapy was protective (HR: 0.4, p < 0.001).ConclusionOur findings acknowledge the limitations of clinically measured factors used to ascertain response to NAT and underline the need for individualized molecular markers that take into consideration the specific tumor biology.
| S-EPMC8653430 | biostudies-literature
Project description:Despite surgical treatment of stage I non-small cell lung cancer (NSCLC), one third of patients will eventually have a recurrence. Robust prognostic markers are required to better manage therapy options. MicroRNAs play important roles in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina). The expression differences between cancer subtypes were compared by t tests. The association of miRNA expression profile with recurrence free survival (RFS) was assessed using partial Cox regression models. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 32 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype; while the second containing 27 miRNAs was adenocarcinoma specific. Both of them were validated using FFPE and/or fresh frozen tissues in independent data set with 170 stage I patients. This has important prognostic or therapeutic implications for the management of patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina).
2012-03-26 | E-GEOD-29135 | biostudies-arrayexpress
Project description:This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple instance learning with final FC layer's features from a model from all patches. Analysis of the clinicopathological and genetic characteristics of the model-based risk group was performed. For predicting recurrence, the model had an area under the curve score of 0.763 with 0.750, 0.633 and 0.680 of sensitivity, specificity, and accuracy in the test set, respectively. High-risk cases for recurrence predicted by the model (HR group) were significantly associated with shorter recurrence-free survival and a higher stage (both, p < 0.001). The HR group was associated with specific histopathological features such as poorly differentiated components, complex glandular pattern components, tumor spread through air spaces, and a higher grade. In the HR group, pleural invasion, necrosis, and lymphatic invasion were more frequent, and the size of the invasion was larger (all, p < 0.001). Several genetic mutations, including TP53 (p = 0.007) mutations, were more frequently found in the HR group. The results of stages I-II were similar to those of the general cohort. DL-based model can predict the recurrence risk of LUAD and identify the presence of the TP53 gene mutation by analyzing histopathologic features.
| S-EPMC10944489 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a disease with a very unfavorable prognosis. Surgical resection represents the only potentially curative treatment option, but recurrence after complete resection is almost certain. In an exploratory attempt we here aimed at identifying preoperative plasma protein biomarkers with the potential to predict early recurrence after resection of PDAC. Peripheral blood samples from 14 PDAC patients divided into three groups according to their time to tumor recurrence after curatively intended resection (early: < 6 months, medium: 6-12 months, late: > 12 months) underwent targeted proteome analysis. Proteins most strongly discriminating early and late recurrence were then examined in a number of established PDAC cell lines and their culture supernatants. Finally, PDAC organoid lines from primary tumors of patients with early and late recurrence were analyzed for confirmation and validation of results. In total, 23 proteins showed differential abundance in perioperative plasma from PDAC patients with early recurrence when compared to patients with late recurrence. Following confirmation of expression on a transcriptional and translational level in PDAC cell lines we further focused on three upregulated (MAEA, NT5E, AZU1) and two downregulated proteins (ATP6AP2, MICA). Increased expression of NT5E was confirmed in a subset of PDAC organoid cultures from tumors with early recurrence. MICA expression was heterogeneous and ATP6AP2 levels were very similar in both organoids from early and late recurrent tumors. Most strikingly, we observed high MAEA expression in all tested PDAC (n = 7) compared to a non-cancer ductal organoid line. MAEA also demonstrated potential to discriminate early recurrence from late recurrence PDAC organoids. Our study suggests that identification of plasma protein biomarkers released by tumor cells may be feasible and of value to predict the clinical course of patients. Prediction of recurrence dynamics would help to stratify up-front resectable PDAC patients for neoadjuvant chemotherapy approaches in an individualized fashion. Here, MAEA and NT5E were the most promising candidates for further evaluation.
| S-EPMC8021576 | biostudies-literature
Project description:Despite surgical treatment of stage I non-small cell lung cancer (NSCLC), one third of patients will eventually have a recurrence. Robust prognostic markers are required to better manage therapy options. MicroRNAs play important roles in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina). The expression differences between cancer subtypes were compared by t tests. The association of miRNA expression profile with recurrence free survival (RFS) was assessed using partial Cox regression models. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 32 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype; while the second containing 27 miRNAs was adenocarcinoma specific. Both of them were validated using FFPE and/or fresh frozen tissues in independent data set with 170 stage I patients. This has important prognostic or therapeutic implications for the management of patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease.
2012-03-27 | GSE29135 | GEO
Project description:BackgroundThe high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters.MethodsDatasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk.FindingsThe developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set.InterpretationThis study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies.FundingThis work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].
| S-EPMC8649647 | biostudies-literature