scholarly journals Differentiating TP53 Mutation Status in Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI-Derived Radiomics

2021 ◽  
Vol 11 ◽  
Author(s):  
Jing Gao ◽  
Xiahan Chen ◽  
Xudong Li ◽  
Fei Miao ◽  
Weihuan Fang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tp53 Mutation ◽  
Curve Analysis ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Support Vector ◽  
Two Dimensional ◽  
Decision Curve Analysis ◽  
Mutation Status ◽  
Tp53 Status

ObjectivesThis study assessed the preoperative prediction of TP53 status based on multiparametric magnetic resonance imaging (mpMRI) radiomics extracted from two-dimensional (2D) and 3D images.Methods57 patients with pancreatic cancer who underwent preoperative MRI were included. The diagnosis and TP53 gene test were based on resections. Of the 57 patients included 37 mutated TP53 genes and the remaining 20 had wild-type TP53 genes. Two radiologists performed manual tumour segmentation on seven different MRI image acquisition sequences per patient, including multi-phase [pre-contrast, late arterial phase (ap), portal venous phase, and delayed phase] dynamic contrast enhanced (DCE) T1-weighted imaging, T2-weighted imaging (T2WI), Diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC). PyRadiomics-package was used to generate 558 two-dimensional (2D) and 994 three-dimensional (3D) image features. Models were constructed by support vector machine (SVM) for differentiating TP53 status and DX score method were used for feature selection. The evaluation of the model performance included area under the curve (AUC), accuracy, calibration curves, and decision curve analysis.ResultsThe 3D ADC-ap-DWI-T2WI model with 11 selected features yielded the best performance for differentiating TP53 status, with accuracy = 0.91 and AUC = 0.96. The model showed the good calibration. The decision curve analysis indicated that the radiomics model had clinical utility.ConclusionsA non-invasive and quantitative mpMRI-based radiomics model can accurately predict TP53 mutation status in pancreatic cancer patients and contribute to the precision treatment.

Determination of Both TP53 Mutation Status and the Amount of p53 Protein Has Limited Diagnostic Importance for Patients with Ovarian Cancer

2022 ◽  
Vol 29 ◽  
Author(s):  
Sebastian M. Klein ◽  
Maria Bozko ◽  
Astrid Toennießen ◽  
Nisar P. Malek ◽  
Przemyslaw Bozko
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Tp53 Mutation ◽  
P53 Protein ◽  
Mutation Status ◽  
Tumor Tissues ◽  
Tp53 Status ◽  
Diagnostic Importance

Background: Ovarian cancer is one of the most aggressive types of gynecologic cancers. Many patients have a relapse within two years after diagnosis and subsequent therapy. Among different genetic changes generally believed to be important for the development of cancer, TP53 is the most common mutation in the case of ovarian tumors. Objective: Our work aims to compare the outcomes of different comparisons based on the overall survival of ovarian cancer patients, determination of TP53 status, and amount of p53 protein in tumor tissues. Methods: We analyzed and compared a collective of 436 ovarian patient’s data. Extracted data include TP53 mutation status, p53 protein level, and information on the overall survival. Values for p53 protein level in dependence of TP53 mutation status were compared using the Independent-Samples t-Test. Survival analyses were displayed by Kaplan-Meier plots, using the log-rank test to check for statistical significance. Results: We have not found any statistically significant correlations between determination of TP53 status, amount of p53 protein in tumor tissues, and overall survival of ovarian cancer patients. Conclusion: In ovarian tumors both determination of TP53 status as well as p53 protein amount has only limited diagnostic importance.

scholarly journals Prediction and clinical utility of a contralateral breast cancer risk model

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniele Giardiello ◽  
Ewout W. Steyerberg ◽  
Michael Hauptmann ◽  
Muriel A. Adank ◽  
Delal Akdeniz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Germline Mutation ◽  
Clinical Utility ◽  
Contralateral Breast Cancer ◽  
Contralateral Breast ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Mutation Status

Abstract Background Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52–0.74; at 10 years, 0.53–0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62–1.37), and the calibration slope was 0.90 (95% PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4–10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

scholarly journals Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249885
Author(s):  
Toshitaka Sugawara ◽  
Daisuke Ban ◽  
Jo Nishino ◽  
Shuichi Watanabe ◽  
Aya Maekawa ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Unmet Need ◽  
Early Recurrence ◽  
Curve Analysis ◽  
Elastic Net ◽  
High Rate ◽  
Ductal Adenocarcinoma ◽  
Inflammatory Factors ◽  
Decision Curve Analysis ◽  
Preoperative Factors

Background Even 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. Methods Data 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. Results A 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. Conclusion A model using multiple preoperative factors can improve prediction of early resectable PDAC recurrence.

scholarly journals Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wilson Bakasa ◽  
Serestina Viriri
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cancer Survival ◽  
State Of The Art ◽  
The State ◽  
Ductal Adenocarcinoma ◽  
Support Vector ◽  
Survival Prediction ◽  
Use Of Resources

Cancer early detection increases the chances of survival. Some cancer types, like pancreatic cancer, are challenging to diagnose or detect early, and the stages have a fast progression rate. This paper presents the state-of-the-art techniques used in cancer survival prediction, suggesting how these techniques can be implemented in predicting the overall survival of pancreatic ductal adenocarcinoma cancer (pdac) patients. Because of bewildering and high volumes of data, the recent studies highlight the importance of machine learning (ML) algorithms like support vector machines and convolutional neural networks. Studies predict pancreatic ductal adenocarcinoma cancer (pdac) survival is within the limits of 41.7% at one year, 8.7% at three years, and 1.9% at five years. There is no significant correlation found between the disease stages and the overall survival rate. The implementation of ML algorithms can improve our understanding of cancer progression. ML methods need an appropriate level of validation to be considered in everyday clinical practice. The objective of these techniques is to perform classification, prediction, and estimation. Accurate predictions give pathologists information on the patient’s state, surgical treatment to be done, optimal use of resources, individualized therapy, drugs to prescribe, and better patient management.

Frequency of longitudinal changes in TP53 mutation status from gene sequencing of serial tumor biopsies from a large cohort of cancer patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3124-3124
Author(s):  
D. Allen Allen Annis ◽  
Dean C. Pavlick ◽  
Garrett M. Frampton ◽  
Lee A. Albacker ◽  
Vojislav M. Vukovic ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Tp53 Mutation ◽  
Gene Sequencing ◽  
Large Cohort ◽  
Cancer Type ◽  
Tumor Biopsy ◽  
Assay Sensitivity ◽  
Mutation Status ◽  
Tumor Biopsies ◽  
Tp53 Status

3124 Background: The p53 pathway is one of the most important in cancer biology, with mutation of the TP53 gene that encodes the p53 tumor suppressor protein observed in ≈50% of all cancers. We evaluated the frequency of changes in TP53 mutation status in a large cohort of serial tumor biopsies. Methods: From a database of >200,000 next-generation gene sequencing results we identified 16,592 samples arising from repeat biopsies from 7840 patients (pts), average 2.12 per pt, 1007 pts with ≥3, max 11; over an interval up to 234 months (mos), average 11.0 mos. TP53 mutations with known or unknown significance in successive biopsies and changes in assignment from TP53-Wild-Type (WT) to Mutant (Mut), or Mut to WT, were evaluated vs. cancer type and time between biopsies. Results: Table: N (%) of samples vs. change in TP53 status from previous biopsy vs. mos from initial biopsy in all samples (7840 initial + 8752 successive, 46% TP53-Mut) and the three most represented cancers: non-small cell lung cancer (NSCLC, 1189 initial + 1268 successive, 60% Mut), breast (947 initial + 993 successive, 55% Mut), multiple myeloma (MM, 578 initial + 981 successive, 18% Mut). Conclusions: Changes in TP53 status were rare (<10% of samples). Differences may occur in serial biopsy samples for pathophysiological reasons, e.g., a mutant clone becoming dominant and/or heterogeneity at different tumor biopsy sites, or analytical differences in biopsy tumor content or assay sensitivity between samples. In this analysis, WT-to-Mut changes were more frequent (5.9%) than Mut-to-WT changes (3.3%), suggesting a small selection pressure for TP53 alterations later in oncogenesis and indicating that these alterations are truncal. Mut-to-WT changes are not readily explained physiologically and may suggest these infrequent changes are mostly due to sampling or analytical variability, and genuine changes in TP53 mutation status are quite rare.[Table: see text]

Effect of TP53 mutation status on survival in the MAGIC trial.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 71-71 ◽  
Author(s):  
Elizabeth Catherine Smyth ◽  
Sanna Hulkki Wilson ◽  
Matthew Guy Nankivell ◽  
David Gonzalez de Castro ◽  
Andrew Wotherspoon ◽  
...  
Keyword(s):  
Genomic Dna ◽  
Tp53 Mutation ◽  
Pcr Amplification ◽  
Ffpe Tissue ◽  
Degraded Dna ◽  
P Value ◽  
Sequencing Analysis ◽  
Mutation Status ◽  
Tp53 Mutations ◽  
Tp53 Status

71 Background: In oesophagogastric cancer TP53 mutation may be prognostic and/or predict for chemoresistance, however available data are conflicting. We hypothesised that TP53 status would impact on survival for patients (pts) randomised to surgery alone or perioperative ECF chemotherapy in the MRC MAGIC trial. Methods: Genomic DNA FFPE tissue sections were extracted with QIAmp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). Mutations in exons 4-9 were screened for by fluorescent PCR -amplification of genomic DNA, followed by Capillary Electrophoresis-Single Strand Conformational Analysis (CE-SSCA). Mutations were characterised by bi -directional Sanger sequencing analysis performed on an independent PCR -reaction and the sequencing results were compared to the reference sequences in the COSMIC database. TP53 status was correlated with demographics and survival. Results: TP53 results were available on 154 pts (50% of pts with available DNA). The remainder failed CE-SSCA due to degraded DNA. Pts with/without TP53 data had similar demographics and survival. TP53 mutation was detected in 51/154 (33%) samples. Exon 4, 5, 6, 7 and 8 mutations occurred in 4%, 42%, 8%, 36% and 10% of specimens with a TP53 result. Pts with TP53 mutations were more likely to have an oesophageal or junctional tumor (vs. gastric) than TP53 wild type (WT) pts (38% vs. 17% respectively p = 0.016). Survival from surgery was comparable for pts with mutant and WT TP53 tumors in both arms of the trial (See Table). P-value for interaction between treatment group and TP53 status was 0.776, indicating no interaction was present. Conclusions: In the MAGIC trial, TP53 mutation status was not prognostic, and did not predict for lack of benefit from chemotherapy. Further study is required in order to evaluate the effect of differing TP53 mutations on treatment and survival. [Table: see text]

scholarly journals Subgroup-Specific Prognostic Implications of TP53 Mutation in Medulloblastoma

2013 ◽  
Vol 31 (23) ◽  
pp. 2927-2935 ◽  
Author(s):  
Nataliya Zhukova ◽  
Vijay Ramaswamy ◽  
Marc Remke ◽  
Elke Pfaff ◽  
David J.H. Shih ◽  
...  
Keyword(s):  
Tp53 Mutation ◽  
Survival Rates ◽  
Prognostic Impact ◽  
Molecular Subgroup ◽  
Mutation Status ◽  
Tp53 Mutations ◽  
Specific Analysis ◽  
Prognostic Implications ◽  
Tp53 Status ◽  
Independent Cohort

Purpose Reports detailing the prognostic impact of TP53 mutations in medulloblastoma offer conflicting conclusions. We resolve this issue through the inclusion of molecular subgroup profiles. Patients and Methods We determined subgroup affiliation, TP53 mutation status, and clinical outcome in a discovery cohort of 397 medulloblastomas. We subsequently validated our results on an independent cohort of 156 medulloblastomas. Results TP53 mutations are enriched in wingless (WNT; 16%) and sonic hedgehog (SHH; 21%) medulloblastomas and are virtually absent in subgroups 3 and 4 tumors (P < .001). Patients with SHH/TP53 mutant tumors are almost exclusively between ages 5 and 18 years, dramatically different from the general SHH distribution (P < .001). Children with SHH/TP53 mutant tumors harbor 56% germline TP53 mutations, which are not observed in children with WNT/TP53 mutant tumors. Five-year overall survival (OS; ± SE) was 41% ± 9% and 81% ± 5% for patients with SHH medulloblastomas with and without TP53 mutations, respectively (P < .001). Furthermore, TP53 mutations accounted for 72% of deaths in children older than 5 years with SHH medulloblastomas. In contrast, 5-year OS rates were 90% ± 9% and 97% ± 3% for patients with WNT tumors with and without TP53 mutations (P = .21). Multivariate analysis revealed that TP53 status was the most important risk factor for SHH medulloblastoma. Survival rates in the validation cohort mimicked the discovery results, revealing that poor survival of TP53 mutations is restricted to patients with SHH medulloblastomas (P = .012) and not WNT tumors. Conclusion Subgroup-specific analysis reconciles prior conflicting publications and confirms that TP53 mutations are enriched among SHH medulloblastomas, in which they portend poor outcome and account for a large proportion of treatment failures in these patients.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

scholarly journals Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

2015 ◽  
Vol 143 (11-12) ◽  
pp. 681-687 ◽  
Author(s):  
Tomislav Pejovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Bile Duct ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Open Cholecystectomy ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Scoring Model ◽  
Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

scholarly journals Chylous Ascites, Unusual Association with Ductal Pancreatic Adenocarcinoma with Plasmacytoid Morphology: A Case Report and Literature Review

2021 ◽  
Vol 07 (03) ◽  
pp. e158-e162
Author(s):  
Catalin Bogdan Satala ◽  
Ioan Jung ◽  
Tivadar Jr. Bara ◽  
Vlad Tudorache ◽  
Simona Gurzu
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Cells ◽  
Surgical Intervention ◽  
Chylous Ascites ◽  
Distal Portion ◽  
Ductal Adenocarcinoma ◽  
Lymph Vessels ◽  
Lymphangiosis Carcinomatosa ◽  
Ductal Pancreatic Adenocarcinoma ◽  
Tumor Emboli

AbstractChylous ascites represents a relatively uncommon condition. In this paper, we present a case of chyloperitoneum associated with pancreatic ductal adenocarcinoma (PDAC) and a review of literature regarding chylous ascites. A 76-year-old male patient was admitted in emergency department with acute abdomen. A pancreatic cancer was suspected. Subtotal spleno-pancreatectomy, for a nodular mass infiltrating the mild and distal portion of the pancreas, was necessary. During surgical intervention in the peritoneal cavity, a moderate quantity of whitish and thick consistency fluid with milk-like appearance was observed to be accumulated. After examination of the fluid, chyloperitoneum was diagnosed. The histologic examination showed a PDAC, with multiple emboli in lymph vessels, with tumor cells with plasmacytoid morphology, diagnosed as lymphangiosis carcinomatosa. The patient died at 3 weeks after surgical intervention. In patients with pancreatic cancer and chylous ascites, suspicion of tumor-related blockage of the lymphatic flow should be suspected. Prognosis of PDAC should be evaluated not only based on the number of lymph node metastases, but also considering the number of lymph vessels with tumor emboli and the architecture of tumor cells. This is the first reported case of a PDAC with plasmacytoid morphology of lymphangiosis carcinomatosa.

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