Computational and systems oncology最新文献

{"title":"Bayesian competing risk analysis: An application to nasopharyngeal carcinoma patients data","authors":"Rakesh Kumar Saroj,&nbsp;K. Narasimha Murthy,&nbsp;Mukesh Kumar,&nbsp;Atanu Bhattacharjee,&nbsp;Kamalesh Kumar Patel","doi":"10.1002/cso2.1006","DOIUrl":"10.1002/cso2.1006","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The Cox proportional hazard (CPH) model is normally used to study the death event data. The presence of competing risk (CR) is often encountered in health data, hence it becomes difficult to manage time to event data in clinical study. Bayesian approach is considered to manage the CR events in clinical data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The objective of study is to find the predictors associated with overall survival of nasopharyngeal carcinoma (NPC) patients. Further, our purpose is to use a Bayesian model that can analyze time to event data in the presence of CR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Total 245 patients with NPC were taken (https://www.ncbi.nlm.nih.gov/geo/). The sociodemographic and clinical variables were considered for analysis purposes. R software and openBUGS were used to overcome the computational problems of CPH and Bayesian models. The Markov chain Monte Carlo (MCMC) method was used to compute the regression coefficients of Bayesian model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The study shows that among NPC patients, the covariates chemotherapy, smoking, N-stage, and tumor site are associated with the higher risk for the deaths occurring in the cancer patients. The posterior mean estimates of proposed Bayesian model for significant factors have been obtained. The posterior mean and standard deviation estimates help to improve the survival of patients in the presence of CR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>It is very difficult to use the CR model with Bayesian approach in health research for nonstatistical researcher due to lack of information. This paper is dedicated to the application of Bayesian approach for CR analysis on NPC data.</p>\u0000 </section>\u0000 </div>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"111288474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational and systems biology of cancer","authors":"David Dingli MD, PhD","doi":"10.1002/cso2.1005","DOIUrl":"https://doi.org/10.1002/cso2.1005","url":null,"abstract":"<p>Ever since the war on cancer was declared in 1971, there has been an explosion in our understanding of this diverse group of diseases. The application of molecular genetics and molecular biology technologies have enabled a deep understanding of the genetic, epigenetic, signaling cascades, survival pathways, and invasive mechanisms that underlie the cancer phenotype [<span>1, 2</span>]. Concomitantly this has translated in the development of ever more effective and safe medications that work through different mechanisms of action and target fundamental aspects of the biology of the tumor. The paradigm has been chronic myeloid leukemia where the discovery of the Philadelphia chromosome [<span>3</span>] ultimately led to the identification of the <i>BCR-ABL</i> oncogene and the development of tyrosine kinase inhibitors such as imatinib, nilotinib, dasatinib, and so on led to rapid, deep, and long-lasting remissions in this disease [<span>4-6</span>]. Another success story has been acute promyelocytic leukemia with the vast majority of patients now being cured of the disease without the need for any classical chemotherapy [<span>7</span>].</p><p>The rapid development of deep sequencing technologies has enabled the discovery of multiple mutations and a deeper understanding of the complex “structure” of the tumor as being composed of multiple subclones that are competing with each other for resources [<span>8-15</span>]. The subclones are being selected for or against by therapy [<span>16</span>]. Principles from evolutionary biology have been applied to understand the dynamics of how these clones change in time [<span>16, 17</span>]. It appears that in the absence of therapy, neutral evolution is very important for the development of the tumor [<span>18</span>], but in the presence of therapy, the potential fitness advantage of resistant clones dominates. The identification of a specific tumor sequence also enables monitoring of patients using simple blood tests (liquid biopsy) [<span>19</span>] for the presence of disease and its burden and perhaps will be used in the future to screen people for premalignant or early malignant processes.</p><p>Naturally over the years, a major focus has been on the tumor cells themselves leading to major advances in understanding of signaling pathways that are critical for tumor cell replication, growth, survival, and cell cycle regulation. This leads to the discovery of important pathways such as the Janus kinases/signal transducer and activator of transcription proteins (JAK/STAT), phosphoinositide-3 (PI-3) kinase, Protein kinase B (AKT), and receptor tyrosine kinases (RTK) [<span>1, 2</span>]. All of this knowledge has been translated into effective therapies for a wide variety of tumors including myeloproliferative neoplasms, hepatocellular carcinoma, renal cell carcinoma, nonsmall cell lung cancer, and so on. The discovery of potent anti-apoptotic mechanisms that are overexpressed in tumor cells has led to th","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137662588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational and systems biology of cancer","authors":"D. Dingli","doi":"10.22541/au.159592715.53358895","DOIUrl":"https://doi.org/10.22541/au.159592715.53358895","url":null,"abstract":"Ever since the war on cancer was declared in 1971, there has been an explosion in our understanding of this diverse group of diseases. The application of molecular genetics and molecular biology technologies have enabled a deep understanding of the genetic, epigenetic, signaling cascades, survival pathways, and invasive mechanisms that underlie the cancer phenotype. Concomitantly this has translated in the development of ever more effective and safe medications that work through different mechanisms of action and target fundamental aspects of the biology of the tumor. The paradigm has been chronic myeloid leukemia where the discovery of the Philadelphia chromosome, ultimately led to the identification of the BCR-ABL oncogene and the development of tyrosine kinase inhibitors such as imatinib, nilotinib, dasatinib and others and lead to rapid, deep and long-lasting remissions in this disease. Another success story has been acute promyelocytic leukemia with the vast majority of patients now being cured of the disease without the need for any classical chemotherapy.","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45010638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}