Long non-coding RNAs in cutaneous melanoma : clinical perspectives

Metastatic melanoma of the skin has a high mortality despite the recent introduction of targeted therapy and immunotherapy. Long non-coding RNAs (lncRNAs) are defined as transcripts of more than 200 nucleotides in length that lack protein-coding potential. There is growing evidence that lncRNAs play an important role in gene regulation, including oncogenesis. We present 13 lncRNA genes involved in the pathogenesis of cutaneous melanoma through a variety of pathways and molecular interactions. Some of these lncRNAs are possible biomarkers or therapeutic targets for malignant melanoma

Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

Optimizing multi-dimensional terahertz imaging analysis for colon cancer diagnosis

Peer reviewedPublisher PD

Cellular interactions in the tumor microenvironment: the role of secretome

Over the past years, it has become evident that cancer initiation and progression depends on several components of the tumor microenvironment, including inflammatory and immune cells, fibroblasts, endothelial cells, adipocytes, and extracellular matrix. These components of the tumor microenvironment and the neoplastic cells interact with each other providing pro and antitumor signals. The tumor-stroma communication occurs directly between cells or via a variety of molecules secreted, such as growth factors, cytokines, chemokines and microRNAs. This secretome, which derives not only from tumor cells but also from cancer-associated stromal cells, is an important source of key regulators of the tumorigenic process. Their screening and characterization could provide useful biomarkers to improve cancer diagnosis, prognosis, and monitoring of treatment responses.Agência financiadora Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) FAPESP 10/51168-0 12/06048-2 13/03839-1 National Council for Scientific and Technological Development (CNPq) CNPq 306216/2010-8 Fundacao para a Ciencia e a Tecnologia (FCT) UID/BIM/04773/2013 CBMR 1334info:eu-repo/semantics/publishedVersio

Quantification and expert evaluation of evidence for chemopredictive biomarkers to personalize cancer treatment.

Predictive biomarkers have the potential to facilitate cancer precision medicine by guiding the optimal choice of therapies for patients. However, clinicians are faced with an enormous volume of often-contradictory evidence regarding the therapeutic context of chemopredictive biomarkers.We extensively surveyed public literature to systematically review the predictive effect of 7 biomarkers claimed to predict response to various chemotherapy drugs: ERCC1-platinums, RRM1-gemcitabine, TYMS-5-fluorouracil/Capecitabine, TUBB3-taxanes, MGMT-temozolomide, TOP1-irinotecan/topotecan, and TOP2A-anthracyclines. We focused on studies that investigated changes in gene or protein expression as predictors of drug sensitivity or resistance. We considered an evidence framework that ranked studies from high level I evidence for randomized controlled trials to low level IV evidence for pre-clinical studies and patient case studies.We found that further in-depth analysis will be required to explore methodological issues, inconsistencies between studies, and tumor specific effects present even within high evidence level studies. Some of these nuances will lend themselves to automation, others will require manual curation. However, the comprehensive cataloging and analysis of dispersed public data utilizing an evidence framework provides a high level perspective on clinical actionability of these protein biomarkers. This framework and perspective will ultimately facilitate clinical trial design as well as therapeutic decision-making for individual patients

The use of knowledge discovery databases in the identification of patients with colorectal cancer

Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000.Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country.Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer.This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection.A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP.Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes.The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7.The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients

A text mining based approach for biomarker discovery

Dissertação de mestrado em BioinformáticaBiomarkers have long been heralded as potential motivators for the emergence of new treatment and diagnostic procedures for disease conditions. However, for many years, the biomarker discovery process could only be achieved through experimental means, serving as a deterrent for their increase in popularity as the usually large number of candidates resulted in a costly and time-consuming discovery process. The increase in computational capabilities has led to a change in the paradigm of biomarker discovery, migrating from the clinical laboratory to in silico environments. Furthermore, text mining, the act of automatically extracting information from text through computational means, has seen a rise in popularity in the biomedical fields. The number of studies and clinical trials in these fields has greatly increased in the past years, making the task of manually examining and annotating these, at the very least, incredibly cumbersome. Adding to this, even though the development of efficient and thorough natural language processing is still an on-going process, the potential for the discovery of common reported and hidden behaviours in the scientific literature is too high to be ignored. Several tools, technologies, pipelines and frameworks already exist capable of, at least, giving a glimpse on how the analysis of the available pile of scientific literature can pave the way for the development of novel medical techniques that might help in the prevention, diagnostic and treatment of diseases. As such, a novel approach is presented in this work for achieving biomarker discov ery, one that integrates both gene-disease associations extracted from current biomedical literature and RNA-Seq gene expression data in an L1-regularization mixed-integer linear programming model for identifying potential biomarkers, potentially providing an optimal and robust genetic signature for disease diagnostic and helping identify novel biomarker candidates. This analysis was carried out on five publicly available RNA-Seq datasets ob tained from the Genomic Data Commons Data Portal, related to breast, colon, lung and prostate cancer, and head and neck squamous cell carcinoma. Hyperparameter optimiza tion was also performed for this approach, and the performance of the optimal set of pa rameters was compared against other machine learning methods.Os biomarcadores há muito que são considerados como os motivadores principais para o desenvolvimento de novos procedimentos de diagnóstico e tratamento de doenças. No entanto, ate há relativamente pouco tempo, o processo de descoberta de biomarcadores estava dependente de métodos experimentais, sendo este um elemento dissuasor da sua aplicação e estudo em massa dado que o número elevado de candidatos implicava um processo de averiguação extremamente dispendioso e demorado. O grande aumento do poder computacional nas últimas décadas veio contrariar esta tendência, levando a migração do processo de descoberta de biomarcadores do laboratório para o ambiente in silico. Para além disso, a aplicação de processos de mineração de textos, que consistem na extração de informação de documentos através de meios computacionais, tem visto um aumento da sua popularidade na comunidade biomédica devido ao aumento exponencial do número de estudos e ensaios clínicos nesta área, tornando todo o processo de analise e anotação manual destes bastante laborioso. A adicionar a isto, apesar do desenvolvimento de métodos eficientes capazes de processar linguagem natural na sua plenitude seja um processo que ainda esteja a decorrer, o potencial para a descoberta de comportamentos reportados e escondidos na literatura e demasiado elevado para ser ignorado. Já existem diversas ferramentas e tecnologias capazes de, pelo menos, dar uma indicação de como a análise da literatura científica disponível pode abrir o caminho para o desenvolvimento de novas técnicas e procedimentos médicos que poder ao auxiliar na prevenção, diagnóstico e tratamento de doenças. Como tal, e apresentado neste trabalho um novo método para realizar a descoberta de biomarcadores, que considera simultaneamente associações entre genes e doenças, já extraídas da literatura biomédica e dados de expressão de genes RNA-Seq num modelo de otimização linear com regularização L1 com variáveis contínuas e inteiras (MILP) para identificar possíveis biomarcadores, sendo capaz potencialmente de providenciar assinaturas genéticas ótimas e robustas para o diagnostico de doenças e ajudar a identificar novos candidatos a biomarcador. Esta análise foi levada a cabo em cinco conjuntos de dados RNA-Seq obtidos através do Portal de Dados do Genomic Data Commons (GDC) relacionados com os cancros da mama, colon, pulmão, próstata, e carcinoma escamoso da cabeça e pescoço. Realizou-se também uma otimização dos hiperparâmetros deste método, e o desempenho do conjunto ideal de parâmetros foi comparado com o de outros métodos de aprendizagem máquina

Clinical decision support system, a potential solution for diagnostic accuracy improvement in oral squamous cell carcinoma: A systematic review

BACKGROUND AND AIM: Oral squamous cell carcinoma (OSCC) is a rapidly progressive disease and despite the progress in the treatment of cancer, remains a life-threatening illness with a poor prognosis. Diagnostic techniques of the oral cavity are not painful, non-invasive, simple and inexpensive methods. Clinical decision support systems (CDSSs) are the most important diagnostic technologies used to help health professionals to analyze patients’ data and make decisions. This paper, by studying CDSS applications in the process of providing care for the cancer patients, has looked into the CDSS potentials in OSCC diagnosis. METHODS: We retrieved relevant articles indexed in MEDLINE/PubMed database using high-quality keywords. First, the title and then the abstract of the related articles were reviewed in the step of screening. Only research articles which had designed clinical decision support system in different stages of providing care for the cancer patient were retained in this study according to the input criteria. RESULTS: Various studies have been conducted about the important roles of CDSS in health processes related to different types of cancer. According to the aim of studies, we categorized them into several groups including treatment, diagnosis, risk assessment, screening, and survival estimation. CONCLUSION: Successful experiences in the field of CDSS applications in different types of cancer have indicated that machine learning methods have a high potential to manage the data and diagnostic improvement in OSCC intelligently and accurately. KEYWORDS: Squamous Cell Carcinoma; Clinical Decision Support System; Neoplasm; Dental Informatic