logo
    Targeted next generation sequencing of endoscopic ultrasound acquired cytology from ampullary and pancreatic adenocarcinoma has the potential to aid patient stratification for optimal therapy selection
    93
    Citation
    27
    Reference
    10
    Related Paper
    Citation Trend
    Abstract:
    // Ferga C. Gleeson 1 , Sarah E. Kerr 2 , Benjamin R. Kipp 2 , Jesse S. Voss 2 , Douglas M. Minot 2 , Zheng Jin Tu 3 , Michael R. Henry 2 , Rondell P. Graham 2 , George Vasmatzis 4 , John C. Cheville 4 , Konstantinos N. Lazaridis 1, 4 , Michael J. Levy 1 1 Division of Gastroenterology & Hepatology, Mayo Clinic Rochester, MN, USA 2 Department of Laboratory Medicine & Pathology, Mayo Clinic Rochester, MN, USA 3 Division of Biomedical Statics & Informatics, Department of Health Sciences Research, Mayo Clinic Rochester, MN, USA 4 Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA Correspondence to: Ferga C. Gleeson, email: gleeson.ferga@mayo.edu Keywords: endoscopic ultrasound fine needle aspiration, pancreatic adenocarcinoma, targeted next-generation sequencing, mutation concordance, personalized medicine Received: April 04, 2016 Accepted: April 24, 2016 Published: May 18, 2016 ABSTRACT Background & Aims: Less than 10% of registered drug intervention trials for pancreatic ductal adenocarcinoma (PDAC) include a biomarker stratification strategy. The ability to identify distinct mutation subsets via endoscopic ultrasound fine needle aspiration (EUS FNA) molecular cytology could greatly aid clinical trial patient stratification and offer predictive markers. We identified chemotherapy treatment naïve ampullary adenocarcinoma and PDAC patients who underwent EUS FNA to assess multigene mutational frequency and diversity with a surgical resection concordance assessment, where available. Methods: Following strict cytology smear screening criteria, targeted next generation sequencing (NGS) using a 160 cancer gene panel was performed. Results: Complete sequencing was achieved in 29 patients, whereby 83 pathogenic alterations were identified in 21 genes. Cytology genotyping revealed that the majority of mutations were identified in KRAS (93%), TP53 (72%), SMAD4 (31%), and GNAS (10%). There was 100% concordance for the following pathogenic alterations: KRAS, TP53, SMAD4, KMT2D, NOTCH2, MSH2, RB1, SMARCA4, PPP2R1A, PIK3R1, SCL7A8, ATM, and FANCD2. Absolute multigene mutational concordance was 83%. Incremental cytology smear mutations in GRIN2A, GATA3 and KDM6A were identified despite re-examination of raw sequence reads in the corresponding resection specimens. Conclusions: EUS FNA cytology genotyping using a 160 cancer gene NGS panel revealed a broad spectrum of pathogenic alterations. The fidelity of cytology genotyping to that of paired surgical resection specimens suggests that EUS FNA represents a suitable surrogate and may complement the conventional stratification criteria in decision making for therapies and may guide future biomarker driven therapeutic development.
    Keywords:
    Endoscopic Ultrasound
    Concordance
    HRAS
    Personalized Medicine
    In today's ever-evolving medical landscape, the groundbreaking concept of precision medicine is revolutionizing the way we approach healthcare. By focusing on the unique characteristics of each patient, precision medicine aims to deliver personalized and targeted treatments that can provide superior outcomes. This personalized healthcare breakthrough has the potential to transform the medical field, ushering in a new era of tailored and effective treatments. Traditional medicine has been largely empirical, where physicians rely on patterns and past experience to diagnose and treat patients. Treatment decisions are often made based on the physician's familiarity with similar cases. In this approach, a single treatment or medication may be prescribed for a
    Personalized Medicine
    Healthcare system
    Cancer is one of the major causes of death by disease and treatment of cancer is one of the most crucial phases of oncology. Precision medicine for cancer treatment is an approach that uses the genetic profile of individual patients. Researchers have not yet discovered all the genetic changes that causes cancer to develop, grow and spread. The Neuro-Genetic model is proposed here for the prediction and recommendation of precision medicine. The proposed work attempts to recommend precision medicine to cancer patients based upon the past genomic data of patient’s survival. The work will employ machine learning (ML) approaches to provide recommendations for different gene expressions. This work can be used in caner hospitals, research institutions for providing personalized treatment to the patient using precision medicine. Precision medicine can even be used to treat other complex diseases like diabetes, dentistry, cardiovascular diseases etc. Precision medicine is the kind of treatment to be offered in the near future.
    Personalized Medicine
    Cancer Treatment
    Cancer Medicine
    genomic medicine
    Targeted therapy is the foundation of personalized medicine in cancer, which is often understood as the right patient using the right drug. Thinking from the viewpoint of determinants during personalized drug treatment, the genetics, epigenetics and metagenomics would provide individual-specific biological elements to characterize the personalized responses for therapy.Such personalized determinants should be not only understood as specific to one person, while they should have certain replicate observations in a group of individuals but not all, which actually provide more credible and reproducible personalized biological features. The requirement of detecting personalized determinants is well supported by novel high-throughput sequencing technologies and newly temporal-spatial experimental protocols, which quickly produce the omics big data.In this mini-review, we would like to give a brief introduction firstly on the advanced drug or drug-like therapy with genetics, epigenetics and metagenomics, respectively, from the viewpoint of personalized determinants; then summarize the computational methods helpful to analyze the corresponding omics data under the consideration of personalized biological context; and particularly focus on metagenomics to discuss current data, method, and opportunity for personalized medicine.Totally, detecting personalized determinants during drug treatment from omics big data will bring the precision medicine or personalized medicine from concept to application. More and more inspiring biotechnologies, data resources, and analytic approaches will benefit All of US in the near future.
    Personalized Medicine
    Omics
    Drug response
    In the era of advanced biotechnology and genomics, precision medicine has emerged as a transformative approach to healthcare. This paper explores the concept of precision medicine, with a particular focus on its application in tailoring treatments for individuals based on their genomic profiles. By leveraging cutting-edge technologies such as genome sequencing and personalized therapies, precision medicine holds immense promise for improving outcomes in a wide range of diseases. In this 10,000-word paper, we delve into the principles, challenges, and potential of precision medicine in the context of Type 2 Diabetes (T2D) as a prominent example. We also discuss the ethical and societal implications of implementing precision medicine and the need for broader integration into healthcare systems.
    Personalized Medicine
    Transformative Learning
    genomic medicine
    Citations (0)
    Precision medicine can be defined as personalized medicine enhanced by technology. In the past, medicine has, in some cases, been personalized. For example, some drugs are dosed on an individualized basis based on age, body-mass index, comorbidities and other clinical parameters. However, overall, medicine has largely followed the ‘one-size-fits-all' paradigm as exemplified in the treatment of essential hypertension or type 2 diabetes mellitus. What has changed in the past few years is that technologies such as high throughput sequencing, mass spectrometry, microfluidics, and imaging can help conduct a multitude of complex measurements on clinical samples. Aided by analytics, these technologies have been providing an increasingly detailed picture of molecular and cellular alterations underlying numerous diseases and have revealed tremendous variability between individuals and patients at the molecular and cellular level. These findings have motivated a more personalized or ‘precision' approach to medicine, in which molecular and cellular markers help tailor patient management to each individual. Here we provide an overview of the key factors driving adoption of precision medicine and highlight current research that may soon make precision medicine more predictive.
    Personalized Medicine
    Systems medicine
    Citations (47)
    Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.
    Personalized Medicine
    Biomarker Discovery
    Citations (35)
    “Personalized medicine” and the related concepts of “precision medicine” and “precision health” refer to the idea that healthcare approaches should not be one‐size‐fits‐all but instead tailored to a person's unique biological, behavioral, and environmental factors. A central focus of personalized medicine is genomic testing to generate more accurate disease risk profiles and to guide gene‐targeted therapies (i.e., pharmacogenomics). Personalized medicine also relies on individuals' digital health engagement and on healthcare informatics to generate personalized guidance. Health communication scholars have an important role to play in the success of personalized medicine. From research to clinical implementation, effective communication is key to enabling and implementing the discoveries that support a personalized approach to health. Important areas for communication research include research participant recruitment, digital health engagement and bioinformatics, conveying complex health information to patients and consumers, and the portrayal of personalized medicine in the media.
    Personalized Medicine
    Pharmacogenomics
    Digital Health
    Translational research informatics