scholarly journals Identification of targeted therapy options for gastric adenocarcinoma by comprehensive analysis of genomic data

2020 ◽  
Vol 23 (4) ◽  
pp. 627-638
Author(s):  
Daniel A. Hescheler ◽  
Patrick S. Plum ◽  
Thomas Zander ◽  
Alexander Quaas ◽  
Michael Korenkov ◽  
...  
Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5436
Author(s):  
Miriam Gutiérrez-Jimeno ◽  
Piedad Alba-Pavón ◽  
Itziar Astigarraga ◽  
Teresa Imízcoz ◽  
Elena Panizo-Morgado ◽  
...  

Genomic techniques enable diagnosis and management of children and young adults with sarcomas by identifying high-risk patients and those who may benefit from targeted therapy or participation in clinical trials. Objective: to analyze the performance of an NGS gene panel for the clinical management of pediatric sarcoma patients. We studied 53 pediatric and young adult patients diagnosed with sarcoma, from two Spanish centers. Genomic data were obtained using the Oncomine Childhood Cancer Research Assay, and categorized according to their diagnostic, predictive, or prognostic value. In 44 (83%) of the 53 patients, at least one genetic alteration was identified. In 80% of these patients, the diagnosis was obtained (n = 11) or changed (n = 9), and thus genomic data affected therapy. The most frequent initial misdiagnosis was Ewing’s sarcoma, instead of myxoid liposarcoma (FUS-DDDIT3), rhabdoid soft tissue tumor (SMARCB1), or angiomatoid fibrous histiocytoma (EWSR1-CREB1). In our series, two patients had a genetic alteration with an FDA-approved targeted therapy, and 30% had at least one potentially actionable alteration. NGS-based genomic studies are useful and feasible in diagnosis and clinical management of pediatric sarcomas. Genomic characterization of these rare and heterogeneous tumors also helps in the search for prognostic biomarkers and therapeutic opportunities.


2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 2039-2039
Author(s):  
Lee A. Albacker ◽  
Dean Pavlick ◽  
Jeffrey S. Ross ◽  
Glenn Jay Lesser ◽  
Robert John Corona ◽  
...  

2017 ◽  
Vol 60 (2) ◽  
Author(s):  
Izumi C. Mori ◽  
Yoko Ikeda ◽  
Takakazu Matsuura ◽  
Takashi Hirayama ◽  
Koji Mikami

AbstractEmerging studies suggest that seaweeds contain phytohormones; however, their chemical entities, biosynthetic pathways, signal transduction mechanisms, and physiological roles are poorly understood. Until recently, it was difficult to conduct comprehensive analysis of phytohormones in seaweeds because of the interfering effects of cellular constituents on fine quantification. In this review, we discuss the details of the latest method allowing simultaneous profiling of multiple phytohormones in red seaweeds, while avoiding the effects of cellular factors. Recent studies have confirmed the presence of indole-3-acetic acid (IAA),


2015 ◽  
Vol 33 (15_suppl) ◽  
pp. 5602-5602 ◽  
Author(s):  
Julia Andrea Elvin ◽  
Mark Bailey ◽  
Benedito A. Carneiro ◽  
Siraj Mahamed Ali ◽  
Jo-Anne Vergilio ◽  
...  

Database ◽  
2010 ◽  
Vol 2010 ◽  
Author(s):  
Marc Bouffard ◽  
Michael S. Phillips ◽  
Andrew M.K. Brown ◽  
Sharon Marsh ◽  
Jean-Claude Tardif ◽  
...  

2021 ◽  
Vol 11 (8) ◽  
pp. 730
Author(s):  
Elena Rojano ◽  
José Córdoba-Caballero ◽  
Fernando M. Jabato ◽  
Diana Gallego ◽  
Mercedes Serrano ◽  
...  

Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets.


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