Prospective molecular profiling of canine cancers provides a clinically relevant comparative model for evaluating precision medicine (PMed) trials
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ABSTRACT:
ORGANISM(S): Canis lupus familiaris Canis lupus
PROVIDER: GSE51131 | GEO | 2014/04/28
SECONDARY ACCESSION(S): PRJNA221340
REPOSITORIES: GEO
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Project description:Feasibility trial to examine the ability to conduct molecular guided therapy in a geographically distributed systems within strict time constraints Canine patients were enrolled across a broad panel of tumor types where tumor samples were acquired and processed to preserve RNA quality for genomic analysis at distributed locations to predict possible effective therapies. Genomic driven therapy suggestion derived using a collection of algorithms based on tumor expression levels.
2014-04-28 | E-GEOD-51131 | biostudies-arrayexpress
Project description:BackgroundMolecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting.MethodologyA proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type.ConclusionsCollection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development.
| S-EPMC3956546 | biostudies-literature
Project description:In this chapter, we introduce a new area of social pharmacology that encompasses the study of the role of neuromodulators in modulating a wide range of social behaviors and brain function, with the interplay of genetic and epigenetic factors. There are increasing evidences for the role of the neuropeptide oxytocin in modulating a wide range of social behaviors, in reducing anxiety, and in impacting the social brain network. Oxytocin also promotes social functions in patients with neuropsychiatric disorders, such as autism and reduces anxiety and fear in anxiety disorders. In this chapter, we will emphasize the importance of integrating basic research and clinical human research in determining optimal strategies for drug discoveries for social dysfunctions and anxiety disorders. We will highlight the significance of adopting a precision medicine approach to optimize targeted treatments with oxytocin in neuropsychiatry. Oxytocin effects on social behavior and brain function can vary from one individual to another based on external factors, such as heterogeneity in autism phenotype, childhood experiences, personality, attachment style, and oxytocin receptor polymorphisms. Hence, targeted therapies for subgroups of patients can help alleviating some of the core symptoms and lead to a better future for these patients and their families.
| S-EPMC5815942 | biostudies-literature
Project description:Immune responses to human cytomegalovirus (CMV) can be used to assess immune fitness in an individual. Further to its clinical significance in posttransplantation settings, emerging clinical and translational studies provide examples of immune correlates of protection pertaining to anti-CMV immune responses in the context of cancer or infectious diseases, e.g., tuberculosis. In this viewpoint, we provide a brief overview about CMV-directed immune reactivity and immune fitness in a clinical context and incorporate some of our own findings obtained from peripheral blood or tumour-infiltrating lymphocytes (TIL) from patients with advanced cancer. Observations in patients with solid cancers whose lesions contain both CMV and tumour antigen-specific T-cell subsets are highlighted, due to a possible CMV-associated "bystander" effect in amplifying local inflammation and subsequent tumour rejection. The role of tumour-associated antibodies recognising diverse CMV-derived epitopes is also discussed in light of anti-cancer immune responses. We discuss here the use of anti-CMV immune responses as a theranostic tool-combining immunodiagnostics with a personalised therapeutic potential-to improve treatment outcomes in oncological indications.
| S-EPMC6514820 | biostudies-literature
Project description:OBJECTIVES:To discuss the role of clinical trials in the changing landscape of cancer care resulting in individualized cancer treatment plans including a discussion of several innovative randomized studies designed to evaluate multiple targeted therapies in molecularly defined subsets of individuals. DATA SOURCES:Medical and nursing literature, research articles, and clinicaltrials.gov. CONCLUSION:Recent advancements in cancer biomarkers and biomedical technology have begun to transform fundamentals of cancer therapeutics and clinical trials through innovative adaptive trial designs. The goal of these studies is to learn not only if a drug is safe and effective but also how it is best delivered and who will derive the most benefit. IMPLICATIONS FOR NURSING PRACTICE:Implementation of clinical trials in the cancer biomarker era requires knowledge, skills, and expertise related to the use of biomarkers and molecularly defined processes underlying a malignancy, as well as an understanding of associated ethical, legal, and social issues to provide competent, safe, and effective health care and patient communication.
| S-EPMC4280791 | biostudies-literature
Project description:Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.
| S-EPMC7211185 | biostudies-literature
Project description:IntroductionPatients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities.MethodsWe estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry. We screened predictors to identify reasons for discriminatory model performance.ResultsThe precision medicine algorithm performed worse predicting a PEx among Black patients when compared with White patients or to patients of another race for all three prediction horizons. There was little to no difference in prediction accuracies among Hispanic and non-Hispanic patients for the same prediction horizons. Differences in F508del, smoking households, secondhand smoke exposure, primary and secondary road densities, distance and drive time to the CF center, and average number of clinical evaluations were key factors associated with race.ConclusionsRacial differences in prediction accuracies from our PEx precision medicine algorithm exist. Misclassification of future PEx was attributable to several underlying factors that correspond to race: CF mutation, location where the patient lives, and clinical awareness. Associations of our proxies with race for CF-related health outcomes can lead to systemic racism in data collection and in prediction accuracies from precision medicine algorithms constructed from it.
| S-EPMC11362628 | biostudies-literature
Project description:ObjectiveThe classical description of Dravet syndrome, the prototypic developmental and epileptic encephalopathy, is of a normal 6-month-old infant presenting with a prolonged, febrile, hemiclonic seizure and showing developmental slowing after age 1 year. SCN1A pathogenic variants are found in >80% of patients. Many patients have atypical features resulting in diagnostic delay and inappropriate therapy. We aimed to provide an evidence-based definition of SCN1A-Dravet syndrome in readiness for precision medicine trials.MethodsEpilepsy patients were recruited to the University of Melbourne Epilepsy Genetics Research Program between 1995 and 2020 by neurologists from around the world. Patients with SCN1A pathogenic variants were reviewed and only those with Dravet syndrome were included. Clinical data, including seizure and developmental course, were analyzed in all patients with SCN1A-Dravet syndrome.ResultsTwo hundred and five patients were studied at a median age of 8.5 years (range 10 months to 60 years); 25 were deceased. The median seizure-onset age was 5.7 months (range 1.5-20.6 months). Initial seizures were tonic-clonic (52%) and hemiclonic (35%), with only 55% being associated with fever. Only 34% of patients presented with status epilepticus (seizure lasting ≥30 minutes). Median time between first and second seizure was 30 days (range 4 hours to 8 months), and seven patients (5%) had at least 6 months between initial seizures. Median ages at onset of second and third seizure types were 9.1 months (range 3 months-25.4 years) and 15.5 months (range 4 months-8.2 years), respectively. Developmental slowing occurred prior to 12 months in 27%.SignificanceAn evidence-based definition of SCN1A-Dravet syndrome is essential for early diagnosis. We refine the spectrum of Dravet syndrome, based on patterns of seizure onset, type, and progression. Understanding of the full spectrum of SCN1A-Dravet syndrome presentation is essential for early diagnosis and optimization of treatment, especially as precision medicine trials become available.
| S-EPMC9291974 | biostudies-literature
Project description: Not available
| S-EPMC7049931 | biostudies-literature
Project description:BackgroundGene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more 'precision' approach that integrates individual variability including 'omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression needs to be interpreted meaningfully for single subjects. We propose an "all-against-one" framework that uses biological replicates in isogenic conditions for testing differentially expressed genes (DEGs) in a single subject (ss) in the absence of an appropriate external reference standard or replicates. To evaluate our proposed "all-against-one" framework, we construct reference standards (RSs) with five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2) and the remainder were treated separately as single-subject sample pairs for ss analyses (without replicates).ResultsEight ss methods (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) for identifying genes with differential expression were compared in Yeast (parental line versus snf2 deletion mutant; n = 42/condition) and a MCF7 breast-cancer cell line (baseline versus stimulated with estradiol; n = 7/condition). Receiver-operator characteristic (ROC) and precision-recall plots were determined for eight ss methods against each of the five RSs in both datasets. Consistent with prior analyses of these data, ~ 50% and ~ 15% DEGs were obtained in Yeast and MCF7 datasets respectively, regardless of the RSs method. NOISeq, edgeR, and DESeq were the most concordant for creating a RS. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the RS method and dataset (> 90% in Yeast, > 0.75 in MCF7). Further, distinct specific single-subject methods perform better according to different proportions of DEGs.ConclusionsThe "all-against-one" framework provides a honest evaluation framework for single-subject DEG studies since these methods are evaluated, by design, against reference standards produced by unrelated DEG methods. The ss-ensemble method was the only one to reliably produce higher accuracies in all conditions tested in this conservative evaluation framework. However, single-subject methods for identifying DEGs from paired samples need improvement, as no method performed with precision> 90% and obtained moderate levels of recall. http://www.lussiergroup.org/publications/EnsembleBiomarker.
| S-EPMC6624180 | biostudies-literature