Project description:Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for LMS. We demonstrate the existence of 3 molecular subtypes in a cohort of 99 cases and an independent cohort of 82 LMS. Two new FFPE tissue-compatible diagnostic immunohistochemical markers are identified: LMOD1 for subtype I LMS and ARL4C for subtype II LMS. Subtype I and subtype II LMS are associated with good and poor prognosis, respectively. The LMS subtypes show significant differences in expression levels for genes for which novel targeted therapies are being developed. Gene expression profiling was performed by 3' End RNA Sequencing (3SEQ), a next generation sequencing approach that does not rely on frozen tissue but can be performed on archival FFPE tissue. Samples included 99 LMS, 6 Undifferentiated Pleomorphic Sarcomas (UPS), 3 leiomyomas, 4 normal myometrium samples, and 1 case of Lymphangioleiomyomatosis (LAM). This study only includes the 99 LMS Samples. After gene expression levels were quantified by 3SEQ analysis pipeline, Consensus Clustering with bootstrap method was used to determine that the dataset contained three robust subtypes, and Silhouette analysis was performed to validate the subtype assignments. Two class SAM analysis (Significance Analysis of Microarrays) was performed to identify genes expressed differentially between each subtype of LMS with FDR of 0.05. Immunohistochemical staining was used to validate the potential diagnostic and prognostic markers from 3SEQ data on a tissue microarray.
Project description:Owing to the advancement of technology combined with our deeper knowledge of human nature and diseases, we are able to move towards precision medicine, where patients are treated at the individual level in concordance with their genetic profiles. Lately, the integration of nanoparticles in biotechnology and their applications in medicine has allowed us to diagnose and treat disease better and more precisely. As a model disease, we used a grade IV malignant brain tumor (glioblastoma). Significant improvements in diagnosis were achieved with the application of fluorescent nanoparticles for intraoperative magnetic resonance imaging (MRI), allowing for improved tumor cell visibility and increasing the extent of the surgical resection, leading to better patient response. Fluorescent probes can be engineered to be activated through different molecular pathways, which will open the path to individualized glioblastoma diagnosis, monitoring, and treatment. Nanoparticles are also extensively studied as nanovehicles for targeted delivery and more controlled medication release, and some nanomedicines are already in early phases of clinical trials. Moreover, sampling biological fluids will give new insights into glioblastoma pathogenesis due to the presence of extracellular vesicles, circulating tumor cells, and circulating tumor DNA. As current glioblastoma therapy does not provide good quality of life for patients, other approaches such as immunotherapy are explored. To conclude, we reason that development of personalized therapies based on a patient's genetic signature combined with pharmacogenomics and immunogenomic information will significantly change the outcome of glioblastoma patients.
Project description:Although genome profiling provides important genetic and phenotypic details for applying precision medicine to diabetes, it is imperative to integrate in vitro human cell models, accurately recapitulating the genetic alterations associated with diabetes. The absence of the appropriate preclinical human models and the unavailability of genetically relevant cells substantially limit the progress in developing personalized treatment for diabetes. Human pluripotent stem cells (hPSCs) provide a scalable source for generating diabetes-relevant cells carrying the genetic signatures of the patients. Remarkably, allogenic hPSC-derived pancreatic progenitors and β cells are being used in clinical trials with promising preliminary results. Autologous hiPSC therapy options exist for those with monogenic and type 2 diabetes; however, encapsulation or immunosuppression must be accompanied with in the case of type 1 diabetes. Furthermore, genome-wide association studies-identified candidate variants can be introduced in hPSCs for deciphering the associated molecular defects. The hPSC-based disease models serve as excellent resources for drug development facilitating personalized treatment. Indeed, hPSC-based diabetes models have successfully provided valuable knowledge by modeling different types of diabetes, which are discussed in this review. Herein, we also evaluate their strengths and shortcomings in dissecting the underlying pathogenic molecular mechanisms and discuss strategies for improving hPSC-based disease modeling investigations.
Project description:Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for LMS. We demonstrate the existence of 3 molecular subtypes in a cohort of 99 cases and an independent cohort of 82 LMS. Two new FFPE tissue-compatible diagnostic immunohistochemical markers are identified: LMOD1 for subtype I LMS and ARL4C for subtype II LMS. Subtype I and subtype II LMS are associated with good and poor prognosis, respectively. The LMS subtypes show significant differences in expression levels for genes for which novel targeted therapies are being developed.
Project description:PurposeThis paper reports on a novel measure, attitudes toward genomics and precision medicine (AGPM), which evaluates attitudes toward activities such as genetic testing, collecting information on lifestyle, and genome editing - activities necessary to achieve the goals of precision medicine.DiscussionThe AGPM will be useful for researchers who want to explore attitudes toward genomics and precision medicine. The association of concerns about precision medicine activities with demographic variables such as religion and politics, as well as higher levels of education, suggests that further education on genomic and precision activities alone is unlikely to shift AGPM scores significantly.MethodsWe wrote items to represent psychological and health benefits of precision medicine activities, and concerns about privacy, social justice, harm to embryos, and interfering with nature. We validated the measure through factor analysis of its structure, and testing associations with trust in the health information system and demographic variables such as age, sex, education, and religion.ResultsThe AGPM had excellent alpha reliability (.92) and demonstrated good convergent validity with existing measures. Variables most strongly associated with higher levels of concern with precision medicine activities included: regular religious practice, republican political leanings, and higher levels of education.
Project description:ObjectivesTo 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 sourcesMedical and nursing literature, research articles, and clinicaltrials.gov.ConclusionRecent 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 practiceImplementation 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.
Project description:Inflammatory breast cancer (IBC) is an aggressive, although infrequent form of invasive breast cancer. Despite some advances in systemic treatment, even in the early setting, with combined-modality approach being the current recommended standard of care, the prognosis of IBC still remains unfavorable and has not significantly improved over time. Thus, a better understanding of the biology of IBC is eagerly awaited in order to identify possible targets for new drug development. This paper aims to provide an overview on recent data on the molecular and biological features of IBC and on possible targetable pathways. Molecular subtypes of IBC, similarly to other forms of breast cancer, have both therapeutic and prognostic implications. Moreover, few activated pathways have been described in IBC, including angiogenesis, epidermal growth factor receptor (EGFR), Janus kinase/signal transducer of activation (JAK/STAT) signaling and phosphoinositide 3-kinase/Akt/mTOR (PI3K/AKT/mTOR) pathways. However, when tested in clinical trials, agents targeting these pathways have provided only small benefit. Several clinical trials are currently ongoing investigating combination of standard chemotherapeutics, new targeted agents and immunotherapy. Moreover, tumor microenvironment (TME) is likely to play a central role in the disease; targeting the components of the tumor stroma may represent an interesting therapeutic strategy.
Project description:Precision medicine is an innovative approach that uses emerging biomedical technologies to deliver optimally targeted and timed interventions, customized to the molecular drivers of an individual's disease. This approach is only just beginning to be considered for treating amyotrophic lateral sclerosis (ALS). The clinical and biological complexities of ALS have hindered development of effective therapeutic strategies. In this review we consider applying the key elements of precision medicine to ALS: phenotypic classification, comprehensive risk assessment, presymptomatic period detection, potential molecular pathways, disease model development, biomarker discovery and molecularly tailored interventions. Together, these would embody a precision medicine approach, which may provide strategies for optimal targeting and timing of efforts to prevent, stop or slow progression of ALS.
Project description:AimsTo identify clinically meaningful clusters of patients with similar glycated hemoglobin (HbA1c) trajectories among patients with type 2 diabetes.MethodsA retrospective cohort study using unsupervised machine learning clustering methodologies to determine clusters of patients with similar longitudinal HbA1c trajectories. Stability of these clusters was assessed and supervised random forest analysis verified the clusters' reproducibility. Clinical relevance of the clusters was assessed through multivariable analysis, comparing differences in risk for a composite outcome (macrovascular and microvascular outcomes, hypoglycemic events, and all-cause mortality) at HbA1c thresholds for each cluster.ResultsAmong 60,423 patients, three clusters of HbA1c trajectories were generated: stable (n = 45,679), descending (n = 6,084), and ascending (n = 8,660) trends, which were reproduced with 99.8% accuracy using a random forest model. In the clinical relevance assessment, HbA1c levels demonstrated a J-shape association with the risk for outcomes. HbA1c level thresholds for minimizing outcomes' risk differed by cluster: 6.0-6.4% for the stable cluster, <8.0% for the descending cluster, and <9.0 for the ascending cluster.ConclusionsBy applying unsupervised machine learning to longitudinal HbA1c trajectories, we have identified clusters of patients who have distinct risk for diabetes-related complications. These clusters can be the basis for developing individualized models to personalize glycemic targets.