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Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

ABSTRACT: With the emergence of high-throughput discovery platforms, robust preclinical small-animal models, and efficient clinical trial pipelines, it is becoming possible to envision a time when the treatment of human neurologic diseases will become personalized. The emergence of precision medicine will require the identification of subgroups of patients most likely to respond to specific biologically based therapies. This stratification only becomes possible when the determinants that contribute to disease heterogeneity become more fully elucidated. This review discusses the defining factors that underlie disease heterogeneity relevant to the potential for individualized brain tumor (optic pathway glioma) treatments arising in the common single-gene cancer predisposition syndrome, neurofibromatosis type 1 (NF1). In this regard, NF1 is posited as a model genetic condition to establish a workable paradigm for actualizing precision therapeutics for other neurologic disorders.

SUBMITTER: Gutmann DH

PROVIDER: S-EPMC4132567 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

Gutmann David H DH

Neurology 20140627 5

With the emergence of high-throughput discovery platforms, robust preclinical small-animal models, and efficient clinical trial pipelines, it is becoming possible to envision a time when the treatment of human neurologic diseases will become personalized. The emergence of precision medicine will require the identification of subgroups of patients most likely to respond to specific biologically based therapies. This stratification only becomes possible when the determinants that contribute to dis ...[more]

PMID: 24975854

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Project description:BackgroundCognitive difficulties are the most common neurological complications in neurofibromatosis type 1 (NF1) patients. Recent animal models proposed increased GABA-mediated inhibition as one underlying mechanism directly affecting the induction of long-term potentiation (LTP) and learning. In most adult NF1 patients, apparent cognitive and attentional deficits, tumors affecting the nervous system and other confounding factors for neuroscientific studies are difficult to control for. Here we used a highly specific group of adult NF1 patients without cognitive or nervous system impairments. Such selected NF1 patients allowed us to address the following open questions: Is the learning process of acquiring a challenging motor skill impaired in NF1 patients? And is such an impairment in relation to differences in intracortical inhibition?MethodsWe used an established non-invasive, double-pulse transcranial magnetic stimulation (dp-TMS) paradigm to assess practice-related modulation of intracortical inhibition, possibly mediated by gamma-minobutyric acid (GABA)ergic-neurotransmission. This was done during an extended learning paradigm in a group of NF1 patients without any neuropsychological deficits, functioning normally in daily life and compared them to healthy age-matched controls.FindingsNF1 patients experienced substantial decline in motor skill acquisition (F = 9.2, p = 0.008) over five-consecutives training days mediated through a selective reduction in the early acquisition (online) and the consolidation (offline) phase. Furthermore, there was a consistent decrease in task-related intracortical inhibition as a function of the magnitude of learning (T = 2.8, p = 0.014), especially evident after the early acquisition phase.InterpretationsCollectively, the present results provide evidence that learning of a motor skill is impaired even in clinically intact NF1 patients based, at least partially, on a GABAergic-cortical dysfunctioning as suggested in previous animal work.

Dataset Information

Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

Publications

Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

Similar Datasets

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