Clinicians' understanding of RLS enigma has recently improved due to the increased intensity of RLS research over the past decade. This review summarizes the current findings in the RLS field as well as providing guidelines for future RLS-related research.

Purpose Tumor gene mutation status is becoming increasingly important in the treatment of patients with cancer. A comprehensive catalog of tumor gene–response outcomes from individual patients is needed, especially for actionable mutations and rare variants. We created a proof-of-principle database [DNA-mutation Inventory to Refine and Enhance Cancer Treatment (DIRECT)], starting with lung cancer-associated EGF receptor (EGFR) mutations, to provide a resource for clinicians to prioritize treatment decisions based on a patient’s tumor mutations at the point of care. Methods A systematic search of literature published between June 2005 and May 2011 was conducted through PubMed to identify patient-level, mutation–drug response in patients with non–small cell lung cancer (NSCLC) with EGFR mutant tumors. Minimum inclusion criteria included patient’s EGFR mutation, corresponding treatment, and an associated radiographic outcome. Results A total of 1,021 patients with 1,070 separate EGFR tyrosine kinase inhibitor therapy responses from 116 different publications were included. About 188 unique EGFR mutations occurring in 207 different combinations were identified: 149 different mutation combinations were associated with disease control and 42 were associated with disease progression. Four secondary mutations, in 16 different combinations, were associated with acquired resistance. Conclusions As tumor sequencing becomes more common in oncology, this comprehensive electronic catalog can enable genome-directed anticancer therapy. DIRECT will eventually encompass all tumor mutations associated with clinical outcomes on targeted therapies. Users can make specific queries at http:// www.mycancergenome.org/about/direct to obtain clinically relevant data associated with various mutations.

Lung cancer is the leading cause of cancer-related mortality in the United States with 222,520 new cases and 157,300 deaths anticipated in 2010. The primary objective of any cancer treatment is to improve patient outcomes including overall survival and quality of life while minimizing treatment toxicity. As our knowledge of the molecular mechanisms involved in the pathogenesis of lung cancer evolves, improved methods of therapeutic selection may help clinicians better realize these goals. Such selection may be accomplished by examining biomarkers within patients' tumors that may provide prognostic information such as risk of recurrence in early stage disease or predict benefit from specific therapies regardless of disease stage. Three such biomarkers have emerged--excision repair cross-complementation group 1, the regulatory subunit of the ribonucleotide reductase enzyme, and thymidylate synthase--and are actively being evaluated in patients with non-small cell lung cancer. This review will focus on the role of these biomarkers as predictive and/or prognostic markers in the selection of chemotherapy regimens in non-small cell lung cancer patients.

BackgroundMany cancer clinical trials now specify the particular status of a genetic lesion in a patient's tumor in the inclusion or exclusion criteria for trial enrollment. To facilitate search and identification of gene-associated clinical trials by potential participants and clinicians, it is important to develop automated methods to identify genetic information from narrative trial documents.MethodsWe developed a two-stage classification method to identify genes and genetic lesion statuses in clinical trial documents extracted from the National Cancer Institute's (NCI's) Physician Data Query (PDQ) cancer clinical trial database. The method consists of two steps: 1) to distinguish gene entities from non-gene entities such as English words; and 2) to determine whether and which genetic lesion status is associated with an identified gene entity. We developed and evaluated the performance of the method using a manually annotated data set containing 1,143 instances of the eight most frequently mentioned genes in cancer clinical trials. In addition, we applied the classifier to a real-world task of cancer trial annotation and evaluated its performance using a larger sample size (4,013 instances from 249 distinct human gene symbols detected from 250 trials).ResultsOur evaluation using a manually annotated data set showed that the two-stage classifier outperformed the single-stage classifier and achieved the best average accuracy of 83.7% for the eight most frequently mentioned genes when optimized feature sets were used. It also showed better generalizability when we applied the two-stage classifier trained on one set of genes to another independent gene. When a gene-neutral, two-stage classifier was applied to the real-world task of cancer trial annotation, it achieved a highest accuracy of 89.8%, demonstrating the feasibility of developing a gene-neutral classifier for this task.ConclusionsWe presented a machine learning-based approach to detect gene entities and the genetic lesion statuses from clinical trial documents and demonstrated its use in cancer trial annotation. Such methods would be valuable for building information retrieval tools targeting gene-associated clinical trials.

Study Objectives: To determine the depth and distribution of sensory discomfort in idiopathic restless legs syndrome/Willis-Ekbom disease (RLS) and RLS concurrent with other leg conditions, specifically peripheral neuropathy, sciatica, leg cramps, and arthritis. Methods: RLS subjects (n = 122) were divided into 71 idiopathic RLS and 51 RLS-C, or Comorbid, groups. All subjects were examined by an RLS expert, answered standardized RLS questionnaires, and received a body diagram to draw the location and depth of their symptoms. Results: Age was 63.04 ± 12.84 years, with 77 females and 45 males. All patients had lower limb involvement and 43/122 (35.25%) also had upper limb involvement. Of the 122 subjects, 42.62% felt that the RLS discomfort was only deep, 9.84% felt that the discomfort was only superficial, and 47.54% felt both superficial and deep discomfort. There were no defining characteristics in depth or distribution of RLS sensations that differentiated those patients with idiopathic RLS from those patients with RLS associated with other comorbid leg conditions. The sensation of arthritis was felt almost exclusively in the joints and not in the four quadrants of the leg, whereas the exact opposite was true of RLS sensations. Conclusions: Depth and distribution cannot be used as a discriminative mechanism to separate out idiopathic RLS from RLS comorbid with other leg conditions. Although seen in clinical practice, the total absence of patients with non-painful RLS only in the joints in the current study attests to the rarity of this presentation and raises the possibility of misdiagnosis under these circumstances. We recommend that such patients not be admitted to genetic or epidemiological studies.