Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search

Radiology reports contain information that can be mined using a search engine for teaching, research, and quality assurance purposes. Current search engines look for exact matches to the search term, but they do not differentiate between reports in which the search term appears in a positive context (i.e., being present) from those in which the search term appears in the context of negation and uncertainty. We describe RadReportMiner, a context-aware search engine, and compare its retrieval performance with a generic search engine, Google Desktop. We created a corpus of 464 radiology reports which described at least one of five findings (appendicitis, hydronephrosis, fracture, optic neuritis, and pneumonia). Each report was classified by a radiologist as positive (finding described to be present) or negative (finding described to be absent or uncertain). The same reports were then classified by RadReportMiner and Google Desktop. RadReportMiner achieved a higher precision (81%), compared with Google Desktop (27%; p < 0.0001). RadReportMiner had a lower recall (72%) compared with Google Desktop (87%; p = 0.006). We conclude that adding negation and uncertainty identification to a word-based radiology report search engine improves the precision of search results over a search engine that does not take this information into account. Our approach may be useful to adopt into current report retrieval systems to help radiologists to more accurately search for radiology reports

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research

Gene expression profiling may improve diagnosis in patients with carcinoma of unknown primary

Carcinomas of unknown primary (CUP) represent between 3 and 10% of malignancies. Treatment with nonspecific chemotherapy is commonly unhelpful and the median survival is between 3 and 6 months. Gene expression microarray (GEM) analysis has demonstrated that molecular signatures can aid in tumour classification and propose foster primaries. In this study, we demonstrate the clinical utility of a diagnostic gene expression profiling tool and discuss its potential implications for patient management strategies. Paraffin tumour samples from 21 cases of ‘true' CUP patients in whom standard investigation had failed to determine a primary site of malignancy were investigated using diagnostic gene profiling. The results were reviewed in the context of histology and clinical history. Classification of tumour origin using the GEM method confirmed the clinicians' suspicion in 16 out of 21 cases. There was a clinical/GEM inconsistency in 4 out of 21 patients and a pathological/GEM inconsistency in 1 patient. The improved diagnoses by the GEM method would have influenced the management in 12 out of 21 cases. Genomic profiling and cancer classification tools represent a promising analytical approach to assist with the management of CUP patients. We propose that GEM diagnosis be considered when the primary clinical algorithm has failed to provide a diagnosis

Mining expressed sequence tags identifies cancer markers of clinical interest

BACKGROUND: Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers. RESULTS: We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered. CONCLUSION: These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies

Drug resistance mutations and heteroresistance detected using the GenoType MTBDRplus assay and their implication for treatment outcomes in patients from Mumbai, India

<p>Abstract</p> <p>Background</p> <p>Only 5% of the estimated global multidrug resistant TB (MDRTB) load is currently detected. Endemic Mumbai with increasing MDR would benefit from the introduction of molecular methods to detect resistance.</p> <p>Methods</p> <p>The GenoType MTBDR<it>plus </it>assay was used to determine mutations associated with isoniazid and rifampicin resistance and their correlation with treatment outcomes. It was performed on a convenience sample comprising 88 onset and 67 fifth month isolates for which phenotypic drug susceptibility testing (DST) was determined by the Buddemeyer technique for an earlier study. Simultaneous presence of wild type and mutant bands was referred to as "mixed patterns" (heteroresistance).</p> <p>Results</p> <p>Phenotypically 41 isolates were sensitive; 11 isoniazid, 2 rifampicin, 2 pyrazinamide and 5 ethambutol monoresistant; 16 polyresistant and 78 MDR. The agreement between both methods was excellent (kappa = 0.72-0.92). Of 22 rifampicin resistant onset isolates, the predominant <it>rpoB </it>mutations were the singular lack of WT8 (n = 8) and mixed D516V patterns (n = 9). Of the 64 rifampicin resistant fifth month isolates, the most frequent mutations were in WT8 (n = 31) with a further 9 showing the S531L mutation. Mixed patterns were seen in 22 (34%) isolates, most frequently for the D516V mutation (n = 21). Of the 22 onset and 35 fifth month <it>katG </it>mutants, 13 and 12 respectively showed the S315T1 mutation with loss of the WT. Mixed patterns involving both S315T1 and S315T2 were seen in 9 and 23 isolates respectively. Seventeen of 23 and 23/35 <it>inhA </it>mutant onset and fifth month isolates showed mixed A16G profiles. Additionally, 10 fifth month isolates lacked WT2. Five onset and 6 fifth month isolates had both <it>katG </it>and <it>inhA </it>mutations. An association was noted between only <it>katG </it>but not only <it>inhA </it>resistance and poor outcome (<it>p </it>= 0.037); and additional resistance to ethambutol (<it>p </it>= 0.0033). More fifth month than onset isolates had mixed profiles for at least 1 gene (<it>p </it>= 0.000001).</p> <p>Conclusions</p> <p>The use of the assay to rapidly diagnose MDR could guide simultaneous first- and second-line DST, and reduce the delay in administering appropriate regimens. Furthermore, detection of heteroresistance could prevent inaccurate "cured" treatment outcomes documented through smear microscopy and permit more sensitive detection of neonascent resistance.</p

CD1d-Expressing Breast Cancer Cells Modulate NKT Cell-Mediated Antitumor Immunity in a Murine Model of Breast Cancer Metastasis

Tumor tolerance and immune suppression remain formidable obstacles to the efficacy of immunotherapies that harness the immune system to eradicate breast cancer. A novel syngeneic mouse model of breast cancer metastasis was developed in our lab to investigate mechanisms of immune regulation of breast cancer. Comparative analysis of low-metastatic vs. highly metastatic tumor cells isolated from these mice revealed several important genetic alterations related to immune control of cancer, including a significant downregulation of cd1d1 in the highly metastatic tumor cells. The cd1d1 gene in mice encodes the MHC class I-like molecule CD1d, which presents glycolipid antigens to a specialized subset of T cells known as natural killer T (NKT) cells. We hypothesize that breast cancer cells, through downregulation of CD1d and subsequent evasion of NKT-mediated antitumor immunity, gain increased potential for metastatic tumor progression.In this study, we demonstrate in a mouse model of breast cancer metastasis that tumor downregulation of CD1d inhibits iNKT-mediated antitumor immunity and promotes metastatic breast cancer progression in a CD1d-dependent manner in vitro and in vivo. Using NKT-deficient transgenic mouse models, we demonstrate important differences between type I and type II NKT cells in their ability to regulate antitumor immunity of CD1d-expressing breast tumors.The results of this study emphasize the importance of determining the CD1d expression status of the tumor when tailoring NKT-based immunotherapies for the prevention and treatment of metastatic breast cancer

Human Mesenchymal Stem Cells Prolong Survival and Ameliorate Motor Deficit through Trophic Support in Huntington's Disease Mouse Models

We investigated the therapeutic potential of human bone marrow-derived mesenchymal stem cells (hBM-MSCs) in Huntington's disease (HD) mouse models. Ten weeks after intrastriatal injection of quinolinic acid (QA), mice that received hBM-MSC transplantation showed a significant reduction in motor function impairment and increased survival rate. Transplanted hBM-MSCs were capable of survival, and inducing neural proliferation and differentiation in the QA-lesioned striatum. In addition, the transplanted hBM-MSCs induced microglia, neuroblasts and bone marrow-derived cells to migrate into the QA-lesioned region. Similar results were obtained in R6/2-J2, a genetically-modified animal model of HD, except for the improvement of motor function. After hBM-MSC transplantation, the transplanted hBM-MSCs may integrate with the host cells and increase the levels of laminin, Von Willebrand Factor (VWF), stromal cell-derived factor-1 (SDF-1), and the SDF-1 receptor Cxcr4. The p-Erk1/2 expression was increased while Bax and caspase-3 levels were decreased after hBM-MSC transplantation suggesting that the reduced level of apoptosis after hBM-MSC transplantation was of benefit to the QA-lesioned mice. Our data suggest that hBM-MSCs have neural differentiation improvement potential, neurotrophic support capability and an anti-apoptotic effect, and may be a feasible candidate for HD therapy

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc.</p> <p>Description</p> <p>We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (<it>Drosophila melanogaster, Caenorhabditis elegans</it>) and vertebrates (<it>Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus</it>). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.</p> <p>Conclusions</p> <p>Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.</p