Accuracy and self correction of information received from an internet breast cancer list: content analysis.

OBJECTIVES: To determine the prevalence of false or misleading statements in messages posted by internet cancer support groups and whether these statements were identified as false or misleading and corrected by other participants in subsequent postings. DESIGN: Analysis of content of postings. SETTING: Internet cancer support group Breast Cancer Mailing List. MAIN OUTCOME MEASURES: Number of false or misleading statements posted from 1 January to 23 April 2005 and whether these were identified and corrected by participants in subsequent postings. RESULTS: 10 of 4600 postings (0.22%) were found to be false or misleading. Of these, seven were identified as false or misleading by other participants and corrected within an average of four hours and 33 minutes (maximum, nine hours and nine minutes). CONCLUSIONS: Most posted information on breast cancer was accurate. Most false or misleading statements were rapidly corrected by participants in subsequent postings

Translational Research from an Informatics Perspective

Clinical and translational research (CTR) is an essential part of a sustainable global health system. Informatics is now recognized as an important en-abler of CTR and informaticians are increasingly called upon to help CTR efforts. The US National Institutes of Health mandated biomedical informatics activity as part of its new national CTR grant initiative, the Clinical and Translational Science Award (CTSA). Traditionally, translational re-search was defined as the translation of laboratory discoveries to patient care (bench to bedside). We argue, however, that there are many other kinds of translational research. Indeed, translational re-search requires the translation of knowledge dis-covered in one domain to another domain and is therefore an information-based activity. In this panel, we will expand upon this view of translational research and present three different examples of translation to illustrate the point: 1) bench to bedside, 2) Earth to space and 3) academia to community. We will conclude with a discussion of our local translational research efforts that draw on each of the three examples

The rapamycin-regulated gene expression signature determines prognosis for breast cancer

<p>Abstract</p> <p>Background</p> <p>Mammalian target of rapamycin (mTOR) is a serine/threonine kinase involved in multiple intracellular signaling pathways promoting tumor growth. mTOR is aberrantly activated in a significant portion of breast cancers and is a promising target for treatment. Rapamycin and its analogues are in clinical trials for breast cancer treatment. Patterns of gene expression (metagenes) may also be used to simulate a biologic process or effects of a drug treatment. In this study, we tested the hypothesis that the gene-expression signature regulated by rapamycin could predict disease outcome for patients with breast cancer.</p> <p>Results</p> <p>Colony formation and sulforhodamine B (IC₅₀< 1 nM) assays, and xenograft animals showed that MDA-MB-468 cells were sensitive to treatment with rapamycin. The comparison of <it>in vitro </it>and <it>in vivo </it>gene expression data identified a signature, termed rapamycin metagene index (RMI), of 31 genes upregulated by rapamycin treatment <it>in vitro </it>as well as <it>in vivo </it>(false discovery rate of 10%). In the Miller dataset, RMI did not correlate with tumor size or lymph node status. High (>75th percentile) RMI was significantly associated with longer survival (<it>P </it>= 0.015). On multivariate analysis, RMI (<it>P </it>= 0.029), tumor size (<it>P </it>= 0.015) and lymph node status (<it>P </it>= 0.001) were prognostic. In van 't Veer study, RMI was not associated with the time to develop distant metastasis (<it>P </it>= 0.41). In the Wang dataset, RMI predicted time to disease relapse (<it>P </it>= 0.009).</p> <p>Conclusion</p> <p>Rapamycin-regulated gene expression signature predicts clinical outcome in breast cancer. This supports the central role of mTOR signaling in breast cancer biology and provides further impetus to pursue mTOR-targeted therapies for breast cancer treatment.</p

Translational Research in Space Exploration

This viewgraph presentation reviews NASA's role in medical translational research, and the importance in research for space exploration. The application of medical research for space exploration translates to health care in space medicine, and on earth

Phase Ib/II Study of the Safety and Efficacy of Combination Therapy with Multikinase VEGF Inhibitor Pazopanib and MEK Inhibitor Trametinib In Advanced Soft Tissue Sarcoma.

Purpose: Pazopanib, a multireceptor tyrosine kinase inhibitor targeting primarily VEGFRs1–3, is approved for advanced soft tissue sarcoma (STS) and renal cell cancer. Downstream of VEGFR, trametinib is an FDA-approved MEK inhibitor used for melanoma. We hypothesized that vertical pathway inhibition using trametinib would synergize with pazopanib in advanced STS. Experimental Design: In an open-label, multicenter, investigator-initiated National Comprehensive Cancer Network (NCCN)-sponsored trial, patients with metastatic or advanced STS received pazopanib 800 mg and 2 mg of trametinib continuously for 28-day cycles. The primary endpoint was 4-month progression-free survival (PFS). Secondary endpoints were overall survival, response rate, and disease control rate. Results: Twenty-five patients were enrolled. The median age was 49 years (range, 22–77 years) and 52% were male. Median PFS was 2.27 months [95% confidence interval (CI), 1.9–3.9], and the 4-month PFS rate was 21.1% (95% CI, 9.7–45.9), which was not an improvement over the hypothesized null 4-month PFS rate of 28.3% (P ¼ 0.79). Median overall survival was 9.0 months (95% CI, 5.7–17.7). A partial response occurred in 2 (8%) of the evaluable patients (95% CI, 1.0–26.0), one with PIK3CA E542K-mutant embryonal rhabdomyosarcoma and another with spindle cell sarcoma. The disease control rate was 14/25 (56%; 95% CI, 34.9–75.6). The most common adverse events were diarrhea (84%), nausea (64%), and fatigue (56%). Conclusions: The combination of pazopanib and trametinib was tolerable without indication of added activity of the combination in STS. Further study may be warranted in RAS/RAF aberrant sarcomas. ©2017 AACR

Predicting multiple sclerosis disease severity with multimodal deep neural networks

Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale (EDSS), composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) creates opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to the data insufficiency or model simplicity. In this paper, we proposed an idea of using patients' multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity at the hospital visit. This work has two important contributions. First, we describe a pilot effort to leverage structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient's MS disease severity. The proposed pipeline demonstrates up to 25% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes