Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE).

PURPOSE: Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. EXPERIMENTAL DESIGN: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. RESULTS: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. CONCLUSIONS: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.Core funding for this project was provided by the National Institutes of Health (R01-CA172404, PI: S.J. Ramus; and R01-CA168758, PIs: J.A. Doherty and M.A.Rossing), the Canadian Institutes for Health Research (Proof-of-Principle I program, PIs: D.G.Huntsman and M.S. Anglesio), the United States Department of Defense Ovarian Cancer Research Program (OC110433, PI: D.D. Bowtell). A. Talhouk is funded through a Michael Smith Foundation for Health Research Scholar Award. M.S. Anglesio is funded through a Michael Smith Foundation for Health Research Scholar Award and the Janet D. Cottrelle Foundation Scholars program managed by the BC Cancer Foundation. J. George was partially supported by the NIH/National Cancer Institute award number P30CA034196. C. Wang was a Career Enhancement Awardee of the Mayo Clinic SPORE in Ovarian Cancer (P50 CA136393). D.G. Huntsman receives support from the Dr. Chew Wei Memorial Professorship in Gynecologic Oncology, and the Canada Research Chairs program (Research Chair in Molecular and Genomic Pathology). M. Widschwendter receives funding from the European Union’s Horizon 2020 European Research Council Programme, H2020 BRCA-ERC under Grant Agreement No. 742432 as well as the charity, The Eve Appeal (https://eveappeal.org.uk/), and support of the National Institute for Health Research (NIHR) and the University College London Hospitals (UCLH) Biomedical Research Centre. G.E. Konecny is supported by the Miriam and Sheldon Adelson Medical Research Foundation. B.Y. Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. H.R. Harris is 20 supported by the NIH/National Cancer Institute award number K22 CA193860. OVCARE (including the VAN study) receives support through the BC Cancer Foundation and The VGH+UBC Hospital Foundation (authors AT, BG, DGH, and MSA). The AOV study is supported by the Canadian Institutes of Health Research (MOP86727). The Gynaecological Oncology Biobank at Westmead, a member of the Australasian Biospecimen Network-Oncology group, was funded by the National Health and Medical Research Council Enabling Grants ID 310670 & ID 628903 and the Cancer Institute NSW Grants ID 12/RIG/1-17 & 15/RIG/1-16. The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command under DAMD17-01-1-0729, The Cancer Council Victoria, Queensland Cancer Fund, The Cancer Council New South Wales, The Cancer Council South Australia, The Cancer Council Tasmania and The Cancer Foundation of Western Australia (Multi-State Applications 191, 211 and 182) and the National Health and Medical Research Council of Australia (NHMRC; ID199600; ID400413 and ID400281). BriTROC-1 was funded by Ovarian Cancer Action (to IAM and JDB, grant number 006) and supported by Cancer Research UK (grant numbers A15973, A15601, A18072, A17197, A19274 and A19694) and the National Institute for Health Research Cambridge and Imperial Biomedical Research Centres. Samples from the Mayo Clinic were collected and provided with support of P50 CA136393 (E.L.G., G.L.K, S.H.K, M.E.S.)

A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

Background High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ −6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. Methods The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. Findings In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17–21%), 2% (1–3%), 8% (7–10%) and 6% (3–9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75–0.81), 0.58 (0.53–0.64), 0.71 (0.69–0.74) and 0.67 (0.62–0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92–1.24). Interpretation Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted fo

Mechanically assisted walking with body weight support results in more independent walking than assisted overground walking in non-ambulatory patients early after stroke : a systematic review

Question: Does mechanically assisted walking with body weight support result in more independent walking and is it detrimental to walking speed or capacity in non-ambulatory patients early after stroke? Design: Systematic review with meta-analysis of randomised trials. Participants: Non-ambulatory adult patients undergoing inpatient rehabilitation up to 3 months after stroke. Intervention: Mechanically assisted walking (eg, treadmill, electromechanical gait trainer, robotic device, servo-motor) with body weight support (eg, harness with or without handrail, but not handrail alone) versus assisted overground walking of longer than 15 min duration. Outcome measures: The primary outcome was the proportion of participants achieving independent walking. Secondary outcomes were walking speed measured as m/s during the 10-m Walk Test and walking capacity measured as distance in m during the 6-min Walk Test. Results: Six studies comprising 549 participants were identified and included in meta-analyses. Mechanically assisted walking with body weight support resulted in more people walking independently at 4 weeks (RD 0.23, 95% CI 0.15 to 0.30) and at 6 months (RD 0.23, 95% CI 0.07 to 0.39), faster walking at 6 months (MD 0.12 m/s, 95% CI 0.02 to 0.21), and further walking at 6 months (MD 55 m, 95% CI 15 to 96) than assisted overground walking. Conclusion: Mechanically assisted walking with body weight support is more effective than overground walking at increasing independent walking in non-ambulatory patients early after stroke. Furthermore, it is not detrimental to walking speed or capacity and clinicians should therefore be confident about implementing this intervention.9 page(s

Mechanically assisted walking with body weight support results in more independent walking than assisted overground walking in non-ambulatory patients early after stroke: a systematic review

Question: Does mechanically assisted walking with body weight support result in more independent walking and is it detrimental to walking speed or capacity in non-ambulatory patients early after stroke? Design: Systematic review with meta-analysis of randomised trials. Participants: Non-ambulatory adult patients undergoing inpatient rehabilitation up to 3 months after stroke. Intervention: Mechanically assisted walking (eg, treadmill, electromechanical gait trainer, robotic device, servo-motor) with body weight support (eg, harness with or without handrail, but not handrail alone) versus assisted overground walking of longer than 15 min duration. Outcome measures: The primary outcome was the proportion of participants achieving independent walking. Secondary outcomes were walking speed measured as m/s during the 10-m Walk Test and walking capacity measured as distance in m during the 6-min Walk Test. Results: Six studies comprising 549 participants were identified and included in meta-analyses. Mechanically assisted walking with body weight support resulted in more people walking independently at 4 weeks (RD 0.23, 95% CI 0.15 to 0.30) and at 6 months (RD 0.23, 95% CI 0.07 to 0.39), faster walking at 6 months (MD 0.12 m/s, 95% CI 0.02 to 0.21), and further walking at 6 months (MD 55 m, 95% CI 15 to 96) than assisted overground walking. Conclusion: Mechanically assisted walking with body weight support is more effective than overground walking at increasing independent walking in non-ambulatory patients early after stroke. Furthermore, it is not detrimental to walking speed or capacity and clinicians should therefore be confident about implementing this intervention

Design and rationale of a pilot randomized clinical trial investigating the use of a mHealth app for sarcoidosis-associated fatigue

Fatigue is the most reported symptom in patients with sarcoidosis (SPs) and is a significant predictor of decreased quality of life that is strongly associated with stress and negative mood states. Few medications exist for treating fatigue in SPs, and outpatient physical rehabilitation programs are limited by availability and cost. Sarcoidosis in the US predominantly impacts minorities and underserved populations who are of working age and often have limited resources (e.g., financial, transportation, time off work) that may prevent them from attending in-person programs. The use of mobile health (mHealth) is emerging as a viable alternative to provide access to self-management resources to improve quality of life. The Sarcoidosis Patient Assessment and Resource Companion (SPARC) App is a sarcoidosis-specific mHealth App intended to improve fatigue and stress in SPs. It prompts SPs to conduct breathing awareness meditation (BAM) and contains educational modules aimed at improving self-efficacy.Herein we describe the design and methods of a 3-month randomized control trial comparing use of the SPARC App (10-min BAM twice daily) to standard care in 50 SPs with significant fatigue (FAS ≥22). A Fitbit® watch will provide immediate heartrate feedback after BAM sessions to objectively monitor adherence. The primary outcomes are feasibility and usability of the SPARC App (collected monthly). Secondary endpoints include preliminary efficacy at improving fatigue, stress, and quality of life. We expect the SPARC App to be a useable and feasible intervention that has potential to overcome barriers of more traditional in-person programs

Genome-wide association study of age-related macular degeneration identifies associated variants in the TNXB–FKBPL–NOTCH4 region of chromosome 6p21.3

Age-related macular degeneration (AMD) is a leading cause of visual loss in Western populations. Susceptibility is influenced by age, environmental and genetic factors. Known genetic risk loci do not account for all the heritability. We therefore carried out a genome-wide association study of AMD in the UK population with 893 cases of advanced AMD and 2199 controls. This showed an association with the well-established AMD risk loci ARMS2 (age-related maculopathy susceptibility 2)-HTRA1 (HtrA serine peptidase 1) (P =2.7 × 10(-72)), CFH (complement factor H) (P =2.3 × 10(-47)), C2 (complement component 2)-CFB (complement factor B) (P =5.2 × 10(-9)), C3 (complement component 3) (P =2.2 × 10(-3)) and CFI (P =3.6 × 10(-3)) and with more recently reported risk loci at VEGFA (P =1.2 × 10(-3)) and LIPC (hepatic lipase) (P =0.04). Using a replication sample of 1411 advanced AMD cases and 1431 examined controls, we confirmed a novel association between AMD and single-nucleotide polymorphisms on chromosome 6p21.3 at TNXB (tenascin XB)-FKBPL (FK506 binding protein like) [rs12153855/rs9391734; discovery P =4.3 × 10(-7), replication P =3.0 × 10(-4), combined P =1.3 × 10(-9), odds ratio (OR) = 1.4, 95% confidence interval (CI) = 1.3-1.6] and the neighbouring gene NOTCH4 (Notch 4) (rs2071277; discovery P =3.2 × 10(-8), replication P =3.8 × 10(-5), combined P =2.0 × 10(-11), OR = 1.3, 95% CI = 1.2-1.4). These associations remained significant in conditional analyses which included the adjacent C2-CFB locus. TNXB, FKBPL and NOTCH4 are all plausible AMD susceptibility genes, but further research will be needed to identify the causal variants and determine whether any of these genes are involved in the pathogenesis of AMD

Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ):Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.</p