Frequency of false-positive FISH 1p/19q codeletion in adult diffuse astrocytic gliomas

Oligodendroglioma is genetically defined by concomitant IDH (IDH1/IDH2) mutation and whole-arm 1p/19q codeletion. Codeletion of 1p/19q traditionally evaluated by fluorescence in situ hybridization (FISH) cannot distinguish partial from whole-arm 1p/19q codeletion. Partial 1p/19q codeletion called positive by FISH is diagnostically a "false-positive" result. Chromosomal microarray (CMA) discriminates partial from whole-arm 1p/19q codeletion. Herein, we aimed to estimate the frequency of partial 1p/19q codeletion that would lead to a false-positive FISH result

SeekFusion - A Clinically Validated Fusion Transcript Detection Pipeline for PCR-Based Next-Generation Sequencing of RNA

Detecting gene fusions involving driver oncogenes is pivotal in clinical diagnosis and treatment of cancer patients. Recent developments in next-generation sequencing (NGS) technologies have enabled improved assays for bioinformatics-based gene fusions detection. In clinical applications, where a small number of fusions are clinically actionable, targeted polymerase chain reaction (PCR)-based NGS chemistries, such as the QIAseq RNAscan assay, aim to improve accuracy compared to standard RNA sequencing. Existing informatics methods for gene fusion detection in NGS-based RNA sequencing assays traditionally use a transcriptome-based spliced alignment approach or a de-novo assembly approach. Transcriptome-based spliced alignment methods face challenges with short read mapping yielding low quality alignments. De-novo assembly-based methods yield longer contigs from short reads that can be more sensitive for genomic rearrangements, but face performance and scalability challenges. Consequently, there exists a need for a method to efficiently and accurately detect fusions in targeted PCR-based NGS chemistries. We describe SeekFusion, a highly accurate and computationally efficient pipeline enabling identification of gene fusions from PCR-based NGS chemistries. Utilizing biological samples processed with the QIAseq RNAscan assay and in-silico simulated data we demonstrate that SeekFusion gene fusion detection accuracy outperforms popular existing methods such as STAR-Fusion, TOPHAT-Fusion and JAFFA-hybrid. We also present results from 4,484 patient samples tested for neurological tumors and sarcoma, encompassing details on some novel fusions identified

Targeted deep sequencing of mucinous ovarian tumors reveals multiple overlapping RAS-pathway activating mutations in borderline and cancerous neoplasms

Background: Mucinous ovarian tumors represent a distinct histotype of epithelial ovarian cancer. The rarest (2-4 % of ovarian carcinomas) of the five major histotypes, their genomic landscape remains poorly described. We undertook hotspot sequencing of 50 genes commonly mutated in human cancer across 69 mucinous ovarian tumors. Our goals were to establish the overall frequency of cancer-hotspot mutations across a large cohort, especially those tumors previously thought to be “RAS-pathway alteration negative”, using highly-sensitive next-generation sequencing as well as further explore a small number of cases with apparent heterogeneity in RAS-pathway activating alterations. Methods: Using the Ion Torrent PGM platform, we performed next generation sequencing analysis using the v2 Cancer Hotspot Panel. Regions of disparate ERBB2-amplification status were sequenced independently for two mucinous carcinoma (MC) cases, previously established as showing ERBB2 amplification/overexpression heterogeneity, to assess the hypothesis of subclonal populations containing either KRAS mutation or ERBB2 amplification independently or simultaneously. Results: We detected mutations in KRAS, TP53, CDKN2A, PIK3CA, PTEN, BRAF, FGFR2, STK11, CTNNB1, SRC, SMAD4, GNA11 and ERBB2. KRAS mutations remain the most frequently observed alteration among MC (64.9 %) and mucinous borderline tumors (MBOT) (92.3 %). TP53 mutation occurred more frequently in carcinomas than borderline tumors (56.8 % and 11.5 %, respectively), and combined IHC and mutation data suggest alterations occur in approximately 68 % of MC and as many as 20 % of MBOT. Proven and potential RAS-pathway activating changes were observed in all but one MC. Concurrent ERBB2 amplification and KRAS mutation were observed in a substantial number of cases (7/63 total), as was co-occurrence of KRAS and BRAF mutations (one case). Microdissection of ERBB2-amplified regions of tumors harboring KRAS mutation suggests these alterations are occurring in the same cell populations, while consistency of KRAS allelic frequency in both ERBB2 amplified and non-amplified regions suggests this mutation occurred in advance of the amplification event. Conclusions: Overall, the prevalence of RAS-alteration and striking co-occurrence of pathway “double-hits” supports a critical role for tumor progression in this ovarian malignancy. Given the spectrum of RAS-activating mutations, it is clear that targeting this pathway may be a viable therapeutic option for patients with recurrent or advanced stage mucinous ovarian carcinoma, however caution should be exercised in selecting one or more personalized therapeutics given the frequency of non-redundant RAS-activating alterations

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

Background Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR). Methods The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes. Results Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)). Conclusions Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness

Translocator protein is a marker of activated microglia in rodent models but not human neurodegenerative diseases

Microglial activation plays central roles in neuroinflammatory and neurodegenerative diseases. Positron emission tomography (PET) targeting 18 kDa Translocator Protein (TSPO) is widely used for localising inflammation in vivo, but its quantitative interpretation remains uncertain. We show that TSPO expression increases in activated microglia in mouse brain disease models but does not change in a non-human primate disease model or in common neurodegenerative and neuroinflammatory human diseases. We describe genetic divergence in the TSPO gene promoter, consistent with the hypothesis that the increase in TSPO expression in activated myeloid cells depends on the transcription factor AP1 and is unique to a subset of rodent species within the Muroidea superfamily. Finally, we identify LCP2 and TFEC as potential markers of microglial activation in humans. These data emphasise that TSPO expression in human myeloid cells is related to different phenomena than in mice, and that TSPO-PET signals in humans reflect the density of inflammatory cells rather than activation state.Published versionThe authors thank the UK MS Society for financial support (grant number: C008-16.1). DRO was funded by an MRC Clinician Scientist Award (MR/N008219/1). P.M.M. acknowledges generous support from Edmond J Safra Foundation and Lily Safra, the NIHR Senior Investigator programme and the UK Dementia Research Institute which receives its funding from DRI Ltd., funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. P.M.M. and D.R.O. thank the Imperial College Healthcare Trust-NIHR Biomedical Research Centre for infrastructure support and the Medical Research Council for support of TSPO studies (MR/N016343/1). E.A. was supported by the ALS Stichting (grant “The Dutch ALS Tissue Bank”). P.M. and B.B.T. are funded by the Swiss National Science Foundation (projects 320030_184713 and 310030_212322, respectively). S.T. was supported by an “Early Postdoc.Mobility” scholarship (P2GEP3_191446) from the Swiss National Science Foundation, a “Clinical Medicine Plus” scholarship from the Prof Dr. Max Cloëtta Foundation (Zurich, Switzerland), from the Jean et Madeleine Vachoux Foundation (Geneva, Switzerland) and from the University Hospitals of Geneva. This work was funded by NIH grants U01AG061356 (De Jager/Bennett), RF1AG057473 (De Jager/Bennett), and U01AG046152 (De Jager/Bennett) as part of the AMP-AD consortium, as well as NIH grants R01AG066831 (Menon) and U01AG072572 (De Jager/St George-Hyslop)