The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition

IntroductionSarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes.MethodsTo explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME).ResultsA multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important.DiscussionThe outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI

Methods for Analysis of Big Data.: A Combination of the Lasso Method and the Case-Crossover Design for Investigating Potential Drug Side Effects on Myocardial Infarction

The current master thesis was written during the academic year 2013 − 2014 atthe Norwegian University of Science and Technology (NTNU). It concerns the im-plementation of the case-crossover design in a combination with the lasso method,for investigating potential effects of a set of drugs on myocardial infarction. Thedatasets that were used in the analysis were generated based on information aboutthe usage frequencies of the drugs in the period from 2008 to 2012. Furthermore,the thesis provides a brief explanation of how the lasso method can be used in thecase of generalized linear models, as well as the case-crossover design. The mainanalysis was based on two datasets such that probably weak aspects of the lassocould be discovered. Another aspect of the current thesis was the implementationof the relatively new inference method for the lasso, as well as the implementa-tion of two forms of the lasso method: simple lasso and bootstrap lasso. Finally,the current thesis shows, with numerical results, that the bootstrap form of lassois an effective variable selection method, and that the lasso inference is not yetsufficiently developed

Experiment_ENCSR000FDD

Processed data for experiment ENCSR000FDD.<br><br>Files:<br><br>ENCSR000FDD_MACPET_Peaks.csv (peaks from MACPET)<br><br>ENCSR000FDD_MACS_Peaks.xls (peaks from MACS)<br><br>ENCSR000FDD_MACPET_interactions_window_i are the interactions using the MACPET peaks with windows i=0,...,1000<br><br>ENCSR000FDD_MACS_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.05.<div><br></div><div>ENCSR000FDD_MACS_0.01_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.01</div><div><br></div>ENCSR000FDD_MACS_N_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, using the first N most significant peaks from MACS, where N=total significant MACPET peaks at 0.05 p-value<br><br><br

Experiment_ENCSR000BZY

Processed data for experiment ENCSR000BZY.<br><br>Files:<br><br>ENCSR000BZY_MACPET_Peaks.csv (peaks from MACPET)<br><br>ENCSR000BZY_MACS_Peaks.xls (peaks from MACS)<br><br>ENCSR000BZY_MACPET_interactions_window_i are the interactions using the MACPET peaks with windows i=0,...,1000<br><br>ENCSR000BZY_MACS_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.05.<div><br></div><div>ENCSR000BZY_MACS_0.01_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.01</div><div><br></div>ENCSR000BZY_MACS_N_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, using the first N most significant peaks from MACS, where N=total significant MACPET peaks at 0.05 p-value<br><br><br><br

Experiment_ENCSR000CAC

Processed data for experiment ENCSR000CAC.<br><br>Files:<br><br>ENCSR000CAC_MACPET_Peaks.csv (peaks from MACPET)<br><br>ENCSR000CAC_MACS_Peaks.xls (peaks from MACS)<br><br>ENCSR000CAC_MACS_par2.Peaks.xls (peaks from MACS parameters bw=600, slocal )<br><br>ENCSR000CAC_MACS_par3.Peaks.xls (peaks from MACS parameters bw=1000, llocal )<br><br>ENCSR000CAC_MACS_par4.Peaks.xls (peaks from MACS parameters bw=300, llocal )<br><br>ENCSR000CAC_MACPET_interactions_window_i are the interactions using the MACPET peaks with windows i=0,...,1000<br><br>ENCSR000CAC_MACS_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.05.<div><br></div><div>ENCSR000CAC_MACS_0.01_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.01</div><div><br></div>ENCSR000CAC_MACS_N_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, using the first N most significant peaks from MACS, where N=total significant MACPET peaks at 0.05 p-value<br><br>ENCSR000CAC_MACPET_MotIV_run_i , i=1-5, results from MACPET peaks on a MotIV object.<br><br>ENCSR000CAC_MACS_MotIV_run_i , i=1-5, results from MACS peaks on a MotIV object.<br><br>ENCSR000CAC_MACS_par2_MotIV_run_i , i=1-5, results from MACS parameters 2 peaks on a MotIV object.<br><br>ENCSR000CAC_MACS_par3_MotIV_run_i , i=1-5, results from MACS parameters 3 peaks on a MotIV object.<br><br>ENCSR000CAC_MACS_par4_MotIV_run_i , i=1-5, results from MACS parameters 4 peaks on a MotIV object.<br><br><br>ENCSR000CAC_MACPET_rGADEM_run_i , i=1-5, results from MACPET peaks on a rGADEM object.<br><br>ENCSR000CAC_MACS_rGADEM_run_i , i=1-5, results from MACS peaks on a rGADEM object.<br><br>ENCSR000CAC_MACS_par2_rGADEM_run_i , i=1-5, results from MACS parameters 2 peaks on a rGADEM object.<br><br>ENCSR000CAC_MACS_par3_rGADEM_run_i , i=1-5, results from MACS parameters 3 peaks on a rGADEM object.<br><br>ENCSR000CAC_MACS_par4_rGADEM_run_i , i=1-5, results from MACS parameters 4 peaks on a rGADEM object.<br><br><br

Experiment_ENCSR000FDG

Processed data for experiment ENCSR000FDG.<br><br>Files:<br><br>ENCSR000FDG_MACPET_Peaks.csv (peaks from MACPET)<br><br>ENCSR000FDG_MACS_Peaks.xls (peaks from MACS)<br><br>ENCSR000FDG_MACPET_interactions_window_i are the interactions using the MACPET peaks with windows i=0,...,1000<br><br><div>ENCSR000FDG_MACS_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.05.</div><div><br></div><div>ENCSR000FDG_MACS_0.01_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.01</div><div><br></div><div>ENCSR000FDG_MACS_N_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, using the first N most significant peaks from MACS, where N=total significant MACPET peaks at 0.05 p-value<br></div><br

Experiment_ENCSR000CAD

Processed data for experiment ENCSR000CAD.<br><br>Files:<br><br>ENCSR000CAD_MACPET_Peaks.csv (peaks from MACPET)<br><br>ENCSR000CAD_MACS_Peaks.xls (peaks from MACS)<br><br>ENCSR000CAD_MACS_par2.Peaks.xls (peaks from MACS parameters bw=600, slocal )<br><br>ENCSR000CAD_MACS_par3.Peaks.xls (peaks from MACS parameters bw=1000, llocal )<br><br>ENCSR000CAD_MACS_par4.Peaks.xls (peaks from MACS parameters bw=300, llocal )<br><br>ENCSR000CAD_MACPET_interactions_window_i are the interactions using the MACPET peaks with windows i=0,...,1000<br><br>ENCSR000CAD_MACS_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.05.<div><br></div><div>ENCSR000CAD_MACS_0.01_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, for MACS peaks on p-value 0.01</div><div><br></div>ENCSR000CAD_MACS_N_interactions_window_i are the interactions using the MACS peaks with windows i=0,...,1000, using the first N most significant peaks from MACS, where N=total significant MACPET peaks at 0.05 p-value<br><br>ENCSR000CAD_MACPET_MotIV_run_i , i=1-5, results from MACPET peaks on a MotIV object.<br><br>ENCSR000CAD_MACS_MotIV_run_i , i=1-5, results from MACS peaks on a MotIV object.<br><br>ENCSR000CAD_MACS_par2_MotIV_run_i , i=1-5, results from MACS parameters 2 peaks on a MotIV object.<br><br>ENCSR000CAD_MACS_par3_MotIV_run_i , i=1-5, results from MACS parameters 3 peaks on a MotIV object.<br><br>ENCSR000CAD_MACS_par4_MotIV_run_i , i=1-5, results from MACS parameters 4 peaks on a MotIV object.<br><br>ENCSR000CAD_MACPET_rGADEM_run_i , i=1-5, results from MACPET peaks on a rGADEM object.<br><br>ENCSR000CAD_MACS_rGADEM_run_i , i=1-5, results from MACS peaks on a rGADEM object.<br><br>ENCSR000CAD_MACS_par2_rGADEM_run_i , i=1-5, results from MACS parameters 2 peaks on a rGADEM object.<br><br>ENCSR000CAD_MACS_par3_rGADEM_run_i , i=1-5, results from MACS parameters 3 peaks on a rGADEM object.<br><br>ENCSR000CAD_MACS_par4_rGADEM_run_i , i=1-5, results from MACS parameters 4 peaks on a rGADEM object.<br><br><br><br><br

MACPET: model-based analysis for ChIA-PET

We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared with MACS in terms of motif occurrence and spatial resolution. Furthermore, significant binding sites discovered by MACPET are involved in a higher number of significant three-dimensional interactions than those discovered by MACS. MACPET is freely available on Bioconductor. ChIA-PET; MACPET; Model-based clustering; Paired-end tags; Peak-calling algorithm

Design and development of a powerlines fault detection system based on a drone equipped with thermal camera

Περίληψη: Τα σύγχρονα drones κάνουν χρήση και αξιοποιούν τις τεράστιες δυνατότητες επεξεργαστικής ισχύος των microcomputers, την τεράστια τεχνολογική εξέλιξη στους δορυφόρους και στις τηλεπικοινωνίες για τον προσδιορισμό θέσης, την εξέλιξη των δικτύων κινητής τηλεφωνίας και τέλος τις σημαντικές επιστημονικές γνώσεις και εφαρμογές στην τηλεμετρία, στην επεξεργασία και μεταφορά εικόνας και βίντεο, καθώς και στην ασύρματη μεταφορά δεδομένων. Εκτός από κάμερες για καταγραφή εικόνας και βίντεο τα drones μπορούν να εφοδιαστούν με θερμικές κάμερες αυξάνοντας θεαματικά τις δυνατότητες πρακτικής εφαρμογής τους. Η παρούσα εργασία εξετάζει την αποτελεσματικότητα της χρήσης βασικών μεθόδων επεξεργασίας εικόνας και την εύρεση των περιορισμών τους σε γλώσσα Matlab για την ανίχνευση βλαβών στις γραμμές μεταφοράς ηλεκτρικής ενέργειας, η οποία γίνεται με τη χρήση drone εξοπλισμένου με θερμική κάμερα. Προτείνονται τρεις μεθοδολογίες, μία για τον εντοπισμό των καλωδίων, μία για την εύρεση της θερμοκρασίας τους και μια για τον εντοπισμό των θερμών σημείων στο δίκτυο μεταφοράς ηλεκτρικής ενέργειας. Οι δύο πρώτες μεθοδολογίες έχουν ως κοινά χαρακτηριστικά την ανίχνευση ακμών με τη μέθοδο Canny, την εφαρμογή του Μετασχηματισμού Hough και τη δημιουργία περιοχής ενδιαφέροντος γύρω από κάθε εντοπισμένο καλώδιο. Στη μεθοδολογία για την εύρεση της θερμοκρασίας των καλωδίων γίνεται επιπλέον χρήση των συναρτήσεων roipoly και sort για τον ακριβέστερο εντοπισμό των καλωδίων. Στην περίπτωση ύπαρξης βλάβης είτε αυτή είναι κομμένο καλώδιο, είτε θερμοκρασιακή διαφορά μεταξύ των καλωδίων, γίνεται ακριβής προσδιορισμός της συγκεκριμένης θέσης με τη βοήθεια των συντεταγμένων GPS. Η τρίτη μεθοδολογία προτείνει μια τιμή κατωφλίου για την τμηματοποίηση της εικόνας χρησιμοποιώντας διάφορες μεθόδους επεξεργασίας και ανάλυσης εικόνων. Ο αλγόριθμος βασίζεται στον εντοπισμό της μέγιστης έντασης μιας εικόνας μετά από ορισμένα βήματα προεπεξεργασίας της. Όλοι οι αλγόριθμοι έχουν επιτύχει ικανοποιητικά αποτελέσματα και λόγω των γρήγορων αποδόσεών τους, θα μπορούσαν να χρησιμοποιηθούν και on-line, κατά τη διάρκεια επιθεωρήσεων.Summarization: Modern drones use and exploit the vast potential of microprocessor processing power, the tremendous technological advancement in satellites and telecoms to determine the location, the evolution of mobile telephone networks and, finally, the important scientific knowledge and applications in telemetry, image and video processing and transfer, and wireless data transfer. In addition to cameras for image and video recording, the drones can be equipped with thermal cameras to dramatically increase their practical application capabilities. This paper examines the effectiveness of using basic image processing methods and finding their limitations in Matlab in order to detect damage or malfunction to powerlines, with the help of a drone equipped with thermal camera. Three methodologies are proposed, one for locating the cables, one for finding their temperature and one for hot spot detection in the power transmission network. The first two methodologies have in common features like Canny edge detection, application of Hough Transform and creation of areas of interest around each detected cable. In the methodology of finding the cable temperature, two more functions are used, roipoly and sort, in order to more accurately locate the powerlines. In the event of a fault, whether it is a cable cut or a temperature difference between the cables, precise determination of the specific position is made with the help of GPS coordinates. The third methodology proposes a selection of a threshold value for image segmentation using several methods of image processing and analysis. The algorithm is based on finding the max intensity of input image after certain pre-processing procedures. The results have brought satisfying effects and the algorithms, owing to their fast performance, could be used on-line, during vision inspections

Assessing short-term risk of ischemic stroke in relation to all prescribed medications

We examined the short-term risk of stroke associated with drugs prescribed in Norway or Sweden in a comprehensive, hypothesis-free manner using comprehensive nation-wide data. We identified 27,680 and 92,561 cases with a first ischemic stroke via the patient- and the cause-of-death registers in Norway (2004–2014) and Sweden (2005–2014), respectively, and linked these data to prescription databases. A case-crossover design was used that compares the drugs dispensed within 1 to 14 days before the date of ischemic stroke occurrence with those dispensed 29 to 42 days before the index event. A Bolasso approach, a version of the Lasso regression algorithm, was used to select drugs that acutely either increase or decrease the apparent risk of ischemic stroke. Application of the Bolasso regression algorithm selected 19 drugs which were associated with increased risk for ischemic stroke and 11 drugs with decreased risk in both countries. Morphine in combination with antispasmodics was associated with a particularly high risk of stroke (odds ratio 7.09, 95% confidence intervals 4.81–10.47). Several potentially intriguing associations, both within and across pharmacological classes, merit further investigation in focused, follow-up studies.publishedVersio