scROSHI: robust supervised hierarchical identification of single cells.

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.

Machine learning analysis of humoral and cellular responses to SARS-CoV-2 infection in young adults.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces B and T cell responses, contributing to virus neutralization. In a cohort of 2,911 young adults, we identified 65 individuals who had an asymptomatic or mildly symptomatic SARS-CoV-2 infection and characterized their humoral and T cell responses to the Spike (S), Nucleocapsid (N) and Membrane (M) proteins. We found that previous infection induced CD4 T cells that vigorously responded to pools of peptides derived from the S and N proteins. By using statistical and machine learning models, we observed that the T cell response highly correlated with a compound titer of antibodies against the Receptor Binding Domain (RBD), S and N. However, while serum antibodies decayed over time, the cellular phenotype of these individuals remained stable over four months. Our computational analysis demonstrates that in young adults, asymptomatic and paucisymptomatic SARS-CoV-2 infections can induce robust and long-lasting CD4 T cell responses that exhibit slower decays than antibody titers. These observations imply that next-generation COVID-19 vaccines should be designed to induce stronger cellular responses to sustain the generation of potent neutralizing antibodies.

gExcite: a start-to-end framework for single-cell gene expression, hashing, and antibody analysis.

SUMMARY: Recently, CITE-seq emerged as a multimodal single-cell technology capturing gene expression and surface protein information from the same single cells, which allows unprecedented insights into disease mechanisms and heterogeneity, as well as immune cell profiling. Multiple single-cell profiling methods exist, but they are typically focused on either gene expression or antibody analysis, not their combination. Moreover, existing software suites are not easily scalable to a multitude of samples. To this end, we designed gExcite, a start-to-end workflow that provides both gene and antibody expression analysis, as well as hashing deconvolution. Embedded in the Snakemake workflow manager, gExcite facilitates reproducible and scalable analyses. We showcase the output of gExcite on a study of different dissociation protocols on PBMC samples. AVAILABILITY AND IMPLEMENTATION: gExcite is open source available on github at https://github.com/ETH-NEXUS/gExcite_pipeline. The software is distributed under the GNU General Public License 3 (GPL3).

scAmpi-A versatile pipeline for single-cell RNA-seq analysis from basics to clinics.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

Drug screening and genome editing in human pancreatic cancer organoids identifies drug-gene interactions and candidates for off-label treatment.

Pancreatic cancer (PDAC) is a highly aggressive malignancy for which the identification of novel therapies is urgently needed. Here, we establish a human PDAC organoid biobank from 31 genetically distinct lines, covering a representative range of tumor subtypes, and demonstrate that these reflect the molecular and phenotypic heterogeneity of primary PDAC tissue. We use CRISPR-Cas9 genome editing and drug screening to characterize drug-gene interactions with ARID1A and BRCA2. We find that missense- but not frameshift mutations in the PDAC driver gene ARID1A are associated with increased sensitivity to the kinase inhibitors dasatinib (p < 0.0001) and VE-821 (p < 0.0001). We conduct an automated drug-repurposing screen with 1,172 FDA-approved compounds, identifying 26 compounds that effectively kill PDAC organoids, including 19 chemotherapy drugs currently approved for other cancer types. We validate the activity of these compounds in vitro and in vivo. The in vivo validated hits include emetine and ouabain, compounds which are approved for non-cancer indications and which perturb the ability of PDAC organoids to respond to hypoxia. Our study provides proof-of-concept for advancing precision oncology and identifying candidates for drug repurposing via genome editing and drug screening in tumor organoid biobanks.

Impact of retrograde intrarenal surgery on biomarkers that are associated with renal parenchyma injury, a preliminary study.

PURPOSE: The primary objective of this preliminary study was to assess the changes in concentration of biomarkers, which indicate renal injury, after RIRS. MATERIALS AND METHODS: Within this prospective study, we included 21 patients with nephrolithiasis requiring treatment with RIRS. From each patient, blood and urine samples were taken at fixed intervals before and after RIRS. Kidney injury molecule-1 (KIM-1), monocyte chemoattractant protein 1 (MCP-1), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), calbindin, albumin, clusterin, gluthation S-transferase-π (GST-π), beta-2-microglobulin (B2M), osteopontin, cystatin c, and trefoil-factor-3 (TFF3) were measured in urine. Creatinine, cystatin c and uric acid were analyzed in the blood samples. RESULTS: A significant increase of the biomarkers clusterin, GST-π, B2M, NGAL and cystatin c was observed after RIRS. However, the biomarkers gradually normalized during the first 14 postoperative days. The parameters surgery time, cumulative stone volume, and BMI did not significantly influence the biomarker concentrations. In the case of GST-π and NGAL a significant positive, yet minuscule effect of age was observed. CONCLUSIONS: With our study, we identified 5 out of 12 assessed renal injury biomarkers that showed a significant increase after RIRS. The increase was only temporary and all markers normalized within 14 days. Further studies are needed to determine the clinical value of these identified markers to assess the long-term impact of intrarenal pressure elevation during RIRS.

A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer.

Improved and cheaper molecular diagnostics allow the shift from "one size fits all" therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients-including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support.

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

Text-Mining Services of the Swiss Variant Interpretation Platform for Oncology.

The Swiss Variant Interpretation Platform for Oncology is a centralized, joint and curated database for clinical somatic variants piloted by a board of Swiss healthcare institutions and operated by the SIB Swiss Institute of Bioinformatics. To support this effort, SIB Text Mining designed a set of text analytics services. This report focuses on three of those services. First, the automatic annotations of the literature with a set of terminologies have been performed, resulting in a large annotated version of MEDLINE and PMC. Second, a generator of variant synonyms for single nucleotide variants has been developed using publicly available data resources, as well as patterns of non-standard formats, often found in the literature. Third, a literature ranking service enables to retrieve a ranked set of MEDLINE abstracts given a variant and optionally a diagnosis. The annotation of MEDLINE and PMC resulted in a total of respectively 785,181,199 and 1,156,060,212 annotations, which means an average of 26 and 425 annotations per abstract and full-text article. The generator of variant synonyms enables to retrieve up to 42 synonyms for a variant. The literature ranking service reaches a precision (P10) of 63%, which means that almost two-thirds of the top-10 returned abstracts are judged relevant. Further services will be implemented to complete this set of services, such as a service to retrieve relevant clinical trials for a patient and a literature ranking service for full-text articles.

Lower urinary tract symptoms (LUTS) before and after robotic-assisted laparoscopic prostatectomy: does improvement of LUTS mitigate worsened incontinence after robotic prostatectomy?

BACKGROUND: Urinary incontinence is a major concern for patients scheduled for radical prostatectomy. However, after prostatectomy lower urinary tract symptoms (LUTS) may improve and thus mitigate this concern. We assessed LUTS and its interference with the quality of life (QoL) using the short form of the international continence society male questionnaire (ICSMALESF-Q) in patients before and after robot-assisted radical prostatectomy (RARP). Furthermore, we aimed to identify risk factors for postoperative urinary incontinence. METHODS: Data of all patients who underwent RARP from 2009 to 2014 were prospectively collected in our customized database. We identified 453 eligible patients for whom a preoperative and at least two postoperative datasets including ICSMALESF-Q were available. RESULTS: Both the ICSMALESF-Q at 6 months (P<0.001) and the related QoL at 12 months (P<0.01) have significantly improved after RARP (P<0.001). Two years after RARP ICSMALESF-Q and thus LUTS have improved in 64%, remained unchanged in 18% and worsened in 18% of patients. The daily pad use was 0 in 79% and 0 or 1 pad in 95.6%, respectively. Increased patient age (P<0.05) was significantly associated with an increased average number of pads used per day (multiplicative effect: +2.1% pads for each year). Being in the D'Amico low-risk group reduced the average number of pads used by 22% (P<0.05, multiplicative effect 0.780). The prostate volume, planned nerve sparing, adjuvant or salvage radiotherapy, body mass index (BMI), or a history of transurethral resection of the prostate (TUR-P) before radical prostatectomy were not associated with the postoperative pad use or changes in LUTS. CONCLUSIONS: The ICSMALESF-Q and thus LUTS have significantly improved in a majority of patients after RARP and hence the associated QoL improved as well. Preoperative D'Amico low-risk group significantly reduced pad use after RARP, whereas increased age significantly increased postoperative pad use. These results will help providers counsel their patients more appropriately before prostatectomy by focusing not only on pad use and incontinence after RARP, but also on changes of the bothersomeness of LUTS and risk factors in general.

Antimicrobial Prophylaxis Redosing Reduces Surgical Site Infection Risk in Prolonged Duration Surgery Irrespective of Its Timing.

BACKGROUND: Long-duration surgery requires repeated administration of antimicrobial prophylaxis (amp). Amp "redosing" reduces incidence of surgical site infections (SSI) but is frequently omitted. Clinical relevance of redosing timing needs to be investigated. Here, we evaluated the effects of compliance with amp redosing and its timing on SSI incidence in prolonged duration surgery. METHODS: Data from >9000 patients undergoing visceral, trauma, or vascular surgery with elective or emergency treatment in two tertiary referral Swiss hospitals were analyzed. All patients had to receive amp preoperatively and redosing, if indicated. Antibiotics used were cefuroxime (1.5 or 3 g, if weight >80 kg), or cefuroxime and metronidazole (1.5 and 0.5 g, or 3 and 1 g doses, if weight >80 kg). Alternatively, in cases of known or suspected allergies, vancomycin (1 g), gentamicin (4 mg/Kg), and metronidazole or clindamycin (300 mg) with or without ciprofloxacin (400 mg) were used. Association of defined parameters, including wound class, ASA scores, and duration of operation, with SSI incidence was explored. RESULTS: In the whole cohort, SSI incidence significantly correlated with duration of surgery (ρ = 0.73, p = 0.031). In 593 patients undergoing >240 min long interventions, duration of surgery was the only parameter significantly (p < 0.001) associated with increased SSI risk, whereas wound class, ASA scores, treatment areas, and emergency versus elective hospital entry were not. Redosing significantly reduced SSI incidence as shown by multivariate analysis (OR 0.60, 95% CI 0.37-0.96, p = 0.034), but exact timing had no significant impact. CONCLUSIONS: Long-duration surgery associates with higher SSI incidence. Irrespective of its exact timing, amp redosing significantly decreases SSI risk.

Bioinformatics for precision oncology.

Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. Here, we discuss the specific requirements of bioinformatics methods and software that arise in the setting of clinical oncology, owing to a stricter regulatory environment and the need for rapid, highly reproducible and robust procedures. We describe the workflow of a molecular tumor board and the specific bioinformatics support that it requires, from the primary analysis of raw molecular profiling data to the automatic generation of a clinical report and its delivery to decision-making clinical oncologists. Such workflows have to various degrees been implemented in many clinical trials, as well as in molecular tumor boards at specialized cancer centers and university hospitals worldwide. We review these and more recent efforts to include other high-dimensional multi-omics patient profiles into the tumor board, as well as the state of clinical decision support software to translate molecular findings into treatment recommendations.

SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics.

BACKGROUND: Molecular precision oncology is an emerging practice to improve cancer therapy by decreasing the risk of choosing treatments that lack efficacy or cause adverse events. However, the challenges of integrating molecular profiling into routine clinical care are manifold. From a computational perspective these include the importance of a short analysis turnaround time, the interpretation of complex drug-gene and gene-gene interactions, and the necessity of standardized high-quality workflows. In addition, difficulties faced when integrating molecular diagnostics into clinical practice are ethical concerns, legal requirements, and limited availability of treatment options beyond standard of care as well as the overall lack of awareness of their existence. METHODS: To the best of our knowledge, we are the first group in Switzerland that established a workflow for personalized diagnostics based on comprehensive high-throughput sequencing of tumors at the clinic. Our workflow, named SwissMTB (Swiss Molecular Tumor Board), links genetic tumor alterations and gene expression to therapeutic options and clinical trial opportunities. The resulting treatment recommendations are summarized in a clinical report and discussed in a molecular tumor board at the clinic to support therapy decisions. RESULTS: Here we present results from an observational pilot study including 22 late-stage cancer patients. In this study we were able to identify actionable variants and corresponding therapies for 19 patients. Half of the patients were analyzed retrospectively. In two patients we identified resistance-associated variants explaining lack of therapy response. For five out of eleven patients analyzed before treatment the SwissMTB diagnostic influenced treatment decision. CONCLUSIONS: SwissMTB enables the analysis and clinical interpretation of large numbers of potentially actionable molecular targets. Thus, our workflow paves the way towards a more frequent use of comprehensive molecular diagnostics in Swiss hospitals.

From hype to reality: data science enabling personalized medicine.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

Linear Mixed Effects Analysis Reveals the Significant Impact of Preoperative Diet Success on Postoperative Weight Loss in Gastric Bypass Surgery.

BACKGROUND: Several studies investigated the impact of preoperative weight loss on bariatric surgery outcome. However, they mostly focus on small groups of patients or lack updated statistical support. METHODS: Two hundred and thirty-nine consecutive patients undergoing laparoscopic, proximal Roux-en-Y gastric bypass at our institution between September 2009 and November 2015 were studied. Patients were operated by the same surgeon, applying a standardized technique and followed a 500-kcal/day preoperative diet, starting 14 days before surgery. Body weight was measured before diet, at surgery, and at least three times postoperatively. A linear mixed effects (LME) model and Benedict and Harris formula were used to assess association of pre- and postoperative weight loss up to 2 years postoperatively. RESULTS: Patients' (184 females) initial weight was 121.7 kg (females 117.2 kg; males 136.6 kg). They lost on average 5.3 kg (females 4.7 kg; males 7.0 kg) pre- and 36.8 kg (females 36.7 kg; males 37.0 kg) postoperatively, within 2 years. Average excess weight loss (EWL) was 67.2% (females 66.6%; males 67.4%). In 205 patients (154 females), EWL exceeded 50%. Longitudinal data analysis according to LME showed a significant impact of pre- on postoperative weight loss (p < 0.001, likelihood-ratio test, LRT). These effects were undetectable if patients were evaluated by non-parametric analysis based on application of the Benedict and Harris formula. CONCLUSIONS: Preoperative dietary success is associated with postoperative weight loss. Effects predicted by the LME model are most pronounced in the first 4-6 months after surgery and are fading away within 24 months postoperatively. External factors not considered in this study might dominate in later phases.

NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis.

Motivation: Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility and documentation are missing. Results: We developed NGS-pipe, a flexible, transparent and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization and easy adaptation to other NGS experiments. Availability and implementation: https://github.com/cbg-ethz/NGS-pipe. Contact: niko.beerenwinkel@bsse.ethz.ch.

Implementation of an IT-guided checklist to improve the quality of medication history records at hospital admission.

Background Medication discrepancies often occur at transition of care such as hospital admission and discharge. Obtaining a complete and accurate medication history on admission is essential as further treatment is based on it. Objective The goal of this study was to reduce the proportion of patients with at least one medication discrepancy in the medication history at admission by implementing an IT-guided checklist. Setting Surgery ward focused on vascular and visceral surgery at a Swiss Cantonal Hospital. Method The study was divided into two phases, before and after implementation of an IT-guided checklist. For both phases a pharmacist collected and compared the medication history (defined as gold standard) with that of the admitting physician. Medication discrepancies were subdivided in omissions and commissions, incorrect medications or dose changes, and incorrect dosage forms or strength. Main outcome measure The proportion of patients with at least one medication discrepancy in the medication history before and after intervention was assessed. Results Out of 415 admissions, 228 patients that met the inclusion criteria were enrolled in the study, 113 before and 115 patients after intervention. After intervention, medication discrepancies declined from 69.9 to 29.6% (p < 0.0001) of patients, the mean medication discrepancy per patient was reduced from 2.3 to 0.6 (p < 0.0001), and the most common error, omission of a regularly used medication, was reduced from 76.4 to 44.1% (p < 0.001). Conclusion The implementation of the IT-guided checklist is associated with a significant reduction of medication discrepancies at admission and potentially improves the medication safety for the patient.

Hypothesis-free analyses from a large psoriatic arthritis cohort support merger to consolidated peripheral arthritis definition without subtyping.

Current ClASsification criteria for Psoriatic ARthritis classification criteria for psoriatic arthritis (PsA) provide a preliminary definition of inflammatory articular disease. This study aimed to further characterize PsA peripheral arthritis using purely data-driven approaches for the affected joint distribution pattern. PsA patients from the Swiss Clinical Quality Management in Rheumatic Diseases (SCQM) database were clustered according to similarities in 66 swollen and in 68 tender joints. Clusters were compared in terms of other disease characteristics and studied for coincidence with traditional PsA subtypes, stability over time and treatment response upon first tumour necrosis factor alpha (TNF-α) therapy. Clustering of 957 patients resulted in an oligoarticular, a polyarticular hand dominated, a polyarticular foot dominated and a fourth cluster which was characterized by polyarticular involvement of the hands and feet. Of the traditional PsA subtypes, only a non-PsA-specific oligoarticular joint involvement pattern was retrieved by clustering. When comparing clusters in other disease manifestations, only minor and clinically probably irrelevant differences occurred. Over time, clusters were more robust than traditional PsA subtypes. Patients in different joint clusters had similar response rates upon first anti-TNF-α therapy, and minimal disease activity was achieved in 56% of 285 patients, irrespective of cluster membership. Hypothesis-free approaches to group PsA patients yield clusters with improved consistency, but without clinically important differences. Taken together, the current peripheral arthritis definition by GRAPPA without further specification into subtypes is strongly supported by the data.

Genomic variant annotation workflow for clinical applications.

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different resources and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, rDGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.