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 focussed 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

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).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma

Clinical management of melanomas with NRAS mutations is challenging. Targeting MAPK signaling is only beneficial to a small subset of patients due to resistance that arises through genetic, transcriptional, and metabolic adaptation. Identification of targetable vulnerabilities in NRAS-mutated melanoma could help improve patient treatment. Here, we used multiomics analyses to reveal that NRAS-mutated melanoma cells adopt a mesenchymal phenotype with a quiescent metabolic program to resist cellular stress induced by MEK inhibition. The metabolic alterations elevated baseline reactive oxygen species (ROS) levels, leading these cells to become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intratumor heterogeneity necessitates the combination of a ROS inducer and a MEK inhibitor to inhibit both tumor growth and metastasis. Ex vivo pharmacoscopy of 62 human metastatic melanomas confirmed that MEK inhibitor-resistant tumors significantly benefited from the combination therapy. Finally, oxidative stress response and translational suppression corresponded with ROS-inducer sensitivity in 486 cancer cell lines, independent of cancer type. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS inducers in melanoma and other cancers.

SIGNIFICANCE

Metabolic reprogramming in drug-resistant NRAS-mutated melanoma cells confers sensitivity to ROS induction, which suppresses tumor growth and metastasis in combination with MAPK pathway inhibitors.

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.

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.

Impact of bodyweight-adjusted antimicrobial prophylaxis on surgical-site infection rates

Background

Antimicrobial prophylaxis (AMP) adjustment according to bodyweight to prevent surgical-site infections (SSI) is controversial. The impact of weight-adjusted AMP dosing on SSI rates was investigated here.

Methods

Results from a first study of patients undergoing visceral, vascular or trauma operations, and receiving standard AMP, enabled retrospective evaluation of the impact of bodyweight and BMI on SSI rates, and identification of patients eligible for weight-adjusted AMP. In a subsequent observational prospective study, patients weighing at least 80 kg were assigned to receive double-dose AMP. Risk factors for SSI, including ASA classification, duration and type of surgery, wound class, diabetes, weight in kilograms, BMI, age, and AMP dose, were evaluated in multivariable analysis.

Results

In the first study (3508 patients), bodyweight and BMI significantly correlated with higher rates of all SSI subclasses (both P < 0.001). An 80-kg cut-off identified patients receiving single-dose AMP who were at higher risk of SSI. In the prospective study (2161 patients), 546 patients weighing 80 kg or more who received only single-dose AMP had higher rates of all SSI types than a group of 1615 who received double-dose AMP (odds ratio (OR) 4.40, 95 per cent c.i. 3.18 to 6.23; P < 0.001). In multivariable analysis including 5021 patients from both cohorts, bodyweight (OR 1.01, 1.00 to 1.02; P = 0.008), BMI (OR 1.01, 1.00 to 1.02; P = 0.007) and double-dose AMP (OR 0.33, 0.23 to 0.46; P < 0.001) among other variables were independently associated with SSI rates.

Conclusion

Double-dose AMP decreases SSI rates in patients weighing 80 kg or more.

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.

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.

Electronic supplementary material

The online version of this article (10.1186/s12911-018-0680-0) contains supplementary material, which is available to authorized users.

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

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.

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 source databases and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, DGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

Proteome-wide identification of predominant subcellular protein localizations in a bacterial model organism

Proteomics data provide unique insights into biological systems, including the predominant subcellular localization (SCL) of proteins, which can reveal important clues about their functions. Here we analyzed data of a complete prokaryotic proteome expressed under two conditions mimicking interaction of the emerging pathogen Bartonella henselae with its mammalian host. Normalized spectral count data from cytoplasmic, total membrane, inner and outer membrane fractions allowed us to identify the predominant SCL for 82% of the identified proteins. The spectral count proportion of total membrane versus cytoplasmic fractions indicated the propensity of cytoplasmic proteins to co-fractionate with the inner membrane, and enabled us to distinguish cytoplasmic, peripheral inner membrane and bona fide inner membrane proteins. Principal component analysis and k-nearest neighbor classification training on selected marker proteins or predominantly localized proteins, allowed us to determine an extensive catalog of at least 74 expressed outer membrane proteins, and to extend the SCL assignment to 94% of the identified proteins, including 18% where in silico methods gave no prediction. Suitable experimental proteomics data combined with straightforward computational approaches can thus identify the predominant SCL on a proteome-wide scale. Finally, we present a conceptual approach to identify proteins potentially changing their SCL in a condition-dependent fashion.

BIOLOGICAL SIGNIFICANCE

The work presented here describes the first prokaryotic proteome-wide subcellular localization (SCL) dataset for the emerging pathogen B. henselae (Bhen). The study indicates that suitable subcellular fractionation experiments combined with straight-forward computational analysis approaches assessing the proportion of spectral counts observed in different subcellular fractions are powerful for determining the predominant SCL of a large percentage of the experimentally observed proteins. This includes numerous cases where in silico prediction methods do not provide any prediction. Avoiding a treatment with harsh conditions, cytoplasmic proteins tend to co-fractionate with proteins of the inner membrane fraction, indicative of close functional interactions. The spectral count proportion (SCP) of total membrane versus cytoplasmic fractions allowed us to obtain a good indication about the relative proximity of individual protein complex members to the inner membrane. Using principal component analysis and k-nearest neighbor approaches, we were able to extend the percentage of proteins with a predominant experimental localization to over 90% of all expressed proteins and identified a set of at least 74 outer membrane (OM) proteins. In general, OM proteins represent a rich source of candidates for the development of urgently needed new therapeutics in combat of resurgence of infectious disease and multi-drug resistant bacteria. Finally, by comparing the data from two infection biology relevant conditions, we conceptually explore methods to identify and visualize potential candidates that may partially change their SCL in these different conditions. The data are made available to researchers as a SCL compendium for Bhen and as an assistance in further improving in silico SCL prediction algorithms.

Directed shotgun proteomics guided by saturated RNA-seq identifies a complete expressed prokaryotic proteome

Prokaryotes, due to their moderate complexity, are particularly amenable to the comprehensive identification of the protein repertoire expressed under different conditions. We applied a generic strategy to identify a complete expressed prokaryotic proteome, which is based on the analysis of RNA and proteins extracted from matched samples. Saturated transcriptome profiling by RNA-seq provided an endpoint estimate of the protein-coding genes expressed under two conditions which mimic the interaction of Bartonella henselae with its mammalian host. Directed shotgun proteomics experiments were carried out on four subcellular fractions. By specifically targeting proteins which are short, basic, low abundant, and membrane localized, we could eliminate their initial underrepresentation compared to the estimated endpoint. A total of 1250 proteins were identified with an estimated false discovery rate below 1%. This represents 85% of all distinct annotated proteins and ∼90% of the expressed protein-coding genes. Genes that were detected at the transcript but not protein level, were found to be highly enriched in several genomic islands. Furthermore, genes that lacked an ortholog and a functional annotation were not detected at the protein level; these may represent examples of overprediction in genome annotations. A dramatic membrane proteome reorganization was observed, including differential regulation of autotransporters, adhesins, and hemin binding proteins. Particularly noteworthy was the complete membrane proteome coverage, which included expression of all members of the VirB/D4 type IV secretion system, a key virulence factor.

Causal stability ranking

Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs, and informative prioritization of experiments is desirable. We therefore consider predicting stable rankings of genes (covariates), according to their total causal effects on a phenotype (response), from observational data. Since causal effects are generally non-identifiable from observational data only, we use a method that can infer lower bounds for the total causal effect under some assumptions. We validated our method, which we call Causal Stability Ranking (CStaR), in two situations. First, we performed knock-out experiments with Arabidopsis thaliana according to a predicted ranking based on observational gene expression data, using flowering time as phenotype of interest. Besides several known regulators of flowering time, we found almost half of the tested top ranking mutants to have a significantly changed flowering time. Second, we compared CStaR to established regression-based methods on a gene expression dataset of Saccharomyces cerevisiae. We found that CStaR outperforms these established methods. Our method allows for efficient design and prioritization of future intervention experiments, and due to its generality it can be used for a broad spectrum of applications.