Neoadjuvant nivolumab with or without relatlimab in resectable non-small-cell lung cancer: a randomized phase 2 trial.

Antibodies targeting the immune checkpoint molecules PD-1, PD-L1 and CTLA-4, administered alone or in combination with chemotherapy, are the standard of care in most patients with metastatic non-small-cell lung cancers. When given before curative surgery, tumor responses and improved event-free survival are achieved. New antibody combinations may be more efficacious and tolerable. In an ongoing, open-label phase 2 study, 60 biomarker-unselected, treatment-naive patients with resectable non-small-cell lung cancer were randomized to receive two preoperative doses of nivolumab (anti-PD-1) with or without relatlimab (anti-LAG-3) antibody therapy. The primary study endpoint was the feasibility of surgery within 43 days, which was met by all patients. Curative resection was achieved in 95% of patients. Secondary endpoints included pathological and radiographic response rates, pathologically complete resection rates, disease-free and overall survival rates, and safety. Major pathological (≤10% viable tumor cells) and objective radiographic responses were achieved in 27% and 10% (nivolumab) and in 30% and 27% (nivolumab and relatlimab) of patients, respectively. In 100% (nivolumab) and 90% (nivolumab and relatlimab) of patients, tumors and lymph nodes were pathologically completely resected. With 12 months median duration of follow-up, disease-free survival and overall survival rates at 12 months were 89% and 93% (nivolumab), and 93% and 100% (nivolumab and relatlimab). Both treatments were safe with grade ≥3 treatment-emergent adverse events reported in 10% and 13% of patients per study arm. Exploratory analyses provided insights into biological processes triggered by preoperative immunotherapy. This study establishes the feasibility and safety of dual targeting of PD-1 and LAG-3 before lung cancer surgery.ClinicalTrials.gov Indentifier: NCT04205552 .

Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEKi) failed due to inherent and acquired resistance mechanisms. To gain insights into molecular changes during the formation of resistance to oncogenic MAPK pathway inhibition, we utilized short-term passaged primary tumor cells from ten PDACs of genetically engineered mice. We followed gain and loss of resistance upon MEKi exposure and withdrawal by longitudinal integrative analysis of whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and mass spectrometry data. RESULTS: We found that resistant cell populations under increasing MEKi treatment evolved by the expansion of a single clone but were not a direct consequence of known resistance-conferring mutations. Rather, resistant cells showed adaptive DNA hypermethylation of 209 and hypomethylation of 8 genomic sites, most of which overlap with regulatory elements known to be active in murine PDAC cells. Both DNA methylation changes and MEKi resistance were transient and reversible upon drug withdrawal. Furthermore, MEKi resistance could be reversed by DNA methyltransferase inhibition with remarkable sensitivity exclusively in the resistant cells. CONCLUSION: Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.

A virally encoded GPCR drives glioblastoma through feed-forward activation of the SK1-S1P1 signaling axis.

The G protein-coupled receptor (GPCR) US28 encoded by the human cytomegalovirus (HCMV) is associated with accelerated progression of glioblastomas, aggressive brain tumors with a generally poor prognosis. Here, we showed that US28 increased the malignancy of U251 glioblastoma cells by enhancing signaling mediated by sphingosine-1-phosphate (S1P), a bioactive lipid that stimulates oncogenic pathways in glioblastoma. US28 expression increased the abundance of the key components of the S1P signaling axis, including an enzyme that generates S1P [sphingosine kinase 1 (SK1)], an S1P receptor [S1P receptor 1 (S1P1)], and S1P itself. Enhanced S1P signaling promoted glioblastoma cell proliferation and survival by activating the kinases AKT and CHK1 and the transcriptional regulators cMYC and STAT3 and by increasing the abundance of cancerous inhibitor of PP2A (CIP2A), driving several feed-forward signaling loops. Inhibition of S1P signaling abrogated the proliferative and anti-apoptotic effects of US28. US28 also activated the S1P signaling axis in HCMV-infected cells. This study uncovers central roles for S1P and CIP2A in feed-forward signaling that contributes to the US28-mediated exacerbation of glioblastoma.

Insane in the vembrane: filtering and transforming VCF/BCF files.

SUMMARY: We present vembrane as a command line variant call format (VCF)/binary call format (BCF) filtering tool that consolidates and extends the filtering functionality of previous software to meet any imaginable filtering use case. Vembrane exposes the VCF/BCF file type specification and its inofficial extensions by the annotation tools VEP and SnpEff as Python data structures. vembrane filter enables filtration by Python expressions, requiring only basic knowledge of the Python programming language. vembrane table allows users to generate tables from subsets of annotations or functions thereof. Finally, it is fast, by using pysam and relying on lazy evaluation. AVAILABILITY AND IMPLEMENTATION: Source code and installation instructions are available at github.com/vembrane/vembrane (doi: 10.5281/zenodo.7003981). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plasma Sequencing for Patients with GIST-Limitations and Opportunities in an Academic Setting.

Circulating tumor DNA (ctDNA) from circulating free DNA (cfDNA) in GIST is of interest for the detection of heterogeneous resistance mutations and treatment monitoring. However, methodologies for use in a local setting are not standardized and are error-prone and difficult to interpret. We established a workflow to evaluate routine tumor tissue NGS (Illumina-based next generation sequencing) panels and pipelines for ctDNA sequencing in an academic setting. Regular blood collection (Sarstedt) EDTA tubes were sufficient for direct processing whereas specialized tubes (STRECK) were better for transportation. Mutation detection rate was higher in automatically extracted (AE) than manually extracted (ME) samples. Sensitivity and specificity for specific mutation detection was higher using digital droplet (dd)PCR compared to NGS. In a retrospective analysis of NGS and clinical data (133 samples from 38 patients), cfDNA concentration correlated with tumor load and mutation detection. A clinical routine pipeline and a novel research pipeline yielded different results, but known and resistance-mediating mutations were detected by both and correlated with the resistance spectrum of TKIs used. In conclusion, NGS routine panel analysis was not sensitive and specific enough to replace solid biopsies in GIST. However, more precise methods (hybridization capture NGS, ddPCR) may comprise important research tools to investigate resistance. Future clinical trials need to compare methodology and protocols.

Detection and Validation of Circular DNA Fragments Using Nanopore Sequencing.

Occurrence of extra-chromosomal circular DNA is a phenomenon frequently observed in tumor cells, and the presence of such DNA has been recognized as a marker of adverse outcome across cancer types. We here describe a computational workflow for identification of DNA circles from long-read sequencing data. The workflow is implemented based on the Snakemake workflow management system. Its key step uses a graph-theoretic approach to identify putative circular fragments validated on simulated reads. We then demonstrate robustness of our approach using nanopore sequencing of selectively enriched circular DNA by highly sensitive and specific recovery of plasmids and the mitochondrial genome, which is the only circular DNA in normal human cells. Finally, we show that the workflow facilitates detection of larger circular DNA fragments containing extrachromosomal copies of the MYCN oncogene and the respective breakpoints, which is a potentially useful application in disease monitoring of several cancer types.

Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo.

Accurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable-because computationally efficient-manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo.

VISPR-online: a web-based interactive tool to visualize CRISPR screening experiments.

BACKGROUND: VISPR is an interactive visualization and analysis framework for CRISPR screening experiments. However, it only supports the output of MAGeCK, and requires installation and manual configuration. Furthermore, VISPR is designed to run on a single computer, and data sharing between collaborators is challenging. RESULTS: To make the tool easily accessible to the community, we present VISPR-online, a web-based general application allowing users to visualize, explore, and share CRISPR screening data online with a few simple steps. VISPR-online provides an exploration of screening results and visualization of read count changes. Apart from MAGeCK, VISPR-online supports two more popular CRISPR screening analysis tools: BAGEL and JACKS. It provides an interactive environment for exploring gene essentiality, viewing guide RNA (gRNA) locations, and allowing users to resume and share screening results. CONCLUSIONS: VISPR-online allows users to visualize, explore and share CRISPR screening data online. It is freely available at http://vispr-online.weililab.org , while the source code is available at https://github.com/lemoncyb/VISPR-online .

Sustainable data analysis with Snakemake.

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

Go Get Data (GGD) is a framework that facilitates reproducible access to genomic data.

The rapid increase in the amount of genomic data provides researchers with an opportunity to integrate diverse datasets and annotations when addressing a wide range of biological questions. However, genomic datasets are deposited on different platforms and are stored in numerous formats from multiple genome builds, which complicates the task of collecting, annotating, transforming, and integrating data as needed. Here, we developed Go Get Data (GGD) as a fast, reproducible approach to installing standardized data recipes. GGD is available on Github ( https://gogetdata.github.io/ ), is extendable to other data types, and can streamline the complexities typically associated with data integration, saving researchers time and improving research reproducibility.

Machine learning reveals a PD-L1-independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context.

OBJECTIVE: Current predictive biomarkers for PD-1 (programmed cell death protein 1)/PD-L1 (programmed death-ligand 1)-directed immunotherapy in non-small cell lung cancer (NSCLC) mostly focus on features of tumour cells. However, the tumour microenvironment and immune context are expected to play major roles in governing therapy response. Against this background, we set out to apply context-sensitive feature selection and machine learning approaches on expression profiles of immune-related genes in diagnostic biopsies of patients with stage IV NSCLC. METHODS: RNA expression levels were determined using the NanoString nCounter platform in formalin-fixed paraffin-embedded tumour biopsies obtained during the diagnostic workup of stage IV NSCLC from two thoracic oncology centres. A 770-gene panel covering immune-related genes and control genes was used. We applied supervised machine learning methods for feature selection and generation of predictive models. RESULTS: Feature selection and model creation were based on a training cohort of 55 patients with recurrent NSCLC treated with PD-1/PD-L1 antibody therapy. Resulting models identified patients with superior outcomes to immunotherapy, as validated in two subsequently recruited, separate patient cohorts (n = 67, hazard ratio = 0.46, p = 0.035). The predictive information obtained from these models was orthogonal to PD-L1 expression as per immunohistochemistry: Selecting by PD-L1 positivity at immunohistochemistry plus model prediction identified patients with highly favourable outcomes. Independence of PD-L1 positivity and model predictions were confirmed in multivariate analysis. Visualisation of the models revealed the predictive superiority of the entire 7-gene context over any single gene. CONCLUSION: Using context-sensitive assays and bioinformatics capturing the tumour immune context allows precise prediction of response to PD-1/PD-L1-directed immunotherapy in NSCLC.

Targeting the innate immunoreceptor RIG-I overcomes melanoma-intrinsic resistance to T cell immunotherapy.

Understanding tumor resistance to T cell immunotherapies is critical to improve patient outcomes. Our study revealed a role for transcriptional suppression of the tumor-intrinsic HLA class I (HLA-I) antigen processing and presentation machinery (APM) in therapy resistance. Low HLA-I APM mRNA levels in melanoma metastases before immune checkpoint blockade (ICB) correlated with nonresponsiveness to therapy and poor clinical outcome. Patient-derived melanoma cells with silenced HLA-I APM escaped recognition by autologous CD8+ T cells. However, targeted activation of the innate immunoreceptor RIG-I initiated de novo HLA-I APM transcription, thereby overcoming T cell resistance. Antigen presentation was restored in interferon-sensitive (IFN-sensitive) but also immunoedited IFN-resistant melanoma models through RIG-I-dependent stimulation of an IFN-independent salvage pathway involving IRF1 and IRF3. Likewise, enhanced HLA-I APM expression was detected in RIG-Ihi (DDX58hi) melanoma biopsies, correlating with improved patient survival. Induction of HLA-I APM by RIG-I synergized with antibodies blocking PD-1 and TIGIT inhibitory checkpoints in boosting the antitumor T cell activity of ICB nonresponders. Overall, the herein-identified IFN-independent effect of RIG-I on tumor antigen presentation and T cell recognition proposes innate immunoreceptor targeting as a strategy to overcome intrinsic T cell resistance of IFN-sensitive and IFN-resistant melanomas and improve clinical outcomes in immunotherapy.

Eleven grand challenges in single-cell data science.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Full-length de novo viral quasispecies assembly through variation graph construction.

MOTIVATION: Viruses populate their hosts as a viral quasispecies: a collection of genetically related mutant strains. Viral quasispecies assembly is the reconstruction of strain-specific haplotypes from read data, and predicting their relative abundances within the mix of strains is an important step for various treatment-related reasons. Reference genome independent ('de novo') approaches have yielded benefits over reference-guided approaches, because reference-induced biases can become overwhelming when dealing with divergent strains. While being very accurate, extant de novo methods only yield rather short contigs. The remaining challenge is to reconstruct full-length haplotypes together with their abundances from such contigs. RESULTS: We present Virus-VG as a de novo approach to viral haplotype reconstruction from preassembled contigs. Our method constructs a variation graph from the short input contigs without making use of a reference genome. Then, to obtain paths through the variation graph that reflect the original haplotypes, we solve a minimization problem that yields a selection of maximal-length paths that is, optimal in terms of being compatible with the read coverages computed for the nodes of the variation graph. We output the resulting selection of maximal length paths as the haplotypes, together with their abundances. Benchmarking experiments on challenging simulated and real datasets show significant improvements in assembly contiguity compared to the input contigs, while preserving low error rates compared to the state-of-the-art viral quasispecies assemblers. AVAILABILITY AND IMPLEMENTATION: Virus-VG is freely available at https://bitbucket.org/jbaaijens/virus-vg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Bayesian model for single cell transcript expression analysis on MERFISH data.

MOTIVATION: Multiplexed error-robust fluorescence in-situ hybridization (MERFISH) is a recent technology to obtain spatially resolved gene or transcript expression profiles in single cells for hundreds to thousands of genes in parallel. So far, no statistical framework to analyze MERFISH data is available. RESULTS: We present a Bayesian model for single cell transcript expression analysis on MERFISH data. We show that the model successfully captures uncertainty in MERFISH data and eliminates systematic biases that can occur in raw RNA molecule counts obtained with MERFISH. Our model accurately estimates transcript expression and additionally provides the full probability distribution and credible intervals for each transcript. We further show how this enables MERFISH to scale towards the whole genome while being able to control the uncertainty in obtained results. AVAILABILITY AND IMPLEMENTATION: The presented model is implemented on top of Rust-Bio (Köster, 2016) and available open-source as MERFISHtools (https://merfishtools.github.io). It can be easily installed via Bioconda (Grüning et al., 2018). The entire analysis performed in this paper is provided as a fully reproducible Snakemake (Köster and Rahmann, 2012) workflow via Zenodo (https://doi.org/10.5281/zenodo.752340). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Practical Computational Reproducibility in the Life Sciences.

Many areas of research suffer from poor reproducibility, particularly in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but implementation of these practices remains difficult due to the challenge of assembling software tools plus associated libraries, connecting tools together into pipelines, and specifying parameters. Here, we discuss a suite of cutting-edge technologies that make computational reproducibility not just possible, but practical in both time and effort. This suite combines three well-tested components-a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and workflow systems that automatically orchestrate the composition of these packages for entire pipelines-to achieve an unprecedented level of computational reproducibility. We also provide a practical implementation and five recommendations to help set a typical researcher on the path to performing data analyses reproducibly.