Genome-Wide Gene-Set Analysis Identifies Molecular Mechanisms Associated with ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal late-onset motor neuron disease characterized by the loss of the upper and lower motor neurons. Our understanding of the molecular basis of ALS pathology remains elusive, complicating the development of efficient treatment. Gene-set analyses of genome-wide data have offered insight into the biological processes and pathways of complex diseases and can suggest new hypotheses regarding causal mechanisms. Our aim in this study was to identify and explore biological pathways and other gene sets having genomic association to ALS. Two cohorts of genomic data from the dbGaP repository were combined: (a) the largest available ALS individual-level genotype dataset (N = 12,319), and (b) a similarly sized control cohort (N = 13,210). Following comprehensive quality control pipelines, imputation and meta-analysis, we assembled a large European descent ALS-control cohort of 9244 ALS cases and 12,795 healthy controls represented by genetic variants of 19,242 genes. Multi-marker analysis of genomic annotation (MAGMA) gene-set analysis was applied to an extensive collection of 31,454 gene sets from the molecular signatures database (MSigDB). Statistically significant associations were observed for gene sets related to immune response, apoptosis, lipid metabolism, neuron differentiation, muscle cell function, synaptic plasticity and development. We also report novel interactions between gene sets, suggestive of mechanistic overlaps. A manual meta-categorization and enrichment mapping approach is used to explore the overlap of gene membership between significant gene sets, revealing a number of shared mechanisms.

Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis.

The rapid increase in the number of genetic variants identified to be associated with Amyotrophic Lateral Sclerosis (ALS) through genome-wide association studies (GWAS) has created an emerging need to understand the functional pathways that are implicated in the pathology of ALS. Gene-set analysis (GSA) is a powerful method that can provide insight into the associated biological pathways, determining the joint effect of multiple genetic markers. The main contribution of this review is the collection of ALS GSA studies that employ GWAS or individual-based genotype data, investigating their methodology and results related to ALS-associated molecular pathways. Furthermore, the limitations in standard single-gene analyses are summarized, highlighting the power of gene-set analysis, and a brief overview of the statistical properties of gene-set analysis and related concepts is provided. The main aims of this review are to investigate the reproducibility of the collected studies and identify their strengths and limitations, in order to enhance the experimental design and therefore the quality of the results of future studies, deepening our understanding of this devastating disease.

snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data.

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

Effects of PI3K and FGFR inhibitors alone and in combination, and with/without cytostatics in childhood neuroblastoma cell lines.

Neuroblastoma (NB) is a heterogenous disease with treatment varying from observation for low­risk tumors, to extensive therapy with chemotherapy, surgery, radiotherapy, and autologous bone­marrow­transplantation and immunotherapy. However, a high frequency of primary­chemo­refractory disease and recurrences urgently require novel treatment strategies. The present study therefore investigated the anti­NB efficacy of the recently FDA­approved phosphoinositide 3­kinase (PI3K) and fibroblast growth factor receptor (FGFR) inhibitors, alpelisib (BYL719) and erdafitinib (JNJ­42756493), alone and in combination with or without cisplatin, vincristine, or doxorubicin on 5 NB cell lines. For this purpose, the NB cell lines, SK­N­AS, SK­N­BE(2)­C, SK­N­DZ, SK­N­FI and SK­N­SH (where SK­N­DZ had a deletion of PIK3C2G and none had FGFR mutations according to the Cancer Program's Dependency Map, although some were chemoresistant), were tested for their sensitivity to FDA­approved inhibitors alone or in combination, or together with cytostatic drugs by viability, cytotoxicity, apoptosis and proliferation assays. The results revealed that monotherapy with alpelisib or erdafitinib resulted in a dose­dependent inhibition of cell viability and proliferation. Notably, the combined use of PI3K and FGFR inhibitors resulted in an enhanced efficacy, while their combined use with the canonical cytotoxic agents, cisplatin, vincristine and doxorubicin, resulted in variable synergistic, additive and antagonistic effects. Collectively, the present study provides pre­clinical evidence that PI3K and FGFR inhibitors exhibit promising anti­NB activity. The data presented herein also indicate that the incorporation of these inhibitors into chemotherapeutic regimens requires careful consideration and further research in order to obtain a beneficial efficacy. Nevertheless, the addition of PI3K and FGFR inhibitors to the treatment arsenal might reduce the occurrence of refractory and relapsing disease in NB without FGFR and PI3K mutations.

What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis?

Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.

Optimization of the Enrichment of Circulating Tumor Cells for Downstream Phenotypic Analysis in Patients with Non-Small Cell Lung Cancer Treated with Anti-PD-1 Immunotherapy.

The current study aimed at the optimization of circulating tumor cell (CTC) enrichment for downstream protein expression analyses in non-small cell lung cancer (NSCLC) to serve as a tool for the investigation of immune checkpoints in real time. Different enrichment approaches-ficoll density, erythrolysis, their combination with magnetic separation, ISET, and Parsortix-were compared in spiking experiments using the A549, H1975, and SKMES-1 NSCLC cell lines. The most efficient methods were tested in patients (n = 15) receiving immunotherapy targeting programmed cell death-1 (PD-1). Samples were immunofluorescently stained for a) cytokeratins (CK)/epithelial cell adhesion molecule (EpCAM)/leukocyte common antigen (CD45), and b) CK/programmed cell death ligand-1 (PD-L1)/ indoleamine-2,3-dioxygenase (IDO). Ficoll, ISET, and Parsortix presented the highest yields and compatibility with phenotypic analysis; however, at the patient level, they provided discordant CTC positivity (13%, 33%, and 60% of patients, respectively) and enriched for distinct CTC populations. IDO and PD-L1 were expressed in 44% and 33% and co-expressed in 19% of CTCs. CTC detection was associated with progressive disease (PD) (p = 0.006), reduced progression-free survival PFS (p = 0.007), and increased risk of relapse (hazard ratio; HR: 10.733; p = 0.026). IDO-positive CTCs were associated with shorter PFS (p = 0.039) and overall survival OS (p = 0.021) and increased risk of death (HR: 5.462; p = 0.039). The current study indicates that CTC analysis according to distinct immune checkpoints is feasible and may provide valuable biomarkers to monitor NSCLC patients treated with anti-PD-1 agents.