To identify key connections between DNA-damage repair and checkpoint pathways, we performed RNA interference screens for regulators of the ionizing radiation-induced G2 checkpoint, and we identified the breast cancer gene BRCA2. The checkpoint was also abrogated following depletion of PALB2, an interaction partner of BRCA2. BRCA2 and PALB2 depletion led to premature checkpoint abrogation and earlier activation of the AURORA A-PLK1 checkpoint-recovery pathway. These results indicate that the breast cancer tumour suppressors and homologous recombination repair proteins BRCA2 and PALB2 are main regulators of G2 checkpoint maintenance following DNA-damage.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML. 1.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

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Background:Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation.Findings:We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered.Conclusions:Through a combination of streamlined data acquisition, interoperable representation of dataset collections, and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no.

BackgroundPseudomyxoma peritonei (PMP) is a rare, slow-growing abdominal cancer with no efficacious treatment options in non-resectable and recurrent cases. Otherwise, rare activating mutations in the GNAS oncogene are remarkably frequent in PMP and the mutated gene product, guanine nucleotide-binding protein α subunit (Gsα), is a potential tumor neoantigen, presenting an opportunity for targeting by a therapeutic cancer vaccine.MethodsTumor and blood samples were collected from 25 patients undergoing surgery for PMP (NCT02073500). GNAS mutation analysis was performed by next-generation targeted sequencing or digital droplet PCR. Responses to stimulation with Gsα mutated (point mutations R201H and R201C) 30 mer peptides were analyzed in peripheral blood T cells derived from patients with PMP and healthy donors. Fresh PMP tumor samples were analyzed by mass cytometry using a panel of 35 extracellular markers, and cellular subpopulations were clustered and visualized using the visual stochastic network embedding analysis tool.ResultsGNAS mutations were detected in 22/25 tumor samples (88%; R201H and R201C mutations detected in 16 and 6 cases, respectively). Strong T cell proliferation against Gsα mutated peptides was observed in 18/24 patients with PMP. Mass cytometry analysis of tumor revealed infiltration of CD3 +T cells in most samples, with variable CD4+:CD8 + ratios. A large proportion of T cells expressed immune checkpoint molecules, in particular programmed death receptor-1 and T cell immunoreceptor with Ig and ITIM, indicating that these T cells were antigen experienced.ConclusionThese findings point to the existence of a pre-existing immunity in patients with PMP towards mutated Gsα, which has been insufficient to control tumor growth, possibly because of inhibition of antitumor T cells by upregulation of immune checkpoint molecules. The results form a rationale for exploring peptide vaccination with Gsα peptides in combination with immune checkpoint inhibiton as a possible curative treatment for PMP and other GNAS mutated cancers.

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