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Platinum-based chemotherapy in combination with anti-PD-L1 antibodies has shown promising results in mesothelioma. However, the immunological mechanisms underlying its efficacy are not well understood and there are no predictive biomarkers to guide treatment decisions. Here, we combine time course RNA sequencing (RNA-seq) of peripheral blood mononuclear cells with pre-treatment tumor transcriptome data from the single-arm, phase 2 DREAM trial (N = 54). Single-cell RNA-seq and T cell receptor sequencing (TCR-seq) reveal that CD8 + T effector memory (TEM) cells with stem-like properties are more abundant in peripheral blood of responders and that this population expands upon treatment. These peripheral blood changes are linked to the transcriptional state of the tumor microenvironment. Combining information from both compartments, rather than individually, is most predictive of response. Our study highlights complex interactions between the tumor and immune cells in peripheral blood during objective tumor responses to chemoimmunotherapy. This trial is registered with the Australian New Zealand Clinical Trials Registry, number ACTRN12616001170415., Competing Interests: Declaration of interests W.J.L. declares consultancy for Douglas Pharmaceuticals and research funding from Douglas Pharmaceuticals, AstraZeneca, and ENA therapeutics. W.J.L. is a director of the Cancer Research Trust, which has funded parts of this work, but is not involved in any funding decisions, which are based on independent external peer review. W.J.L. is a founder and director of Setonix Pharmaceuticals, which is not related to the presented work. W.J.L. is an inventor on patent applications unrelated to the presented work. A.K.N. declares consultancy for Douglas Pharmaceuticals and Bristol-Myers Squibb, institutional research funding from AstraZeneca, and consultancy (unrelated to this work) from AstraZeneca., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Motivation: Over the last two decades, transcriptomics has become a standard technique in biomedical research. We now have large databases of RNA-seq data, accompanied by valuable metadata detailing scientific objectives and the experimental procedures used. The metadata is crucial in understanding and replicating published studies, but so far has been underutilized in helping researchers to discover existing datasets., Results: We present SampleExplorer, a tool allowing researchers to search for relevant data using both text and gene set queries. SampleExplorer embeds sample metadata and uses a transformer-based language model to retrieve similar datasets. Extensive benchmarking (see Supplementary Materials and Methods) using the ARCHS4 database demonstrates that SampleExplorer provides an effective approach for retrieving biologically relevant samples from large-scale transcriptomicdata. This tool provides an efficient approach for discovering relevant gene expression datasets in large public repositories. It improves sample and dataset identification across diverse experimental contexts, helping researchers leverage existing transcriptomic data for potential replication or verification studies., Availability and implementation: SampleExplorer is available as a Python package compatible with versions 3.9 to 3.11, available for installation via the Python Package Index (PyPI). The codebase and documentation are accessible at https://github.com/wlchin/SampleExplorer. Supplementary data (Supplementary Materials and Methods) provides detailed methodological information, including an algorithmic description of the retrieval process and data preparation steps., (© The Author(s) 2024. Published by Oxford University Press.)

A robust understanding of the cellular mechanisms underlying diseases sets the foundation for the effective design of drugs and other interventions. The wealth of existing single-cell atlases offers the opportunity to uncover high-resolution information on expression patterns across various cell types and time points. To better understand the associations between cell types and diseases, we leveraged previously developed tools to construct a standardized analysis pipeline and systematically explored associations across four single-cell datasets, spanning a range of tissue types, cell types and developmental time periods. We utilized a set of existing tools to identify co-expression modules and temporal patterns per cell type and then investigated these modules for known disease and phenotype enrichments. Our pipeline reveals known and novel putative cell type-disease associations across all investigated datasets. In addition, we found that automatically discovered gene co-expression modules and temporal clusters are enriched for drug targets, suggesting that our analysis could be used to identify novel therapeutic targets., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Background: SETBP1 Haploinsufficiency Disorder (SETBP1-HD) is characterised by mild to moderate intellectual disability, speech and language impairment, mild motor developmental delay, behavioural issues, hypotonia, mild facial dysmorphisms, and vision impairment. Despite a clear link between SETBP1 mutations and neurodevelopmental disorders the precise role of SETBP1 in neural development remains elusive. We investigate the functional effects of three SETBP1 genetic variants including two pathogenic mutations p.Glu545Ter and SETBP1 p.Tyr1066Ter, resulting in removal of SKI and/or SET domains, and a point mutation p.Thr1387Met in the SET domain., Methods: Genetic variants were introduced into induced pluripotent stem cells (iPSCs) and subsequently differentiated into neurons to model the disease. We measured changes in cellular differentiation, SETBP1 protein localisation, and gene expression changes., Results: The data indicated a change in the WNT pathway, RNA polymerase II pathway and identified GATA2 as a central transcription factor in disease perturbation. In addition, the genetic variants altered the expression of gene sets related to neural forebrain development matching characteristics typical of the SETBP1-HD phenotype., Limitations: The study investigates changes in cellular function in differentiation of iPSC to neural progenitor cells as a human model of SETBP1 HD disorder. Future studies may provide additional information relevant to disease on further neural cell specification, to derive mature neurons, neural forebrain cells, or brain organoids., Conclusions: We developed a human SETBP1-HD model and identified perturbations to the WNT and POL2RA pathway, genes regulated by GATA2. Strikingly neural cells for both the SETBP1 truncation mutations and the single nucleotide variant displayed a SETBP1-HD-like phenotype., (© 2024. Crown.)

Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: "Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature". Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of "a globally coordinated diagnostic and research pipeline". To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient's diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease., (© 2024. The Author(s).)

Time-critical transcriptional events in the immune microenvironment are important for response to immune checkpoint blockade (ICB), yet these events are difficult to characterise and remain incompletely understood. Here, we present whole tumor RNA sequencing data in the context of treatment with ICB in murine models of AB1 mesothelioma and Renca renal cell cancer. We sequenced 144 bulk RNAseq samples from these two cancer types across 4 time points prior and after treatment with ICB. We also performed single-cell sequencing on 12 samples of AB1 and Renca tumors an hour before ICB administration. Our samples were equally distributed between responders and non-responders to treatment. Additionally, we sequenced AB1-HA mesothelioma tumors treated with two sample dissociation protocols to assess the impact of these protocols on the quality transcriptional information in our samples. These datasets provide time-course information to transcriptionally characterize the ICB response and provide detailed information at the single-cell level of the early tumor microenvironment prior to ICB therapy., (© 2024. The Author(s).)

Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRβ repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRβ clonotypes was observed in responding tumours. Machine learning identified TCRβ CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRβ signatures associated with ICT response., Competing Interests: LB is employed by the company JJP Biologics. WJL received research funding from Douglas Pharmaceuticals, AstraZeneca, ENA therapeutics, consultancy for Douglas Pharmaceuticals and MSD. AN is on the advisory board of Boehringer Ingelheim, Bayer, Roche, BMS; and received research funding from AstraZeneca. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.)

An estimated 3.5%-5.9% of the global population live with rare diseases, and approximately 80% of these diseases have a genetic cause. Rare genetic diseases are difficult to diagnose, with some affected individuals experiencing diagnostic delays of 5-30 years. Next-generation sequencing has improved clinical diagnostic rates to 33%-48%. In a majority of cases, novel variants potentially causing the disease are discovered. These variants require functional validation in specialist laboratories, resulting in a diagnostic delay. In the interim, the finding is classified as a genetic variant of uncertain significance (VUS) and the affected individual remains undiagnosed. A VUS (PTCHD1 c. 2489T>G) was identified in a child with autistic behavior, global developmental delay, and hypotonia. Loss of function mutations in PTCHD1 are associated with autism spectrum disorder and intellectual disability; however, the molecular function of PTCHD1 and its role in neurodevelopmental disease is unknown. Here, we apply CRISPR gene editing and induced pluripotent stem cell (iPSC) neural disease modeling to assess the variant. During differentiation from iPSCs to neural progenitors, we detect subtle but significant gene signatures in synaptic transmission and muscle contraction pathways. Our work supports the causal link between the genetic variant and the child's phenotype, providing evidence for the variant to be considered a pathogenic variant according to the American College of Medical Genetics and Genomics guidelines. In addition, our study provides molecular data on the role of PTCHD1 in the context of other neurodevelopmental disorders., Competing Interests: Declaration of interests The authors declare no competing interests., (Crown Copyright © 2023. Published by Elsevier Inc. All rights reserved.)

Background: Genomic sequencing in congenital heart disease (CHD) patients often discovers novel genetic variants, which are classified as variants of uncertain significance (VUS). Functional analysis of each VUS is required in specialised laboratories, to determine whether the VUS is disease causative or not, leading to lengthy diagnostic delays. We investigated stem cell cardiac disease modelling and transcriptomics for the purpose of genetic variant classification using a GATA4 (p.Arg283Cys) VUS in a patient with CHD., Methods: We performed high efficiency CRISPR gene editing with homology directed repair in induced pluripotent stem cells (iPSCs), followed by rapid clonal selection with amplicon sequencing. Genetic variant and healthy matched control cells were compared using cardiomyocyte disease modelling and transcriptomics., Results: Genetic variant and healthy cardiomyocytes similarly expressed Troponin T (cTNNT), and GATA4. Transcriptomics analysis of cardiomyocyte differentiation identified changes consistent with the patient's clinical human phenotype ontology terms. Further, transcriptomics revealed changes in calcium signalling, and cardiomyocyte adrenergic signalling in the variant cells. Functional testing demonstrated, altered action potentials in GATA4 genetic variant cardiomyocytes were consistent with patient cardiac abnormalities., Conclusions: This work provides in vivo functional studies supportive of a damaging effect on the gene or gene product. Furthermore, we demonstrate the utility of iPSCs, CRISPR gene editing and cardiac disease modelling for genetic variant interpretation. The method can readily be applied to other genetic variants in GATA4 or other genes in cardiac disease, providing a centralised assessment pathway for patient genetic variant interpretation., (© 2023. The Author(s).)

Motivation: SAMStat is an efficient program to extract quality control metrics from fastq and SAM/BAM files. A distinguishing feature is that it displays sequence composition, base quality composition and mapping error profiles split by mapping quality. This allows users to rapidly identify reasons for poor mapping including the presence of untrimmed adapters or poor sequencing quality at individual read positions., Results: Here, we present a major update to SAMStat. The new version now supports paired-end and long-read data. Quality control plots are drawn using the ploty javascript library., Availability and Implementation: The source code of SAMStat and code to reproduce the results are found here: https://github.com/timolassmann/samstat., (© The Author(s) 2023. Published by Oxford University Press.)

While chemotherapy remains the first-line treatment for many cancers, it is still unclear what distinguishes responders from non-responders. Here, we characterize the chemotherapy-responsive tumor microenvironment in mice, using RNA sequencing on tumors before and after cyclophosphamide, and compare the gene expression profiles of responders with progressors. Responsive tumors have an inflammatory and highly immune infiltrated pre-treatment tumor microenvironment characterized by the enrichment of pathways associated with CD4 + T cells, interferons (IFNs), and tumor necrosis factor alpha (TNF-α). The same gene expression profile is associated with response to cyclophosphamide-based chemotherapy in patients with breast cancer. Finally, we demonstrate that tumors can be sensitized to cyclophosphamide and 5-FU chemotherapy by pre-treatment with recombinant TNF-α, IFNγ, and poly(I:C). Thus, a CD4 + T cell-inflamed pre-treatment tumor microenvironment is necessary for response to chemotherapy, and this state can be therapeutically attained by targeted immunotherapy., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

The biological determinants of the response to immune checkpoint blockade (ICB) in cancer remain incompletely understood. Little is known about dynamic biological events that underpin therapeutic efficacy due to the inability to frequently sample tumours in patients. Here, we map the transcriptional profiles of 144 responding and non-responding tumours within two mouse models at four time points during ICB. We find that responding tumours display on/fast-off kinetics of type-I-interferon (IFN) signaling. Phenocopying of this kinetics using time-dependent sequential dosing of recombinant IFNs and neutralizing antibodies markedly improves ICB efficacy, but only when IFNβ is targeted, not IFNα. We identify Ly6C + /CD11b + inflammatory monocytes as the primary source of IFNβ and find that active type-I-IFN signaling in tumour-infiltrating inflammatory monocytes is associated with T cell expansion in patients treated with ICB. Together, our results suggest that on/fast-off modulation of IFNβ signaling is critical to the therapeutic response to ICB, which can be exploited to drive clinical outcomes towards response., (© 2022. The Author(s).)

There are an estimated > 400 million people living with a rare disease globally, with genetic variants the cause of approximately 80% of cases. Next Generation Sequencing (NGS) rapidly identifies genetic variants however they are often of unknown significance. Low throughput functional validation in specialist laboratories is the current ad hoc approach for functional validation of genetic variants, which creating major bottlenecks in patient diagnosis. This study investigates the application of CRISPR gene editing followed by genome wide transcriptomic profiling to facilitate patient diagnosis. As proof-of-concept, we introduced a variant in the Euchromatin histone methyl transferase (EHMT1) gene into HEK293T cells. We identified changes in the regulation of the cell cycle, neural gene expression and suppression of gene expression changes on chromosome 19 and chromosome X, that are in keeping with Kleefstra syndrome clinical phenotype and/or provide insight into disease mechanism. This study demonstrates the utility of genome editing followed by functional readouts to rapidly and systematically validating the function of variants of unknown significance in patients suffering from rare diseases., (Copyright © 2022. Published by Elsevier B.V.)

Identifying the causal variant for diagnosis of genetic diseases is challenging when using next-generation sequencing approaches and variant prioritization tools can assist in this task. These tools provide in silico predictions of variant pathogenicity, however they are agnostic to the disease under study. We previously performed a disease-specific benchmark of 24 such tools to assess how they perform in different disease contexts. We found that the tools themselves show large differences in performance, but more importantly that the best tools for variant prioritization are dependent on the disease phenotypes being considered. Here we expand the assessment to 37 tools and refine our assessment by separating performance for nonsynonymous single nucleotide variants (nsSNVs) and missense variants (i.e., excluding nonsense variants). We found differences in performance for missense variants compared to nsSNVs and recommend three tools that stand out in terms of their performance (BayesDel, CADD, and ClinPred)., (© 2022 The Authors. Human Mutation published by Wiley Periodicals LLC.)