Genome sequencing reanalysis increases the diagnostic yield in dystonia.

PURPOSE: We investigated the contribution of genomic data reanalysis to the diagnostic yield of dystonia patients who remained undiagnosed after prior genome sequencing. METHODS: Probands with heterogeneous dystonia phenotypes who underwent initial genome sequencing (GS) analysis in 2019 were included in the reanalysis, which was performed through gene-specific discovery collaborations and systematic genomic data reanalysis. RESULTS: Initial GS analysis in 2019 (n = 111) identified a molecular diagnosis in 11.7 % (13/111) of cases. Reanalysis between 2020 and 2023 increased the diagnostic yield by 7.2 % (8/111); 3.6 % (4/111) through focused gene-specific clinical correlation collaborative efforts [VPS16 (two probands), AOPEP and POLG], and 3.6 % (4/111) by systematic reanalysis completed in 2023 [NUS1 (two probands) and DDX3X variants, and a microdeletion encompassing VPS16]. Seven of these patients had a high phenotype-based dystonia score ≥3. Notable unverified findings in four additional cases included suspicious variants of uncertain significance in FBXL4 and EIF2AK2, and potential phenotypic expansion associated with SLC2A1 and TREX1 variants. CONCLUSION: GS data reanalysis increased the diagnostic yield from 11.7 % to 18.9 %, with potential extension up to 22.5 %. While optimal timing for diagnostic reanalysis remains to be determined, this study demonstrates that periodic re-interrogation of dystonia GS datasets can provide additional genetic diagnoses, which may have significant implications for patients and their families.

Next-Generation Sequencing and Emerging Technologies.

Genetic sequencing technologies are evolving at a rapid pace with major implications for research and clinical practice. In this review, the authors provide an updated overview of next-generation sequencing (NGS) and emerging methodologies. NGS has tremendously improved sequencing output while being more time and cost-efficient in comparison to Sanger sequencing. The authors describe short-read sequencing approaches, such as sequencing by synthesis, ion semiconductor sequencing, and nanoball sequencing. Third-generation long-read sequencing now promises to overcome many of the limitations of short-read sequencing, such as the ability to reliably resolve repeat sequences and large genomic rearrangements. By combining complementary methods with massively parallel DNA sequencing, a greater insight into the biological context of disease mechanisms is now possible. Emerging methodologies, such as advances in nanopore technology, in situ nucleic acid sequencing, and microscopy-based sequencing, will continue the rapid evolution of this area. These new technologies hold many potential applications for hematological disorders, with the promise of precision and personalized medical care in the future.

CRUX, a platform for visualising, exploring and analysing cancer genome cohort data.

To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).

Precision Medicine Is Changing the Roles of Healthcare Professionals, Scientists, and Research Staff: Learnings from a Childhood Cancer Precision Medicine Trial.

Precision medicine programs aim to utilize novel technologies to identify personalized treatments for children with cancer. Delivering these programs requires interdisciplinary efforts, yet the many groups involved are understudied. This study explored the experiences of a broad range of professionals delivering Australia's first precision medicine trial for children with poor-prognosis cancer: the PRecISion Medicine for Children with Cancer (PRISM) national clinical trial of the Zero Childhood Cancer Program. We conducted semi-structured interviews with 85 PRISM professionals from eight professional groups, including oncologists, surgeons, clinical research associates, scientists, genetic professionals, pathologists, animal care technicians, and nurses. We analyzed interviews thematically. Professionals shared that precision medicine can add complexity to their role and result in less certain outcomes for families. Although many participants described experiencing a greater emotional impact from their work, most expressed very positive views about the impact of precision medicine on their profession and its future potential. Most reported navigating precision medicine without formal training. Each group described unique challenges involved in adapting to precision medicine in their profession. Addressing training gaps and meeting the specific needs of many professional groups involved in precision medicine will be essential to ensure the successful implementation of standard care.

High-Throughput Drug Screening of Primary Tumor Cells Identifies Therapeutic Strategies for Treating Children with High-Risk Cancer.

For one-third of patients with pediatric cancer enrolled in precision medicine programs, molecular profiling does not result in a therapeutic recommendation. To identify potential strategies for treating these high-risk pediatric patients, we performed in vitro screening of 125 patient-derived samples against a library of 126 anticancer drugs. Tumor cell expansion did not influence drug responses, and 82% of the screens on expanded tumor cells were completed while the patients were still under clinical care. High-throughput drug screening (HTS) confirmed known associations between activating genomic alterations in NTRK, BRAF, and ALK and responses to matching targeted drugs. The in vitro results were further validated in patient-derived xenograft models in vivo and were consistent with clinical responses in treated patients. In addition, effective combinations could be predicted by correlating sensitivity profiles between drugs. Furthermore, molecular integration with HTS identified biomarkers of sensitivity to WEE1 and MEK inhibition. Incorporating HTS into precision medicine programs is a powerful tool to accelerate the improved identification of effective biomarker-driven therapeutic strategies for treating high-risk pediatric cancers. SIGNIFICANCE: Integrating HTS with molecular profiling is a powerful tool for expanding precision medicine to support drug treatment recommendations and broaden the therapeutic options available to high-risk pediatric cancers.

Atypical splicing variants in PKD1 explain most undiagnosed typical familial ADPKD.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of kidney failure and is primarily associated with PKD1 or PKD2. Approximately 10% of patients remain undiagnosed after standard genetic testing. We aimed to utilise short and long-read genome sequencing and RNA studies to investigate undiagnosed families. Patients with typical ADPKD phenotype and undiagnosed after genetic diagnostics were recruited. Probands underwent short-read genome sequencing, PKD1 and PKD2 coding and non-coding analyses and then genome-wide analysis. Targeted RNA studies investigated variants suspected to impact splicing. Those undiagnosed then underwent Oxford Nanopore Technologies long-read genome sequencing. From over 172 probands, 9 met inclusion criteria and consented. A genetic diagnosis was made in 8 of 9 (89%) families undiagnosed on prior genetic testing. Six had variants impacting splicing, five in non-coding regions of PKD1. Short-read genome sequencing identified novel branchpoint, AG-exclusion zone and missense variants generating cryptic splice sites and a deletion causing critical intron shortening. Long-read sequencing confirmed the diagnosis in one family. Most undiagnosed families with typical ADPKD have splice-impacting variants in PKD1. We describe a pragmatic method for diagnostic laboratories to assess PKD1 and PKD2 non-coding regions and validate suspected splicing variants through targeted RNA studies.

Parents' expectations, preferences, and recall of germline findings in a childhood cancer precision medicine trial.

BACKGROUND: Germline genome sequencing in childhood cancer precision medicine trials may reveal pathogenic or likely pathogenic variants in cancer predisposition genes in more than 10% of children. These findings can have implications for diagnosis, treatment, and the child's and family's future cancer risk. Understanding parents' perspectives of germline genome sequencing is critical to successful clinical implementation. METHODS: A total of 182 parents of 144 children (<18 years of age) with poor-prognosis cancers enrolled in the Precision Medicine for Children with Cancer trial completed a questionnaire at enrollment and after the return of their child's results, including clinically relevant germline findings (received by 13% of parents). Parents' expectations of germline genome sequencing, return of results preferences, and recall of results received were assessed. Forty-five parents (of 43 children) were interviewed in depth. RESULTS: At trial enrollment, most parents (63%) believed it was at least "somewhat likely" that their child would receive a clinically relevant germline finding. Almost all expressed a preference to receive a broad range of germline genomic findings, including variants of uncertain significance (88%). Some (29%) inaccurately recalled receiving a clinically relevant germline finding. Qualitatively, parents expressed confusion and uncertainty after the return of their child's genome sequencing results by their child's clinician. CONCLUSIONS: Many parents of children with poor-prognosis childhood cancer enrolled in a precision medicine trial expect their child may have an underlying cancer predisposition syndrome. They wish to receive a wide scope of information from germline genome sequencing but may feel confused by the reporting of trial results.

Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications.

Predicting the impact of coding and noncoding variants on splicing is challenging, particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing splice prediction tools are complementary but knowing which to use for each splicing context remains difficult. Here, we describe Introme, which uses machine learning to integrate predictions from several splice detection tools, additional splicing rules, and gene architecture features to comprehensively evaluate the likelihood of a variant impacting splicing. Through extensive benchmarking across 21,000 splice-altering variants, Introme outperformed all tools (auPRC: 0.98) for the detection of clinically significant splice variants. Introme is available at https://github.com/CCICB/introme .

A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer.

BACKGROUND: Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. METHODS: We combined immunohistochemistry (IHC) with RNA sequencing and whole-genome sequencing across a diverse spectrum of high-risk paediatric cancers to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME. Furthermore, we explored transcriptional features of immune archetypes and T-cell receptor sequencing diversity, assessed the relationship between CD8+ and CD4+ abundance by IHC and deconvolution predictions and assessed the common adult biomarkers such as neoantigen load and TMB. RESULTS: A novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), was identified. Using this signature, we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. In addition, we showed that PD-L1 protein expression is poorly correlated with PD-L1 RNA expression and TMB and neoantigen load are not predictive of T-cell infiltration in paediatrics. Furthermore, deconvolution algorithms are only weakly correlated with IHC measurements of T-cells. CONCLUSIONS: Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.

Histone H3-wild type diffuse midline gliomas with H3K27me3 loss are a distinct entity with exclusive EGFR or ACVR1 mutation and differential methylation of homeobox genes.

Diffuse midline gliomas (DMG) harbouring H3K27M mutation are paediatric tumours with a dismal outcome. Recently, a new subtype of midline gliomas has been described with similar features to DMG, including loss of H3K27 trimethylation, but lacking the canonical H3K27M mutation (H3-WT). Here, we report a cohort of five H3-WT tumours profiled by whole-genome sequencing, RNA sequencing and DNA methylation profiling and combine their analysis with previously published cases. We show that these tumours have recurrent and mutually exclusive mutations in either ACVR1 or EGFR and are characterised by high expression of EZHIP associated to its promoter hypomethylation. Affected patients share a similar poor prognosis as patients with H3K27M DMG. Global molecular analysis of H3-WT and H3K27M DMG reveal distinct transcriptome and methylome profiles including differential methylation of homeobox genes involved in development and cellular differentiation. Patients have distinct clinical features, with a trend demonstrating ACVR1 mutations occurring in H3-WT tumours at an older age. This in-depth exploration of H3-WT tumours further characterises this novel DMG, H3K27-altered sub-group, characterised by a specific immunohistochemistry profile with H3K27me3 loss, wild-type H3K27M and positive EZHIP. It also gives new insights into the possible mechanism and pathway regulation in these tumours, potentially opening new therapeutic avenues for these tumours which have no known effective treatment. This study has been retrospectively registered on clinicaltrial.gov on 8 November 2017 under the registration number NCT03336931 ( https://clinicaltrials.gov/ct2/show/NCT03336931 ).

Memory of stochastic single-cell apoptotic signaling promotes chemoresistance in neuroblastoma.

Gene expression noise is known to promote stochastic drug resistance through the elevated expression of individual genes in rare cancer cells. However, we now demonstrate that chemoresistant neuroblastoma cells emerge at a much higher frequency when the influence of noise is integrated across multiple components of an apoptotic signaling network. Using a JNK activity biosensor with longitudinal high-content and in vivo intravital imaging, we identify a population of stochastic, JNK-impaired, chemoresistant cells that exist because of noise within this signaling network. Furthermore, we reveal that the memory of this initially random state is retained following chemotherapy treatment across a series of in vitro, in vivo, and patient models. Using matched PDX models established at diagnosis and relapse from individual patients, we show that HDAC inhibitor priming cannot erase the memory of this resistant state within relapsed neuroblastomas but improves response in the first-line setting by restoring drug-induced JNK activity within the chemoresistant population of treatment-naïve tumors.

In vivo loss of tumorigenicity in a patient-derived orthotopic xenograft mouse model of ependymoma.

Introduction: Ependymomas (EPN) are the third most common malignant brain cancer in children. Treatment strategies for pediatric EPN have remained unchanged over recent decades, with 10-year survival rates stagnating at just 67% for children aged 0-14 years. Moreover, a proportion of patients who survive treatment often suffer long-term neurological side effects as a result of therapy. It is evident that there is a need for safer, more effective treatments for pediatric EPN patients. There are ten distinct subgroups of EPN, each with their own molecular and prognostic features. To identify and facilitate the testing of new treatments for EPN, in vivo laboratory models representative of the diverse molecular subtypes are required. Here, we describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of posterior fossa A (PFA) EPN, derived from a metastatic cranial lesion. Methods: Patient and PDOX tumors were analyzed using immunohistochemistry, DNA methylation profiling, whole genome sequencing (WGS) and RNA sequencing. Results: Both patient and PDOX tumors classified as PFA EPN by methylation profiling, and shared similar histological features consistent with this molecular subgroup. RNA sequencing revealed that gene expression patterns were maintained across the primary and metastatic tumors, as well as the PDOX. Copy number profiling revealed gains of chromosomes 7, 8 and 19, and loss of chromosomes 2q and 6q in the PDOX and matched patient tumor. No clinically significant single nucleotide variants were identified, consistent with the low mutation rates observed in PFA EPN. Overexpression of EZHIP RNA and protein, a common feature of PFA EPN, was also observed. Despite the aggressive nature of the tumor in the patient, this PDOX was unable to be maintained past two passages in vivo. Discussion: Others who have successfully developed PDOX models report some of the lowest success rates for EPN compared to other pediatric brain cancer types attempted, with loss of tumorigenicity not uncommon, highlighting the challenges of propagating these tumors in the laboratory. Here, we discuss our collective experiences with PFA EPN PDOX model generation and propose potential approaches to improve future success in establishing preclinical EPN models.

Diagnostic classification of childhood cancer using multiscale transcriptomics.

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

They Can Have Our Cake - But Can We Eat It? Access to Raw Genomic Data under Australian Privacy Law.

There is an increasing demand for the return of raw genomic data by research participants in translational genomic research. This article discusses the scope and application of privacy and freedom of information legislative provisions in Australia. Whether there is a right to access a copy of such data under Australian privacy legislation is contingent on whether raw genomic data can identify an individual and this article explores the opportunities for genomic data to be linked to individuals. We conclude that despite the complexity and overlapping nature of privacy laws in Australia, there is a clear right on the part of research participants to access their raw genomic data.

RaScALL: Rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL).

RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, integrating RNA-seq in a clinical setting requires rapid detection and accurate reporting of clinically relevant alterations. Here we present RaScALL, an implementation of the k-mer based variant detection tool km, capable of identifying more than 100 prognostically significant lesions observed in ALL, including gene fusions, single nucleotide variants and focal gene deletions. We compared genomic alterations detected by RaScALL and those reported by alignment-based de novo variant detection tools in a study cohort of 180 Australian patient samples. Results were validated using 100 patient samples from a published North American cohort. RaScALL demonstrated a high degree of accuracy for reporting subtype defining genomic alterations. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% of reported cases in the study cohort (n = 164) and 95% of samples (n = 63) in the validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n = 12) and IKZF1 p.N159Y (n = 4). Intragenic IKZF1 deletions resulting in aberrant transcript isoforms were also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches. The application of RaScALL enables rapid identification and reporting of previously identified genomic alterations of known clinical relevance.

Biallelic pathogenic variants in COX11 are associated with an infantile-onset mitochondrial encephalopathy.

Primary mitochondrial diseases are a group of genetically and clinically heterogeneous disorders resulting from oxidative phosphorylation (OXPHOS) defects. COX11 encodes a copper chaperone that participates in the assembly of complex IV and has not been previously linked to human disease. In a previous study, we identified that COX11 knockdown decreased cellular adenosine triphosphate (ATP) derived from respiration, and that ATP levels could be restored with coenzyme Q10 (CoQ10 ) supplementation. This finding is surprising since COX11 has no known role in CoQ10 biosynthesis. Here, we report a novel gene-disease association by identifying biallelic pathogenic variants in COX11 associated with infantile-onset mitochondrial encephalopathies in two unrelated families using trio genome and exome sequencing. Functional studies showed that mutant COX11 fibroblasts had decreased ATP levels which could be rescued by CoQ10 . These results not only suggest that COX11 variants cause defects in energy production but reveal a potential metabolic therapeutic strategy for patients with COX11 variants.

Whole exome and genome sequencing in mendelian disorders: a diagnostic and health economic analysis.

Whole genome sequencing (WGS) improves Mendelian disorder diagnosis over whole exome sequencing (WES); however, additional diagnostic yields and costs remain undefined. We investigated differences between diagnostic and cost outcomes of WGS and WES in a cohort with suspected Mendelian disorders. WGS was performed in 38 WES-negative families derived from a 64 family Mendelian cohort that previously underwent WES. For new WGS diagnoses, contemporary WES reanalysis determined whether variants were diagnosable by original WES or unique to WGS. Diagnostic rates were estimated for WES and WGS to simulate outcomes if both had been applied to the 64 families. Diagnostic costs were calculated for various genomic testing scenarios. WGS diagnosed 34% (13/38) of WES-negative families. However, contemporary WES reanalysis on average 2 years later would have diagnosed 18% (7/38 families) resulting in a WGS-specific diagnostic yield of 19% (6/31 remaining families). In WES-negative families, the incremental cost per additional diagnosis using WGS following WES reanalysis was AU$36,710 (£19,407;US$23,727) and WGS alone was AU$41,916 (£22,159;US$27,093) compared to WES-reanalysis. When we simulated the use of WGS alone as an initial genomic test, the incremental cost for each additional diagnosis was AU$29,708 (£15,705;US$19,201) whereas contemporary WES followed by WGS was AU$36,710 (£19,407;US$23,727) compared to contemporary WES. Our findings confirm that WGS is the optimal genomic test choice for maximal diagnosis in Mendelian disorders. However, accepting a small reduction in diagnostic yield, WES with subsequent reanalysis confers the lowest costs. Whether WES or WGS is utilised will depend on clinical scenario and local resourcing and availability.