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Models that recapitulate the complexity of human tumors are urgently needed to develop more effective cancer therapies. We report a bank of human patient-derived xenografts (PDXs) and matched organoid cultures from tumors that represent the greatest unmet need: endocrine-resistant, treatment-refractory and metastatic breast cancers. We leverage matched PDXs and PDX-derived organoids (PDxO) for drug screening that is feasible and cost-effective with in vivo validation. Moreover, we demonstrate the feasibility of using these models for precision oncology in real time with clinical care in a case of triple-negative breast cancer (TNBC) with early metastatic recurrence. Our results uncovered a Food and Drug Administration (FDA)-approved drug with high efficacy against the models. Treatment with this therapy resulted in a complete response for the individual and a progression-free survival (PFS) period more than three times longer than their previous therapies. This work provides valuable methods and resources for functional precision medicine and drug development for human breast cancer.

The capacity for nuclear RNA measurements to recapitulate results from whole cells is essential to the utility of single-nucleus RNA-seq. Early studies argued that nuclear samples could yield comparable results to single-cell RNA-seq if intronic reads from pre-mRNAs were included in the analysis of both assays. While pre-mRNA sampling has since been acknowledged to be subject to sampling bias related to gene length, the impact of this phenomenon across cell types has been largely ignored. Here, we describe the contrasting effects of mRNA and pre-mRNA sampling on the concordance of gene expression estimates between cells and nuclei. We also address the generalizability of a recently published normalization method intended to maximize assay similarity by removing gene length bias from pre-mRNA sampling. Comparing nuclei to cells among cell types of the cortex, we show that pre-mRNA (intron) abundances are much more similar than mRNA (exon) abundances. When comparing overall gene expression, the magnitude of gene length bias reflects the relative enrichment of pre-mRNAs in nuclei, which varies considerably among cell types of the cortex. This variability leads to unreliable performance of the normalization method, which emphasizes mRNA measurements by downweighting pre-mRNA measurements according to gene length. As a potential alternative, we demonstrate adaptation of an existing method for removing systematic bias from gene set enrichment analysis results. Broadly, our analysis provides a mechanistic explanation for variation in assay similarity across cell types and argues for the application ofpost hocnormalization approaches as an avenue to improved biological interpretation.

While mobile elements are largely inactive in healthy somatic tissues, increased activity has been found in cancer tissues, with significant variation among different cancer types. In addition to insertion events, mobile elements have also been found to mediate many structural variation events in the genome. Here, to better understand the timing and impact of mobile element insertions and structural variants involving existing mobile elements in cancer, we examined their activity in longitudinal samples of four metastatic breast cancer patients. With whole-genome sequencing data from multiple timepoints through tumor progression, we used mobile element detection software followed by visual confirmation of the insertions. We identified 11 mobile element insertions or structural variants involving existing elements and found that the majority of these occurred early in tumor progression. Two of the identified insertions were SVA elements, which have rarely been found in previous cancer studies. Most of the variants appear to impact intergenic regions; however, we identified a translocation interrupting MAP2K4 involving Alu elements and a deletion in YTHDF2 involving mobile elements that likely inactivate reported tumor suppressor genes. The high variant allele fraction of the MAP2K4 translocation, the loss of the other copy of MAP2K4, the recurrent loss-of-function mutations found in this gene in other human cancers, and the important function of MAP2K4 indicate that this translocation is potentially a driver mutation. Overall, using a unique longitudinal dataset, we find that most variants are likely passenger mutations in the four patients we examined, but some variants impact tumor progression.

In pleiotropic diseases, multiple organ systems are affected causing a variety of clinical manifestations. Here, we report a pleiotropic disorder with a unique constellation of neurological, endocrine, exocrine, and haematological findings that is caused by biallelic MADD variants. MADD, the mitogen-activated protein kinase (MAPK) activating death domain protein, regulates various cellular functions, such as vesicle trafficking, activity of the Rab3 and Rab27 small GTPases, tumour necrosis factor-α (TNF-α)-induced signalling and prevention of cell death. Through national collaboration and GeneMatcher, we collected 23 patients with 21 different pathogenic MADD variants identified by next-generation sequencing. We clinically evaluated the series of patients and categorized the phenotypes in two groups. Group 1 consists of 14 patients with severe developmental delay, endo- and exocrine dysfunction, impairment of the sensory and autonomic nervous system, and haematological anomalies. The clinical course during the first years of life can be potentially fatal. The nine patients in Group 2 have a predominant neurological phenotype comprising mild-to-severe developmental delay, hypotonia, speech impairment, and seizures. Analysis of mRNA revealed multiple aberrant MADD transcripts in two patient-derived fibroblast cell lines. Relative quantification of MADD mRNA and protein in fibroblasts of five affected individuals showed a drastic reduction or loss of MADD. We conducted functional tests to determine the impact of the variants on different pathways. Treatment of patient-derived fibroblasts with TNF-α resulted in reduced phosphorylation of the extracellular signal-regulated kinases 1 and 2, enhanced activation of the pro-apoptotic enzymes caspase-3 and -7 and increased apoptosis compared to control cells. We analysed internalization of epidermal growth factor in patient cells and identified a defect in endocytosis of epidermal growth factor. We conclude that MADD deficiency underlies multiple cellular defects that can be attributed to alterations of TNF-α-dependent signalling pathways and defects in vesicular trafficking. Our data highlight the multifaceted role of MADD as a signalling molecule in different organs and reveal its physiological role in regulating the function of the sensory and autonomic nervous system and endo- and exocrine glands.

A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). To assess the contribution of noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; P = 8.7 × 10-4). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, P = 1 × 10-5). We observed significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1-5.0, P = 5.4 × 10-3). CHD DNVs altered transcription levels in 5 of 31 enhancers assayed. Finally, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1-1.2, P = 8.8 × 10-5). Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs.

Evolve and resequencing (E&R) was applied to lab adaptation of Toxoplasma gondii for over 1,500 generations with the goal of mapping host-independent in vitro virulence traits. Phenotypic assessments of steps across the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Nonsynonymous single-nucleotide polymorphisms (SNPs) in only one gene, a P4 flippase, fixated across two different evolving populations, whereas dramatic changes in the transcriptional signature of extracellular parasites were identified. Newly developed computational tools correlated phenotypes evolving at different rates with specific transcriptomic changes. A set of 300 phenotype-associated genes was mapped, of which nearly 50% is annotated as hypothetical. Validation of a select number of genes by knockouts confirmed their role in lab adaptation and highlights novel mechanisms underlying in vitro virulence traits. Further analyses of differentially expressed genes revealed the development of a “pro-tachyzoite” profile as well as the upregulation of the fatty acid biosynthesis (FASII) pathway. The latter aligned with the P4 flippase SNP and aligned with a low abundance of medium-chain fatty acids at low passage, indicating this is a limiting factor in extracellular parasites. In addition, partial overlap with the bradyzoite differentiation transcriptome in extracellular parasites indicated that stress pathways are involved in both situations. This was reflected in the partial overlap between the assembled ApiAP2 and Myb transcription factor network underlying the adapting extracellular state with the bradyzoite differentiation program. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii. IMPORTANCE It has been well established that prolonged in vitro cultivation of Toxoplasma gondii augments progression of the lytic cycle. This lab adaptation results in increased capacities to divide, migrate, and survive outside a host cell, all of which are considered host-independent virulence factors. However, the mechanistic basis underlying these enhanced virulence features is unknown. Here, E&R was utilized to empirically characterize the phenotypic, genomic, and transcriptomic changes in the non-lab-adapted strain, GT1, during 2.5 years of lab adaptation. This identified the shutdown of stage differentiation and upregulation of lipid biosynthetic pathways as the key processes being modulated. Furthermore, lab adaptation was primarily driven by transcriptional reprogramming, which rejected the starting hypothesis that genetic mutations would drive lab adaptation. Overall, the work empirically shows that lab adaptation augments T. gondii’s in vitro virulence by transcriptional reprogramming and that E&R is a powerful new tool to map multigenic traits.

MotivationIn time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not, and consequently has now turned into the primary bottleneck. Modern computational hardware are capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, for example, is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices.ResultsIn an effort to stimulate the development of a new generation of faster sequence analysis tools, We developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 4 minutes while the original version took 1.5 hours, using the same storage solution.Availability and ImplementationOpen source and freely available athttps://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high performance informatics tools, either directly boosting their performance if they are currently dataaccess bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.Contactyi.qiao@genetics.utah.edu.

Background. Autism Spectrum Disorder (ASD) is a neurodevelopmental diagnosis that encompasses deficits in social communication in addition to repetitive and restrictive behaviors and interests. Accumulated evidence implicates over 100 risk genes and suggests possible genetic subtypes. We tested one previously characterized subtype relating to high maternal body mass index (BMI) as an enhancing risk factor in genetically vulnerable offspring. Methods. Using 1,300 families from the Simons Simplex Collection (SSC), we created an objectively defined subgroup of mothers in the highest quartile of the distribution of derived BMI polygenic risk scores. Polygenic risk for BMI reflects background genetic risk independent of the many environmental modifiers of BMI.Results. In the ASD offspring of mothers in this highest quartile, we found significant associations with de novo, putatively functional variants in genes in pathways related to chromatin state, chromatin structure, histone activity, and microtubule function. These gene pathways represent potential epigenetic vulnerability to alterations in the metabolic prenatal environment and/or alterations in microtubule-related brain development processes. The observed pathway enrichments were maternal-specific, and were not observed in neurotypical offspring. Two-thirds of the 36 genes in the significant epigenetic pathways and over half of the 33 genes in the significant microtubule pathways had existing ASD or neurodevelopmental risk evidence. Limitations. Though tests and simulations were done to ensure robustness of results, these findings have not been replicated in an external cohort.Conclusions. Our results suggest that epigenetic modification and/or microtubule deficits may be unique to a subset of ASD probands of mothers at increased genetic metabolic risk, pending external replication. Beyond the current application of these methods, our approach presents a strategy to reveal genetic subsets through polygenic risk stratification across phenotypic domains.

BackgroundMetastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult, and thus poorly understood.ResultsVia rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor’s evolution at subclonal resolution. The most unique, previously not reported aspect of the tumor’s evolution we observed in this patient was the presence of “subclone incubators”, i.e. already metastatic sites where substantial tumor evolution occurred before colonization of additional sites and organs by subclones that evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g. abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion; subclones at this site were the source of all four subsequent metastatic waves; and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known driver or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient’s metastases for effective clinical intervention.ConclusionsOur analysis revealed the presence of substantial tumor evolution at metastatic incubator sites, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

With increasing utilization of comprehensive genomic data to guide clinical care, anticipated to become the standard of care in many clinical settings, the practice of diagnostic medicine is undergoing a notable shift. However, the move from single-gene or panel-based genetic testing to exome and genome sequencing has not been matched by the development of tools to enable diagnosticians to interpret increasingly complex genomic findings. A new paradigm has emerged, where genome-based tests are often evaluated by a large multi-disciplinary collaborative team, typically including a diagnostic pathologist, a bioinformatician, a genetic counselor, and often a subspeciality clinician. This team-based approach calls for new computational tools to allow every member of the clinical care provider team, at varying levels of genetic knowledge and diagnostic expertise, to quickly and easily analyze and interpret complex genomic data. Here, we present gene.iobio, a real-time, intuitive and interactive web application for clinically-driven variant interrogation and prioritization. We show gene.iobio is a novel and effective approach that significantly improves upon and reimagines existing methods. In a radical departure from existing methods that present variants and genomic data in text and table formats, gene.iobio provides an interactive, intuitive and visually-driven analysis environment. We demonstrate that adoption of gene.iobio in clinical and research settings empowers clinical care providers to interact directly with patient genomic data both for establishing clinical diagnoses and informing patient care, using sophisticated genomic analyses that previously were only accessible via complex command line tools.

Selection of the treatments most likely to combat a patient’s tumor is a central aim of precision oncology. We are currently developing a functional precision oncology program at the University of Utah and Huntsman Cancer Institute that combines the multi-omic characterization of a patient's tumor with the functional screening of relevant drug candidates in patient-derived organoid tumor models to identify which drugs are uniquely capable of combating the patient’s cancer. Here we present our model-based approach for utilizing the multi-omic tumor data to computationally predict the patient’s response to each drug. In this approach, we first identify the genomic and transcriptomic vulnerabilities of the tumor as genes harboring somatic DNA mutations or copy number changes (from paired tumor/normal WGS/WES DNA sequencing data), as well as genes whose expression levels have been significantly altered in the tumor (based on bulk or single-cell RNA sequencing data). Second, using targeting information from drug-gene interaction databases (DGIdb) we compile a list of genes targeted by each drug relevant in the patient’s treatment. Third, we utilize gene interaction database knowledge (KEGG) to construct a gene interaction graph to link each drug’s gene targets with all of the tumor’s vulnerability genes. We then identify all gene interaction paths connecting specific drug target genes with specific vulnerability genes; and score each path and combine all path-specific scores for the target-vulnerability gene pair. Subsequently, we further combine all pairwise scores across all drug target genes and all tumor vulnerability genes and determine the statistical significance of the resulting score by sampling the background distribution of such scores across random drugs, target genes, and vulnerability genes. We have applied our algorithm to predict drug response in advanced breast cancer patients as well as using publicly available tumor cell line data (GDSC2). We have found a high level of concordance between our computational prediction and organoid/cell line screening results, clearly separating sensitive and non-sensitive models. Using Bayesian probability, we compare the drug-specific score distributions of sensitive and non-sensitive models and are able to identify sensitive cell lines/patients with high accuracy. By encapsulating available information on direct gene-gene interactions, the drug’s direct gene targets and the collected omic vulnerabilities of the tumor, our model is not only capable of predicting sensitivity to both targeted and chemotherapy agents, but can also provide a mechanistic understanding for the targeting of the tumor. Our approach will be validated and fine-tuned on a large cohort of breast and brain cancer patients as well as patient PDX models, interrogating gene-target interactions to identify novel relevant target genes/pathways. Citation Format: Szabolcs Tarapcsak, Yi Qiao, Xiaomeng Huang, Tony Di Sera, Matthew H. Bailey, Bryan E. Welm, Alana L. Welm, Gabor T. Marth. Model-based cancer therapy selection by linking tumor vulnerabilities to drug mechanism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2723.

To map host-independent in vitro virulence traits of Toxoplasma gondii, evolve and resequencing (E&R) during the lab-adaption was applied. Phenotypic assessments of the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Surprisingly, only non-synonymous mutations in a P4 flippase fixed in two populations. However, dramatic changes in the transcriptional signature of extracellular parasites revealed a “pro-tachyzoite” profile as well as upregulation of fatty acid biosynthesis (FASII) pathway genes. More general, a set of 300 genes which expression profile changes during evolution mapped to specific traits. Validation of a select number of genes in this set by knock-outs indeed confirmed their role in lab-adaptation. Finally, assembly of an ApiAP2 and Myb transcription factor network revealed the transcriptional program underlying the adapting extracellular state. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii.

Model systems that recapitulate the complexity of human tumors and the reality of variable treatment responses are urgently needed to better understand cancer biology and to develop more effective cancer therapies. Here we report development and characterization of a large bank of patient-derived xenografts (PDX) and matched organoid cultures from tumors that represent some of the greatest unmet needs in breast cancer research and treatment. These include endocrine-resistant, treatment-refractory, and metastatic breast cancers and, in some cases, multiple tumor collections from the same patients. The models can be grown long-term with high fidelity to the original tumors. We show that development of matched PDX and PDX-derived organoid (PDxO) models facilitates high-throughput drug screening that is feasible and cost-effective, while also allowing in vivo validation of results. Our data reveal consistency between drug screening results in organoids and drug responses in breast cancer PDX. Moreover, we demonstrate the feasibility of using these patient-derived models for precision oncology in real time with patient care, using a case of a triple negative breast cancer with early metastatic recurrence as an example. Our results uncovered an FDA-approved drug with high efficacy against the models. Treatment with the PDxO-directed therapy resulted in a complete response for the patient and a progression-free survival period more than three times longer than her previous therapies. This work provides valuable new methods and resources for functional precision medicine and drug development for human breast cancer.Graphical Abstract

Additional file 1. : Supplementary Methods for the running of FreeBayes.

Summary: The diaphragm is critical for respiration and separation of the thoracic and abdominal cavities, and defects in diaphragm development are the cause of congenital diaphragmatic hernias (CDH), a common and often lethal birth defect. The genetic etiology of CDH is complex. Single-nucleotide variants (SNVs), insertions/deletions (indels), and structural variants (SVs) in more than 150 genes have been associated with CDH, although few genes are recurrently mutated in multiple individuals and mutated genes are incompletely penetrant. This suggests that multiple genetic variants in combination, other not-yet-investigated classes of variants, and/or nongenetic factors contribute to CDH etiology. However, no studies have comprehensively investigated in affected individuals the contribution of all possible classes of variants throughout the genome to CDH etiology. In our study, we used a unique cohort of four individuals with isolated CDH with samples from blood, skin, and diaphragm connective tissue and parental blood and deep whole-genome sequencing to assess germline and somatic de novo and inherited SNVs, indels, and SVs. In each individual we found a different mutational landscape that included germline de novo and inherited SNVs and indels in multiple genes. We also found in two individuals a 343 bp deletion interrupting an annotated enhancer of the CDH-associated gene GATA4, and we hypothesize that this common SV (found in 1%–2% of the population) acts as a sensitizing allele for CDH. Overall, our comprehensive reconstruction of the genetic architecture of four CDH individuals demonstrates that the etiology of CDH is heterogeneous and multifactorial.

Background When interpreting sequencing data from multiple spatial or longitudinal biopsies, detecting sample mix-ups is essential, yet more difficult than in studies of germline variation. In most genomic studies of tumors, genetic variation is detected through pairwise comparisons of the tumor and a matched normal tissue from the sample donor. In many cases, only somatic variants are reported, which hinders the use of existing tools that detect sample swaps solely based on genotypes of inherited variants. To address this problem, we have developed Somalier, a tool that operates directly on alignments and does not require jointly called germline variants. Instead, Somalier extracts a small sketch of informative genetic variation for each sample. Sketches from hundreds of germline or somatic samples can then be compared in under a second, making Somalier a useful tool for measuring relatedness in large cohorts. Somalier produces both text output and an interactive visual report that facilitates the detection and correction of sample swaps using multiple relatedness metrics. Results We introduce the tool and demonstrate its utility on a cohort of five glioma samples each with a normal, tumor, and cell-free DNA sample. Applying Somalier to high-coverage sequence data from the 1000 Genomes Project also identifies several related samples. We also demonstrate that it can distinguish pairs of whole-genome and RNA-seq samples from the same individuals in the Genotype-Tissue Expression (GTEx) project. Conclusions Somalier is a tool that can rapidly evaluate relatedness from sequencing data. It can be applied to diverse sequencing data types and genome builds and is available under an MIT license at github.com/brentp/somalier.

The diaphragm is a mammalian muscle critical for respiration and separation of the thoracic and abdominal cavities. Defects in the development of the diaphragm are the cause of congenital diaphragmatic hernia (CDH), a common birth defect. In CDH, weaknesses in the developing diaphragm allow abdominal contents to herniate into the thoracic cavity and impair lung development, leading to a high neonatal mortality. The genetic etiology of CDH is complex. Single nucleotide variants (SNVs), insertion/deletions (indels), and structural/copy number variants in more than 150 genes have been associated with CDH, although few genes are recurrently mutated in multiple patients and recurrently mutated genes can be incompletely penetrant. This suggests that multiple genetic variants in combination, other not yet investigated classes of variants, and/or nongenetic factors contribute to CDH susceptibility. However, to date no studies have comprehensively investigated the contribution of all possible classes of variants throughout the genome to the etiology of CDH. In our study, we used a unique cohort of four patients with isolated CDH with samples from blood, skin, and diaphragm connective tissue and parental blood samples and deep whole genome sequencing to assess germline and somaticde novoand inherited variants of various sizes (SNVs, indels, and structural variants) in exons, introns, UTRs, and intergenic regions. In each patient we found a different mutational landscape that included germlinede novo,and inherited SNVs and indels in multiple genes. We also found in two patients an inherited 343 bp deletion interrupting an annotated enhancer of the CDH associated gene,GATA4, and we hypothesize that this common deletion (found in 1-2% of the population) acts as a sensitizing allele for CDH. Overall, our comprehensive reconstruction of the genetic architecture of four CDH individuals demonstrates that the etiology of CDH is heterogeneous and multifactorial.AUTHOR SUMMARYDeep whole genome sequencing of family trios shows that etiology of congenital diaphragmatic hernias is heterogeneous and multifactorial.

Background Pedigree files are ubiquitously used within bioinformatics and genetics studies to convey critical information about relatedness, sex and affected status of study samples. While the text based format of ped files is efficient for computational methods, it is not immediately intuitive to a bioinformatician or geneticist trying to understand family structures, many of which encode the affected status of individuals across multiple generations. The visualization of pedigrees into connected nodes with descriptive shapes and shading provides a far more interpretable format to recognize visual patterns and intuit family structures. Despite these advantages of a visual pedigree, it remains difficult to quickly and accurately visualize a pedigree given a pedigree text file. Results Here we describe ped_draw a command line and web tool as a simple and easy solution to pedigree visualization. Ped_draw is capable of drawing complex multi-generational pedigrees and conforms to the accepted standards for depicting pedigrees visually. The command line tool can be used as a simple one liner command, utilizing graphviz to generate an image file. The web tool, https://peddraw.github.io, allows the user to either: paste a pedigree file, type to construct a pedigree file in the text box or upload a pedigree file. Users can save the generated image file in various formats. Conclusions We believe ped_draw is a useful pedigree drawing tool that improves on current methods due to its ease of use and approachability. Ped_draw allows users with various levels of expertise to quickly and easily visualize pedigrees.

Challenges with distinguishing circulating tumor DNA (ctDNA) from next-generation sequencing (NGS) artifacts limits variant searches to established solid tumor mutations. Here we show early and random PCR errors are a principal source of NGS noise that persist despite duplex molecular barcoding, removal of artifacts due to clonal hematopoiesis of indeterminate potential, and suppression of patterned errors. We also demonstrate sample duplicates are necessary to eliminate the stochastic noise associated with NGS. Integration of sample duplicates into NGS analytics may broaden ctDNA applications by removing NGS-related errors that confound identification of true very low frequency variants during searches for ctDNA without a priori knowledge of specific mutations to target.

IntroductionHereditary haemorrhagic telangiectasia (HHT) is a genetically heterogeneous disorder caused by mutations in the genes ENG, ACVRL1, and SMAD4. Yet the genetic cause remains unknown for some families even after exhaustive exome analysis. We hypothesised that non-coding regions of the known HHT genes may harbour variants that disrupt splicing in these cases.MethodsDNA from 35 individuals with clinical findings of HHT and 2 healthy controls from 13 families underwent whole genome sequencing. Additionally, 87 unrelated cases suspected to have HHT were evaluated using a custom designed next-generation sequencing panel to capture the coding and non-coding regions of ENG, ACVRL1 and SMAD4. Individuals from both groups had tested negative previously for a mutation in the coding region of known HHT genes. Samples were sequenced on a HiSeq2500 instrument and data were analysed to identify novel and rare variants.ResultsEight cases had a novel non-coding ACVRL1 variant that disrupted splicing. One family had an ACVRL1intron 9:chromosome 3 translocation, the first reported case of a translocation causing HHT. The other seven cases had a variant located within a ~300 bp CT-rich ‘hotspot’ region of ACVRL1intron 9 that disrupted splicing.ConclusionsDespite the difficulty of interpreting deep intronic variants, our study highlights the importance of non-coding regions in the disease mechanism of HHT, particularly the CT-rich hotspot region of ACVRL1intron 9. The addition of this region to HHT molecular diagnostic testing algorithms will improve clinical sensitivity.

Small cell lung cancer (SCLC) is a neuroendocrine tumor treated clinically as a single disease with poor outcomes. Distinct SCLC molecular subtypes have been defined based on expression of ASCL1, NEUROD1, POU2F3, or YAP1. Here, we use mouse and human models with a time-series single-cell transcriptome analysis to reveal that MYC drives dynamic evolution of SCLC subtypes. In neuroendocrine cells, MYC activates Notch to dedifferentiate tumor cells, promoting a temporal shift in SCLC from ASCL1+ to NEUROD1+ to YAP1+ states. MYC alternatively promotes POU2F3+ tumors from a distinct cell type. Human SCLC exhibits intratumoral subtype heterogeneity, suggesting that this dynamic evolution occurs in patient tumors. These findings suggest that genetics, cell of origin, and tumor cell plasticity determine SCLC subtype.

When ordering genetic testing or triaging candidate variants in exome and genome sequencing studies, it is critical to generate and test a comprehensive list of candidate genes that succinctly describe the complete and objective phenotypic features of disease. Significant efforts have been made to curate gene:disease associations both in academic research and commercial genetic testing laboratory settings. However, many of these valuable resources exist as islands and must be used independently, generating static, single-resource gene:disease association lists. Here we describe genepanel.iobio (https://genepanel.iobio.io) an easy to use, free and open-source web tool for generating disease- and phenotype-associated gene lists from multiple gene:disease association resources, including the NCBI Genetic Testing Registry (GTR), Phenolyzer, and the Human Phenotype Ontology (HPO). We demonstrate the utility of genepanel.iobio by applying it to complex, rare and undiagnosed disease cases that had reached a diagnostic conclusion. We find that genepanel.iobio is able to correctly prioritize the gene containing the diagnostic variant in roughly half of these challenging cases. Importantly, each component resource contributed diagnostic value, showing the benefits of this aggregate approach. We expect genepanel.iobio will improve the ease and diagnostic value of generating gene:disease association lists for genetic test ordering and whole genome or exome sequencing variant prioritization.

IntroductionComputational analysis of genome or exome sequences may improve inherited disease diagnosis, but is costly and time-consuming.MethodsWe describe the use of iobio, a web-based tool suite for intuitive, real-time genome diagnostic analyses.ResultsWe used iobio to identify the disease-causing variant in a patient with early infantile epileptic encephalopathy with prior nondiagnostic genetic testing.ConclusionsIobio tools can be used by clinicians to rapidly identify disease-causing variants from genomic patient sequencing data.

A comprehensive list of candidate genes that succinctly describe the complete and objective phenotypic features of disease is critical when both ordering genetic testing and when triaging candidate variants in exome and genome sequencing studies. Great efforts have been made to curate gene:disease associations both in academic research and commercial gene testing settings. However, many of these valuable resources exist as islands and must be used independently, generating static, single-resource gene:disease association lists. To more effectively utilize these resources we created genepanel.iobio (https://genepanel.iobio.io), an easy to use, free and open-source web tool for generating disease- and phenotype-associated gene lists from multiple gene:disease association resources, including the NCBI Genetic Testing Registry (GTR), Phenolyzer, and the Human Phenotype Ontology (HPO). We demonstrate the utility of genepanel.iobio by applying it to complex, rare and undiagnosed disease cases which had reached a diagnostic conclusion. We find that genepanel.iobio is able to correctly prioritize the diagnostic variant in over half of these challenging cases. Importantly, each resource contributed diagnostic value, showing the benefits of this aggregate approach. We expect genepanel.iobio will improve the ease and diagnostic value of generating gene:disease association lists for genetic test ordering and whole genome or exome sequencing variant prioritization.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (, Structural variants (SVs) in human genomes contribute diversity and diseases. Here, the authors use a multi-platform strategy to generate haplotype-resolved SVs for three human parent–child trios.

INTRODUCTION The DNA of protein-coding genes is transcribed into mRNA, which is translated into proteins. The “coding genome” describes the DNA that contains the information to make these proteins and represents ~1.5% of the human genome. Newly arising de novo mutations (variants observed in a child but not in either parent) in the coding genome contribute to numerous childhood developmental disorders, including autism spectrum disorder (ASD). Discovery of these effects is aided by the triplet code that enables the functional impact of many mutations to be readily deciphered. In contrast, the “noncoding genome” covers the remaining ~98.5% and includes elements that regulate when, where, and to what degree protein-coding genes are transcribed. Understanding this noncoding sequence could provide insights into human disorders and refined control of emerging genetic therapies. Yet little is known about the role of mutations in noncoding regions, including whether they contribute to childhood developmental disorders, which noncoding elements are most vulnerable to disruption, and the manner in which information is encoded in the noncoding genome. RATIONALE Whole-genome sequencing (WGS) provides the opportunity to identify the majority of genetic variation in each individual. By performing WGS on 1902 quartet families including a child affected with ASD, one unaffected sibling control, and their parents, we identified ~67 de novo mutations across each child’s genome. To characterize the functional role of these mutations, we integrated multiple datasets relating to gene function, genes implicated in neurodevelopmental disorders, conservation across species, and epigenetic markers, thereby combinatorially defining 55,143 categories. The scope of the problem—testing for an excess of de novo mutations in cases relative to controls for each category—is challenging because there are more categories than families. RESULTS Comparing cases to controls, we observed an excess of de novo mutations in cases in individual categories in the coding genome but not in the noncoding genome. To overcome the challenge of detecting noncoding association, we used machine learning tools to develop a de novo risk score to look for an excess of de novo mutations across multiple categories. This score demonstrated a contribution to ASD risk from coding mutations and a weaker, but significant, contribution from noncoding mutations. This noncoding signal was driven by mutations in the promoter region, defined as the 2000 nucleotides upstream of the transcription start site (TSS) where mRNA synthesis starts. The strongest promoter signals were defined by conservation across species and transcription factor binding sites. Well-defined promoter elements (e.g., TATA-box) are usually observed within 80 nucleotides of the TSS; however, the strongest ASD association was observed distally, 750 to 2000 nucleotides upstream of the TSS. CONCLUSION We conclude that de novo mutations in the noncoding genome contribute to ASD. The clearest evidence of noncoding ASD association came from mutations at evolutionarily conserved nucleotides in the promoter region. The enrichment for transcription factor binding sites, primarily in the distal promoter, suggests that these mutations may disrupt gene transcription via their interaction with enhancer elements in the promoter region, rather than interfering with transcriptional initiation directly. Promoter regions in autism. De novo mutations from 1902 quartet families are assigned to 55,143 annotation categories, which are each assessed for autism spectrum disorder (ASD) association by comparing mutation counts in cases and sibling controls. A de novo risk score demonstrated a noncoding contribution to ASD driven by promoter mutations, especially at sites conserved across species, in the distal promoter or targeted by transcription factors.

Detection of tumor-derived circulating cell-free DNA (ccfDNA) in plasma from glioblastoma patients remains elusive. The vast intra-tumor genetic heterogeneity of glioblastoma has limited targeted searches using a priori molecular profiling from a focal tissue sample. Recent data supports the isolation of shorter ccfDNA fragments relative to ccfDNA’s principal mononucleosomal peak (< 150 bp vs. 167 bp, respectively) to both enrich for tumor-derived ccfDNA and reduce false positives associated with next-generation sequencing (NGS). Here, we sought to determine if size selection affords detection of glioblastoma-derived ccfDNA. The ccfDNA from 11 healthy controls and 10 glioblastoma patients was molecularly barcoded, PCR amplified, and short ccfDNA fragments (< 150 bp) isolated using an automated gel-based technology. Both the original and size-selected ccfDNA samples were capture enriched using a custom-designed, glioblastoma-targeted NGS panel (128 genes, 128 kb) followed by paired-end sequencing. A consensus sequence was determined for each group of PCR duplicates with an identical barcode. The number of PCR duplicates used to derive a consensus sequence has been termed family size (FS). Non-reference alleles (NRAs) from consensus sequences in exons were tabulated. In healthy controls, there was a 15-fold reduction (P < 0.001) in NRAs (i.e., false positives) in the short ccfDNA fraction at FS ≥ 20 compared to FS ≥ 1. At FS ≥ 20 in the short ccfDNA fraction, there were significantly more NRAs in glioblastoma patients (i.e., potential tumor-derived variants) compared to healthy controls (1,065 ± 406 vs. 174 ± 75 NRAs, respectively; P < 0.001). In glioblastoma patients, there were significantly more NRAs in the short ccfDNA fraction compared to the original unselected ccfDNA sample at FS ≥ 20 (1,065 ± 406 vs. 165 ± 135 NRAs, respectively; P < 0.001). Selection for short ccfDNA fragments coupled with molecular barcoding detects glioblastoma-derived ccfDNA through concomitant improvements in both sensitivity and specificity during untargeted searches employing panel-based NGS.

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor that is treated clinically as a single disease with poor outcomes. However, SCLC is recently recognized to comprise multiple molecular subsets with unique therapeutic vulnerabilities. Four distinct subtypes of SCLC have been defined based on expression of lineage-related transcription factors: ASCL1, NEUROD1, POU2F3 or YAP1. The origins of these subtypes remain unknown. We use mouse and human SCLC models with a time-series analysis of single-cell transcriptome profiling to reveal that the oncogene MYC drives the dynamic evolution of SCLC subtypes by activation of Notch signaling. MYC cooperates with Notch signaling to promote a temporal shift from an ASCL1-to-NEUROD1-to-YAP1-positive state from a neuroendocrine cell of origin, whereas MYC promotes POU2F3+ tumors from a distinct cell type. SCLC molecular subtypes are therefore not distinct, but rather represent dynamic stages of MYC-driven tumor evolution. Treatment-naive human SCLC exhibits intratumoral heterogeneity in SCLC subtypes, suggesting this dynamic evolution occurs in patient tumors. These findings demonstrate that genetics, cell of origin, and tumor cell plasticity determine SCLC subtype. Given the reported unique therapeutic vulnerabilities of each subtype, we postulate that SCLC tumors represent a “moving therapeutic target” that may require more general, combinatorial, or plasticity-directed therapeutic approaches to combat this transcriptional flexibility. We anticipate that molecular subsets of other cancer types may also represent dynamic stages of tumor evolution. Citation Format: Abbie S. Ireland, Alexi M. Micinski, David W. Kastner, Bingqian Guo, Sarah J. Wait, Kyle B. Spainhower, Christopher C. Conley, Opal S. Chen, Matthew R. Guthrie, Danny Soltero, Yi Qiao, Xiaomeng Huang, Szabolcs Tarapcsak, Siddhartha Devarakonda, Milind D. Chalishazar, Jason Gertz, Justin C. Moser, Gabor Marth, Sonam Puri, Benjamin L. Witt, Benjamin T. Spike, Trudy G. Oliver. MYC drives temporal evolution of small cell lung cancer subtypes by reprogramming neuroendocrine fate [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-120.

Rapid conversion of chronic myeloid leukemia to chronic myelomonocytic leukemia in a patient on imatinib therapy

Chronic lymphocytic leukemia (CLL) is the most common type of adult leukemia and is considered incurable. Hyperactivity of B-cell receptor signaling is a central driver of CLL's molecular pathogenesis. Targeting this pathway with Bruton's tyrosine kinase inhibitors (BTKi) has improved clinical outcomes for CLL patients when compared to standard chemo-immunotherapies (Woyach, NEJM 2018; Shanafelt, NEJM 2019). To characterize cancer evolution in CLL patients who received BTKi treatment, we collected blood samples from 4 CLL patients at 3 timepoints: pre-treatment, 1 year, and 2 years after initiation of treatment. Sorted B and T cells (normal control) were subjected to 200X WES and 60X WGS. Using somatic mutations and their allele frequencies from WGS/WES data across the 3 timepoints, we reconstructed the subclonal evolution of the malignant B cell population using our subclone deconvolution algorithm SubcloneSeeker (Qiao, Genome Biol. 2014). Our analysis revealed diverse patterns of subclonal genomic evolution across patients in terms of (1) presence or absence of subclonal evolution between time points; (2) emergence of new subclones across time points; and (3) clonal replacement across time points.To elucidate subclone-specific phenotypic behavior in each patient, we collected single-cell RNA sequencing (sc-RNA-seq) and IgG VDJ sequencing (sc-VDJ-seq) from unsorted blood samples at the same time points at which we had the genomic data. Using our new scBayes algorithm (manuscript in preparation), we assigned individual cells in the scRNA-seq data to genomic subclones based on the presence or absence of subclone-defining DNA mutations. This allowed us to isolate and examine the expression phenotype corresponding to subclonal populations. We observed three patterns: 1) no/minimal genomic and transcriptomic evolution across time points; 2) no/minimal genomic subclonal evolution but transcriptomic shift of the same subclones across time points; 3) clonal/subclonal replacement with distinct transcriptomic and VDJ profiles. We found that the two patients in our study with no/minimal genomic and transcriptomic evolution had progressive disease. In contrast, the two other patients whose tumor showed transcriptomic shift and/or clonal replacement had clinically complete remission. Although larger sample sizes are needed to draw further biological conclusions, this study highlights the power of multi-omic, and especially single-cell transcriptional profiling of BTKi treatment impact on CLL patients. Citation Format: Xiaomeng Huang, Yi Qiao, Philip Moos, Szabolcs Tarapcsák, Jennifer A. Woyach, John C. Byrd, Deborah M. Stephens, Gabor T. Marth. The integration of bulk DNA sequencing and single-cell analysis reveals diverse clonal evolution in CLL patients treated with BTKi [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1517.