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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.

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.

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

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.

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.

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.

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.

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.

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.

Many types of cancer are found to exhibit a high level of intrasample genetic heterogeneity, as a result of the microevolution process. As genomic aberrations accumulate within tumor cells, a subset of the cells may gain a selective advantage and form an outgrowth within the tumor. When chemotherapeutic agents are administered to the patient, these tumor subclones may respond differently to the treatment and may eventually cause relapse. Identifying resistant subclones, and their genotypes, after each chemotherapy would yield valuable insights into how the tumor population reacted to the previously administered chemo-agent, and how the treatment plan should be adjusted based on the genetic features of surviving subclones. Similarly, understanding the relationships between the primary and metastatic tumor cell populations would provide knowledge about the mechanisms driving, and potentially preventing, metastasis. Our method (SubcloneSeeker) is able to reconstruct subclone structures jointly across all samples (temporal or spatial), using somatic variants jointly clustered with their Cell Prevalence (CP), to infer a unified evolutionary structure. When more than one structure is able to explain the observed somatic variants and their CP values, we can compute a consensus structure that captures the most important features (i.e., which subclones and the corresponding genomic aberrations are responsible for the resistance of a certain chemotherapy drug), as well as suggest a panel of variants to be profiled with single-cell assay in order to reduce computational ambiguity. We applied this method to several datasets, including a breast cancer patient who had received three regimens of chemotherapy and had tumor samples taken at 4 consecutive time points. Our analysis revealed that the tumor population reacted to these treatment in two distinct patterns: 1) although a significant reduction in tumor burden was observed, most of the major subclones survived and expanded again after treatment, indicating that the treatment was effective, but terminated prematurely; 2) a single subclone with extra mutations (e.g., FBXL19, a tumor suppressor by inhibiting E-cadherin downregulation) survived the treatment and became the founding clone for the tumor population post-treatment, and the extra mutations were fixed at very high CP (~100%), indicating that these extra mutations are most likely to be causative for chemoresistance. We also applied our method to breast cancer patient rapid-autopsy datasets with 28 tumor samples. Our method was able to map out subclone evolution across multiple metastatic sites and highlight variants unique to each evolutionary lineage. In conclusion, our method is able to elucidate the dynamics of tumorigenesis, drug response, and metastasis by reconstructing unified subclone evolutionary structures, allowing more informed decisions to be made in adjusting treatment plans in real time and potentially facilitating personalized cancer medicine. Citation Format: Gabor Marth, Yi Qiao, Dillon Lee, Xiaomeng Huang. Real-time spatial and temporal monitoring on tumor subclonal evolution [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 39.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

Early infantile epileptic encephalopathy (EIEE) is a devastating epilepsy syndrome with onset in the first months of life. Although mutations in more than 50 different genes are known to cause EIEE, current diagnostic yields with gene panel tests or whole-exome sequencing are below 60%. We applied whole-genome analysis (WGA) consisting of whole-genome sequencing and comprehensive variant discovery approaches to a cohort of 14 EIEE subjects for whom prior genetic tests had not yielded a diagnosis. We identified both de novo point and INDEL mutations and de novo structural rearrangements in known EIEE genes, as well as mutations in genes not previously associated with EIEE. The detection of a pathogenic or likely pathogenic mutation in all 14 subjects demonstrates the utility of WGA to reduce the time and costs of clinical diagnosis of EIEE. While exome sequencing may have detected 12 of the 14 causal mutations, 3 of the 12 patients received non-diagnostic exome panel tests prior to genome sequencing. Thus, given the continued decline of sequencing costs, our results support the use of WGA with comprehensive variant discovery as an efficient strategy for the clinical diagnosis of EIEE and other genetic conditions., Pediatric neurology: whole-genome analysis finds mutations causing severe newborn seizures Whole-genome sequencing combined with specialized bioinformatics can diagnose disease mutations in newborns with devastating seizures. Josh Bonkowsky, Gabor Marth, Aaron Quinlan, and colleagues from the University of Utah in Salt Lake City, USA, thoroughly detailed the genomic variation of 14 babies with early infantile epileptic encephalopathy (EIEE). EIEE is a severe pediatric neurodevelopmental disorder causing seizures and early death. In the 14 infants, who had previous testing that did not identify the cause, the research team found the genetic cause for all of the infants. Prior to this work, approaches for EIEE diagnosed only up to 60% of infants. With the cost of DNA sequencing continuing to fall, the authors suggest that whole-genome testing could be a cost-effective approach to diagnosing EIEE and other genetic conditions.

SummaryGenomic studies to date in autism spectrum disorder (ASD) have largely focused on newly arising mutations that disrupt protein coding sequence and strongly influence risk. We evaluate the contribution of noncoding regulatory variation across the size and frequency spectrum through whole genome sequencing of 519 ASD cases, their unaffected sibling controls, and parents. Cases carry a small excess of de novo (1.02-fold) noncoding variants, which is not significant after correcting for paternal age. Assessing 51,801 regulatory classes, no category is significantly associated with ASD after correction for multiple testing. The strongest signals are observed in coding regions, including structural variation not detected by previous technologies and missense variation. While rare noncoding variation likely contributes to risk in neurodevelopmental disorders, no category of variation has impact equivalent to loss-of-function mutations. Average effect sizes are likely to be smaller than that for coding variation, requiring substantially larger samples to quantify this risk.

Importance Sensorineural hearing loss (SNHL) is commonly caused by conditions that affect cochlear structures or the auditory nerve, and the genes identified as causing SNHL to date only explain a fraction of the overall genetic risk for this debilitating disorder. It is likely that other genes and mutations also cause SNHL. Objective To identify a candidate gene that causes bilateral, symmetric, progressive SNHL in a large multigeneration family of Northern European descent. Design, Setting, and Participants In this prospective genotype and phenotype study performed from January 1, 2006, through April 1, 2016, a 6-generation family of Northern European descent with 19 individuals having reported early-onset hearing loss suggestive of an autosomal dominant inheritance were studied at a tertiary academic medical center. In addition, 179 unrelated adult individuals with SNHL and 186 adult individuals reporting nondeafness were examined. Main Outcomes and Measures Sensorineural hearing loss. Results Nine family members (5 women [55.6%]) provided clinical audiometric and medical records that documented hearing loss. The hearing loss is characterized as bilateral, symmetric, progressive SNHL that reached severe to profound loss in childhood. Audiometric configurations demonstrated a characteristic dip at 1000 to 2000 Hz. All affected family members wear hearing aids or have undergone cochlear implantation. Exome sequencing and linkage and association analyses identified a fully penetrant sequence variant (rs35725509) on chromosome 12q21 (logarithm of odds, 3.3) in the TMTC2 gene region that segregates with SNHL in this family. This gene explains the SNHL occurrence in this family. The variant is also associated with SNHL in a cohort of 363 unrelated individuals (179 patients with confirmed SNHL and 184 controls, P = 7 × 10 −4 ). Conclusions and Relevance A previously uncharacterized gene, TMTC2 , has been identified as a candidate for causing progressive SNHL in humans. This finding identifies a novel locus that causes autosomal dominant SNHL and therefore a more detailed understanding of the genetic basis of SNHL. Because TMTC2 has not been previously reported to regulate auditory function, the discovery reveals a potentially new, uncharacterized mechanism of hearing loss.

Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer’s ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves., In metastatic breast cancer, subclonal evolution can drive drug resistance. Here, the authors genetically and transcriptionally follow the evolution of four breast cancers over time and treatment, and suggest a phenotype-targeted treatment strategy to adapt to cancer as it evolves.

Intracranial germ cell tumours (IGCTs) are a group of rare heterogeneous brain tumours that are clinically and histologically similar to the more common gonadal GCTs. IGCTs show great variation in their geographical and gender distribution, histological composition and treatment outcomes. The incidence of IGCTs is historically five- to eightfold greater in Japan and other East Asian countries than in Western countries, with peak incidence near the time of puberty. About half of the tumours are located in the pineal region. The male-to-female incidence ratio is approximately 3-4:1 overall, but is even higher for tumours located in the pineal region. Owing to the scarcity of tumour specimens available for research, little is currently known about this rare disease. Here we report the analysis of 62 cases by next-generation sequencing, single nucleotide polymorphism array and expression array. We find the KIT/RAS signalling pathway frequently mutated in more than 50% of IGCTs, including novel recurrent somatic mutations in KIT, its downstream mediators KRAS and NRAS, and its negative regulator CBL. Novel somatic alterations in the AKT/mTOR pathway included copy number gains of the AKT1 locus at 14q32.33 in 19% of patients, with corresponding upregulation of AKT1 expression. We identified loss-of-function mutations in BCORL1, a transcriptional co-repressor and tumour suppressor. We report significant enrichment of novel and rare germline variants in JMJD1C, which codes for a histone demethylase and is a coactivator of the androgen receptor, among Japanese IGCT patients. This study establishes a molecular foundation for understanding the biology of IGCTs and suggests potentially promising therapeutic strategies focusing on the inhibition of KIT/RAS activation and the AKT1/mTOR pathway.

Several approaches are now available for subclonal reconstruction of heterogeneous tumor biopsies from somatic variant allele frequencies measured in bulk DNA sequencing datasets, including our own SubcloneSeeker program. However, to understand how chemoresistance, relapse, or metastasis evolves at a subclonal level, we also need to study the molecular phenotype corresponding to each subclone. Single-cell RNAseq technologies now allow one to study the transcriptomic characteristics and phenotypic behaviors of individual cells, but new algorithms are needed to link these cells to the genomically defined tumor subclones they represent. Here we present a computational approach to make such cell assignments, using a combination of bulk DNA sequencing data (WGS or WES), and single cell RNA sequencing (scRNAseq) collected from the same tissues. Subclones constructed from bulk DNA sequencing data are defined by specific combinations of somatic mutations (SNVs and CNVs). Our algorithm uses the scRNA-seq reads to assess which of these subclone-defining mutations are present in each individual cell, then utilizes a Bayesian probabilistic framework for making the cell-to-subclone assignment. This framework overcomes the challenges of sparse scRNA-seq read coverage, and maximizes the accuracy and efficiency of cell assignment. We have successfully applied this method to multiple longitudinal and metastatic cancer patient datasets, representing both WES and WGS bulk DNA sequencing, as well as datasets collected using the 10X Chromium and Fluidigm C1 technologies. Our algorithm uses both somatically acquired CNV and SNV events in the tumor for cell assignment. Using our approach, as many of 80% of tumor cells could be assigned to specific subclones, enabling comparative gene expression and pathway analysis across subclones. Citation Format: Yi Qiao, Xiaomeng Huang, Samuel Brady, Andrea Bild, David Bowtell, William Johnson, Gabor Marth. Subclone-specific evolution of tumor phenotypes – A framework to study subclone-specific gene expression from a combination of bulk DNA and single cell RNA sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4689.

Purpose: Establishing a reliable bioinformatics variant calling workflow is crucial for untargeted searches of low-frequency variants in plasma circulating cell-free DNA (ccfDNA). We sought to benchmark the performance of nine variant calling algorithms on simulated data and cancer patient ccfDNA sequencing samples. Methods: A simulated tumor dataset was constructed by in-silico introduction of pathogenic variants (303 SNVs and 91 INDELs; VAF of 0.1-12%) into a healthy control sequencing sample (128 gene capture panel; 128kb; 4200X depth). An unspiked replicate served as a control. Tumor and control datasets were analyzed by nine somatic variant calling algorithms available in the public domain (Table 1). Sensitivity and specificity were calculated for each caller. Variant calling algorithms that proved most successful in synthetic data (Lofreq, Mutect) were applied to 24 EGFR-mutant lung cancer patient ccfDNA library samples captured and sequenced under conditions identical to the benchmarking data set. Droplet digital PCR (ddPCR) was used to independently determine the VAFs of two EGFR SNVs (T790M and L858R) and exon 19 deletions in all lung cancer libraries (detected range: 0.1-48.5%). Results: Table 1:Sensitivity (Sens.) and false discovery rate (FDR) as determined for 9 variant callers. For synthetic data, results are shown for 1.6% VAF (NC = no call)CallerSynthetic data (1.6% VAF)Lung cancer ccfDNASNVINDELSNVINDEL% Sens.% FDR% Sens.% FDR% Sens. 1% VAF% Sens. 1% VAFLofreq87.15.792.35.675.094.466.7100Mutect47.52.291.26.762.588.916.787.5Mutect235.63.296.71.1Strelka17.83.57.69.2FreebayesNCNCNCNCVarscanNCNCNCNCVardictNCNCNCNCLumpy13.986.17.792.1DellyNCNCNCNC Conclusion: Performance of variant callers differed greatly, with Lofreq providing the best sensitivity at low VAF. In cancer ccfDNA libraries Lofreq detected most but not all low-frequency variants, indicating that ccfDNA diagnostics require optimized variant calling workflows to improve detection of variants with a VAF Citation Format: Preetida J. Bhetariya, Sabine Hellwig, David A. Nix, Gabor T. Marth, Mary P. Bronner, Hunter R. Underhill. Benchmarking of somatic variant calling algorithms for detection of circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2220.

Comprehensive interpretation of human genome sequencing data is a challenging bioinformatic problem that typically requires weeks of analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source genome analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human genome in

OBJECTIVES/SPECIFIC AIMS: The objective of the study was 2-fold; to identify potentially deleterious alleles in a child with Treacher Collins syndrome, and; to demonstrate the value of the iobio analysis platform for intuitively and rapidly analyzing genomic data. METHODS/STUDY POPULATION: We used the iobio suite of web-based applications to analyze quality metrics for the sequencing data and called variants for the proband and his parents. We then visually interrogated variants in genes potentially associated with the syndrome in real-time, using the intuitive gene.iobio application. We sought high impact variants that demonstrated a predicted impact on the protein function, and were simultaneously at low allele frequency in the general human population. Variants were also compared against the ClinVar database of known mutations to identify variants that have already been associated with this, or related syndromes in the literature or clinical studies. Finally, the gene.iobio tool allows users to interrogate the primary sequencing data to ensure that no variants had been missed by the primary variant calling pipeline. This analysis pipeline was performed using intuitive web-based apps in real time, and consequently represents a system that is available to users that traditionally are excluded from these analyses. RESULTS/ANTICIPATED RESULTS: The iobio suite was used to rapidly assess data quality and interrogate genetic variants for a child with Treacher Collins syndrome. A compound heterozygote consisting of 2 missense alleles in the TCOF1 gene was identified as a compelling pathogenic allele, necessitating further functional investigation. The study helped validate the use of the intuitive iobio tools in such analyses, strengthening the case for greater involvement of medical professionals in data analysis. DISCUSSION/SIGNIFICANCE OF IMPACT: The performed analyses demonstrated that the whole genome sequencing data for the family being studied was of a very high quality, although 1 gene demonstrated a local region of almost zero coverage. This ensured that study conclusions can be presented with confidence. A variant associated with Treacher Collins syndrome 1 in ClinVar was uncovered in the TCOF1 gene, however, given it’s benign rating, this variant was not considered further. The most interesting candidate was a compound heterozygote, consisting of 2 missense mutations, also in the TCOF1 gene. These mutations occurred with allele frequencies of 22% and 8% in the general population, and additional molecular and functional studies are currently being pursued.

Metastatic breast cancer is an advanced-stage disease in which the cancer cells have spread to distant organs. To understand the patterns of metastatic colonization in a patient who presented with aggressive disease, we have collected tumor biopsies at initial diagnosis, at mastectomy necessitated by the patient's relapse, twenty-six metastatic tumors across seven organs and two normal tissue control skin biopsies via a rapid autopsy procedure within hours after the patient's death. All biopsy samples were subjected to 60X Illumina whole genome sequencing. Our analysis revealed extensive chromosomal changes including amplifications, deletions, LOH and translocations, as well as known driver mutations in RB1, TP53, and PTEN in all tumor samples. We used the CNV and LOH data to reconstruct the phylogenetic relationships among the tumor samples. We used the somatic SNV allele frequencies in copy number-normal regions to refine these phylogenetic relationships, and to establish the evolution of the tumor at the subclonal level across different metastatic sites, with an extension of our SubcloneSeeker algorithm. Our analysis reveals the lung as the first metastatic site. In fact, this invasion occurred before relapse at the primary site in the breast, indicating early metastatic escape. This initial invasion was followed by distinct waves of massive metastatic expansion, beginning with the abdominal organs, then the lymph nodes, brain and bones. As the initial invasion, each metastatic wave involved other distinct sites in the lungs, these sites serving as incubators where subclones further evolved prior to seeding new metastatic sites. Finally, we see clear evidence for metastatic “recolonization” where such further evolved subclones invaded already established, earlier metastatic sites. The high number of biopsied sites in this study, 30 in all, allowed us to reconstruct the evolution of the aggressive disease in our patient with unprecedented resolution, and to identify characteristic patterns of metastatic colonization. If confirmed in additional, similarly high resolution datasets, these patterns will lead to better understanding of the metastatic process, and guide effective clinical intervention. Citation Format: Xiaomeng Huang, Yi Qiao, Samuel W. Brady, Adam Cohen, Andrea H. Bild, Gabor T. Marth. Temporal and spatial dynamics of metastatic colonization revealed by 26 rapid-autopsy tumor biopsies from a TNBC patient [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2195.

Current tumor variant detection software tools are focused on the identification of somatic mutations in a single tumor sample (e.g. the primary tumor) compared to normal control tissue, and are not adequate for studies where multiple samples from the same patient, either at consecutive time points or at different metastatic sites are collected. Here we present a software framework that facilitates simultaneous analysis of such multi-sample datasets. Our framework utilizes our FreeBayes variant caller for jointly calling all tumor samples and normal controls from the patient. This ensures that even very low frequency mutations are called because read evidence is aggregated across all samples; and that allele frequency information for a called somatic variant is provided uniformly for all samples. Short and medium-length INDELs, and structural variants are identified by our RUFUS software, a new reference-free mutation calling algorithm for both germline and somatic mutation detection, with extremely high sensitivity and specificity. We use our Graphite tool, a graph-genome based read realignment algorithm, to eliminate false positive somatic calls i.e. inherited variants masquerading as somatic mutations, and to substantially increase accuracy of allele frequency estimation of the true somatic calls, a feature critical for downstream subclone analysis. In addition, we use the FACETS package for CNV and LOH analysis, also critical for downstream subclone analysis. Finally, we utilize SubcloneSeeker, our algorithm to reconstruct the evolution of the cancer at the subclonal level from the somatic variant allele frequencies, to understand how the tumor evolved over time, in response to multiple courses of treatment, or over space, across multiple metastatic sites. We demonstrate the use of this pipeline in published and ongoing studies involving longitudinal and multi-site tumor sample datasets. Citation Format: Yi Qiao, Xiaomeng Huang, Dillon Lee, Andrew Farrell, Thomas Nicholas, Brent Pederson, Aaron Quinlan, Gabor Marth. Utah somatic variant calling pipeline featuring multi-sample joint calling, variant-graph based accurate allele frequency estimation and subclone analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3280.

Patient-derived xenografts (PDX) are valuable, clinically-relevant models of cancer. Their close genomic, phenotypic, and temporal association with patient tumors makes them well-suited for pre-clinical and co-clinical studies that assess the potential of new therapeutics. However, PDX models are not amenable to large-scale drug sensitivity studies due to their high cost and low throughput capacity. To address this, we established and characterized long-term PDX organoid (PDxO) cultures from breast cancer (BC) PDXs and evaluated their utility in therapeutic studies. To establish PDxO culture conditions, we extensively tested medium supplements, including growth factors, kinase inhibitors, conditioned medium, and antioxidants, along with extracellular matrix composition in 3D gels. Using optimized culture conditions for each BC subtype, we established PDxOs from 16 PDX lines in the HCI series, with a PDX to PDxO success rate of 85%. Around 20% of PDxOs contained aggressive mouse stroma, which had to be eliminated by FACS for long-term culture success. PDxOs were maintained for over 1 year, during which we tracked viability, doubling time, organoid size, genomics, epithelial character, and tumorigenicity. Doubling times stabilized after 60 days of initial culture, with a mean of 6.4±1.7 days for triple negative (TN) lines and 7.2±1 days for ER+ lines. Mean organoid size remained stable, with ER+ PDxOs significantly smaller than TN PDxOs, at 91 and 262 cells/organoid respectively (p=0.0012). PDxOs histologically resembled their PDX counterparts when stained for H&E, Vimentin, and CAM 5.2. RNA-Seq and copy-number variation analyses showed that PDxOs clustered with their PDXs and patient tumors. To assess if culturing affected tumorigenicity, we re-implanted PDxOs into mice following early and late passaging. 5/5 early passage and 5/6 late passage PDxOs engrafted and formed tumors. Resulting tumors showed similar gene expression profiles as their original PDXs by RNA-Seq. These data suggest that PDxOs generally recapitulate the molecular and genomic features of their originating PDXs and patient tumors. Having established PDxO cultures, we evaluated their utility as a therapeutic screening platform. We developed a screen to differentiate compounds with cytotoxic and cytostatic effects. NCI CTEP and clinically-approved BC therapies (n=40) were screened in quadruplicate 8-point dose response curves on all 16 PDxOs across three biological replicates. Drug response profiles were stable across biological replicates, spanning up to 1 year in culture. PDxOs exhibited diverse responses to therapies targeting cIAP1, PI3K, and CHK1/2. Initial work to evaluate concordance between PDxO and in vivo PDX responses returned trending linear correlations between PDxO dose response data and change in PDX growth rate following treatment (R2 = 0.52-0.77; p = 0.045-0.12, across 3 PDXs and 9 compounds). Ongoing work aims to confirm concordance between PDX and PDxO models. Our work demonstrates that PDxO models are cost-efficient, easy to maintain, and grow indefinitely - making them renewable and accessible cancer models. PDxOs are a powerful parallel resource to PDX models, especially useful for efficient determination of PDX drug response. We are currently expanding our PDxO bank to include 100 models which will be deposited with the NCI as part of the PDXNet effort. Citation Format: Katrin P. Guillen, Sandra D. Scherer, Yi Qiao, Satya S. Pathi, James M. Graham, Maihi Fujita, Yoko S. DeRose, Jason Gertz, Gabor T. Marth, Katherine E. Varley, Alana L. Welm, Bryan E. Welm. Predicting breast cancer therapy response using a patient-derived xenograft organoid screening platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-038.

We report the identification and characterization of 2,000 human diallelic insertion/deletion polymorphisms (indels) distributed throughout the human genome. Candidate indels were identified by comparison of overlapping genomic or cDNA sequences. Average confirmation rate for indels with aor =2-nt allele-length difference was 58%, but the confirmation rate for indels with a 1-nt length difference was only 14%. The vast majority of the human diallelic indels were monomorphic in chimpanzees and gorillas. The ratio of deletionrcolon;insertion mutations was 4.1. Allele frequencies for the indels were measured in Europeans, Africans, Japanese, and Native Americans. New alleles were generally lower in frequency than old alleles. This tendency was most pronounced for the Africans, who are likely to be closest among the four groups to the original modern human population. Diallelic indels comprise approximately 8% of all human polymorphisms. Their abundance and ease of analysis make them useful for many applications.

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me).

Many tumors are composed of genetically divergent cell subpopulations. We report SubcloneSeeker, a package capable of exhaustive identification of subclone structures and evolutionary histories with bulk somatic variant allele frequency measurements from tumor biopsies. We present a statistical framework to elucidate whether specific sets of mutations are present within the same subclones, and the order in which they occur. We demonstrate how subclone reconstruction provides crucial information about tumorigenesis and relapse mechanisms; guides functional study by variant prioritization, and has the potential as a rational basis for informed therapeutic strategies for the patient. SubcloneSeeker is available at: https://github.com/yiq/SubcloneSeeker. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0443-x) contains supplementary material, which is available to authorized users.