The mouse (Mus musculus) is the premier animal model for understanding human disease and development. Here we show that a comprehensive understanding of mouse biology is only possible with the availability of a finished, high-quality genome assembly. The finished clone-based assembly of the mouse strain C57BL/6J reported here has over 175,000 fewer gaps and over 139 Mb more of novel sequence, compared with the earlier MGSCv3 draft genome assembly. In a comprehensive analysis of this revised genome sequence, we are now able to define 20,210 protein-coding genes, over a thousand more than predicted in the human genome (19,042 genes). In addition, we identified 439 long, non-protein-coding RNAs with evidence for transcribed orthologs in human. We analyzed the complex and repetitive landscape of 267 Mb of sequence that was missing or misassembled in the previously published assembly, and we provide insights into the reasons for its resistance to sequencing and assembly by whole-genome shotgun approaches. Duplicated regions within newly assembled sequence tend to be of more recent ancestry than duplicates in the published draft, correcting our initial understanding of recent evolution on the mouse lineage. These duplicates appear to be largely composed of sequence regions containing transposable elements and duplicated protein-coding genes; of these, some may be fixed in the mouse population, but at least 40% of segmentally duplicated sequences are copy number variable even among laboratory mouse strains. Mouse lineage-specific regions contain 3,767 genes drawn mainly from rapidly-changing gene families associated with reproductive functions. The finished mouse genome assembly, therefore, greatly improves our understanding of rodent-specific biology and allows the delineation of ancestral biological functions that are shared with human from derived functions that are not.

Despite high initial efficacy, targeted therapies eventually fail in advanced cancers, as tumors develop resistance and relapse. In contrast to the substantial body of research on the molecular mechanisms of resistance, understanding of how resistance evolves remains limited. Using an experimental model of ALK positive NSCLC, we explored the evolution of resistance to different clinical ALK inhibitors. We found that resistance can originate from heterogeneous, weakly resistant subpopulations with variable sensitivity to different ALK inhibitors. Instead of the commonly assumed stochastic single hit (epi) mutational transition, or drug-induced reprogramming, we found evidence for a hybrid scenario involving the gradual, multifactorial adaptation to the inhibitors through acquisition of multiple cooperating genetic and epigenetic adaptive changes. Additionally, we found that during this adaptation tumor cells might present unique, temporally restricted collateral sensitivities, absent in therapy naïve or fully resistant cells, suggesting the potential for new therapeutic interventions, directed against evolving resistance.

Bone morphogenetic proteins (BMPs) were first described over 50 years ago as potent inducers of ectopic bone formation when administrated subcutaneously. Preclinical studies have extensively examined the osteoinductive properties of BMPs in vitro and new bone formation in vivo. BMPs (BMP-2, BMP-7) have been used in orthopedics over 15 years. While osteogenic function of BMPs has been widely accepted, our previous studies demonstrated that loss-of-function of BMP receptor type IA (BMPR1A), a potent receptor for BMP-2, increased net bone mass by significantly inhibiting bone resorption in mice, indicating a positive role of BMP signaling in bone resorption. The physiological role of BMPs (i.e. osteogenic vs. osteoclastogenic) is still largely unknown. The purpose of this study was to investigate the physiological role of BMP signaling in endogenous long bones during adult stages. For this purpose, we conditionally and constitutively activated the Smad-dependent canonical BMP signaling thorough BMPR1A in osteoblast lineage cells using the mutant mice (Col1CreER™:caBmpr1a). Because trabecular bones were largely increased in the loss-of-function mouse study for BMPR1A, we hypothesized that the augmented BMP signaling would affect endogenous trabecular bones. In the mutant bones, the Smad phosphorylation was enhanced within physiological level three-fold while the resulting gross morphology, bodyweights, bone mass/shape/length, serum calcium/phosphorus levels, collagen cross-link patterns, and healing capability were all unchanged. Interestingly, we found; 1) increased expressions of both bone formation and resorption markers in femoral bones, 2) increased osteoblast and osteoclast numbers together with dynamic bone formation parameters by trabecular bone histomorphometry, 3) modest bone architectural phenotype with reduced bone quality (i.e. reduced trabecular bone connectivity, larger diametric size but reduced cortical bone thickness, and reduced bone mechanical strength), and 4) increased expression of SOST, a downstream target of the Smad-dependent BMPR1A signaling, in the mutant bones. This study is clinically insightful because gain-of-function of BMP signaling within a physiological window does not increase bone mass while it alters molecular and cellular aspects of osteoblast and osteoclast functions as predicted. These findings help explain the high-doses of BMPs (i.e. pharmacological level) in clinical settings required to substantially induce a bone formation, concurrent with potential unexpected side effects (i.e. bone resorption, inflammation) presumably due to a broader population of cell-types exposed to the high-dose BMPs rather than osteoblastic lineage cells.

Molecular mechanisms characterizing cancer development and progression are complex and process through thousands of interacting elements in the cell. Understanding the underlying structure of interactions requires the integration of cellular networks with extensive combinations of dysregulation patterns. Recent pan-cancer studies focused on identifying common dysregulation patterns in a confined set of pathways or targeting a manually curated set of genes. However, the complex nature of the disease presents a challenge for finding pathways that would constitute a basis for tumor progression and requires evaluation of subnetworks with functional interactions. Uncovering these relationships is critical for translational medicine and the identification of future therapeutics. We present a frequent subgraph mining algorithm to find functional dysregulation patterns across the cancer spectrum. We mined frequent subgraphs coupled with biased random walks utilizing genomic alterations, gene expression profiles, and protein-protein interaction networks. In this unsupervised approach, we have recovered expert-curated pathways previously reported for explaining the underlying biology of cancer progression in multiple cancer types. Furthermore, we have clustered the genes identified in the frequent subgraphs into highly connected networks using a greedy approach and evaluated biological significance through pathway enrichment analysis. Gene clusters further elaborated on the inherent heterogeneity of cancer samples by both suggesting specific mechanisms for cancer type and common dysregulation patterns across different cancer types. Survival analysis of sample level clusters also revealed significant differences among cancer types (p < 0.001). These results could extend the current understanding of disease etiology by identifying biologically relevant interactions.Supplementary Information: Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/FSM_Pancancer.

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general may play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Even when positive ecological interactions between mutants and ancestors are rare, these clones provide the primary mode of evolved resistance because their positive interaction leads to significantly longer extinction times. Next, we find that even in the case where mutation supply is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects. Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance, and where our theory predicts positive ecological interactions to be common. We find that all three engineered mutants display a positive ecological interaction with their ancestor, as predicted. Strikingly, as with our originally evolved resistant mutant, two of the three engineered mutants have ecological interactions that fully compensate for their substantial fitness costs. As a whole, these results suggest that frequency-dependent ecological effects may provide the primary mode by which pre-existing resistance emerges.

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds ("drugs") previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms.

Bone morphogenetic protein (BMP) signaling in osteoblasts plays critical roles in skeletal development and bone homeostasis. Our previous studies showed loss of function of BMPR1A, one of the type 1 receptors for BMPs, in osteoblasts results in increased trabecular bone mass in long bones due to an imbalance between bone formation and bone resorption. Decreased bone resorption was associated with an increased mature-to-immature collagen cross-link ratio and mineral-matrix ratios in the trabecular compartments, and increased tissue-level biomechanical properties. Here, we investigated the bone mass, bone composition and biomechanical properties of ribs and spines in the same genetically altered mouse line to compare outcomes by loss of BMPR1A functions in bones from different anatomic sites and developmental origins. Bone mass was significantly increased in both cortical and trabecular compartments of ribs with minimal to modest changes in compositions. While tissue-levels of biomechanical properties were not changed between control and mutant animals, whole bone levels of biomechanical properties were significantly increased in association with increased bone mass in the mutant ribs. For spines, mutant bones showed increased bone mass in both cortical and trabecular compartments with an increase of mineral content. These results emphasize the differential role of BMP signaling in osteoblasts in bones depending on their anatomical locations, functional loading requirements and developmental origin.

Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference.

Patient-derived xenografts established from human cancers are important tools for investigating novel anti-cancer therapies. Establishing PDXs requires a significant investment and many PDXs may be used infrequently due to their similarity to existing models, their growth rate, or the lack of relevant mutations. We performed this study to determine whether we could efficiently establish PDXs after cryopreservation to allow molecular profiling to be completed prior to implanting the human cancer.

Complete or partial deletions of chromosome 7 (-7/del7q) belong to the most frequent chromosomal abnormalities in myeloid neoplasm (MN) and are associated with a poor prognosis. The disease biology of -7/del7q and the genes responsible for the leukemogenic properties have not been completely elucidated. Chromosomal deletions may create clonal vulnerabilities due to haploinsufficient (HI) genes contained in the deleted regions. Therefore, HI genes are potential targets of synthetic lethal strategies. Through the most comprehensive multimodal analysis of more than 600 -7/del7q MN samples, we elucidated the disease biology and qualified a list of most consistently deleted and HI genes. Among them, 27 potentially synthetic lethal target genes were identified with the following properties: (i) unaffected genes by hemizygous/homozygous LOF mutations; (ii) prenatal lethality in knockout mice; and (iii) vulnerability of leukemia cells by CRISPR and shRNA knockout screens. In -7/del7q cells, we also identified 26 up or down-regulated genes mapping on other chromosomes as downstream pathways or compensation mechanisms. Our findings shed light on the pathogenesis of -7/del7q MNs, while 27 potential synthetic lethal target genes and 26 differential expressed genes allow for a therapeutic window of -7/del7q.

The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly.

Background: TP53 mutations ( TP53 MT ) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model to properly resolve the allelic configuration of TP53 MT and assess prognosis more precisely. Results: Overall, TP53 MT were found in 1010 patients. Following the traditional criteria, 36% of cases were classified as single hits while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53 MT . Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. These results were recapitulated by single-cell DNA studies, which unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

PHF6 mutations (PHF6MT) are identified in various myeloid neoplasms (MN). However, little is known about the precise function and consequences of PHF6 in MN. Here we show three main findings in our comprehensive genomic and proteomic study. Firstly, we show a different pattern of genes correlating with PHF6MT in male and female cases. When analyzing male and female cases separately, in only male cases, RUNX1 and U2AF1 are co-mutated with PHF6. In contrast, female cases reveal co-occurrence of ASXL1 mutations and X-chromosome deletions with PHF6MT. Next, proteomics analysis reveals a direct interaction between PHF6 and RUNX1. Both proteins co-localize in active enhancer regions that define the context of lineage differentiation. Finally, we demonstrate a negative prognostic role of PHF6MT, especially in association with RUNX1. The negative effects on survival are additive as PHF6MT cases with RUNX1 mutations have worse outcomes when compared to cases carrying single mutation or wild-type.

Solid tumors present a panoply of genomic alterations, from single base changes to the gain or loss of entire chromosomes. Although aberrations at the two extremes of this spectrum are readily defined, comprehensive discernment of the complex and disperse mutational spectrum of cancer genomes remains a significant challenge for current genome analysis platforms. In this context, high throughput, single molecule platforms like Optical Mapping offer a unique perspective.

TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis.

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25-31, 2020, then rose rapidly during September 1-11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.

Methylation of CpG dinucleotides is a fundamental mechanism of epigenetic regulation in eukaryotic genomes. Development of methods for rapid genome wide methylation profiling will greatly facilitate both hypothesis and discovery driven research in the field of epigenetics. In this regard, a single molecule approach to methylation profiling offers several unique advantages that include elimination of chemical DNA modification steps and PCR amplification.

Non-small cell lung cancer (NSCLC) brain metastasis cell lines and in vivo models are not widely accessible. Herein we report on a direct-from patient-derived xenograft (PDX) model system of NSCLC brain metastases with genomic annotation useful for translational and mechanistic studies. Both heterotopic and orthotopic intracranial xenografts were established and RNA and DNA sequencing was performed on patient and matching tumors. Morphologically, strong retention of cytoarchitectural features was observed between original patient tumors and PDXs. Transcriptome and mutation analysis revealed high correlation between matched patient and PDX samples with more than more than 95% of variants detected being retained in the matched PDXs. PDXs demonstrated response to radiation, response to selumetinib in tumors harboring KRAS G12C mutations and response to savolitinib in a tumor with MET exon 14 skipping mutation. Savolitinib also demonstrated in vivo radiation enhancement in our MET exon 14 mutated PDX. Early passage cell strains showed high consistency between patient and PDX tumors. Together, these data describe a robust human xenograft model system for investigating NSCLC brain metastases. These PDXs and cell lines show strong phenotypic and molecular correlation with the original patient tumors and provide a valuable resource for testing preclinical therapeutics.