Medulloblastomas Initiated by Homologous Recombination Defects in Mice.

Germline mutations of homologous-recombination (HR) genes are among the top contributors to medulloblastomas. A significant portion of human medulloblastomas exhibited genomic signatures of HR defects. We asked whether ablation of Brca2, Palb2, and their related Brca1 and Bccip genes, in the mouse brain can differentially initiate medulloblastomas. We established conditional knockout mouse models of these HR-genes, and a conditional knockdown of Bccip (shBccip-KD). Deletion of any of these genes led to microcephaly and neurological defects, with Brca1- and Bccip- producing the worst. Trp53 co-deletion significantly rescued the microcephaly with Brca1, Palb2, and Brca2 deficiency, but it exhibited limited impact on Bccip- mice. For the first time, inactivation of either Brca1 or Palb2 with Trp53 was found to induce medulloblastomas. Bccip/Trp53 deletions failed, despite that shBccip-CKD was highly penetrative, to induce medulloblastomas. The tumors displayed diverse immunohistochemical features and chromosome copy number variation. While there were widespread upregulations of cell proliferative pathways, most of the tumors expressed biomarkers of the Sonic Hedgehog subgroup. The MBs developed from Brca1-, Palb2-, and Brca2- mice were highly sensitive to a PARP inhibitor, but not the ones from shBccip-CKD mice. Our models recapitulate the spontaneous medulloblastoma development with high penetrance and narrow time-window, providing ideal platforms to test therapeutic agents with the ability to differentiate HR defective and proficient tumors.

Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.

MOTIVATION: Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. RESULTS: We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. AVAILABILITY AND IMPLEMENTATION: Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.

DYNLL1 binds to MRE11 to limit DNA end resection in BRCA1-deficient cells.

Limited DNA end resection is the key to impaired homologous recombination in BRCA1-mutant cancer cells. Here, using a loss-of-function CRISPR screen, we identify DYNLL1 as an inhibitor of DNA end resection. The loss of DYNLL1 enables DNA end resection and restores homologous recombination in BRCA1-mutant cells, thereby inducing resistance to platinum drugs and inhibitors of poly(ADP-ribose) polymerase. Low BRCA1 expression correlates with increased chromosomal aberrations in primary ovarian carcinomas, and the junction sequences of somatic structural variants indicate diminished homologous recombination. Concurrent decreases in DYNLL1 expression in carcinomas with low BRCA1 expression reduced genomic alterations and increased homology at lesions. In cells, DYNLL1 limits nucleolytic degradation of DNA ends by associating with the DNA end-resection machinery (MRN complex, BLM helicase and DNA2 endonuclease). In vitro, DYNLL1 binds directly to MRE11 to limit its end-resection activity. Therefore, we infer that DYNLL1 is an important anti-resection factor that influences genomic stability and responses to DNA-damaging chemotherapy.

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.

Requirement of Bccip for the Regeneration of Intestinal Progenitors.

BCCIP was originally identified as a BRCA2 and CDKN1A/p21 interaction protein. Although a partial loss of BCCIP function is sufficient to trigger genomic instability and tumorigenesis, complete deletion of BCCIP is lethal to cells. Using Rosa26-CreERT2 mouse models, we found that induced Bccip deletion in adult mice caused an acute intestinal epithelial denudation that cannot be relieved by co-deletion of Trp53. The critical role of Bccip in intestine epithelial renewal was verified with a Villin-CreERT2 mouse model. The epithelium degeneration was associated with a rapid loss of the proliferative capability of the crypt progenitor cells in vivo, lack of crypt base columnar stem cell markers, and a failure of in vitro crypt organoid growth. RNA-Seq analysis of freshly isolated intestinal crypt cells showed that Bccip deletion caused an overwhelming down-regulation of genes involved in mitotic cell division but an up-regulation of genes involved in apoptosis and stress response to microbiomes. Our data not only indicate that intestinal epithelium is the most sensitive tissue to whole-body deletion of Bccip but also point to Bccip as a novel and critical factor for the proliferation of the intestinal progenitors. These findings have significant implications for understanding why a hypomorphic loss of BCCIP functions is more relevant to tumorigenesis.

Drivers of dynamic intratumor heterogeneity and phenotypic plasticity.

Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves-which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.

Polyploidy can drive rapid adaptation in yeast.

Polyploidy is observed across the tree of life, yet its influence on evolution remains incompletely understood. Polyploidy, usually whole-genome duplication, is proposed to alter the rate of evolutionary adaptation. This could occur through complex effects on the frequency or fitness of beneficial mutations. For example, in diverse cell types and organisms, immediately after a whole-genome duplication, newly formed polyploids missegregate chromosomes and undergo genetic instability. The instability following whole-genome duplications is thought to provide adaptive mutations in microorganisms and can promote tumorigenesis in mammalian cells. Polyploidy may also affect adaptation independently of beneficial mutations through ploidy-specific changes in cell physiology. Here we perform in vitro evolution experiments to test directly whether polyploidy can accelerate evolutionary adaptation. Compared with haploids and diploids, tetraploids undergo significantly faster adaptation. Mathematical modelling suggests that rapid adaptation of tetraploids is driven by higher rates of beneficial mutations with stronger fitness effects, which is supported by whole-genome sequencing and phenotypic analyses of evolved clones. Chromosome aneuploidy, concerted chromosome loss, and point mutations all provide large fitness gains. We identify several mutations whose beneficial effects are manifest specifically in the tetraploid strains. Together, these results provide direct quantitative evidence that in some environments polyploidy can accelerate evolutionary adaptation.

Mutational signatures and mutable motifs in cancer genomes.

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Understanding the role of phenotypic switching in cancer drug resistance.

The emergence of acquired drug resistance in cancer represents a major barrier to treatment success. While research has traditionally focused on genetic sources of resistance, recent findings suggest that cancer cells can acquire transient resistant phenotypes via epigenetic modifications and other non-genetic mechanisms. Although these resistant phenotypes are eventually relinquished by individual cells, they can temporarily 'save' the tumor from extinction and enable the emergence of more permanent resistance mechanisms. These observations have generated interest in the potential of epigenetic therapies for long-term tumor control or eradication. In this work, we develop a mathematical model to study how phenotypic switching at the single-cell level affects resistance evolution in cancer. We highlight unique features of non-genetic resistance, probe the evolutionary consequences of epigenetic drugs and explore potential therapeutic strategies. We find that even short-term epigenetic modifications and stochastic fluctuations in gene expression can drive long-term drug resistance in the absence of any bona fide resistance mechanisms. We also find that an epigenetic drug that slightly perturbs the average retention of the resistant phenotype can turn guaranteed treatment failure into guaranteed success. Lastly, we find that combining an epigenetic drug with an anti-cancer agent can significantly outperform monotherapy, and that treatment outcome is heavily affected by drug sequencing.

Dissecting the sources of gene expression variation in a pan-cancer analysis identifies novel regulatory mutations.

Although the catalog of cancer-associated mutations in protein-coding regions is nearly complete for all major cancer types, an assessment of regulatory changes in cancer genomes and their clinical significance remain largely preliminary. Adopting bottom-up approach, we quantify the effects of different sources of gene expression variation in a cohort of 3899 samples from 10 cancer types. We find that copy number alterations, epigenetic changes, transcription factors and microRNAs collectively explain, on average, only 31-38% and 18-26% expression variation for cancer-associated and other genes, respectively, and that among these factors copy number alteration has the highest effect. We show that the genes with systematic, large expression variation that could not be attributed to these factors are enriched for pathways related to cancer hallmarks. Integrating whole genome sequencing data and focusing on genes with systematic expression variation we identify novel, recurrent regulatory mutations affecting known cancer genes such as NKX2-1 and GRIN2D in multiple cancer types. Nonetheless, at a genome-wide scale proportions of gene expression variation attributed to recurrent point mutations appear to be modest so far, especially when compared to that attributed to copy number changes - a pattern different from that observed for other complex diseases and traits. We suspect that, owing to plasticity and redundancy in biological pathways, regulatory alterations show complex combinatorial patterns, modulating gene expression in cancer genomes at a finer scale.

HUWE1 interacts with PCNA to alleviate replication stress.

Defects in DNA replication, DNA damage response, and DNA repair compromise genomic stability and promote cancer development. In particular, unrepaired DNA lesions can arrest the progression of the DNA replication machinery during S-phase, causing replication stress, mutations, and DNA breaks. HUWE1 is a HECT-type ubiquitin ligase that targets proteins involved in cell fate, survival, and differentiation. Here, we report that HUWE1 is essential for genomic stability, by promoting replication of damaged DNA We show that HUWE1-knockout cells are unable to mitigate replication stress, resulting in replication defects and DNA breakage. Importantly, we find that this novel role of HUWE1 requires its interaction with the replication factor PCNA, a master regulator of replication fork restart, at stalled replication forks. Finally, we provide evidence that HUWE1 mono-ubiquitinates H2AX to promote signaling at stalled forks. Altogether, our work identifies HUWE1 as a novel regulator of the replication stress response.

Expression Profiling of Macrophages Reveals Multiple Populations with Distinct Biological Roles in an Immunocompetent Orthotopic Model of Lung Cancer.

Macrophages represent an important component of the tumor microenvironment and play a complex role in cancer progression. These cells are characterized by a high degree of plasticity, and they alter their phenotype in response to local environmental cues. Whereas the M1/M2 classification of macrophages has been widely used, the complexity of macrophage phenotypes has not been well studied, particularly in lung cancer. In this study we employed an orthotopic immunocompetent model of lung adenocarcinoma in which murine lung cancer cells are directly implanted into the left lobe of syngeneic mice. Using multimarker flow cytometry, we defined and recovered several distinct populations of monocytes/macrophages from tumors at different stages of progression. We used RNA-seq transcriptional profiling to define distinct features of each population and determine how they change during tumor progression. We defined an alveolar resident macrophage population that does not change in number and expresses multiple genes related to lipid metabolism and lipid signaling. We also defined a population of tumor-associated macrophages that increase dramatically with tumor and selectively expresses a panel of chemokine genes. A third population, which resembles tumor-associated monocytes, expresses a large number of genes involved in matrix remodeling. By correlating transcriptional profiles with clinically prognostic genes, we show that specific monocyte/macrophage populations are enriched in genes that predict outcomes in lung adenocarcinoma, implicating these subpopulations as critical determinants of patient survival. Our data underscore the complexity of monocytes/macrophages in the tumor microenvironment, and they suggest that distinct populations play specific roles in tumor progression.

The landscape of somatic mutations in protein coding genes in apparently benign human tissues carries signatures of relaxed purifying selection.

Mutations acquired during development and aging lead to inter- and intra-tissue genetic variations. Evidence linking such mutations to complex traits and diseases is rising. We detected somatic mutations in protein-coding regions in 140 benign tissue samples representing nine tissue-types (bladder, breast, liver, lung, prostate, stomach, thyroid, head and neck) and paired blood from 70 donors. A total of 80% of the samples had 2-39 mutations detectable at tissue-level resolution. Factors such as age and smoking were associated with increased burden of detectable mutations, and tissues carried signatures of distinct mutagenic processes such as oxidative DNA damage and transcription-coupled repair. Using mutational signatures, we predicted that majority of the mutations in blood originated in hematopoietic stem and early progenitor cells. Missense to silent mutations ratio and the persistence of potentially damaging mutations in expressed genes carried signatures of relaxed purifying selection. Our findings have relevance for etiology, diagnosis and treatment of diseases including cancer.

Rational Design of a Parthenolide-based Drug Regimen That Selectively Eradicates Acute Myelogenous Leukemia Stem Cells.

Although multidrug approaches to cancer therapy are common, few strategies are based on rigorous scientific principles. Rather, drug combinations are largely dictated by empirical or clinical parameters. In the present study we developed a strategy for rational design of a regimen that selectively targets human acute myelogenous leukemia (AML) stem cells. As a starting point, we used parthenolide, an agent shown to target critical mechanisms of redox balance in primary AML cells. Next, using proteomic, genomic, and metabolomic methods, we determined that treatment with parthenolide leads to induction of compensatory mechanisms that include up-regulated NADPH production via the pentose phosphate pathway as well as activation of the Nrf2-mediated oxidative stress response pathway. Using this knowledge we identified 2-deoxyglucose and temsirolimus as agents that can be added to a parthenolide regimen as a means to inhibit such compensatory events and thereby further enhance eradication of AML cells. We demonstrate that the parthenolide, 2-deoxyglucose, temsirolimus (termed PDT) regimen is a potent means of targeting AML stem cells but has little to no effect on normal stem cells. Taken together our findings illustrate a comprehensive approach to designing combination anticancer drug regimens.

An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples.

Solid tumor samples typically contain multiple distinct clonal populations of cancer cells, and also stromal and immune cell contamination. A majority of the cancer genomics and transcriptomics studies do not explicitly consider genetic heterogeneity and impurity, and draw inferences based on mixed populations of cells. Deconvolution of genomic data from heterogeneous samples provides a powerful tool to address this limitation. We discuss several computational tools, which enable deconvolution of genomic and transcriptomic data from heterogeneous samples. We also performed a systematic comparative assessment of these tools. If properly used, these tools have potentials to complement single-cell genomics and immunoFISH analyses, and provide novel insights into tumor heterogeneity.

SomVarIUS: somatic variant identification from unpaired tissue samples.

MOTIVATION: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples. RESULTS: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of ∼150 × coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples. AVAILABILITY AND IMPLEMENTATION: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/ CONTACT: subhajyoti.de@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Signatures of accelerated somatic evolution in gene promoters in multiple cancer types.

Cancer-associated somatic mutations outside protein-coding regions remain largely unexplored. Analyses of the TERT locus have indicated that non-coding regulatory mutations can be more frequent than previously suspected and play important roles in oncogenesis. Using a computational method called SASE-hunter, developed here, we identified a novel signature of accelerated somatic evolution (SASE) marked by a significant excess of somatic mutations localized in a genomic locus, and prioritized those loci that carried the signature in multiple cancer patients. Interestingly, even when an affected locus carried the signature in multiple individuals, the mutations contributing to SASE themselves were rarely recurrent at the base-pair resolution. In a pan-cancer analysis of 906 samples from 12 tumor types, we detected SASE in the promoters of several genes, including known cancer genes such as MYC, BCL2, RBM5 and WWOX. Nucleotide substitution patterns consistent with oxidative DNA damage and local somatic hypermutation appeared to contribute to this signature in selected gene promoters (e.g. MYC). SASEs in selected cancer gene promoters were associated with over-expression, and also correlated with the age of onset of cancer, aggressiveness of the disease and survival. Taken together, our work detects a hitherto under-appreciated and clinically important class of regulatory changes in cancer genomes.

The dilemma of choosing the ideal permutation strategy while estimating statistical significance of genome-wide enrichment.

Integrative analyses of genomic, epigenomic and transcriptomic features for human and various model organisms have revealed that many such features are nonrandomly distributed in the genome. Significant enrichment (or depletion) of genomic features is anticipated to be biologically important. Detection of genomic regions having enrichment of certain features and estimation of corresponding statistical significance rely on the expected null distribution generated by a permutation model. We discuss different genome-wide permutation approaches, present examples where the permutation strategy affects the null model and show that the confidence in estimating statistical significance of genome-wide enrichment might depend on the choice of the permutation approach. In those cases, where biologically relevant constraints are unclear, it is preferable to examine whether key conclusions are consistent, irrespective of the choice of the randomization strategy.

Variability in DNA methylation defines novel epigenetic subgroups of DLBCL associated with different clinical outcomes.

Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive form of non-Hodgkin lymphoma with variable biology and clinical behavior. The current classification does not fully explain the biological and clinical heterogeneity of DLBCLs. In this study, we carried out genomewide DNA methylation profiling of 140 DLBCL samples and 10 normal germinal center B cells using the HpaII tiny fragment enrichment by ligation-mediated polymerase chain reaction assay and hybridization to a custom Roche NimbleGen promoter array. We defined methylation disruption as a main epigenetic event in DLBCLs and designed a method for measuring the methylation variability of individual cases. We then used a novel approach for unsupervised hierarchical clustering based on the extent of DNA methylation variability. This approach identified 6 clusters (A-F). The extent of methylation variability was associated with survival outcomes, with significant differences in overall and progression-free survival. The novel clusters are characterized by disruption of specific biological pathways such as cytokine-mediated signaling, ephrin signaling, and pathways associated with apoptosis and cell-cycle regulation. In a subset of patients, we profiled gene expression and genomic variation to investigate their interplay with methylation changes. This study is the first to identify novel epigenetic clusters of DLBCLs and their aberrantly methylated genes, molecular associations, and survival.

Aberration in DNA methylation in B-cell lymphomas has a complex origin and increases with disease severity.

Despite mounting evidence that epigenetic abnormalities play a key role in cancer biology, their contributions to the malignant phenotype remain poorly understood. Here we studied genome-wide DNA methylation in normal B-cell populations and subtypes of B-cell non-Hodgkin lymphoma: follicular lymphoma and diffuse large B-cell lymphomas. These lymphomas display striking and progressive intra-tumor heterogeneity and also inter-patient heterogeneity in their cytosine methylation patterns. Epigenetic heterogeneity is initiated in normal germinal center B-cells, increases markedly with disease aggressiveness, and is associated with unfavorable clinical outcome. Moreover, patterns of abnormal methylation vary depending upon chromosomal regions, gene density and the status of neighboring genes. DNA methylation abnormalities arise via two distinct processes: i) lymphomagenic transcriptional regulators perturb promoter DNA methylation in a target gene-specific manner, and ii) aberrant epigenetic states tend to spread to neighboring promoters in the absence of CTCF insulator binding sites.