Single-nucleotide variant calling in single-cell sequencing data with Monopogen.

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Archival single-cell genomics reveals persistent subclones during DCIS progression.

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

A spatially resolved single-cell genomic atlas of the adult human breast.

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

A spatially resolved single cell genomic atlas of the adult human breast.

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing.

BACKGROUND: Single-cell RNA-seq has emerged as an innovative technology used to study complex tissues and characterize cell types, states, and lineages at a single-cell level. Classification of bulk tumors by their individual cellular constituents has also created new opportunities to generate single-cell atlases for many organs, cancers, and developmental models. Despite the tremendous promise of this technology, recent evidence studying epithelial tissues and diverse carcinomas suggests the methods used for tissue processing, cell disaggregation, and preservation can significantly bias gene expression and alter the observed cell types. To determine whether sarcomas - tumors of mesenchymal origin - are subject to the same technical artifacts, we profiled patient-derived tumor explants (PDXs) propagated from three aggressive subtypes: osteosarcoma (OS), Ewing sarcoma (ES), desmoplastic small round cell tumor (DSRCT). Given the rarity of these sarcoma subtypes, we explored whether single-nuclei RNA-seq from more widely available archival frozen specimens could accurately be identified by gene expression signatures linked to tissue phenotype or pathognomonic fusion proteins. RESULTS: We systematically assessed dissociation methods across different sarcoma subtypes. We compared gene expression from single-cell and single-nucleus RNA-sequencing of 125,831 whole-cells and nuclei from ES, DSRCT, and OS PDXs. We detected warm dissociation artifacts in single-cell samples and gene length bias in single-nucleus samples. Classic sarcoma gene signatures were observed regardless of the dissociation method. In addition, we showed that dissociation method biases could be computationally corrected. CONCLUSIONS: We highlighted transcriptional biases, including warm dissociation and gene-length biases, introduced by the dissociation method for various sarcoma subtypes. This work is the first to characterize how the dissociation methods used for sc/snRNA-seq may affect the interpretation of the molecular features in sarcoma PDXs.

Dimeric p53 Mutant Elicits Unique Tumor-Suppressive Activities through an Altered Metabolic Program.

Cancer-related alterations of the p53 tetramerization domain (TD) abrogate wild-type (WT) p53 function. They result in a protein that preferentially forms monomers or dimers, which are also normal p53 states under basal cellular conditions. However, their physiologic relevance is not well understood. We have established in vivo models for monomeric and dimeric p53, which model Li-Fraumeni syndrome patients with germline p53 TD alterations. p53 monomers are inactive forms of the protein. Unexpectedly, p53 dimers conferred some tumor suppression that is not mediated by canonical WT p53 activities. p53 dimers upregulate the PPAR pathway. These activities are associated with lower prevalence of thymic lymphomas and increased CD8+ T-cell differentiation. Lymphomas derived from dimeric p53 mice show cooperating alterations in the PPAR pathway, further implicating a role for these activities in tumor suppression. Our data reveal novel functions for p53 dimers and support the exploration of PPAR agonists as therapies. SIGNIFICANCE: New mouse models with TP53R342P (monomer) or TP53A347D (dimer) mutations mimic Li-Fraumeni syndrome. Although p53 monomers lack function, p53 dimers conferred noncanonical tumor-suppressive activities. We describe novel activities for p53 dimers facilitated by PPARs and propose these are "basal" p53 activities. See related commentary by Stieg et al., p. 1046. See related article by Choe et al., p. 1250. This article is highlighted in the In This Issue feature, p. 1027.

Anaplastic transformation in thyroid cancer revealed by single-cell transcriptomics.

The deadliest anaplastic thyroid cancer (ATC) often transforms from indolent differentiated thyroid cancer (DTC); however, the complex intratumor transformation process is poorly understood. We investigated an anaplastic transformation model by dissecting both cell lineage and cell fate transitions using single-cell transcriptomic and genetic alteration data from patients with different subtypes of thyroid cancer. The resulting spectrum of ATC transformation included stress-responsive DTC cells, inflammatory ATC cells (iATCs), and mitotic-defective ATC cells and extended all the way to mesenchymal ATC cells (mATCs). Furthermore, our analysis identified 2 important milestones: (a) a diploid stage, in which iATC cells were diploids with inflammatory phenotypes and (b) an aneuploid stage, in which mATCs gained aneuploid genomes and mesenchymal phenotypes, producing excessive amounts of collagen and collagen-interacting receptors. In parallel, cancer-associated fibroblasts showed strong interactions among mesenchymal cell types, macrophages shifted from M1 to M2 states, and T cells reprogrammed from cytotoxic to exhausted states, highlighting new therapeutic opportunities for the treatment of ATC.

Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing.

Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) scDNA-seq method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 patients with triple negative-breast cancer (TNBC), which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From these data, we reconstructed mutational lineages and identified early mutational and copy-number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggest that MPT can overcome a major technical obstacle for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.

Targeting T cell checkpoints 41BB and LAG3 and myeloid cell CXCR1/CXCR2 results in antitumor immunity and durable response in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is considered non-immunogenic, with trials showing its recalcitrance to PD1 and CTLA4 immune checkpoint therapies (ICTs). Here, we sought to systematically characterize the mechanisms underlying de novo ICT resistance and to identify effective therapeutic options for PDAC. We report that agonist 41BB and antagonist LAG3 ICT alone and in combination, increased survival and antitumor immunity, characterized by modulating T cell subsets with antitumor activity, increased T cell clonality and diversification, decreased immunosuppressive myeloid cells and increased antigen presentation/decreased immunosuppressive capability of myeloid cells. Translational analyses confirmed the expression of 41BB and LAG3 in human PDAC. Since single and dual ICTs were not curative, T cell-activating ICTs were combined with a CXCR1/2 inhibitor targeting immunosuppressive myeloid cells. Triple therapy resulted in durable complete responses. Given similar profiles in human PDAC and the availability of these agents for clinical testing, our findings provide a testable hypothesis for this lethal disease.

EGFR is a master switch between immunosuppressive and immunoactive tumor microenvironment in inflammatory breast cancer.

Inflammatory breast cancer (IBC), the most aggressive breast cancer subtype, is driven by an immunosuppressive tumor microenvironment (TME). Current treatments for IBC have limited efficacy. In a clinical trial (NCT01036087), an anti-EGFR antibody combined with neoadjuvant chemotherapy produced the highest pathological complete response rate ever reported in patients with IBC having triple-negative receptor status. We determined the molecular and immunological mechanisms behind this superior clinical outcome. Using novel humanized IBC mouse models, we discovered that EGFR-targeted therapy remodels the IBC TME by increasing cytotoxic T cells and reducing immunosuppressive regulatory T cells and M2 macrophages. These changes were due to diminishing immunosuppressive chemokine expression regulated by transcription factor EGR1. We also showed that induction of an immunoactive IBC TME by an anti-EGFR antibody improved the antitumor efficacy of an anti-PD-L1 antibody. Our findings lay the foundation for clinical trials evaluating EGFR-targeted therapy combined with immune checkpoint inhibitors in patients with cancer.

Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients.

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution.

Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.

Learning to distinguish progressive and non-progressive ductal carcinoma in situ.

Ductal carcinoma in situ (DCIS) is a non-invasive breast neoplasia that accounts for 25% of all screen-detected breast cancers diagnosed annually. Neoplastic cells in DCIS are confined to the ductal system of the breast, although they can escape and progress to invasive breast cancer in a subset of patients. A key concern of DCIS is overtreatment, as most patients screened for DCIS and in whom DCIS is diagnosed will not go on to exhibit symptoms or die of breast cancer, even if left untreated. However, differentiating low-risk, indolent DCIS from potentially progressive DCIS remains challenging. In this Review, we summarize our current knowledge of DCIS and explore open questions about the basic biology of DCIS, including those regarding how genomic events in neoplastic cells and the surrounding microenvironment contribute to the progression of DCIS to invasive breast cancer. Further, we discuss what information will be needed to prevent overtreatment of indolent DCIS lesions without compromising adequate treatment for high-risk patients.

Single-Cell Sequencing Reveals Trajectory of Tumor-Infiltrating Lymphocyte States in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) has few effective treatments. Immunotherapy, an attractive alternative strategy, remains challenging with the lack of knowledge on the tumor-infiltrating lymphocyte (TIL) landscape in PDAC. To generate a reference of T-cell subpopulations, we profiled 80,000 T cells from 57 PDAC samples, 22 uninvolved/normal samples, and cultured TIL using single-cell transcriptomic and T-cell receptor analysis. These data revealed 20 cell states and heterogeneous distributions of TIL populations. The CD8+ TIL contained a putative transitional GZMK+ population based on T-cell receptor clonotype sharing, and cell-state trajectory analysis showed similarity to a GZMB+PRF1+ cytotoxic and a CXCL13+ dysfunctional population. Statistical analysis suggested that certain TIL states, such as dysfunctional and inhibitory populations, often occurred together. Finally, analysis of cultured TIL revealed that high-frequency clones from effector populations were preferentially expanded. These data provide a framework for understanding the PDAC TIL landscape for future TIL use in immunotherapy for PDAC. SIGNIFICANCE: To improve the efficacy of immunotherapy in PDAC, there is a great need to understand the PDAC TIL landscape. This study represents a reference of PDAC TIL subpopulations and their relationships and provides a foundation upon which to base future immunotherapeutic efforts. This article is highlighted in the In This Issue feature, p. 2221.

A Decade's Experience in Pediatric Chromosomal Microarray Reveals Distinct Characteristics Across Ordering Specialties.

Chromosomal microarray (CMA) is a testing modality frequently used in pediatric patients; however, published data on its utilization are limited to the genetic setting. We performed a database search for all CMA testing performed from 2010 to 2020, and delineated the diagnostic yield based on patient characteristics, including sex, age, clinical specialty of providers, indication of testing, and pathogenic finding. The indications for testing were further categorized into Human Phenotype Ontology categories for analysis. This study included a cohort of 14,541 patients from 29 different medical specialties, of whom 30% were from the genetics clinic. The clinical indications for testing suggested that neonatology patients demonstrated the greatest involvement of multiorgan systems, involving the most Human Phenotype Ontology categories, compared with developmental behavioral pediatrics and neurology patients being the least. The top pathogenic findings for each specialty differed, likely due to the varying clinical features and indications for testing. Deletions involving the 22q11.21 locus were the top pathogenic findings for patients presenting to genetics, neonatology, cardiology, and surgery. Our data represent the largest pediatric cohort published to date. This study is the first to demonstrate the diagnostic utility of this assay for patients seen in the setting of different specialties, and it provides normative data of CMA results among a general pediatric population referred for testing because of variable clinical presentations.

Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.

Mesenchymal and stem-like prostate cancer linked to therapy-induced lineage plasticity and metastasis.

Bioinformatic analysis of 94 patient-derived xenografts (PDXs), cell lines, and organoids (PCOs) identifies three intrinsic transcriptional subtypes of metastatic castration-resistant prostate cancer: androgen receptor (AR) pathway + prostate cancer (PC) (ARPC), mesenchymal and stem-like PC (MSPC), and neuroendocrine PC (NEPC). A sizable proportion of castration-resistant and metastatic stage PC (M-CRPC) cases are admixtures of ARPC and MSPC. Analysis of clinical datasets and mechanistic studies indicates that MSPC arises from ARPC as a consequence of therapy-induced lineage plasticity. AR blockade with enzalutamide induces (1) transcriptional silencing of TP53 and hence dedifferentiation to a hybrid epithelial and mesenchymal and stem-like state and (2) inhibition of BMP signaling, which promotes resistance to AR inhibition. Enzalutamide-tolerant LNCaP cells re-enter the cell cycle in response to neuregulin and generate metastasis in mice. Combined inhibition of HER2/3 and AR or mTORC1 exhibits efficacy in models of ARPC and MSPC or MSPC, respectively. These results define MSPC, trace its origin to therapy-induced lineage plasticity, and reveal its sensitivity to HER2/3 inhibition.

Spatial charting of single-cell transcriptomes in tissues.

Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections, but do not have single-cell resolution. Here, we developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. We benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. We then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. We performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data show that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.

Single-cell multimodal glioma analyses identify epigenetic regulators of cellular plasticity and environmental stress response.

Glioma intratumoral heterogeneity enables adaptation to challenging microenvironments and contributes to therapeutic resistance. We integrated 914 single-cell DNA methylomes, 55,284 single-cell transcriptomes and bulk multi-omic profiles across 11 adult IDH mutant or IDH wild-type gliomas to delineate sources of intratumoral heterogeneity. We showed that local DNA methylation disorder is associated with cell-cell DNA methylation differences, is elevated in more aggressive tumors, links with transcriptional disruption and is altered during the environmental stress response. Glioma cells under in vitro hypoxic and irradiation stress increased local DNA methylation disorder and shifted cell states. We identified a positive association between genetic and epigenetic instability that was supported in bulk longitudinally collected DNA methylation data. Increased DNA methylation disorder associated with accelerated disease progression and recurrently selected DNA methylation changes were enriched for environmental stress response pathways. Our work identified an epigenetically facilitated adaptive stress response process and highlights the importance of epigenetic heterogeneity in shaping therapeutic outcomes.