Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes

Learning Phenotype Associated Signature in Spatial Transcriptomics with PASSAGE

Spatially resolved transcriptomics (SRT) is poised to advance the understanding of cellular organization within complex tissues under various physiological and pathological conditions at unprecedented resolution. Despite the development of numerous computational tools that facilitate the automatic identification of statistically significant intra-/inter-slice patterns (like spatial domains), these methods typically operate in an unsupervised manner, without leveraging sample characteristics like physiological/pathological states. Here PASSAGE (Phenotype Associated Spatial Signature Analysis with Graph-based Embedding), a rationally-designed deep learning framework is presented for characterizing phenotype-associated signatures across multiple heterogeneous spatial slices effectively. In addition to its outstanding performance in systematic benchmarks, PASSAGE's unique capability in calling sophisticated signatures has been demonstrated in multiple real-world cases. The full package of PASSAGE is available at https://github.com/gao-lab/PASSAGE.

Conserved patterns of transcriptional dysregulation, heterogeneity, and cell states in clear cell kidney cancer

Clear cell kidney cancers are characterized both by conserved oncogenic driver events and by marked intratumor genetic and phenotypic heterogeneity, which help drive tumor progression, metastasis, and resistance to therapy. How these are reflected in transcriptional programs within the cancer and stromal cell components remains an important question with the potential to drive novel therapeutic approaches to treating cancer. To better understand these programs, we perform single-cell transcriptomics on 75 multi-regional biopsies from kidney tumors and normal kidney. We identify conserved patterns of transcriptional dysregulation and their upstream regulators within the tumor and associated vasculature. We describe recurrent subclonal transcriptional consequences of Chr14q loss linked to metastatic potential. We identify prognostically significant conserved patterns of intratumor transcriptional heterogeneity. These reflect co-existing cell states found in both cancer cells and normal kidney cells, indicating that rather than arising from genetic heterogeneity they are a consequence of lineage plasticity.

Detecting copy-number alterations from single-cell chromatin sequencing data by AtaCNA

Single-cell assay of transposase-accessible chromatin sequencing (scATAC-seq) unbiasedly profiles genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often relies on copy-number alterations (CNAs). However, CNA detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNA, a computational algorithm that accurately detects high-resolution CNAs from scATAC-seq data. We benchmark AtaCNA using simulation and real data and find AtaCNA's superior performance. Analyses of 10 scATAC-seq datasets show that AtaCNA could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial, and ovarian cancer samples, AtaCNA identifies subclones at distinct cellular states, suggesting an important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale (10-20 Mb) CNAs, demonstrating the importance of high-resolution CNA detection. These data show that AtaCNA can aid in integrative analysis to understand the complex heterogeneity in cancer.

Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy

Understanding how intratumoral immune populations coordinate antitumor responses after therapy can guide treatment prioritization. We systematically analyzed an established immunotherapy, donor lymphocyte infusion (DLI), by assessing 348,905 single-cell transcriptomes from 74 longitudinal bone marrow samples of 25 patients with relapsed leukemia; a subset was evaluated by both protein- and transcriptome-based spatial analysis. In acute myeloid leukemia (AML) DLI responders, we identified clonally expanded ZNF683+ CD8+ cytotoxic T lymphocytes with in vitro specificity for patient-matched AML. These cells originated primarily from the DLI product and appeared to coordinate antitumor immune responses through interaction with diverse immune cell types within the marrow microenvironment. Nonresponders lacked this cross-talk and had cytotoxic T lymphocytes with elevated TIGIT expression. Our study identifies recipient bone marrow microenvironment differences as a determinant of an effective antileukemia response and opens opportunities to modulate cellular therapy.

Single-cell analysis of the multiple myeloma microenvironment after γ-secretase inhibition and CAR T-cell therapy

ABSTRACT

Chimeric antigen receptor (CAR) T cells and bispecific antibodies targeting B-cell maturation antigen (BCMA) have significantly advanced the treatment of relapsed and refractory multiple myeloma. Resistance to BCMA-targeting therapies, nonetheless, remains a significant challenge. BCMA shedding by γ-secretase is a known resistance mechanism, and preclinical studies suggest that inhibition may improve anti-BCMA therapy. Leveraging a phase 1 clinical trial of the γ-secretase inhibitor (GSI), crenigacestat, with anti-BCMA CAR T cells (FCARH143), we used single-nuclei RNA sequencing and assay for transposase-accessible chromatin sequencing to characterize the effects of GSI on the tumor microenvironment. The most significant impacts of GSI involved effects on monocytes, which are known to promote tumor growth. In addition to observing a reduction in the frequency of nonclassical monocytes, we also detected significant changes in gene expression, chromatin accessibility, and inferred cell-cell interactions after exposure to GSI. Although many genes with altered expression are associated with γ-secretase-dependent signaling, such as Notch, other pathways were affected, indicating GSI has far-reaching effects. Finally, we detected monoallelic deletion of the BCMA locus in some patients with prior exposure to anti-BCMA therapy, which significantly correlated with reduced progression-free survival (PFS; median PFS, 57 vs 861 days). GSIs are being explored in combination with the full spectrum of BCMA-targeting agents, and our results reveal widespread effects of GSI on both tumor and immune cell populations, providing insight into mechanisms for enhancing BCMA-directed therapies.

Inactivation of TACC2 epigenetically represses CDKN1A and confers sensitivity to CDK inhibitors

BACKGROUND

The genomic landscape of esophageal squamous cell carcinoma (ESCC) has been characterized extensively, but there remains a significant need for actionable targets and effective therapies.

METHODS

Here, we perform integrative analysis of genome-wide loss of heterozygosity and expression to identify potential tumor suppressor genes. The functions and mechanisms of one of the candidates, TACC2, are then explored both in vitro and in vivo, leading to the proposal of a therapeutic strategy based on the concept of synthetic lethality.

FINDINGS

We reveal that the inactivation of TACC2, due to copy number loss and promoter hypermethylation, is associated with poor prognosis in ESCC patients. TACC2 depletion enhances ESCC tumorigenesis and progression, as demonstrated in Tacc2 knockout mouse models and by increased growth abilities of ESCC cells. Mechanistically, TACC2 interacts with components of the NuRD and CoREST co-repressor complexes, including MTA1, MBD3, and HMG20B, in the cytoplasm. TACC2 loss leads to the translocation of these proteins into the nucleus, facilitating the formation of functional NuRD and CoREST complexes and the epigenetic repression of CDKN1A. This repression results in elevated CDK1/2 activation. Furthermore, TACC2-deficient cells and ESCC patient-derived organoids with reduced TACC2 expression show increased sensitivity to CDK inhibitors, particularly dinaciclib, which is currently in a phase III trial. Notably, the combination of TACC2-specific RNAi and dinaciclib in subcutaneous ESCC models significantly impairs tumor growth.

CONCLUSIONS

The findings suggest a strategy for cancer treatment based on synthetic lethality.

FUNDING

Funded by NKRDP, NSFC, GDIIET, GDBABRF, GDECISTP, and SYSUTP.

Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. While these inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumour phylogenies reconstruct ground truth DNA-based tumour phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumour phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

Deeper response predicts better outcomes in high-risk-smoldering-myeloma: results of the I-PRISM phase II clinical trial

Early therapeutic intervention in high-risk smoldering multiple myeloma (HR-SMM) has shown benefits, however, no studies have assessed whether biochemical progression or response depth predicts long-term outcomes. The single-arm I-PRISM phase II trial (NCT02916771) evaluated ixazomib, lenalidomide, and dexamethasone in 55 patients with HR-SMM. The primary endpoint, median progression-free survival (PFS), was not reached (NR) (95% CI: 57.7-NR, median follow-up 50 months). The secondary endpoint, biochemical PFS, was 48.6 months (95% CI: 39.9-NR) and coincided with or preceded SLiM-CRAB in eight patients. For additional secondary objectives, the overall response rate was 93% with 31% achieving complete response (CR) and 45% very good partial response (VGPR) or better. CR correlated strongly with the absence of SLiM-CRAB and biochemical progression. MRD-negativity (10-5 sensitivity) predicted a 5-year biochemical PFS of 100% versus 40% in MRD-positive patients (p = 0.051), demonstrating that deep responses significantly improve time to progression. Exploratory single-cell RNA sequencing linked tumor MHC class I expression to proteasome inhibitor response, and a lower proportion of GZMB+ T cells within clonally expanded CD8+ T cells associated with suboptimal outcomes.

Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data

Cancer is an evolutionary process involving the accumulation of diverse somatic mutations and clonal evolution over time. Phylogenetic inference from samples obtained from an individual patient offers a powerful approach to unraveling the intricate evolutionary history of cancer and provides insights that can inform cancer treatment. Somatic copy number alterations (CNAs) are important in cancer evolution and are often used as markers, alone or with other somatic mutations, for phylogenetic inferences, particularly in low-coverage DNA sequencing data. Many phylogenetic inference methods using CNAs detected from bulk or single-cell DNA sequencing data have been developed over the years. However, there have been no systematic reviews on these methods. To summarize the state-of-the-art of the field and inform future development, this review presents a comprehensive survey on the major challenges in inference, different types of methods, and applications of these methods. The challenges are discussed from the aspects of input data, models of evolution, and inference algorithms. The different methods are grouped according to the markers used for inference and the types of the reconstructed trees. The applications include using phylogenetic inference to understand intra-tumor heterogeneity, metastasis, treatment resistance, and early cancer development. This review also sheds light on future directions of cancer phylogenetic inference using CNAs, including the improvement of scalability, the utilization of new types of data, and the development of more realistic models of evolution.

Case study of a neuroendocrine tumor of uncertain origin: single-cell transcriptomics unravels potential primary location

PURPOSE

Determining the primary origin of non-organ-confined neuroendocrine tumors (NETs) for accurate diagnosis and management. Neuroendocrine tumors are rare neoplasms with diverse clinical behaviors. Determining their primary origin remains challenging in cases of non-organ-confined NETs. This study explores the histogenesis of a retroperitoneal, non-functional NET localized between the duodenum and pancreatic head, utilizing advanced molecular diagnostics to elucidate its probable primary source.

METHODS

Initial diagnostic methods, including imaging and histopathology, failed to resolve the tumor's origin. The tumor was subjected to single-cell RNA sequencing (scRNA-seq) and whole exome sequencing (WES). Publicly available transcriptomic datasets from pancreatic and small intestine NETs were used to develop and validate a molecular gene signature for tissue-of-origin identification.

RESULTS

The gene signature distinguished pancreatic and small intestine NETs with high accuracy. The tumor cells presented a molecular profile consistent with a pancreatic origin, likely derived from ectopic pancreatic tissue.

CONCLUSIONS

This case demonstrates the value of integrating scRNA-seq and WES for the molecular characterization of complex NETs. Identifying the tumor's pancreatic origin informed a targeted management approach, avoiding unnecessary systemic treatment and underscoring the potential of single-cell approaches in personalized oncology.

HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on a modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 17,000 cells.

Pancreatic Neuroendocrine Tumor: The Case Report of a Patient with Germline FANCD2 Mutation and Tumor Analysis Using Single-Cell RNA Sequencing

Background: Neuroendocrine neoplasms are a rare and heterogeneous group of neoplasms. Small-sized (≤2 cm) pancreatic neuroendocrine tumors (PanNETs) are of particular interest as they are often associated with aggressive behavior, with no specific prognostic or progression markers.

METHODS

This article describes a clinical case characterized by a progressive growth of nonfunctional PanNET requiring surgical treatment in a patient with a germline FANCD2 mutation, previously not reported in PanNETs. The patient underwent whole exome sequencing and single-cell RNA sequencing.

RESULTS

The patient underwent surgical treatment. We confirmed the presence of the germline mutation FANCD2 and also detected the germline mutation WNT10A. The cellular composition of the PanNET was analyzed using single-cell sequencing, and the main cell clusters were identified. We analyzed the tumor genomics, and used the data to define the effect the germline FANCD2 mutation had.

CONCLUSIONS

Analysis of the mutational status of patients with PanNET may provide additional data that may influence treatment tactics, refine the plan for monitoring such patients, and provide more information about the pathogenesis of PanNET. PanNET research using scRNA-seq data may help in predicting the effect of therapy on neuroendocrine cells with FANCD2 mutations.

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics

Analyzing somatic evolution within a tumor over time and across space is a key challenge in cancer research. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genomic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and reconstruct spatial tumor evolution, or phylogeography, from SRT data. CalicoST identifies important classes of CNAs-including copy-neutral loss of heterozygosity and mirrored subclonal CNAs-that are invisible to total copy number analysis. Using nine patients' data from the Human Tumor Atlas Network, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. CalicoST reconstructs a tumor phylogeography in three-dimensional space for two patients with multiple adjacent slices. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals mirrored subclonal CNAs on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.

Tumor Expression of CD83 Reduces Glioma Progression and Is Associated with Reduced Immunosuppression

SIGNIFICANCE

Immunosuppression in malignant glioma remains a barrier to therapeutic development. CD83 overexpression in human and mouse glioma increases survival. CD83+ tumor cells promote signatures related to cytotoxic T cells, enhanced activation of CD8+ T cells, and increased proinflammatory cytokines. These findings suggest that tumor-expressed CD83 could mediate tumor-immune communications.

Copy number variation heterogeneity reveals biological inconsistency in hierarchical cancer classifications

Cancers are heterogeneous diseases with unifying features of abnormal and consuming cell growth, where the deregulation of normal cellular functions is initiated by the accumulation of genomic mutations in cells of - potentially - any organ. At diagnosis malignancies typically present with patterns of somatic genome variants on diverse levels of heterogeneity. Among the different types of genomic alterations, copy number variants (CNV) represent a distinct, near-ubiquitous class of structural variants. Cancer classifications are foundational for patient care and oncology research. Terminologies such as the National Cancer Institute Thesaurus provide large sets of hierarchical cancer classification vocabularies and promote data interoperability and ontology-driven computational analysis. To find out how categorical classifications correspond to genomic observations, we conducted a meta-analysis of inter-sample genomic heterogeneity for classification hierarchies on CNV profiles from 97,142 individual samples across 512 cancer entities, and evaluated recurring CNV signatures across diagnostic subsets. Our results highlight specific biological mechanisms across cancer entities with the potential for improvement of patient stratification and future enhancement of cancer classification systems and provide some indications for cooperative genomic events across distinct clinical entities.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

High-confidence calling of normal epithelial cells allows identification of a novel stem-like cell state in the colorectal cancer microenvironment

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer cells in single-cell data. We find that our method and others based on gene expression or allelic imbalances identify overlapping sets of colorectal cancer versus normal colon epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible.

Molecular Basis of Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors (NETs) are rare well-differentiated neoplasms with limited therapeutic options and unknown cells of origin. The current classification of pancreatic neuroendocrine tumors is based on proliferative grading, and guides therapeutic strategies, however, tumors within grades exhibit profound heterogeneity in clinical manifestation and outcome. Manifold studies have highlighted intra-patient differences in tumors at the genetic and transcriptomic levels. Molecular classification might become an alternative or complementary basis for treatment decisions and reflect tumor biology, actionable cellular processes. Here, we provide a comprehensive review of genomic, transcriptomic, proteomic and epigenomic studies of pancreatic NETs to elucidate patterns shared between proposed subtypes that could form a foundation for new classification. We denote four NET subtypes with distinct molecular features, which were consistently reproduced using various omics technologies.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain.

Single-Cell Profiling of Sarcomas from Archival Tissue Reveals Programs Associated with Resistance to Immune Checkpoint Blockade

PURPOSE

Sarcoma encompasses a diverse group of cancers that are typically resistant to current therapies, including immune checkpoint blockade (ICB), and underlying mechanisms are poorly understood. The contexture of sarcomas limits generation of high-quality data using cutting-edge molecular profiling methods, such as single-cell RNA-sequencing, thus hampering progress in understanding these understudied cancers.

EXPERIMENTAL DESIGN

Here, we demonstrate feasibility of producing multimodal single-cell genomics and whole-genome sequencing data from frozen tissues, profiling 75,716 cell transcriptomes of five undifferentiated pleomorphic sarcoma and three intimal sarcoma samples, including paired specimens from two patients treated with ICB.

RESULTS

We find that genomic diversity decreases in patients with response to ICB, and, in unbiased analyses, identify cancer cell programs associated with therapy resistance. Although interactions of tumor-infiltrating T lymphocytes within the tumor ecosystem increase in ICB responders, clonal expansion of CD8+ T cells alone was insufficient to predict drug responses.

CONCLUSIONS

This study provides a framework for studying rare tumors and identifies salient and treatment-associated cancer cell intrinsic and tumor microenvironmental features in sarcomas.

Recent Advances in Genomic Approaches for the Detection of Homologous Recombination Deficiency

Accurate detection of homologous recombination deficiency (HRD) in cancer patients is paramount in clinical applications, as HRD confers sensitivity to poly(ADP-ribose) polymerase (PARP) inhibitors. With the advances in genome sequencing technology, mutational profiling on a genome-wide scale has become readily accessible, and our knowledge of the genomic consequences of HRD has been greatly expanded and refined. Here, we review the recent advances in HRD detection methods. We examine the copy number and structural alterations that often accompany the genome instability that results from HRD, describe the advantages of mutational signature-based methods that do not rely on specific gene mutations, and review some of the existing algorithms used for HRD detection. We also discuss the choice of sequencing platforms (panel, exome, or whole-genome) and catalog the HRD detection assays used in key PARP inhibitor trials.

Aneuploidy as a driver of human cancer

Aneuploidy, an abnormal chromosome composition, is a major contributor to cancer development and progression and an important determinant of cancer therapeutic responses and clinical outcomes. Despite being recognized as a hallmark of human cancer, the exact role of aneuploidy as a 'driver' of cancer is still largely unknown. Identifying the specific genetic elements that underlie the recurrence of common aneuploidies remains a major challenge of cancer genetics. In this Review, we discuss recurrent aneuploidies and their function as drivers of tumor development. We then delve into the context-dependent identification and functional characterization of the driver genes underlying driver aneuploidies and examine emerging strategies to uncover these driver genes using cancer genomics data and cancer models. Lastly, we explore opportunities for targeting driver aneuploidies in cancer by leveraging the functional consequences of these common genetic alterations.

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) data are widely used to study cancer cell states and their heterogeneity. However, the tumour microenvironment is usually a mixture of healthy and cancerous cells and it can be difficult to fully separate these two populations based on transcriptomics alone. If available, somatic single-nucleotide variants (SNVs) observed in the scRNA-seq data could be used to identify the cancer population and match that information with the single cells' expression profile. However, calling somatic SNVs in scRNA-seq data is a challenging task, as most variants seen in the short-read data are not somatic, but can instead be germline variants, RNA edits or transcription, sequencing, or processing errors. In addition, only variants present in actively transcribed regions for each individual cell will be seen in the data.

RESULTS

To address these challenges, we develop CCLONE (Cancer Cell Labelling On Noisy Expression), an interpretable tool adapted to handle the uncertainty and sparsity of SNVs called from scRNA-seq data. CCLONE jointly identifies cancer clonal populations, and their associated variants. We apply CCLONE on two acute myeloid leukaemia datasets and one lung adenocarcinoma dataset and show that CCLONE captures both genetic clones and somatic events for multiple patients. These results show how CCLONE can be used to gather insight into the course of the disease and the origin of cancer cells in scRNA-seq data.

AVAILABILITY AND IMPLEMENTATION

Source code is available at github.com/HaghverdiLab/CCLONE.

Single-cell RNA sequencing reveals melanoma cell state-dependent heterogeneity of response to MAPK inhibitors

BACKGROUND

Melanoma is a heterogeneous cancer influenced by the plasticity of melanoma cells and their dynamic adaptations to microenvironmental cues. Melanoma cells transition between well-defined transcriptional cell states that impact treatment response and resistance.

METHODS

In this study, we applied single-cell RNA sequencing to interrogate the molecular features of immunotherapy-naive and immunotherapy-resistant melanoma tumours in response to ex vivo BRAF/MEK inhibitor treatment.

FINDINGS

We confirm the presence of four distinct melanoma cell states - melanocytic, transitory, neural-crest like and undifferentiated, and identify enrichment of neural crest-like and undifferentiated melanoma cells in immunotherapy-resistant tumours. Furthermore, we introduce an integrated computational approach to identify subsets of responding and nonresponding melanoma cells within the transcriptional cell states.

INTERPRETATION

Nonresponding melanoma cells are identified in all transcriptional cell states and are predisposed to BRAF/MEK inhibitor resistance due to pro-inflammatory IL6 and TNFɑ signalling. Our study provides a framework to study treatment response within distinct melanoma cell states and indicate that tumour-intrinsic pro-inflammatory signalling contributes to BRAF/MEK inhibitor resistance.

FUNDING

This work was supported by Macquarie University, Melanoma Institute Australia, and the National Health and Medical Research Council of Australia (NHMRC; grant 2012860, 2028055).

Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone

Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

Copy number alterations: a catastrophic orchestration of the breast cancer genome

Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact.

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

Somatic mutations associate with clonal expansion of CD8+ T cells

Somatic mutations in T cells can cause cancer but also have implications for immunological diseases and cell therapies. The mutation spectrum in nonmalignant T cells is unclear. Here, we examined somatic mutations in CD4+ and CD8+ T cells from 90 patients with hematological and immunological disorders and used T cell receptor (TCR) and single-cell sequencing to link mutations with T cell expansions and phenotypes. CD8+ cells had a higher mutation burden than CD4+ cells. Notably, the biggest variant allele frequency (VAF) of non-synonymous variants was higher than synonymous variants in CD8+ T cells, indicating non-random occurrence. The non-synonymous VAF in CD8+ T cells strongly correlated with the TCR frequency, but not age. We identified mutations in pathways essential for T cell function and often affected lymphoid neoplasia. Single-cell sequencing revealed cytotoxic TEMRA phenotypes of mutated T cells. Our findings suggest that somatic mutations contribute to CD8+ T cell expansions without malignant transformation.

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons, however, the electrophysiological properties endogenous to tumor cells remain obscure. To address this, we employed Patch-sequencing on human glioma specimens and found that one third of patched cells in IDH mutant (IDH mut ) tumors demonstrate properties of both neurons and glia by firing single, short action potentials. To define these hybrid cells (HCs) and discern if they are tumor in origin, we developed a computational tool, Single Cell Rule Association Mining (SCRAM), to annotate each cell individually. SCRAM revealed that HCs represent tumor and non-tumor cells that feature GABAergic neuron and oligodendrocyte precursor cell signatures. These studies are the first to characterize the combined electrophysiological and molecular properties of human glioma cells and describe a new cell type in human glioma with unique electrophysiological and transcriptomic properties that are likely also present in the non-tumor mammalian brain.

Phasing millions of samples achieves near perfect accuracy, enabling parent-of-origin classification of variants

Haplotype phasing, the process of determining which genetic variants are physically located on the same chromosome, is crucial for various genetic analyses. In this study, we first benchmark SHAPEIT and Beagle, two state-of-the-art phasing methods, on two large datasets: > 8 million diverse, research-consented 23andMe, Inc. customers and the UK Biobank (UKB). We find that both perform exceptionally well. Beagle's median switch error rate (SER) (after excluding single SNP switches) in white British trios from UKB is 0.026% compared to 0.00% for European ancestry 23andMe research participants; 55.6% of European ancestry 23andMe research participants have zero non-single SNP switches, compared to 42.4% of white British trios. South Asian ancestry 23andMe research participants have the highest median SER amongst the 23andMe populations, but it is still remarkably low at 0.46%. We also investigate the relationship between identity-by-descent (IBD) and SER, finding that switch errors tend to occur in regions of little or no IBD segment coverage. SHAPEIT and Beagle excel at 'intra-chromosomal' phasing, but lack the ability to phase across chromosomes, motivating us to develop an inter-chromosomal phasing method, called HAPTIC ( HAP lotype TI ling and C lustering), that assigns paternal and maternal variants discretely genome-wide. Our approach uses identity-by-descent (IBD) segments to phase blocks of variants on different chromosomes. HAPTIC represents the segments a focal individual shares with their relatives as nodes in a signed graph and performs bipartite clustering on the signed graph using spectral clustering. We test HAPTIC on 1022 UKB trios, yielding a median phase error of 0.08% in regions covered by IBD segments (33.5% of sites). We also ran HAPTIC in the 23andMe database and found a median phase error rate (the rate of mismatching alleles between the inferred and true phase) of 0.92% in Europeans (93.8% of sites) and 0.09% in admixed Africans (92.7% of sites). HAPTIC's precision depends heavily on data from relatives, so will increase as datasets grow larger and more diverse. HAPTIC enables analyses that require the parent-of-origin of variants, such as association studies and ancestry inference of untyped parents.

CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics

MOTIVATION

Copy number variations (CNVs) are common genetic alterations in tumour cells. The delineation of CNVs holds promise for enhancing our comprehension of cancer progression. Moreover, accurate inference of CNVs from single-cell sequencing data is essential for unravelling intratumoral heterogeneity. However, existing inference methods face limitations in resolution and sensitivity.

RESULTS

To address these challenges, we present CopyVAE, a deep learning framework based on a variational autoencoder architecture. Through experiments, we demonstrated that CopyVAE can accurately and reliably detect CNVs from data obtained using single-cell RNA sequencing. CopyVAE surpasses existing methods in terms of sensitivity and specificity. We also discussed CopyVAE's potential to advance our understanding of genetic alterations and their impact on disease advancement.

AVAILABILITY AND IMPLEMENTATION

CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE.

Early Immune Remodeling Steers Clinical Response to First-Line Chemoimmunotherapy in Advanced Gastric Cancer

Adding anti-programmed cell death protein 1 (anti-PD-1) to 5-fluorouracil (5-FU)/platinum improves survival in some advanced gastroesophageal adenocarcinomas (GEA). To understand the effects of chemotherapy and immunotherapy, we conducted a phase II first-line trial (n = 47) sequentially adding pembrolizumab to 5-FU/platinum in advanced GEA. Using serial biopsy of the primary tumor at baseline, after one cycle of 5-FU/platinum, and after the addition of pembrolizumab, we transcriptionally profiled 358,067 single cells to identify evolving multicellular tumor microenvironment (TME) networks. Chemotherapy induced early on-treatment multicellular hubs with tumor-reactive T-cell and M1-like macrophage interactions in slow progressors. Faster progression featured increased MUC5A and MSLN containing treatment resistance programs in tumor cells and M2-like macrophages with immunosuppressive stromal interactions. After pembrolizumab, we observed increased CD8 T-cell infiltration and development of an immunity hub involving tumor-reactive CXCL13 T-cell program and epithelial interferon-stimulated gene programs. Strategies to drive increases in antitumor immune hub formation could expand the portion of patients benefiting from anti-PD-1 approaches.

SIGNIFICANCE

The benefit of 5-FU/platinum with anti-PD-1 in first-line advanced gastric cancer is limited to patient subgroups. Using a trial with sequential anti-PD-1, we show coordinated induction of multicellular TME hubs informs the ability of anti-PD-1 to potentiate T cell-driven responses. Differential TME hub development highlights features that underlie clinical outcomes. This article is featured in Selected Articles from This Issue, p. 695.

De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

Long-Term Follow-Up Defines the Population That Benefits from Early Interception in a High-Risk Smoldering Multiple Myeloma Clinical Trial Using the Combination of Ixazomib, Lenalidomide, and Dexamethasone

Background

Early therapeutic intervention in high-risk SMM (HR-SMM) has demonstrated benefit in previous studies of lenalidomide with or without dexamethasone. Triplets and quadruplet studies have been examined in this same population. However, to date, none of these studies examined the impact of depth of response on long-term outcomes of participants treated with lenalidomide-based therapy, and whether the use of the 20/2/20 model or the addition of genomic alterations can further define the population that would benefit the most from early therapeutic intervention. Here, we present the results of the phase II study of the combination of ixazomib, lenalidomide, and dexamethasone in patients with HR-SMM with long-term follow-up and baseline single-cell tumor and immune sequencing that help refine the population to be treated for early intervention studies.

Methods

This is a phase II trial of ixazomib, lenalidomide, and dexamethasone (IRD) in HR-SMM. Patients received 9 cycles of induction therapy with ixazomib 4mg on days 1, 8, and 15; lenalidomide 25mg on days 1-21; and dexamethasone 40mg on days 1, 8, 15, and 22. The induction phase was followed by maintenance with ixazomib 4mg on days 1, 8, and 15; and lenalidomide 15mg d1-21 for 15 cycles for 24 months of treatment. The primary endpoint was progression-free survival after 2 years of therapy. Secondary endpoints included depth of response, biochemical progression, and correlative studies included single-cell RNA sequencing and/or whole-genome sequencing of the tumor and single-cell sequencing of immune cells at baseline.

Results

Fifty-five patients, with a median age of 64, were enrolled in the study. The overall response rate was 93%, with 31% of patients achieving a complete response and 45% achieving a very good partial response or better. The most common grade 3 or greater treatment-related hematologic toxicities were neutropenia (16 patients; 29%), leukopenia (10 patients; 18%), lymphocytopenia (8 patients; 15%), and thrombocytopenia (4 patients; 7%). Non-hematologic grade 3 or greater toxicities included hypophosphatemia (7 patients; 13%), rash (5 patients; 9%), and hypokalemia (4 patients; 7%). After a median follow-up of 50 months, the median progression-free survival (PFS) was 48.6 months (95% CI: 39.9 - not reached; NR) and median overall survival has not been reached. Patients achieving VGPR or better had a significantly better progression-free survival (p<0.001) compared to those who did not achieve VGPR (median PFS 58.2 months vs. 31.3 months). Biochemical progression preceded or was concurrent with the development of SLiM-CRAB criteria in eight patients during follow-up, indicating that biochemical progression is a meaningful endpoint that correlates with the development of end-organ damage. High-risk 20/2/20 participants had the worst PFS compared to low- and intermediate-risk participants. The use of whole genome or single-cell sequencing of tumor cells identified high-risk aberrations that were not identified by FISH alone and aided in the identification of participants at risk of progression. scRNA-seq analysis revealed a positive correlation between MHC class I expression and response to proteasome inhibition and at the same time a decreased proportion of GZMB+ T cells within the clonally expanded CD8+ T cell population correlated with suboptimal response.

Conclusions

Ixazomib, lenalidomide and dexamethasone in HR-SMM demonstrates significant clinical activity with an overall favorable safety profile. Achievement of VGPR or greater led to significant improvement in time to progression, suggesting that achieving deep response is beneficial in HR-SMM. Biochemical progression correlates with end-organ damage. Patients with high-risk FISH and lack of deep response had poor outcomes. ClinicalTrials.gov identifier: (NCT02916771).

Investigation of cellular communication and signaling pathways in tumor microenvironment for high TP53-expressing osteosarcoma cells through single-cell RNA sequencing

Osteosarcoma (OS) stands as the most prevalent primary bone cancer in children and adolescents, and its limited treatment options often result in unsatisfactory outcomes, particularly for metastatic cases. The tumor microenvironment (TME) has been recognized as a crucial determinant in OS progression. However, the intercellular dynamics between high TP53-expressing OS cells and neighboring cell types within the TME are yet to be thoroughly understood. In our study, we harnessed the single-cell RNA sequencing (scRNA-seq) technology in combination with the computational tool-Cellchat, aiming to elucidate the intercellular communication networks present within OS. Through meticulous quantitative inference and subsequent analysis of these networks, we succeeded in identifying significant signaling pathways connecting high TP53-expressing OS cells with proximate cell types, namely Macrophages, Monocytes, Endothelial Cells, and PVLs. This research brings forth a nuanced understanding of the intricate patterns and coordination involved in the TME's intercellular communication signals. These findings not only provide profound insights into the molecular mechanisms underpinning OS but also indicate potential therapeutic targets that could revolutionize treatment strategies.

Allele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells

Subclonal copy number alterations are a prevalent feature in tumors with high chromosomal instability and result in heterogeneous cancer cell populations with distinct phenotypes. However, the extent to which subclonal copy number alterations contribute to clone-specific phenotypes remains poorly understood. We develop TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population. TreeAlign accurately encodes dosage effects from subclonal copy number alterations, the impact of allelic imbalance on allele-specific transcription, and obviates the need to define genotypic clones from a phylogeny a priori, leading to highly granular definitions of clones with distinct expression programs. These improvements enable clone-clone gene expression comparisons with higher resolution and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer.

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy

Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.

Somatic cancer driver mutations are enriched and associated with inflammatory states in Alzheimer's disease microglia

Alzheimer's disease (AD) is an age-associated neurodegenerative disorder characterized by progressive neuronal loss and pathological accumulation of the misfolded proteins amyloid-β and tau1,2. Neuroinflammation mediated by microglia and brain-resident macrophages plays a crucial role in AD pathogenesis1-5, though the mechanisms by which age, genes, and other risk factors interact remain largely unknown. Somatic mutations accumulate with age and lead to clonal expansion of many cell types, contributing to cancer and many non-cancer diseases6,7. Here we studied somatic mutation in normal aged and AD brains by three orthogonal methods and in three independent AD cohorts. Analysis of bulk RNA sequencing data from 866 samples from different brain regions revealed significantly higher (~two-fold) overall burdens of somatic single-nucleotide variants (sSNVs) in AD brains compared to age-matched controls. Molecular-barcoded deep (>1000X) gene panel sequencing of 311 prefrontal cortex samples showed enrichment of sSNVs and somatic insertions and deletions (sIndels) in cancer driver genes in AD brain compared to control, with recurrent, and often multiple, mutations in genes implicated in clonal hematopoiesis (CH)8,9. Pathogenic sSNVs were enriched in CSF1R+ microglia of AD brains, and the high proportion of microglia (up to 40%) carrying some sSNVs in cancer driver genes suggests mutation-driven microglial clonal expansion (MiCE). Analysis of single-nucleus RNA sequencing (snRNAseq) from temporal neocortex of 62 additional AD cases and controls exhibited nominally increased mosaic chromosomal alterations (mCAs) associated with CH10,11. Microglia carrying mCA showed upregulated pro-inflammatory genes, resembling the transcriptomic features of disease-associated microglia (DAM) in AD. Our results suggest that somatic driver mutations in microglia are common with normal aging but further enriched in AD brain, driving MiCE with inflammatory and DAM signatures. Our findings provide the first insights into microglial clonal dynamics in AD and identify potential new approaches to AD diagnosis and therapy.

Genomic tumor evolution dictates human medulloblastoma progression

Background

Medulloblastoma (MB) is the most common high-grade pediatric brain tumor, comprised of 4 main molecular subgroups-sonic-hedgehog (SHH), Wnt, Group 3, and Group 4. Group 3 and Group 4 tumors are the least characterized MB subgroups, despite Group 3 having the worst prognosis (~50% survival rate), and Group 4 being the most prevalent. Such poor characterization can be attributed to high levels of inter- and intratumoral heterogeneity, making it difficult to identify common therapeutic targets.

Methods

In this study, we generated single-cell sequencing data from 14 MB patients spanning all subgroups that we complemented with publicly available single-cell data from Group 3 patients. We used a ligand-receptor analysis tool (CellChat), expression- and allele-based copy-number variation (CNV) detection methods, and RNA velocity analysis to characterize tumor cell-cell interactions, established a connection between CNVs and temporal tumor progression, and unraveled tumor evolution.

Results

We show that MB tumor cells follow a temporal trajectory from those with low CNV levels to those with high CNV levels, allowing us to identify early and late markers for SHH, Group 3, and Group 4 MBs. Our study also identifies SOX4 upregulation as a major event in later tumor clones for Group 3 and Group 4 MBs, suggesting it as a potential therapeutic target for both subgroups.

Conclusion

Taken together, our findings highlight MB's inherent tumor heterogeneity and offer promising insights into potential drivers of MB tumor evolution particularly in Group 3 and Group 4 MBs.

Chromothripsis orchestrates leukemic transformation in blast phase MPN through targetable amplification of DYRK1A

Chromothripsis, the process of catastrophic shattering and haphazard repair of chromosomes, is a common event in cancer. Whether chromothripsis might constitute an actionable molecular event amenable to therapeutic targeting remains an open question. We describe recurrent chromothripsis of chromosome 21 in a subset of patients in blast phase of a myeloproliferative neoplasm (BP-MPN), which alongside other structural variants leads to amplification of a region of chromosome 21 in ∼25% of patients ('chr21amp'). We report that chr21amp BP-MPN has a particularly aggressive and treatment-resistant phenotype. The chr21amp event is highly clonal and present throughout the hematopoietic hierarchy. DYRK1A , a serine threonine kinase and transcription factor, is the only gene in the 2.7Mb minimally amplified region which showed both increased expression and chromatin accessibility compared to non-chr21amp BP-MPN controls. We demonstrate that DYRK1A is a central node at the nexus of multiple cellular functions critical for BP-MPN development, including DNA repair, STAT signalling and BCL2 overexpression. DYRK1A is essential for BP-MPN cell proliferation in vitro and in vivo , and DYRK1A inhibition synergises with BCL2 targeting to induce BP-MPN cell apoptosis. Collectively, these findings define the chr21amp event as a prognostic biomarker in BP-MPN and link chromothripsis to a druggable target.

STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer

Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alterations in Visium data. STmut is tested on fresh-frozen Visium data, formalin-fixed paraffin-embedded (FFPE) Visium data, and tumors with and without matching DNA sequencing data. Copy number is inferred on all conditions, but the chemistry of the FFPE platform does not permit analyses of single nucleotide variants. Taken together, we propose solutions to add the genetic dimension to spatial transcriptomic data and describe the limitations of different datatypes.

A pan-cancer landscape of pathogenic somatic copy number variations

OBJECTIVE

Copy number variations (CNVs) play crucial roles in physiological and pathological processes, including cancer. However, the functional implications of somatic CNVs in tumor progression and evolution remain unclear. This study focuses on identifying CNV alterations with high pathogenic potential that drive and sustain tumorigenesis, distinguishing them from passenger alterations that accumulate during tumor growth. Our goal is to explore the variability of CNVs across different tumor types and infer their impact on tumor cell functions.

METHODS

Starting from 7352 copy number profiles across 33 different cancer types, we infer the pathogenicity of each CNV and perform both intra- and inter-tumor analyses to predict the functional impact of different genomic patterns. We evaluate the actionability of genes belonging to altered regions and we correlate the presence of pathogenic regions with genome instability patterns and patients' survival.

RESULTS

Our analysis uncovered large heterogeneity among different tumors suggesting in many cases distinct genetic drivers of tumorigenesis. Recurrent genomic alterations frequently coincide with dysfunctional homologous recombination pathways and negative regulation of the immune system. In certain tumors, the number of pathogenic CNVs emerged as a prognostic biomarker, highlighting their significance in cancer progression.

CONCLUSION

This study contributes to elucidate the functional impact of pathogenic CNVs in tumor progression and sheds light on their potential as prognostic markers in specific cancer types.

Acquired resistance to a GPRC5D-directed T-cell engager in multiple myeloma is mediated by genetic or epigenetic target inactivation

Bispecific antibodies targeting GPRC5D demonstrated promising efficacy in multiple myeloma, but acquired resistance usually occurs within a few months. Using a single-nucleus multi-omic strategy in three patients from the MYRACLE cohort (ClinicalTrials.gov registration: NCT03807128 ), we identified two resistance mechanisms, by bi-allelic genetic inactivation of GPRC5D or by long-range epigenetic silencing of its promoter and enhancer regions. Molecular profiling of target genes may help to guide the choice of immunotherapy and early detection of resistance in multiple myeloma.

A pan-tissue survey of mosaic chromosomal alterations in 948 individuals

Genetic mutations accumulate in an organism's body throughout its lifetime. While somatic single-nucleotide variants have been well characterized in the human body, the patterns and consequences of large chromosomal alterations in normal tissues remain largely unknown. Here, we present a pan-tissue survey of mosaic chromosomal alterations (mCAs) in 948 healthy individuals from the Genotype-Tissue Expression project, augmenting RNA-based allelic imbalance estimation with haplotype phasing. We found that approximately a quarter of the individuals carry a clonally-expanded mCA in at least one tissue, with incidence strongly correlated with age. The prevalence and genome-wide patterns of mCAs vary considerably across tissue types, suggesting tissue-specific mutagenic exposure and selection pressures. The mCA landscapes in normal adrenal and pituitary glands resemble those in tumors arising from these tissues, whereas the same is not true for the esophagus and skin. Together, our findings show a widespread age-dependent emergence of mCAs across normal human tissues with intricate connections to tumorigenesis.

Deciphering the heterogeneity and immunosuppressive function of regulatory T cells in osteosarcoma using single-cell RNA transcriptome

Osteosarcoma (OS) is a highly invasive malignant neoplasm with poor prognosis. The tumor microenvironment (TME) plays an essential role in the occurrence and development of OS. Regulatory T cells (Tregs) are known to facilitate immunosuppression, tumor progression, invasion, and metastasis. However, the effect of Tregs in the TME of OS remains unclear. In this study, single-cell RNA sequencing (scRNA-seq) data was used to identify Tregs and various other cell clusters in the TME of OS. Gene set variation analysis (GSVA) was used to investigate the signaling pathways in Tregs from OS and adjacent tissues. The CellChat and iTALK packages were used to analyze cellular communication. In addition, a prognostic model was established based on the Tregs-specific genes using bulk RNA-seq from the TARGET database, and it was verified using a Gene Expression Omnibus dataset. The pRRophetic package was used to predict drug sensitivity. Immunohistochemistry was used to verify the expression of candidate genes in OS. Based on the above methods, we showed that the OS samples were highly infiltrated with Tregs. GSVA revealed that oxidative phosphorylation, angiogenesis and mammalian target of rapamycin complex 1 (mTORC1) were highly activated in Tregs from OS compared with those from adjacent tissues. Using cellular communication analysis, we found that Tregs interacted with osteoblastic, endothelial, and myeloid cells via C-X-C motif chemokine ligand (CXCL) signaling; particularly, they strongly affected the expression of C-X-C motif chemokine receptor 4 (CXCR4) and interacted with other cell clusters through CXCL12/transforming growth factor β1 (TGFB1) to collectively enable tumor growth and progression. Subsequently, two Tregs-specific genes-CD320 and MAF-were screened through univariate, least absolute shrinkage and selection operator regression (LASSO) and multivariate analysis to construct a prognostic model, which showed excellent prognostic accuracy in two independent cohorts. In addition, drug sensitivity analysis demonstrated that OS patients at high Tregs risk were sensitive to sunitinib, sorafenib, and axitinib. We also used immunohistochemistry to validate that CD320 and MAF were significantly upregulated in OS tissues compared with adjacent tissues. Overall, this study reveals the heterogeneity of Tregs in the OS TME, providing new insights into the invasion and treatment of this cancer.

Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration

Persistent DNA double-strand breaks (DSBs) in neurons are an early pathological hallmark of neurodegenerative diseases including Alzheimer's disease (AD), with the potential to disrupt genome integrity. We used single-nucleus RNA-seq in human postmortem prefrontal cortex samples and found that excitatory neurons in AD were enriched for somatic mosaic gene fusions. Gene fusions were particularly enriched in excitatory neurons with DNA damage repair and senescence gene signatures. In addition, somatic genome structural variations and gene fusions were enriched in neurons burdened with DSBs in the CK-p25 mouse model of neurodegeneration. Neurons enriched for DSBs also had elevated levels of cohesin along with progressive multiscale disruption of the 3D genome organization aligned with transcriptional changes in synaptic, neuronal development, and histone genes. Overall, this study demonstrates the disruption of genome stability and the 3D genome organization by DSBs in neurons as pathological steps in the progression of neurodegenerative diseases.

Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches

Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.

Modeling specific aneuploidies: from karyotype manipulations to biological insights

An abnormal chromosome number, or aneuploidy, underlies developmental disorders and is a common feature of cancer, with different cancer types exhibiting distinct patterns of chromosomal gains and losses. To understand how specific aneuploidies emerge in certain tissues and how they contribute to disease development, various methods have been developed to alter the karyotype of mammalian cells and mice. In this review, we provide an overview of both classic and novel strategies for inducing or selecting specific chromosomal gains and losses in human and murine cell systems. We highlight how these customized aneuploidy models helped expanding our knowledge of the consequences of specific aneuploidies to (cancer) cell physiology.