Single-cell genomic variation induced by mutational processes in cancer

Tumor break load quantitates structural variant-associated genomic instability with biological and clinical relevance across cancers

While structural variants (SVs) are a clear sign of genomic instability, they have not been systematically quantified per patient since declining costs have only recently enabled large-scale profiling. Therefore, the biological and clinical impact of high numbers of SVs in patients is unknown. We introduce tumor break load (TBL), defined as the sum of unbalanced SVs, as a measure for SV-associated genomic instability. Using pan-cancer data from TCGA, PCAWG, and CCLE, we show that a high TBL is associated with significant changes in gene expression in 26/31 cancer types that consistently involve upregulation of DNA damage repair and downregulation of immune response pathways. Patients with a high TBL show a higher risk of recurrence and shorter median survival times for 5/15 cancer types. Our data demonstrate that TBL is a biologically and clinically relevant feature of genomic instability that may aid patient prognostication and treatment stratification. For the datasets analyzed in this study, TBL has been made available in cBioPortal.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling

Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with realistic evolutionary constraints. We introduce evolution-aware copy number calling via deep reinforcement learning (CNRein). Our simulations demonstrate CNRein infers more accurate copy-number profiles and better recapitulates ground truth clonal structure than existing methods. On sequencing data of breast and ovarian cancer, CNRein produces more parsimonious solutions than existing methods while maintaining agreement with single-nucleotide variants. Additionally, CNRein shows consistency on a breast cancer patient sequenced with distinct low-pass technologies.

CINner: Modeling and simulation of chromosomal instability in cancer at single-cell resolution

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG, [Formula: see text]). We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Analysis of inference results using CINner across cancer types in The Cancer Genome Atlas ([Formula: see text]) further reveals that the inferred selection parameters reflect the bias between tumor suppressor genes and oncogenes on specific genomic regions. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) in PCAWG uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions (chronic lymphocytic leukemia in PCAWG, [Formula: see text]). Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability

Chromosomal instability (CIN) is pervasive in human tumours and often leads to structural or numerical chromosomal aberrations. Somatic structural variants (SVs) are intimately related to copy number alterations but the two types of variant are often studied independently. Additionally, despite numerous studies on detecting various SV patterns, there are still no general quantitative models of SV generation. To address this issue, we develop a computational cell-cycle model for the generation of SVs from end-joining repair and replication after double-strand break formation. Our model provides quantitative information on the relationship between breakage fusion bridge cycle, chromothripsis, seismic amplification, and extra-chromosomal circular DNA. Given whole-genome sequencing data, the model also allows us to infer important parameters in SV generation with Bayesian inference. Our quantitative framework unifies disparate genomic patterns resulted from CIN, provides a null mutational model for SV, and reveals deeper insights into the impact of genome rearrangement on tumour evolution.

Identification of intratumoral bacteria that enhance breast tumor metastasis

The central, mortality-associated hallmark of cancer is the process of metastasis. It is increasingly recognized that bacteria influence multiple facets of cancer progression, but the extent to which tumor microenvironment-associated bacteria control metastasis in cancer is poorly understood. To identify tumor-associated bacteria and their role in metastasis, we utilized established murine models of non-metastatic and metastatic breast tumors to identify bacteria capable of driving metastatic disease. We found several species of the Bacillus genus that were unique to metastatic tumors, and found that breast tumor cells cultured with a Bacillus bacterium isolated from metastatic tumors, Bacillus thermoamylovorans, produced nearly 3× the metastatic burden as control cells or cells cultured with bacteria from non-metastatic breast tumors. We then performed targeted metabolomics on tumor cells cultured with different bacterial species and found that B. thermoamylovorans differentially regulated tumor cell metabolite profiles compared to bacteria isolated from non-metastatic tumors. Using these bacteria, we performed de novo sequencing and tested for the presence of genes that were unique to the bacterium isolated from metastatic tumors in a patient population to provide a proof-of-concept for identifying how specific bacterial functions are associated with the metastatic process in cancer independent of bacterial species. Together, our data directly demonstrate the ability of specific bacteria to promote metastasis through interaction with cancer cells.

IMPORTANCE

Metastasis is a major barrier to long-term survival for cancer patients, and therapeutic options for patients with aggressive, metastatic forms of breast cancer remain limited. It is therefore critical to understand the differences between non-metastatic and metastatic disease to identify potential methods for slowing or even stopping metastasis. In this work, we identify a bacterial species present with metastatic breast tumors capable of increasing the metastatic capabilities of tumor cells. We isolated and sequenced this bacteria, as well as a control species which failed to promote metastasis, and identified specific bacterial genes that were unique to the metastasis-promoting species. We tested for the presence of these bacterial genes in patient tumor samples and found they were more likely to be associated with mortality. We also identified enrichment of specific bacterial functions, providing insight into possible sources of bacteria-driven increases in the metastatic potential of multiple cancer types.

Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single-cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming-based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants, copy number alterations, and structural variations into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness of real data in resolving clonal evolution in the presence of multiple variant types, providing a path toward more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.

p53 enhances DNA repair and suppresses cytoplasmic chromatin fragments and inflammation in senescent cells

Genomic instability and inflammation are distinct hallmarks of aging, but the connection between them is poorly understood. Here we report a mechanism directly linking genomic instability and inflammation in senescent cells through a mitochondria-regulated molecular circuit involving p53 and cytoplasmic chromatin fragments (CCF) that are enriched for DNA damage signaling marker γH2A.X. We show that p53 suppresses CCF accumulation and its downstream inflammatory phenotype. p53 activation suppresses CCF formation linked to enhanced DNA repair and genome integrity. Activation of p53 in aged mice by pharmacological inhibition of MDM2 reverses transcriptomic signatures of aging and age-associated accumulation of monocytes and macrophages in liver. Mitochondrial ablation in senescent cells suppresses CCF formation and activates p53 in an ATM-dependent manner, suggesting that mitochondria-dependent formation of γH2A.X + CCF dampens nuclear DNA damage signaling and p53 activity. These data provide evidence for a mitochondria-regulated p53 signaling circuit in senescent cells that controls DNA repair, genome integrity, and senescence- and age-associated inflammation, with relevance to therapeutic targeting of age-associated disease.

DICE: fast and accurate distance-based reconstruction of single-cell copy number phylogenies

Somatic copy number alterations (sCNAs) are valuable phylogenetic markers for inferring evolutionary relationships among tumor cell subpopulations. Advances in single-cell DNA sequencing technologies are making it possible to obtain such sCNAs datasets at ever-larger scales. However, existing methods for reconstructing phylogenies from sCNAs are often too slow for large datasets. We propose two new distance-based methods, DICE-bar and DICE-star, for reconstructing single-cell tumor phylogenies from sCNA data. Using carefully simulated datasets, we find that DICE-bar matches or exceeds the accuracies of all other methods on noise-free datasets and that DICE-star shows exceptional robustness to noise and outperforms all other methods on noisy datasets. Both methods are also orders of magnitude faster than many existing methods. Our experimental analysis also reveals how noise/error in copy number inference, as expected for real datasets, can drastically impact the accuracies of most methods. We apply DICE-star, the most accurate method on error-prone datasets, to several real single-cell breast and ovarian cancer datasets and find that it rapidly produces phylogenies of equivalent or greater reliability compared with existing methods.

Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints

Ongoing mutagenesis in cancer drives genetic diversity throughout the natural history of cancers. As the activities of mutational processes are dynamic throughout evolution, distinguishing the mutational signatures of 'active' and 'historical' processes has important implications for studying how tumors evolve. This can aid in understanding mutagenic states at the time of presentation, and in associating active mutational process with therapeutic resistance. As bulk sequencing primarily captures historical mutational processes, we studied whether ultra-low-coverage single-cell whole-genome sequencing (scWGS), which measures the distribution of mutations across hundreds or thousands of individual cells, could enable the distinction between historical and active mutational processes. While technical challenges and data sparsity have limited mutation analysis in scWGS, we show that these data contain valuable information about dynamic mutational processes. To robustly interpret single nucleotide variants (SNVs) in scWGS, we introduce ArtiCull, a method to identify and remove SNV artifacts by leveraging evolutionary constraints, enabling reliable detection of mutations for signature analysis. Applying this approach to scWGS data from pancreatic ductal adenocarcinoma (PDAC), triple-negative breast cancer (TNBC), and high-grade serous ovarian cancer (HGSOC), we uncover temporal and spatial patterns in mutational processes. In PDAC, we observe a temporal increase in mismatch repair deficiency (MMRd). In cisplatin-treated TNBC patient-derived xenografts, we identify therapy-induced mutagenesis and inactivation of APOBEC3 activity. In HGSOC, we show distinct patterns of APOBEC3 mutagenesis, including late tumor-wide activation in one case and clade-specific enrichment in another. Additionally, we detect a clone-specific increase in SBS17 activity, in a clone previously linked to recurrence. Our findings establish ultra-low-coverage scWGS as a powerful approach for studying active mutational processes that may influence ongoing clonal evolution and therapeutic resistance.

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression

Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell copy number data to accurately perform missing value imputation, identify cell replication states, and detect genomic heterogeneity. This allows us to separate somatic copy number alterations from copy number changes resulting from DNA replication. Our methodology brings critical insights into chromosomal aberrations and highlights the ubiquitous aneuploidy process during tumorigenesis. The copy number and scRT profiles obtained by analysing >119,000 high-quality human single cells from different cell lines, patient tumours and patient-derived xenograft samples leads to a multi-sample heterogeneity-resolved scRT atlas. This atlas is an important resource for cancer research and demonstrates that scRT profiles can be used to study replication timing heterogeneity in cancer. Our findings also highlight the importance of studying cancer tissue samples to comprehensively grasp the complexities of DNA replication because cell lines, although convenient, lack dynamic environmental factors. These results facilitate future research at the interface of genomic instability and replication stress during cancer progression.

Early tolerance and late persistence as alternative drug responses in cancer

Bacteria withstand antibiotic treatment through three alternative mechanisms: resistance, persistence or tolerance. While resistance and persistence have been described, whether drug-induced tolerance exists in cancer cells remains largely unknown. Here, we show that human cancer cells elicit a tolerant response when exposed to commonly used chemotherapy regimens, propelled by the pervasive activation of autophagy, leading to the comprehensive activation of DNA damage repair pathways. After prolonged drug exposure, such tolerant responses morph into persistence, whereby the increased DNA damage repair is entirely reversed. The central regulator of mitophagy PINK1 drives this reduction in DNA repair via the cytoplasmic relocalization of the cell identity master HNF4A, thus hampering HNF4A transcriptional activation of DNA repair genes. We conclude that exposing cancer cells to relevant standard-of-care antitumour therapies induces a pervasive drug-induced tolerant response that might be broadly exploited to increase the impact of first-line, adjuvant treatments and debulking in advanced cancers.

Complex rearrangements fuel ER+ and HER2+ breast tumours

Breast cancer is a highly heterogeneous disease whose prognosis and treatment as defined by the expression of three receptors-oestrogen receptor (ER), progesterone receptor and human epidermal growth factor receptor 2 (HER2; encoded by ERBB2)-is insufficient to capture the full spectrum of clinical outcomes and therapeutic vulnerabilities. Previously, we demonstrated that transcriptional and genomic profiles define eleven integrative subtypes with distinct clinical outcomes, including four ER+ subtypes with increased risk of relapse decades after diagnosis1,2. Here, to determine whether these subtypes reflect distinct evolutionary histories, interactions with the immune system and pathway dependencies, we established a meta-cohort of 1,828 breast tumours spanning pre-invasive, primary invasive and metastatic disease with whole-genome and transcriptome sequencing. We demonstrate that breast tumours fall along a continuum constrained by three genomic archetypes. The ER+ high-risk integrative subgroup is characterized by complex focal amplifications, similar to HER2+ tumours, including cyclic extrachromosomal DNA amplifications induced by ER through R-loop formation and APOBEC3B-editing, which arise in pre-invasive lesions. By contrast, triple-negative tumours exhibit genome-wide instability and tandem duplications and are enriched for homologous repair deficiency-like signatures, whereas ER+ typical-risk tumours are largely genomically stable. These genomic archetypes, which replicate in an independent cohort of 2,659 primary tumours, are established early during tumorigenesis, sculpt the tumour microenvironment and are conserved in metastatic disease. These complex structural alterations contribute to replication stress and immune evasion, and persist throughout tumour evolution, unveiling potential vulnerabilities.

Deconvolution and Phylogeny Inference of Diverse Variant Types Integrating Bulk DNA-seq with Single-cell RNA-seq

Motivation

Reconstructing clonal lineage trees ("tumor phylogenetics") has become a core tool of cancer genomics. Earlier approaches based on bulk DNA sequencing (DNA-seq) have largely given way to single-cell DNA-seq (scDNA-seq), which offers far greater resolution for clonal substructure. Available data has lagged behind computational theory, though. While single-cell RNA-seq (scRNA-seq) has become widely available, scDNA-seq is still sufficiently costly and technically challenging to preclude routine use on large cohorts. This forces difficult tradeoffs between the limited genome coverage of scRNA-seq, limited availability of scDNA-seq, and limited clonal resolution of bulk DNA-seq. These limitations are especially problematic for studying structural variations and focal copy number variations that are crucial to cancer progression but difficult to observe in RNA-seq.

Results

We develop a method, TUSV-int, combining advantages of these various genomic technologies by integrating bulk DNA-seq and scRNA-seq data into a single deconvolution and phylogenetic inference computation while allowing for single nucleotide variant (SNV), copy number alteration (CNA) and structural variant (SV) data. We accomplish this by using integer linear programming (ILP) to deconvolve heterogeneous variant types and resolve them into a clonal lineage tree. We demonstrate improved deconvolution performance over comparative methods lacking scRNA-seq data or using more limited variant types. We further demonstrate the power of the method to better resolve clonal structure and mutational histories through application to a previously published DNA-seq/scRNA-seq breast cancer data set.

Availability

The source code for TUSV-int is available at https://github.com/CMUSchwartzLab/TUSV-INT.git.

Ongoing chromothripsis underpins osteosarcoma genome complexity and clonal evolution

Osteosarcoma is the most common primary cancer of the bone, with a peak incidence in children and young adults. Using multi-region whole-genome sequencing, we find that chromothripsis is an ongoing mutational process, occurring subclonally in 74% of osteosarcomas. Chromothripsis generates highly unstable derivative chromosomes, the ongoing evolution of which drives the acquisition of oncogenic mutations, clonal diversification, and intra-tumor heterogeneity across diverse sarcomas and carcinomas. In addition, we characterize a new mechanism, termed loss-translocation-amplification (LTA) chromothripsis, which mediates punctuated evolution in about half of pediatric and adult high-grade osteosarcomas. LTA chromothripsis occurs when a single double-strand break triggers concomitant TP53 inactivation and oncogene amplification through breakage-fusion-bridge cycles. It is particularly prevalent in osteosarcoma and is not detected in other cancers driven by TP53 mutation. Finally, we identify the level of genome-wide loss of heterozygosity as a strong prognostic indicator for high-grade osteosarcoma.

Cancer Research in the Age of Spatial Omics: Lessons from IMAXT

The Imaging and Molecular Annotation of Xenografts and Tumors Cancer Grand Challenges team was set up with the objective of developing the "next generation" of pathology and cancer research by using a combination of single-cell and spatial omics tools to produce 3D molecularly annotated maps of tumors. Its activities overlapped, and in some cases catalyzed, a spatial revolution in biology that saw new technologies being deployed to investigate the roles of tumor heterogeneity and of the tumor micro-environment. See related article by Stratton et al., p. 22 See related article by Bhattacharjee et al., p. 28 See related article by Goodwin et al., p. 34.

Characterizing the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER

Proliferation is a key hallmark of cancer, but whether it differs between evolutionarily distinct clones co-existing within a tumor is unknown. We introduce the Single-cell Proliferation Rate Inference in Non-homogeneous Tumors through Evolutionary Routes (SPRINTER) algorithm that uses single-cell whole-genome DNA sequencing data to enable accurate identification and clone assignment of S- and G2-phase cells, as assessed by generating accurate ground truth data. Applied to a newly generated longitudinal, primary-metastasis-matched dataset of 14,994 non-small cell lung cancer cells, SPRINTER revealed widespread clone proliferation heterogeneity, orthogonally supported by Ki-67 staining, nuclei imaging and clinical imaging. We further demonstrated that high-proliferation clones have increased metastatic seeding potential, increased circulating tumor DNA shedding and clone-specific altered replication timing in proliferation- or metastasis-related genes associated with expression changes. Applied to previously generated datasets of 61,914 breast and ovarian cancer cells, SPRINTER revealed increased single-cell rates of different genomic variants and enrichment of proliferation-related gene amplifications in high-proliferation clones.

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.

Normal breast tissues harbour rare populations of aneuploid epithelial cells

Aneuploid epithelial cells are common in breast cancer1,2; however, their presence in normal breast tissues is not well understood. To address this question, we applied single-cell DNA sequencing to profile copy number alterations in 83,206 epithelial cells from the breast tissues of 49 healthy women, and we applied single-cell DNA and assay for transposase-accessible chromatin sequencing co-assays to the samples of 19 women. Our data show that all women harboured rare aneuploid epithelial cells (median 3.19%) that increased with age. Many aneuploid epithelial cells (median 82.22%) in normal breast tissues underwent clonal expansions and harboured copy number alterations reminiscent of invasive breast cancers (gains of 1q; losses of 10q, 16q and 22q). Co-assay profiling showed that the aneuploid cells were mainly associated with the two luminal epithelial lineages, and spatial mapping showed that they localized in ductal and lobular structures with normal histopathology. Collectively, these data show that even healthy women have clonal expansions of rare aneuploid epithelial cells in their breast tissues.

Luminal breast epithelial cells of BRCA1 or BRCA2 mutation carriers and noncarriers harbor common breast cancer copy number alterations

The prevalence and nature of somatic copy number alterations (CNAs) in breast epithelium and their role in tumor initiation and evolution remain poorly understood. Using single-cell DNA sequencing (49,238 cells) of epithelium from BRCA1 and BRCA2 carriers or wild-type individuals, we identified recurrent CNAs (for example, 1q-gain and 7q, 10q, 16q and 22q-loss) that are present in a rare population of cells across almost all samples (n = 28). In BRCA1/BRCA2 carriers, these occur before loss of heterozygosity (LOH) of wild-type alleles. These CNAs, common in malignant tumors, are enriched in luminal cells but absent in basal myoepithelial cells. Allele-specific analysis of prevalent CNAs reveals that they arose by independent mutational events, consistent with convergent evolution. BRCA1/BRCA2 carriers contained a small percentage of cells with extreme aneuploidy, featuring loss of TP53, BRCA1/BRCA2 LOH and multiple breast cancer-associated CNAs. Our findings suggest that CNAs arising in normal luminal breast epithelium are precursors to clonally expanded tumor genomes.

Long-term breast cancer response to CDK4/6 inhibition defined by TP53-mediated geroconversion

Inhibition of CDK4/6 kinases has led to improved outcomes in breast cancer. Nevertheless, only a minority of patients experience long-term disease control. Using a large, clinically annotated cohort of patients with metastatic hormone receptor-positive (HR+) breast cancer, we identify TP53 loss (27.6%) and MDM2 amplification (6.4%) to be associated with lack of long-term disease control. Human breast cancer models reveal that p53 loss does not alter CDK4/6 activity or G1 blockade but instead promotes drug-insensitive p130 phosphorylation by CDK2. The persistence of phospho-p130 prevents DREAM complex assembly, enabling cell-cycle re-entry and tumor progression. Inhibitors of CDK2 can overcome p53 loss, leading to geroconversion and manifestation of senescence phenotypes. Complete inhibition of both CDK4/6 and CDK2 kinases appears to be necessary to facilitate long-term response across genomically diverse HR+ breast cancers.

Coordinated inheritance of extrachromosomal DNAs in cancer cells

The chromosomal theory of inheritance dictates that genes on the same chromosome segregate together while genes on different chromosomes assort independently1. Extrachromosomal DNAs (ecDNAs) are common in cancer and drive oncogene amplification, dysregulated gene expression and intratumoural heterogeneity through random segregation during cell division2,3. Distinct ecDNA sequences, termed ecDNA species, can co-exist to facilitate intermolecular cooperation in cancer cells4. How multiple ecDNA species within a tumour cell are assorted and maintained across somatic cell generations is unclear. Here we show that cooperative ecDNA species are coordinately inherited through mitotic co-segregation. Imaging and single-cell analyses show that multiple ecDNAs encoding distinct oncogenes co-occur and are correlated in copy number in human cancer cells. ecDNA species are coordinately segregated asymmetrically during mitosis, resulting in daughter cells with simultaneous copy-number gains in multiple ecDNA species before any selection. Intermolecular proximity and active transcription at the start of mitosis facilitate the coordinated segregation of ecDNA species, and transcription inhibition reduces co-segregation. Computational modelling reveals the quantitative principles of ecDNA co-segregation and co-selection, predicting their observed distributions in cancer cells. Coordinated inheritance of ecDNAs enables co-amplification of specialized ecDNAs containing only enhancer elements and guides therapeutic strategies to jointly deplete cooperating ecDNA oncogenes. Coordinated inheritance of ecDNAs confers stability to oncogene cooperation and novel gene regulatory circuits, allowing winning combinations of epigenetic states to be transmitted across cell generations.

Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data

Dysregulated DNA replication is a cause and a consequence of aneuploidy in cancer, yet the interplay between copy number alterations (CNAs), replication timing (RT) and cell cycle dynamics remain understudied in aneuploid tumors. We developed a probabilistic method, PERT, for simultaneous inference of cell-specific replication and copy number states from single-cell whole genome sequencing (scWGS) data. We used PERT to investigate clone-specific RT and proliferation dynamics in  >50,000 cells obtained from aneuploid and clonally heterogeneous cell lines, xenografts and primary cancers. We observed bidirectional relationships between RT and CNAs, with CNAs affecting X-inactivation producing the largest RT shifts. Additionally, we found that clone-specific S-phase enrichment positively correlated with ground-truth proliferation rates in genomically stable but not unstable cells. Together, these results demonstrate robust computational identification of S-phase cells from scWGS data, and highlight the importance of RT and cell cycle properties in studying the genomic evolution of aneuploid tumors.

The evolutionary theory of cancer: challenges and potential solutions

The clonal evolution model of cancer was developed in the 1950s-1970s and became central to cancer biology in the twenty-first century, largely through studies of cancer genetics. Although it has proven its worth, its structure has been challenged by observations of phenotypic plasticity, non-genetic forms of inheritance, non-genetic determinants of clone fitness and non-tree-like transmission of genes. There is even confusion about the definition of a clone, which we aim to resolve. The performance and value of the clonal evolution model depends on the empirical extent to which evolutionary processes are involved in cancer, and on its theoretical ability to account for those evolutionary processes. Here, we identify limits in the theoretical performance of the clonal evolution model and provide solutions to overcome those limits. Although we do not claim that clonal evolution can explain everything about cancer, we show how many of the complexities that have been identified in the dynamics of cancer can be integrated into the model to improve our current understanding of cancer.

Linked regulation of genome integrity and senescence-associated inflammation by p53

Genomic instability and inflammation are distinct hallmarks of aging, but the connection between them is poorly understood. Understanding their interrelationship will help unravel new mechanisms and therapeutic targets of aging and age-associated diseases. Here we report a novel mechanism directly linking genomic instability and inflammation in senescent cells through a mitochondria-regulated molecular circuit driven by p53 and cytoplasmic chromatin fragments (CCF). We show, through activation or inactivation of p53 by genetic and pharmacologic approaches, that p53 suppresses CCF accumulation and the downstream inflammatory senescence-associated secretory phenotype (SASP), without affecting cell cycle arrest. p53 activation suppressed CCF formation by promoting DNA repair, and this is reflected in maintenance of genomic integrity, particularly in subtelomeric regions, as shown by single cell genome resequencing. Activation of p53 in aged mice by pharmacological inhibition of MDM2 reversed signatures of aging, including age- and senescence-associated transcriptomic signatures of inflammation and age-associated accumulation of monocytes and macrophages in liver. Remarkably, mitochondria in senescent cells suppressed p53 activity by promoting CCF formation and thereby restricting ATM-dependent nuclear DNA damage signaling. These data provide evidence for a mitochondria-regulated p53 signaling circuit in senescent cells that controls DNA repair, genome integrity, and senescence- and age-associated inflammation. This pathway is immunomodulatory in mice and a potential target for healthy aging interventions by small molecules already shown to activate p53.

Targeting chromosomal instability in patients with cancer

Chromosomal instability (CIN) is a hallmark of cancer and a driver of metastatic dissemination, therapeutic resistance, and immune evasion. CIN is present in 60-80% of human cancers and poses a formidable therapeutic challenge as evidenced by the lack of clinically approved drugs that directly target CIN. This limitation in part reflects a lack of well-defined druggable targets as well as a dearth of tractable biomarkers enabling direct assessment and quantification of CIN in patients with cancer. Over the past decade, however, our understanding of the cellular mechanisms and consequences of CIN has greatly expanded, revealing novel therapeutic strategies for the treatment of chromosomally unstable tumours as well as new methods of assessing the dynamic nature of chromosome segregation errors that define CIN. In this Review, we describe advances that have shaped our understanding of CIN from a translational perspective, highlighting both challenges and opportunities in the development of therapeutic interventions for patients with chromosomally unstable cancers.

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing

MOTIVATION

Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline.

RESULTS

We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON uses a Gaussian Mixture Model to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high-quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses.

AVAILABILITY AND IMPLEMENTATION

SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008.

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.

Proteomic landscape of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data

Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.

SCCNAInfer: a robust and accurate tool to infer the absolute copy number on scDNA-seq data

MOTIVATION

Copy number alterations (CNAs) play an important role in disease progression, especially in cancer. Single-cell DNA sequencing (scDNA-seq) facilitates the detection of CNAs of each cell that is sequenced at a shallow and uneven coverage. However, the state-of-the-art CNA detection tools based on scDNA-seq are still subject to genome-wide errors due to the wrong estimation of the ploidy.

RESULTS

We developed SCCNAInfer, a computational tool that utilizes the subclonal signal inside the tumor cells to more accurately infer each cell's ploidy and CNAs. Given the segmentation result of an existing CNA detection method, SCCNAInfer clusters the cells, infers the ploidy of each subclone, refines the read count by bin clustering, and accurately infers the CNAs for each cell. Both simulated and real datasets show that SCCNAInfer consistently improves upon the state-of-the-art CNA detection tools such as Aneufinder, Ginkgo, SCOPE and SeCNV.

AVAILABILITY AND IMPLEMENTATION

SCCNAInfer is freely available at https://github.com/compbio-mallory/SCCNAInfer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

BACKGROUND

The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states.

RESULTS

We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure.

CONCLUSIONS

Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.

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.

Genome-scale mutational signature analysis in archived fixed tissues

Mutation spectra and mutational signatures in cancerous and non-cancerous tissues can be identified by various established techniques of massively parallel sequencing (or next-generation sequencing) including whole-exome or whole-genome sequencing, and more recently by error-corrected/duplex sequencing. One rather underexplored area has been the genome-scale analysis of mutational signatures as markers of mutagenic exposures, and their impact on cancer driver events applied to formalin-fixed or alcohol-fixed paraffin embedded archived biospecimens. This review showcases successful applications of the next-generation sequencing methodologies in archived fixed tissues, including the delineation of the specific tissue fixation-related DNA damage manifesting as artifactual signatures, distinguishable from the true signatures that arise from biological mutagenic processes. Overall, we discuss and demonstrate how next-generation sequencing techniques applied to archived fixed biospecimens can enhance our understanding of cancer causes including mutagenic effects of extrinsic cancer risk agents, and the implications for prevention efforts aimed at reducing avoidable cancer-causing exposures.

Mechanism of the Radioresistant Colorectal Cancer Cell Line SW480RR Established after Fractionated X Irradiation

Radioresistant cancer cells are risk factors for recurrence and are occasionally detected in recurrent tumors after radiotherapy. Intratumor heterogeneity is believed to be a potential cause of treatment resistance. Heterogeneity in DNA content has also been reported in human colorectal cancer; however, little is known about how such heterogeneity changes with radiotherapy or how it affects cancer radioresistance. In the present study, we established radioresistant clone SW480RR cells after fractionated X-ray irradiation of human colorectal cancer-derived SW480.hu cells, which are composed of two cell populations with different chromosome numbers, and examined how cellular radioresistance changed with fractionated radiotherapy. Compared with the parental cell population, which mostly comprised cells with higher ploidy, the radioresistant clones showed lower ploidy and less initial DNA damage. The lower ploidy cells in the parental cell population were identified as having radioresistance prior to irradiation; thus, SW480RR cells were considered intrinsically radioresistant cells selected from the parental population through fractionated irradiation. This study presents a practical example of the emergence of radioresistant cells from a cell population with ploidy heterogeneity after irradiation. The most likely mechanism is the selection of an intrinsically radioresistant population after fractionated X-ray irradiation, with a background in which lower ploidy cells exhibit lower initial DNA damage.

Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer

Cancer-associated mutations have been documented in normal tissues, but the prevalence and nature of somatic copy number alterations and their role in tumor initiation and evolution is not well understood. Here, using single cell DNA sequencing, we describe the landscape of CNAs in >42,000 breast epithelial cells from women with normal or high risk of developing breast cancer. Accumulation of individual cells with one or two of a specific subset of CNAs (e.g. 1q gain and 16q, 22q, 7q, and 10q loss) is detectable in almost all breast tissues and, in those from BRCA1 or BRCA2 mutations carriers, occurs prior to loss of heterozygosity (LOH) of the wildtype alleles. These CNAs, which are among the most common associated with ductal carcinoma in situ (DCIS) and malignant breast tumors, are enriched almost exclusively in luminal cells not basal myoepithelial cells. Allele-specific analysis of the enriched CNAs reveals that each allele was independently altered, demonstrating convergent evolution of these CNAs in an individual breast. Tissues from BRCA1 or BRCA2 mutation carriers contain a small percentage of cells with extreme aneuploidy, featuring loss of TP53 , LOH of BRCA1 or BRCA2 , and multiple breast cancer-associated CNAs in addition to one or more of the common CNAs in 1q, 10q or 16q. Notably, cells with intermediate levels of CNAs are not detected, arguing against a stepwise gradual accumulation of CNAs. Overall, our findings demonstrate that chromosomal alterations in normal breast epithelium partially mirror those of established cancer genomes and are chromosome- and cell lineage-specific.

Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes

The extent of cell-to-cell variation in tumor mitochondrial DNA (mtDNA) copy number and genotype, and the phenotypic and evolutionary consequences of such variation, are poorly characterized. Here we use amplification-free single-cell whole-genome sequencing (Direct Library Prep (DLP+)) to simultaneously assay mtDNA copy number and nuclear DNA (nuDNA) in 72,275 single cells derived from immortalized cell lines, patient-derived xenografts and primary human tumors. Cells typically contained thousands of mtDNA copies, but variation in mtDNA copy number was extensive and strongly associated with cell size. Pervasive whole-genome doubling events in nuDNA associated with stoichiometrically balanced adaptations in mtDNA copy number, implying that mtDNA-to-nuDNA ratio, rather than mtDNA copy number itself, mediated downstream phenotypes. Finally, multimodal analysis of DLP+ and single-cell RNA sequencing identified both somatic loss-of-function and germline noncoding variants in mtDNA linked to heteroplasmy-dependent changes in mtDNA copy number and mitochondrial transcription, revealing phenotypic adaptations to disrupted nuclear/mitochondrial balance.

Chromosome evolution screens recapitulate tissue-specific tumor aneuploidy patterns

Whole chromosome and arm-level copy number alterations occur at high frequencies in tumors, but their selective advantages, if any, are poorly understood. Here, utilizing unbiased whole chromosome genetic screens combined with in vitro evolution to generate arm- and subarm-level events, we iteratively selected the fittest karyotypes from aneuploidized human renal and mammary epithelial cells. Proliferation-based karyotype selection in these epithelial lines modeled tissue-specific tumor aneuploidy patterns in patient cohorts in the absence of driver mutations. Hi-C-based translocation mapping revealed that arm-level events usually emerged in multiples of two via centromeric translocations and occurred more frequently in tetraploids than diploids, contributing to the increased diversity in evolving tetraploid populations. Isogenic clonal lineages enabled elucidation of pro-tumorigenic mechanisms associated with common copy number alterations, revealing Notch signaling potentiation as a driver of 1q gain in breast cancer. We propose that intrinsic, tissue-specific proliferative effects underlie tumor copy number patterns in cancer.

CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions. Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology

Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.

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.

Joint analysis of mutational and transcriptional landscapes in human cancer reveals key perturbations during cancer evolution

BACKGROUND

Tumors are able to acquire new capabilities, including traits such as drug resistance and metastasis that are associated with unfavorable clinical outcomes. Single-cell technologies have made it possible to study both mutational and transcriptomic profiles, but as most studies have been conducted on model systems, little is known about cancer evolution in human patients. Hence, a better understanding of cancer evolution could have important implications for treatment strategies.

RESULTS

Here, we analyze cancer evolution and clonal selection by jointly considering mutational and transcriptomic profiles of single cells acquired from tumor biopsies from 49 lung cancer samples and 51 samples with chronic myeloid leukemia. Comparing the two profiles, we find that each clone is associated with a preferred transcriptional state. For metastasis and drug resistance, we find that the number of mutations affecting related genes increases as the clone evolves, while changes in gene expression profiles are limited. Surprisingly, we find that mutations affecting ligand-receptor interactions with the tumor microenvironment frequently emerge as clones acquire drug resistance.

CONCLUSIONS

Our results show that lung cancer and chronic myeloid leukemia maintain a high clonal and transcriptional diversity, and we find little evidence in favor of clonal sweeps. This suggests that for these cancers selection based solely on growth rate is unlikely to be the dominating driving force during cancer evolution.

scAbsolute: measuring single-cell ploidy and replication status

Cancer cells often exhibit DNA copy number aberrations and can vary widely in their ploidy. Correct estimation of the ploidy of single-cell genomes is paramount for downstream analysis. Based only on single-cell DNA sequencing information, scAbsolute achieves accurate and unbiased measurement of single-cell ploidy and replication status, including whole-genome duplications. We demonstrate scAbsolute's capabilities using experimental cell multiplets, a FUCCI cell cycle expression system, and a benchmark against state-of-the-art methods. scAbsolute provides a robust foundation for single-cell DNA sequencing analysis across different technologies and has the potential to enable improvements in a number of downstream analyses.

Aneuploidy and complex genomic rearrangements in cancer evolution

Mutational processes that alter large genomic regions occur frequently in developing tumors. They range from simple copy number gains and losses to the shattering and reassembly of entire chromosomes. These catastrophic events, such as chromothripsis, chromoplexy and the formation of extrachromosomal DNA, affect the expression of many genes and therefore have a substantial effect on the fitness of the cells in which they arise. In this review, we cover large genomic alterations, the mechanisms that cause them and their effect on tumor development and evolution.

BRCA1 mutation promotes sprouting angiogenesis in inflammatory cancer-associated fibroblast of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with inferior outcomes owing to its low treatment response and high invasiveness. Based on abundant cancer-associated fibroblasts (CAFs) and frequent mutation of breast cancer-associated 1 (BRCA1) in TNBC, the characteristics of CAFs in TNBC patients with BRCA1 mutation compared to wild-type were investigated using single-cell analysis. Intriguingly, we observed that characteristics of inflammatory CAFs (iCAFs) were enriched in patients with BRCA1 mutation compared to the wild-type. iCAFs in patients with BRCA1 mutation exhibited outgoing signals to endothelial cells (ECs) clusters, including chemokine (C-X-C motif) ligand (CXCL) and vascular endothelial growth factor (VEGF). During CXCL signaling, the atypical chemokine receptor 1 (ACKR1) mainly interacts with CXCL family members in tumor endothelial cells (TECs). ACKR1-high TECs also showed high expression levels of angiogenesis-related genes, such as ANGPT2, MMP1, and SELE, which might lead to EC migration. Furthermore, iCAFs showed VEGF signals for FLT1 and KDR in TECs, which showed high co-expression with tip cell marker genes, including ZEB1 and MAFF, involved in sprouting angiogenesis. Moreover, BRCA1 mutation patients with relatively abundant iCAFs and tip cell gene expression exhibited a limited response to neoadjuvant chemotherapy, including cisplatin and bevacizumab. Importantly, our study observed the intricate link between iCAFs-mediated angiogenesis and chemoresistance in TNBC with BRCA1 mutation.

Sc-TUSV-ext: Single-cell clonal lineage inference from single nucleotide variants (SNV), copy number alterations (CNA) and structural variants (SV)

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming (ILP) based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants (SNV), copy number alterations (CNA) and structural variations (SV) into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness on real data in resolving clonal evolution in the presence of multiple variant types, providing a path towards more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.