Clonal fitness inferred from time-series modelling of single-cell cancer genomes

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.

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.

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.

The intricate dance of tumor evolution: Exploring immune escape, tumor migration, drug resistance, and treatment strategies

Recent research has unveiled fascinating insights into the intricate mechanisms governing tumor evolution. These studies have illuminated how tumors adapt and proliferate by exploiting various factors, including immune evasion, resistance to therapeutic drugs, genetic mutations, and their ability to adapt to different environments. Furthermore, investigations into tumor heterogeneity and chromosomal aberrations have revealed the profound complexity that underlies the evolution of cancer. Emerging findings have also underscored the role of viral influences in the development and progression of cancer, introducing an additional layer of complexity to the field of oncology. Tumor evolution is a dynamic and complex process influenced by various factors, including immune evasion, drug resistance, tumor heterogeneity, and viral influences. Understanding these elements is indispensable for developing more effective treatments and advancing cancer therapies. A holistic approach to studying and addressing tumor evolution is crucial in the ongoing battle against cancer. The main goal of this comprehensive review is to explore the intricate relationship between tumor evolution and critical aspects of cancer biology. By delving into this complex interplay, we aim to provide a profound understanding of how tumors evolve, adapt, and respond to treatment strategies. This review underscores the pivotal importance of comprehending tumor evolution in shaping effective approaches to cancer treatment.

Nanoparticles targeting mutant p53 overcome chemoresistance and tumor recurrence in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) shows high drug resistance and leads to low survival due to the high level of mutated Tumor Protein p53 (TP53). Cisplatin is a first-line treatment option for NSCLC, and the p53 mutation is a major factor in chemoresistance. We demonstrate that cisplatin chemotherapy increases the risk of TP53 mutations, further contributing to cisplatin resistance. Encouragingly, we find that the combination of cisplatin and fluvastatin can alleviate this problem. Therefore, we synthesize Fluplatin, a prodrug consisting of cisplatin and fluvastatin. Then, Fluplatin self-assembles and is further encapsulated with poly-(ethylene glycol)-phosphoethanolamine (PEG-PE), we obtain Fluplatin@PEG-PE nanoparticles (FP NPs). FP NPs can degrade mutant p53 (mutp53) and efficiently trigger endoplasmic reticulum stress (ERS). In this study, we show that FP NPs relieve the inhibition of cisplatin chemotherapy caused by mutp53, exhibiting highly effective tumor suppression and improving the poor NSCLC prognosis.

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.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Correlating somatic copy number alteration in aqueous humour cfDNA with chemotherapy history, eye salvage and pathological features in retinoblastoma

BackgroundThis study determined to probe the potential association between somatic copy number alteration (SCNA) in retinoblastoma (RB) aqueous humour (AH) and pathological high-risk factors, clinical features and previous chemotherapy history.

METHODS

Single-centre retrospective cohort study from including 58 AH samples collected from 58 patients diagnosed. Among them, 41 samples were collected after enucleation and 17 samples were collected before intravitreal chemotherapy. SCNAs were accessed by conducting shallow whole-genome sequencing in cell-free (cf) DNA of AH. HRs and ORs were applied to measure risk factors.

RESULTS

Canonical RB SCNAs including 1q gain (87%), 2p gain (50%), 6p gain (76%), 16q loss (69%) were frequently detected. Non-classical RB SCNAs in AH including 17q gain (53%), 19q loss (43%), 7q gain (35%) were also commonly observed. 19q loss was significantly more common in patients with cT3c or worse stage than others (p=0.034). 2p gain(p=0.001) and 7q gain(p=0.001) were both more common in patients with primary enucleation than those with previous chemotherapy. Interestingly, both 2p gain (HR=1.933, p=0.027) and 7q gain (HR=2.394, p=0.005) might predict enucleation. Correlation analysis with pathological features among enucleated eyes showed that 19q loss can predict a higher risk for both massive choroid invasion (OR=4.909, p=0.038) and postlaminar optic nerve invasion (OR=4.250, p=0.043).

DISCUSSION

Sequencing of AH cfDNA in RB can provide sufficient in vivo information. 19q loss was a potential signature of advanced cases clinically and pathologically.Repeated sampling from eyes receiving sequential chemotherapy should be conducted to evaluate fluctuation of SCNA in future study.

Natural History of Germline BRCA1 Mutated and BRCA Wild-type Triple-negative Breast Cancer

We report a deep next-generation sequencing analysis of 13 sequentially obtained tumor samples, eight sequentially obtained circulating tumor DNA (ctDNA) samples and three germline DNA samples over the life history of 3 patients with triple-negative breast cancer (TNBC), 2 of whom had germline pathogenic BRCA1 mutation, to unravel tumor evolution. Tumor tissue from all timepoints and germline DNA was subjected to whole-exome sequencing (WES), custom amplicon deep sequencing (30,000X) of a WES-derived somatic mutation panel, and SNP arrays for copy-number variation (CNV), while whole transcriptome sequencing (RNA-seq) was performed only on somatic tumor.There was enrichment of homologous recombination deficiency signature in all tumors and widespread CNV, which remained largely stable over time. Somatic tumor mutation numbers varied between patients and within each patient (range: 70-216, one outlier). There was minimal mutational overlap between patients with TP53 being the sole commonly mutated gene, but there was substantial overlap in sequential samples in each patient. Each patient's tumor contained a founding ("stem") clone at diagnosis, which persisted over time, from which all other clones ("subclone") were derived ("branching evolution"), which contained mutations in well-characterized cancer-related genes like PDGFRB, ARID2, TP53 (Patient_02), TP53, BRAF, BRIP1, CSF3R (Patient_04), and TP53, APC, EZH2 (Patient_07). Including stem and subclones, tumors from all patients were polyclonal at diagnosis and during disease progression. ctDNA recapitulated most tissue-derived stem clonal and subclonal mutations while detecting some additional subclonal mutations. RNA-seq revealed a stable basal-like pattern, with most highly expressed variants belonging to stem clone.

SIGNIFICANCE

In germline BRCA1 mutated and BRCA wild-type patients, TNBC shows a branching evolutionary pattern of mutations with a single founding clone, are polyclonal throughout their disease course, and have widespread copy-number aberrations. This evolutionary pattern may be associated with treatment resistance or sensitivity and could be therapeutically exploited.

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.

Measures of genetic diversification in somatic tissues at bulk and single-cell resolution

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

LASS2 enhances chemosensitivity to cisplatin by inhibiting PP2A-mediated β-catenin dephosphorylation in a subset of stem-like bladder cancer cells

BACKGROUND

The benefits of first-line, cisplatin-based chemotherapy for muscle-invasive bladder cancer are limited due to intrinsic or acquired resistance to cisplatin. Increasing evidence has revealed the implication of cancer stem cells in the development of chemoresistance. However, the underlying molecular mechanisms remain to be elucidated. This study investigates the role of LASS2, a ceramide synthase, in regulating Wnt/β-catenin signaling in a subset of stem-like bladder cancer cells and explores strategies to sensitize bladder cancer to cisplatin treatment.

METHODS

Data from cohorts of our center and published datasets were used to evaluate the clinical characteristics of LASS2. Flow cytometry was used to sort and analyze bladder cancer stem cells (BCSCs). Tumor sphere formation, soft agar colony formation assay, EdU assay, apoptosis analysis, cell viability, and cisplatin sensitivity assay were used to investigate the functional roles of LASS2. Immunofluorescence, immunoblotting, coimmunoprecipitation, LC-MS, PCR array, luciferase reporter assays, pathway reporter array, chromatin immunoprecipitation, gain-of-function, and loss-of-function approaches were used to investigate the underlying mechanisms. Cell- and patient-derived xenograft models were used to investigate the effect of LASS2 overexpression and a combination of XAV939 on cisplatin sensitization and tumor growth.

RESULTS

Patients with low expression of LASS2 have a poorer response to cisplatin-based chemotherapy. Loss of LASS2 confers a stem-like phenotype and contributes to cisplatin resistance. Overexpression of LASS2 results in inhibition of self-renewal ability of BCSCs and increased their sensitivity to cisplatin. Mechanistically, LASS2 inhibits PP2A activity and dissociates PP2A from β-catenin, preventing the dephosphorylation of β-catenin and leading to the accumulation of cytosolic phospho-β-catenin, which decreases the transcription of the downstream genes ABCC2 and CD44 in BCSCs. Overexpression of LASS2 combined with a tankyrase inhibitor (XAV939) synergistically inhibits tumor growth and restores cisplatin sensitivity.

CONCLUSIONS

Targeting the LASS2 and β-catenin pathways may be an effective strategy to overcome cisplatin resistance and inhibit tumor growth in bladder cancer patients.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma

Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.

Transforming ovarian cancer care by targeting minimal residual disease

Frontline treatment and resultant cure rates in patients with advanced ovarian cancer have changed little over the past several decades. Here, we outline a multidisciplinary approach aimed at gaining novel therapeutic insights by focusing on the poorly understood minimal residual disease phase of ovarian cancer that leads to eventual incurable recurrences.

Clonal tracking in cancer and metastasis

The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.

Permission to pass: on the role of p53 as a gatekeeper for aneuploidy

Aneuploidy-the karyotype state in which the number of chromosomes deviates from a multiple of the haploid chromosome set-is common in cancer, where it is thought to facilitate tumor initiation and progression. However, it is poorly tolerated in healthy cells: during development and tissue homeostasis, aneuploid cells are efficiently cleared from the population. It is still largely unknown how cancer cells become, and adapt to being, aneuploid. P53, the gatekeeper of the genome, has been proposed to guard against aneuploidy. Aneuploidy in cancer genomes strongly correlates with mutations in TP53, and p53 is thought to prevent the propagation of aneuploid cells. Whether p53 also participates in preventing the mistakes in cell division that lead to aneuploidy is still under debate. In this review, we summarize the current understanding of the role of p53 in protecting cells from aneuploidy, and we explore the consequences of functional p53 loss for the propagation of aneuploidy in cancer.

Refphase: Multi-sample phasing reveals haplotype-specific copy number heterogeneity

Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patient's disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample phasing. We demonstrate Refphase's ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours.

Single-cell DNA replication dynamics in genomically unstable cancers

Dysregulated DNA replication is both a cause and a consequence of aneuploidy, yet the dynamics of DNA replication in aneuploid cell populations remains understudied. We developed a new method, PERT, for inferring cell-specific DNA replication states from single-cell whole genome sequencing, and investigated clone-specific DNA replication dynamics in >50,000 cells obtained from a collection of aneuploid and clonally heterogeneous cell lines, xenografts and primary cancer tissues. Clone replication timing (RT) profiles correlated with future copy number changes in serially passaged cell lines. Cell type was the strongest determinant of RT heterogeneity, while whole genome doubling and mutational process were associated with accumulation of late S-phase cells and weaker RT associations. Copy number changes affecting chromosome X had striking impact on RT, with loss of the inactive X allele shifting replication earlier, and loss of inactive Xq resulting in reactivation of Xp. Finally, analysis of time series xenografts illustrate how cell cycle distributions approximate clone proliferation, recapitulating expected relationships between proliferation and fitness in treatment-naive and chemotherapeutic contexts.

Cracking the pattern of tumor evolution based on single-cell copy number alterations

Copy number alterations (CNAs) are a key characteristic of tumor development and progression. The accumulation of various CNAs during tumor development plays a critical role in driving tumor evolution. Heterogeneous clones driven by distinct CNAs have different selective advantages, leading to differential patterns of tumor evolution that are essential for developing effective cancer therapies. Recent advances in single-cell sequencing technology have enabled genome-wide copy number profiling of tumor cell populations at single-cell resolution. This has made it possible to explore the evolutionary patterns of CNAs and accurately discover the mechanisms of intra-tumor heterogeneity. Here, we propose a two-step statistical approach that distinguishes neutral, linear, branching and punctuated evolutionary patterns for a tumor cell population based on single-cell copy number profiles. We assessed our approach using a variety of simulated and real single-cell genomic and transcriptomic datasets, demonstrating its high accuracy and robustness in predicting tumor evolutionary patterns. We applied our approach to single-cell DNA sequencing data from 20 breast cancer patients and observed that punctuated evolution is the dominant evolutionary pattern in breast cancer. Similar conclusions were drawn when applying the approach to single-cell RNA sequencing data obtained from 132 various cancer patients. Moreover, we found that differential immune cell infiltration is associated with specific evolutionary patterns. The source code of our study is available at https://github.com/FangWang-SYSU/PTEM.

Disentangling the roles of aneuploidy, chromosomal instability and tumour heterogeneity in developing resistance to cancer therapies

Aneuploidy is defined as the cellular state of having a number of chromosomes that deviates from a multiple of the normal haploid chromosome number of a given organism. Aneuploidy can be present in a static state: Down syndrome individuals stably maintain an extra copy of chromosome 21 in their cells. In cancer cells, however, aneuploidy is usually present in combination with chromosomal instability (CIN) which leads to a continual generation of new chromosomal alterations and the development of intratumour heterogeneity (ITH). The prevalence of cells with specific chromosomal alterations is further shaped by evolutionary selection, for example, during the administration of cancer therapies. Aneuploidy, CIN and ITH have each been individually associated with poor prognosis in cancer, and a wealth of evidence suggests they contribute, either alone or in combination, to cancer therapy resistance by providing a reservoir of potential resistant states, or the ability to rapidly evolve resistance. A full understanding of the contribution and interplay between aneuploidy, CIN and ITH is required to tackle therapy resistance in cancer patients. However, these characteristics often co-occur and are intrinsically linked, presenting a major challenge to defining their individual contributions. Moreover, their accurate measurement in both experimental and clinical settings is a technical hurdle. Here, we attempt to deconstruct the contribution of the individual and combined roles of aneuploidy, CIN and ITH to therapy resistance in cancer, and outline emerging approaches to measure and disentangle their roles as a step towards integrating these principles into cancer therapeutic strategy.

Maintaining, masking, and mimicking selection: the interplay of cell-intrinsic and cell-extrinsic effects upon eco-evolutionary dynamics

Evolution is a stochastic yet inevitable process that lies at the heart of biology yet in the multi-cellular environments within patients, ecological complexities arise via heterogeneity and microenvironments. The interplay of ecology and mutation is thus fundamental to predicting the evolution of complex diseases and engineering optimal treatment solutions. As experimental evidence of ecological interactions between disease agents continues to grow, so does the need for evolutionary theory and modeling that incorporates these interaction effects. Inspired by experimental cell biology, we transform the variables in the interaction payoff matrix to encode cell-cell interactions in our mathematical approach as growth-rate modifying, frequency-dependent interactions. In this way, we can show the extent to which the presence of these cell-extrinsic ecological interactions can modify the evolutionary trajectories that would be predicted from cell-intrinsic properties alone. To do this we form a Fokker-Planck equation for a genetic population undergoing diffusion, drift, and interactions and generate a novel, analytic solution for the stationary distribution. We use this solution to determine when these interactions can modify evolution in such ways as to maintain, mask, or mimic mono-culture fitness differences. This work has implications for the interpretation and understanding of experimental and patient evolution and is a result that may help to explain the abundance of apparently neutral evolution in cancer systems and heterogeneous populations in general. In addition, the derivation of an analytical result for stochastic, ecologically dependent evolution paves the way for treatment approaches requiring knowledge of a stationary solution for the development of control protocols.

A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy

We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a T cell exhaustion topic that independently recapitulates the previously identified LAG-3+ immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a T cell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity.

Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

Cancer aneuploidies are shaped primarily by effects on tumour fitness

Aneuploidies-whole-chromosome or whole-arm imbalances-are the most prevalent alteration in cancer genomes1,2. However, it is still debated whether their prevalence is due to selection or ease of generation as passenger events1,2. Here we developed a method, BISCUT, that identifies loci subject to fitness advantages or disadvantages by interrogating length distributions of telomere- or centromere-bounded copy-number events. These loci were significantly enriched for known cancer driver genes, including genes not detected through analysis of focal copy-number events, and were often lineage specific. BISCUT identified the helicase-encoding gene WRN as a haploinsufficient tumour-suppressor gene on chromosome 8p, which is supported by several lines of evidence. We also formally quantified the role of selection and mechanical biases in driving aneuploidy, finding that rates of arm-level copy-number alterations are most highly correlated with their effects on cellular fitness1,2. These results provide insight into the driving forces behind aneuploidy and its contribution to tumorigenesis.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Deterministic evolution and stringent selection during preneoplasia

The earliest events during human tumour initiation, although poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult preneoplasia by biallelic inactivation of TP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over 2 years. TP53 loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single-cell sequencing of TP53-deficient gastric organoids similarly indicates progression towards malignant transcriptional programmes. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programmes repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference and a marked degree of phenotypic convergence in premalignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and show evolutionary constraints and barriers to malignant transformation, with implications for earlier detection and interception of aggressive, genome-instable tumours.

Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach

Immune checkpoint inhibitors (ICIs), now mainstays in the treatment of cancer treatment, show great potential but only benefit a subset of patients. A more complete understanding of the immunological mechanisms and pharmacodynamics of ICI in cancer patients will help identify the patients most likely to benefit and will generate knowledge for the development of next-generation ICI regimens. We set out to interrogate the early temporal evolution of T cell populations from longitudinal single-cell flow cytometry data. We developed an innovative statistical and computational approach using a Latent Dirichlet Allocation (LDA) model that extends the concept of topic modeling used in text mining. This powerful unsupervised learning tool allows us to discover compositional topics within immune cell populations that have distinct functional and differentiation states and are biologically and clinically relevant. To illustrate the model's utility, we analyzed ∼17 million T cells obtained from 138 pre- and on-treatment peripheral blood samples from a cohort of melanoma patients treated with ICIs. We identified three latent dynamic topics: a T-cell exhaustion topic that recapitulates a LAG3+ predominant patient subgroup with poor clinical outcome; a naive topic that shows association with immune-related toxicity; and an immune activation topic that emerges upon ICI treatment. We identified that a patient subgroup with a high baseline of the naïve topic has a higher toxicity grade. While the current application is demonstrated using flow cytometry data, our approach has broader utility and creates a new direction for translating single-cell data into biological and clinical insights.

Application of high-throughput single-nucleus DNA sequencing in pancreatic cancer

Despite insights gained by bulk DNA sequencing of cancer it remains challenging to resolve the admixture of normal and tumor cells, and/or of distinct tumor subclones; high-throughput single-cell DNA sequencing circumvents these and brings cancer genomic studies to higher resolution. However, its application has been limited to liquid tumors or a small batch of solid tumors, mainly because of the lack of a scalable workflow to process solid tumor samples. Here we optimize a highly automated nuclei extraction workflow that achieves fast and reliable targeted single-nucleus DNA library preparation of 38 samples from 16 pancreatic ductal adenocarcinoma patients, with an average library yield per sample of 2867 single nuclei. We demonstrate that this workflow not only performs well using low cellularity or low tumor purity samples but reveals genomic evolution patterns of pancreatic ductal adenocarcinoma as well.

The role of structural variations in Alzheimer's disease and other neurodegenerative diseases

Dozens of single nucleotide polymorphisms (SNPs) related to Alzheimer's disease (AD) have been discovered by large scale genome-wide association studies (GWASs). However, only a small portion of the genetic component of AD can be explained by SNPs observed from GWAS. Structural variation (SV) can be a major contributor to the missing heritability of AD; while SV in AD remains largely unexplored as the accurate detection of SVs from the widely used array-based and short-read technology are still far from perfect. Here, we briefly summarized the strengths and weaknesses of available SV detection methods. We reviewed the current landscape of SV analysis in AD and SVs that have been found associated with AD. Particularly, the importance of currently less explored SVs, including insertions, inversions, short tandem repeats, and transposable elements in neurodegenerative diseases were highlighted.

Intra-tumor heterogeneity, turnover rate and karyotype space shape susceptibility to missegregation-induced extinction

The phenotypic efficacy of somatic copy number alterations (SCNAs) stems from their incidence per base pair of the genome, which is orders of magnitudes greater than that of point mutations. One mitotic event stands out in its potential to significantly change a cell's SCNA burden-a chromosome missegregation. A stochastic model of chromosome mis-segregations has been previously developed to describe the evolution of SCNAs of a single chromosome type. Building upon this work, we derive a general deterministic framework for modeling missegregations of multiple chromosome types. The framework offers flexibility to model intra-tumor heterogeneity in the SCNAs of all chromosomes, as well as in missegregation- and turnover rates. The model can be used to test how selection acts upon coexisting karyotypes over hundreds of generations. We use the model to calculate missegregation-induced population extinction (MIE) curves, that separate viable from non-viable populations as a function of their turnover- and missegregation rates. Turnover- and missegregation rates estimated from scRNA-seq data are then compared to theoretical predictions. We find convergence of theoretical and empirical results in both the location of MIE curves and the necessary conditions for MIE. When a dependency of missegregation rate on karyotype is introduced, karyotypes associated with low missegregation rates act as a stabilizing refuge, rendering MIE impossible unless turnover rates are exceedingly high. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering MIE unlikely.

Exploiting allele-specific transcriptional effects of subclonal copy number alterations for genotype-phenotype mapping in cancer cell populations

Somatic copy number alterations drive aberrant gene expression in cancer cells. In tumors with high levels of chromosomal instability, subclonal copy number alterations (CNAs) are a prevalent feature which often result in heterogeneous cancer cell populations with distinct phenotypes1. However, the extent to which subclonal CNAs contribute to clone-specific phenotypes remains poorly understood, in part due to the lack of methods to quantify how CNAs influence gene expression at a subclone level. We developed TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population and explicitly models gene dosage effects from subclonal alterations. We show through quantitative benchmarking data and application to human cancer data with single cell DNA and RNA libraries that TreeAlign accurately encodes clone-specific transcriptional effects of subclonal CNAs, the impact of allelic imbalance on allele-specific transcription, and obviates the need to arbitrarily define genotypic clones from a phylogenetic tree a priori. Combined, these advances lead to highly granular definitions of clones with distinct copy-number driven expression programs with increased resolution and accuracy over competing methods. The resulting improvement in assignment of transcriptional phenotypes to genomic clones enables clone-clone gene expression comparisons and explicit inference of genes that are mechanistically altered through CNAs, 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.

Spatial biology of cancer evolution

The natural history of cancers can be understood through the lens of evolution given that the driving forces of cancer development are mutation and selection of fitter clones. Cancer growth and progression are spatial processes that involve the breakdown of normal tissue organization, invasion and metastasis. For these reasons, spatial patterns are an integral part of histological tumour grading and staging as they measure the progression from normal to malignant disease. Furthermore, tumour cells are part of an ecosystem of tumour cells and their surrounding tumour microenvironment. A range of new spatial genomic, transcriptomic and proteomic technologies offers new avenues for the study of cancer evolution with great molecular and spatial detail. These methods enable precise characterizations of the tumour microenvironment, cellular interactions therein and micro-anatomical structures. In conjunction with spatial genomics, it emerges that tumours and microenvironments co-evolve, which helps explain observable patterns of heterogeneity and offers new routes for therapeutic interventions.

Single-cell genomic variation induced by mutational processes in cancer

How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.

Mastering the use of cellular barcoding to explore cancer heterogeneity

Tumours are often composed of a multitude of malignant clones that are genomically unique, and only a few of them may have the ability to escape cancer therapy and grow as symptomatic lesions. As a result, tumours with a large degree of genomic diversity have a higher chance of leading to patient death. However, clonal fate can be driven by non-genomic features. In this context, new technologies are emerging not only to track the spatiotemporal fate of individual cells and their progeny but also to study their molecular features using various omics analysis. In particular, the recent development of cellular barcoding facilitates the labelling of tens to millions of cancer clones and enables the identification of the complex mechanisms associated with clonal fate in different microenvironments and in response to therapy. In this Review, we highlight the recent discoveries made using lentiviral-based cellular barcoding techniques, namely genetic and optical barcoding. We also emphasize the strengths and limitations of each of these technologies and discuss some of the key concepts that must be taken into consideration when one is designing barcoding experiments. Finally, we suggest new directions to further improve the use of these technologies in cancer research.

Towards precision oncology with patient-derived xenografts

Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.

Malignant clonal evolution drives multiple myeloma cellular ecological diversity and microenvironment reprogramming

Background

Multiple myeloma (MM) is a heterogeneous disease with different patterns of clonal evolution and a complex tumor microenvironment, representing a challenge for clinicians and pathologists to understand and dissect the contribution and impact of polyclonality on tumor progression.

Methods

In this study, we established a global cell ecological landscape of the bone marrow (BM) from MM patients, combining single-cell RNA sequencing and single-molecule long-read genome sequencing data.

Results

The malignant mutation event was localized to the tumor cell clusters with shared mutation of ANK1 and IFITM2 in all malignant subpopulations of all MM patients. Therefore, these two variants occur in the early stage of malignant clonal origin to mediate the malignant transformation of proplasmacytes or plasmacytes to MM cells. Tumor cell stemness index score and pseudo-sequential clonal evolution analysis can be used to divide the evolution model of MM into two clonal origins: types I and IX. Notably, clonal evolution and the tumor microenvironment showed an interactive relationship, in which the evolution process is not only selected by but also reacts to the microenvironment; thus, vesicle secretion enriches immune cells with malignant-labeled mRNA for depletion. Interestingly, microenvironmental modification exhibited significant heterogeneity among patients.

Conclusions

This characterization of the malignant clonal evolution pattern of MM at the single-cell level provides a theoretical basis and scientific evidence for a personalized precision therapy strategy and further development of a potential new adjuvant strategy combining epigenetic agent and immune checkpoint blockade.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01648-z.

Triple negative breast cancer (TNBC): Non-genetic tumor heterogeneity and immune microenvironment: Emerging treatment options

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by extensive intra-tumoral heterogeneity, and frequently develops resistance to therapies. Tumor heterogeneity and lack of biomarkers are thought to be some of the most difficult challenges driving therapeutic resistance and relapse. This review will summarize current therapy for TNBC, studies in treatment resistance and relapse, including data from recent single cell sequencing. We will discuss changes in both the transcriptome and epigenome of TNBC, and we will review mechanisms regulating the immune microenvironment. Lastly, we will provide new perspective in patient stratification, and treatment options targeting transcriptome dysregulation and the immune microenvironment of TNBC patients.

Lineage tracing in human tissues

The dynamical process of cell division that underpins homeostasis in the human body cannot be directly observed in vivo, but instead is measurable from the pattern of somatic genetic or epigenetic mutations that accrue in tissues over an individual's lifetime. Because somatic mutations are heritable, they serve as natural lineage tracing markers that delineate clonal expansions. Mathematical analysis of the distribution of somatic clone sizes gives a quantitative readout of the rates of cell birth, death, and replacement. In this review we explore the broad range of somatic mutation types that have been used for lineage tracing in human tissues, introduce the mathematical concepts used to infer dynamical information from these clone size data, and discuss the insights of this lineage tracing approach for our understanding of homeostasis and cancer development. We use the human colon as a particularly instructive exemplar tissue. There is a rich history of human somatic cell dynamics surreptitiously written into the cell genomes that is being uncovered by advances in sequencing and careful mathematical analysis lineage of tracing data. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Whole-Genome Duplication Shapes the Aneuploidy Landscape of Human Cancers

Aneuploidy is a hallmark of cancer with tissue-specific prevalence patterns that suggest it plays a driving role in cancer initiation and progression. However, the contribution of aneuploidy to tumorigenesis depends on both cellular and genomic contexts. Whole-genome duplication (WGD) is a common macroevolutionary event that occurs in more than 30% of human tumors early in tumorigenesis. Although tumors that have undergone WGD are reported to be more permissive to aneuploidy, it remains unknown whether WGD also affects aneuploidy prevalence patterns. Here we analyzed clinical tumor samples from 5,586 WGD- tumors and 3,435 WGD+ tumors across 22 tumor types and found distinct patterns of aneuploidy in WGD- and WGD+ tumors. WGD+ tumors were characterized by more promiscuous aneuploidy patterns, in line with increased aneuploidy tolerance. Moreover, the genetic interactions between chromosome arms differed between WGD- and WGD+ tumors, giving rise to distinct cooccurrence and mutual exclusivity aneuploidy patterns. The proportion of whole-chromosome aneuploidy compared with arm-level aneuploidy was significantly higher in WGD+ tumors, indicating distinct dominant mechanisms for aneuploidy formation. Human cancer cell lines successfully reproduced these WGD/aneuploidy interactions, confirming the relevance of studying this phenomenon in culture. Finally, induction of WGD and assessment of aneuploidy in isogenic WGD-/WGD+ human colon cancer cell lines under standard or selective conditions validated key findings from the clinical tumor analysis, supporting a causal link between WGD and altered aneuploidy landscapes. We conclude that WGD shapes the aneuploidy landscape of human tumors and propose that this interaction contributes to tumor evolution.

SIGNIFICANCE

These findings suggest that the interactions between whole-genome duplication and aneuploidy are important for tumor evolution, highlighting the need to consider genome status in the analysis and modeling of cancer aneuploidy.

Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution

The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.

Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers

Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.

Unravelling the tumour genome: the evolutionary and clinical impacts of structural variants in tumourigenesis

Structural variants (SVs) represent a major source of aberration in tumour genomes. Given the diversity in the size and type of SVs present in tumours, the accurate detection and interpretation of SVs in tumours is challenging. New classes of complex structural events in tumours are discovered frequently, and the definitions of the genomic consequences of complex events are constantly being refined. Detailed analyses of short‐read whole‐genome sequencing (WGS) data from large tumour cohorts facilitate the interrogation of SVs at orders of magnitude greater scale and depth. However, the inherent technical limitations of short‐read WGS prevent us from accurately detecting and investigating the impact of all the SVs present in tumours. The expanded use of long‐read WGS will be critical for improving the accuracy of SV detection, and in fully resolving complex SV events, both of which are crucial for determining the impact of SVs on tumour progression and clinical outcome. Despite the present limitations, we demonstrate that SVs play an important role in tumourigenesis. In particular, SVs contribute significantly to late‐stage tumour development and to intratumoural heterogeneity. The evolutionary trajectories of SVs represent a window into the clonal dynamics in tumours, a comprehensive understanding of which will be vital for influencing patient outcomes in the future. Recent findings have highlighted many clinical applications of SVs in cancer, from early detection to biomarkers for treatment response and prognosis. As the methods to detect and interpret SVs improve, elucidating the full breadth of the complex SV landscape and determining how these events modulate tumour evolution will improve our understanding of cancer biology and our ability to capitalise on the utility of SVs in the clinical management of cancer patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Lymph Node Microenvironment May Invigorate Cancer Cells With Enhanced Metastatic Capacities

Cancer metastasis, a typical malignant biological behavior involving the distant migration of tumor cells from the primary site to other organs, contributed majorly to cancer-related deaths of patients. Although constant efforts have been paid by researchers to elucidate the mechanisms of cancer metastasis, we are still far away from the definite answer. Recently, emerging evidence demonstrated that cancer metastasis is a continuous coevolutionary process mediated by the interactions between tumor cells and the host organ microenvironment, and epigenetic reprogramming of metastatic cancer cells may confer them with stronger metastatic capacities. The lymph node served as the first metastatic niche for many types of cancer, and the appearance of lymph node metastasis predicted poor prognosis. Importantly, multiple immune cells and stromal cells station and linger in the lymph nodes, which constitutes the complexity of the lymph node microenvironment. The active cross talk between cancer cells and immune cells could happen unceasingly within the metastatic environment of lymph nodes. Of note, diverse immune cells have been found to participate in the formation of malignant properties of tumor, including stemness and immune escape. Based on these available evidence and data, we hypothesize that the metastatic microenvironment of lymph nodes could drive cancer cells to metastasize to further organs through epigenetic mechanisms.

Understanding and targeting prostate cancer cell heterogeneity and plasticity

Prostate cancer (PCa) is a prevalent malignancy that occurs primarily in old males. Prostate tumors in different patients manifest significant inter-patient heterogeneity with respect to histo-morphological presentations and molecular architecture. An individual patient tumor also harbors genetically distinct clones in which PCa cells display intra-tumor heterogeneity in molecular features and phenotypic marker expression. This inherent PCa cell heterogeneity, e.g., in the expression of androgen receptor (AR), constitutes a barrier to the long-term therapeutic efficacy of AR-targeting therapies. Furthermore, tumor progression as well as therapeutic treatments induce PCa cell plasticity such that AR-positive PCa cells may turn into AR-negative cells and prostate tumors may switch lineage identity from adenocarcinomas to neuroendocrine-like tumors. This induced PCa cell plasticity similarly confers resistance to AR-targeting and other therapies. In this review, I first discuss PCa from the perspective of an abnormal organ development and deregulated cellular differentiation, and discuss the luminal progenitor cells as the likely cells of origin for PCa. I then focus on intrinsic PCa cell heterogeneity in treatment-naïve tumors with the presence of prostate cancer stem cells (PCSCs). I further elaborate on PCa cell plasticity induced by genetic alterations and therapeutic interventions, and present potential strategies to therapeutically tackle PCa cell heterogeneity and plasticity. My discussions will make it clear that, to achieve enduring clinical efficacy, both intrinsic PCa cell heterogeneity and induced PCa cell plasticity need to be targeted with novel combinatorial approaches.

Ginsenoside (20S)-protopanaxatriol induces non-protective autophagy and apoptosis by inhibiting Akt/mTOR signaling pathway in triple-negative breast cancer cells

Triple-negative breast cancer (TNBC) lacks a recognized therapeutic molecular target and has an unfavorable prognosis. (20S)-Protopanaxatriol (g-PPT, PPT) is an active metabolite extracted from ginseng. Accumulating evidence suggests that it has good anti-cancer activity in vivo and in vitro. In this study, we aimed to elucidate the anti-tumor effects of PPT in TNBC cells and tumor-bearing mice, as well as the relevant molecular mechanisms of autophagy and apoptosis. In vitro, we have found that PPT is capable of inducing non-protective autophagy and apoptosis, thus exerting some anti-proliferative and anti-migration activity in TNBC cells. And in vivo, the therapeutic effects of PPT were evaluated by xenograft mouse models. The potential binding mode of PPT and Akt was predicted by molecular docking. Our findings indicated that PPT treatment induced non-protective autophagy in TNBC cells by inhibiting the Akt/mTOR signaling pathway. Therefore, PPT may be a potential treatment for TNBC in the future.

TP53 loss initiates chromosomal instability in fallopian tube epithelial cells

High-grade serous ovarian cancer (HGSOC) originates in the fallopian tube epithelium and is characterized by ubiquitous TP53 mutation and extensive chromosomal instability (CIN). However, direct causes of CIN, such as mutations in DNA replication and mitosis genes, are rare in HGSOC. We therefore asked whether oncogenic mutations that are common in HGSOC can indirectly drive CIN in non-transformed human fallopian tube epithelial cells. To model homologous recombination deficient HGSOC, we sequentially mutated TP53 and BRCA1 then overexpressed MYC. Loss of p53 function alone was sufficient to drive the emergence of subclonal karyotype alterations. TP53 mutation also led to global gene expression changes, influencing modules involved in cell cycle commitment, DNA replication, G2/M checkpoint control and mitotic spindle function. Both transcriptional deregulation and karyotype diversity were exacerbated by loss of BRCA1 function, with whole-genome doubling events observed in independent p53/BRCA1-deficient lineages. Thus, our observations indicate that loss of the key tumour suppressor TP53 is sufficient to deregulate multiple cell cycle control networks and thereby initiate CIN in pre-malignant fallopian tube epithelial cells. This article has an associated First Person interview with the first author of the paper.

Evolution's cartographer: Mapping the fitness landscape in cancer

Cancer treatment effectiveness could be improved if it were possible to accurately anticipate the response of the tumor to treatment. Writing in Nature, Salehi et al. combine single-cell genomics and mathematical modeling to measure cancer subclone fitness and use these measurements to accurately predict the future trajectory of cancer evolution.