Single-cell analysis of clonal maintenance of transcriptional and epigenetic states in cancer cells

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.

The up-regulation of SYNCRIP promotes the proliferation and tumorigenesis via DNMT3A/p16 in colorectal cancer

Heterogeneous nuclear ribonucleoproteins (hnRNPs), a group of proteins that control gene expression, have been implicated in many post-transcriptional processes. SYNCRIP (also known as hnRNP Q), a subtype of hnRNPs, has been reported to be involved in mRNA splicing and translation. In addition, the deregulation of SYNCRIP was found in colorectal cancer (CRC). However, the role of SYNCRIP in regulating CRC growth remains largely unknown. Here, we found that SYNCRIP was highly expressed in colorectal cancer by analyzing TCGA and GEPIA database. Furthermore, we confirmed the expression of SYNCRIP expression in CRC tumor and CRC cell lines. Functionally, SYNCRIP depletion using shRNA in CRC cell lines (SW480 and HCT 116) resulted in increased caspase3/7 activity and decreased cell proliferation, as well as migration. Meanwhile, overexpression of SYNCRIP showed opposite results. Mechanistically, SYNCRIP regulated the expression of DNA methyltransferases (DNMT) 3A, but not DNMT1 or DNMT3B, which affected the expression of tumor suppressor, p16. More importantly, our in vivo experiments showed that SYNCRIP depletion significantly inhibited colorectal tumor growth. Taken all together, our results suggest SYNCRIP as a potent therapeutic target in colorectal cancer.

Star wars against leukemia: attacking the clones

Leukemia, although most likely starts as a monoclonal genetic/epigenetic anomaly, is a polyclonal disease at manifestation. This polyclonal nature results from ongoing evolutionary changes in the genome/epigenome of leukemia cells to promote their survival and proliferation advantages. We discuss here how genetic and/or epigenetic aberrations alter intracellular microenvironment in individual leukemia clones and how extracellular microenvironment selects the best fitted clones. This dynamic polyclonal composition of leukemia makes designing an effective therapy a challenging task especially because individual leukemia clones often display substantial differences in response to treatment. Here, we discuss novel therapeutic approach employing single cell multiomics to identify and eradicate all individual clones in a patient.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Computational single-cell methods for predicting cancer risk

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

Phenotypic noise and plasticity in cancer evolution

Non-genetic alterations can produce changes in a cell's phenotype. In cancer, these phenomena can influence a cell's fitness by conferring access to heritable, beneficial phenotypes. Herein, we argue that current discussions of 'phenotypic plasticity' in cancer evolution ignore a salient feature of the original definition: namely, that it occurs in response to an environmental change. We suggest 'phenotypic noise' be used to distinguish non-genetic changes in phenotype that occur independently from the environment. We discuss the conceptual and methodological techniques used to identify these phenomena during cancer evolution. We propose that the distinction will guide efforts to define mechanisms of phenotype change, accelerate translational work to manipulate phenotypes through treatment, and, ultimately, improve patient outcomes.

Heterogeneity generating capacity in tumorigenesis and cancer therapeutics

Cells of multicellular organisms generate heterogeneity in a controlled and transient fashion during embryogenesis, which can be reactivated in pathologies such as cancer. Although genomic heterogeneity is an important part of tumorigenesis, continuous generation of phenotypic heterogeneity is central for the adaptation of cancer cells to the challenges of tumorigenesis and response to therapy. Here I discuss the capacity of generating heterogeneity, hereafter called cell hetness, in cancer cells both as the activation of hetness oncogenes and inactivation of hetness tumor suppressor genes, which increase the generation of heterogeneity, ultimately producing an increase in adaptability and cell fitness. Transcriptomic high hetness states in therapy-tolerant cell states denote its importance in cancer resistance to therapy. The definition of the concept of hetness will allow the understanding of its origins, its control during embryogenesis, its loss of control in tumorigenesis and cancer therapeutics and its active targeting.

Two ARGONAUTE proteins loaded with transposon-derived small RNAs are associated with the reproductive cell lineage in Arabidopsis

In sexually propagating organisms, genetic, and epigenetic mutations are evolutionarily relevant only if they occur in the germline and are hence transmitted to the next generation. In contrast to most animals, plants are considered to lack an early segregating germline, implying that somatic cells can contribute genetic information to progeny. Here we demonstrate that 2 ARGONAUTE proteins, AGO5 and AGO9, mark cells associated with sexual reproduction in Arabidopsis (Arabidopsis thaliana) throughout development. Both AGOs are loaded with dynamically changing small RNA populations derived from highly methylated, pericentromeric, long transposons. Sequencing of single stem cell nuclei revealed that many of these transposons are co-expressed within an AGO5/9 expression domain in the shoot apical meristem (SAM). Co-occurrence of transposon expression and specific ARGONAUTE (AGO) expression in the SAM is reminiscent of germline features in animals and supports the existence of an early segregating germline in plants. Our results open the path to investigating transposon biology and epigenome dynamics at cellular resolution in the SAM stem cell niche.

Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets

Assigning single cell transcriptomes to cellular lineage trees by lineage tracing has transformed our understanding of differentiation during development, regeneration, and disease. However, lineage tracing is technically demanding, often restricted in time-resolution, and most scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly identify small to medium-sized cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable gene expression, to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human breast cancer biopsies, GEMLI reveals previously unknown gene expression changes at the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the role of small cell lineages in a wide range of physiological and pathological contexts, notably in vivo. GEMLI is available as an R package on GitHub ( https://github.com/UPSUTER/GEMLI ).

Clonally heritable gene expression imparts a layer of diversity within cell types

Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.

Fluorescent Reporter Systems to Investigate Chromatin Effector Proteins in Living Cells

Epigenetic research faces the challenge of the high complexity and tight regulation in chromatin modification networks. Although many isolated mechanisms of chromatin-mediated gene regulation have been described, solid approaches for the comprehensive analysis of specific processes as parts of the bigger epigenome network are missing. In order to expand the toolbox of methods by a system that will help to capture and describe the complexity of transcriptional regulation, we describe here a robust protocol for the generation of stable reporter systems for transcriptional activity and summarize their applications. The system allows for the induced recruitment of a chromatin regulator to a fluorescent reporter gene, followed by the detection of transcriptional changes using flow cytometry. The reporter gene is integrated into an endogenous chromatin environment, thus enabling the detection of regulatory dependencies of the investigated chromatin regulator on endogenous cofactors. The system allows for an easy and dynamic readout at the single-cell level and the ability to compensate for cell-to-cell variances of transcription. The modular design of the system enables the simple adjustment of the method for the investigation of different chromatin regulators in a broad panel of cell lines. We also summarize applications of this technology to characterize the silencing velocity of different chromatin effectors, removal of activating histone modifications, analysis of stability and reversibility of epigenome modifications, the investigation of the effects of small molecule on chromatin effectors and of functional effector-coregulator relationships. The presented method allows to investigate the complexity of transcriptional regulation by epigenetic effector proteins in living cells.

EDARADD promotes colon cancer progression by suppressing E3 ligase Trim21-mediated ubiquitination and degradation of Snail

Tumor cell migration, specifically epithelial-mesenchymal transition (EMT), serves as a key contributor to treatment failure in colon cancer patients. However, the limited comprehension of its genetic and biological aspects presents challenges for its investigation. EDAR-associated death domain (EDARADD), an important TNFR superfamily member, is elevated in colon cancer. However, it remains unclear about the exact role of EDARADD in the progression of colon cancer metastasis. In this study, we initially demonstrated that both protein and mRNA levels of EDDARADD are elevated in colon cancer tissues and cells, associated with reduced overall survival. Furthermore, functional experiments demonstrated that EDARADD promotes colon cancer cell proliferation and participates in EMT both in vitro and vivo. Mechanistically, Co-IP verified EDARADD could stabilize Snail1 by interacting with E3 ubiquitin ligase Trim21 to inhibit ubiquitination of Snail1. Interestingly, RNA-seq and ubiquitination assay revealed EDARADD's dual downregulation of Trim21 expression at the translational level via Cul1-mediated ubiquitin degradation, and at the transcriptional level through PPARa regulation. Moreover, EDARADD activates NF-κB signaling and experiences feedback transcriptional regulation by p65. In conclusion, this study highlights the signal pathway of EDARADD-PPARa-Trim21-Snail1-EMT and a feedback regulation of NF-κB signaling on EDARADD, which indicated EDARADD as an emerging therapeutic target for colon cancer.

Decoding Cancer Evolution: Integrating Genetic and Non-Genetic Insights

The development of cancer begins with cells transitioning from their multicellular nature to a state akin to unicellular organisms. This shift leads to a breakdown in the crucial regulators inherent to multicellularity, resulting in the emergence of diverse cancer cell subpopulations that have enhanced adaptability. The presence of different cell subpopulations within a tumour, known as intratumoural heterogeneity (ITH), poses challenges for cancer treatment. In this review, we delve into the dynamics of the shift from multicellularity to unicellularity during cancer onset and progression. We highlight the role of genetic and non-genetic factors, as well as tumour microenvironment, in promoting ITH and cancer evolution. Additionally, we shed light on the latest advancements in omics technologies that allow for in-depth analysis of tumours at the single-cell level and their spatial organization within the tissue. Obtaining such detailed information is crucial for deepening our understanding of the diverse evolutionary paths of cancer, allowing for the development of effective therapies targeting the key drivers of cancer evolution.

Deciphering a global source of non-genetic heterogeneity in cancer cells

Cell-to-cell variability within a clonal population, also known as non-genetic heterogeneity, has created significant challenges for intervening with diseases such as cancer. While non-genetic heterogeneity can arise from the variability in the expression of specific genes, it remains largely unclear whether and how clonal cells could be heterogeneous in the expression of the entire transcriptome. Here, we showed that gene transcriptional activity is globally modulated in individual cancer cells, leading to non-genetic heterogeneity in the global transcription rate. Such heterogeneity contributes to cell-to-cell variability in transcriptome size and displays both dynamic and static characteristics, with the global transcription rate temporally modulated in a cell-cycle-coupled manner and the time-averaged rate being distinct between cells and heritable across generations. Additional evidence indicated the role of ATP metabolism in this heterogeneity, and suggested its implication in intrinsic cancer drug tolerance. Collectively, our work shed light on the mode, mechanism, and implication of a global but often hidden source of non-genetic heterogeneity.

MYB regulates the SUMO protease SENP1 and its novel interaction partner UXT, modulating MYB target genes and the SUMO landscape

SUMOylation is a post-translational modification frequently found on nuclear proteins, including transcription factors (TFs) and coactivators. By controlling the activity of several TFs, SUMOylation may have far-reaching effects. MYB is an example of a developmental TF subjected to SUMO-mediated regulation, through both SUMO conjugation and SUMO binding. How SUMO affects MYB target genes is unknown. Here, we explored the global effect of reduced SUMOylation of MYB on its downstream gene programs. RNA-Seq in K562 cells after MYB knockdown and rescue with mutants having an altered SUMO status revealed a number of differentially regulated genes and distinct gene ontology term enrichments. Clearly, the SUMO status of MYB both quantitatively and qualitatively affects its regulome. The transcriptome data further revealed that MYB upregulates the SUMO protease SENP1, a key enzyme that removes SUMO conjugation from SUMOylated proteins. Given this role of SENP1 in the MYB regulome, we expanded the analysis, mapped interaction partners of SENP1, and identified UXT as a novel player affecting the SUMO system by acting as a repressor of SENP1. MYB inhibits the expression of UXT suggesting that MYB is able not only to control a specific gene program directly but also indirectly by affecting the SUMO landscape through SENP1 and UXT. These findings suggest an autoactivation loop whereby MYB, through enhancing SENP1 and reducing UXT, is itself being activated by a reduced level of repressive SUMOylation. We propose that overexpressed MYB, seen in multiple cancers, may drive this autoactivation loop and contribute to oncogenic activation of MYB.

Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells

Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1-7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7-9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.

Single-cell profiling in multiple myeloma: insights, problems, and promises

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.

Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.

Epigenetic targets in B- and T-cell lymphomas: latest developments

Non-Hodgkin's lymphomas (NHLs) comprise a diverse group of diseases, either of mature B-cell or of T-cell derivation, characterized by heterogeneous molecular features and clinical manifestations. While most of the patients are responsive to standard chemotherapy, immunotherapy, radiation and/or stem cell transplantation, relapsed and/or refractory cases still have a dismal outcome. Deep sequencing analysis have pointed out that epigenetic dysregulations, including mutations in epigenetic enzymes, such as chromatin modifiers and DNA methyltransferases (DNMTs), are prevalent in both B- cell and T-cell lymphomas. Accordingly, over the past decade, a large number of epigenetic-modifying agents have been developed and introduced into the clinical management of these entities, and a few specific inhibitors have already been approved for clinical use. Here we summarize the main epigenetic alterations described in B- and T-NHL, that further supported the clinical development of a selected set of epidrugs in determined diseases, including inhibitors of DNMTs, histone deacetylases (HDACs), and extra-terminal domain proteins (bromodomain and extra-terminal motif; BETs). Finally, we highlight the most promising future directions of research in this area, explaining how bioinformatics approaches can help to identify new epigenetic targets in B- and T-cell lymphoid neoplasms.

Cell-to-cell variability in Myc dynamics drives transcriptional heterogeneity in cancer cells

Cell-to-cell heterogeneity is vital for tumor evolution and survival. How cancer cells achieve and exploit this heterogeneity remains an active area of research. Here, we identify c-Myc as a highly heterogeneously expressed transcription factor and an orchestrator of transcriptional and phenotypic diversity in cancer cells. By monitoring endogenous c-Myc protein in individual living cells, we report the surprising pulsatile nature of c-Myc expression and the extensive cell-to-cell variability in its dynamics. We further show that heterogeneity in c-Myc dynamics leads to variable target gene transcription and that timing of c-Myc expression predicts cell-cycle progression rates and drug sensitivities. Together, our data advocate for a model in which cancer cells increase the heterogeneity of functionally diverse transcription factors such as c-Myc to rapidly survey transcriptional landscapes and survive stress.

Technologies and applications of single-cell DNA methylation sequencing

DNA methylation is the most stable epigenetic modification. In mammals, it usually occurs at the cytosine of CpG dinucleotides. DNA methylation is essential for many physiological and pathological processes. Aberrant DNA methylation has been observed in human diseases, particularly cancer. Notably, conventional DNA methylation profiling technologies require a large amount of DNA, often from a heterogeneous cell population, and provide an average methylation level of many cells. It is often not realistic to collect sufficient numbers of cells, such as rare cells and circulating tumor cells in peripheral blood, for bulk sequencing assays. It is therefore essential to develop sequencing technologies that can accurately profile DNA methylation using small numbers of cells or even single cells. Excitingly, many single-cell DNA methylation sequencing and single-cell omics sequencing technologies have been developed, and applications of these methods have greatly expanded our understanding of the molecular mechanism of DNA methylation. Here, we summaries single-cell DNA methylation and multi-omics sequencing methods, delineate their applications in biomedical sciences, discuss technical challenges, and present our perspective on future research directions.

An all-in-one strategy for bisulfite-free DNA methylation detection by temperature-programmed enzymatic reactions

The fragmentation and low concentration of cell-free DNA (cfDNA) pose higher challenges for the cfDNA methylation detection technologies. Conventional bisulfite conversion-based methods are inadequate for cfDNA methylation analysis due to cumbersome operation and exacerbating cfDNA degradation. Herein, we proposed temperature-programmed enzymatic reactions for cfDNA methylation analysis in a single tube. Endonuclease was used to mildly recognize DNA methylation to avoid the degradation of cfDNA. And two stages of amplification reactions significantly improved the detection sensitivity for GC-rich sequence. With vimentin as the target, the detection sensitivity was 10 copies of methylated DNA. Meanwhile, the proposed method can accurately quantify the methylation level of target sequence from 1000-fold of unmethylated DNA background. Further, the methylated vimentin gene in 20 clinical plasma samples was successfully detected. The results shown significant differences in methylation levels of the vimentin gene between healthy volunteers and colorectal cancer patients. These results lead us to believe that the proposed method has great application potential for DNA methylation analysis as a complement to bisulfite conversion-based methods.

Tumor Evolution Models of Phase-Field Type with Nonlocal Effects and Angiogenesis

In this survey article, a variety of systems modeling tumor growth are discussed. In accordance with the hallmarks of cancer, the described models incorporate the primary characteristics of cancer evolution. Specifically, we focus on diffusive interface models and follow the phase-field approach that describes the tumor as a collection of cells. Such systems are based on a multiphase approach that employs constitutive laws and balance laws for individual constituents. In mathematical oncology, numerous biological phenomena are involved, including temporal and spatial nonlocal effects, complex nonlinearities, stochasticity, and mixed-dimensional couplings. Using the models, for instance, we can express angiogenesis and cell-to-matrix adhesion effects. Finally, we offer some methods for numerically approximating the models and show simulations of the tumor's evolution in response to various biological effects.

Epigenetics as a mediator of plasticity in cancer

The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.

Transiently heritable fates and quorum sensing drive early IFN-I response dynamics

Type I interferon (IFN-I)-mediated antiviral responses are central to host defense against viral infections. Crucial is the tight and well-orchestrated control of cellular decision-making leading to the production of IFN-Is. Innovative single-cell approaches revealed that the initiation of IFN-I production is limited to only fractions of 1-3% of the total population, both found in vitro, in vivo, and across cell types, which were thought to be stochastically regulated. To challenge this dogma, we addressed the influence of various stochastic and deterministic host-intrinsic factors on dictating early IFN-I responses, using a murine fibroblast reporter model. Epigenetic drugs influenced the percentage of responding cells. Next, with the classical Luria-Delbrück fluctuation test, we provided evidence for transient heritability driving responder fates, which was verified with mathematical modeling. Finally, while studying varying cell densities, we substantiated an important role for cell density in dictating responsiveness, similar to the phenomenon of quorum sensing. Together, this systems immunology approach opens up new avenues to progress the fundamental understanding on cellular decision-making during early IFN-I responses, which can be translated to other (immune) signaling systems.

Pan-cancer surveys indicate cell cycle-related roles of primate-specific genes in tumors and embryonic cerebrum

BACKGROUND

Despite having been extensively studied, it remains largely unclear why humans bear a particularly high risk of cancer. The antagonistic pleiotropy hypothesis predicts that primate-specific genes (PSGs) tend to promote tumorigenesis, while the molecular atavism hypothesis predicts that PSGs involved in tumors may represent recently derived duplicates of unicellular genes. However, these predictions have not been tested.

RESULTS

By taking advantage of pan-cancer genomic data, we find the upregulation of PSGs across 13 cancer types, which is facilitated by copy-number gain and promoter hypomethylation. Meta-analyses indicate that upregulated PSGs (uPSGs) tend to promote tumorigenesis and to play cell cycle-related roles. The cell cycle-related uPSGs predominantly represent derived duplicates of unicellular genes. We prioritize 15 uPSGs and perform an in-depth analysis of one unicellular gene-derived duplicate involved in the cell cycle, DDX11. Genome-wide screening data and knockdown experiments demonstrate that DDX11 is broadly essential across cancer cell lines. Importantly, non-neutral amino acid substitution patterns and increased expression indicate that DDX11 has been under positive selection. Finally, we find that cell cycle-related uPSGs are also preferentially upregulated in the highly proliferative embryonic cerebrum.

CONCLUSIONS

Consistent with the predictions of the atavism and antagonistic pleiotropy hypotheses, primate-specific genes, especially those PSGs derived from cell cycle-related genes that emerged in unicellular ancestors, contribute to the early proliferation of the human cerebrum at the cost of hitchhiking by similarly highly proliferative cancer cells.

DNA methyltransferases 3A and 3B target specific sequences during mouse gastrulation

In mammalian embryos, DNA methylation is initialized to maximum levels in the epiblast by the de novo DNA methyltransferases DNMT3A and DNMT3B before gastrulation diversifies it across regulatory regions. Here we show that DNMT3A and DNMT3B are differentially regulated during endoderm and mesoderm bifurcation and study the implications in vivo and in meso-endoderm embryoid bodies. Loss of both Dnmt3a and Dnmt3b impairs exit from the epiblast state. More subtly, independent loss of Dnmt3a or Dnmt3b leads to small biases in mesoderm-endoderm bifurcation and transcriptional deregulation. Epigenetically, DNMT3A and DNMT3B drive distinct methylation kinetics in the epiblast, as can be predicted from their strand-specific sequence preferences. The enzymes compensate for each other in the epiblast, but can later facilitate lineage-specific methylation kinetics as their expression diverges. Single-cell analysis shows that differential activity of DNMT3A and DNMT3B combines with replication-linked methylation turnover to increase epigenetic plasticity in gastrulation. Together, these findings outline a dynamic model for the use of DNMT3A and DNMT3B sequence specificity during gastrulation.

Phenotypic selection during laboratory evolution of yeast populations leads to a genome-wide sustainable chromatin compaction shift

In a previous study, we have shown how microbial evolution has resulted in a persistent reduction in expression after repeatedly selecting for the lowest PGAL1-YFP-expressing cells. Applying the ATAC-seq assay on samples collected from this 28-day evolution experiment, here we show how genome-wide chromatin compaction changes during evolution under selection pressure. We found that the chromatin compaction was altered not only on GAL network genes directly impacted by the selection pressure, showing an example of selection-induced non-genetic memory, but also at the whole-genome level. The GAL network genes experienced chromatin compaction accompanying the reduction in PGAL1-YFP reporter expression. Strikingly, the fraction of global genes with differentially compacted chromatin states accounted for about a quarter of the total genome. Moreover, some of the ATAC-seq peaks followed well-defined temporal dynamics. Comparing peak intensity changes on consecutive days, we found most of the differential compaction to occur between days 0 and 3 when the selection pressure was first applied, and between days 7 and 10 when the pressure was lifted. Among the gene sets enriched for the differential compaction events, some had increased chromatin availability once selection pressure was applied and decreased availability after the pressure was lifted (or vice versa). These results intriguingly show that, despite the lack of targeted selection, transcriptional availability of a large fraction of the genome changes in a very diverse manner during evolution, and these changes can occur in a relatively short number of generations.

Metacells untangle large and complex single-cell transcriptome networks

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization.

RESULTS

We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

CONCLUSIONS

SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Untangling the web of intratumour heterogeneity

Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.

The intrinsic and extrinsic effects of TET proteins during gastrulation

Mice deficient for all ten-eleven translocation (TET) genes exhibit early gastrulation lethality. However, separating cause and effect in such embryonic failure is challenging. To isolate cell-autonomous effects of TET loss, we used temporal single-cell atlases from embryos with partial or complete mutant contributions. Strikingly, when developing within a wild-type embryo, Tet-mutant cells retain near-complete differentiation potential, whereas embryos solely comprising mutant cells are defective in epiblast to ectoderm transition with degenerated mesoderm potential. We map de-repressions of early epiblast factors (e.g., Dppa4 and Gdf3) and failure to activate multiple signaling from nascent mesoderm (Lefty, FGF, and Notch) as likely cell-intrinsic drivers of TET loss phenotypes. We further suggest loss of enhancer demethylation as the underlying mechanism. Collectively, our work demonstrates an unbiased approach for defining intrinsic and extrinsic embryonic gene function based on temporal differentiation atlases and disentangles the intracellular effects of the demethylation machinery from its broader tissue-level ramifications.

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Chimeras with full or partial Tet deficiency are mapped over the course of gastrulation

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Tet-TKO cells disrupt signaling, leading to skewed whole-embryo mutant gastrulation

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Tet-TKO cells retain near-complete differentiation potential in a chimera context

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Loss of TET leads to pervasive hypermethylation and mildly perturbed gene expression

A Review of Mathematical and Computational Methods in Cancer Dynamics

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems medicine. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems, and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

Intrinsic transcriptional heterogeneity in neuroblastoma guides mechanistic and therapeutic insights

Cell state is controlled by master transcription factors (mTFs) that determine the cellular gene expression program. Cancer cells acquire dysregulated gene expression programs by mutational and non-mutational processes. Intratumoral heterogeneity can result from cells displaying distinct mTF-regulated cell states, which co-exist within the tumor. One archetypal tumor associated with transcriptionally regulated heterogeneity is high-risk neuroblastoma (NB). Patients with NB have poor overall survival despite intensive therapies, and relapsed patients are commonly refractory to treatment. The cellular populations that comprise NB are marked by different cohorts of mTFs and differential sensitivity to conventional therapies. Recent studies have highlighted mechanisms by which NB cells dynamically shift the cell state with treatment, revealing new opportunities to control the cellular response to treatment by manipulating cell-state-defining transcriptional programs. Here, we review recent advances in understanding transcriptionally defined cancer heterogeneity. We offer challenges to the field to encourage translation of basic science into clinical benefit.

The Genetic Evolution of Metastasis

Cancer is an evolutionary process that is characterized by the emergence of multiple genetically distinct populations or clones within the primary tumor. Intratumor heterogeneity provides a substrate for the selection of adaptive clones, such as those that lead to metastasis. Comparative molecular studies of primary tumors and metastases have identified distinct genomic features associated with the development of metastases. In this review, we discuss how these insights could inform clinical decision-making and uncover rational antimetastasis treatment strategies.

Development of Low-Grade Serous Ovarian Carcinoma from Benign Ovarian Serous Cystadenoma Cells

Simple Summary

Low-grade serous ovarian carcinoma (LGSOC) is thought to progress from benign cystadenoma in a stepwise fashion via serous borderline tumors (SBTs). This hypothesis is based on pathological and molecular evidence obtained following the genetic analysis of clinical samples from LGSOCs, SBTs, and cystadenomas. However, there have been no reports on the occurrence of LGSOCs following the introduction of oncogenes into benign serous cystadenoma cells. This study successfully developed an in vitro carcinogenic model of LGSOCs by introducing oncogenic KRAS and PIK3CA gene mutations in immortalized HOVs-cyst-1 cells from serous cystadenomas. The established mouse xenograft tumors resulting from the inoculation of HOVs-cyst-1 cells with KRAS and PIK3CA mutations exhibited the micropapillary invasive pattern of LGSOCs with low nuclear atypia without alveoli.

Abstract

Despite the knowledge about numerous genetic mutations essential for the progression of low-grade serous ovarian carcinoma (LGSOC), the specific combination of mutations required remains unclear. Here, we aimed to recognize the oncogenic mutations responsible for the stepwise development of LGSOC using immortalized HOVs-cyst-1 cells, developed from ovarian serous cystadenoma cells, and immortalized via cyclin D1, CDK4^R24C, and hTERT gene transfection. Furthermore, oncogenic mutations, KRAS and PIK3CA, were individually and simultaneously introduced in immortalized HOV-cyst-1 cells. Cell functions were subsequently analyzed via in vitro assays. KRAS or PIK3CA double mutant HOV-cyst-1 cells exhibited higher cell proliferation and migration capacity than the wild-type cells, or those with either a KRAS or a PIK3CA mutation, indicating that these mutations play a causative role in LGSOC tumorigenesis. Moreover, KRAS and PIK3CA double mutants gained tumorigenic potential in nude mice, whereas the cells with a single mutant exhibited no signs of tumorigenicity. Furthermore, the transformation of HOV-cyst-1 cells with KRAS and PIK3CA mutants resulted in the development of tumors that were grossly and histologically similar to human LGSOCs. These findings suggest that simultaneous activation of the KRAS/ERK and PIK3CA/AKT signaling pathways is essential for LGSOC development.

Towards targeting of shared mechanisms of cancer metastasis and therapy resistance

Resistance to therapeutic treatment and metastatic progression jointly determine a fatal outcome of cancer. Cancer metastasis and therapeutic resistance are traditionally studied as separate fields using non-overlapping strategies. However, emerging evidence, including from in vivo imaging and in vitro organotypic culture, now suggests that both programmes cooperate and reinforce each other in the invasion niche and persist upon metastatic evasion. As a consequence, cancer cell subpopulations exhibiting metastatic invasion undergo multistep reprogramming that - beyond migration signalling - supports repair programmes, anti-apoptosis processes, metabolic adaptation, stemness and survival. Shared metastasis and therapy resistance signalling are mediated by multiple mechanisms, such as engagement of integrins and other context receptors, cell-cell communication, stress responses and metabolic reprogramming, which cooperate with effects elicited by autocrine and paracrine chemokine and growth factor cues present in the activated tumour microenvironment. These signals empower metastatic cells to cope with therapeutic assault and survive. Identifying nodes shared in metastasis and therapy resistance signalling networks should offer new opportunities to improve anticancer therapy beyond current strategies, to eliminate both nodular lesions and cells in metastatic transit.

Combined inhibition of G9a and EZH2 suppresses tumor growth via synergistic induction of IL24-mediated apoptosis

G9a and EZH2 are two histone methyltransferases commonly upregulated in several cancer types, yet the precise roles that these enzymes play cooperatively in cancer is unclear. We demonstrate here that frequent concurrent upregulation of both G9a and EZH2 occurs in several human tumors. These methyltransferases cooperatively repressed molecular pathways responsible for tumor cell death. In genetically distinct tumor subtypes, concomitant inhibition of G9a and EZH2 potently induced tumor cell death, highlighting the existence of tumor cell survival dependency at the epigenetic level. G9a and EZH2 synergistically repressed expression of genes involved in the induction of endoplasmic reticulum (ER) stress and the production of reactive oxygen species. IL24 was essential for the induction of tumor cell death and was identified as a common target of G9a and EZH2. Loss-of-function of G9a and EZH2 activated the IL24-ER stress axis and increased apoptosis in cancer cells while not affecting normal cells. These results indicate that G9a and EZH2 promotes the evasion of ER stress-mediated apoptosis by repressing IL24 transcription, therefore suggesting that their inhibition may represent a potential therapeutic strategy for solid cancers.

piRNA-14633 promotes cervical cancer cell malignancy in a METTL14-dependent m6A RNA methylation manner

Background

Cervical cancer (CC) is one of the most common gynecological tumors that threatens women's health and lives. Aberrant expression of PIWI-interacting RNA (piRNA) is closely related with a range of cancers and can serve as a tumor promoter or suppressor in proliferation, migration and invasion. In this study, the aim was not only to discover differential expression of piRNA in CC tissue (CC cells) and normal cervical tissue (normal cervical epithelium cells), but also to investigate the biological function and action mechanism of piRNA in CC.

Methods

The DESeq2 approach was used to estimate fold change in piRNA between CC tissue and normal cervical tissue. The relative expressions of piRNAs (piRNA-20657, piRNA-20497, piRNA-14633 and piRNA-13350) and RNA m6A methyltransferases/demethylases were detected using RT-qPCR. After intervention with piRNA-14633 and METTL14 expression, the viability of CaSki cells and SiHa cells was detected by CCK8. CC cell proliferation was detected by colony formation assay. Apoptosis rate and cell cycle were detected by flow cytometry. Transwell assay was performed to detect cell migration and invasion. EpiQuik m6A RNA Methylation Quantification Kit was used to evaluate m6A RNA methylation levels. Expression of methyltransferase-like protein 14 (METTL14), PIWIL-proteins and CYP1B1 were detected by RT-qPCR and western blot. The effect of piRNA-14633 on METTL14 was evaluated by a dual-luciferase reporter assay. The in vivo effects of piRNA-14633 on CC was assessed by nude mice experiments.

Results

piRNA-14633 showed high expression in CC tissues and cells, piRNA-14633 mimic (piRNA-14633 overexpression) promoted viability, proliferation, migration and invasion of CaSki cells and SiHa cells. Besides, piRNA-14633 mimic increased m6A RNA methylation levels and METTL14 mRNA stability. Results of dual luciferase reporter assays indicated that METTL14 was a directed target gene of piRNA-14633. Knockdown of METTL14 with siRNA attenuated proliferation, migration and invasion of CC cells. piRNA-14633 increased CYP1B1 expression, while silencing of METTL14 impaired its expression. The effect of piRNA overexpression on METTL14 expression has concentration-dependent characteristics. Results from in vivo experiment indicated that piRNA-14633 promoted cervical tumor growth.

Conclusion

piRNA-14633 promotes proliferation, migration and invasion of CC cells by METTL14/CYP1B1 signaling axis, highlighting the important role of piRNA-14633 in CC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03257-2.

Enhancer-promoter interactions and transcription are largely maintained upon acute loss of CTCF, cohesin, WAPL or YY1

It remains unclear why acute depletion of CTCF (CCCTC-binding factor) and cohesin only marginally affects expression of most genes despite substantially perturbing three-dimensional (3D) genome folding at the level of domains and structural loops. To address this conundrum, we used high-resolution Micro-C and nascent transcript profiling in mouse embryonic stem cells. We find that enhancer-promoter (E-P) interactions are largely insensitive to acute (3-h) depletion of CTCF, cohesin or WAPL. YY1 has been proposed as a structural regulator of E-P loops, but acute YY1 depletion also had minimal effects on E-P loops, transcription and 3D genome folding. Strikingly, live-cell, single-molecule imaging revealed that cohesin depletion reduced transcription factor (TF) binding to chromatin. Thus, although CTCF, cohesin, WAPL or YY1 is not required for the short-term maintenance of most E-P interactions and gene expression, our results suggest that cohesin may facilitate TFs to search for and bind their targets more efficiently.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer

Background

In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design.

Objective

In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches in light of their application to therapeutic response modeling in cancer.

Conclusion

We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

The Origins of Phenotypic Heterogeneity in Cancer

Heterogeneity is a pervasive feature of cancer, and understanding the sources and regulatory mechanisms underlying heterogeneity could provide key insights to help improve the diagnosis and treatment of cancer. In this review, we discuss the origin of heterogeneity in the phenotype of individual cancer cells. Genotype-phenotype (G-P) maps are widely used in evolutionary biology to represent the complex interactions of genes and the environment that lead to phenotypes that impact fitness. Here, we present the rationale of an extended G-P (eG-P) map with a cone structure in cancer. The eG-P cone is formed by cells that are similar at the genome layer but gradually increase variability in the epigenome, transcriptome, proteome, metabolome and signalome layers to produce large variability at the phenome layer. Experimental evidence from single-cell -omics analyses supporting the cancer eG-P cone concept is presented, and the impact of epimutations and the interaction of cancer and tumor microenvironmental eG-P cones are integrated with the current understanding of cancer biology. The eG-P cone concept uncovers potential therapeutic strategies to reduce cancer evolution and improve cancer treatment. More methods to study phenotypes in single cells will be key to better understand cancer cell fitness in tumor biology and therapeutics.

An Epigenetic Perspective on Intra-Tumour Heterogeneity: Novel Insights and New Challenges from Multiple Fields

Simple Summary

Although research on cancer biology in recent decades has unveiled the main genetic perturbations driving the onset of tumorigenesis, we are still far from properly treating this disease without the occurrence of drug resistance and metastatic burden. This achievement is hampered by the onset of intra-tumour heterogeneity (ITH), which increases cancer cell fitness and plasticity, thereby fostering cell adaptation to foreign environments and stimuli. In this review, we discuss the contribution of the epigenetic factors in sustaining ITH and their interplay with the tumour microenvironment. We also highlight the recent technological advancements that are contributing to defining the epigenetic mechanisms governing tumour heterogeneity at the single-cell level.

Abstract

Cancer is a group of heterogeneous diseases that results from the occurrence of genetic alterations combined with epigenetic changes and environmental stimuli that increase cancer cell plasticity. Indeed, multiple cancer cell populations coexist within the same tumour, favouring cancer progression and metastatic dissemination as well as drug resistance, thereby representing a major obstacle for treatment. Epigenetic changes contribute to the onset of intra-tumour heterogeneity (ITH) as they facilitate cell adaptation to perturbation of the tumour microenvironment. Despite being its central role, the intrinsic multi-layered and reversible epigenetic pattern limits the possibility to uniquely determine its contribution to ITH. In this review, we first describe the major epigenetic mechanisms involved in tumourigenesis and then discuss how single-cell-based approaches contribute to dissecting the key role of epigenetic changes in tumour heterogeneity. Furthermore, we highlight the importance of dissecting the interplay between genetics, epigenetics, and tumour microenvironments to decipher the molecular mechanisms governing tumour progression and drug resistance.

Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states

Single-cell RNA sequencing has revealed extensive transcriptional cell state diversity in cancer, often observed independently of genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, here we performed multiomics single-cell profiling-integrating DNA methylation, transcriptome and genotype within the same cells-of diffuse gliomas, tumors characterized by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to directly measure cell state heritability and transition dynamics based on high-resolution lineage trees in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal and plastic cell state architectures in IDH-mutant glioma versus IDH-wild-type glioblastoma, respectively. This work provides a framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.

Cancer cell states and emergent properties of the dynamic tumor system

Phenotypic heterogeneity within malignant cells of a tumor is emerging as a key property of tumorigenesis. Recent work using single-cell transcriptomics has led to the identification of distinct cancer cell states across a range of cancer types, but their functional relevance and the advantage that they provide to the tumor as a system remain elusive. We present here a definition of cancer cell states in terms of coherently and differentially expressed gene modules and review the origins, dynamics, and impact of states on the tumor system as a whole. The spectrum of cell states taken on by a malignant population may depend on cellular lineage, epigenetic history, genetic mutations, or environmental cues, which has implications for the relative stability or plasticity of individual states. Finally, evidence has emerged that malignant cells in different states may cooperate or compete within a tumor niche, thereby providing an evolutionary advantage to the tumor through increased immune evasion, drug resistance, or invasiveness. Uncovering the mechanisms that govern the origin and dynamics of cancer cell states in tumorigenesis may shed light on how heterogeneity contributes to tumor fitness and highlight vulnerabilities that can be exploited for therapy.

The role of dynamic phenotypes in cancer

The question of whether cancer recurrence is mediated by a process that is exclusively Darwinian or that involves both Darwinian and Lamarckian processes is long standing and far from answered. The major open question is the origin of variation, whether it relays exclusively on stable, mostly genetic, mechanisms or whether it can also involve dynamic processes. Recent evidence with single-cell epigenomic and transcriptomic profiling and measurement of phenotypes in colonies indicate that several phenotypes quickly change with a few cell divisions. Most importantly, cell fitness under basal as well as in the presence of chemotherapeutic agents changes considerably over short periods of time and this dynamic is reduced by epigenetic modulators. These studies contribute to establish the dynamic nature of fitness and are key for the interplay between cancer cell dynamics and stable genetic and epigenetic alterations in the survival of a few cancer cells after therapy.

DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation

DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis-specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors.

IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA

Effective and powerful survival mediation models are currently lacking. To partly fill such knowledge gap, we particularly focus on the mediation analysis that includes multiple DNA methylations acting as exposures, one gene expression as the mediator and one survival time as the outcome. We proposed IUSMMT (intersection-union survival mixture-adjusted mediation test) to effectively examine the existence of mediation effect by fitting an empirical three-component mixture null distribution. With extensive simulation studies, we demonstrated the advantage of IUSMMT over existing methods. We applied IUSMMT to ten TCGA cancers and identified multiple genes that exhibited mediating effects. We further revealed that most of the identified regions, in which genes behaved as active mediators, were cancer type-specific and exhibited a full mediation from DNA methylation CpG sites to the survival risk of various types of cancers. Overall, IUSMMT represents an effective and powerful alternative for survival mediation analysis; our results also provide new insights into the functional role of DNA methylation and gene expression in cancer progression/prognosis and demonstrate potential therapeutic targets for future clinical practice.

Intratumor CMS Heterogeneity Impacts Patient Prognosis in Localized Colon Cancer

PURPOSE

The consensus molecular subtypes (CMS) represent a significant advance in the understanding of intertumor heterogeneity in colon cancer. Intratumor heterogeneity (ITH) is the new frontier for refining prognostication and understanding treatment resistance. This study aims at deciphering the transcriptomic ITH of colon cancer and understanding its potential prognostic implications.

EXPERIMENTAL DESIGN

We deconvoluted the transcriptomic profiles of 1,779 tumors from the PETACC8 trial and 155 colon cancer cell lines as weighted sums of the four CMSs, using the Weighted In Silico Pathology (WISP) algorithm. We assigned to each tumor and cell line a combination of up to three CMS subtypes with a threshold above 20%.

RESULTS

Over 55% of tumors corresponded to mixtures of at least two CMSs, demonstrating pervasive ITH in colon cancer. Of note, ITH was associated with shorter disease-free survival (DFS) and overall survival, [HR, 1.34; 95% confidence interval (CI; 1.12-1.59), 1.40, 95% CI (1.14-1.71), respectively]. Moreover, we uncovered specific combinations of CMS associated with dismal prognosis. In multivariate analysis, ITH represents the third parameter explaining DFS variance, after T and N stages. At a cellular level, combined WISP and single-cell transcriptomic analysis revealed that most colon cancer cell lines are a mixture of cells falling into different CMSs, indicating that ITH may correspond to distinct functional statuses of colon cancer cells.

CONCLUSIONS

This study shows that CMS-based transcriptomic ITH is frequent in colon cancer and impacts its prognosis. CMS-based transcriptomic ITH may correspond to distinct functional statuses of colon cancer cells, suggesting plasticity between CMS-related cell populations. Transcriptomic ITH deserves further assessment in the context of personalized medicine.