At the time a patient is first diagnosed with cancer, the tumor may be composed of tens of millions of cells. These cell populations have already diversified, producing a tumor that can be highly heterogeneous. Such intratumoral heterogeneity (ITH) has been observed among spatially distinct regions of solid tumors and among individual cells in solid tumors or leukemias (). Profiling ITH provides a powerful opportunity to trace back through the formation of the malignancy and reconstruct the tumor's evolution, from the tumor-initiating events through subsequent stepwise development of malignant clones (). The models of tumor evolution, or tumor phylogenies, derived from ITH have improved our understanding of tumorigenesis. Despite the increased understanding, a majority of cancer therapies fail to achieve durable responses, which is often attributed to ITH. Importantly, most clinical trials still do not assess ITH and miss an opportunity to examine the prognostic value of ITH in a controlled setting.

ITH at diagnosis may be altered by selective pressures of cytotoxic or targeted cancer therapies, promoting outgrowth of one or more therapy-resistant tumor cell clones (). Therapeutic interventions could lead to contraction of ITH in some cases or expansion in others, influencing subsequent response and outcome. In most ITH studies, however, only a small fraction of the tumor is available for analysis. Furthermore, tumor samples typically lack information on from where within the heterogeneous tumor they were obtained. Using image-guided biopsies instead of random samples, molecular ITH could be compared with ITH charted by advanced imaging of tumors in patients ().

ITH and tumor evolution have historically been assessed with genetic alterations such as somatic mutation and copy-number alteration (CNA). However, an increasing number of studies have shown that in cell lines with a high degree of genetic homogeneity, epigenetic heterogeneity leads to cell-to-cell variability in response to therapy (). Epigenetic mechanisms that may contribute to ITH include DNA methylation, post-translational modifications of histones, and chromatin remodeling, which are essential for genome organization, gene expression, and cell function ().

Studies that examined the evolution of DNA methylation from initial tumors to recurrence found that overall DNA methylation levels can be lower at recurrence (), higher at recurrence (), or exhibit patterns that do not change across time (). These seemingly conflicting findings may be related to differences in the tumor type, the choice of the CpG sites analyzed, or selective pressures of different therapies. Changes in methylation in samples obtained at different times in the course of a patient's disease could reflect clonal evolution or mITH. Here we focus specifically on studies that have explicitly examined mITH, either through multiple intratumoral samplings or quantification of mITH from a single sample per tumor.

eITH can be examined at the level of histone modifications, chromatin conformation, or DNA methylation. To date, DNA methylation has been the main focus due to the quantitative nature of DNA methylation assays and the relative ease of obtaining sufficient genomic DNA compared with chromatin. The following sections thus focus on key issues surrounding ITH of DNA methylation (mITH).

Recurrent epigenetic alterations are well characterized in many tumor types, but only recently has eITH been profiled on a genome-wide scale. Intratumoral studies of the epigenome have unique advantages because they eliminate major variables that confound other study designs. For example, in contrast to interindividual comparisons, intratumoral studies control for variables such as age, gender, germline differences, and accumulated effects of dietary and environmental exposures, each of which can alter the epigenome. Moreover, interpretations of epigenetic differences that were identified by comparing tumor with normal tissues have been repeatedly questioned because the cell of origin of the tumor, representing the ideal normal tissue control, is often not known or hotly debated. Even when the cell of origin is known, the precise stage of differentiation at which a cell is first transformed affects the tumor methylation state (). Thus, investigation of eITH provides unique opportunities to identify epigenetic alterations associated with tumor evolution.

In contrast to genetic alterations, epigenetic modifications are enzymatically reversible and their maintenance may have lower fidelity through DNA replication and mitosis. It had therefore been unclear whether epigenetic ITH (eITH) would be useful to infer tumor evolution. In normal adult cells, at least, epigenomic patterns in gene-regulatory regions contain information related to their embryonic developmental history (). Early ancestral information may therefore be encoded in epigenome patterns of subsequent cell generations.

Several of these epigenetic defects may be linked to genetic mutations. Genes encoding regulators of the epigenome, including the writers, readers, and erasers of epigenetic marks, are among the most commonly mutated genes observed across cancer types (). Thus, epigenetic alterations may be a common mechanism linking genetic mutations to cancer phenotypes, although the details on how they are linked are just beginning to be explored. Indeed, recent work suggests that reprogramming of the epigenome to a progenitor-like state may be required for driver mutations to induce tumorigenesis (). This work highlights the importance of studying premalignant cells and model systems to better understand when epigenomic changes arise and how stable they are over time. Naturally, clinical trials using new “epigenetic therapies” have been initiated to target the genetically mutant epigenetic regulators and their associated proteins ().

Alteration to the epigenome is a fundamental characteristic of nearly all human cancers. Pioneering studies focused on DNA methylation and identified decreased 5-methylcytosine content in tumors compared with normal tissue (), further loss of 5-methylcytosine during tumor progression (), and increased methylation in normally unmethylated CpG islands and promoter regions of a wide variety of genes including tumor suppressors (), metastasis genes (), and DNA repair genes (). The changes were hypothesized to affect gene expression and chromosomal stability. Indeed, induction of genome-wide hypomethylation via reduction in DNA methyltransferase levels was associated with chromosomal abnormalities and was sufficient to induce aggressive T cell lymphoma in mice (), suggesting a potentially causal role.

took a different approach to mITH in colorectal cancer by analyzing DNA methylation patterns at two genomic loci that were assumed to have no role in gene regulation. In contrast to driver methylation changes (), methylation at such neutral loci were unlikely to be under selective pressure, and therefore could serve as a “molecular clock” to measure mitotic divisions based on the higher error rate of DNA methylation maintenance relative to the error rate of DNA polymerase. In this initial studyprofiled eight alleles/cells per sample and 10–14 samples per patient, while follow-up studies used an average of 1,500 cells per sample in colorectal cancer () and glioblastoma (). This molecular clock analysis revealed highly heterogeneous tumors, suggesting that the tumors had not undergone any recent clonal expansion or selective sweep (). This conclusion, based on neutral sites of DNA methylation, similar to the common practice of including silent somatic mutations in genomic evolutionary analyses, suggests an increase in power to differentiate evolutionary branch points by including non-regulatory domains in epigenomic analyses.

Quantitative methods for measuring mITH highlighted a high frequency of mITH across cancer types (). However, it remained difficult to compare the relative degree of mITH due to the lack of standardized methods and thresholds for calling mITH. For example, bothandexamined mITH in breast cancer using valid measures, but methodological differences complicated comparison. Moreover, the methods implemented in these studies were unable to distinguish mITH from normal cell contamination.

Initial studies of mITH analyzed the promoters of individual tumor-suppressor genes and other cancer-related genes. Early studies used methylation-sensitive restriction enzyme digestion followed by southern blotting, which was sufficient to identify samples with both methylated and unmethylated signal, but not to determine the extent of mITH across the genome (). Many subsequent studies identified mITH using either the methylation-specific PCR method (MSP), a non-quantitative method with a binarized output (), or a related quantitative assay (). A common practice has been to classify a gene as methylated when both methylated and unmethylated alleles are detected. In many of these “methylated” cases, however, the evidence was also consistent with mITH. Indeed, while many studies examined multiple samples per tumor to identify mITH, few interpreted mixed methylated and unmethylated signal from a single sample as evidence of mITH. Mixed methylated and unmethylated signal was more often attributed to normal cells within the bulk tumor sample.

While multiple samplings from a tumor is a powerful approach to profiling spatial heterogeneity and inferring tumor populations based on their spatial distributions, new analytical methods () have made it possible to infer clonal and subclonal structures from exome or whole-genome sequencing of a single sample (). Moreover, by profiling a patient's tumor over time, it is possible to monitor clonal dynamics and infer the evolutionary history of the malignancy ().

Clonal evolutionary theory () provides a basis to infer the order in which molecular alterations were acquired. Inference of the “evolutionary history” of a tumor from somatic mutations relies on the pattern of shared mutations across multiple samples of a tumor: mutations present in all samples of a tumor are inferred to be acquired by early precursor cells which clonally expanded (clonal mutations); in contrast, mutations present in only a subset of samples are inferred to be later events, acquired at some point during or after the initial clonal expansion (subclonal mutations). A seminal publication () integrated the next-generation sequencing of tumors with principles from the clonal evolution theory of tumors.performed exome sequencing of 14 and 10 spatially distinct biopsies from two individuals with metastatic renal cell carcinoma. Taking advantage of the genetic ITH (gITH) delineated by the multiple samplings per tumor and analyzing the patterns of shared and unique mutations, early and late events were distinguished. Together the events revealed a branched evolutionary history, with several instances of convergent evolution in which the same gene was mutated independently in multiple subclones within a single tumor. For each patient, these findings were presented with a phylogenetic tree, a graphical representation of the evolutionary history of a patient's tumor, as inferred from somatic mutations ( Figure 1 A ). This approach to tracing tumor evolutionary history has since been applied across a wide range of malignancies ().

(B) Phylogenies have traditionally been built from genetic alterations, including somatic mutations and copy-number alterations. Phyloepigenetic trees have been built from genome-wide DNA methylation data and could be similarly derived from other epigenetic marks, including histone modification patterns, open chromatin, or RNA expression levels. Representative patterns of somatic mutations, copy-number alterations, and DNA methylation (black, methylated CpG site; yellow, unmethylated CpG site) are shown. To date, tumor histories derived from genetic alterations and DNA methylation show similar evolutionary patterns, raising the question of the extent to which these alterations are functionally related. Further work is still required to determine whether phylogenies derived from histone modifications and RNA expression also reflect similar evolutionary histories.

(A) Analysis of spatially distinct biopsies can be used to build a phylogeny that represents the evolutionary history of a tumor. Phylogeny tree branches are colored according to the contributions of each cell population: the black branch contains alterations that are shared among all biopsies, the red/blue branch contains alterations shared between the red and blue biopsies, and the red, blue, and purple branches represent those alterations uniquely present in a single biopsy.

Despite the success of previous studies identifying the relationship between a mutation and specific methylation changes, these statistical analyses required large cohorts of cases with and without each mutation to overcome the inherent interindividual variability of DNA methylation arising from germline epigenetic differences, age, gender, and other covariates. One approach to identify genetic-epigenetic associations in a smaller cohort is to use ITH of mutations and DNA methylation. For example, chromatin-modifier genes, including SMARCA4 and BAP1, are often mutated as late events in tumorigenesis and therefore are present heterogeneously within a tumor (). By profiling mITH and contrasting the samples with and without mutation in a particular gene and then extending the analysis across a cohort of patients with similarly heterogeneous mutations in the same gene, substantial interpatient heterogeneity can be excluded to then identify DNA methylation changes that result from the specific mutation.

Aberrant epigenetic states may promote genetic instability or may arise from specific genetic alterations (). As an example, by using single samples per patient from a large cohort of patients, comparison of samples with and without particular somatic mutations have identified associations between mutations and DNA methylation patterns. These associations reflect both mutations that drove altered DNA methylation, as with IDH1 mutation in glioma (), and altered DNA methylation landscapes that promoted the acquisition of particular mutations, as with BRAF mutation in colorectal cancer ().

Together, these studies support the concept of co-dependency of aberrant DNA methylation and genetic alterations, including either CNAs or somatic point mutation. Moreover, evolutionary histories could potentially be inferred from a range of additional data types, allowing future research to address the question of the extent to which other types of epigenetic marks evolve along similar evolutionary paths. Other datasets that could be used to construct phylogenies and infer a tumor's evolutionary history include, but are not limited to, histone modifications or gene expression ( Figure 1 B). Additional research is still needed to determine the extent to which gITH or eITH reflects differences in regional or clone-specific driver or passenger events, and to what extent the alterations to genetics and epigenetics may be functionally related. It is also not yet known whether the patterns in prostate and glioma data will be widely generalizable to other cancer types.

Independent genetic and epigenetic approaches were also applied to brain tumors (). Genome-wide patterns of DNA methylation across six individuals, including multiple samplings of paired initial and recurrent gliomas, were compared with somatic mutations derived from exome sequencing. Construction of phylogenies independently from the DNA methylation (phyloepigenetic tree) and somatic mutations (phylogenetic tree) yielded highly concordant as well as complementary evolutionary histories.

andused arrays to simultaneously profile genome-wide DNA methylation and CNA in prostate cancer.examined multiple samples from the primary tumor site as well as premalignant lesions and metastases from five individuals, whileexamined metastases from 13 subjects. These studies revealed that while prostate-relevant enhancers frequently demonstrated mITH (), the sites of promoter mITH and the expression of target genes did not correlate well (). This may indicate that DNA methylation changes that alter gene expression are more likely to be selected for and become relatively homogenously present across the tumor (). In both studies, parallel analysis of the genome-wide DNA methylation and CNA produced highly similar tumor evolutionary histories.

Recent work in solid tumors has extended genome-wide profiling of multiple intratumoral samples from genomics to epigenomics. Given that DNA methylation is reversible and more error prone than DNA replication, the evolutionary history of a tumor might appear different when inferred from genetic versus epigenetic data from the same intratumoral samples. ITH analysis in prostate cancer () and glioma () have shown, somewhat surprisingly, that the inferred histories from DNA methylation are highly similar to those from CNA or somatic mutation profiles ( Figure 1 B).

further correlated mITH at promoters with gene-expression data from single-cell RNA sequencing. They noted that promoters with high mITH showed high cell-to-cell expression heterogeneity of the corresponding gene. This discrepancy with the finding byin prostate cancer may reflect a functional distinction between solid and liquid malignancies, differences in genomic regions profiled, or the power of single-cell RNA sequencing to identify signals that are obscured in bulk sampling.

In contrast to the spatial heterogeneity of solid tumors, samples from distinct locations (peripheral blood and lymph node) in blood cell malignancies share similar epigenomic patterns (), suggesting that a single sample represents the full diversity of tumor cell populations. Using an array-based approach,calculated the mITH levels of 68 chronic lymphocytic leukemia (CLL) and found that overall CLL had low mITH relative to several solid tumors. As with the analysis of multiple samplings,noted that the level of mITH within an individual's leukemia was positively correlated with the level of gITH.used reduced representation bisulfite sequencing (RRBS), also in CLL, and found a similar correlation between high numbers of subclonal mutations and high mITH. They also discovered a different kind of correlation between genetic and epigenetic heterogeneity. Looking at the genomic locations exhibiting mITH, they noted that CpG sites with high mITH were found in regions of high genetic variation, such as regions of late DNA replication and gene-poor regions. Interestingly,also investigated genomic locations with high mITH and found lymphomas to have high mITH in gene-rich regions, suggesting that the association of genomic features and levels of mITH might be variable between different types of malignancies.

mITH can be further tracked for evolution over time. Intriguingly, mITH can increase or decrease at relapse.classified 14 cases of CLL into those that did or did not undergo genetic evolution and found that those patients with genetic evolution showed higher mITH at relapse. Looking in diffuse large B cell lymphoma (DLBCL),also calculated mITH from RRBS data in 11 paired diagnostic and relapse samples, but found that the level of mITH decreased at relapse in all but one case. They interpreted this result as reflecting a series of clonal outgrowths from more diverse cell populations, as diagnostic samples showed lower mITH than the normal B cell population from which they arose, and a further decrease in mITH was apparent at relapse.

A novel analytical approach combining genetic and epigenetic data is required to better understand tumor heterogeneity between tumor subclones and build a comprehensive evolutionary history of cancer progression. Although several studies have found substantial agreement between tumor evolution traced from DNA methylation compared with somatic mutations or CNAs, it is not yet possible to create a theoretical mathematical model to understand how much co-dependency exists between the genetics and epigenetics, as the rate, timing, and location of exact DNA methylation changes is not well known.

While important steps have been taken to understand heterogeneity from both multi-sample and single-sample data, additional work is still necessary to compute an overall measure of heterogeneity for a spatially distributed tumor. Thus an essential next step is to develop methods for inferring tumor-wide heterogeneity across multiple spatially distinct samples from the same individual.demonstrated the effectiveness of one approach for determining heterogeneity across multiple samples. The authors built a gene co-expression network from 96 serial samplings of normal brain tissue. They then identified modules of genes with similar expression profiles across the 96 samples. Finally, using the number of separate modules that the genes were separated into, the authors estimated the number of subtypes present within this tissue. This method has been applied to gene expression, but the underlying technique could be applied to data types including DNA methylation and other epigenetic marks. While the utility of this approach has been demonstrated for normal tissue, further work is required to extend this method to cancer. It is also important to note that even though accurately estimating a large correlation matrix from a small sample size is mathematically difficult, this analysis led to new insights that were independently validated.

In contrast, reference-based approaches can be applied to deconvoluting normal contaminating cells from the tumor population, especially for those tumor samples for which tumor purity is low. Such an approach has already been developed for expression datasets (). Methylation data has been used to identify component populations of normal tissue (), suggesting that a reference-based approach may be successful in identifying contaminating normal cells within DNA methylation datasets from tumors. This approach is especially useful for identifying the amount of signal originating from non-tumor cells with well-characterized epigenetic profiles.

An alternative reference-free approach applicable to array data uses the distribution of methylation proportions across all probes to impute the amount of heterogeneity (). This method takes into account the enrichment of probes centered at 0 and 1 in normal tissue and calculates the deviation from that enrichment in tumor samples. Within a single cell, or a homogeneous population of cells, each CpG site can only exist in three states: methylated, unmethylated, or heterozygously methylated. Therefore any intermediate methylation in a tumor, excluding allele-specific methylation, may be considered mITH.

In a reference-free approach applicable to sequencing data,used the methylation status of CpG sites across a single read from RRBS to calculate the epipolymorphism, a measure of mITH. For any four CpG sites that are close enough to be covered by a single sequencing read, the epipolymorphism is defined as the probability of any two reads having a different pattern of methylation across those sites, given the overall methylation level. While heavily methylated or unmethylated loci always have a low level of epipolymorphism, because most alleles are identical, intermediately methylated loci can have low (e.g., an imprinted locus) or high values, indicating the heterogeneity of methylation patterns ().

Many computational methods have been developed to quantify tissue heterogeneity or remove the influence of mixed cell populations when conducting hypothesis testing in both microarray and high-throughput sequencing data.andprovide a comprehensive review and simulation results on this topic. In brief, methods to infer ITH can be generally divided into two categories: reference-based approaches that separate an observed heterogeneous sample into proportions of previously profiled cell types and any remaining signal of unknown origin; and reference-free methods that attempt to identify unmeasured confounding variables, such as ITH, with an unsupervised approach.

A major benefit of understanding the phylogeny of a tumor is better knowledge of the genetic and epigenetic signatures underlying subclones of a tumor. In evolutionary analysis, simple set-difference calculation can often be used to identify the specific mutations responsible for tree branch points (). However, identifying the CpG sites responsible for a bifurcation in a phyloepigenetic tree is not as straightforward.calculated the singular vectors along the samples and determined the weighting of CpG sites when projected onto those directions. A heuristic method was then applied to determine how many CpG sites were most important for forming that bifurcation. Additional work is still required to identify the most appropriate computational techniques for uncovering the epigenetic signature underlying branch points in a phyloepigenetic tree.

The availability of multiple samples from individual tumors leads to a natural application of clustering to infer the evolutionary history of tumors. While mutation information is often simplified to a binary readout, DNA methylation data, which measures the fraction of methylated alleles at a given CpG site, is typically represented on a continuous scale from 0 to 1, and often requires heuristic choices in the data analysis that could alter the biological interpretation of the results. Moreover, the few published phyloepigenetic trees from DNA methylation data are built by a distance-based clustering method. In these instances, the choice of distance metric not only dictates the topology and branch lengths but also determines the relative importance of each CpG site. In contrast, building a distance matrix from binary mutation data weighs clonal and subclonal mutations equally. Thus it is important to understand the biological implications of each distance metric and data type.

The biological conclusions in these studies are strongly influenced by the computational methods used in the analysis. Thus, it is necessary to understand not just the differences between the analytical methods but also the advantages and drawbacks in each experimental design.

These models also highlight one of the problems with profiling bulk tumor samples. Measurements from bulk samples are a population average, which will underestimate heterogeneity by masking rare subpopulations even though rare populations can have a significant impact on treatment response. For full understanding of eITH and the profiling and study of common and rare populations, emerging technologies for single-cell sequencing will be useful.

These persister cells may model observations that some patients responded initially to therapy, developed resistance, and then responded again to the same chemotherapy after a drug holiday (). Thus, one hypothesis is that eITH at the single-cell level may play a role in therapy responses in patients, and concurrent treatment with epigenetic therapies may improve drug responses (). This also raises the question of how eITH is altered in tumors as a result of therapy. As in the in vitro cell models, a tumor may return to a state similar to the pretreatment tumor ( Figure 2 C, center). Alternatively, a tumor may display increased eITH ( Figure 2 C, right) (), or decreased eITH due to outgrowth of a small number of resistant cells that dominate the recurrence ( Figure 2 C, left) (). Additional in vitro experiments with genomic and epigenomic profiling, along with increased analysis of patient tumors before and after therapy, will help determine how often each of these models applies.

Powerful in vitro and in vivo models have shown that epigenetic heterogeneity can drive variable responses to therapy and differences in tumor-propagating potential.showed that upon separation of a breast cancer cell line into its basal, luminal, and stem-like cell populations, each cell type expanded into a heterogeneous culture that fully recapitulated the initial cell-type heterogeneity through cell-state interconversions.further showed that after isolating individual cells from the same genetic background and transplanting them into mice, the separate transplants displayed differences in growth dynamics and treatment response. Similarly,found that while the majority of cells in a single cell derived non-small cell lung cancer subline were drug sensitive, a small subpopulation of cells were drug tolerant. Following removal of drug, these drug-tolerant persister cells expanded and reacquired drug-sensitivity. Persister cells displayed an altered chromatin landscape, suggesting that concurrent epigenetic therapies could reduce or eliminate them. Indeed, treatment of cell lines with histone deacetylase inhibitors or knockdown of the histone demethylase KDM5A reduced the emergence of persister cells.

These analyses examined mITH at an early time point and calculated how it relates to PFS. A related question is how heterogeneity of a tumor might change over time: given a particular state of heterogeneity of an initial tumor, is there a positive or negative correlation between the duration of PFS and the level of mITH at recurrence? Given that more heterogeneous initial tumors recur more quickly, it could be argued that continued high levels of mITH would be associated with shorter time to recurrence ( Figure 2 B). An alternative hypothesis is that the high level of mITH in the initial tumor could provide a larger population from which a single, most-fit subclone can emerge and rapidly regrow into a less heterogeneous recurrence in a shorter time. Future mITH studies with large cohorts of initial and recurrent tumors will be required to address this question across a range of malignancies.

Application of mITH in a clinical setting will require the development of clinically tractable assays. Methylation at MGMT provides a case study for bringing DNA methylation analyses into a clinical setting. The current standard is non-quantitative MSP, but several groups have investigated methods for quantifying methylation at the MGMT promoter, including pyrosequencing () and real-time MSP (), the latter of which has been used in clinical trials (). Such clinically tractable and quantitative methods could be applied to additional loci, as determined by genome-wide assays, for quantitative measures of mITH and their relationship to response and outcome in patients.

It is unclear how mITH and patient outcome will relate in solid tumors. Increased gITH in solid tumors does correlate with poorer outcomes, with some contribution of aberrant DNA methylation (). Single-cell analysis of gene expression in glioblastoma showed that detection of multiple expression-based subtypes () in an individual tumor is associated with worse overall survival (). However, analysis of mITH at the single-gene level suggests that this may not be universal. DNA methylation at the promoter/enhancer of O-methylguanine DNA methyltransferase (MGMT) is a prognostic marker in glioblastoma (). Several studies found that the MGMT methylation level is relatively consistent throughout individual gliomas (). This homogeneity may be a unique feature of MGMT or may be indicative of a wider epigenetic pattern. Further analysis will be required to address the potential correlation between mITH and outcome in solid tumors.

Several groups have investigated a potential relationship between mITH and outcome ( Figure 2 A ). In CLL, high mITH at diagnosis correlated with a shorter interval until treatment is required () and, furthermore, higher mITH also correlated with shorter progression-free survival (PFS) (). In DLBCL, patients who had not relapsed in 5 years after treatment had lower mITH at diagnosis than those who did relapse within 5 years (). Furthermore, lower mITH correlated with longer PFS following initial treatment. In a cohort including follicular lymphoma and DLBCL,similarly showed that higher mITH predicts poor PFS. While these initial results are promising, larger studies will be required to determine the value of mITH as a prognostic feature.

(C) The impact of therapy on mITH is understudied. For a given number of subclones at diagnosis (top), therapy may selectively kill some subclones, leading to decreased mITH at recurrence (left), or it may not alter the diversity of subclones, leading to a recurrence with similar subclones as the diagnostic tumor (center), or it may promote increased mITH and expansion of novel subclones, leading to a more heterogeneous recurrence (right).

(B) Two different timeline models of the relationship between PFS and mITH. A tumor initiates (far left) and expands while acquiring a series of epigenetic alterations, leading to three distinct subclones (shades of blue) at the time of surgical resection (vertical black line). In both scenarios, identical subclones are present at initial resection. Surgical resection removes the majority of tumor cells, although a small number remain which continue to evolve and eventually develop into the recurrence after a short (top) or long (bottom) period of progression-free survival (PFS). Here, we question whether the duration of PFS may correlate with the levels of mITH in the recurrent tumor (cell populations on right).

One provocative question is the possibility that mITH may be tied to patient outcomes, and moreover that it may be used as a prognostic feature. Analysis of gITH has shown promise as a prognostic feature in a variety of malignancies. In the premalignant condition Barrett's esophagus, increased gITH of lesions correlated with progression to malignant esophageal adenocarcinoma (). Similarly, studies in hematopoietic malignancies including acute myeloid leukemia and CLL have shown that increased gITH, as measured by the number of subclones or subclonal mutations, is associated with worse patient outcomes ().

Coming Soon: ITH at the Single-Cell Level

While numerous ITH-related discoveries have been made, current methods for determining the eITH profile of cancer are limited. Rare cell populations such as persister cells will be undetectable because profiling measures the dominant tumor cell population predominantly, while the rare populations contribute relatively little signal. In addition, the presence of non-tumor cells can confound and mask signal from the tumor. The advancement of single-cell genome-wide profiling techniques has the potential to overcome the drawbacks associated with the standard population profiling methods.

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Tang F. Profiling DNA methylome landscapes of mammalian cells with single-cell reduced-representation bisulfite sequencing. While valuable knowledge has been gained from single-cell genomic technologies, few studies have explored the single-cell epigenomic landscape of cancer tissues. Two recent studies measured chromatin accessibility by applying assay for transposase-accessible chromatin using sequencing to single cells () and identified classes of transcription factors with high cell-to-cell variability in the accessibility of their binding motifs within cancer cell lines.further found that transcription factor binding sites had the most variable accessibility in cell types for which those transcription factors drive cell state, such as GATA transcription factors in K562 cells or Nanog in mouse embryonic stem cells, suggesting an important functional role for this variability. Single-cell chromatin immunoprecipitation sequencing has also been developed and used to explore the variable chromatin landscape within populations of embryonic stem cells (). However, the low complexity of reads produced by the approach, on the order of 800 peaks per cell, hampers its application to study ITH in cancer samples. The current state of the technology does not provide sufficient statistical power to reliably differentiate rare cell types in tumors from the technique-specific artifacts that can arise. Whole-genome bisulfite sequencing (WGBS) has been performed on the single-cell level (), but exact DNA methylation differences between cells cannot be made at base-pair resolution and therefore regulatory region sets were used to differentiate cell populations. Thus, while single-cell WGBS is available, the resolution of the technique is currently too sparse to reliably identify differentially methylated CpG sites. Higher-coverage sequencing of DNA methylation can be obtained with single-cell RRBS (), although in far fewer CpG sites compared with WGBS. While to date these methods have only been applied to cell lines, the next step will be to profile single cells dissociated from primary tumor tissue.

While there have been important discoveries from the application of single-cell technologies to study cancer biology, it is also important to consider their current limitations. Single-cell approaches require novel computational frameworks to take into account their relatively low signal resolution when compared with traditional bulk tumor sample sequencing. However, a main limitation is the potential for artifact introduced by the amplification of low amounts of nucleic acid input, at times making it difficult to distinguish a mutation or methylation call identified in a single cell from an error during amplification or sequencing. In addition, it is currently not possible to determine whether an event (e.g., expression of a given gene) that is not observed in a particular cell reflects true lack of expression or lack of sampling of that particular gene. As the field continues to improve the experimental techniques and analysis methods, the power of these methods will transform our understanding of ITH of the genome, epigenome, and transcriptome, and will affect how tumors are classified and which therapies are indicated.