Tumour heterogeneity describes the observation that different tumour cells can show distinct morphological and phenotypic profiles, including cellular morphology, gene expression, metabolism, motility, proliferation, and metastatic potential. [1] This phenomenon occurs both between tumours (inter-tumour heterogeneity) and within tumours (intra-tumour heterogeneity). A minimal level of intra-tumour heterogeneity is a simple consequence of the imperfection of DNA replication: whenever a cell (normal or cancerous) divides, a few mutations are acquired [2] —leading to a diverse population of cancer cells. [3] The heterogeneity of cancer cells introduces significant challenges in designing effective treatment strategies. However, research into understanding and characterizing heterogeneity can allow for a better understanding of the causes and progression of disease. In turn, this has the potential to guide the creation of more refined treatment strategies that incorporate knowledge of heterogeneity to yield higher efficacy. [4]
Tumour heterogeneity has been observed in leukemias, [5] breast, [6] prostate, [7] [8] [9] colon, [10] [11] [12] brain, [13] esophagus, [14] head and neck, [15] bladder [16] and gynecological carcinomas, [17] liposarcoma, [18] and multiple myeloma. [19]
There are two models used to explain the heterogeneity of tumour cells. These are the cancer stem cell model and the clonal evolution model. The models are not mutually exclusive, and it is believed that they both contribute to heterogeneity in varying amounts across different tumour types. [20]
The cancer stem cell model asserts that within a population of tumour cells, there is only a small subset of cells that are tumourigenic (able to form tumours). These cells are termed cancer stem cells (CSCs), and are marked by the ability to both self-renew and differentiate into non-tumourigenic progeny. The CSC model posits that the heterogeneity observed between tumour cells is the result of differences in the stem cells from which they originated. Stem cell variability is often caused by epigenetic changes, but can also result from clonal evolution of the CSC population where advantageous genetic mutations can accumulate in CSCs and their progeny (see below). [20]
Evidence of the cancer stem cell model has been demonstrated in multiple tumour types including leukemias, [21] [22] glioblastoma, [23] breast cancer, [24] and prostate cancer. [25]
However, the existence of CSCs is still under debate. One reason for this is that markers for CSCs have been difficult to reproduce across multiple tumours. Further, methods for determining tumourigenic potential utilize xenograft models. These methods suffer from inherent limitations such as the need to control immune response in the transplant animal, and the significant difference in environmental conditions from the primary tumour site to the xenograft site (e.g. absence of required exogenous molecules or cofactors). [26] This has caused some doubt about the accuracy of CSC results and the conclusions about which cells have tumourigenic potential.[ citation needed ]
The clonal evolution model was first proposed in 1976 by Peter Nowell. [27] In this model, tumours arise from a single mutated cell, accumulating additional mutations as it progresses. These changes give rise to additional subpopulations, and each of these subpopulations has the ability to divide and mutate further. This heterogeneity may give rise to subclones that possess an evolutionary advantage over the others within the tumour environment, and these subclones may become dominant in the tumour over time. [28] [29] When proposed, this model allowed for the understanding of tumour growth, treatment failure, and tumour aggression that occurs during the natural process of tumour formation. [28]
Evolution of the initial tumour cell may occur by two methods:
Sequentially ordered mutations accumulate in driver genes, tumour suppressor genes, and DNA repair enzymes, resulting in clonal expansion of tumour cells. Linear expansion is less likely to reflect the endpoint of a malignant tumour [30] because the accumulation of mutations is stochastic in heterogeneic tumours.
Expansion into multiple subclonal populations occurs through a splitting mechanism. [28] This method is more associated with tumour heterogeneity than linear expansion. The acquisition of mutations is random as a result of increased genomic instability with each successive generation. The long-term mutational accumulation may provide a selective advantage during certain stages of tumour progression. The tumor microenvironment may also contribute to tumour expansion, as it is capable of altering the selective pressures that the tumour cells are exposed to. [30]
Multiple types of heterogeneity have been observed between tumour cells, stemming from both genetic and non-genetic variability. [31]
Genetic heterogeneity is a common feature of tumour genomes, and can arise from multiple sources. Some cancers are initiated when exogenous factors introduce mutations, such as ultraviolet radiation (skin cancers) and tobacco (lung cancer). A more common source is genomic instability, which often arises when key regulatory pathways are disrupted in the cells. Some examples include impaired DNA repair mechanisms which can lead to increased replication errors, and defects in the mitosis machinery that allow for large-scale gain or loss of entire chromosomes. [32] Furthermore, it is possible for genetic variability to be further increased by some cancer therapies (e.g. treatment with temozolomide and other chemotherapy drugs). [33] [34]
Mutational tumor heterogeneity refers to variations in mutation frequency in different genes and samples and can be explored by MutSig Archived 2017-10-03 at the Wayback Machine . The etiology of mutational processes can considerably vary between tumor samples from the same or different cancer types and can be manifested in different context-dependent mutational profiles. It can be explored by COSMIC mutational signatures or MutaGene.
Tumour cells can also show heterogeneity between their expression profiles. This is often caused by underlying epigenetic changes. [31] Variation in expression signatures have been detected in different regions of tumour samples within an individual. Researchers have shown that convergent mutations affecting H3K36 methyltransferase SETD2 and histone H3K4 demethylase KDM5C arose in spatially separated tumour sections. Similarly, MTOR, a gene encoding a cell regulatory kinase, has shown to be constitutively active, thereby increasing S6 phosphorylation. This active phosphorylation may serve as a biomarker in clear-cell carcinoma. [30]
Mechanochemical heterogeneity is a hallmark of living eukaryotic cells. It has an impact on epigenetic gene regulation. The heterogeneous dynamic mechanochemical processes regulate interrelationships within the group of cellular surfaces through adhesion. [35] Tumour development and spreading is accompanied by change in heterogeneous chaotic dynamics of mechanochemical interaction process in the group cells, including cells within tumour, and is hierarchical for the host of cancer patients. [36] The biological phenomena of mechanochemical heterogeneity maybe used for differential gastric cancer diagnostics against patients with inflammation of gastric mucosa [37] and for increasing antimetastatic activity of dendritic cells based on vaccines when mechanically heterogenized microparticles of tumor cells are used for their loading. [38] There is also a possible methodical approach based on the simultaneous ultrasound imaging diagnostic techniques and therapy, regarding the mechanochemical effect on nanobubles conglomerates with drugs in the tumour. [39] [40]
Redox processes in cancer cells induce changes in mechanochemical tumor heterogeneity by modifying bonds which influence the spatial arrangement of molecules in cell structures. This leads to the formation of regions with different biomechanical and biochemical properties within the tumor. [41]
Heterogeneity between tumour cells can be further increased due to heterogeneity in the tumour microenvironment. Regional differences in the tumour (e.g. availability of oxygen) impose different selective pressures on tumour cells, leading to a wider spectrum of dominant subclones in different spatial regions of the tumour. The influence of microenvironment on clonal dominance is also a likely reason for the heterogeneity between primary and metastatic tumours seen in many patients, as well as the inter-tumour heterogeneity observed between patients with the same tumour type. [42]
Heterogeneic tumours may exhibit different sensitivities to cytotoxic drugs among different clonal populations. This is attributed to clonal interactions that may inhibit or alter therapeutic efficacy, posing a challenge for successful therapies in heterogeneic tumours (and their heterogeneic metastases). [1]
Drug administration in heterogeneic tumours will seldom kill all tumour cells. The initial heterogeneic tumour population may bottleneck, such that few drug resistant cells (if any) will survive. This allows resistant tumour populations to replicate and grow a new tumour through the branching evolution mechanism (see above). The resulting repopulated tumour is heterogeneic and resistant to the initial drug therapy used. The repopulated tumour may also return in a more aggressive manner.[ citation needed ]
The administration of cytotoxic drugs often results in initial tumour shrinkage. This represents the destruction of initial non-resistant subclonal populations within a heterogeneic tumour, leaving only resistant clones. These resistant clones now contain a selective advantage and can replicate to repopulate the tumour. Replication will likely occur through branching evolution, contributing to tumour heterogeneity. The repopulated tumour may appear to be more aggressive. This is attributed to the drug-resistant selective advantage of the tumour cells.[ citation needed ]
In multiple myeloma, genetic analyzes of the tumor is used to detect risks markers such as specific mutation, deletion, insertion etc. Helping to assess the Prognosis of the patient. But there is a discrepancy between patients, some patients associated with a good risk will relapse earlier than expected. In addition, in some patients, risks anomaly will only be observed at relapse. A study from 2023 [43] using single cell showed that subclones with risks marker are present in some patients from the diagnosis but in such low frequency that they are not detectable by standard genetic routine assessment. Furthermore, this study indicated that patients with risks markers detectable only at relapse are indeed associated with a bad prognosis. With some risk anomaly there is no difference in the life expectancy (overall survival) between patients with the anomaly detected from the diagnosis and those with the anomaly only detected at relapse. Open question remains about the effect of the treatment on clonal selection. The therapeutic implication of this result is extensively developed in a paper : "Thus, sensitive detection approaches are required to detect these subclones at diagnosis together with innovative treatment strategies to eradicate low-frequency, high-risk subclones and prevent them from becoming dominant. [...] Finally, the described phenomenon is highly unlikely to be restricted to MM" [44] (Multiple Myeloma).
Due to the genetic differences within and between tumours, biomarkers that may predict treatment response or prognosis may not be widely applicable. However, it has been suggested that the level of heterogeneity can itself be used as a biomarker since more heterogeneous tumours may be more likely to contain treatment-resistant subclones. [31] Further research into developing biomarkers that account for heterogeneity is still in progress.
Current model systems typically lack the heterogeneity seen in human cancers. [45] In order to accurately study tumour heterogeneity, we must develop more accurate preclinical models. One such model, the patient derived tumour xenograft, has shown excellent utility in preserving tumour heterogeneity whilst allowing detailed study of the drivers of clonal fitness. [46] However, even this model cannot capture the full complexity of cancer.
While the problem of identifying, characterizing, and treating tumour heterogeneity is still under active research, some effective strategies have been proposed, including both experimental and computational solutions.[ citation needed ]
A neoplasm is a type of abnormal and excessive growth of tissue. The process that occurs to form or produce a neoplasm is called neoplasia. The growth of a neoplasm is uncoordinated with that of the normal surrounding tissue, and persists in growing abnormally, even if the original trigger is removed. This abnormal growth usually forms a mass, which may be called a tumour or tumor.
Cancer stem cells (CSCs) are cancer cells that possess characteristics associated with normal stem cells, specifically the ability to give rise to all cell types found in a particular cancer sample. CSCs are therefore tumorigenic (tumor-forming), perhaps in contrast to other non-tumorigenic cancer cells. CSCs may generate tumors through the stem cell processes of self-renewal and differentiation into multiple cell types. Such cells are hypothesized to persist in tumors as a distinct population and cause relapse and metastasis by giving rise to new tumors. Therefore, development of specific therapies targeted at CSCs holds hope for improvement of survival and quality of life of cancer patients, especially for patients with metastatic disease.
Perfect phylogeny is a term used in computational phylogenetics to denote a phylogenetic tree in which all internal nodes may be labeled such that all characters evolve down the tree without homoplasy. That is, characteristics do not hold to evolutionary convergence, and do not have analogous structures. Statistically, this can be represented as an ancestor having state "0" in all characteristics where 0 represents a lack of that characteristic. Each of these characteristics changes from 0 to 1 exactly once and never reverts to state 0. It is rare that actual data adheres to the concept of perfect phylogeny.
Oncogenomics is a sub-field of genomics that characterizes cancer-associated genes. It focuses on genomic, epigenomic and transcript alterations in cancer.
The Cancer Genome Atlas (TCGA) is a project to catalogue the genomic alterations responsible for cancer using genome sequencing and bioinformatics. The overarching goal was to apply high-throughput genome analysis techniques to improve the ability to diagnose, treat, and prevent cancer through a better understanding of the genetic basis of the disease.
Somatic evolution is the accumulation of mutations and epimutations in somatic cells during a lifetime, and the effects of those mutations and epimutations on the fitness of those cells. This evolutionary process has first been shown by the studies of Bert Vogelstein in colon cancer. Somatic evolution is important in the process of aging as well as the development of some diseases, including cancer.
RNA-Seq is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome.
Cancer genome sequencing is the whole genome sequencing of a single, homogeneous or heterogeneous group of cancer cells. It is a biochemical laboratory method for the characterization and identification of the DNA or RNA sequences of cancer cell(s).
COSMIC is an online database of somatically acquired mutations found in human cancer. Somatic mutations are those that occur in non-germline cells that are not inherited by children. COSMIC, an acronym of Catalogue Of Somatic Mutations In Cancer, curates data from papers in the scientific literature and large scale experimental screens from the Cancer Genome Project at the Sanger Institute. The database is freely available to academic researchers and commercially licensed to others.
SNV calling from NGS data is any of a range of methods for identifying the existence of single nucleotide variants (SNVs) from the results of next generation sequencing (NGS) experiments. These are computational techniques, and are in contrast to special experimental methods based on known population-wide single nucleotide polymorphisms. Due to the increasing abundance of NGS data, these techniques are becoming increasingly popular for performing SNP genotyping, with a wide variety of algorithms designed for specific experimental designs and applications. In addition to the usual application domain of SNP genotyping, these techniques have been successfully adapted to identify rare SNPs within a population, as well as detecting somatic SNVs within an individual using multiple tissue samples.
Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.
The Network of Cancer Genes (NCG) is a freely accessible web resource of genes that, when altered in their sequence, drive clonal expansion of normal tissues or cancer. The project was launched in 2010 and has reached its 7th release in 2022. In 2023 the additional annotation of cancer drivers that interact with the tumour immune microenvironment was added. NCG7.1 reports information on 3,347 cancer drivers and 95 healthy drivers. Of these, 596 are also TIME drivers. NCG7.1 also reports the system-level properties and the associated publications of each driver, as well as a list possible false positives. NCG7.1 enables advanced searches on the primary anatomical site, cancer type, type of sequencing screens and literature supports.
Circulating tumor DNA (ctDNA) is tumor-derived fragmented DNA in the bloodstream that is not associated with cells. ctDNA should not be confused with cell-free DNA (cfDNA), a broader term which describes DNA that is freely circulating in the bloodstream, but is not necessarily of tumor origin. Because ctDNA may reflect the entire tumor genome, it has gained traction for its potential clinical utility; "liquid biopsies" in the form of blood draws may be taken at various time points to monitor tumor progression throughout the treatment regimen.
Breast cancer metastatic mouse models are experimental approaches in which mice are genetically manipulated to develop a mammary tumor leading to distant focal lesions of mammary epithelium created by metastasis. Mammary cancers in mice can be caused by genetic mutations that have been identified in human cancer. This means models can be generated based upon molecular lesions consistent with the human disease.
G&T-seq is a novel form of single cell sequencing technique allowing one to simultaneously obtain both transcriptomic and genomic data from single cells, allowing for direct comparison of gene expression data to its corresponding genomic data in the same cell...
CAPP-Seq is a next-generation sequencing based method used to quantify circulating DNA in cancer (ctDNA). The method was introduced in 2014 by Ash Alizadeh and Maximilian Diehn’s laboratories at Stanford, as a tool for measuring Cell-free tumor DNA which is released from dead tumor cells into the blood and thus may reflect the entire tumor genome. This method can be generalized for any cancer type that is known to have recurrent mutations. CAPP-Seq can detect one molecule of mutant DNA in 10,000 molecules of healthy DNA. The original method was further refined in 2016 for ultra sensitive detection through integration of multiple error suppression strategies, termed integrated Digital Error Suppression (iDES). The use of ctDNA in this technique should not be confused with circulating tumor cells (CTCs); these are two different entities.
PyClone is a software that implements a Hierarchical Bayes statistical model to estimate cellular frequency patterns of mutations in a population of cancer cells using observed alternate allele frequencies, copy number, and loss of heterozygosity (LOH) information. PyClone outputs clusters of variants based on calculated cellular frequencies of mutations.
Personalized onco-genomics (POG) is the field of oncology and genomics that is focused on using whole genome analysis to make personalized clinical treatment decisions. The program was devised at British Columbia's BC Cancer Agency and is currently being led by Marco Marra and Janessa Laskin. Genome instability has been identified as one of the underlying hallmarks of cancer. The genetic diversity of cancer cells promotes multiple other cancer hallmark functions that help them survive in their microenvironment and eventually metastasise. The pronounced genomic heterogeneity of tumours has led researchers to develop an approach that assesses each individual's cancer to identify targeted therapies that can halt cancer growth. Identification of these "drivers" and corresponding medications used to possibly halt these pathways are important in cancer treatment.
Tumour mutational burden is a genetic characteristic of tumorous tissue that can be informative to cancer research and treatment. It is defined as the number of non-inherited mutations per million bases (Mb) of investigated genomic sequence, and its measurement has been enabled by next generation sequencing. High TMB and DNA damage repair mutations were discovered to be associated with superior clinical benefit from immune checkpoint blockade therapy by Timothy Chan and colleagues at the Memorial Sloan Kettering Cancer Center.
Single-cell genome and epigenome by transposases sequencing (scGET-seq) is a DNA sequencing method for profiling open and closed chromatin. In contrast to single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), which only targets active euchromatin, scGET-seq is also capable of probing inactive heterochromatin.
{{cite book}}
: |journal=
ignored (help)