| Literature DB >> 30386335 |
Ibrahim Al Bakir1,2, Kit Curtius1, Trevor A Graham1.
Abstract
Patients with inflammatory bowel disease have an increased risk of developing colorectal cancer, and this risk is related to disease duration, extent, and cumulative inflammation burden. Carcinogenesis follows the principles of Darwinian evolution, whereby somatic cells acquire genomic alterations that provide them with a survival and/or growth advantage. Colitis represents a unique situation whereby routine surveillance endoscopy provides a serendipitous opportunity to observe somatic evolution over space and time in vivo in a human organ. Moreover, somatic evolution in colitis is evolution in the 'fast lane': the repeated rounds of inflammation and mucosal healing that are characteristic of the disease accelerate the evolutionary process and likely provide a strong selective pressure for inflammation-adapted phenotypic traits. In this review, we discuss the evolutionary dynamics of pre-neoplastic clones in colitis with a focus on how measuring their evolutionary trajectories could deliver a powerful way to predict future cancer occurrence. Measurements of somatic evolution require an interdisciplinary approach that combines quantitative measurement of the genotype, phenotype and the microenvironment of somatic cells-paying particular attention to spatial heterogeneity across the colon-together with mathematical modeling to interpret these data within an evolutionary framework. Here we take a practical approach in discussing how and why the different "evolutionary ingredients" can and should be measured, together with our viewpoint on subsequent translation into clinical practice. We highlight the open questions in the evolution of colitis-associated cancer as a stimulus for future work.Entities:
Keywords: biomarker development; cancer evolution; colorectal cancer; inflammatory bowel disease (IBD); risk stratification; surveillance colonoscopy
Mesh:
Substances:
Year: 2018 PMID: 30386335 PMCID: PMC6198656 DOI: 10.3389/fimmu.2018.02368
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 7.561
Figure 1Clonal evolution in the colitic bowel. (A) The normal colon. Wild type (blue), phenotypically normal epithelial cells within crypts acquire mutations occur through chance (DNA replication error at mitosis) or due to microenvironmental pressures (e.g., mutagens in the diet or microbiome). Most mutations (pink crypts, arrows) are evolutionary neutral (i.e., do not change phenotype), or in fact disadvantageous leading to eventual cell death, through the disruption of critical cellular mechanisms or the activation of senescence/apoptosis. Occasionally, a positively selected driver mutation will be acquired. Through either positive selection or neutral drift, the mutant cell population can grow and take over the whole population of cells within the crypt (green crypt). (B) Field cancerization. A crypt wholly populated by mutated cells may have an increased crypt fission rate and/or lowered crypt death rate (green crypt). This leads to the expansion of the mutant crypt through the epithelium (green patch), causing a “field” of crypts bearing the same base change, heritable epigenetic change or copy number alteration, which is disposed to subsequent neoplastic transformation. (C) Neoplastic initiation. IBD generates a selective pressure favoring clones capable of surviving repeated cycles of inflammation and more rapidly repopulating the healing mucosa during remission. These selected mutant clones, while remaining histopathologically unremarkable, continue to evolve and expand at varying rates (crypts in various shades of green). Expansion remains limited by environmental constraints (e.g., a reliance on paracrine signaling from the stroma or a limit to the tolerance of cell crowding). Within this cancerised field, a frankly dysplastic sub-clone (yellow crypt) eventually arises. (D) Neoplastic promotion. The dysplastic clone (yellow with black outline) grows at a much more rapid pace than non-dysplastic crypts, resulting in an accelerated rate of evolution (crypts with black outline and shades of orange, brown, gray or pink); this genetic progression may be accompanied by progression in dysplasia grading. Potential biological mechanisms are diverse and include a loss of dependence on morphogen gradients and an altered metabolism that is better adapted to a hypoxic and nutrient-poor environment. (E) Malignant transformation and progression. Transformation to a malignant phenotype (red gland) produces a clone that can undergo uncontrolled cell division and is capable of invasion. Further progression in clone size results in a symptomatic, clinically detectable cancer. Potential biological mechanisms for this malignant transformation include a loss of critical DNA repair mechanisms and escape from immune surveillance. (F) Phylogenetic representation. Through multi-region sequencing, quantification of genomic divergence, and mathematical modeling, the phylogenetic relationship between clones can be understood. Here the evolutionary history of an IBD-CRC and its precursor lesions is depicted, with branch lengths corresponding to the amount of genetic change from the bifurcation node of clone branches.
Figure 2Screening and surveillance intervals in IBD using an evolutionary approach. Patients in a population develop IBD at a known age; the aim for prevention is to provide a baseline endoscopic screen before some of those patients will have developed neoplasia. Hypothetical “windows of opportunities” for surveillance quantify timescales during which (A) pre-initiated cells gain alterations (early diagnosis), (B) initiated clones expand (promotion) and become more genetically diverse (early intervention), and (C) preclinical malignant clones evolve (progression) and could be thwarted (early detection). Levels of diversity increase in an evolving inflamed microenvironment and may act as a biomarker of evolutionary trajectory position. We aim to relate biomarker levels (b) measured during surveillance screens at specified ages (a) to multistage model parameters via variables in a theoretical function Fbiomarker. As a theoretical example, if the relationship between diversity and size of premalignant clone (yellow) is known, then we can measure the rate of growth of the clone during surveillance and predict at what age the clone will be a certain size and likely harbor a detectable phenotype change of preclinical malignancy.