Genetic and phenotypic attributes of splenic marginal zone lymphoma
Ferdinando BonfiglioAlessio BruscagginFrancesca GuidettiLodovico Terzi di BergamoMartin FaderlValeria SpinaAdalgisa CondoluciLuisella BonominiGabriela ForestieriRicardo KochDeborah PiffarettiKatia PiniMaria Cristina PirosaMicol Giulia CittoneAlberto J. ArribasMarco LucioniGuido GhilardiWei WuLuca ArcainiMaria João BaptistaGabriela BastidasSı́lvia BeàRenzo BoldoriniAlessandro BroccoliMarco BuehlerVincenzo CanzonieriLuciano CascioneLuca CerianiSergio CogliattiPaolo CorradiniEnrico DerenziniLiliana DevizziSascha DietrichAngela Rita EliaFabio FacchettiGianluca GaïdanoJuan F. Garcı́aB. GerberPaolo GhiaMaría Gomes da SilvaGiuseppe GrittiAnna GuidettiFelicitas HitzGiorgio InghiramiMarco LadettoArmando López‐GuillermoElisa LucchiniAntonino MaioranaRoberto MarascaEstella MatutesVéronique MeigninMichele MerliAlden A. MocciaManuela MollejoCarlos MontalbánUrban NovakDavid OscierFrancesco PassamontiFrancesco PiazzaStefano PizzolittoAlessandro RambaldiElena SabattiniGilles SallesElisa SantambrogioLydia ScarfòAnastasios StathisGeorg StüssiJulia T. GeyerGustavo TapiaCorrado TarellaCatherine ThiéblemontThomas TousseynAlessandra TucciGiorgio VaniniCarlo ViscoUmberto VitoloRenata WalewskaFrancesco ZajaThorsten ZenzPier Luigi ZinzaniHossein KhiabanianArianna CalcinottoFrancesco BertoniGovind BhagatElı́as CampoLaurence de LevalStefan DirnhoferStefano PileriMiguel Á. PirisAlexandra Traverse‐GlehenAlexandar TzankovMarco PaulliMaurilio PonzoniLuca MazzucchelliFranco CavalliEmanuele ZuccaDavide Rossi
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Abstract:
Splenic marginal zone B-cell lymphoma (SMZL) is a heterogeneous clinico-biological entity. The clinical course is variable, multiple genes are mutated with no unifying mechanism, and essential regulatory pathways and surrounding microenvironments are diverse. We sought to clarify the heterogeneity of SMZL by resolving different subgroups and their underlying genomic abnormalities, pathway signatures, and microenvironment compositions to uncover biomarkers and therapeutic vulnerabilities. We studied 303 SMZL spleen samples collected through the IELSG46 multicenter international study (NCT02945319) by using a multiplatform approach. We carried out genetic and phenotypic analyses, defined self-organized signatures, validated the findings in independent primary tumor metadata and in genetically modified mouse models, and determined correlations with outcome data. We identified 2 prominent genetic clusters in SMZL, termed NNK (58% of cases, harboring NF-κB, NOTCH, and KLF2 modules) and DMT (32% of cases, with DNA-damage response, MAPK, and TLR modules). Genetic aberrations in multiple genes as well as cytogenetic and immunogenetic features distinguished NNK- from DMT-SMZLs. These genetic clusters not only have distinct underpinning biology, as judged by differences in gene-expression signatures, but also different outcomes, with inferior survival in NNK-SMZLs. Digital cytometry and in situ profiling segregated 2 basic types of SMZL immune microenvironments termed immune-suppressive SMZL (50% of cases, associated with inflammatory cells and immune checkpoint activation) and immune-silent SMZL (50% of cases, associated with an immune-excluded phenotype) with distinct mutational and clinical connotations. In summary, we propose a nosology of SMZL that can implement its classification and also aid in the development of rationally targeted treatments.The review describes the results of the investigations into molecular heterogeneity of monogenic inherited diseases which could be revealed at the levels of the structure of mutant genes, biochemical phenotype of disease and its clinical manifestations. The multilocus origin of some diseases, the patterns of gene mutations, their types, the blocks of the gene expression and the variability of the biochemical phenotypes of diseases are characterized. The data concerning trans-acting mutations causing the specific secondary alterations of the biochemical phenotype and corresponding clinical manifestations are summarized. Some mechanisms of the suppression of pathological phenotype are analyzed.
Clinical phenotype
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Abstract Phenotypic (non-genetic) heterogeneity has significant implications for development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unravelled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other but members of a team activate one another, forming a ‘toggle switch’ between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their ‘latent’ design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.
Multistability
Gene regulatory network
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Purpose The corneal dystrophies are a group of genetically determined diseases usually characterized by loss of corneal transparency, which may be caused by a progressive accumulation of abnormal material within the cornea. The genetic characterization of corneal dystrophies revealed both genetic heterogeneity, that is, different genes (KRT3 and KRT12) causing a single dystrophy phenotype (Meesmann dystrophy), and phenotypic heterogeneity with a single gene (TGFBI) causing different allelic dystrophy phenotypes (RBCD, TBCD, granular type 1, granular type 2, and lattice type 1).But less is known about the evolution of the phenotype during life. Methods We were interesting in following the corneal phénotype progressive evolution during childhood. During several years we analyzed corneal phénotype of families of Lattice type 1 and granular type 1, using Scheimpflug camera. Results We were able to follow the accumulation of abnormal material, his corneal localisation and evolution during years. Conclusion The phenotype of both Lattice type 1 and granular type 1 is totally different in childhood, with subepithelial localization.
Corneal dystrophy
Scheimpflug principle
TGFBI
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Follicular lymphoma
Lymphoplasmacytic Lymphoma
Mucosa-associated lymphoid tissue
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Prevalence of hepatitis B and hepatitis C viral infections in various subtypes of B-cell non-Hodgkin lymphoma: confirmation of the association with splenic marginal zone lymphoma
Viral Hepatitis
Hepatitis B
B-cell lymphoma
Hepatitis C
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Lymphoma, a cancer of lymphoid tissue, is a relatively common form of canine malignancy, typically affecting middle-aged to older dogs, with a higher prevalance among certain breeds. Treatment choices and more precise diagnosis and classification for this disease are currently limited by deficiencies in our understanding of the underlying pathogenic mechanisms and by a lack of useful biomarkers. As with other forms of cancer, canine lymphoma is likely to be a molecular disease resulting from the abnormal expression of genes involved in fundamental cell processes such as cell differentiation, proliferation and apoptosis. Thus, molecular tools such as gene expression microarrays, which permit the analysis of thousands of genes in parallel, have the potential to greatly enhance our understanding of lymphoma pathogenesis in the dog, as previously achieved for human lymphoma.
In this study, gene expression profiling (GEP) of 45 canine lymphomas (36 B cell and 9 T cell) and 10 clinically normal canine lymph nodes was carried out using the Affymetrix Canine Genome 2.0 GeneChip microarray system. This showed that canine B cell lymphoma and T cell lymphoma could easily be distinguished from each other, as well as from non-diseased lymph node tissue. Within the B lymphoma specimens, which were dominated by DLBCL cases, there was a close molecular similarity and B lymphoma samples with a histological diagnosis of Burkitt-like (BL) were generally indiscernible as a subgroup. Significantly, however, gene expression profiling could subdivide the B cell lymphomas overall into two closely related molecular subgroups (designated B1 and B2). Direct comparison of this data with previously published canine lymphoma microarray data (the Frantz cohort), upheld these B lymphoma subgroups and confirmed the existence of two previously identified T lymphoma subgroups (high grade T-LBL and low grade TZL), in which all 9 T lymphoma specimens in this study corresponded to the high grade T-LBL subgroup.
Comparison of gene expression patterns of both the B cell and T cell lymphomas with non-diseased lymph nodes, revealed a preponderance of cell cycle genes and genes encoding components of various lymphocyte signalling pathways. A similar result was seen within the T lymphomas when the high-grade (T-LBL) subgroup was compared with the low grade (TZL) specimens. This was strongly indicative of tumour cells with drastically altered proliferative and survival capabilities. The B lymphomas also showed a marked decrease in gene transcripts associated with cell adhesion. An attempt to identify a gene signature common to both B cell and T cell lymphomas identified relatively few genes. However, among these were several coding for haemoglobin molecules, suggesting that an ability to scavenge oxygen in a hypoxic tumour environment might be an important feature of lymphomas in general. The discovery of highly related, yet discernible, canine B lymphoma (mainly DLBCL) subgroups prompted additional assessment of whether the B lymphomas would segregate according to two key molecular signatures defined for human DLBCL, namely stromal-1 (mesenchymal-like with strong extracellular matrix representation) and stromal-2 (angiogenic with increased blood vessel density). While a direct correspondence between human and canine was not readily apparent, a canine B lymphoma subgroup, largely congruent with identified subgroup B2 and referred to as the stromal subgroup, was revealed which appeared somewhat analogous to human stromal-1 signature with very high expression of genes associated with the extracellular matrix together with diffuse collagen staining histologically. This signature, which included the key regulator CTGF (connective tissue growth factor) as well as genes involved in cell adhesion, may underlie those canine tumours exhibiting a fibrotic reaction.
Using a set of 25 genes (16 on the full cohort of specimens and 9 on subsets of the cohort), the microarray data were validated by use of the independent technique of quantitative reverse transcription PCR (qRT-PCR) analysis. To further extend the study, the possibility of using routine archival formalin-fixed paraffin-embedded (FFPE) lymphoma specimens for qRT-PCR was explored. Using modified techniques for extraction of suitable RNA from FFPE tissue, highly valid results were obtained, although a lower signal-to-noise ratio compared to using unfixed tissue necessitated the use of higher cDNA template concentrations and effectively precluded the analysis of genes that exhibited only a low fold-change in mRNA expression. The ability to use archival FFPE tissues for measurement of gene expression has great potential for large-scale retrospective studies to aid the discovery of disease classifiers and therapeutic targets in canine lymphoma.
In summary, this study has shown at a molecular level that canine lymphoma has some degree of similarity with its human counterpart and that it can be stratified into distinct molecular subgroups on the basis of gene expression profiling. A better understanding of the pathogenesis of this tumour type will facilitate efforts to improve classification, discover diagnostic and prognostic biomarkers, and guide treatment decision-making, with the overall aim of improving lymphoma management and clinical outcomes.
Canine Lymphoma
BCL10
B-cell lymphoma
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Abstract Populations of microbes are constantly evolving heterogeneity that selection acts upon, yet heterogeneity is nontrivial to assess methodologically. The necessary practice of isolating single‐cell colonies and thus subclone lineages for establishing, transferring, and using a strain results in single‐cell bottlenecks with a generally neglected effect on the characteristics of the strain itself. Here, we present evidence that various subclone lineages for industrial yeasts sequenced for recent genomic studies show considerable differences, ranging from loss of heterozygosity to aneuploidies. Subsequently, we assessed whether phenotypic heterogeneity is also observable in industrial yeast, by individually testing subclone lineages obtained from products. Phenotyping of industrial yeast samples and their newly isolated subclones showed that single‐cell bottlenecks during isolation can indeed considerably influence the observable phenotype. Next, we decoupled fitness distributions on the level of individual cells from clonal interference by plating single‐cell colonies and quantifying colony area distributions. We describe and apply an approach using statistical modeling to compare the heterogeneity in phenotypes across samples and subclone lineages. One strain was further used to show how individual subclonal lineages are remarkably different not just in phenotype but also in the level of heterogeneity in phenotype. With these observations, we call attention to the fact that choosing an initial clonal lineage from an industrial yeast strain may vastly influence downstream performances and observations on karyotype, on phenotype, and also on heterogeneity.
Strain (injury)
Phenotypic trait
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Mucosa-associated lymphoid tissue
Hematology
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Phenotypic heterogeneity of glioblastomas is a leading determinant of therapeutic resistance and treatment failure. However, functional assessment of the heterogeneity of glioblastomas is lacking. We developed a self-assembly-based assessment system that predicts inter/intracellular heterogeneity and phenotype associations, such as cell proliferation, invasiveness, drug responses, and gene expression profiles. Under physical constraints for cellular interactions, mixed populations of glioblastoma cells are sorted to form a segregated architecture, depending on their preference for binding to cells of the same phenotype. Cells distributed at the periphery exhibit a reduced temozolomide (TMZ) response and are associated with poor patient survival, whereas cells in the core of the aggregates exhibit a significant response to TMZ. Our results suggest that the multicellular self-assembly pattern is indicative of the intertumoral and intra-patient heterogeneity of glioblastomas, and is predictive of the therapeutic response.
Temozolomide
Multicellular organism
Tumour heterogeneity
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A tumour is a heterogeneous population of cells that competes for limited resources. In the clinic, we typically probe the tumour by biopsy, and then characterize it by the dominant genetic clone. But genotypes are only the first link in the chain of hierarchical events that leads to a specific cell phenotype. The relationship between genotype and phenotype is not simple, and the so-called genotype to phenotype map is poorly understood. Many genotypes can produce the same phenotype, so genetic heterogeneity may not translate directly to phenotypic heterogeneity. We therefore choose to focus on the functional endpoint, the phenotype as defined by a collection of cellular traits (e.g. proliferative and migratory ability). Here, we will examine how phenotypic heterogeneity evolves in space and time and how the way in which phenotypes are inherited will drive this evolution. A tumour can be thought of as an ecosystem, which critically means that we cannot just consider it as a collection of mutated cells but more as a complex system of many interacting cellular and microenvironmental elements. At its simplest, a growing tumour with increased proliferation capacity must compete for space as a limited resource. Hypercellularity leads to a contact-inhibited core with a competitive proliferating rim. Evolution and selection occurs, and an individual cell's capacity to survive and propagate is determined by its combination of traits and interaction with the environment. With heterogeneity in phenotypes, the clone that will dominate is not always obvious as there are both local interactions and global pressures. Several combinations of phenotypes can coexist, changing the fitness of the whole. To understand some aspects of heterogeneity in a growing tumour, we build an off-lattice agent-based model consisting of individual cells with assigned trait values for proliferation and migration rates. We represent heterogeneity in these traits with frequency distributions and combinations of traits with density maps. How the distributions change over time is dependent on how traits are passed on to progeny cells, which is our main enquiry. We bypass the translation of genetics to behaviour by focusing on the functional end result of inheritance of the phenotype combined with the environmental influence of limited space.
Phenotypic trait
clone (Java method)
Phenotypic switching
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