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 GhilardiWenping WuLuca ArcainiMaria João BaptistaGabriela BastidasSı́lvia BeàRenzo BoldoriniAlessandro BroccoliVincenzo CanzonieriLuciano CascioneLuca CerianiSergio CogliattiEnrico DerenziniLiliana DevizziSascha DietrichAngela Rita EliaFabio FacchettiGianluca GaïdanoJuan F. Garcı́aB GerberPaolo GhiaM. G. da SilvaGiuseppe GrittiAnna GuidettiFelicitas HitzGiorgio InghiramiMarco LadettoArmando López‐GuillermoElisa LucchiniAntonino MaioranaRoberto MarascaEstella MatutesVéronique MeigninMichele MerliAlden A. MocciaManuela MollejoCarlos MontalbánUrban NovakDavid OscierFrancesco PassamontiF. Di PiazzaStefano PizzolittoElena SabattiniGilles SallesElisa SantambrogioLydia ScarfòAnastasios StathisGeorg StüssiJulia T. GeyerGustavo TapiaCatherine ThiéblemontThomas TousseynAlessandra TucciCarlo ViscoUmberto VitoloThorsten ZenzPier Luigi ZinzaniHossein KhiabanianArianna CalcinottoFrancesco BertoniGovind BhagatElı́as CampoLaurence de LevalS DirnhoferStefano PileriM. A. gel PirisAlexandra Traverse‐GlehenAlexandar TzankovMarco PaulliMaurilio PonzoniLuca MazzucchelliF CavalliEmanuele ZuccaDavide Rossi
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Introduction: Splenic marginal zone B-cell lymphoma (SMZL) is a heterogeneous entity. The clinical course is variable, mutated genes are multiple with no unifying mechanism, essential regulatory pathways and surrounding microenvironments are diverse. We sought to provide a unifying view of SMZL by resolving its heterogeneity in subgroups sharing genomic abnormalities, pathway signatures and microenvironment compositions to uncover biomarkers and therapeutic vulnerabilities. Methods: We studied 303 pathologically confirmed SMZL spleen samples collected through the IELSG46 multicenter, international study (NCT02945319). The study cohort was representative of SMZL in terms of demographics, clinical features and outcome. We carried out a genetic and phenotypic analysis, defined self-organized signatures, validated them in independent primary tumors meta-data and in genetically modified mouse models, and correlated them with outcome data. Results: We identified and validated two prominent and self-aggregating genetic clusters in SMZL, termed NNK (58% of cases, from NF-kB, NOTCH and KLF2 modules) and DMT (32% of cases, from DNA-damage response, MAPK and TLR modules). NNK-SMZLs were dominated by mutations of NF-κB (e.g., TNFAIP3, TRAF3, BIRC3), NOTCH (e.g., NOTCH2, NOTCH1, SPEN) and KLF2. DMT-SMZLs were characterized by mutations in DNA damage response pathway genes (e.g., TP53, ATM). Mutations in MAPK (e.g., BRAF) and TLR genes (e.g., MYD88, all involving positions other than p.L265) were also enriched in DMT-SMZLs (Figure A and B). These genetic clusters have distinct underpinning biology. NNK-SMZLs were enriched of IGHV1-2*04 allele usage and of 7q deletion, while conversely DMT-SMZLs were depleted of both of them (Figure C). NNK-SMZL expressed significantly higher levels of genes belonging to NOTCH2 pathway and of genes that are activated by non-canonical NF-κB transcription factors. Conversely, DMT-SMZL had a signature of TP53 and apoptosis impairment (Figure D). Digital cytometry and in situ profiling segregated two basic types of SMZL immune microenvironment termed inflamed SMZL (50% of cases, associated with inflammatory cells and immune checkpoint activation) and non-inflamed SMZL (50% of cases) (Figure E and F). The combination of molecular and phenotypic profiling allowed to sort out a high risk clinical subset of patients whose tumor was characterized by having both NNK genotype and ‘’inflamed’’ microenvironment (Figure G). The research was funded by: Swiss Cancer League, ID 3746, 4395 4660, and 4705, Bern, Switzerland; Research Advisory Board of the Ente Ospedaliero Cantonale, ABREOC 2019-22514, Bellinzona, Switzerland; European Research Council (ERC) Consolidator Grant CLLCLONE, ID: 772051; Swiss National Science Foundation, ID 320030_169670/1, 310030_192439, 320036_179318, Berne, Switzerland; Fondazione Ticinese Contro il Cancro; Fondazione Fidinam, Lugano, Switzerland; Nelia & Amadeo Barletta Foundation, Lausanne, Switzerland; Fond’Action, Lausanne, Switzerland; The Leukemia & Lymphoma Society, Translational Research Program, ID 6594-20, New York; AFRI, Ente Ospedaliero Cantonale, Bellinzona, Switzerland; Fondazione Dr. Ettore Balli. Keywords: Diagnostic and Prognostic Biomarkers, Indolent non-Hodgkin lymphoma, Pathology and Classification of Lymphomas Conflicts of interests pertinent to the abstract P. Ghia Honoraria: AbbVie, ArQule/MSD, AstraZeneca, Beigene, Celgene/Juno(BMS, Gilead, Janssen, Loxo/Lilly, Roche Research funding: AbbVie, AstraZeneca, Gilead, Janssen, Sunesis G. Gritti Consultant or advisory role: Takeda, IQvia, Gilead Sciences Research funding: Gilead Sciences Educational grants: Roche, Abbvie, Gilead Sciences, Abbvie A. Moccia Consultant or advisory role: Roche, Janssen and Takeda L. Scarfó Honoraria: AbbVie, AstraZeneca and Janssen D. Rossi Honoraria: AbbVie, AstraZeneca, Janssen Research funding: AbbVie, AstraZeneca, JanssenIntroduction: Splenic marginal zone B-cell lymphoma (SMZL) is a heterogeneous entity. The clinical course is variable, mutated genes are multiple with no unifying mechanism, essential regulatory pathways and surrounding microenvironments are diverse. We sought to provide a unifying view of SMZL by resolving its heterogeneity in subgroups sharing genomic abnormalities, pathway signatures and microenvironment compositions to uncover biomarkers and therapeutic vulnerabilities. Methods: We studied 303 pathologically confirmed SMZL spleen samples collected through the IELSG46 multicenter, international study (NCT02945319). The study cohort was representative of SMZL in terms of demographics, clinical features and outcome. We carried out a genetic and phenotypic analysis, defined self-organized signatures, validated them in independent primary tumors meta-data and in genetically modified mouse models, and correlated them with outcome data. Results: We identified and validated two prominent and self-aggregating genetic clusters in SMZL, termed NNK (58% of cases, from NF-kB, NOTCH and KLF2 modules) and DMT (32% of cases, from DNA-damage response, MAPK and TLR modules). NNK-SMZLs were dominated by mutations of NF-κB (e.g., TNFAIP3, TRAF3, BIRC3), NOTCH (e.g., NOTCH2, NOTCH1, SPEN) and KLF2. DMT-SMZLs were characterized by mutations in DNA damage response pathway genes (e.g., TP53, ATM). Mutations in MAPK (e.g., BRAF) and TLR genes (e.g., MYD88, all involving positions other than p.L265) were also enriched in DMT-SMZLs (Figure A and B). These genetic clusters have distinct underpinning biology. NNK-SMZLs were enriched of IGHV1-2*04 allele usage and of 7q deletion, while conversely DMT-SMZLs were depleted of both of them (Figure C). NNK-SMZL expressed significantly higher levels of genes belonging to NOTCH2 pathway and of genes that are activated by non-canonical NF-κB transcription factors. Conversely, DMT-SMZL had a signature of TP53 and apoptosis impairment (Figure D). Digital cytometry and in situ profiling segregated two basic types of SMZL immune microenvironment termed inflamed SMZL (50% of cases, associated with inflammatory cells and immune checkpoint activation) and non-inflamed SMZL (50% of cases) (Figure E and F). The combination of molecular and phenotypic profiling allowed to sort out a high risk clinical subset of patients whose tumor was characterized by having both NNK genotype and ‘’inflamed’’ microenvironment (Figure G). The research was funded by: Swiss Cancer League, ID 3746, 4395 4660, and 4705, Bern, Switzerland; Research Advisory Board of the Ente Ospedaliero Cantonale, ABREOC 2019-22514, Bellinzona, Switzerland; European Research Council (ERC) Consolidator Grant CLLCLONE, ID: 772051; Swiss National Science Foundation, ID 320030_169670/1, 310030_192439, 320036_179318, Berne, Switzerland; Fondazione Ticinese Contro il Cancro; Fondazione Fidinam, Lugano, Switzerland; Nelia & Amadeo Barletta Foundation, Lausanne, Switzerland; Fond’Action, Lausanne, Switzerland; The Leukemia & Lymphoma Society, Translational Research Program, ID 6594-20, New York; AFRI, Ente Ospedaliero Cantonale, Bellinzona, Switzerland; Fondazione Dr. Ettore Balli. Keywords: Diagnostic and Prognostic Biomarkers, Indolent non-Hodgkin lymphoma, Pathology and Classification of Lymphomas Conflicts of interests pertinent to the abstract P. Ghia Honoraria: AbbVie, ArQule/MSD, AstraZeneca, Beigene, Celgene/Juno(BMS, Gilead, Janssen, Loxo/Lilly, Roche Research funding: AbbVie, AstraZeneca, Gilead, Janssen, Sunesis G. Gritti Consultant or advisory role: Takeda, IQvia, Gilead Sciences Research funding: Gilead Sciences Educational grants: Roche, Abbvie, Gilead Sciences, Abbvie A. Moccia Consultant or advisory role: Roche, Janssen and Takeda L. Scarfó Honoraria: AbbVie, AstraZeneca and Janssen D. Rossi Honoraria: AbbVie, AstraZeneca, Janssen Research funding: AbbVie, AstraZeneca, Janssen
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Lymphocytosis
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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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We studied the cases of 353 patients with lymphoma involving the ocular adnexa diagnosed at the Massachusetts General Hospital between 1974 and 2005. The patients included 153 males and 200 females, aged 7 to 95 years, with a mean age of 64 years. In 277 cases, there was no known history of lymphoma. Seventy-six patients had a history of lymphoma, with the ocular adnexa being involved at relapse or with progression of the previously diagnosed lymphoma. The patients had marginal zone lymphoma (182 cases), follicular lymphoma (80 cases), mantle cell lymphoma (18 cases), small lymphocytic lymphoma/chronic lymphocytic leukemia (13 cases), lymphoplasmacytic lymphoma (4 cases), splenic marginal zone lymphoma (2 cases), low-grade B cell, not subclassified (19 cases), precursor B lymphoblastic lymphoma (3 cases), diffuse large B-cell lymphoma (26 cases), and 1 case each of high-grade B-cell lymphoma, not subclassified, peripheral T-cell lymphoma, unspecified type, extranodal NK/T-cell lymphoma, nasal type, and Hodgkin lymphoma, nodular sclerosis type. Almost all marginal zone lymphoma patients (168 of 182, 92%) had primary ocular adnexal lymphoma. Fourteen marginal zone lymphoma patients (8%) had a prior history of lymphoma, usually arising in another extranodal site. Twenty-five of 80 (31%) follicular lymphoma patients had a prior history of lymphoma, usually arising in lymph nodes. Patients with mantle cell lymphoma, chronic lymphocytic leukemia, lymphoplasmacytic lymphoma, and splenic marginal zone lymphoma almost always had a prior history of lymphoma or were known to have widespread disease at the time of diagnosis of ocular adnexal lymphoma. A subset of the diffuse large B-cell lymphomas were associated with large destructive masses involving adjacent structures such as paranasal sinuses, raising the possibility that they may have arisen from one of the adjacent structures and involved the ocular adnexa by direct extension. The relatively high proportion of low-grade lymphoma, not subclassified, highlights the difficulty that may arise in distinguishing different types of low-grade lymphoma, particularly when biopsies are small and artifactually distorted. Ocular adnexal lymphoma is primarily a disease of older adults, with a slight female preponderance. Most lymphomas are low-grade B-cell lymphomas, with marginal zone lymphoma being by far the most common type. Marginal zone lymphoma typically involves the ocular adnexa primarily, whereas other types of low-grade B-cell lymphoma often involve the ocular adnexa secondarily. High-grade B-cell lymphomas only occasionally involve the ocular adnexa, and T-cell lymphoma, NK-cell lymphoma, and Hodgkin lymphoma are only rarely encountered in this site.
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Lymphoplasmacytic Lymphoma
Lymphoblastic 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.
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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.
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clone (Java method)
Phenotypic switching
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