Clinical and Biological Subtypes of B-cell Lymphoma Revealed by Microenvironmental Signatures

Kotlov, Nikita; Bagaev, Alexander; Revuelta, María V.; Phillip, Jude M.; Cacciapuoti, Maria Teresa; Antysheva, Zoya; Svekolkin, Viktor; Tikhonova, Ekaterina; Miheecheva, Natalia; Kuzkina, Natalia; Nos, Grigorii; Tabbò, Fabrizio; Frenkel, Felix; Ghione, Paola; Tsiper, Maria; Almog, Nava; Fowler, Nathan; Melnick, Ari; Leonard, John P.; Inghirami, Giorgio; Cerchietti, Leandro

doi:10.1158/2159-8290.cd-20-0839

Cited by 157 publications

(230 citation statements)

References 86 publications

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“…B cell lymphomas are among the most frequent hematopoietic malignancies and represent a molecularly heterogeneous group of diseases with different therapeutic vulnerabilities (26,117) and clinical outcomes that largely depend on a complex interplay between a multitude of genomic alterations and heterogeneous tumor microenvironment signatures (118,119). Sequencing studies allowed the identification of recurrently mutated genes that drive lymphomagenesis and led to refine DLBCL classification that pave the way for personalized therapeutic strategies (14,19,118).…”

Section: Discussionmentioning

confidence: 99%

“…The latest large-scale genome sequencing studies identified up to seven different molecular subtypes of DLBCL (118) which represent an interesting framework for the development of models mimicking each molecular subtype. Now, recent DLBCL classifications based of TME composition revealed four distinct microenvironment compositions which provide independent contributions to clinical outcome regardless of the GCB/ABC cell-of-origin or the genetic DLBCL subtype classification (119). The challenge of modeling future preclinical mouse models will be to recapitulate both the genomic features of the tumors and the parallel remodeling of the tumor microenvironment.…”

Section: Discussionmentioning

confidence: 99%

“…GEM models may recapitulate the natural history and histological properties of human tumors. However, they display a limited mutational and immunological complexity compared to the human tumors they are intended to model (119). Novel sophisticated tools for engineering mouse models such as CRISPR-Cas9 gene editing techniques or patient-derived xenografts (especially those mimicking indolent lymphomas such as FL), including in humanized mice, will be useful to fill this gap and efficiently generate preclinical models that reflect the complex genetics of the human tumors (61,(123)(124)(125).…”

Section: Discussionmentioning

confidence: 99%

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Human B Lymphomas Reveal Their Secrets Through Genetic Mouse Models

et al. 2021

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Lymphomas are cancers deriving from lymphocytes, arising preferentially in secondary lymphoid organs, and represent the 6th cancer worldwide and the most frequent blood cancer. The majority of B cell Non-Hodgkin lymphomas (B-NHL) develop from germinal center (GC) experienced mature B cells. GCs are transient structures that form in lymphoid organs in response to antigen exposure of naive B cells, and where B cell receptor (BCR) affinity maturation occurs to promote B cell differentiation into memory B and plasma cells producing high-affinity antibodies. Genomic instability associated with the somatic hypermutation (SHM) and class-switch recombination (CSR) processes during GC transit enhance susceptibility to malignant transformation. Most B cell differentiation steps in the GC are at the origin of frequent B cell malignant entities, namely Follicular Lymphoma (FL) and GCB diffuse large B cell lymphomas (GCB-DLBCL). Over the past decade, large sequencing efforts have provided a great boost in the identification of candidate oncogenes and tumor suppressors involved in FL and DLBCL oncogenesis. Mouse models have been instrumental to accurately mimic in vivo lymphoma-specific mutations and interrogate their normal function in the GC context and their oncogenic function leading to lymphoma onset. The limited access of biopsies during the initiating steps of the disease, the cellular and (epi)genetic heterogeneity of individual tumors across and within patients linked to perturbed dynamics of GC ecosystems make the development of genetically engineered mouse models crucial to decipher lymphomagenesis and disease progression and eventually to test the effects of novel targeted therapies. In this review, we provide an overview of some of the important genetically engineered mouse models that have been developed to recapitulate lymphoma-associated (epi)genetic alterations of two frequent GC-derived lymphoma entities: FL and GCB-DLCBL and describe how those mouse models have improved our knowledge of the molecular processes supporting GC B cell transformation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Human B Lymphomas Reveal Their Secrets Through Genetic Mouse Models

et al. 2021

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“…A recent large cohort study of lymphoma is showing the importance of a tumor microenvironment and its clinical importance. They identified novel microenvironments subtypes by defining 25 functional gene expression signatures reflecting pathway activities from transcriptome data [38]. Genomic data was analyzed to identify cancer clones by their unique mutational signatures [39].…”

Section: Tumor Heterogeneity: Cancer Genomics To Translational Medicinementioning

confidence: 99%

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Park

Heider

Hauschild

2021

Cancers

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The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.

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“…Schematic representation of the predominant cells in the microenvironment of the four DLBCL subgroups with prognostic significance, described by Kotlov et al . 70 (GC-like, germinal center-like; MS, mesenchymal; IN, inflammatory; and DP, depleted).…”

Section: The Tumor Microenvironmentmentioning

confidence: 99%

Prognostic molecular biomarkers in diffuse large B-cell lymphoma in the rituximab era and their therapeutic implications

Papageorgiou

Thomopoulos

Katagas

et al. 2021

Therapeutic Advances in Hematology

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Diffuse large B-cell lymphoma (DLBCL) represents a group of tumors characterized by substantial heterogeneity in terms of their pathological and biological features, a causal factor of their varied clinical outcome. This variation has persisted despite the implementation of rituximab in treatment regimens over the last 20 years. In this context, prognostic biomarkers are of great importance in order to identify high-risk patients that might benefit from treatment intensification or the introduction of novel therapeutic agents. Herein, we review current knowledge on specific immunohistochemical or genetic biomarkers that might be useful in clinical practice. Gene-expression profiling is a tool of special consideration in this effort, as it has enriched our understanding of DLBCL biology and has allowed for the classification of DLBCL by cell-of-origin as well as by more elaborate molecular signatures based on distinct gene-expression profiles. These subgroups might outperform individual biomarkers in terms of prognostication; however, their use in clinical practice is still limited. Moreover, the underappreciated role of the tumor microenvironment in DLBCL prognosis is discussed in terms of prognostic gene-expression signatures, as well as in terms of individual biomarkers of prognostic significance. Finally, the efficacy of novel therapeutic agents for the treatment of DLBCL patients are discussed and an evidence-based therapeutic approach by specific genetic subgroup is suggested.

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Clinical and Biological Subtypes of B-cell Lymphoma Revealed by Microenvironmental Signatures

Cited by 157 publications

References 86 publications

Human B Lymphomas Reveal Their Secrets Through Genetic Mouse Models

Human B Lymphomas Reveal Their Secrets Through Genetic Mouse Models

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Prognostic molecular biomarkers in diffuse large B-cell lymphoma in the rituximab era and their therapeutic implications

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