ReviewBlood2021
Understanding CLL biology through mouse models of human genetics.
Review in Blood, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- A question of TiME: how microenvironmental interactions shape response to immunotherapy in CLL and Richter Transformation.Frontiers in immunology · 2025Review
- The molecular map of CLL and Richter's syndrome.Seminars in hematology · 2024Review
- Chronic lymphocytic leukemia patient-derived xenografts recapitulate clonal evolution to Richter transformation.Leukemia · 2024Article
- Article
- Mutation-specific CAR T cells as precision therapy for IGLV3-21Nature communications · 2024Article
- Mice Overexpressing Wild-TypeCancers · 2023Article
- Mitochondrial DNA Mutations as Natural Barcodes for Lineage Tracing of Murine Tumor Models.Cancer research · 2023Article
- In Vivo Modeling of CLL Transformation to Richter Syndrome Reveals Convergent Evolutionary Paths and Therapeutic Vulnerabilities.Blood cancer discovery · 2023Article
- The Broad Spectrum ofHuman mutation · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Rapid advances in large-scale next-generation sequencing studies of human samples have progressively defined the highly heterogeneous genetic landscape of chronic lymphocytic leukemia (CLL). At the same time, the numerous challenges posed by the difficulties in rapid manipulation of primary B cells and the paucity of CLL cell lines have limited the ability to interrogate the function of the discovered putative disease "drivers," defined in human sequencing studies through statistical inference. Mouse models represent a powerful tool to study mechanisms of normal and malignant B-cell biology and for preclinical testing of novel therapeutics. Advances in genetic engineering technologies, including the introduction of conditional knockin/knockout strategies, have opened new opportunities to model genetic lesions in a B-cell-restricted context. These new studies build on the experience of generating the MDR mice, the first example of a genetically faithful CLL model, which recapitulates the most common genomic aberration of human CLL: del(13q). In this review, we describe the application of mouse models to the studies of CLL pathogenesis and disease transformation from an indolent to a high-grade malignancy (ie, Richter syndrome [RS]) and treatment, with a focus on newly developed genetically inspired mouse lines modeling recurrent CLL genetic events. We discuss how these novel mouse models, analyzed using new genomic technologies, allow the dissection of mechanisms of disease evolution and response to therapy with greater depth than previously possible and provide important insight into human CLL and RS pathogenesis and therapeutic vulnerabilities. These models thereby provide valuable platforms for functional genomic analyses and treatment studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.