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Record W2968109725 · doi:10.1182/blood-2018-99-111726

Literature Review of All Cases of Aggressive T-Cell Large Granular Lymphocytic Leukemia Cases and Report of an Additional Case

2018· article· en· W2968109725 on OpenAlexaff
Vanessa Brunet, Michel Pavic, Sofia Marouan, Isabelle Fleury, Jean‐François Castilloux

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-RosemontHôpital FleurimontUniversité de Sherbrooke
Fundersnot available
KeywordsLeukemiaMedicineImmunologyImmunophenotypingLymphoproliferative disordersCytopeniaChronic lymphocytic leukemiaPathologyLymphomaBone marrowAntigen

Abstract

fetched live from OpenAlex

Abstract Large granular lymphocyte (LGL) leukemia is a spectrum of rare lymphoproliferative disorders, classified into T-cell LGL leukemia, chronic lymphoproliferative disorder of NK-cells and aggressive NK-cell leukemia; chronic NK-cell leukemia is a provisional diagnosis. However, we identified fourteen cases of aggressive T-LGL leukemia retrieved in the literature. Considering this unusual and rare clinical presentation, we are reporting a literature review and presenting an additional case. Leukemic cells of T-LGL leukemia have a characteristic phenotype (CD3+CD8+CD16+CD57+) and show clonal TCR gene rearrangement, while leukemic cells of aggressive NK cell leukemia show a distinguishable phenotype (CD3-CD4-CD8-CD16+CD56+CD57-) and are EBV-related. In contrast with the aggressive NK cell leukemia, the chronic lymphoproliferative disorder of T-cell is not EBV-associated and has a distinguishable immunophenotype (CD16+CD56−CD57+). While T-LGL leukemia and chronic lymphoproliferative disorder of NK-cells have a more chronic disease (years), mainly reported with autoimmune disease (rheumatoid arthritis), numerous infections due to neutropenia and a mild-to-moderate splenomegaly, aggressive NK-cell leukemia is characterized by systemic manifestations and a disseminated disease after a few weeks of presentation despite treatment instauration. In contrast to the T-LGL leukemia, aggressive T-LGL leukemia has a clinical presentation similar to the aggressive NK-cell leukemia, characterized by constitutional symptoms, rapidly progressive hepatosplenomegaly, cytopenia and organ infiltration. The atypical clinical presentation and pathological findings of the aggressive T-LGL leukemia explain the diagnostic challenge of this entity for clinicians. In fact, cases of aggressive T- LGL leukemia retrieved atypical size, irregular nuclei and atypical immunophenotype. Some cases had features similar to those described for patients with NK-cell leukemia (CD56+CD57-) while others did not present neither NK nor T-cell classical immunophenotype (CD56-57-). Facing the heterogeneity of aggressive LGL leukemia, the rapidly evolutive disease (multi-organic infiltration) and the absence of randomized trials on large numbers of patients, no consensus on the treatment approach exists. (Table 1) Our patient presented, at the age of 24 yo, with transitory and autonomous resolution of hepatosplenomegaly and pancytopenia. Almost 30 years later, the patient developed a similar and persistent episode, which lead to a diagnosis of aggressive T-cell LGL leukemia. Was this first episode the early and indolent presentation of his T- LGL leukemia or was it only related to an indolent and transitory etiology? Considering the clinical evolution of previously reported case and of our patient, LGL leukemia tends to evolve in many ways; resolution, indolent and chronic or aggressive evolution and transformation into a lymphoma. As the cases retrieved in literature, the diagnosis of our patient was complicated by atypical clinical presentation and unusual pathological findings; massive medullary involvement without real images of intra-sinusal lymphocytosis and atypical T- LGL based on their small-medium size with slightly irregular nuclei and the lack of expression of CD56/CD57. Facing the heterogeneity of treatments attempted for aggressive T-LGL leukemia and their unpredictable response, we believe that the treatments given to our patient were consistent with the current literature and did not add an additional mortality risk (3 cycles of CHOP, 4 cycles of ESHAP, methotrexate, splenectomy). As reported in literature, even though our patient was treated with varying regimens, his disease rapidly evolved into a multi-organic infiltration (skin, lungs, liver, kidney, facial cranial nerves, conus medullaris and bone marrow involvement) Large granular lymphocyte (LGL) leukemia represent a spectrum of indolent and aggressive diseases, whereby an indolent form can evolve into an aggressive form. Aggressive T-cell LGL leukemia are characterized by a multisystem disease, an atypical immunophenotype (CD56-CD57- or CD56+CD57-) and are associated with an uncertainty regarding therapeutics.Our case report of an aggressive T-cell LGL leukemia adds to the few available studies on the subject. Disclosures Pavic: Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.311
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2018
Admission routes1
Has abstractyes

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