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Record W2563468727 · doi:10.1158/1538-7445.am2015-1627

Abstract 1627: Phenotypic and functional evaluation of peripheral blood cell subsets in children at the completion of induction therapy for acute lymphoblastic leukemia

2015· article· en· W2563468727 on OpenAlexaff
Nina Rolf, Amina Kariminia, Kinga K. Smolen, Caron Strahlendorf, Gregor S. D. Reid

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineImmune systemImmunologyBone marrowCancerPeripheral blood cellLymphocyteOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Acute lymphoblastic leukemia (ALL) is the most common malignancy in children, accounting for more than one quarter of the new cases of pediatric cancer each year. Despite significant improvements in cure rates, recurrent ALL causes 10% of childhood cancer deaths and the need for new treatment strategies has not diminished. Although ALL has long been viewed as a poor target for immune therapy, it has recently been reported that the peripheral blood absolute lymphocyte count (ALC) at completion of induction chemotherapy is prognostic for outcome. This finding suggests that the immune environment during therapy may play a role in protecting against disease progression. It is currently unknown, however, whether ALC is simply a marker of bone marrow recovery or whether particular immune cell subsets at this early time-point confer a beneficial effect. To address this question, we have initiated a flow-cytometric study of 20 peripheral blood leukocyte populations, obtained at Day 29 from children undergoing therapy for precursor B cell ALL. The goal of this study is to correlate the size and responsiveness of the cell subsets with ALC in order to identify potential immune-mediators of the high ALC-associated favorable outcome. To date we have evaluated 8 patients for T cells (5 subsets), B cells (6 subsets), NK cells (3 subsets), NKT cells (2 subsets), dendritic cells (2 subsets), monocytes, and granulocytes. In addition to this extensive phenotypic characterization, all Day 29 blood samples have been stimulated with a panel of Toll-like receptor (TLR) ligands to evaluate functional responsiveness. Although we have not yet identified a single lymphocyte population that significantly correlates with ALC, clear trends, most notably with myeloid dendritic cells, have emerged that suggest that ALC may be indicative of specific immune activity. TLR-induced cytokine production is currently being quantified to determine whether higher ALC correlates with a specific pattern of response. While a large-scale prospective study to validate prognostic significance of ALC in children with high-risk ALL is underway, to our knowledge our ongoing study is the first to investigate the specific leukocyte composition of Day 29 peripheral blood for significant links with ALC. The identification of the mechanism(s) underlying ALC-associated outcomes may reveal novel strategies for enhanced immune-mediated control of ALL after chemotherapy. Citation Format: Nina Rolf, Amina Kariminia, Kinga K. Smolen, Caron Strahlendorf, Gregor S. Reid. Phenotypic and functional evaluation of peripheral blood cell subsets in children at the completion of induction therapy for acute lymphoblastic leukemia. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1627. doi:10.1158/1538-7445.AM2015-1627

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.096
GPT teacher head0.368
Teacher spread0.272 · 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 designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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