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Defining Functional Heterogeneity In Acute Lymphoblastic Leukemia

2013· article· en· W343231960 on OpenAlexaff
Stephanie M. Dobson, Esmé Waanders, Jessica McLeod, Ildiko Grandal, Olga I. Gan, Faiyaz Notta, Jessica Loo-Young-Kee, Mark D. Minden, Cynthia J. Guidos, Jayne S. Danska, Charles G. Mullighan, John E. Dick

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsAcute lymphocytic leukemiaTransplantationclone (Java method)LeukemiaMedicineSomatic evolution in cancerOncologyImmunophenotypingDiseaseMinimal residual diseaseCytogeneticsAcute leukemiaImmunologyCancerBiologyInternal medicineLymphoblastic LeukemiaGeneticsAntigenChromosomeGene

Abstract

fetched live from OpenAlex

Abstract Despite high survival rates for children with acute lymphoblastic leukemia (ALL), only 40% of adult patients will achieve long-term disease-free survival, and relapses in both pediatric and adult ALL are often fatal. Most current therapies are directed at molecular markers or dominant pathways present in the bulk of neoplastic cells, yet recent studies have identified many genetically distinct subclones co-existing within a single neoplasm. The functional properties and clinical relevance of these neoplastic subclones remain undefined. Genome wide copy number analysis of matched diagnostic and relapse ALL samples identified that in 50% of patients, the clones present at relapse are not the dominant clones at diagnosis, but have evolved from an ancestral pre-leukemic clone (Mullighan et al., 2008). In order to investigate the functional consequences of clonal evolution in disease progression and therapy resistance, we performed limiting dilution analysis of 3 diagnostic and 14 paired diagnostic/relapse samples from adult and pediatric B-ALL patients of varying cytogenetics, by transplantation into immune-deficient mice (xenografts). In one patient, the leukemia-initiating cell (LIC) frequency was 7.65 fold higher in the relapse sample than at diagnosis, while another patient showed the reverse with a 5.81 fold higher LIC frequency in the diagnostic sample. Two patients showed no significant differences in LIC frequency from diagnosis to relapse. LIC frequency varied from 1 in 14.2 to 1 in 4802 CD19+ blast cells. Interestingly, in 50% of the paired patient samples, transplantation of cells from the relapse sample gave rise to greater leukemic dissemination to the spleen and/or central nervous system of recipient mice in comparison to the diagnostic sample, despite similar levels of engraftment in the bone marrow. This data suggests that although the LIC frequency in B-ALL remains high and relatively static between diagnosis and relapse, relapse cells acquire increased invasive properties. To investigate the clonal composition of 3 diagnostic B-ALL samples, we undertook copy number variation (CNV) analysis of xenografts generated at both limiting and high transplanted cell doses. In all 3 samples, we detected subclones in the xenografts that were distinct from the predominant clone in the primary patient sample. We performed network analysis on these subclones and identified differentially enriched pathways, including differential expression of anti-apoptotic and apoptosis regulation pathways, providing evidence of putative functional differences. These results support the existence of functionally diverse subclones within diagnostic samples as well as functional diversity between the subclones present at diagnosis and relapse. Ongoing in depth genomic analysis of the diagnosis/relapse paired samples will add to our understanding of the functional role of the subclones identified at diagnosis in the establishment of disease relapse. In summary, these experiments will provide further insight into the functional heterogeneity present in B-ALL and the drivers of lymphoid leukemogenesis that lead to therapy failure and disease relapse. Disclosures: Danska: Trillium Therapeutics/Stem Cell Therapeutics: 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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