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Record W7132972755

Transcriptomic Characterization of Primary B Cell Acute Lymphoblastic Leukemia Identifies Novel Protein Biomarkers of High-risk Disease and Novel Mechanisms of L-asparaginase Resistance

2021· dissertation· W7132972755 on OpenAlexaff
Mark William Gower

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTranscriptomeGeneDiseaseGene expression profilingAsparaginaseB cellLeukemiaGene signatureImmune system
DOInot available

Abstract

fetched live from OpenAlex

There is an unmet medical need to improve identification of B cell acute lymphoblastic leukemia (B-ALL) patients at high-risk (HR) for relapse, so that they can receive more aggressive or experimental therapies. Treatment with ABL-targeted therapies has improved outcomes for patients with the Philadelphia (Ph) chromosome that encodes BCR-Abl fusions. “Ph-like” ALL is also characterized by kinase activating mutations that are therapeutically targetable, but they cannot be readily identified using routine clinical methods. The overall goal of my project was to use transcriptomics and single cell immune profiling to improve identification of HR “Ph-like” ALL cases and to identify mechanisms of ALL chemo-resistance. I developed an RNA-Sequencing (RNA-seq) data analysis pipeline and identified fusion transcripts, mutations and a gene expression signature associated with Ph-like cases in a cohort of 45 “B-Other” ALL cases that lacked clinically detected mutations. I then used single cell flow and mass cytometry to show that expression patterns of five surface protein encoding genes (SPEG) robustly distinguished Ph-like cases with ABL1 or CRLF2/JAK2 mutations that promote STAT5 activation from non-Ph-like cases of “B-Other” ALL. Thus, by integrating data from several “multi-omic” approaches, I defined a novel SPEG classification method that can rapidly identify Ph-like ALL cases with targetable signaling mutations. Unfortunately, about 12% of relapsed B-ALL patients are refractory to treatment with L-asparaginase (L-asp), long used as a front-line chemotherapy for ALL cells because they lack Asparagine synthetase (ASNS) and thus require extracellular L-Asparagine. However, few studies have identified mechanisms of L-asp resistance in primary ALL cells. Through bioinformatic and in vitro approaches, I show that deregulation of ASNS expression by promoter fusions or by RAS/MAPK pathway activation promotes L-asp resistance in B-ALL. These data suggest that RAS-targeted therapies could improve outcomes for RAS-mutated ALL. Overall, my work has provided a new method that could be used for routine diagnosis of HR Ph-like ALL cases likely to have targetable signaling mutations. Additionally, I have identified RAS/MAPK signaling as a potential therapeutic target to enhance induction of remission and more effectively treat relapses in patients with RAS-mutated B-ALL.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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