Transcriptomic Characterization of Primary B Cell Acute Lymphoblastic Leukemia Identifies Novel Protein Biomarkers of High-risk Disease and Novel Mechanisms of L-asparaginase Resistance
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".