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Record W2562019564 · doi:10.1158/1538-7445.am2015-2075

Abstract 2075: Signaling thresholds and negative B cell selection in acute lymphoblastic leukemia

2015· article· en· W2562019564 on OpenAlexaff
Zhengshan Chen, Seyedmehdi Shojaee, Maike Buchner, Huimin Geng, Jae Woong Lee, Lars Klemm, Eugene Park, Ying Xim Tan, Anne B. Satterthwaite, Elisabeth Paietta, Stephen P. Hunger, Mignon L. Loh, Jae U. Jung, John E. Coligan, Silvia Bolland, Tak W. Mak, André Limnander, Hassan Jumaa, Michael Reth, Arthur Weiss, Clifford A. Lowell, Markus Müschen

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldChemistry
TopicSynthesis and Biological Evaluation
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
Keywordsbreakpoint cluster regionSykBruton's tyrosine kinaseCancer researchImmunoreceptor tyrosine-based activation motifTyrosine kinaseSignal transductionB-cell receptorBiologyReceptorTyrosine phosphorylationPhosphorylationTyrosineCell biologyChronic lymphocytic leukemiaLeukemiaImmunologyB cellAntibodyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Introduction: Unlike other cell types, B cells are selected for an intermediate level of signaling strength. Critical survival and proliferation signals emanate from the (pre-) B cell receptor (BCR): Both attenuation below minimum (e.g. non-functional pre-BCR) and hyperactivation above maximum (e.g. autoreactive pre-BCR) thresholds of signaling strength trigger negative selection and cell death. The oncogenic BCR-ABL1 tyrosine kinase mimics active pre-BCR signaling in Ph+ acute lymphoblastic leukemia (ALL) which defines the ALL subgroup with the worst clinical outcome. Current therapy approaches are largely focused on the development of more potent tyrosine kinase inhibitors (TKI) to suppress oncogenic signaling. However resistance to TKI is developed invariably. Here, we test the hypothesis that targeting hyperactivation above a maximum threshold will selectively kill Ph+ ALL cells, similar to removal of self-reactive B cells. Results: The Ph+ ALL cells don not express ITAM (immunoreceptor tyrosine-based activation motif) receptor Igα or Igβ on the cell surface, indicating defects for a functional pre-BCR. Reconstitution of ITAM receptor was sufficient to induce cell death through increasing pre-BCR signaling strength indicated by phosphorylation of SYK, SRC, BTK and PLCγ2. TKI-treatment, while designed to kill leukemia cells, seemingly paradoxically rescued Ph+ ALL cells in this experimental setting. Surprisingly, patient-derived Ph+ ALL cells express the ITIM (immunoreceptor tyrosine-based inhibitory motif) receptors PECAM1, CD300A and LAIR1 at high levels compared to normal pre-B cells. Importantly, high expression levels of ITIM-receptors are predictive of poor outcome in two clinical trials, including both pediatric and adult ALL patients. Genetic studies revealed that Pecam1, Cd300a and Lair1 were critical to calibrate pre-BCR signaling strength through recruitment of the inhibitory phosphatases Ptpn6 (Shp1) and Inpp5d (Ship1). Genetic deletion of Lair1, Ptpn6 or Inpp5d in BCR-ABL1 ALL caused cell death in vitro and in vivo through hyperactivation of pre-BCR signaling. Testing various components of proximal pre-BCR signaling, we found that an incremental increase of SYK tyrosine kinase activity was required and sufficient to induce cell death. Hyperactive SYK was functionally equivalent to acute activation of a self-reactive BCR on ALL cells. Using chimeric PECAM1, CD300A and LAIR1 receptor decoys and a novel small molecule inhibitor of INPP5D, we demonstrated that pharmacological hyperactivation of pre-BCR signaling and engagement of negative B cell selection represents a promising new strategy to overcome drug-resistance in human Ph+ ALL. Conclusion: These results indicated that inhibitory receptors and downstream phosphatases are critical regulators of pre-BCR signaling strength in Ph+ ALL, and identified targeting hyperactivation of pre-BCR signaling as a potential novel class of therapeutic strategy. Note: This abstract was not presented at the meeting. Citation Format: Zhengshan Chen, Seyedmehdi Shojaee, Maike Buchner, Huimin Geng, Jae Woong Lee, Lars Klemm, Eugene Park, Ying Xim Tan, Anne Satterthwaite, Elisabeth Paietta, Stephen P. Hunger, Mignon L. Loh, Jae U. Jung, John E. Coligan, Silvia Bolland, Tak W. Mak, Andre Limnander, Hassan Jumaa, Michael Reth, Arthur Weiss, Clifford A. Lowell, Markus Müschen. Signaling thresholds and negative B cell selection in 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 2075. doi:10.1158/1538-7445.AM2015-2075

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.007
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.137
GPT teacher head0.395
Teacher spread0.257 · 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 teacher head, 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
Published2015
Admission routes1
Has abstractyes

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