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Distinct Proteomic Signaling Networks Stratify Responses to Ara-C Based Induction Therapy in Patients with Acute Myeloid Leukemia (AML)

2008· article· en· W2559793479 on OpenAlexaff
Mark D. Minden, David B. Rosen, Santosh Putta, Aileen Cleary Cohen, Helen Francis-Lang, Todd Covey, John Woronicz, David M. Soper, Brian J. Long, James Cordeiro, Lootsie Panganiban-Lustan, Steve Banville, Urte Gayko, Alessandra Cesano, Wendy J. Fantl

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDaunorubicinCytarabineMyeloid leukemiaCytokineCancer researchMedicineLeukemiaSignal transductionImmunologyBiologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Abstract Background: AML represents a group of clonal hematopoietic stem cell disorders in which aberrantly regulated signaling pathways lead to oncogenic progression. In a prior study, a panel of 36 cytokine response nodes was measured in single cells by multi-parameter phosphoflow cytometry in newly diagnosed AML patient samples. Unique cancer network profiles were revealed that correlated with response to chemotherapy (Irish et al, Cell (2004) 118, p217). Objectives: Given the heterogeneity of AML, the current study was designed to expand the number of signaling nodes to 152 per patient sample. The signaling nodes were organized into 4 biological categories: Protein expression of receptors and drug transporters Response to cytokines and growth factors, Phosphatase activity, Apoptotic signaling pathways. Methods: Multi-parameter flow cytometric analysis was performed on peripheral blasts taken at diagnosis from 33 AML patients who attained a complete response (CR, n=9) or no-remission (NR, n=24) to one cycle of standard 7 + 3 induction therapy (100–200mg/m2 cytarabine and 60mg/m2 daunorubicin). Results: The data show that expression of the receptors for c-Kit and FLT3L and the drug transporter ABCG2 were increased in patients who attained an NR versus CR. Readouts from the cytokine-Stat response panels and the growth factor-Map kinase and PI3-Kinase response panels (see Table 1) revealed increased signaling in blasts taken from NR patients versus blasts taken from patients who clinically responded to therapy. To determine the role of phosphatases, a physiologic phosphatase inhibitor, peroxide, (H2O2) revealed increased phosphatase activity in CRs versus NRs. In the absence of treatment with H2O2, CRs had lower levels of phosphorylated PLCƒ×2 and SLP-76 versus NRs, and attained higher levels of phosphorylated PLCƒ×2 and SLP-76 upon H2O2 treatment. Lastly, interrogation of the apoptotic machinery using agents such as staurosporine and etoposide showed that NR patient blasts failed to undergo cell death, as determined by cleaved PARP and cleaved Caspase 8. Of note, in NR patient blasts, these agents did promote an increase in phosphorylated Chk2 suggesting a communication breakdown between the DNA damage response pathway and the apoptotic machinery. In contrast, blasts from CR patients showed significant populations of cells with cleaved PARP and caspase 8 consistent with their clinical response outcomes. Conclusions : In this study, 152 signaling nodes per patient sample were measured by multi-parameter flow cytometry and revealed distinct signaling profiles that correlate with patient response to ara-C based induction therapy. Alterations were seen in expression for the c-Kit and Flt-3L receptors, the ABCG2 drug transporter, cytokine and growth factor pathway response, phosphatase activity and apoptotic response, all of which could stratify the NR from the CR patient subsets. Whether there is a combination of hierarchical nodes to best predict response to therapy is currently under investigation.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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".

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Citations0
Published2008
Admission routes1
Has abstractyes

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