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Record W2987692177 · doi:10.1182/blood-2019-131444

Establishing a Novel in Vitro Informed Precision Clinical Trial Pathway for Refractory Pediatric Leukemia

2019· article· en· W2987692177 on OpenAlexaff
Chunfen Zhang, Ritul Sharma, Satbir Thakur, Allison Cheney, Olena M. Vaske, Lauren Sanders, Jessica Boklan, Victor Lewis, Ronald Anderson, Olga Kovalchuk, Norman J. Lacayo, Aru Narendran, Kevin Bielamowicz

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsAlberta Children's HospitalUniversity of LethbridgeUniversity of Calgary
Fundersnot available
KeywordsDruggabilityMedicineLeukemiaClinical trialStromal cellBone marrowCancer researchBioinformaticsOncologyImmunologyBiologyInternal medicineGene

Abstract

fetched live from OpenAlex

Introduction: In children diagnosed with leukemia, relapse and its associated morbidity and mortality remain the most dreaded consequences of the disease. Therefore, the discovery and implementation of novel and broadly applicable therapeutic strategies for these patients are urgently needed. Currently, a number of precision therapeutic approaches have been formulated where molecular analyses of the malignant cells have been used to inform, often multiple, probable targets and potential therapeutic agents. However, a common drawback of this approach has been the uncertainty involved in selecting the drug with the most and clinically relevant cytotoxic potential. In vitro xenograft approaches, although can provide key information on drug activity and side effects, are time consuming and impractical and cumbersome in most cases. We have recently demonstrated the ability of a bone marrow stromal derived cell line to sustain the growth and survival of patient leukemic cells in culture that has allowed in vitro evaluations of drug response.This methodology was combined with a previously validated molecular pathway analysis program to identify effective agents or combinations for a subsequent informed precision clinical trial. Methods: Gene expression profiles from refractory pediatric leukemic cells were analyzed against similar data from more than 12, 000 tumors and outlier analyses were carried out to generate a list of overexpressed genes. This information was computed to identify hypothetically activated pathways, druggable targets and potential agents from a panel of FDA approved drugs. A bone marrow stromal cell line was established and characterized that has been shown to support leukemia cell proliferation in vitro. Briefly, stromal cells were co-cultured with leukemic cells at pre-determined ratios with and without the drugs identified in the genomic analysis. After four days in culture, leukemic cells were re-suspended and analyzed for proliferation. Target modulation and activated cell death pathways were queried by Western blot analyses. Results: Multiple targets and potential agents for effective therapeutics were identified against an initial set of relapsed leukemia specimens. For example, in patient # P700491 (pre B-ALL) gene expression data sets revealed clustering within the area of ALL and AML in the reference cancer genomics data. Comparative tumor RNA seq outlier analysis showed molecular abnormalities in BTK, JAK3 and PIK3CD, corresponding to molecular categories of RTK, JAK-SAT and PI3K-AKT-mTOR pathways targetable by the drugs Ibrutinib, WHI-P131 and Idelalisib, respectively. However, in vitro studies showed significant cell killing with Idelalisib and not with the other two agents. Target modulation assays showed effective induction of apoptosis including PARP cleavage in Idelalisib treated leukemia cells compared to controls, indicating the feasibility of this approach to effectively identify potentially applicable agents for this individual patient. Conclusions: We demonstrate the ability of a newly cloned bone marrow stromal derived cell line to sustain the growth and survival of patient leukemic cells in culture that has allowed in vitro target modulation and target validation analyses for cytotoxicity. This methodology was combined with a previously demonstrated molecular pathway interrogation program to ascertain effective agents or combinations for an experimentally informed precision clinical trial. Importantly, our data showed that genomically identified actionable targets are not universally predictive of tumor response and an in vitro cytotoxicity analysis step may enhance the accuracy of this approach. We describe the practical advantages and versatility of this work-flow to inform the selection of agents in future umbrella trials. It is anticipated that the information obtained will lead to an applicable clinical trial the near future. Disclosures Narendran: Bayer: Honoraria, Other: CANTRK Advisory Board .

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)0.012
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.352
Teacher spread0.304 · 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 designNon-randomized trial
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

Citations1
Published2019
Admission routes1
Has abstractyes

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