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Record W2485994046 · doi:10.1158/1538-7445.am2016-4177

Abstract 4177: Dynamic visualization of cancer cell engraftment into immune compromised zebrafish

2016· article· en· W2485994046 on OpenAlexaff
John C. Moore, Qin Tang, Nora Torres Yordán, Timothy S. Mulligan, Finola E. Moore, Riadh Lobbardi, Ashwin Ramakrishnan, Anthony Anselmo, Ruslan I. Sadreyev, Jason N. Berman, Robert Liwski, Brant M. Weinstein, John F. Rawls, David M. Langenau

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsZebrafishBiologyImmune systemCancer researchT cellRecombination-activating geneTransplantationCell biologyCellImmunologyGeneGeneticsMedicine

Abstract

fetched live from OpenAlex

Abstract Cell transplantation into immune compromised mice has transformed our understanding of cancer and is now the gold standard for assessing therapeutic responses in vivo. However, mouse models are expensive and engraftment is often difficult to visualize directly. To overcome these challenges, we have developed immune compromised zebrafish (ICZ) in the transparent casper background using genome editing techniques. We have successfully targeted genes required for immune cell function and are well known to cause immune deficiency in human and mice. To date, we have developed homozygous viable mutants for recombination-activating gene 2 (rag2), DNA-dependent protein kinase (prkdc), janus kinase 3 (jak3), interleukin 2 receptor gamma (Il2rg), zeta-chain (TCR) associated protein kinase 70 (zap70), and forkhead box N1 (foxn1/nude). Gene expression analysis of marrow cells using RNAseq has identified novel transcript changes correlated with loss of specific cell types, and in conjunction with large-scale single cell transcriptional profiling, has identified specific cellular defects associated with T, B, and NK cell loss. For example, homozygous prkdc (SCID) mutant fish lack mature T and B cells, but have intact NK cell signaling. By contrast, il2rg-deficient zebrafish lack T and NK cells. Importantly, these ICZ models accurately recapitulate known human severe combined immune deficiencies and established mouse models that are commonly used for cell transplantation. Thus, it is not unexpected that a subset of zebrafish mutants have reduced immune cell function, permitting engraftment of normal hematopoietic and muscle satellite cells from allogeneic donors. Additionally, we have demonstrated robust and persistent engraftment of fluorescently labeled leukemia, rhabdomyosarcoma, neuroblastoma, and melanoma from a wide range of zebrafish strains. Because mutations have been created in optically-clear, casper-strain zebrafish and cancers are fluorescently labeled, we now have unprecedented access to directly visualize tumor cells at single cell resolution in live animals. To date, we have optimized our models to visualize neovascularization, intratumoral cell heterogeneity, clonal evolution and metastisis. The ability to transplant non-immune matched cell types will likely revolutionize the types and scale of cell transplantation experiments performed in the zebrafish and will likely permit engraftment of mouse and human cells into compound mutant ICZ models in the near future. Citation Format: John C. Moore, Qin Tang, Nora Torres Yordan, Timothy Mulligan, Finola E. Moore, Riadh Lobbardi, Ashwin Ramakrishnan, Anthony Anselmo, Ruslan Sadreyev, Jason Berman, Robert Liwski, Brant Weinstein, John Rawls, David M. Langenau. Dynamic visualization of cancer cell engraftment into immune compromised zebrafish. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4177.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.407
Teacher spread0.383 · 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
Published2016
Admission routes1
Has abstractyes

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