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Record W3047410483 · doi:10.1158/1538-7445.pedca19-b67

Abstract B67: EGFR as a target in pediatric solid tumors

2020· article· en· W3047410483 on OpenAlexaboutno aff
Catherine M. Albert, Navin Pinto, Erin R. Rudzinski, Julie R. Park

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsEpidermal growth factor receptorDesmoplastic small-round-cell tumorCancer researchImmunohistochemistryCancerEpitopeTissue microarrayMonoclonal antibodyMalignancyMedicineBiologyPathologyAntibodyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract The epidermal growth factor receptor (EGFR or HER1) is a cell surface tyrosine kinase receptor that is expressed in a diverse group of epithelial and nonepithelial tissues and broadly associated with cell proliferation and differentiation. Expression of wild-type EGFR and activating mutations are described in many malignancies, including a variety of pediatric solid tumors. In the setting of malignancy, EGFR has been associated with aggressive disease, chemotherapy resistance, and increased metastatic potential. Employment of anti-EGFR agents has yielded some promising results, but they are not always effective against all cancer-associated aberrant EGFR expression and are frequently associated with notable off-tumor toxicities due to the expression of EGFR in normal tissue. The unique EGFR monoclonal antibody (mAb) 806 selectively binds to an epitope on the extracellular portion of human EGFR expressed on the surface of tumor cells. This antibody recognizes the deletion mutant EGFRvIII commonly present in glioblastomas and some other solid tumors as well as wild-type EGFR. We designed a phase I trial for pediatric and young adult patients with recurrent or refractory solid tumors expressing epidermal growth factor (EGFR) to examine the safety and feasibility of administering autologous, peripheral blood-derived T cells that have been genetically modified to express a second-generation (2G) EGFR806-specific chimeric antigen receptor (CAR). Literature review and evaluation of EGFR immunohistochemistry (IHC) performed on several tissue microarrays were used to estimate the percentage of EGFR positivity among common subtypes of pediatric cancer. Using these tools, we hypothesized that 15-40% of otherwise eligible patients would meet the eligibility criteria of EGFR positivity. The trial opened in August of 2018. To date, a total of 33 patient tumors have undergone EGFR IHC to determine eligibility for the trial. 9/33 (27.3%) have been EGFR+. EGFR+ histologies include BCOR fusion sarcoma (3), germ cell tumor (2), osteosarcoma, Ewing sarcoma, desmoplastic small round cell tumor, and synovial sarcoma. Our eligibility testing thus far demonstrates EGFR expression by IHC in a variety of histologies. Two rare diseases, recurrent BCOR fusion sarcoma and refractory germ cell tumors, have demonstrated a significantly higher rate of EGFR IHC positivity, (3/3)100% and (2/3) 66.7%, respectively. These early results indicate that this trial and other anti-EGFR therapies may be most effectively targeted to certain subgroups of pediatric cancer patients. Citation Format: Catherine M. Albert, Navin R. Pinto, Erin R. Rudzinski, Julie R. Park. EGFR as a target in pediatric solid tumors [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B67.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.114
GPT teacher head0.456
Teacher spread0.342 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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