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Record W2328965374 · doi:10.1006/mthe.2002.0570

Improved Treatment of Pancreatic Cancer by IL-12 and B7.1 Costimulation: Antitumor Efficacy and Immunoregulation in a Nonimmunogenic Tumor Model

2002· article· en· W2328965374 on OpenAlexaff
Brigitte M. Pützer, Florian Rödicker, Mary Hitt, Thorsten Stiewe, Helmut Esche

Bibliographic record

VenueMolecular Therapy · 2002
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPancreatic cancerImmune systemImmunotherapyMedicineDownregulation and upregulationAdenocarcinomaCancer researchCancerImmunologyOncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Ductal pancreatic adenocarcinoma is one of the commonest and most lethal cancers in the Western world. Unfortunately, recent advances in diagnostics, staging, and therapy in pancreatic carcinoma have not resulted in significant improvements in long-term survival. We have previously shown that adenovirus (Ad)-mediated coexpression of interleukin-12 (IL-12) and the costimulatory molecule B7.1 is extremely efficient in inducing regression of highly immunogenic transplanted and nontransplanted tumors. Here, we examined the antitumor efficacy of IL-12- and B7.1-based immunotherapy against a nonimmunogenic murine model of ductal pancreatic cancer. Compared with AdIL-12 treatment alone, single intratumoral injection of AdIL-12/B7.1 led to a prolonged immune response and mediated complete regression in 80% of treated animals. After rechallenge with parental tumor cells, 70% of cured mice remained tumor-free, suggesting that protective immunity had been induced. The antitumoral response was associated with upregulation of H-2K(b) and Abcb2 expression, whereas other components of the proteasome (Abcb3, Psmb9, and Psmb8) were not affected. These data indicate that upregulation of the antigen presentation machinery by AdIL-12/B7.1 may be a therapeutic rationale for nonimmunogenic, therapy-resistant pancreatic cancer.

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)0.000
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 score0.156
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations39
Published2002
Admission routes1
Has abstractyes

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