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Abstract A040: Assessing the ability of Fc3TSR to remodel the tumor microenvironment and enhance efficacy of immunotherapies and chemotherapy in a murine model of pancreatic ductal adenocarcinoma

2024· article· en· W4390915129 on OpenAlexaff
Bianca Garlisi, Caroline Aitken, Sylvia Lauks, Julia Stewart, Duncan Petrik, Jack Lawler, Jim Petrik

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineGemcitabineTumor microenvironmentPancreatic cancerCancer researchCancerMetastasisAngiogenesisAdenocarcinomaInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Introduction: Pancreatic Ductal Adenocarcinoma (PDAC) has a poor survival rate due to late diagnosis where metastasis has often occurred. Angiogenesis, the process by which new vessels form from pre-existing vasculature, is crucial for tumor growth and metastasis. Tumors aggressively upregulate expression of pro-angiogenic factors, stimulating rapid vessel formation. These vessels often lack pericyte coverage and have dysfunctional morphology, leading to high interstitial fluid pressure (IFP). Impaired perfusion and high IFP impede therapy uptake. Fc3TSR is a novel fusion protein derived from the potent angiogenic inhibitor thrombospondin 1, which we have shown to normalize vasculature in ovarian cancer, significantly decreasing tumor size and improving uptake of many therapies. In this study, we evaluated Fc3TSR’s ability to remodel the tumor microenvironment to induce tumor regression and to enhance efficacy of other therapies. Methods: We developed an orthotopic syngeneic murine model of PDAC, whereby we surgically injected 2.5 × 104 murine PDAC cells (KPC) into the tail of the pancreas of C57BL/6 mice. Tumors were allowed to progress for 14 days before intraperitoneal (IP) administration of Fc3TSR (0.158mg/kg) or PBS (control) on day 14 and 21. Gemcitabine (GEM) chemotherapy treated mice received either daily, metronomic dosages of GEM, or weekly, maximum tolerated dosages GEM starting on day 23. Checkpoint inhibitor mice received either PD-L1 (25ug) or CTLA4 (25ug) checkpoint inhibitors on day 23 and 26. Mice were euthanized on day 30 and tumors and the draining lymph nodes were collected and weighed. 1 hour before euthanasia, Fc3TSR and PBS mice were IP injected with Hypoxyprobe (Pimonidazole Hydrochloride) and tumors were immunostained for markers of blood vessels and tumor hypoxia. Results: Fc3TSR induced tumor regression when compared to PBS, but GEM at either dosage did not further enhance this effect. Following Fc3TSR, PD-L1, but not CTLA-4 checkpoint inhibitors significantly reduced tumor size when compared to mice pre-treated with PBS. Fc3TSR treatment also reduced the area of tumor hypoxia. Tumor vasculature is currently being imaged and analyzed for Fc3TSR’s effect on vessel morphology. Conclusion: Our data suggests that normalizing the tumor microenvironment can enhance the uptake and efficacy of combination therapies. Enhanced perfusion could facilitate the migration of activated immune cells and may enhance immune responses in PDAC patients. Here we demonstrate a novel approach to optimize therapeutic efficacy in advanced stage PDAC. Citation Format: Bianca Garlisi, Caroline Aitken, Sylvia Lauks, Julia Stewart, Duncan Petrik, Jack Lawler, Jim Petrik. Assessing the ability of Fc3TSR to remodel the tumor microenvironment and enhance efficacy of immunotherapies and chemotherapy in a murine model of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A040.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.035
GPT teacher head0.373
Teacher spread0.338 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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