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Abstract A013: Targeting perivascular macrophages with an orally bioavailable heme oxygenase-1 inhibitor improves responses to chemotherapeutic drugs in cancer

2023· article· en· W4389239913 on OpenAlexaboutno aff
M. Bahri, Taha Al‐Adhami, Emre Demirel, Joanne E. Anstee, Karen Feehan, Cheryl Gillett, Dominika Sosnowska, Renee Gitsaki-Taylor, Tik Shing Cheung, James Spicer, Khondaker Miraz Rahman, James N. Arnold

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeme Oxygenase-1 and Carbon Monoxide
Canadian institutionsnot available
Fundersnot available
KeywordsCancer researchTumor microenvironmentStromal cellHeme oxygenaseImmune systemAngiogenesisCancerCancer cellImmunotherapyCancer immunotherapyFerroportinBiologyImmunologyChemistryHemeMedicineInflammationInternal medicineBiochemistryEnzyme

Abstract

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Abstract The tumor microenvironment (TME) plays a crucial role in cancer progression. It consists of various elements including stromal cells, proteins, and physical factors that support the growth and spread of cancer. Targeting the TME has become an appealing strategy for cancer treatment. Tumor-associated macrophages (TAMs) are an abundant stromal cell type in the TME and, due to their plasticity, TAMs can be exploited by cancer cells and the broader stromal reaction to promote a pro-tumoral response. TAMs have been implicated in facilitating a variety of pro-tumoral pathways including resistance to anti-cancer therapeutics, immune suppression, angiogenesis, and metastasis. Our group has recently characterized a subpopulation of pro-angiogenic and immune-suppressive TAMs which can be identified by their expression of the lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1). LYVE-1+ TAMs reside spatially proximal to blood vasculature and adopt a collaborative multi-cellular ‘nest’ arrangement which may support their biological activity. LYVE-1+ TAMs selectively express high levels of the immunomodulatory enzyme heme oxygenase-1 (HO-1), an enzyme which catabolizes heme to generate the biologically active catabolites carbon monoxide (CO), biliverdin and ferrous iron. Genetic inactivation of HO-1 in LYVE-1+ TAMs results in improved anti-tumoral CD8 T-cell responses and enhanced response to chemotherapy, resulting in prolonged tumor control in a spontaneous MMTV-PyMT murine model of breast cancer. Unfortunately, current HO-1 inhibitors are not orally bioavailable, which limits their utility as immunotherapeutics for the treatment of cancer. In response to this hurdle, we have developed a next generation HO-1 inhibitor called KCL-HO-1i. We demonstrate in pharmacokinetic studies that KCL-HO-1i is orally bioavailable in murine models with a serum half-life of around 3hours. Furthermore, orally delivering KCL-HO-1i alongside standard of care chemotherapy is able to deliver durable tumor control in MMTV-PyMT mice. We have characterized the response of the TME to KCL-HO-1i using flow cytometry, immunofluorescence and RNA sequencing approaches and demonstrate that KCL-HO-1i supports a broader switch from an immunological ‘cold’ to ‘hot’ TME which provides a more favourable immune landscape for improving the response to chemotherapeutic drugs. Taken together, our data support the use of KCL-HO-1i as novel immunotherapeutic for the treatment of cancer. Citation Format: Meriem Bahri, Taha Al-Adhami, Emre Demirel, Joanne E. Anstee, Karen T. Feehan, James Rosekilly, Cheryl E. Gillett, Dominika Sosnowska, Renee Gitsaki-Taylor, Tik Shing Cheung, James F. Spicer, Khondaker Miraz Rahman, James N. Arnold. Targeting perivascular macrophages with an orally bioavailable heme oxygenase-1 inhibitor improves responses to chemotherapeutic drugs in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A013.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.359
Teacher spread0.321 · 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.

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
Published2023
Admission routes1
Has abstractyes

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