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Record W3182080430 · doi:10.1158/1538-7445.am2021-1778

Abstract 1778: Bacterial regulation of innate and adaptive tumor immunity in metastatic breast cancer

2021· article· en· W3182080430 on OpenAlexaff
Zachary J. Gerbec, Shoukat Dedhar, B. Brett Finlay

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInflammationTumor microenvironmentImmune systemAcquired immune systemBreast cancerImmunologyCancerMicrobiomeInnate immune systemCancer researchMetastatic breast cancerTumor progressionMedicineBiologyInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Abstract In the tumor microenvironment (TME), innate and adaptive immune populations produce inflammatory cytokines that, while often critical for anti-tumor immunity, also mediate tumor-promoting functions. This paradoxical role of inflammation highlights the importance of defining the factors that control inflammatory signaling during tumor progression to optimize treatment of inflammation driven tumors and enhance our understanding of the role of inflammation in cancer immunology. Similar to inflammation, recent exploration of bacteria-mediated immune regulation reveals the microbiome mediates both anti-tumor and protumorigenic functions. In breast cancer for example, Helicobacter infection-induced inflammation augments primary tumor growth in a neutrophil-dependent manner. Similarly, an exogenously induced shift toward a more inflammatory microbiome in breast tumor-bearing mice leads to systemic inflammation and myeloid cell infiltration into the tumor that enhances dissemination into the lungs. Considering the lack of surgical options for patients with highly metastatic breast cancer (MBC), we seek to define how microbiome-mediated inflammation affects tumor progression in metastatic forms of the disease. Using a microbiome depletion model, our labs found bacterial depletion inhibited primary tumor growth of metastatic 4T1 breast cancer tumors specifically, while having no effect on non-metastatic 67NR tumors. When we compared immune populations in metastatic and non-metastatic tumors, we found that following microbiome depletion, MHCII expression was significantly reduced on macrophages in metastatic 4T1 tumors specifically. We also found CD4 Th17 populations were decreased both in terms of percentage and number, and that reduced Th17 numbers robustly correlated with reduced tumor volume. We then determined if the decrease in MHCII expression was due to M2 polarization. Analysis of CD206+ cells (M2 macrophages) revealed no differences between macrophages from control and microbiome-depleted animals, and when we analyzed MHCII expression in accordance with CD206, we found that reduced MHCII expression was associated specifically with CD206- cells, showing that down regulation occurs on the M1 population primarily responsible for activating Th17 cells. Additionally, rank correlation analyses revealed that reduced MHCII expression levels robustly correlated reduced tumor volumes in 4T1 tumors. These data suggest the microbiome induces tumor-promoting inflammation in MBC, and suggest a model whereby the microbiome regulates protumorigenic macrophage function by maintaining MHCII expression in MBC, and that bacterial-dependent macrophage activity augments tumor-promoting inflammation from Th17 cells to drive disease progression. Further analysis of this axis will identify pathways that can be targeted during treatment of inflammation-driven tumors. Citation Format: Zachary Gerbec, Shoukat Dedhar, Brett Finlay. Bacterial regulation of innate and adaptive tumor immunity in metastatic breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1778.

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 score0.368
Threshold uncertainty score0.997

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)0.0010.000

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 head0.053
GPT teacher head0.389
Teacher spread0.335 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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