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Abstract B041: Defining the Determinants of Immune Response in DNA Homologous Recombination Deficient Tumors

2023· article· en· W4389240415 on OpenAlexaboutno aff
Natalie Vaninov, Robert Samstein

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
Fundersnot available
KeywordsImmune checkpointCXCL10Tumor microenvironmentImmune systemImmunotherapyCancer researchChemokineCXCR3Cancer immunotherapyBiologyCancerImmunologyChemokine receptorMedicineGenetics

Abstract

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Abstract Immune checkpoint blockade (ICB) has shown unprecedented success in improving clinical outcomes for numerous cancer patients, but many patients still fail to respond to treatment. Studies have demonstrated that the tumor microenvironment, tumor mutational burden, and patient DNA Damage Repair (DDR) deficiency may play a key role in determining response to ICB. To better characterize this and define key determinants of ICB response, we created an isogenic knockout BRCA2 in a murine 4T1 metastatic TNBC background to model these differential clinical responses and to assess how these tumor-intrinsic programs influence the tumor immune microenvironment to poise tumors for immunotherapy response. Differential immune landscapes were found via scRNAseq at baseline and with ICB driven by tumor DDR status with notable differences in the myeloid compartment and CXCR3-expressing T cell, NK, pDC, DCs, and plasma cells. Differential expression and interferon stimulated gene (ISG) metagene analysis showed notable differences in ISG expression in DDR-deficient samples at baseline, particularly in clusters containing monocytes and macrophages. Additionally, differential expression of CXCL10 in monocytes/macrophages and assessment of ligand-receptor interactions by CellChat similarly showed enhanced contribution CXCL10-CXCR3. Parallel bulk RNAseq analysis showed increased production of T cell trafficking chemokines that would poise the tumor for response to ICB. Our lab established a genetic interferon reporter system to measure both tumor intrinsic interferon-driven inflammation in cis and in trans with tumor associated macrophages, which enables multidimensional tracking of bidirectional signaling between tumor cells and macrophages by flow. This system enabled screening of inhibitors and tumor knockout cell lines to identify that tumor-intrinsic cGAS/STING and IFNb1 reinforce production of tumor and myeloid CXCL10/11 to poise the tumor for response to ICB. Further assessment of tumor cell lines via cellular fractionation experiments identified the presence of both gDNA and R-loops in the cytoplasm of BRCA2-mutant mammary cancer cell lines that may be detected as damage associated molecular patterns by tumor cells or myeloid cells in the tumor bed. Future studies aim at validating this DDR-driven production of IFNb1 as a driver of T cell chemokines in vivo; tumor-intrinsic knockout of IFNb1 and other T cell promoting chemokines (CXCL10/11/CCL5) to assess tumor or myeloid driven nature of this response to ICB and depletion of myeloid cells are being performed in parallel to assess contribution of tumor and myeloid cells, respectively. Taken together, our results have important implications for understanding the key drivers of ICB response in the tumor microenvironment and how patient-intrinsic mutations differentially poise the microenvironment for immunotherapy response. Citation Format: Natalie Vaninov, Robert Samstein. Defining the Determinants of Immune Response in DNA Homologous Recombination Deficient Tumors [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 B041.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.070
GPT teacher head0.404
Teacher spread0.334 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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