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Abstract A02: Oncogenic drivers of lung cancer induce production of CCL5 and recruitment of regulatory T-cells

2018· article· en· W2890008571 on OpenAlexaff
Elizabeth Franks, Elizabeth C. Halvorsen, Etienne Melese, Unni Arun, Jenna L. Collier, Bryant Harbourne, Min Hee Oh, Lam Vivian, Gerry Krystal, John C. English, Wan L. Lam, Stephen Lam, N. B. Abraham, Kevin L. Bennewith, William W. Lockwood

Bibliographic record

VenueClinical Cancer Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsImmune systemCancer researchBiologyCarcinogenesisChemokineLung cancerTumor microenvironmentAdenocarcinomaKRASCytokineImmunologyOncogeneCancerMedicinePathologyCell cycle

Abstract

fetched live from OpenAlex

Abstract Lung cancer development is driven by the expression of mutant oncogenes, with EGFR and KRAS being the most frequent mutations in lung adenocarcinoma. However, additional factors may influence lung tumor development and progression, including the balance of antitumor immune effector cells and pro-tumorigenic immune suppressor cells within the lung and lung tumor microenvironment. Tumor cells can evade immune attack by producing cytokines that recruit immune modulatory cells, such as regulatory T cells (Tregs), that promote a localized immune suppressive environment We hypothesized that oncogene signaling regulates the production of cytokines by tumor cells at the earliest stages of transformation that can recruit immune suppressive cells and promote lung tumour development. We used CIBERSORT-based analysis of gene expression data to quantify 22 different immune cell types from over 300 human lung adenocarcinomas and 100 matched normal lung tissues. We found that Tregs were significantly enriched in early-stage lung adenocarcinoma tumors compared to matched normal tissue from the same patient, and validated these findings with immunohistochemistry staining of lung sections. To identify cytokines that could recruit Tregs early in lung tumorigenesis, we used normal cells expressing doxycycline-inducible wild-type EGFR, mutant EGFRL858R or mutant KRASG12V. Secreted cytokines were quantified using a multiplex LUMINEX assay with subsequent validation by ELISA. Induction of EGFRL858R and KRASG12V expression in normal cells rapidly increased the production of CCL5 (RANTES), as did expression of wild-type EGFR in the presence of exogenous EGF. To elucidate the mechanism of oncogene-driven CCL5 secretion, we treated lung cancer cells harboring EGFR or KRAS mutations with a MEK inhibitor (trametinib) to disrupt oncogenic signaling downstream of EGFR and KRAS. In KRAS mutant lung cancer cells, treatment with trametinib decreased CCL5 production and inhibited both ERK and AKT signaling. To determine if oncogene-driven cytokines could induce migration of Tregs ex vivo, we used a trans-well assay with conditioned media from cells expressing doxycycline inducible EGFRL858R or KRASG12V. Conditioned media from EGFRL858R and KRASG12V-expressing cells induced Treg migration, which was mitigated by the addition of an anti-CCL5 antibody. These data indicate that oncogenic EGFR and KRAS signaling regulates expression of CCL5 in lung tumor cells, and that CCL5-mediated Treg recruitment to lung tumors may occur in early stages of lung tumor development. Therefore, targeted inhibition of CCL5, Tregs, and/or oncogenic EGFR and KRAS signaling may represent therapeutic strategies to block recruitment and function of immunosuppressive Tregs during lung tumor development. Citation Format: Elizabeth Franks, Elizabeth C. Halvorsen, Etienne Melese, Unni Arun, Jenna L. Collier, Bryant T. Harbourne, Min Hee Oh, Lam Vivian, Gerry Krystal, John C. English, Wan L. Lam, Stephen Lam, Ninan Abraham, Kevin L. Bennewith, William W. Lockwood. Oncogenic drivers of lung cancer induce production of CCL5 and recruitment of regulatory T-cells [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr A02.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.238
GPT teacher head0.520
Teacher spread0.282 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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