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Record W3183091224 · doi:10.1158/1538-7445.am2021-2701

Abstract 2701: Molecular profiling to assess the immune response to neoadjuvant SABR in early breast cancer

2021· article· en· W3183091224 on OpenAlexaff
Melanie Spears, Vida Talebian, Linda M. Liao, Megan Hopkins, Kalan Lynn, Michael Lock, Anat Kornecki, John M.S. Bartlett, Muriel Brackstone

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsWestern UniversityLondon Health Sciences CentreLawson Health Research InstituteOntario Institute for Cancer Research
Fundersnot available
KeywordsTumor microenvironmentImmune systemBreast cancerCancer researchMedicineAbscopal effectImmune checkpointGene expression profilingRadiation therapyOncologyImmunologyImmunotherapyCancerBiologyInternal medicineGene expressionGene

Abstract

fetched live from OpenAlex

Abstract Radiation therapy is used successfully in the treatment of breast cancer. Stereotactic radiation therapy (SBRT) is well established in the treatment of lung cancer, brain metastases and various other metastatic sites. Localized radiotherapy can promote dendritic cells maturations and activation and enhances phagocytosis of cells by antigen-presenting cells (APCs) increased presentation of tumor-associated antigenic peptides, and T cells being primed from naïve towards memory phenotypes In this study we evaluated the ability to implement a three fraction SBRT regimen for low-risk primary carcinoma of the breast prior to lumpectomy and profiled the immune microenvironment using NanoString's GeoMx Digital Spatial Profiling (DSP) platform and we performed NanoString gene expression profiling using the Human v.1.1 PanCancer immune profiling panel. NanoString's DSP platform was used to analyze 25 patient samples from the SIGNAL 2.0 clinical trial. For analysis, region of interest were selected comparing the tumor microenvironment (TME, CD45+ve) and tumor (pan cytokeratin) in pre and post treated FFPE slides. A panel of 60-antibodies were analyzed in each region. For gene expression profiling total RNA was extracted from the frozen tissue and the human V.1.1 PanCancer Immune Profiling Panel was used. We observed notable differences in the immune microenvironment gene expression patterns in samples pre- and post-treatment. We identified a total of 175 differentially expressed genes (DEGs) using a 5% adjusted p-value across the entire patient population regardless of which radiation arm. Pathway analysis identified genes associated with activation of immune response, signaling by interleukins and adaptive immune response were significantly changed in the post treatment samples. Deconvolution of the immune cell mRNA gene expression data demonstrated that there were significant changes in the cellular composition after radiotherapy. There were significant increase in expression levels of macrophages, dendritic cells, neutrophils and CD8 T cells post radiation treatment. In conclusion we have demonstrated through proteomic and transcriptomic profiling, SBRT elicits an immune response with increases in the innate response. Citation Format: Melanie Spears, Vida Talebian, Linda Liao, Megan Hopkins, Kalan Lynn, Michael Lock, Anat Kornecki, John M. Bartlett, Muriel Brackstone. Molecular profiling to assess the immune response to neoadjuvant SABR in early 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 2701.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.052
GPT teacher head0.365
Teacher spread0.313 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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