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Record W3203998457 · doi:10.3390/cancers13194798

The Breast Tumor Microenvironment: A Key Player in Metastatic Spread

2021· review· en· W3203998457 on OpenAlexafffund
Lucas E. L. Terceiro, Chidalu A. Edechi, Nnamdi Ikeogu, Barbara E. Nickel, Sabine Hombach‐Klonisch, Tanveer Sharif, Etienne Leygue, Yvonne Myal

Bibliographic record

VenueCancers · 2021
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsCancerCare ManitobaSt. Boniface HospitalUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTumor microenvironmentStromal cellMicrovesiclesParacrine signallingMetastasisCancer researchAutocrine signallingBreast cancerMedicineCirculating tumor cellCancer-Associated FibroblastsMetastatic breast cancerCarcinogenesisTumor progressionPrimary tumorCancer cellCancerBiologymicroRNAInternal medicineTumor cellsReceptor

Abstract

fetched live from OpenAlex

The tumor microenvironment plays a pivotal role in the tumorigenesis, progression, and metastatic spread of many cancers including breast. There is now increasing evidence to support the observations that a bidirectional interplay between breast cancer cells and stromal cells exists within the tumor and the tumor microenvironment both at the primary tumor site and at the metastatic site. This interaction occurs through direct cell to cell contact, or by the release of autocrine or paracrine factors which can activate pro-tumor signaling pathways and modulate tumor behavior. In this review, we will highlight recent advances in our current knowledge about the multiple interactions between breast cancer cells and neighboring cells (fibroblasts, endothelial cells, adipocytes, innate and adaptive immune cells) in the tumor microenvironment that coordinate to regulate metastasis. We also highlight the role of exosomes and circulating tumor cells in facilitating breast cancer metastasis. We discuss some key markers associated with stromal cells in the breast tumor environment and their potential to predict patient survival and guide treatment. Finally, we will provide some brief perspectives on how current technologies may lead to the development of more effective therapies for the clinical management of breast cancer patients.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.320
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2021
Admission routes2
Has abstractyes

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