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Tumor microenvironment conditions that favor vessel co-option in colorectal cancer liver metastases: A theoretical model

2020· review· en· W3086011748 on OpenAlexaff
Miran Rada, Anthoula Lazaris, Audrey Kapelanski‐Lamoureux, Thomas Z. Mayer, Peter Metrakos

Bibliographic record

VenueSeminars in Cancer Biology · 2020
Typereview
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsColorectal cancerMetastasisCancer researchTumor microenvironmentCancerMedicineInternal medicine

Abstract

fetched live from OpenAlex

Vessel co-option is an alternative strategy by which tumour cells vascularize and gain access to nutrients to support tumour growth, survival and metastasis. In vessel co-option, the cancer cells move towards the pre-existing vasculature and hijack them. Vessel co-option is adopted by a wide range of human tumours including colorectal cancer liver metastases (CRCLM) and is responsible for the effectiveness of treatment in CRCLM. Furthermore, vessel co-option is an intrinsic feature and an acquired mechanism of resistance to anti-angiogenic treatment. In this review, we describe the microenvironment, the molecular players, discovered thus far of co-opting CRCLM lesions and propose a theoretical model. We also highlight key unanswered questions that are critical to improving our understanding of CRCLM vessel co-option and for the development of effective approaches for the treatment of co-opting tumours.

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 score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.319
Teacher spread0.290 · 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

Citations47
Published2020
Admission routes1
Has abstractyes

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