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Record W2345959150 · doi:10.1093/neuonc/nov208.31

BMET-31DEVELOPMENT AND APPLICATION OF A NOVEL MODEL OF HUMAN LUNG-TO-BRAIN METASTASIS TO IDENTIFY UNIQUE METASTATIC GENE SIGNATURES

2015· article· en· W2345959150 on OpenAlexaff
Mohini Singh, Chitra Venugopal, Nicole McFarlane, David Bakhshinyan, Sujeivan Mahendram, Kevin R. Brown, Amy H.Y. Tong, Kathrin Durrer, Robin Hallett, John A. Hassell, Jason Moffat, Sheila K. Singh

Bibliographic record

VenueNeuro-Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsBrain metastasisMetastasisHuman lungMedicineLungGeneComputational biologyOncologyCancer researchInternal medicineBiologyCancerGenetics

Abstract

fetched live from OpenAlex

Brain Metastases (BM) are the most common type of cerebral tumor in adult, occurring at a rate 10 times greater than that of primary brain cancers. The inherent abilities of a primary tumor cell capable of initiating a BM resembles that of a cancer stem cell (CSCs). Previous work conducted in our lab identified a population of CSCs within lung-derived brain metastases. We hypothesize that a subgroup of CSCs, termed brain metastasis-initiating cells (BMICs), are responsible for the initiation of BM and are identifiable by an exclusive subset of genes that regulate self-renewal and metastasis. Despite the prevalence and lethality of BM, there is no clinically relevant model that fully reflects metastasis in patients. We recently generated a novel human-mouse xenotransplantation model of BM that allows for interrogation of each phase of the metastatic process from lung to brain, through injection of human patient-derived GFP-expressing BMICs into immunocompromised mice via three routes: 1) intracranial (IC), 2) intrathoracic (IT) and 3) intravascular/intracardiac (IV). GFP+ cells were harvested from the lungs and/or brains from each injection route, and RNA was submitted for microarray analysis to identify a unique metastatic and tissue-specific gene signature. In order to identify genes involved in self-renewal and tumor initiation, we also performed RNA interference screens in vitro and in vivo on BMIC lines against 150 genes implicated in BM formation. Further in vitro validation of select hit genes identified SPOCK1 and TWIST2 as essential regulators of cell proliferation, sphere formation, and migration. Future work will validate the role of these genes in our established in vivo metastasis models. We also aim to examine their expression in primary lung FFPE samples to determine if they are predictive biomarkers of lung-to-brain metastasis in prospective cohorts of newly diagnosed lung cancer patients, and to determine their potential as therapeutic targets.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.388
Teacher spread0.318 · 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
Published2015
Admission routes1
Has abstractyes

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