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Record W2922708500 · doi:10.1038/s41598-019-38736-y

Oncosuppressor-Mutated Cells as a Liquid Biopsy Test for Cancer-Screening

2019· article· en· W2922708500 on OpenAlexaff
Mohamed Abdouh, Zu‐Hua Gao, Vincenzo Arena, Manuel Arena, Miguel N. Burnier, Goffredo Arena

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsCancerBiopsyLiquid biopsyCancer cellPTENImmunohistochemistryCancer researchPathologyDysplasiaMedicineBiologyInternal medicineCell biologySignal transduction

Abstract

fetched live from OpenAlex

We reported on the ability of immortalized or oncosuppressor-mutated cells (OMCs) to uptake circulating cancer-factors and give tumors when transplanted into mice. This led to the first biological based liquid biopsy test, which we called MATER-D platform. In the present study, we showed for the first time that a different type of OMCs (PTEN-deficient human epithelial MCF10A cells) turn malignant when exposed to cancer patient's sera, confirming the concept that different cells with diverse oncosuppressor mutations can uptake cancer factors and be used in biological based liquid biopsy tests. Our observations were confirmed in a large variety of solid and haematological malignancies. This test was able to detect dysplasia and carcinomas in situ lesions in different organs and circulating factors in cancer patients years after the removal of their lesions. To our knowledge, this ability is unique and not shared by other liquid biopsy platforms. Immunohistochemistry analysis of the xenotransplants revealed identical patterns of differentiation regardless of the cancer type, showing that differentiation through horizontal transfer might be dependent on the nature of the target cells rather than the type of cancer factors. These data strengthen the notion that OMC-based liquid biopsy tests might be promising platforms for cancer screening.

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 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 score0.333
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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.

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

Citations12
Published2019
Admission routes1
Has abstractyes

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