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Record W4414043305 · doi:10.1101/2025.09.03.673759

Inhibition of PTCH1 drug efflux activity enhances chemotherapy efficacy against triple negative breast cancer

2025· preprint· en· W4414043305 on OpenAlexaff
Sarah Cogoluegnes, Sandra Kovachka, Thierry Dubois, Roberto Würth, Elisa Donato, Andreas Trumpp, Michel Franco, Frédéric Luton, Stéphane Azoulay, Isabelle Mus‐Veteau

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsCanadian Nautical Research Society
FundersCentre National de la Recherche ScientifiqueCHIST-ERAAgence Nationale de la Recherche
KeywordsPTCH1DoxorubicinTriple-negative breast cancerEffluxPaclitaxelChemotherapyMultiple drug resistanceDrug resistance

Abstract

fetched live from OpenAlex

Abstract Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subgroup characterized by a high risk of resistance to chemotherapies and high relapse potential. High levels of mRNA from the Hedgehog receptor PTCH1 are associated with poor prognosis in TNBC. PTCH1 is overexpressed in many aggressive cancers. We previously reported that PTCH1 is a multidrug transporter that triggers resistance to chemotherapy of adrenocortical carcinoma and melanoma cells, and that inhibiting PTCH1 drug efflux strongly enhanced chemotherapy efficacy on these cell lines both in vitro and in vivo . In the present study, we found that PTCH1 inhibition also significantly inhibited doxorubicin efflux in three TNBC cell lines leading to a strong increase of the cytotoxic effect of doxorubicin and docetaxel, and an inhibition of cell migration. Altogether, our data highlight the therapeutic potential of targeting PTCH1 drug efflux activity using drug association strategies for the treatment of TNBC 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 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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.007
GPT teacher head0.239
Teacher spread0.231 · 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
Published2025
Admission routes1
Has abstractyes

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