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Abstract C122: Effects of standard-of-care therapies on tumor growth, tumor-induced bone loss and bone pain in preclinical models of breast and prostate cancer bone metastasis and multiple myeloma bone disease

2025· article· en· W4415444204 on OpenAlexaff
Tiina E. Kähkönen, Jie Wen, Ru Yang, Jussi M. Halleen

Bibliographic record

VenueMolecular Cancer Therapeutics · 2025
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMicropharma (Canada)
Fundersnot available
KeywordsProstate cancerBreast cancerMultiple myelomaBone metastasisBone marrowBone painBortezomibBone diseaseZoledronic acid

Abstract

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Abstract Bone metastases are a significant clinical problem in many major cancers, especially in breast and prostate cancer where 70-90% of advanced patients develop bone metastases. Myeloma bone disease is associated with similar clinical problems than bone metastases, including increased risk of fractures and bone pain that decrease the quality of life. The standard-of-care therapy (SOC) to prevent tumor growth depends on the tumor type. In this study, we demonstrated the effects of SOCs in triple-negative breast cancer (TNBC) and castration-resistant prostate cancer (CRPC) bone metastasis models, and in a multiple myeloma (MM) bone disease model. The TNBC model included 4T1 mouse triple-negative breast cancer cells in female BALB/c mice, the CRPC model included RM-1 mouse androgen-insensitive prostate cancer cells in castrated male C57BL/6 mice, and the MM model included human RPMI 8226 cells in immunodeficient female NPG mice. In all models, luciferase-labelled cancer cells were inoculated intratibially into the bone marrow to model tumor growth in bone, mimicking growth of bone metastases in patients. Tumor growth was monitored by bioluminescence imaging (BLI) and cancer-induced bone changes by X-ray imaging. The study lengths were 21, 25 and 56 days in the TNBC, CRPC and MM models, respectively. In the TNBC and CRPC models, bone pain was assessed by Von Frey filaments (mechanical allodynia). Doxorubicin (4 mg/kg, ip, BIW), docetaxel (10 mg/kg, ip, BIW) or bortezomib (0.5 mg/kg, ip, BIW) were used as SOCs in the TNBC, CRPC and MM models, respectively. In the TNBC model, doxorubicin reduced both tumor growth and cancer-induced bone loss at the endpoint, and reduction in bone pain was observed at day 14. In the CRPC model, docetaxel decreased tumor burden at day 18 but the effect was lost at day 25, and no effects were observed on cancer-induces bone loss or bone pain. In the MM model, bortezomib decreased tumor growth at days 28 and 56, but no effects were observed in cancer-induced bone loss. The SOCs decreased tumor growth in all three preclinical models. Doxorubicin also decreased cancer-induced bone loss and bone pain in the TNBC model. In conclusion, different SOCs have varying effects in preclinical bone metastasis models and performance of each SOC needs to be validated separately before they are included as reference compounds or used as combination partners in preclinical studies. Citation Format: Tiina E. Kähkönen, Jie Wen, Ru Yang, Jussi Halleen. Effects of standard-of-care therapies on tumor growth, tumor-induced bone loss and bone pain in preclinical models of breast and prostate cancer bone metastasis and multiple myeloma bone disease [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C122.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.324
Teacher spread0.303 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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