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Record W4409629661 · doi:10.1158/1538-7445.am2025-3927

Abstract 3927: Bone Metastasis Technology Platform: Establishing clinically relevant bone metastasis models for breast and prostate cancer and multiple myeloma

2025· article· en· W4409629661 on OpenAlexaff
Tiina E. Kähkönen, Jie Wen, Ru Yang, Yuyang Xu, Michael Q. Zhang, Jussi M. Halleen

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsMicropharma (Canada)
Fundersnot available
KeywordsProstate cancerMedicineBone metastasisMultiple myelomaMetastasisBreast cancerOncologyProstateBreast cancer metastasisCancerInternal medicineCancer research

Abstract

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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. Current cancer therapies can only partially decrease tumor growth, resulting in only 5% of bone metastatic patients being alive 5 years after the diagnosis. Bone metastases are therefore a high unmet medical need with a high demand for effective therapies. Lack of appropriate preclinical bone metastasis models that would exhibit the same clinical features that are observed in bone metastatic patients has made it difficult to advance therapy development at early stages. This study describes the establishment of three preclinical bone metastasis models, a breast cancer model using 4T1 mouse triple-negative breast cancer cells in BALB/c mice, a prostate cancer model using RM-1 mouse androgen-insensitive prostate cancer cells in C57BL/6 mice, and a multiple myeloma model using human RPMI 8226 cells in immunodeficient NPG mice. The cancer cells were inoculated intratibially into the bone marrow to model tumor growth in bone. Tumor growth was monitored by bioluminescence imaging (BLI), cancer-induced bone changes by X-ray imaging, and bone pain by Von Frey filaments (mechanical allodynia). In the 4T1 breast cancer model, 100% of the mice had bone metastases at day 7, and maximum study duration was 21 days. Osteolytic bone lesions were clearly observed and bone pain was detected at day 7. In the RM-1 prostate cancer model, 83% of the mice had bone metastases at day 7, and 100% of the mice at day 14, and maximum study duration was 28 days. Bone pain was observed at day 7, and osteolytic-mixed bone metastases were visible at day 14. In the RPMI 8226 multiple myeloma model, 100% tumor take rate was detected at day 7. Osteolytic bone metastases were visible at day 21, and maximum study duration was 100 days. We have established a clinically relevant Bone Metastasis Technology Platform (BMTP©) that currently includes preclinical bone metastasis models for breast and prostate cancer and multiple myeloma. These models have clinical features that are similar to those observed in bone metastatic patients. In preclinical models established in BMTP, tumor burden is monitored by BLI, the type and extent of cancer-induced bone loss is visualized by X-ray imaging, and bone pain is analyzed to provide a clinically relevant readout about the quality of life. We conclude that BMTP is a clinically relevant translational tool for evaluating efficacy of cancer therapies on bone metastasizing cancers. Citation Format: Tiina E. Kähkönen, Jie Wen, Ru Yang, Yuyang Xu, Michael Zhang, Jussi M. Halleen. Bone Metastasis Technology Platform: Establishing clinically relevant bone metastasis models for breast and prostate cancer and multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3927.

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)0.001
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.004

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 head0.119
GPT teacher head0.443
Teacher spread0.323 · 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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