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Record W4409628026 · doi:10.1158/1538-7445.am2025-1605

Abstract 1605: Pretreatment of bone anabolic agent delays or prevents multiple myeloma progression

2025· article· en· W4409628026 on OpenAlexaff
Syed Hassan Mehdi, Dongjoon Lee, Alex J. Lee, Paul Lee, Donghoon Yoon

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsCoquitlam College
Fundersnot available
KeywordsAnabolismMultiple myelomaMedicineCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Abstract Multiple myeloma (MM) is the second most common blood cancer. It remains an incurable disease due to relapsing and developing treatment resistance. Treatment resistance and disease progression are not solely due to intrinsic MM characteristics but are also fostered by alterations within the surrounding BM microenvironment. The bone serves as a home for growing myeloma but is also a target organ for myeloma cells. The MM-bone disease (MMBD) is one of the defining features of MM. A previous report showed that myeloma cells colonize the endosteal niche, enter a dormant state, and are activated by bone-lining cells. Many attempts, including ours, were made to test novel or existing bone anabolic drugs for anti-MMBD and found they did not significantly affect myeloma progression, although bone formation has improved. Interestingly, we found that the surviving mice from myeloma transplants become tolerable to subsequent tumor transplantation. We hypothesized that improving the bone environment may lead to the prevention of myeloma growth. We pretreated mice with a bone anabolic agent, Sigma Ant-Bonding Calcium Carbonate (SAC), and then tested myeloma growth. It may mimic a treatment in the pre-emerging or -relapsed period of MM. SAC is a chemically/physically modified calcium carbonate with a unique weak bonding and a commercially available dietary supplement. It induces bone formation by increasing the active ionized calcium in the body. We divided the 8-12-week-old NOD SCID gamma mice into three groups: PBS, the SAC-Treated group before Transplant (TbT), and the SAC-Treated group after Transplant (TaT). SAC was gavaged twice daily, five days/week, for four weeks before the transplantation (TbT) or two weeks after transplant (TaT), while PBS was gavaged to the control group when TbT started. The 1x106 luciferase-expressing 5TGM1 cells were injected into the mice via the tail vein. Mice were imaged weekly by IVIS imager to assess the myeloma progression and terminated when endpoint criteria were met. All surviving mice were sacrificed one week after all PBS group mice died. We found that the median survival of PBS-treated mice was 39 days, while 45 days in the TaT group. To our surprise, 80% of TbT group mice survived until the end of the study. The bioluminescence image analysis showed that myeloma slowly progressed in both treated group mice. DEXA results demonstrated that bone minerals significantly increased in the spine of SAC-treated mice compared to the control mice. Trabecular thickness and bone volume density were significantly higher in the SAC-treated group at the lumbar spine compared to the PBS group. In conclusion, our results showed that improving the bone microenvironment with SAC pretreatment delays or prevents myeloma cell growth in mice. These results may suggest a novel therapeutic regimen for MM during pre-emerging or -remission of multiple myeloma patients. Citation Format: Syed Hassan Mehdi, Dongjoon Lee, Alex Lee, Paul Lee, Donghoon Yoon. Pretreatment of bone anabolic agent delays or prevents multiple myeloma progression [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 1605.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.118
GPT teacher head0.517
Teacher spread0.399 · 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 designObservational
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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