Abstract 5202: Additive benefits of radium-223 dichloride and bortezomib combination in a syngeneic 5TGM1 multiple myeloma mouse model
Bibliographic record
Abstract
Abstract Radium-223 dichloride (radium-223, Xofigo®), a targeted alpha-therapy, improves overall survival in prostate cancer patients with bone metastases. It inhibits disease progression by reducing tumor growth and tumor-induced pathological bone reaction in breast and prostate cancer mouse models. Radium-223 is actively incorporated into the bone matrix by osteoblasts. Multiple myeloma (MM) is characterized by increased osteoclast and reduced or no osteoblast activity. Bortezomib (Velcade®), a treatment for MM, restores the impaired osteoblast activity in MM. Here, we report the effects of radium-223, bortezomib and their combination on myeloma cell proliferation in vitro and on myeloma bone disease model in mice. Proliferation assays were performed with human plasma cell leukemia (JJN-3, L-363), human MM (LP-1, MOLP-8, RPMI-8226 and OPM-2), and mouse MM (5TGM1) cells. Corresponding in vivo effects were studied in a syngeneic 5TGM1 mouse MM model. Female C57BL/KaLwRij mice (7 weeks old, n=15/group) were inoculated with 5TGM1 cells via tail vein and 26 days later, radium-223 (300 kBq/kg, single iv injection) and/or bortezomib (1 mg/kg ip, twice a week; total of 3 doses) or vehicle control were administered. The development of osteolytic lesions was detected by radiography. Hind limbs were used for histological analyses and total activity measurement was performed by a gamma-counter. TRAP-stained osteoclasts were counted at tumor-bone interface. Bortezomib inhibited proliferation of all cancer cell lines tested at 25 nM (JJN3 and OPM-2 at 2.5 nM) and radium-223 at 0.8 kBq/ml (L-363 and MOLP-8 at 0.2 kBq/ml) concentrations. Additive effects were observed with combination treatment in vitro. The 5TGM1 in vivo model demonstrated that both bortezomib and radium-223 decreased osteolytic lesion area as monotherapy (p<0.05 and p<0.01, respectively), with the combination being more effective than either monotherapy alone (p<0.001). Bortezomib decreased the number of osteoclasts at tumor-bone interface (p<0.05) and an additive decrease was observed with combination therapy (p<0.01) resulting in almost complete eradication of osteoclasts. Incorporation of radium-223 to bone was higher with combination therapy based on total activity measurements. All treatments were well tolerated. In conclusion, radium-223 dichloride (Xofigo®) therapy in combination with bortezomib decreased osteolytic lesion area and almost completely eradicated tumor-associated osteoclasts in a mouse model of myeloma bone disease. Incorporation of radium-223 to bone matrix was improved, possibly via induction of osteoblast activity by bortezomib. These data suggest that combination of radium-223 and bortezomib could be a new effective therapy in MM, which is currently being investigated in a Phase Ib/II trial in patients with early relapsed MM (NCT02928029). Citation Format: Mari I. Suominen, Jukka P. Rissanen, Anniina Luostarinen, Katja M. Fagerlund, Birgitta Sjöholm, Esa Alhoniemi, Sanna-Maria Käkönen, Dominik Mumberg, Jussi M. Halleen, Karl Ziegelbauer, Arne Scholz. Additive benefits of radium-223 dichloride and bortezomib combination in a syngeneic 5TGM1 multiple myeloma mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5202. doi:10.1158/1538-7445.AM2017-5202
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".