Intratibial Injection of Human Multiple Myeloma Cells in NOD/SCID IL-2Rγ(Null) Mice Mimics Human Myeloma and Serves as a Valuable Tool for the Development of Anticancer Strategies
Bibliographic record
Abstract
BACKGROUND: We systematically analyzed multiple myeloma (MM) cell lines and patient bone marrow cells for their engraftment capacity in immunodeficient mice and validated the response of the resulting xenografts to antimyeloma agents. DESIGN AND METHODS: Using flow cytometry and near infrared fluorescence in-vivo-imaging, growth kinetics of MM cell lines L363 and RPMI8226 and patient bone marrow cells were investigated with use of a murine subcutaneous bone implant, intratibial and intravenous approach in NOD/SCID, NOD/SCID treated with CD122 antibody and NOD/SCID IL-2Rγ(null) mice (NSG). RESULTS: Myeloma growth was significantly increased in the absence of natural killer cell activity (NSG or αCD122-treated NOD/SCID). Comparison of NSG and αCD122-treated NOD/SCID revealed enhanced growth kinetics in the former, especially with respect to metastatic tumor sites which were exclusively observed therein. In NSG, MM cells were more tumorigenic when injected intratibially than intravenously. In NOD/SCID in contrast, the use of juvenile long bone implants was superior to intratibial or intravenous cancer cell injection. Using the intratibial NSG model, mice developed typical disease symptoms exclusively when implanted with human MM cell lines or patient-derived bone marrow cells, but not with healthy bone marrow cells nor in mock-injected animals. Bortezomib and dexamethasone delayed myeloma progression in L363- as well as patient-derived MM cell bearing NSG. Antitumor activity could be quantified via flow cytometry and in vivo imaging analyses. CONCLUSIONS: Our results suggest that the intratibial NSG MM model mimics the clinical situation of the disseminated disease and serves as a valuable tool in the development of novel anticancer strategies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".