Evolution of Mouse Models for Studying CNS Cancer: a Decade of Progress
Bibliographic record
Abstract
The first Mouse Models for Human Cancer Consortium (MMHCC) meeting for human nervous system cancer convened in New York City in November of 2000, with the proceedings of that meeting subsequently reported in Oncogene (1).The emphasis of the first meeting was on the comparative pathology of mouse models and corresponding human tumors. Recommendations from the meeting included the need to recapitulate human CNS tumor gene alterations in mouse model tumors, a need for increased emphasis on the molecular characterization of mouse model tumors for establishing consistency with corresponding human tumors, and the need to utilize mouse models in the preclinical evaluation of new therapies for treating CNS cancer. As indicated in the report ofthe 5th MMHCC meeting for nervous system cancer (Montreal, November 2010) in the current issue of Neuro-Oncology (2), these recommendations have proven influential, as most mouse models for CNS cancer are now 1) based on gene alterations suspected ofdriving corresponding human tumor development; 2) routinely subjected to extensive molecular characterization; and 3) are experiencingincreased use for therapeutic hypothesis testing.In addition, the sophistication of current models has increased substantially, with many utilizing information regarding potential cell of origin to regulate the expression or inactivation of genes in precursor cells thought to give rise to corresponding human tumors. Perhaps unforeseen at the 2000 MMHCC meeting was the revitalization of xenograft models for studying CNS cancer, which has been driven by the growing appreciation that histopathologically defined classes of CNS cancer, such as glioblastoma, consist of multiple distinct tumor subtypes, many of which can be propagated as xenografts, and the understanding that individual tumors are composed of distinct subpopulations of cells with distinct biologic properties.As a result of such understanding, human tumor xenografts, established either by direct transplantation from surgical specimens or by transplantation from primary cultures of surgical specimens, are viewed as model systems in their own right. Currently available genetically engineered mouse(GEM) models and human tumor xenografts now allow for detailed investigation of tumor initiation, progression, and response to therapy and have provided investigators with a resource armamentarium for the in-depthstudy of the molecular, cellular, and tumor biology of CNS cancer.It will be of interest to follow the progress during the next 10 years, as GEM modelers will undoubtedly utilize the rapidly growing base of information regarding developmental regulation of gene expression to further refine models for appropriate temporal, spatial, and cellular recapitulation of corresponding human tumor development.
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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 | 0.044 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".