Abstract 1183: Establishment,characterization and utilization of models of central nervous system metastasis
Bibliographic record
Abstract
Abstract Metastases to the central nervous system (CNS) are the most common intracranial malignancy and are typically associated with survival times of only a few months. Lung and breast are the most common type of CNS metastasis, representing over half of such tumors. The molecular characteristics of these tumors as well as the factors driving cancers to metastasize to the CNS are poorly understood. This is due in part to inaccessibility of clinical samples and a lack of established, well-characterized models. In order to shed light on the molecular biology of these highly heterogeneous tumors and establish resources for preclinical testing, we are working to create both in vivo and in vitro models of CNS metastases originating from lung or breast cancer. We have thus far received surgical specimens from 7 lung and two breast CNS metastases. Of these, one was a spine metastasis and 6 were brain metastases. Each surgical specimen was used for the generation of patient-derived xenografts (PDX) and cell lines. Of these specimens, we have successfully established continuous cell lines of CNS metastases originating from three lung adenocarcinomas, one squamous lung carcinoma and one ductal breast carcinoma. To verify the presence of metastatic carcinoma cells in culture, cell lines were characterized by immunofluorescence based on staining profiles obtained from each tumor's pathology report. Adenocarcinomas of the lung expressed cytokeratins typical of lung cancer (CK7, 8 and 18), as well as Napsin-A, a highly specific pulmonary marker. The squamous cell carcinoma of the lung expressed these as well as CK5/6 and p63-α, pathological markers of squamous differentiation. A metastatic ductal carcinoma of the breast expressed ER, Her2, GATA-3, and Vimentin. For the development of PDX models, tumors were initially implanted into flank of NOG mice and then passaged orthotopically for preclinical testing. Of nine implants, tumor growth was evident for 3 animals harboring adenocarcinoma of the lung, 2 with squamous cell carcinoma of the lung, and 1 with breast carcinoma. Samples from metastatic tissue, xenograft tissue and primary tumors (when available) were flow sorted to identify different clonal subpopulations and sequenced for exome and RNA level changes. Genomic characterization of the different clones allowed reconstruction of the evolution of metastasis and identified therapeutically targetable genes. Studies are ongoing to develop personalized preclinical mouse studies based on the integration of multi-omics data derived from the models and patient tumors. Citation Format: Kyle N. Johnson, Paul M. Gonzalez, Mario Sepulveda, Loren Gorgol, Jennifer Glen, Danielle M. DiPerna, Mark Bernstein, Steven A. Toms, Bodour Salhia. Establishment,characterization and utilization of models of central nervous system metastasis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1183. doi:10.1158/1538-7445.AM2014-1183
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".