Abstract 2680: Development and application of a novel model of human lung-to-brain metastasis: identification of TWIST2 and SPOCK1 as unique regulators of brain metastases
Bibliographic record
Abstract
Abstract Brain Metastases (BM) are the most common type of cerebral tumor in adult, occurring at a rate ten times greater than that of primary brain cancers. The inherent properties of a primary tumor cell capable of initiating a BM resemble that of a cancer stem cell (CSC). Previous work conducted in our lab identified a CSC population within lung-derived BM. We hypothesize that a subgroup of CSC-like cells, termed brain metastasis-initiating cells (BMICs), are responsible for the initiation of BM and are identifiable by an exclusive subset of genes that regulate self-renewal and metastasis. Despite the prevalence and lethality of BM, there is no clinically relevant model that fully reflects metastasis in patients. The selection of an appropriate metastatic in vivo model is crucial for the identification of genes that regulate BM formation, prognostic and/or predictive markers, and evaluation of anti-metastatic therapeutics. We recently generated a novel human-mouse xenotransplantation model of BM that allows for interrogation of each phase of the metastatic process from lung to brain, through injection of human patient-derived GFP-expressing BMICs into immunocompromised mice via three routes: 1) intracranial (IC), 2) intrathoracic (IT) and 3) intravascular/intracardiac (IV). GFP+ BMICs were harvested from the lungs and/or brains from each injection route, and RNA was submitted for microarray analysis to identify a unique metastatic and tissue-specific gene signature from BMICs isolated from IT injections. We performed RNA interference screens in vitro and in vivo on BMIC lines against 150 genes implicated in BM formation in order to identify genes involved in self-renewal, tumor initiation and metastasis. We validated two top hits, TWIST2 and SPOCK1, using our novel BM mouse model, and determined that their knockdown functionally blocked BM formation. Future work will examine the expression of these genes in primary lung FFPE samples to determine if they are predictive biomarkers of lung-to-brain metastasis in prospective cohorts of newly diagnosed lung cancer patients, and to determine their potential as therapeutic targets. Citation Format: Sheila K. Singh, Mohini Singh, Chitra Venugopal, Nicole McFarlane, David Bakhshinyan, Manvir Dhillon, Sujeivan Mahendram, Kevin Brown, Amy Tong, Kathrin Durrer, Tomas Tokar, Robin Hallett, John Hassell, Igor Jurisica, Jason Moffat. Development and application of a novel model of human lung-to-brain metastasis: identification of TWIST2 and SPOCK1 as unique regulators of brain metastases. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2680.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".