Abstract 976: Interrogating the pre-metastastic gene signature to block brain metastases
Bibliographic record
Abstract
Abstract BACKGROUND: The incidence of brain metastases (BM) is tenfold higher than that of primary brain tumors. BM predominantly originate from primary lung, breast, and melanoma tumors with a 90% mortality rate within one year of diagnosis, posing a large unmet clinical need to identify novel therapies against BM. The goal of this work is to uncover the molecular factors that drive the formation of BM and investigate whether we can slow down and ultimately block BM formation. METHODS: The Singh lab has generated a large in-house biobank of patient-derived BM cell lines that are established from BM patient tumor samples. We use these BM cell lines to generate murine orthotopic xenograft models of BM and interrogate the biological processes that lead to BM. These models have successfully recapitulated all the stages of their respective BM cascades and additionally captured a “pre-metastatic” population of BM cells that have just seeded the brains of mice before forming mature, clinically detectable tumors. Pre-metastatic cell populations are impossible to detect in human patients but represent a therapeutic window wherein metastasizing cells can be targeted and eradicated before establishing clinically detectable and difficult to treat brain tumors. RESULTS: RNA sequencing of pre-metastatic BM cells revealed a unique deregulated transcriptomic profile that is specific to pre-metastatic cells despite the tumor of origin. Subsequent Connectivity Map analysis revealed compounds that we biologically characterized in vitro for selective anti-BMIC phenotypes. This effort led to a lead compound that exhibits anti-BM activity in vitro, while remaining ineffective against normal brain cell controls. Preliminary in vivo work has shown that following both orthotopic and intracardiac injection of BM cells, treatment with this lead compound reduces the tumor burden compared to mice being treated by a vehicle control, while providing a significant survival advantage. Ongoing mechanistic investigations aim to delineate the protein target of this compound in the context of the observed selective anti-BM phenotype. CONCLUSION: Identification of novel small molecules that target premetastatic BM cells could slow or prevent the formation of BM and dramatically improve the prognosis of at-risk cancer patients. Citation Format: Agata Kieliszek, Blessing Bassey-Archibong, Chitra Venugopal, Sheila Singh. Interrogating the pre-metastastic gene signature to block brain metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 976.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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 teacher head, 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".