Abstract 2474: Uncovering a new therapeutic vulnerability for preventing brain metastases
Bibliographic record
Abstract
Abstract INTRO: Patients with brain metastases (BM) face a 90% mortality rate within one year of their diagnosis and they lack targeted therapeutic options, particularly preventative or interceptional ones. METHODS: The Singh lab has generated a large in-house biobank of patient-derived BM cell lines that are established from patient-derived BM from primary lung and breast cancers and melanoma. 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 metastatic cascades and allowed characterization of a “premetastatic” 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 present a therapeutic window wherein metastasizing cells can be targeted and eradicated before establishing clinically detectable and difficult to treat brain tumors. RESULTS: Targeting premetastatic BM cells is a feasible interceptional strategy to block BM, but druggable targets are still very limited. Here, we applied RNA sequencing of premetastatic BM cells to reveal a unique deregulated transcriptomic profile that is specific to premetastatic cells regardless of primary tumor origin. Subsequent Connectivity Map analysis revealed compounds that we biologically characterized in vitro for selective anti-BMIC phenotypes. This effort led us to identify a tool compound that exhibits anti-BM activity in vitro, while remaining ineffective against normal brain cell controls. Follow up preclinical studies showed that treatment with this tool compound reduces the tumor burden of mice compared to placebo, while providing a significant survival advantage. Mass spectrometry-based metabolomics and CRISPR knock-out studies directly validated our tool compound’s target, Target X, as a targetable therapeutic vulnerability in BM, where pharmacological and genetic perturbation of Target X attenuates BM cell proliferation both in vitro and in vivo. We have now begun a large-scale medicinal chemistry campaign to develop a novel, brain penetrant Target X-inhibitor with a drug-like pre-clinical profile validated by our in vivo experimental models. This advanced drug candidate will be ready for later stage preclinical development and subsequent clinical development. CONCLUSION: This potential first-in-class anti-metastatic therapy may provide an alternative interceptional treatment strategy for patients experiencing BM that are otherwise limited to palliation. Our work provides a new model for target discovery and validation to develop more effective preventative therapeutic strategies for patients with metastatic disease. Citation Format: Agata M. Kieliszek, Daniel Mobilio, Blessing I. Bassey-Archibong, Jarrod Johnson, Nikoo Aghaei, William Gwynne, Dillon McKenna, Minomi K. Subapanditha, Chitra Venugopal, Jakob Magolan, Sheila K. Singh. Uncovering a new therapeutic vulnerability for preventing brain metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2474.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".