Abstract 3615: Targeting tumor-brain crosstalk in invasive brain metastases
Bibliographic record
Abstract
Abstract Background: An estimated 20-40% of cancer patients develop brain metastases (BrM), mostly those affected by lung cancer, breast cancer or melanoma. Unfortunately, these patients suffer from poor outcomes and diminished quality of life. Few BrM treatment options beyond local therapy exist and this is often a short-term solution as 60% of resected BrM recur within 1 year. Our group discovered that patient prognosis is linked to BrM invasiveness, with highly invasive (HI) BrM more likely to recur, compared to minimally invasive (MI) BrM. This has presented us the opportunity to investigate exploitable biological mechanisms driving HI BrM. For instance, invasive signaling can be driven by microenvironmental proteins such as growth and immunological factors (cytokines) secreted by surrounding brain or cancer cells, through inter-cellular or self-feeding autocrine loops. Hypothesis: Considering the influence of secreted factors on cancer invasion and the brain microenvironment, I hypothesize that secretory profiling of BrM and brain parenchymal cells will be mechanistically insightful and help identify potential targetable drivers of BrM invasion. Results: To identify BrM invasion-related tumor- and brain-derived factors, I performed human- and mouse-specific high throughput Enzyme-Linked Immunosorbent Assay (ELISA)-based screens on conditioned media from mouse brain slices harboring intracranial MI or HI BrM patient-derived xenografts (PDX). This secretome screen reveals distinct MI and HI secretory profiles for melanoma, breast cancer and lung cancer BrMs and several HI BrM-derived factors of interest have been identified. Such factors are important to investigate as possible drivers of invasiveness in BrM through functional studies. Conclusion: BrM patients currently face a bleak prognosis, with few treatment options and a median survival of only 8-16 months. Considering the clinical availability of targeted therapies including inhibitors for growth factor-binding tyrosine kinases, antibody-drug conjugates, and immunotherapy, this project will help elucidate the cancer-brain crosstalk which may be exploited therapeutically with existing drugs in patients with frequently recurring HI BrM. ADDIN Citation Format: Caitlyn Mourcos, Sarah M. Maritan, Matthew G. Annis, Georgia Kruck, Alexander Nowakowski, Anna-Maria Lazaratos, Kevin Petrecca, Peter Siegel. Targeting tumor-brain crosstalk in invasive 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 3615.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".