Abstract A064: Study of molecular pathways involved in the development of ovarian cancer brain metastases
Bibliographic record
Abstract
Abstract Ovarian cancer is the deadliest gynecological cancer, with a five-year survival rate of 40%. With the advancement of anti-cancer treatments, a modest increase in patient life expectancy has been observed in recent years and seems to be accompanied by an increase in cases of brain metastases (up to 6% of patients). Unfortunately, there are no clinical recommendations for treating this fatal condition. The blood-brain barrier and the brain's unique environment exert selective pressure on invading cancer cells. This can cause phenotypic changes in metastases and render them resistant to the systemic anticancer therapies used to treat the extracranial ovarian tumors. Thus, a better understanding of these phenotypes and the identification of therapeutic vulnerabilities are essential in order to develop new therapeutic approaches against ovarian cancer brain metastases. The aim of this project is to determine the molecular mechanisms involved in the development of brain metastases from ovarian cancer, and to identify therapeutic vulnerabilities that could be exploited to treat ovarian cancer patients with brain metastases. Samples from 22 ovarian cancer patients (primary tumors and associated brain metastases) were analyzed by cyclic immunofluorescence (Cyc-IF). This method enabled the spatial-oriented single-cell analysis of 80 protein markers on two consecutive FFPE sample slides (2 antibody panels of 40 markers). The two antibody panels covered oncogenic signaling pathways, immune cells monitoring and characterization of the tumor microenvironment (MET). An initial t-SNE clustering of the data according to patient and tissue type (primary or metastatic) revealed that certain cells share the same phenotype between primary and metastatic tumor; indicating the possibility to identify precursors of brain metastases from primary tumors. Further clustering analyses have shown that the tumor MET composition differs between the primary tumor and the matched brain metastases, indicating a remodeling of the tumor’s architecture and immune composition. The cancer cells also demonstrated different activity of oncogenic signaling pathways, indicating an adaptation of the tumor cells to the brain environment and revealed the possibility to identify therapeutic opportunities. It has been demonstrated that ovarian cancer brain metastases are lethal and do not respond to conventional treatments used against extracranial ovarian tumors. The identification of therapeutic vulnerabilities in ovarian cancer brain metastases will be the first step essential to improve patients care. Citation Format: Korina Mouzakitis, Nathan McMahon, Hongli Ma, Gordon B. Mills, Tanja Pejovic, Marilyne Labrie. Study of molecular pathways involved in the development of ovarian cancer brain metastases [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A064.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".