Abstract IA001: An overview of applying discovery science to care for patients in need: working together to advance our understanding and treatment of sarcomas
Bibliographic record
Abstract
Abstract There are myriad subtypes and clinical scenarios for “sarcomas.” These heterogeneous malignancies, linked by a mesenchymal phenotype and/or cell of origin, serve as both microcosm and model to study the complexities of all cancers. Sarcomas prove the concept that advanced molecular diagnostics are key enabling factors to allow expert pathologists to make more specific, potentially life-preserving diagnoses. It is clear that molecular diagnostics alone cannot be relied upon to define diagnosis. Translocations of EWSR1 can be found in a multiplicity of sarcoma subtypes as well as carcinomas; thus, an EWSR1 fusion is not diagnostic per se. Expert pathologists remains critical to the first step of care: making the most accurate and clinically meaningful diagnosis. Even within a given diagnostic term (e.g., GIST), there are obviously different oncogenic drivers (KIT vs. PDGFRA vs. SDH(x) vs. NTRK) that require uniquely different management decisions. Splitting, rather than lumping together, molecularly-different sarcomas may be a key success factor to understanding inter-patient differences in outcomes, as well as the clinical impact of the evolution of tumor heterogeneity over time, with progressively more challenging resistance to any therapeutic intervention, in any single individual. The fact that stromal and mesenchymal factors may contribute dramatically to resistance to immunotherapy techniques is also very relevant for sarcomas, as we may learn key lessons to extrapolate into improvement of effective immunotherapy for other more common forms of malignancy. The key elements of merging the best discovery science with focused clinical translation in well-defined clinical investigations are key to making advances in patients who rely upon us; this is also where sarcoma investigators have proven our ability to join forces worldwide in collaborative trials that can provide reliable data to achieve regulatory approvals for new targeted therapies in diseases as rare as PEComa or TGCT, or as common as KIT-driven GIST. New approaches to modifying the epigenetic landscape of fusion-associated sarcomas in patients will add to our knowledge about chromatin remodeling and aberrant transcriptional regulation. Finally, new research initiatives in liposarcomas are bringing together teams of investigators to apply the most sophisticated tools to understand this family of diseases characterized by pathognomonic gene amplifications, aberrantly driven ubiquitin-pathway function with loss of p53, oncogenic fusions, and blocked adipocytic differentiation. New discovery collaborations are needed to bring in the best innovative techniques and investigators to work with expert clinical investigators to drive advances. It is very timely for AACR to host this focused research meeting again as a focal point for new discovery and innovation in sarcomas, and this introduction will serve to set the stage for a week of interactive discussions regarding many lines of relevant research to advance our field. Citation Format: George D. Demetri. An overview of applying discovery science to care for patients in need: working together to advance our understanding and treatment of sarcomas [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA001.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".