Abstract B016: Unravelling personalized drug vulnerabilities in pediatric solid tumors—functional precision medicine approach
Bibliographic record
Abstract
Abstract Many pediatric cancer patients with high-risk primary, refractory, or relapsed tumors still lack effective treatment strategies and frequently suffer from both acute and delayed adverse drug effects, including secondary cancers. These tumors are generally characterized by very few druggable molecular alterations, diminishing the advantage of genetics in defining patient-specific treatments. The purpose of this study is to utilize the functional precision medicine approach to provide improved diagnostics and better therapy options for pediatric solid tumors through deep molecular and functional profiling in a patient-specific manner. Here, we describe the results of our current study, which includes exome sequencing for germline and somatic mutations, and transcriptomics analysis for fusion genes, as well as ex vivo drug testing of patient-derived cancer cells (PDCs) in real-time clinical set-up. Our current clinical data and sample collection, currently from 40 patients, include different types of pediatric solid tumors, such as central nervous system (CNS) tumors, sarcomas, and neuroblastomas. We have prepared PDC cultures and screened them using a 3D-drug sensitivity and resistance testing (DSRT) platform with an internationally defined pediatric solid tumor drug panel. Our results include the drug testing results of control cell lines and primary healthy cells to assess the general cytotoxicity of the drugs and patient-specific responses. We present our "fast-track" functional precision medicine concept, incorporated as part of biobank operations to return clinically relevant findings back to the treating clinicians, and exemplify the data returned to the clinics as well as its potential clinical impact. In conclusion, our findings demonstrate that the functional precision medicine approach can i) help to discover clinically relevant molecular events important for diagnosis and treatment, and ii) increase our biological understanding of drug vulnerabilities in pediatric cancers. Citation Format: Sara Kuusela, Enni Martikainen, Katja Eloranta, Wenyu Wang, Romika Kumari, Swapnil Potdar, Giovanna Dashi, Jouko Lohi, Aino Mutka, Olli Tynninen, Jukka Kanerva, Virve Pentikäinen, Minna Koskenvuo, Vilja M. Pietiäinen. Unravelling personalized drug vulnerabilities in pediatric solid tumors—functional precision medicine approach [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B016.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".