Abstract A040 Tumor educated platelets in detecting pediatric central nervous system tumors
Bibliographic record
Abstract
Abstract Background Central nervous system (CNS) tumors are the most common pediatric solid cancer type. Tissue biopsy is not always feasible for CNS tumors due to difficult location and high complication risk. Tissue biopsies also do not represent fully the heterogeneity of the tumor, which may lead to false negative or incomplete diagnosis. Platelets have been shown to interact with tumors by various mechanisms, including taking up nucleic acids. These interactions educate platelets causing changes in their gene expression leading to development of tumor educated platelets (TEPs). Consequently, these TEPs exhibit altered function and can promote tumor cell survival and metastasis. Previous studies have demonstrated that transcriptomic analysis of TEPs allows discrimination of false positive progression from actual progression in adult glioblastoma patients. In pediatric CNS tumors, TEPs have not been widely studied earlier. The aim of this study is to evaluate the potential of TEP transcriptomic analyses to distinguish the patients with CNS tumor from asymptomatic controls, and to predict tumor progression. Methods We studied eight pediatric CNS tumor patients (aged 0-18 years) including two high grade gliomas, five low grade gliomas, and one atypical teratoid rhabdoid tumor. Blood was drawn into EDTA tubes from pediatric tumor patients before the treatment initiation, and from pediatric controls without any history of tumors (n=8). Platelets were isolated from blood using double centrifugation and CD45+ leukocyte depletion. RNA was extracted from platelets and the RNA integrity and quantity was determined using Agilent 2100 Bioanalyzer. RNA sequencing (RNA-seq) libraries were constructed from 1ng of total RNA. Rsubread was used to assign RNA-seq reads to human reference genome EnsEMBL release 111 and NCBI nuclear rRNA gene models and to determine read counts per genes. Differential expression analysis was made using DESeq2 and pathway analysis was made using Enrichr. Results We observed differential expression of 42 genes (FDR<0.1), of which 27 were protein coding, in platelets from pediatric CNS tumor patients compared to controls. Out of the protein coding genes, 5 genes were down regulated, and 22 were upregulated. Pathway enrichment analysis demonstrated that various pathways were altered, such as pathways related to immune system and interleukin signaling (p<0.001) in TEPs collected from pediatric CNS tumor patients. Furthermore, our results suggested that gene expression signature of TEPs discriminated aggressive pediatric CNS malignancies from low grade tumors. Conclusions In this pilot study we demonstrated that pediatric CNS tumor patients have alterations in TEP transcriptome compared to pediatric controls. Our preliminary results are in line with previous studies of TEPs suggesting that the presence of tumor alters platelets´ gene expression. Moreover, gene expression analysis of TEPs may be utilized as a diagnostic approach for pediatric CNS malignancies, but more validation and larger cohorts are needed before clinical use. Citation Format: Markus Talka, Amanda Holmström, Satu Långström, Matti Kankainen, Mia Westerholm-Ormio, Anu M. Suominen, Pirjo Isohanni, Pia Valle, Atte Karppinen, Päivi Koroknay-Pal, Anna Piippo-Karjalainen, Nuutti Vartiainen, Olli Tynninen, Soili Kytölä, Anna-Kaisa Anttonen, Virve Penteikäinen, Katja Eloranta. Tumor educated platelets in detecting pediatric central nervous system tumors [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 A040.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".