Abstract A040 Tumor educated platelets in detecting pediatric central nervous system tumors
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».