Abstract LB-177: Handling of liquid biopsy samples can dramatically change the numbers of PSMA-positive microparticles
Notice bibliographique
Résumé
Abstract The use of PSA as the gold standard for diagnosing prostate cancer (PCa) has been challenged recently and the search for new PCa biomarkers has increased. Although the PSA test has acceptable sensitivity, it lacks the necessary specificity to discriminate benign prostatic diseases, resulting in overdiagnosis and overtreatment. Thus new, more specific biomarkers for PCa are needed to prevent unnecessary surgical biopsies. The development of fluid biopsies which analyze extracellular microvesicles in plasma, urine or semen are an exciting new area of investigation. Extracellular microvesicles are a new, heterogenous group and while the nomenclature is still being defined by the research community, we define microvesicles as submicron vesicles shed from the plasma membrane. These microvesicles can contain mRNA, microRNA, and membrane proteins - any of which are potential sources for new PCa biomarkers. The diagnostic test must differentiate microvesicles originating from PCa cells versus non-cancerous origins, and also define a biomarker which is enriched in this prostate cancer microvesicle (PCMV) population. In our assay, the prostate specific membrane antigen (PSMA) which has been shown to be highly specific for PCa cells, is used. As the microvesicle field is new, no standard methods for specimen isolation, handling or even proper controls for direct comparisons have yet been accepted. Our initial studies examined plasma preparation and storage as a source of variation. Blood was collected from five prostate cancer patients and plasma prepared under different handling conditions; EDTA was used as the anticoagulant in all cases. Microparticles were assessed directly in plasma using the Apogee A50 micro-flow cytometer and the Nanosight LM10, and ranged in size from ∼80-200nm. Matched isotype controls were used for gating purposes. Significantly, fresh, never frozen plasma had dramatically more PSMA positive microparticles than any other treatment group. However, assaying fresh patient plasma is not practical; development of any biomarker assay will utilize retrospective samples, so frozen samples resemble true sample availability. Plasma prepared within 2hrs of collection, aliquoted and frozen at -80C was considered as “control”. Plasma or whole blood stored at RT overnight significantly increased PCMV (ie PSMA+) counts (2-4 fold respectively) compared to control. Whole blood that was stored at 4C for 30 min or overnight also had significantly more PCMVs. Plasma stored at RT for 6hrs or at -20C O/N and then frozen at -80C had similar PCMPs compared to control. In agreement with other published data, isotype controls yielded significant numbers of “positive” microvesicles. The size of vesicles consistently ranged from 80-200nm with a sharp drop off at both ends and a significant peak at ∼90nm. After freezing, the numbers of microvesicles <80nm increased but the sharp cutoff at 200nm remained. Freeze thaw (10x) of the plasma increased microvesicle size to 400nm. In conclusion, the storage and handling of patient plasma can significantly affect the number of microvesicles which stain positive for PSMA and hence be classified as prostate cancer microvesicles. Standardization of processing and handling of patient samples is necessary to be able to assay the effectiveness any potential biomarker for diagnostic value. Citation Format: Desmond B. Pink, Deborah Sosnowski, Robert Paproski, Andries Zijlstra, John D. Lewis. Handling of liquid biopsy samples can dramatically change the numbers of PSMA-positive microparticles. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-177. doi:10.1158/1538-7445.AM2014-LB-177
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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,003 | 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 ».