An Observational Study to Collect and Assess Tissue Samples from Subjects with One of Three Neoplastic Conditions (ANSWer)
Notice bibliographique
Résumé
Background: Multiple studies have demonstrated that established human cancer cell lines are inadequate to model intra- and inter-patient tumor heterogeneity and resulting mechanisms of disease resistance and progression. Ex vivo drug sensitivity screening (DSS) on primary patient samples may provide a unique approach to overcome these limitations, unlocking insights into drug resistance and identify novel combination therapies that overcome resistance. Notable Labs has built an automated, flow-cytometry-based high-throughput DSS platform that enables testing hundreds of drugs and combinations on individual patient samples at scale. In addition, custom-built software facilitates data analysis and delivery of results within clinically actionable turnaround times.1 This technology platform is in use in the ANSWer study to obtain evidence for potential clinical utility of DSS in clinical practice and management of hematologic malignancies. Study Design and Methods: This is a prospective, multicenter, observational study with collection of de-identified biospecimens with matched clinical data from up to 1000 participants from clinical networks in the United States and Canada. Clinical information, demographics, and medical data relevant to cancer status are collected from all participants and their medical record at baseline (at study entry and time of baseline biospecimen collection), and subsequent visits per patient consent, for up to 1 year. The primary assessment is the establishment of a tumor registry with annotated clinical outcomes. Exploratory assessments include correlation of ex vivo functional testing results with clinical outcomes, as well as identification of potential biomarkers that correlate responses with genotype and/or phenotype. Inclusion Criteria: Provide written informed consent; Age ≥ 18 years, male or female, of any race; Suspected hematologic malignancy (any of the below), in need of starting an active anti-cancer therapy* Acute myelogenous leukemia (AML)a Multiple myeloma (MM) Myelodysplastic syndrome (MDS)a Lymphoma Acute lymphocytic leukemia (ALL) Chronic lymphocytic leukemia (CLL) Chronic myelogenous leukemia (CML) Myeloproliferative Neoplasm (MPN)a Other (upon review and approval by medical monitor) Intent to start anti-cancer therapy within 21 days of biospecimen collection; ≥ 7 days from last anti-cancer therapy; Any number of prior therapies Subject's cohort is currently opena *Supportive care agents including erythropoiesis-stimulating agents (ESAs) such as EPO, Procrit, Aranesp, etc; granulocyte colony stimulating factor (GCSF); hydroxyurea (Hydrea); and luspatercept (Reblozyl) are not considered anti-cancer therapy for this study aCohorts currently open and enrolling: AML, MDS, MPN Exclusion Criteria: Unwilling or unable to give consent; Disease is in remission; Subject's cohort is not open at the time of consent; Subject is restarting an ongoing treatment regimen after a dose interruption Statistical Methods: For the primary assessment, descriptive statistics will be used to summarize baseline patient characteristics, therapies, clinical outcomes, and collection of biospecimens. For exploratory analyses,hierarchical clustering using Euclidean distance metrics and Ward minimum variance will be used to identify patient clusters with distinctex vivodrug sensitivity patterns within a particular treatment cohort. The correlation between clinical response and drug sensitivity will be assessed using the area under the receiver-operator curve (AUROC) and bootstrapping to test for significance. A generalized estimation equation model will be used to identify associations between additional exploratory biomarkers, such as somatic mutations identified via NGS, andex vivosensitivity to various drug classes within specific treatment cohorts. 1Spinner M, et al. Ex Vivo Drug Screening Defines Novel Drug Sensitivity Patterns for Informing Personalized Therapy in Myeloid Neoplasms. Blood. 2020 June 23; 4(12):2768-2778. DOI: 10.1182/bloodadvances.2020001934. The ANSWer Study is currently enrolling at multiple sites across the US with plans to expand in the US and Canada: Figure Disclosures Wen: Notable Labs:Current Employment, Current equity holder in private company.Richardson:Notable Labs:Current Employment, Current equity holder in private company.Kita:Notable Labs:Current Employment, Current equity holder in private company.Tada:Notable Labs:Current Employment, Current equity holder in private company.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».