An Observational Study to Collect and Assess Tissue Samples from Subjects with One of Three Neoplastic Conditions (ANSWer)
Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".