PRESS RELEASE
Notable Labs Presents Clinical Platform Validation Data at the 2023 AACR Conference
100% accuracy predicting clinical responders with Notable’s PPMP with enhanced machine learning…
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Figure 1: Heatmap showing responses to 31 drugs or drug combinations (rows) in 37 blood cancer patient samples (columns). Each colored box represents the level of ex vivo drug sensitivity, with red indicating the highest level of sensitivity and blue the lowest.
Getting the right cancer drugs to the right patients relies on the accurate prediction of clinical response to an ever-increasing array of cancer therapies. Although genomics-based precision medicine approaches fulfill this need for some patients by revealing druggable alterations, they do not always identify the optimal drug or result in a clinical response. To address this critical unmet need, Notable has developed a custom precision medicine platform to directly test patient cancer cell responses to drugs in an ex vivo setting. Historically, ex vivo drug sensitivity testing has relied on a “one-size-fits-all” approach, based primarily on measures of cell viability. However, because cancer drugs work across a wide variety of mechanisms, an ex vivo drug sensitivity platform requires a high degree of versatility. For example, cytotoxic chemotherapies interfere with diverse cellular processes including DNA replication and mitotic spindle formation. Targeted therapies inhibit specific signaling pathways, metabolic processes, and other functions that regulate proliferation, differentiation, and survival. Immunotherapy encompasses both molecular and cellular therapies to target cancer cells directly, interrupt checkpoint inhibition pathways, and/or alter the numbers and activities of immune cells within the tumor microenvironment. How does the Notable platform test patient drug responses across so many diverse mechanisms?
Notable has developed a versatile platform that combines high-throughput flow cytometry, advanced automation, and a streamlined analysis pipeline to improve the scale and precision with which patient responses to current and potential therapeutics are modeled on clinical samples in the laboratory. The high-throughput flow cytometry analysis provides a rich dataset of cellular responses, including measures of apoptosis, proliferation, differentiation, and stemness. We also have the ability to evaluate specific immunotherapy targets by modifying the antibody staining panels used in the analysis. The platform utilizes an automated cell culture process in a 384-well plate format. Once the patient cells are seeded, liquid handling technology introduces a specific matrix of drugs at a specified range of doses and combinations across each plate. This allows us to aliquot and test cells from patient samples across a wide range of culture conditions, drug doses and time points on a scale that cannot be accomplished manually. Following completion of the cell culture and drug treatment process, the plates are automatically stained and transitioned to the flow cytometer for analysis, enabling the collection of 10-20 dimensions of data at the single cell level, and resulting in over 100,000 data points from each test condition. These features enable unparalleled capacity to develop and implement specifically tailored conditions required to capture responses to a wide variety of drugs in research projects and clinical studies.
Notable has focused initially on blood cancers, using blood and bone marrow from diagnostic samples, which provide high numbers of cells without the need for expansion in culture. Blood cancers originate through disruption of hematopoietic stem and progenitor cell differentiation, resulting in abnormal blast proliferation coupled to a reduction in normal populations of differentiated blood cells. Notable’s technology platform is furthest in development in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), where we have evaluated drug sensitivity to a broad array of FDA-approved drugs and investigational agents. Our recent publication1 describes application of the Notable platform to evaluate drug sensitivity patterns for samples from 37 blood cancer patients. Figure 1 illustrates these patterns in a heatmap showing patient sample responses to 31 unique drugs and drug combinations from a broad range of drug classes including cytotoxics, kinase inhibitors, differentiating agents, proteasome inhibitors, histone deacetylases inhibitors, and hypomethylating agents.
When developing assays for specific drugs, Notable recognizes that there are diverse cellular responses to different classes of agents. Cytotoxic agents induce cell death whereas cytostatic agents impede cell proliferation, and still other therapies may promote cancer cell differentiation. Our multiparameter cytometric analysis enables broad characterization of the various cell populations and marker expression patterns to capture the diversity of responses. To learn more about our ongoing assay development process to facilitate and measure these outcomes in our platform, please see our white paper.
Current cancer drugs, both approved and investigational, hold enormous promise for developing precise therapeutic strategies and combinations that produce deep, durable responses for cancer patients. Cancer drugs target a wide range of pathways and mechanisms of action against cancer cells, and approaches that predict clinical responses require precise, accurate assays. Notable’s functional ex vivo drug sensitivity platform offers the flexibility, precision, and scale to capture and measure cellular responses in patient samples across a broad set of drug classes, with the potential to apply the technology in drug development, clinical trials, and ultimately the clinic.
1) Michael A. Spinner, MD, Alexey Aleshin, MD, MBA, Marianne T. Santaguida, PhD, Steven A. Schaffert, PhD, James L. Zehnder, MD, A. Scott Patterson, MA, Christos Gekas, PhD, Diane Heiser, PhD, and Peter L. Greenberg, MD. Ex vivo drug screening defines novel drug sensitivity patterns for informing personalized therapy in myeloid neoplasms. Blood Adv. 2020. 4(12): 2768-2778.