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Introduction Following the advent of cellular therapies, flow cytometry has become a central tool for simultaneous assessment of drug levels and disease burden due to its ability for high dimensional cellular analysis. However, identification and monitoring of myriad immune- and leukemic cell phenotypes requires a manual gating process by subject matter experts. Although this process is the current gold standard for flow data analysis, it is slow, tedious, and prone to subjectivity. Herein, we describe our approach to automate flow data analysis using novel strategies that reduces the reliance on clustering methods that are unable to filter challenging noisy data sets inherent in flow data. Methods We developed DeepCytoFlow, a novel deep learning framework adapted for flow cytometry data analysis. DeepCytoFlow utilizes a convoluted neural network structure to analyze flow cytometry data files to generate appropriately gated cell populations in a matter of minutes significantly reducing the time required for manual analysis. The neural networks were first trained using a unique data set generated manually by multiple operators and tested using an independent data set to establish concordance in assessments. In addition, we evaluated the performance of our method using both accuracy and F1-score. Results Our integrated DeepCytoFlow pipeline employing neural network design shows promise on clustering-based analysis methods in terms of accuracy, precision and recall. More specifically, DeepCytoFlow is capable of correctly classifying various immune cell lineages (e.g., CD4 and CD8 T-cells). However, a limitation was observed on its ability to classify rare cell populations with a very limited prevalence. In an effort to improve performance, we are currently designing a new network architecture, and increasing our training and test dataset's size and quality. Conclusions DeepCytoFlow provides proof of concept of the applicability of deep learning for automation of gating in flow cytometry. The high adaptability of deep learning to a large set of problems suggests that this technique can be broadly applied to a larger set of biomarkers for the purpose for automated gating. With additional improvements, we believe DeepCytoFlow can enable a fast, accurate, and robust platform for analysts to perform consistent and, repeatable flow analysis for biomarker monitoring in clinical trials investigating novel immunotherapies. Citation Format: Arjun Chandran, Jennifer Tsau, Dalia Gaddis, Shyam Sarikonda, Naveen Dakappagari, Christian Laing, Swetha Ann Thomas. Development of DeepCytoFlow: A novel deep learning pipeline enabling automated gating of flow cytometry data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 164.

Propelled by the advent of novel oncogenic pathway-targeting drugs and increasing regulatory expectations to fulfil proof of mechanism endpoints in clinical trials, there is a critical need to reliably evaluate target inhibition by investigational drugs in biologically relevant compartments. However, the ability to monitor modulation of labile oncogenic pathway biomarkers in global hematomalignancy clinical trials is complicated by varying technical expertise at clinical sites, when specimen stability considerations do not permit analysis at a central specialty laboratory. To address this critical gap, we developed a novel preservation system, designated NovaPerm3 (NP3), that enables stabilization of phosphoprotein based biomarker specimens in a single step, requires less than 20 minutes, and can be implemented in routine clinical trial settings. Stabilized specimens can be frozen and analyzed even after 90 days in a specialized laboratory. Using the NP3 preservation system, we systematically developed and validated a flow cytometry assay that not only enables reliable identification of neoplastic plasma cells but also allows for quantitation of phosphoprotein biomarkers in the tumor cells of interest present in both the peripheral blood (PB) and bone marrow (BM). We will describe a) modulation of phosphorylation levels of the S6 ribosomal protein (pS6RP) by a novel anticancer agent in multiple myeloma patients b) qualitative concordance between NP3-flow cytometry and western blot results from multiple drug treated leukemia cell lines c) sensitive detection of neoplastic plasma cells up to 1% of the total white cells and d) specimen (pS6RP) stabilization over a 90 day period. In conclusion, our novel preservation method and clinical trial laboratory validated flow assay enables reliable quantification of signaling biomarkers in hematomalignancy trials with ease in a specialized central laboratory while reducing the procedural burden for collecting biomarker specimens in global clinical trials. Citation Format: Anil Pahuja, Shyam Sarikonda, Benjamin Lee, Armin Graber, Shabnam Tangri, Naveen Dakappagari. Analytical validation and clinical verification of phosphoprotein biomarker modulation using a novel preservation system-based flow cytometry assay in multiple myeloma clinical trials. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3388. doi:10.1158/1538-7445.AM2015-3388

Propelled by the advent of new technologies and an evolving regulatory landscape, the desire to personalize cancer treatments has never been greater. There is a critical need to reliably evaluate target inhibition and pharmacodynamic activity of investigational drugs in biologically relevant compartments. However, the ability to fulfill such a task in global clinical trials is complicated by varying technical expertise available at clinical sites and considerations about specimen stability for centralized laboratory analysis. We developed a novel formalin-based preservation method that enables specimen stabilization in a single step, requiring less than 20 minutes, without the need for specialized training or instrumentation. Stabilized specimens can be frozen for shipping and batched analysis at a later time point, in a specialized laboratory. Using this preservation method, we developed a flow assay enabling identification of multiple cell-types, and quantification of intracellular biomarkers in target cellular compartments, in both the peripheral blood (PB) and bone marrow (BM). We present pharmacodynamic (PD) data for a novel anticancer investigational agent intended for acute myeloid leukemia and multiple myeloma and its effect on multiple target biomarkers of the PI3K signaling pathway, including phosphorylation of the S6 ribosomal protein (S6). Our novel fixation method allowed detection of pS6 modulation in a dose dependent manner in both tumor cells and PB or BM in response to the novel investigational agent. Assay sensitivity and concordance were evaluated by comparing flow assay with western blot analysis, with each assay performed at a different site using the same batched frozen samples; exceptional preservation of phosphorylated proteins for more than 72 hours could be observed, when frozen immediately following fixation. In conclusion, our novel preservation method enables reliable quantification of signaling biomarkers in centralized laboratories at different time points post sample (PB or BM) acquisition requiring minimal processing at collection sites. Citation Format: Anil Pahuja, Abdel Saci, Shyam Sarikonda, Armin Graber, Benjamin Lee, Jelveh Lameh, Shabnam Tangri, Naveen Dakappagari. A novel blood preservation system to study oncogenic signaling pathway biomarkers by flow cytometry in leukemia/lymphoma clinical trials. [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-96. doi:10.1158/1538-7445.AM2014-LB-96