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Functional precision medicine offers a promising complement to genomics-based cancer therapy guidance by testing drug efficacy directly on a patient’s tumor cells. Here, we describe a workflow that utilizes single-cell mass measurements with inline brightfield imaging and machine-learning based image classification to broaden the clinical utility of such functional testing for cancer. Using these image-curated mass measurements, we characterize mass response signals for 60 different drugs with various mechanisms of action across twelve different cell types, demonstrating an improved ability to detect response for several slow acting drugs as compared with standard cell viability assays. Furthermore, we use this workflow to assess drug responses for various primary tumor specimen formats including blood, bone marrow, fine needle aspirates (FNA), and malignant fluids, all with reports generated within two days and with results consistent with patient clinical responses. The combination of high-resolution measurement, broad drug and malignancy applicability, and rapid return of results offered by this workflow suggests that it is well-suited to performing clinically relevant functional assessment of cancer drug response.

Existing predictive biomarkers for immune checkpoint blockade (ICB) therapies that rely on characterization of tumor genomics or immunohistological assessment of the tumor microenvironment have demonstrated mixed success, with no single biomarker proving to be universally predictive of ICB treatment outcome. To complement these existing approaches, we present a workflow that relies on high-resolution single-cell mass measurements curated with CNN-based image classification to characterize T cell activation and functional response to treatment with ICB therapies. As a highly integrative measure of cellular phenotype, cell mass can be used as a label-free biomarker for characterizing immune cell function. For instance, upon activation with anti-CD3/CD28, T cells isolated from human PBMC showed a rapid and significant increase in cell mass. However, in T cells that had been repeatedly stimulated in vitro to generate a PD-1+ exhausted-like phenotype, this mass response to activation was blunted. We hypothesized that this variability in activation-induced mass response was the result of an altered balance of stimulatory and inhibitory “signal two” cues seen by the T cells. Consistent with this hypothesis, purified T cells stimulated with anti-CD3 alone (no co-stimulation) demonstrated a significantly smaller increase in cell mass when compared with those stimulated with both anti-CD3 and anti-CD28. Furthermore, the T cell response measured when unpurified PBMC were activated with anti-CD3 demonstrated an intermediate mass increase, suggesting contributions of both stimulatory and inhibitory signaling from other cells in the milieu. To explore how ICB treatment disrupts this co-stimulatory/inhibitory signaling balance, we measured T cell mass responses for samples treated with anti-CD3 alone or in combination with pembrolizumab, atezolizumab, nivolumab, or dostarlimab. These samples included PBMC isolated from blood and tumor/immune cell milieus isolated from malignant fluids (pleural effusions or abdominal ascites) collected from patients with advanced cancer. We found that T cells isolated from samples treated with both anti-CD3 and an ICB drug often showed a significantly greater mass increase than the same samples treated with anti-CD3 alone. This mass response showed significant heterogeneity across patients, with many samples showing no significant treatment effect. Despite all four of these therapies targeting ostensibly the same PD-1/PD-L1 signaling axis, we also found significant response heterogeneity to these different treatments within individual samples.These results suggest that mass response measurements capture biological heterogeneity associated with ICB response and may serve as a valuable complementary readout to existing biomarkers. Citation Format: Robert Kimmerling, Mark Stevens, Selim Olcum, Anthony Minnah, Madeleine Vacha, Rachel LaBella, Katie Katsis, Audrey Guo, Juanita Fujii, Zayna Shaheen, Srividya Sundaresan, Anobel Tamrazi, Cliff Reid. A cell mass-based platform for characterizing immune cell function and response to checkpoint blockade in primary blood and malignant fluid samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2176.