Abstract 2319: Deep learning-enabled dynamic infiltration and response to NK therapies in solid tumor organoids
Authors: Lonini Luca , Khare Sonal , Lopez Timothy Don , Szydlo Stanislaw , Streit Michael , Karginova Olga A. , Flaherty Mary K. , Salhaudeen Ameen , Kunert Andre , Oja Anna , Sasser Kate , Stumpe Martin C. , Ho Chi-Sing 
Published: March 2024

Patient-derived organoid co-culture models have become a state of the art platform to evaluate the effectiveness of immunotherapeutic agents in preclinical studies. However, preclinical development as well as quantification of therapeutic potency and resistance require differential labeling of cells with fluorescent dyes or transgenic fluorescent proteins, which may dysregulate the effector cell function or produce undesired cytotoxicity. Deep learning-based image analysis allows the use of brightfield images to co-localize tumor organoids (TOs) with effector cells, and measure TO-specific responses to novel candidate immunotherapies in a label-free manner; this can accelerate their evaluation as therapeutic candidates in cancer patients and shed light on tumor-immune interaction mechanisms.

Here we record multi-day time-lapse confocal microscopy images of TOs co-cultured with NK cells at increasing concentrations of target to effector cells. Two pre-trained U-Net convolutional neural networks are used to separately segment tumor and immune cells from the brightfield channel, and quantify immune cell infiltration over time. TO segmentation masks are registered with a vital dye (caspase 3/7) channel to selectively quantify TO cell death and correlate it with estimated immune cell infiltration across a large cohort of patient-derived TO models (including breast, colorectal, endometrial, gastric, head and neck, liver, lung and pancreatic cancer), at increasing concentrations of 6 different NK cell types (identified as A through F).

We find that the estimated density of infiltrating immune cells is highly correlated with TO death, as quantified by fluorescence intensity of caspase over time (median Pearson’s r=0.89, P&lt;0.001). Higher ratios of effector to target cells lead to higher activation of NK cells as measured by infiltrating cell density. Differential infiltration dynamics are observed across TOs and immune cell lines, as peak infiltration density is affected by co-culture time, ratio of target to effector cells, TO line, and NK cell type. These findings highlight that the paired segmentation models are able to measure varying degrees of infiltration across different types of immune cells and TO models.

The approach described here is highly scalable and only requires capturing brightfield images of the assay, therefore eliminating the need to label cells and avoiding phototoxic effects or alteration of the immune cells activity. Time series measurements enable quantification of patterns of immune cell activation, including infiltration, migration and co-localization dynamics, which provide insights into the pharmacokinetics and mechanisms for specific immune therapies. Overall, this methodology enables high throughput screening of many therapeutic candidates across dozens to hundreds of unique TO-models, thereby facilitating targeted precision therapy.

Citation Format: Luca Lonini, Sonal Khare, Timothy Don Lopez, Stanislaw Szydlo, Michael Streit, Olga A. Karginova, Mary K. Flaherty, Ameen Salhaudeen, Andre Kunert, Anna Oja, Kate Sasser, Martin C. Stumpe, Chi-Sing Ho. Deep learning-enabled dynamic infiltration and response to NK therapies in solid tumor organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2319.