KEYWORDS: Digital signal processing, Profiling, Breast cancer, Tumors, Resistance, Antibodies, Ultraviolet radiation, Tissues, Optical signal processing, Optical imaging
NanoString GeoMX Digital Spatial Profiling (DSP) is an emerging optical technology for spatial multi-omics. DSP utilizes probes attached to UV-photocleavable, fluorescent oligonucleotide barcodes indicating target identity and location. These barcodes are counted to quantify gene expression. This high-throughput, single-cell resolution tool exemplifies how optics can elucidate disease mechanisms. Our study employed DSP to understand T-DXd resistance in metastatic breast cancer (mBC). T-DXd is an antibody-drug conjugate that is standard of care in mBC. Patients have few options if T-DXd fails; uncovering resistance mechanisms is crucial for developing life-saving therapies. We used GeoMX DSP to investigate tumor-immune interactions in T-DXd responsive and resistant patient samples. We captured spatially diverse tumor-dense and tumor-sparse regions of interest for proteomics analysis. We found that local stromal remodeling and decreased killer T-cell infiltration are T-DXd resistance characteristics. These results show promise for helping T-DXd resistant-patients and highlight the importance of optical imaging in drug discovery.
Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose.Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, “annotations” refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms.Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency.Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.
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