Improved Biomarker Information Is Provided by Digital Pathology and Spatial Genomics
Specific molecular traits or properties found in tissues that display patterns of spatial organization or distribution are referred to be spatial biomarkers. They offer details on the spatial interactions and configurations of the structures, biomolecules, and cells in a tissue sample. In the field of spatial biomarker research, digital pathology and spatial genomics are complimentary technologies that have the potential to transform our knowledge of illnesses and promote the development of novel treatments. By offering spatially resolved “-omics” data on proteomics, gene expression patterns, cellular interactions, and tissue architecture within the human biological environment, or “space,” they provide new dimensions to clinical research.
Essentially, digital pathology gives the visual context and tissue morphology information, whereas single-cell molecular and spatial profiling is provided by spatial genomics. When combined, these technologies provide complementary and richer biomarker data that is improved by spatial and temporal context (for example, through time-series research design).
Digital Spatial Biomarkers using AI
High-throughput “big data” is produced by both digital pathology and spatial genomics, necessitating advanced data analytics—often requiring the creation of novel algorithms and methodologies—to extract valuable insights. To improve many facets of assay creation, analysis, interpretation, and decision-making, artificial intelligence, machine learning, and deep learning are being used more and more to these digital imaging biomarker data dimensions. In particular, AI provides a number of significant benefits:
Image analysis and pattern recognition: Large-scale digital pictures derived from methods like spatial transcriptomics, IHC (immunohistochemistry), fluorescence in situ hybridization (FISH), or multiplexed biomarker imaging are analyzed using AI/ML/DL algorithms. These techniques include measuring spatial patterns of gene expression, detecting nuclei, and segmenting cells. Convolutional neural networks (CNNs), for instance, are used for object identification, picture segmentation, and the accurate study and measurement of certain cellular or molecular characteristics in both normal and malignant tissues.
Automated categorization and geographic clustering: AI/ML/DL algorithms may be used for supervised or unsupervised learning tasks to quantify molecular biomarkers, identify distinct immune cell subgroups, detect spatial cell states, and describe complicated tumor microenvironments.
Integration of Spatial Biomarker Data: AI/ML/DL approaches allow for the smooth integration of spatial biomarker data with other “-omics” data modalities, such as single-cell RNA next-generation sequencing (NGS) data and spatial proteomics. Both geographical and temporal dimensions are included in this integration, which increases the combined data set’s richness and comprehensiveness and improves biomarker insights and value extraction.
Prognostic and predictive modeling: In fields like precision oncology, AI/ML techniques are especially revolutionary when it comes to utilizing massive data sets to create prognostic and predictive modeling. For the development of precision medicine and clinical decision-making, these models can forecast disease progression, treatment responses, and patient outcomes by digitizing and evaluating multimodal histopathological images in conjunction with multi-omics data (e.g., genomics, proteomics, molecular pathology), clinical data, and real-world data.
Spatial Biomarkers’ Promises
By bridging the gap between histopathology and genomics, the integration of spatial biomarker technologies enables more thorough characterization of tissues and cells. It also opens up new avenues for significant drug discovery and development areas, including early disease detection, novel biomarker discovery, new target identification, and creative precision trial designs. Their combined use in the creation of innovative treatments has great potential in:
Identification of the drug target: Spatial “-omics” profiling can show the target genes’ or proteins’ spatial distribution and patterns of expression within tissues. Such data can direct the development of treatments that target certain cellular populations or microenvironments and aid in the creation of molecularly defined tissue atlases within cellular resolution.
Finding new biomarkers: Digital pathology and spatial genomics make it possible to find and confirm new biomarkers linked to illnesses. Biomarkers with better predictive value for better diagnosis or prognosis can be found by comparing molecular profiles with tissue morphology.
Clarification of disease processes: Tissue architecture analysis and molecular profiling work together to reveal intricate cellular relationships and disease mechanisms. This knowledge may result in the identification of new treatment targets and pathways as well as an earlier or better illness diagnosis.
Precision diagnosis and individualized treatment: By combining spatial context, molecular or histological features, and AI/ML/DL, it is possible to make more precise diagnoses of particular diseases or subtypes, improve the design of precision clinical trials, and ultimately make more individualized treatment choices.
Predicting and tracking therapy response: Real-time tracking of treatment responses at the cellular and molecular levels can be achieved by digital pathology and spatial genomics. This makes it possible to evaluate the effectiveness of the therapy, distinguish between responders and nonresponders, and identify possible resistance mechanisms.
What Obstacles Stand in the Way of Fulfilling This Promise?
As with every invention, these developments have great potential for the advancement of precision medicine, but in order to reach their full potential, some obstacles must be overcome:
Computational complexity and scalability: Complex computational techniques and infrastructure are needed to analyze geographical biomarker data. Large-scale data processing can be laborious and time-consuming, requiring a significant amount of computing power. It will continue to be essential to create scalable and effective AI/ML/DL algorithms that can manage the intricacy of spatial biomarker data.
Analytical and clinical validation: Thorough technical and clinical validation—often involving clinical trials—are necessary to translate encouraging results from spatial biomarker and artificial intelligence research into therapeutic applications. It is difficult yet necessary to adequately demonstrate the clinical utility, dependability, and repeatability of spatial biomarkers and AI algorithms in the target patient populations and intended purpose use settings in order for them to be widely used in clinical applications and drug development.
Cost and resource availability: Access to integrated, high-quality data sets, high-resolution imaging equipment, high-performance computer infrastructure, sophisticated AI-based algorithms, and multidisciplinary knowledge are frequently required for spatial biomarker analysis. However, researchers may encounter difficulties due to the cost and accessibility of these tools, particularly those working in environments with restricted resources.
Regulatory considerations: In order to be used in clinical settings or drug development, AI-driven spatial biomarkers or diagnostics must need regulatory permission. This necessitates clear regulatory standards and frameworks that take into consideration the unique properties of AI algorithms and spatial biomarkers. The FDA is actively involved in regulating and monitoring the use of AI/ML in drug development and diagnostics, which is encouraging. The FDA has taken proactive steps, like publishing a discussion paper to get input from stakeholders on how to overcome the difficulties and create an adaptable framework that encourages creativity.