Insights from Tumor Microenvironment AI-powered digital pathology
25 October 2022
Revealing new insights from the Tumor Microenvironment with AI-powered digital pathology
Cancer treatment has significantly evolved since the approval of the first companion diagnostic, HercepTest, in 1998. Both targeted therapies and immunotherapies have revolutionised cancer treatment, benefiting greatly from precision biomarker strategies that enhance outcomes in selected patient populations. However, we are beginning to reach the limits of existing biomarker-driven treatment efficacy and development.
Broadly, tissue-based biomarkers fall into two main categories, each with innate limitations which present challenges for drug development. Molecular biomarkers (e.g. EGFR, MSI, etc.) indicate a specific genetic alteration that leads to an abnormal phenotype but they fail to consider the heterogeneity of etiologies that can lead to similar abnormal phenotypes. Protein biomarkers are closer to the downstream manifestation of genetic alterations and immune dysregulation but suffer from manual and subjective interpretations to establish score cut-offs. For example, a PD-L1 expression level (i.e., CPS or TPS) offers an imperfect ability to predict treatment response. A significant percentage of potential responders are not identified using existing PD-L1 cut-offs and a portion of PD-L1-positive patients also fail to respond to therapy (1).
The complete picture of disease biology cannot be observed from surveying a very limited amount of specific genetic or protein abnormalities. To unlock new biomarker insights and develop new lifesaving treatments, pharmaceutical manufacturers have been turning to research of the tumor microenvironment (TME) to further evolve an understanding of disease biology.
What is the Tumor Microenvironment?
The TME describes the ecosystem that surrounds and comprises a tumor. (2) Numerous cell and tissue types including immune cells, stromal cells, the blood and vascular networks, as well as various environmental components (e.g., secretory factors), contribute to the regulation of tumor progression and therapeutic response in unique ways. This ecosystem may be differentially represented across disease types and patient populations. There have been various approved therapies directed toward the vasculature, immune checkpoints, and T cells (e.g., aVEGF, PD-L1, CD19) with many treatment targets emerging within the TME (e.g., tertiary lymphoid structures (TLS) and tumor infiltrating lymphocytes (TILs)). (3)
The study of the TME is recognised as one of the most promising areas in oncology drug development to develop a more complete picture of disease biology and tumor progression, particularly around incorporating heterogeneity, morphological patterns, and spatial context into treatment decisions. However, despite the critical role of the TME in tumor progression and regulating treatment efficacy, manual pathology methods have limited the degree to which the TME can be quantified for drug development. Reproducible, scalable, and standardised measurement has been challenging to date due to the sheer amount of data points and time required to systematically characterise each individual cell and feature within the TME.
Utilising AI Technology to Unlock the TME
AI models have been developed to address many of the problems that have limited research of the TME. Models analyse every pixel of an H&E-stained whole slide image (WSI) to exhaustively quantify and characterise the cellular and tissue composition of the TME. These tumor-specific models are developed and trained utilising millions of pathologists’ annotations on a set of images. This ensures the accuracy of cell and feature classification and enables a robust quantification of the most salient features for each cancer type.
Perhaps the biggest challenge to date has been the ability to utilize and quantify pathology outputs to analyse the TME. Particularly, without a standardised approach to measure the spatial organisation of cells and tissue in the TME, analysing changes within the TME is nearly impossible. As first published in Nature Communications by a team from Harvard, Brigham & Womens’ Hospital, Brown, and PathAI, a standardised approach utilising human-interpretable features (HIFs) can be used to predict important clinically-relevant molecular phenotypes (HRD score and expression of PD-1, PD-L1, CTLA-4, and TIGIT). HIFs allow researchers to describe counts, areas, densities, and spatial relationships of cell types and tissue compartments across the TME, enabling quantifiable analysis of data at a speed and scale which was previously inaccessible.
Recent Applications of AI in TME Research
AI technology has demonstrated the ability to quantify the TME at different magnifications of analysis ranging from characterising the TME across several cancer types at once down to assessing each individual cell’s nuclear morphology.
One of the applications of AI technology in TME research which was presented at Pathology Visions 2022 is utilising HIFs to stratify samples based on cancer type and characterise the differences in the TME composition between cancer types. This AI-based characterisation of the TME can be coupled with other modalities, such as RNA-seq, to further investigate the biology of distinct cancer types and the driving factors underlying treatment response. (5)
AI models can also focus on specific features of interest within the TME, such as molecular signatures. In work presented at AACR 2022, AI models were shown to successfully predict TLS gene expression signatures which correlated with overall survival in breast cancer. (6) At WCLC 2022, results showed AI models can predict c-MET over-expression status in NSCLC with high sensitivity. AI models identified that the density of lymphocytes in cancer epithelium was significantly associated with c-MET positivity. (7) Both use-cases demonstrate that H&E data may have a potential for use as a screening tool to more accurately and efficiently identify patients with molecular signatures which can be targeted for treatment.
Powering the Next-Generation of Drug Development
Digital pathology is ready to empower the life sciences industry to utilize the TME to develop the next generation of cancer treatments. The countless archives of existing H&E slide images across hospitals and pharmaceuticals could be utilised with TME AI models immediately to potentially discover new biomarkers or the next therapeutic breakthrough. AI technology will continue to improve upon its already impressive accuracy and speed, paving the way for it to eventually becoming the standard go-to diagnostic and pathology tool for drug developers to utilize in their quest to cure cancer.
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Also published on Fiercebiotech.com
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