Abstract
Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists.
Highlights
Engaging in image analysis methods is becoming increasingly important for pathologists to maintain their role as leaders in precision medicine and diagnostics
This paper provides a comprehensive outline of image analysis and machine learning (ML) applications for precision immunoprofiling
We have recently developed and validated a computational pathology assay that identifies specific PD1-positive subpopulations in nonsmall cell lung cancer (NSCLC) as a powerful predictive indicator of response to immune checkpoint inhibitors (ICI) treatment [76]
Summary
A picture is worth a thousand words. This is the essence of the technological transition from macropathology to. This will allow to better understand the pathophysiology of tumor-host interaction, checkpoint molecule expression, and therapy effects in archival samples and preclinical models [8, 31] Using these modern digital tools to record immune cell infiltrates over the course of therapy will empower data mining with patient characteristics, clinical response profiles, and genomic markers to build novel predictive indicators. PD1T cells were reproducibly detected and quantified in pretreatment biopsies of lung cancer patients and correlated strongly with treatment response to ICI in two independent clinical cohorts This translational approach highlights how digital image analysis can represent a powerful companion diagnostic for cancer immunotherapy applications. The same applies to the majority of immunohistochemical stains, in particular those where expression levels matter
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