Abstract
Immunohistochemical (IHC) analysis of tissue biopsies is currently used for clinical screening of solid cancers to assess protein expression. The large amount of image data produced from these tissue samples requires specialized computational pathology methods to perform integrative analysis. Even though proteins are traditionally studied independently, the study of protein co-expression may offer new insights towards patients' clinical and therapeutic decisions. To explore protein co-expression, we constructed a modular image analysis pipeline to spatially align tissue microarray (TMA) image slides, evaluate alignment quality, define tumor regions, and ultimately quantify protein expression, before and after tumor segmentation. The pipeline was built with open-source tools that can manage gigapixel slides. To evaluate the consensus between pathologist and computer, we characterized a cohort of 142 gastric cancer (GC) cases regarding the extent of E-cadherin and CD44v6 expression. We performed IHC analysis in consecutive TMA slides and compared the automated quantification with the pathologists' manual assessment. Our results show that automated quantification within tumor regions improves agreement with the pathologists' classification. A co-expression map was created to identify the cores co-expressing both proteins. The proposed pipeline provides not only computational tools forwarding current pathology practices to explore co-expression, but also a framework for merging data and transferring information in learning-based approaches to pathology.
Highlights
G ASTRIC cancer (GC) ranks as the fifth most common cancer worldwide and the third most frequent cause of cancer-related mortality [1]
In order to quantify protein co-expression, we developed and present here an image analysis pipeline to perform image registration on tissue microarray (TMA) slides, define tumor regions and quantify protein co-expression, built on such libraries
We developed an image analysis pipeline to align and quantify co-expression of two proteins in GC TMA cores
Summary
G ASTRIC cancer (GC) ranks as the fifth most common cancer worldwide and the third most frequent cause of cancer-related mortality [1]. A better understanding of tumor heterogeneity and local molecular signatures is crucial to guide better clinical and therapeutic decisions, a fact that is true for many types of cancer [3] where the knowledge of underlying mechanisms is limited. Immunohistochemical (IHC) analysis of tissue samples is the mainstream approach for diagnosis and therapeutic decision in solid cancers. IHC is often limited by the subjectivity associated with qualitative visual interpretation of expression levels. In this matter, computational pathology can be an ally, as it objectively assesses, quantifies and relates a large number of features in a systematic and high throughput way
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