Immunoprofiling as a predictor of patient's response to cancer therapy—promises and challenges
D Bethmann, Z Feng, BA Fox - Current opinion in immunology, 2017 - Elsevier
D Bethmann, Z Feng, BA Fox
Current opinion in immunology, 2017•ElsevierHighlights•T cell type, number and location (Immunoscore) in colon cancer predicts
prognosis.•Colon cancer Immunoscore predicted prognosis is superior to AJCC TNM
staging.•Tumor PD-L1 expression allows enrichment of patients who respond to anti-PD-
1.•Multispectral IHC imaging allows for evaluation of cell-to-cell spatial relationships.•Expect
stain automation to allow Multispectral IHC use for clinical decision making.Immune cell
infiltration is common to many tumors and has been recognized by pathologists for more …
prognosis.•Colon cancer Immunoscore predicted prognosis is superior to AJCC TNM
staging.•Tumor PD-L1 expression allows enrichment of patients who respond to anti-PD-
1.•Multispectral IHC imaging allows for evaluation of cell-to-cell spatial relationships.•Expect
stain automation to allow Multispectral IHC use for clinical decision making.Immune cell
infiltration is common to many tumors and has been recognized by pathologists for more …
Highlights
- T cell type, number and location (Immunoscore) in colon cancer predicts prognosis.
- Colon cancer Immunoscore predicted prognosis is superior to AJCC TNM staging.
- Tumor PD-L1 expression allows enrichment of patients who respond to anti-PD-1.
- Multispectral IHC imaging allows for evaluation of cell-to-cell spatial relationships.
- Expect stain automation to allow Multispectral IHC use for clinical decision making.
Immune cell infiltration is common to many tumors and has been recognized by pathologists for more than 100 years. The application of digital imaging and objective assessment software allowed a concise determination of the type and quantity of immune cells and their location relative to the tumor and, in the case of colon cancer, characterized overall survival better than AJCC TNM staging. Subsequently, expression of PD-L1, by 50% or more tumor cells, identified NSCLC patients with double the response rate to anti-PD-1. Soon, automated staining methods will improve reproducibility of multiplex staining and allow for CLIA standards so that multiplex staining can be used to make clinical decisions. Ultimately, machine-learning algorithms will help interpret data from tissue images and lead to improved delivery of precision medicine.
Elsevier