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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2018, Vol. 12 Issue (2) : 229-235    https://doi.org/10.1007/s11684-017-0581-0
COMMENTARY
Molecular classification and precision therapy of cancer: immune checkpoint inhibitors
Yingyan Yu()
Department of Surgery, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Abstract

On May 23, 2017, the US Food and Drug Administration (FDA) approved a treatment for cancer patients with positive microsatellite instability-high (MSI-H) markers or mismatch repair deficient (dMMR) markers. This approach is the first approved tumor treatment using a common biomarker rather than specified tumor locations in the body. FDA previously approved Keytruda for treatment of several types of malignancies, such as metastatic melanoma, metastatic non-small-cell lung cancer, recurrent or metastatic head and neck cancer, refractory Hodgkin lymphoma, and urothelial carcinoma, all of which carry positive programmed death-1/programmed death-ligand 1 biomarkers. Therefore, indications of Keytruda significantly expanded. Several types of malignancies are disclosed by MSI-H status due to dMMR and characterized by increased neoantigen load, which elicits intense host immune response in tumor microenvironment, including portions of colorectal and gastric carcinomas. Currently, biomarker-based patient selection remains a challenge. Pathologists play important roles in evaluating histology and biomarker results and establishing detection methods. Taking gastric cancer as an example, its molecular classification is built on genome abnormalities, but it lacks acceptable clinical characteristics. Pathologists are expected to act as “genetic interpreters” or “genetic translators” and build a link between molecular subtypes with tumor histological features. Subsequently, by using their findings, oncologists will carry out targeted therapy based on molecular classification.

Keywords molecular classification      precision medicine      pembrolizumab      PD-1/PD-L1      MSI-H     
Corresponding Author(s): Yingyan Yu   
Just Accepted Date: 30 October 2017   Issue Date: 02 April 2018
 Cite this article:   
Yingyan Yu. Molecular classification and precision therapy of cancer: immune checkpoint inhibitors[J]. Front. Med., 2018, 12(2): 229-235.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-017-0581-0
https://academic.hep.com.cn/fmd/EN/Y2018/V12/I2/229
Fig.1  Schematic figure explaining the mechanism of action of Keytruda. Top: PD-1 is expressed on effector T cells, whereas PD-L1 is expressed on tumor cells, producing neoantigens by the dMMR system. Binding of PD-1 and PD-L1 inhibits T cell activity. Bottom: Keytruda acts as an antibody to PD-1. After Keytruda treatment, binding of PD-1 and PD-L1 is disturbed and results in T cell activity against cancer cells.
TCGA classification
(case number)
Histological features (% occupied) Indication for Keytruda drug
EBV-positive subtype
(26)
Moderately or poorly differentiated adenocarcinoma with lymphoepithelial lesion (80.77%) Yes
Other histology (19.23%)
MSI subtype
(64)
(A) Low power: expanding growth pattern of bulky tumor; high power: bulky solid or medullary tumor with lymphocyte infiltration, especially in invasive frontier
(B) Low power: expanding growth pattern of bulky tumor; high power: moderately differentiated adenocarcinoma clusters with lymphocyte infiltration, especially in invasive frontier (70.31%)
Other histology (29.69%)
Yes
GS subtype
(58)
Signet-ring cell carcinoma and its variants, including typical signet-ring cell, small-cell variant, large-cell variant, spindle-cell variant, mucinous variant, and undifferentiated beam variant (namely, poorly cohesive carcinoma) (63.79%) Unknown
Other histology (36.21%)
CIN subtype
(147)
Intestinal-type histology (66.67%)
Other histology (33.33%)
Unknown
Tab.1  Phenotypic features of TCGA molecular subtypes for stomach cancer
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