J Gastric Cancer.  2019 Sep;19(3):235-253. 10.5230/jgc.2019.19.e25.

Clinical Implementation of Precision Medicine in Gastric Cancer

• Jeon J
• Cheong JH

• KMID: 2458826
• DOI: http://doi.org/10.5230/jgc.2019.19.e25

Abstract

Gastric cancer (GC) is one of the deadliest malignancies in the world. Currently, clinical treatment decisions are mostly made based on the extent of the tumor and its anatomy, such as tumor-node-metastasis staging. Recent advances in genome-wide molecular technology have enabled delineation of the molecular characteristics of GC. Based on this, efforts have been made to classify GC into molecular subtypes with distinct prognosis and therapeutic response. Simplified algorithms based on protein and RNA expressions have been proposed to reproduce the GC classification in the clinical field. Furthermore, a recent study established a single patient classifier (SPC) predicting the prognosis and chemotherapy response of resectable GC patients based on a 4-gene real-time polymerase chain reaction assay. GC patient stratification according to SPC will enable personalized therapeutic strategies in adjuvant settings. At the same time, patient-derived xenografts and patient-derived organoids are now emerging as novel preclinical models for the treatment of GC. These models recapitulate the complex features of the primary tumor, which is expected to facilitate both drug development and clinical therapeutic decision making. An integrated approach applying molecular patient stratification and patient-derived models in the clinical realm is considered a turning point in precision medicine in GC.

Keyword

Stomach neoplasm; Precision medicine; Tumor biomarkers; Adjuvant chemotherapy; Molecular targeted therapy

MeSH Terms

Biomarkers, Tumor
Chemotherapy, Adjuvant
Classification
Decision Making
Drug Therapy
Heterografts
Humans
Molecular Targeted Therapy
Organoids
Precision Medicine*
Prognosis
Real-Time Polymerase Chain Reaction
RNA
Stomach Neoplasms*
Biomarkers, Tumor
RNA

Figure

• 

  Fig. 1 Clinical perspectives of patient-derived model systems in precision medicine. (A) PDO and PDX biobanks recapitulating a heterogeneous patient population that facilitateshigh-throughput drug screening and preclinical drug testing respectively. (B) Co-clinical trials were conducted on both the patients as well as the models derived from them, enabling drug response validation and molecular mechanism investigation of response/resistance. (C) Avatar models are established from tumor samples of individual gastric cancer patients. Active drugs are identified using drug screening on avatar models, assisting physicians to optimize anti-cancer treatment.PDX = patient-derived xenograft; PDO = patient-derived organoid.

• 

  Fig. 2 Clinical implementation of precision medicine in resectable GC. (A) Resected GC samples are subjected to single patient classifier. GC patients are stratified into three subtypes: immune, epithelial, and stem-like. Optimized treatment strategy is arranged for each patient based on the clinical subtype. Immune subtypes receive no additional treatment after surgery, whereas epithelial subtypes are treated with adjuvant chemotherapy and stem-like subtypes are directly enrolled for the clinical trials. (B) High-risk patients are identified by integrating the clinicopathologic data, TNM staging, and molecular subtypes, such as high TNM stage, diffuse histology, stem-like type. Potential target drug screening is conducted on avatar models of high-risk patients to select personalized target drugs for potential cancer recurrence.mRNA = messenger RNA; RT-PCR = real-time polymerase chain reaction; GC = gastric cancer; FFPE = formalin-fixed, paraffin-embedded tumor tissue; OP = operation; TNM = tumor-node-metastasis.

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