| Literature DB >> 30008616 |
Francesca Battaglin1,2, Madiha Naseem1, Alberto Puccini1,3, Heinz-Josef Lenz1.
Abstract
Gastro-esophageal adenocarcinomas (GEA) represent a severe global health burden and despite improvements in the multimodality treatment of these malignancies the prognosis of patients remains poor. HER2 overexpression/amplification has been the first predictive biomarker approved in clinical practice to guide patient selection for targeted treatment with trastuzumab in advanced gastric and gastro-esophageal junction cancers. More recently, immunotherapy has been approved for the treatment of GEA and PD-L1 expression is now a biomarker required for the administration of pembrolizumab in these diseases. Significant progress has been made in recent years in dissecting the genomic makeup of GEA in order to identify distinct molecular subtypes linked to distinct patterns of molecular alterations. GEA have been found to be highly heterogeneous malignances, representing a challenge for biomarkers discovery and targeted treatment development. The current review focuses on an overview of established and novel promising biomarkers in GEA, covering recent molecular classifications from TCGA and ACRG. Main elements of molecular heterogeneity are discussed, as well as emerging mechanisms of primary and secondary resistance to HER2 targeted treatment and recent biomarker-driven trials. Future perspectives on the role of epigenetics, miRNA/lncRNA and liquid biopsy, and patient-derived xenograft models as a new platform for molecular-targeted drug discovery in GEA are presented. Our knowledge on the genomic landscape of GEA continues to evolve, uncovering the high heterogeneity and deep complexity of these tumors. The availability of new technologies and the identification of promising novel biomarker will be critical to optimize targeted treatment development in a setting where therapeutic options are currently lacking. Nevertheless, clinical validation of novel biomarkers and treatment strategies still represents an issue.Entities:
Keywords: Asian Cancer Research Group (ACRG); Epigenomics; Gastro-esophageal cancer; Genomic profiling; HER-2; Immunotherapy; Liquid biopsy; Molecular biomarkers; The Cancer Genome Atlas (TCGA)
Year: 2018 PMID: 30008616 PMCID: PMC6042434 DOI: 10.1186/s12935-018-0594-z
Source DB: PubMed Journal: Cancer Cell Int ISSN: 1475-2867 Impact factor: 5.722
Main biomarkers and trials of targeted therapies in gastric and esophageal adenocarcinoma
| Target | Biomarker | Targeted agent | Study (treatment line) | Regimen | Primary endpoint | Positive study Y/N | Refs. |
|---|---|---|---|---|---|---|---|
| HER2 | |||||||
| Trastuzumab | ToGA (1st) | Trastuzumab + CX vs CX | OS | Y | [ | ||
| Lapatinib | TRIO-013/LOGiC (1st) | Lapatinib + XELOX vs XELOX | OS | N | [ | ||
| Lapatinib | TyTAN (2nd) | Lapatinib + paclitaxel vs paclitaxel | OS | N | [ | ||
| T-DM1 | GATSBY (2nd) | T-DM1 vs taxane | OS | N | [ | ||
| Pertuzumab | JACOB (1st) | Pertuzumab + trastuzumab + CF vs trastuzumab + CF | OS | N | [ | ||
| VEGF-A and VEGFR-2 | – | ||||||
| Bevacizumab | AVAGAST (1st) | Bevacizumab + CX vs CX | OS | N | [ | ||
| Ramucirumab | REGARD (2nd) | Ramucirumab vs placebo | OS | Y | [ | ||
| Ramucirumab | RAINBOW (2nd) | Paclitaxel + ramucirumab vs Paclitaxel | OS | Y | [ | ||
| EGFR | |||||||
| Cetuximab | EXPAND (1st) | Cetuximab + CX vs CX | PFS | N | [ | ||
| Panitumumab | REAL-3 (1st) | Paniumumab + EOX vs EOX | OS | N | [ | ||
| MET and HGF | |||||||
| Rilotumumab | RILOMET-1 (1st) | Rilotumumab + ECX vs ECX | OS | N | [ | ||
| Onartuzumab | METGastric (1st) | Onartuzumab + FOLFOX vs FOLFOX | OS | N | [ | ||
| FGFR2 | FGFR2 polysomy/gene amplification | ||||||
| AZD4547 | SHINE (2nd) | AZD4547 vs paclitaxel | PFS | N | [ | ||
| PD-1 | – | ||||||
| Nivolumab | ATTRACTION-2 (ONO-4538-12) (≥ 3rd) | Nivolumab vs placebo | OS | Y | [ | ||
| PD-L1 expression | |||||||
| Pembrolizumab | KEYNOTE-059 (≥ 3rd cohort 1; 1st cohort 2 and 3) | Pembrolizumab (cohort 1); Pembrolizumab + CF (cohort 2); Pembrolizumab (cohort 3) | ORR (cohort 1 and 3) | Y | [ | ||
| Pembrolizumab | KEYNOTE-028 (after failure on standard therapy or if standard therapy not appropriate) | Pembrolizumab | ORR | Y | [ | ||
| PD-L1 | – | ||||||
| Avelumab | JAVELIN Gastric 300 (3rd) | Avelumab + BSC vs CT | OS | N | [ |
BSC best supportive care, CF cisplatin + fluoropyrimidine, CT chemotherapy, CX cisplatin + capecitabine, EGFR epidermal growth factor receptor, EOX epirubicin + oxaliplatin + capecitabine, FGFR fibroblast growth factor receptor, FOLFOX 5-fluorouracil + leucovorin + oxaliplatin, HGF hepatocyte growth factor, ORR overall response rate, OS overall survival, PD-1 programmed cell death protein 1, PD-L1 programmed death-ligand 1, PFS progression-free survival, VEGF vascular endothelial growth factor, VEGFR vascular endothelial growth factor receptor, XELOX capecitabine + oxaliplatin
Fig. 1Schematic representation of main biomarkers and molecular characteristics according to tumor location and genomic subtype
Promising future biomarkers
| Biomarker | Description | Potential value | Refs. |
|---|---|---|---|
| HER2 loss | Loss of HER2 overexpression after anti-HER2 treatment | Predictive: secondary resistance to anti-HER2 agents | [ |
| Secondary driver alterations (mutations/amplification) co-occurrent with | Predictive: primary resistance to anti-HER2 | [ | |
| Acquired alterations under anti-HER2 treatment pressure | Predictive: secondary resistance to anti-HER2 agents | [ | |
| Liquid biopsy | Mutational analysis of circulating tumor DNA | Molecular profiling and identification of predictive mutations for targeted treatments at baseline | [ |
| DNA methylation | Aberrant promoter DNA methylation in target genes | Diagnostic value | [ |
| miRNA | Micro RNA: short noncoding single-stranded RNA molecules, with post-transcriptional regulatory functions | Diagnostic and prognostic value | [ |
| lncRNA | Long noncoding RNA: noncoding single-stranded RNA molecules, > 200 nucleotides, involved in cancer development and metastases | Possible diagnostic and prognostic value | [ |
| PDX models | Patient-derived xenograft animal models with defined molecular signatures | Predictive: preclinical studies with targeted drugs | [ |