Gastric cancer is the third leading cause of cancer-related deaths worldwide. Over 95% of gastric cancers are adenocarcinomas, which are typically classified based on anatomic location and histologic type. Gastric cancer generally carries a poor prognosis because it is often diagnosed at an advanced stage. Systemic therapy can provide palliation, improved survival, and enhanced quality of life in patients with locally advanced or metastatic disease. The implementation of biomarker testing, especially analysis of HER2 status, microsatellite instability (MSI) status, and the expression of programmed death-ligand 1 (PD-L1), has had a significant impact on clinical practice and patient care. Targeted therapies including trastuzumab, nivolumab, and pembrolizumab have produced encouraging results in clinical trials for the treatment of patients with locally advanced or metastatic disease. Palliative management, which may include systemic therapy, chemoradiation, and/or best supportive care, is recommended for all patients with unresectable or metastatic cancer. Multidisciplinary team management is essential for all patients with localized gastric cancer. This selection from the NCCN Guidelines for Gastric Cancer focuses on the management of unresectable locally advanced, recurrent, or metastatic disease.

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Cancers originating in the esophagus or esophagogastric junction constitute a major global health problem. Esophageal cancers are histologically classified as squamous cell carcinoma (SCC) or adenocarcinoma, which differ in their etiology, pathology, tumor location, therapeutics, and prognosis. In contrast to esophageal adenocarcinoma, which usually affects the lower esophagus, esophageal SCC is more likely to localize at or higher than the tracheal bifurcation. Systemic therapy can provide palliation, improved survival, and enhanced quality of life in patients with locally advanced or metastatic disease. The implementation of biomarker testing, especially analysis of HER2 status, microsatellite instability status, and the expression of programmed death-ligand 1, has had a significant impact on clinical practice and patient care. Targeted therapies including trastuzumab, nivolumab, ipilimumab, and pembrolizumab have produced encouraging results in clinical trials for the treatment of patients with locally advanced or metastatic disease. Palliative management, which may include systemic therapy, chemoradiation, and/or best supportive care, is recommended for all patients with unresectable or metastatic cancer. Multidisciplinary team management is essential for all patients with locally advanced esophageal or esophagogastric junction cancers. This selection from the NCCN Guidelines for Esophageal and Esophagogastric Junction Cancers focuses on the management of recurrent or metastatic disease.

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

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