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Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery.

ABSTRACT: Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.

SUBMITTER: Vizeacoumar FS

PROVIDER: S-EPMC8027878 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pre- and peri-operative strategies are becoming standard for the management of localized gastro-esophageal cancer. For localized gastric/gastro-esophageal junction (GEJ) cancer there are conflicting data that a peri-operative approach with cisplatin-based chemotherapy improves survival, with the benefits seen in esophageal cancer likely less than a 5-10% incremental improvement. Further trends toward improvement in local control and survival, when combined chemotherapy and radiation therapy are given pre-operatively, are suggested by recent phase III trials. In fit patients, a significant survival benefit with pre-operative chemoradiation is seen in those patients who achieve a pathologic complete response. In esophageal/GEJ cancer, definitive chemoradiation is now considered in medically inoperable patients. In squamous cell carcinoma of the esophagus, surgery after primary chemoradiation is not clearly associated with an improved overall survival, however, local control may be better. In localized gastric/GEJ cancer, the integration of bevacizumab with pre-operative chemotherapy is being explored in large randomized studies, and with chemoradiotherapy in pilot trials. The addition of anti-epidermal growth factor receptor and anti-human epidermal growth factor receptor-2 antibody treatment to pre-operative chemoradiation continues to be explored. Early results show the integration of targeted therapy is feasible. Metabolic imaging can predict early response to pre-operative chemotherapy and biomarkers may further predict response to pre-operative chemo-targeted therapy. A multimodality approach to localized gastro-esophageal cancer has resulted in better outcomes. For T3 or node-positive disease, surgery alone is no longer considered appropriate and neo-adjuvant therapy is recommended. The future of neo-adjuvant strategies in this disease will involve the individualization of therapy with the integration of molecular signatures, targeted therapy, metabolic imaging and predictive biomarkers.

Project description:Preliminary clinical studies of anti-programmed cell death-1 (anti-PD-1) therapy in gastro-esophageal cancers have suggested promising single-agent activity. In patients who received prior treatment for advanced disease, pembrolizumab has been associated with a response rate of 20% in programmed cell death-1 ligand 1 (PD-L1)-positive tumors, and nivolumab with a response rate of 12% in unselected tumors. Both agents yielded a median duration of response lasting ~6-7 months. PD-L1 expression and microsatellite instability (MSI) have emerged as potential predictive markers for PD-1/PD-L1 blockade. PD-L1 expression in tumor cells and in immune cells within the tumor microenvironment has been detected in 14-24% and ~35% of patients with gastro-esophageal cancer, respectively. PD-L1 tumor cell expression appears to be more common in Epstein-Barr virus (EBV)-positive gastric cancers (GCs) and has been associated with an increased density of tumor-infiltrating lymphocytes (TIL). To date, data are too sparse to determine whether PD-L1 expression predicts efficacy of anti-PD-1 therapy in gastro-esophageal cancer, but data from other tumor types have not been consistent regarding its predictive value. MSI occurs in 10-20% of gastro-esophageal cancers and arises from deficient mismatch repair (MMR). MSI is highly correlated with non-synonymous mutation burden, as well as a dense accumulation of TILs. MSI has been associated with improved response to anti-PD-1 therapy in gastrointestinal cancers. Multiple studies are ongoing which examine therapeutic blockade of the PD-1/PD-L1 axis in unselected patients with gastro-esophageal cancer, as well as patients whose tumors express PD-L1 or exhibit MSI. These studies will clarify their activity in this disease and potentially can determine whether identify a strong predictive biomarker can be identified. Checkpoint inhibition is also being studied in combination with curative-intent chemo (radio) therapy and surgery.

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Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery.

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