Observational study (cohort type) of advanced GC patients that will be recruited prospectively to study biological factors associated with the disease and relevant clinical outcomes.
Despite of multiple attempts to improve treatment in recent decades, none strategies has improved prognosis in locally advanced stage III and IV GC. A therapeutic approach to GC based on current histological and image criteria (Tumour Node Metastasis -TNM- stage) is insufficient. Although multiple targeted agents are currently under investigation, so far, only trastuzumab and ramucirumab have demonstrated efficacy in advanced GC and have a regulatory approval. For this reason, the identification of specific targets that could be susceptible for drug inhibition, is an urgent requirement. Moreover, most studies and current international databases on late-stage/advanced GC are largely based on Asian populations, in sharp contrast tumour biology and genome of EU or CELAC populations remain poorly known. The primary objective of this study are to: 1. Characterize a multi-centric cohort including EU and CELAC populations diagnosed with advanced GC through a multi-omic approach including proteomics, genomics, transcriptomics, microbiome and exposome analysis due to study the determinants of GC. 2. Identify the regional differences in EU and CELAC populations recruiting patients for this study for each omic characterization due to identify the high-risk group populations. 3. Identify and select from the multi-omic approach those biomarkers useful for the development of an algorithm to guide the therapeutic approach for advanced GC.
Study Type
OBSERVATIONAL
Enrollment
800
Instituto Alexander Fleming
Buenos Aires, Argentina
Pontificia Universidad Católica de Chile
Santiago, Chile
Instituto Nacional de Cancerología de México
México, Mexico
VU Medical Centre
Amsterdam, Netherlands
Development of a new diagnostic algorithm for gastric cancer that includes molecular landscape, patient history, histopathological and environmental factors, personal microbiome and immune landscape
The project will look for an integrative diagnostic algorithm that incorporates multi-parameter inputs and apply artificial intelligence to provide more personalized risk estimates and which will form the basis for future development of a clinical tool
Time frame: 3 years
Proteomic analysis of gastric cancer tissue
Determine the expression of certain proteins using ICH and ISH
Time frame: 3 years
Genomics (tumour next-generation sequencing)
Determine differences in the genetic mutational profile of different populations
Time frame: 3 years
Transcriptomics (Nanostring immune gene expression panel)
Determine differences in the genetic expression profile of different populations
Time frame: 3 years
Microbiota sequencing (including level of Epstein-Barr virus [EBV] DNA)
Determine differences in the microbiota profile of different populations
Time frame: 3 years
Dietary habits as assessed by a new study-specific questionnaire
Determine differences in dietary habits of different populations and its correlation with risk to develop gastric cancer
Time frame: 3 years
Biological risk factors as assessed through medical chart review
Determine differences in biological risk factors of different populations and its correlation with risk to develop gastric cancer
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GenPat
Asunción, Paraguay
Institute of Pathology and Immunology of University of Porto
Porto, Portugal
Vall d'Hebron Institut d'Oncologia
Barcelona, Spain
Hospital Clínico Univeristario de Valencia
Valencia, Spain
Time frame: 3 years
Daily routines as assessed by a new study-specific questionnaire
Determine differences daily routines of different populations and its correlation with risk to develop gastric cancer
Time frame: 3 years