Approaching recurrence and resistance mechanisms in esophagogastric adenocarcinomas from the prospective MEMORI trial
a) Background and rationale: Many tumors including esophageal and esophagogastric junction adenocarcinomas (EAC) suffer from typical tumor characteristics responsible for poor prognosis: inherent and acquired therapy resistance, early metastasis and recurrence. All of these features are closely linked to inter- and intratumor molecular heterogeneity. b) Hypothesis: We hypothesize to identify robust classifiers to direct therapy based on comprehensive analysis of sequential tumor material from the MEMORI trial (Metabolic and Molecular Response Evaluation for Therapy Individualization in esophageal and esophagogastric junction adenocarcinomas, EUDRA-CT Number 2014-000860-16). In this clinical trial, 14 days before and after induction of neoadjuvant chemotherapy tumor response is evaluated using PET and sequential tumor biopsies obtained. This setting gives the unique opportunity to study the evolution of intratumor heterogeneity of EAC in the context of therapy response through a comprehensive molecular examination of the tissue specimens before, under and after neoadjuvant therapy. c) Aims: We want to characterize and understand intratumor heterogeneity in EAC samples before, under and after therapy within the MEMORI trial on a proteomic, genomic, epigenomic and metabolomic level where mass spectrometry imaging (MSI) will be used to identify tumor-biologically and therapeutically relevant tumor subpopulations. In parallel, we want to compare the natural and therapy-induced molecular heterogeneity using a second, readily available EAC sample collective (n=120) with the aim to get insights into the molecular mechanisms of resistance. d) Methods: Formalin-fixed, paraffin-embedded (FFPE) and PAXgene-based samples of the 75 patients are collected before and at 14 days post chemotherapy and at resection. MSI of these will be used to obtain spatially-resolved molecular profiles (Norris et al. Chem Rev 2013). Histologically annotated tumor areas will then be in-silico segmented on the basis of the molecular profiles for uncovering spatial molecular intratumor heterogeneity and identifying those tumor subpopulations that are associated with the resistance of EAC patients (Balluff et al. J Pathol 2015). MALDI-FTICR MSI and LA-ICP MSI will be performed to visualize the distributions of metabolites and drugs in the same tissues for comparison with the segmented intratumor heterogeneity. In parallel or subsequent MSI-guided next generation sequencing on the full sample or dissected material, respectively, will be performed for genomic and epigenetic (histone profiling) analyses combined with quantitative label-free proteomics. e) Expected results and potential impact: Integrative bioinformatics will allow for a comprehensive understanding of therapy-induced intratumor heterogeneity and its molecular mechanisms of resistance. This will thus deliver potential alternative therapeutic targets for EAC and robust classifiers for therapy decision making.
Many tumors including esophagogastric junction adenocarcinomas (EAC) suffer from characteristics responsible for poor prognosis: resistance to therapy, early metastasis and recurrence. All these features may arise from a small fraction of the tumor cells, and so the identification of these cells is of paramount importance.
A patient’s tumor can be highly heterogeneous, containing many different cell types as well as tumor cells. The challenge is to identify the tumor cells that lead, for instance, to therapy resistance from within this high variability.
In order to understand how this variability contributes to treatment resistance it is necessary to characterize the variability within each patient’s tumor before treatment, during treatment, and after treatment.
In the ARREST project we sought to identify tumor subpopulations associated with response to treatment by analyzing tumor material before, during, and after treatment. In this manner it was possible to study the evolution of tumor variability in the context of therapy response.
The variability in the molecular content of the tumor cells was visualized by mass spectrometry imaging (MSI), an unlabeled molecular imaging technique that can be used to identify tumor subpopulations.
The project identified heterogeneity EAC tumors, in the tumor’s proteins and metabolites, and demonstrated how combining MSI datasets from multiple molecular classes led to improved prognostic performance.
It was also demonstrated how specific tumor regions identified by MSI could be isolated and comprehensively characterized by allied omics technologies.
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 964264.