The presence or absence of viruses affects the characteristics of cancer, the response to anti-cancer treatments, and the prognosis of the disease. A cancerous tumor consists of cancerous cells as well as a variety of non-cancerous cells and molecules, collectively known as the tumor microenvironment. By studying these tumor microenvironments and understanding how our immune system interacts with virus-related and non-virus-related cancers, investigators may discover new approaches to developing more effective anti-cancer treatments. This study aims to characterize the tumor microenvironments specifically associated with viruses.
This is a non-interventional, ambispective multicentre study with 5 complementary workpackages (WP); WP1 clinic, WP2 anatomopathology, WP3 molecular analysis, WP4 proteomic analyses using image-based mass cytometry on tissue sections coupled to the HYPERION imaging system, and WP5 Bioinformatics and Biostatistics analyses. Medical record data will be collected and used for this research. The biological resources required for the study will be one or two additional blood samples of minimal volume or already available in the various tumour banks participating in the study. Blood samples and archived biopsies will be transported to CIMI by private carrier. Clinical data will be collected exclusively from the patient's computerised medical record and entered into an electronic case report form (e-CRF) by the Clinical Study coordinator in each recruiting department, identified by the investigating physicians.
In the analysis, investigators will initially compare the virus-associated and non-virus-associated groups by cancer type, but investigators will also be able to set up a global test by stratifying on cancer type. Investigators will use direct comparisons in the counts from the RNASeq analysis, but also compositional analyses. The remaining statistical evaluations will primarily focus on description and exploration, and will be showcased accordingly. These will involve conventional methods such as computing percentages, means, correlations, along with survival analysis techniques like Kaplan-Meier and Cox regression.