Virus-associated Tumour Microenvironment

Overview

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.

After decades of cancer research targeting tumour cells, strategies aimed at targeting the tumour microenvironment (TME) for therapeutic purposes are expanding rapidly. It has been shown that the composition and quality of the TME have a major prognostic impact and influence the response to anti-tumour treatments, such as immune checkpoint inhibitors (ICIs) and cytotoxic chemotherapies. The immune mechanisms associated with the TME and involved in resistance to anti-tumour treatments have only been partially described, and are currently leading to the development of promising new therapeutic approaches. Virus-associated cancers are common and constitute a major public health problem. Several viruses have been described as associated with cancers, either directly involved in carcinogenesis (oncoviruses, e.g. Human Papilloma Virus (HPV)) or indirectly by inducing, for example, immunodepression that favours the onset of cancer (e.g. Human Immunodeficiency Virus (HIV)). These viruses appear to be capable of affecting the molecular profile and the TME of these cancers, thereby modifying their sensitivity to anti-tumour treatments. Some of the TMEs associated with viruses have been partially described, mainly using transcriptomic approaches and focusing on T lymphocytes. The immune mechanisms specifically associated with viruses and involved in resistance to anti-tumour treatments such as ICIs and chemotherapy have not yet been described. Characterising the TME and the immunomolecular mechanisms involved in resistance to anti-tumour treatments, for both virus-associated and non-virus-associated cancers, could reveal new molecular and immune targets. These discoveries could form the basis of future therapeutic strategies targeting the TME of virus-associated cancers. 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. A total of 254 subjects will be recruited prospectively and retrospectively over two and a half years within 17 recruiting clinical centre. Five groups of patients will be recruited with matching on important clinical characteristics within the groups. All the patients in the study will be patients followed up in the identified AP-HP clinical services for their cancer. This is the prospective arm of the study, patients with a de novo diagnosis of cancer will be recruited via consultations with the study's investigating physicians. The investigating physicians will inform and obtain the non-opposition of the patients who take part in the research during a consultation. 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 Technicians in each recruiting department, identified by the investigating physicians. The e-CRF will be built by the Public Health Department of the Saint-Antoine Hospital, within the Pierre Louis Institute of Epidemiology and Public Health (INSERM UMR S 1136) under the responsibility of Prof. Pierre-Yves BOELLE, then validated by the study coordinators.Omics data will be collected by the participating biological platforms and processed at the same site. 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. 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