Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence

The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells from adrenal medullary chromaffin tissues and extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal treatment options for advanced and metastatic cases have been limited. Hence, understanding the role of the immune system in PPGL tumorigenesis can provide essential knowledge for the development of better therapeutic and tumor management strategies, especially for those with advanced and metastatic PPGLs. The first part of this review outlines the fundamental principles of the immune system and tumor microenvironment, and their role in cancer immunoediting, particularly emphasizing PPGLs. We focus on how the unique pathophysiology of PPGLs, such as their high molecular, biochemical, and imaging heterogeneity and production of several oncometabolites, creates a tumor-specific microenvironment and immunologically "cold" tumors. Thereafter, we discuss recently published studies related to the reclustering of PPGLs based on their immune signature. The second part of this review discusses future perspectives in PPGL management, including immunodiagnostic and promising immunotherapeutic approaches for converting "cold" tumors into immunologically active or "hot" tumors known for their better immunotherapy response and patient outcomes. Special emphasis is placed on potent immune-related imaging strategies and immune signatures that could be used for the reclassification, prognostication, and management of these tumors to improve patient care and prognosis. Furthermore, we introduce currently available immunotherapies and their possible combinations with other available therapies as an emerging treatment for PPGLs that targets hostile tumor environments.

Tumour microenvironment in pheochromocytoma and paraganglioma

Pheochromocytomas and Paragangliomas (Pheo/PGL) are rare catecholamine-producing tumours derived from adrenal medulla or from the extra-adrenal paraganglia respectively. Around 10-15% of Pheo/PGL develop metastatic forms and have a poor prognosis with a 37% of mortality rate at 5 years. These tumours have a strong genetic determinism, and the presence of succinate dehydrogenase B (SDHB) mutations are highly associated with metastatic forms. To date, no effective treatment is present for metastatic forms. In addition to cancer cells, the tumour microenvironment (TME) is also composed of non-neoplastic cells and non-cellular components, which are essential for tumour initiation and progression in multiple cancers, including Pheo/PGL. This review, for the first time, provides an overview of the roles of TME cells such as cancer-associated fibroblasts (CAFs) and tumour-associated macrophages (TAMs) on Pheo/PGL growth and progression. Moreover, the functions of the non-cellular components of the TME, among which the most representatives are growth factors, extracellular vesicles and extracellular matrix (ECM) are explored. The importance of succinate as an oncometabolite is emerging and since Pheo/PGL SDH mutated accumulate high levels of succinate, the role of succinate and of its receptor (SUCNR1) in the modulation of the carcinogenesis process is also analysed. Further understanding of the mechanism behind the complicated effects of TME on Pheo/PGL growth and spread could suggest novel therapeutic targets for further clinical treatments.

Artificial intelligence for prediction of response to cancer immunotherapy

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.