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Biomarkers in Medicine is a comprehensive guide to understanding the current and future status of biomarkers. The book features 27 chapters focusing on disease biomarkers for diseases such as cancer, neurodegenerative diseases, cardiac diseases, metabolic conditions and much more. This book supplies readers with the unique insight of experts in multiple specialties in medicine and life sciences who have extensive experience in diagnostics and clinical laboratories. The book includes case studies and practical examples from different classes of biomarkers on different platforms, including new data for biomarkers in different therapeutic indications. In addition to presenting biomarker information, each chapter covers the relevant pathology and also emphasizes on preclinical and clinical manifestation of the disease process. Clinicians managing patients or clinical trials, clinical researchers, clinical laboratories, diagnostic companies, regulatory agencies, medical school graduate students, academic students, and the general public involved in healthcare delivery will all benefit from information presented in this book.

Many cancer patients are diagnosed at a stage in which the cancer is too far advanced to be cured, and most cancer treatments are effective in only a minority of patients undergoing therapy. Thus, there is tremendous opportunity to improve the outcome for people with cancer by enhancing detection and treatment approaches. Biomarkers will be instrumental in making that transition. Advances in biotechnology and genomics have given scientists new hope that biomarkers can be used to improve cancer screening and detection, to improve the drug development process, and to enhance the effectiveness and safety of cancer care by allowing physicians to tailor treatment for individual patients-an approach known as personalized medicine. However, progress overall has been slow, despite considerable effort and investment, and there are still many challenges and obstacles to overcome before this paradigm shift in oncology can become a reality.

Lung cancer is one of the most common cancers in both men and women worldwide. Early diagnosis of lung cancer can significantly increase the chances of a patient's survival, yet early detection has historically been difficult. As a result, there has been a great deal of progress in the development of accurate and fast diagnostic tools in recent years. Lung Cancer and Imaging provides an introduction to both the methods currently used in lung cancer diagnosis and the promising new techniques that are emerging. Areas covered include the major trends and challenges in lung cancer detection and diagnosis, classification of cancer types, lung feature extraction in joint PET/CT images, and algorithms in the area of low dosage CT lung cancer images. Part of Series in Physics and Engineering in Medicine and Biology.

Gastric cancer (GC) represents the fifth most frequently diagnosed cancer worldwide, with a poor prognosis in patients with advanced disease despite many improvements in systemic treatments in the last decade. In fact, GC has shown resistance to several treatment options, and thus, notable efforts have been focused on the research and identification of novel therapeutic targets in this setting. The tumor microenvironment (TME) has emerged as a potential therapeutic target in several malignancies including GC, due to its pivotal role in cancer progression and drug resistance. Therefore, several agents and therapeutic strategies targeting the TME are currently under assessment in both preclinical and clinical studies. The present study provides an overview of available evidence of the inflammatory TME in GC, highlighting different types of tumor-associated cells and implications for future therapeutic strategies.

Cancer-associated fibroblasts (CAFs), a stromal cell population with cell-of-origin, phenotypic and functional heterogeneity, are the most essential components of the tumor microenvironment (TME). Through multiple pathways, activated CAFs can promote tumor growth, angiogenesis, invasion and metastasis, along with extracellular matrix (ECM) remodeling and even chemoresistance. Numerous previous studies have confirmed the critical role of the interaction between CAFs and tumor cells in tumorigenesis and development. However, recently, the mutual effects of CAFs and the tumor immune microenvironment (TIME) have been identified as another key factor in promoting tumor progression. The TIME mainly consists of distinct immune cell populations in tumor islets and is highly associated with the antitumor immunological state in the TME. CAFs interact with tumor-infiltrating immune cells as well as other immune components within the TIME via the secretion of various cytokines, growth factors, chemokines, exosomes and other effector molecules, consequently shaping an immunosuppressive TME that enables cancer cells to evade surveillance of the immune system. In-depth studies of CAFs and immune microenvironment interactions, particularly the complicated mechanisms connecting CAFs with immune cells, might provide novel strategies for subsequent targeted immunotherapies. Herein, we shed light on recent advances regarding the direct and indirect crosstalk between CAFs and infiltrating immune cells and further summarize the possible immunoinhibitory mechanisms induced by CAFs in the TME. In addition, we present current related CAF-targeting immunotherapies and briefly describe some future perspectives on CAF research in the end.

The tumor microenvironment (TME) is a complex and ever-changing “rogue organ” composed of its own blood supply, lymphatic and nervous systems, stroma, immune cells and extracellular matrix (ECM). These complex components, utilizing both benign and malignant cells, nurture the harsh, immunosuppressive and nutrient-deficient environment necessary for tumor cell growth, proliferation and phenotypic flexibility and variation. An important aspect of the TME is cellular crosstalk and cell-to-ECM communication. This interaction induces the release of soluble factors responsible for immune evasion and ECM remodeling, which further contribute to therapy resistance. Other aspects are the presence of exosomes contributed by both malignant and benign cells, circulating deregulated microRNAs and TME-specific metabolic patterns which further potentiate the progression and/or resistance to therapy. In addition to biochemical signaling, specific TME characteristics such as the hypoxic environment, metabolic derangements, and abnormal mechanical forces have been implicated in the development of treatment resistance. In this review, we will provide an overview of tumor microenvironmental composition, structure, and features that influence immune suppression and contribute to treatment resistance.

Minimally invasive approaches to detect residual disease after surgery are needed to identify patients with cancer who are at risk for metastatic relapse. Circulating tumour DNA (ctDNA) holds promise as a biomarker for molecular residual disease and relapse 1 . We evaluated outcomes in 581 patients who had undergone surgery and were evaluable for ctDNA from a randomized phase III trial of adjuvant atezolizumab versus observation in operable urothelial cancer. This trial did not reach its efficacy end point in the intention-to-treat population. Here we show that ctDNA testing at the start of therapy (cycle 1 day 1) identified 214 (37%) patients who were positive for ctDNA and who had poor prognosis (observation arm hazard ratio = 6.3 (95% confidence interval: 4.45–8.92); P  < 0.0001). Notably, patients who were positive for ctDNA had improved disease-free survival and overall survival in the atezolizumab arm versus the observation arm (disease-free survival hazard ratio = 0.58 (95% confidence interval: 0.43–0.79); P  = 0.0024, overall survival hazard ratio = 0.59 (95% confidence interval: 0.41–0.86)). No difference in disease-free survival or overall survival between treatment arms was noted for patients who were negative for ctDNA. The rate of ctDNA clearance at week 6 was higher in the atezolizumab arm (18%) than in the observation arm (4%) ( P  = 0.0204). Transcriptomic analysis of tumours from patients who were positive for ctDNA revealed higher expression levels of cell-cycle and keratin genes. For patients who were positive for ctDNA and who were treated with atezolizumab, non-relapse was associated with immune response signatures and basal–squamous gene features, whereas relapse was associated with angiogenesis and fibroblast TGFβ signatures. These data suggest that adjuvant atezolizumab may be associated with improved outcomes compared with observation in patients who are positive for ctDNA and who are at a high risk of relapse. These findings, if validated in other settings, would shift approaches to postoperative cancer care. The authors report on prospective exploratory analyses of circulating tumour DNA in an urothelial carcinoma immunotherapy clinical trial.

Prepared by world leaders on this topic, Biomarkers in Cancer Screening and Early Detection offers a comprehensive, state-of-the-art perspective on the various research and clinical aspects of cancer biomarkers, from their discovery and development to their validation, clinical utility, and use in developing personalized cancer treatment. * Offers a comprehensive, state-of-the-art perspective on the various research and clinical aspects of cancer biomarkers * Provides immediately actionable information – and hopefully also inspiration – to move discovery and clinical application forward * Offers vital knowledge to help develop personalized cancer treatment for individual patients with specific cancers

The tumor microenvironment (TME) is an intricate system within solid neoplasms. In this review, we aim to provide an updated insight into the TME with a focus on the effects of tumor necrosis factor-α (TNF-α) on its various components and the use of TNF-α to improve the efficiency of drug delivery. The TME comprises the supporting structure of the tumor, such as its extracellular matrix and vasculature. In addition to cancer cells and cancer stem cells, the TME contains various other cell types, including pericytes, tumor-associated fibroblasts, smooth muscle cells, and immune cells. These cells produce signaling molecules such as growth factors, cytokines, hormones, and extracellular matrix proteins. This review summarizes the intricate balance between pro-oncogenic and tumor-suppressive functions that various non-tumor cells within the TME exert. We focused on the interaction between tumor cells and immune cells in the TME that plays an essential role in regulating the immune response, tumorigenesis, invasion, and metastasis. The multifunctional cytokine, TNF-α, plays essential roles in diverse cellular events within the TME. The uses of TNF-α in cancer treatment and to facilitate cancer drug delivery are discussed. The effects of TNF-α on tumor neovasculature and tumor interstitial fluid pressure that improve treatment efficacy are summarized.

Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response.