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The tumor microenvironment (TME) plays a critical role in tumor proliferation, progression, and therapeutic responses. TME is a complex network of cancer cells, stromal cells, and, most importantly, infiltrating immune cells. Cancer cells regulate numerous biological functions through direct or indirect interaction with TME components. Emerging evidence suggests that TME crucially influences the response to both chemotherapy and immunotherapy. As scientific research has entered the big data era with the fast development of high-throughput sequencing technologies, machine learning has been gradually widely applied to extract important knowledge from big data bioinformatics. Thus, characterizing the TME landscape in cancer and identifying different immune-related TME phenotypes using machine learning-based bioinformatics analyses, in vitro experiments, and in vivo experiments are of great interest and significance.
The tumor microenvironment (TME) plays a critical role in tumor proliferation, progression, and therapeutic responses. TME is a complex network of cancer cells, stromal cells, and, most importantly, infiltrating immune cells. Cancer cells regulate numerous biological functions through direct or indirect interaction with TME components. Emerging evidence suggests that TME crucially influences the response to both chemotherapy and immunotherapy. As scientific research has entered the big data era with the fast development of high-throughput sequencing technologies, machine learning has been gradually widely applied to extract important knowledge from big data bioinformatics. Thus, characterizing the TME landscape in cancer and identifying different immune-related TME phenotypes using machine learning-based bioinformatics analyses, in vitro experiments, and in vivo experiments are of great interest and significance.
Advances in cancer research have led to an improved understanding of the molecular mechanisms underpinning the development of cancer and how the immune system responds to cancer. This influx of research has led to an increasing number and variety of therapies in the drug development pipeline, including targeted therapies and associated biomarker tests that can select which patients are most likely to respond, and immunotherapies that harness the body's immune system to destroy cancer cells. Compared with standard chemotherapies, these new cancer therapies may demonstrate evidence of benefit and clearer distinctions between efficacy and toxicity at an earlier stage of development. However, there is a concern that the traditional processes for cancer drug development, evaluation, and regulatory approval could impede or delay the use of these promising cancer treatments in clinical practice. This has led to a number of effortsâ€"by patient advocates, the pharmaceutical industry, and the Food and Drug Administration (FDA)â€"to accelerate the review of promising new cancer therapies, especially for cancers that currently lack effective treatments. However, generating the necessary data to confirm safety and efficacy during expedited drug development programs can present a unique set of challenges and opportunities. To explore this new landscape in cancer drug development, the National Academies of Sciences, Engineering, and Medicine developed a workshop held in December 2016. This workshop convened cancer researchers, patient advocates, and representatives from industry, academia, and government to discuss challenges with traditional approaches to drug development, opportunities to improve the efficiency of drug development, and strategies to enhance the information available about a cancer therapy throughout its life cycle in order to improve its use in clinical practice. This publication summarizes the presentations and discussions from the workshop.
Cancer cells can change and adapt, especially within the host environment; a phenomenon known as cancer plasticity. Several factors, including the immune system can influence, and be influenced by, cancer plasticity which in turn can impact upon patient responses to treatment. As such, we currently face several challenges for implementing combination therapies as effective cancer treatment strategies. We have compiled a topic with a number of articles that emphasize the various aspects of cancer plasticity, describing in particular the important role of the tumor microenvironment. As we embark on a new era of precision medicine with multi-modal therapies for improving patient outcomes, this topic highlights some relevant points for consideration that are pertinent to the incorporation and effective use of new treatments as part of cancer treatment regimens, including immune-modulating drugs.
In this book we provide insights into liver – cancer and immunology. Experts in the field provide an overview over fundamental immunological questions in liver cancer and tumorimmunology, which form the base for immune based approaches in HCC, which gain increasing interest in the community due to first promising results obtained in early clinical trials. Hepatocellular carcinoma (HCC) is the third most common cause of cancer related death in the United States. Treatment options are limited. Viral hepatitis is one of the major risk factors for HCC, which represents a typical “inflammation-induced” cancer. Immune-based treatment approaches have revolutionized oncology in recent years. Various treatment strategies have received FDA approval including dendritic cell vaccination, for prostate cancer as well as immune checkpoint inhibition targeting the CTLA4 or the PD1/PDL1 axis in melanoma, lung, and kidney cancer. Additionally, cell based therapies (adoptive T cell therapy, CAR T cells and TCR transduced T cells) have demonstrated significant efficacy in patients with B cell malignancies and melanoma. Immune checkpoint inhibitors in particular have generated enormous excitement across the entire field of oncology, providing a significant benefit to a minority of patients.
The field of immuno-oncology continues to rapidly evolve as new insights to fight and treat cancer emerge. The fourth edition of Immunotherapy provides the most current overview of immuno-oncology in different cancer types and toxicities associated with immunotherapy. While immunotherapy has revolutionized the treatment landscape of several solid malignancies, several challenges still exist. Only a subset of patients derive clinical benefits; some do not respond at all, and others respond initially, only for their disease to progress later. Because these drugs can activate a broad range of immune cells, patients suffer from a unique set of side effects known as immune-related adverse events. As more immunotherapeutic agents are used in the clinic, it is important to provide updates about current and ongoing developments in the field to further research efforts and inform treatment decisions. The fourth edition will have a new focus on strategies to overcome the challenges associated with immunotherapy. Chapters will discuss topics such as biomarkers of response, resistance mechanisms, role of imaging in predicting immune-related adverse events, and management of immune-related adverse events. Written by leading experts conducting cutting-edge research, readers will gain up-to-date knowledge on the current state and future of immunotherapy.
This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.
Nowhere is the explosion in comprehensive genomic testing more evident than in oncology. Multiple consensus guidelines now recommend molecular testing as the standard of care for most metastatic tumors. To aid in the advancement of this rapidly changing field, we intend this Special Issue of JPM to focus on technical developments in the genomic profiling of cancer, detail promising somatic alterations that either are, or have a high likelihood of being, relevant in the near future, and to address issues related to the pricing and value of these tests.The last few years have seen the cost of molecular testing decrease by orders of magnitude. In 2018, we saw the first “site-agnostic” drug approvals in cancer (for microsatellite unstable cancer (PD-1 inhibitors) and NTRK-fusions (TRK inhibitors)). Research on targetable mutations, determination of genetic “signatures” that can use multiple individual genes/pathways, development of targeted therapy, and insight into the value of new technology remains at the cutting edge of research in this field. We are soliciting papers that present new technologies to assess predictive biomarkers in cancer, original research (pre-clinical or clinical) that demonstrates promise for particular targeted therapies in cancer, and articles that explore the clinical and financial impacts of this paradigmatic shift in cancer diagnostics and treatment.