Precision medicine is an emerging practice by which clinicians aim to deliver a personalized treatment program to affected patients based on information gained from their individual clinical and biological profiles. In the context of precise cancer immunotherapy, multi-omics, high-throughput sequencing, big data, and other approaches serve to screen new predictive factors for immunotherapy response and prognosis in cancer patients.
This Research Topic is the second volume of the “Community Series in Novel Biomarkers for Predicting Response to Cancer Immunotherapy". Please see Volume I here. Immunotherapy has revolutionized the treatment of malignancies. Targeting of immune checkpoints cytotoxic T-lymphocyte-associated protein 4, programmed cell death protein 1 (PD-1) and its ligand (PD-L1) has led to improving survival in a subset of patients. Despite their remarkable success, clinical benefit remains limited to only a subset of patients. A significant limitation behind these current treatment modalities is an irregularity in clinical response, which is especially pronounced among checkpoint inhibition. Currently, relevant predictors of cancer immunotherapy response include microsatellite instability-high/deficient mismatch repair (MSI-H/dMMR), expression of PD-L1, tumor mutation burden (TMB), immune genomic characteristics, and tumor infiltrating lymphocytes (TILs). However, none of them have sufficient evidence to be a stratification factor. Moreover, as the combined strategies for effective cancer immunotherapy had been developed in multiple tumors, such as Immunotherapy combined with chemotherapy, radiotherapy, targeted therapy and anti-angiogenesis therapy. Therefore, the development of novel biomarkers endowed with high sensitivity, specificity and accuracy able to identify which patients may truly benefit from the treatment with cancer immunotherapy would allow to refine the therapeutic selection and to better tailor the treatment strategy. This research topic aims to focus on the advances in the discoveries of novel biomarkers for predicting response to cancer immunotherapy in various tumors. We welcome the submission of original research and review articles that include biomarkers in clinical study and applications, as well as technologies or discoveries in experimental approaches.
Immunotherapy has revolutionized the treatment of malignancies. Targeting of immune checkpoints cytotoxic T-lymphocyte-associated protein 4, programmed cell death protein 1 (PD-1) and its ligand (PD-L1) has led to improving survival in a subset of patients. Despite their remarkable success, clinical benefit remains limited to only a subset of patients. A significant limitation behind these current treatment modalities is an irregularity in clinical response, which is especially pronounced among checkpoint inhibition. Currently, relevant predictors of cancer immunotherapy response include microsatellite instability-high/deficient mismatch repair (MSI-H/dMMR), expression of PD-L1, tumor mutation burden (TMB), immune genomic characteristics, and tumor infiltrating lymphocytes (TILs). However, none of them have sufficient evidence to be a stratification factor. Moreover, as the combined strategies for effective cancer immunotherapy had been developed in multiple tumors, such as Immunotherapy combined with chemotherapy, radiotherapy, targeted therapy and anti-angiogenesis therapy. Therefore, the development of novel biomarkers endowed with high sensitivity, specificity and accuracy able to identify which patients may truly benefit from the treatment with cancer immunotherapy would allow to refine the therapeutic selection and to better tailor the treatment strategy.
Expert laboratory and clinical researchers from around the world review how to design and evaluate studies of tumor markers and examine their use in breast cancer patients. The authors cover both the major advances in sophisticated molecular methods and the state-of-the-art in conventional prognostic and predictive indicators. Among the topics discussed are the relevance of rigorous study design and guidelines for the validation studies of new biomarkers, gene expression profiling by tissue microarrays, adjuvant systemic therapy, and the use of estrogen, progesterone, and epidermal growth factor receptors as both prognostic and predictive indicators. Highlights include the evaluation of HER2 and EGFR family members, of p53, and of UPA/PAI-1; the detection of rare cells in blood and marrow; and the detection and analysis of soluble, circulating markers.
Topic Editor Dr. Lewis Shi received financial support from Varian Medical System, Inc. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Written with the busy practice in mind, this book delivers clinically focused, evidence-based gynecology guidance in a quick-reference format. It explores etiology, screening, tests, diagnosis, and treatment for a full range of gynecologic health issues. The coverage includes the full range of gynecologic malignancies, reproductive endocrinology and infertility, infectious diseases, urogynecologic problems, gynecologic concerns in children and adolescents, and surgical interventions including minimally invasive surgical procedures. Information is easy to find and absorb owing to the extensive use of full-color diagrams, algorithms, and illustrations. The new edition has been expanded to include aspects of gynecology important in international and resource-poor settings.
This book comprehensively summarizes the biology, etiology, and pathology of ovarian cancer and explores the role of deep molecular and cellular profiling in the advancement of precision medicine. The initial chapter discusses our current understanding of the origin, development, progression and tumorigenesis of ovarian cancer. In turn, the book highlights the development of resistance, disease occurrence, and poor prognosis that are the hallmarks of ovarian cancer. The book then reviews the role of deep molecular and cellular profiling to overcome challenges that are associated with the treatment of ovarian cancer. It explores the use of genome-wide association analysis to identify genetic variants for the evaluation of ovarian carcinoma risk and prognostic prediction. Lastly, it highlights various diagnostic and prognostic ovarian cancer biomarkers for the development of molecular-targeted therapy.
Topic Editor Prof. Aimin Xu receives financial support from Servier Laboratories. The other Topic Editors declare no competing interests with regards to the Research Topic theme.
Get a quick, expert overview of clinically-focused topics and guidelines that are relevant to testing for HER2, which contributes to approximately 25% of breast cancers today. This concise resource by Drs. Sara Hurvitz, and Kelly McCann consolidates today’s available information on this growing topic into one convenient resource, making it an ideal, easy-to-digest reference for practicing and trainee oncologists.
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.
