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It is now well recognized that individualized cancer treatment planning, based on tumor characteristics specific to an individual's cancer, makes it easier to select those most likely to benefit from toxic cancer therapies and to avoid treating those least likely to benefit. In Biomarkers in Breast Cancer: Molecular Diagnostics for Predicting and Monitoring Therapeutic Effect, expert laboratory and clinical researchers from around the world review how to design and evaluate studies of tumor markers, as well as 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 to the validation 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. Authoritative and cutting-edge, Biomarkers in Breast Cancer: Molecular Diagnostics for Predicting and Monitoring Therapeutic Effect offers laboratory investigators developing new tumor markers, clinical investigators testing them, and clinicians using them an up-to-date understanding of both the prognostic/predictive indicators and the novel molecular-targeted therapies suitable for individualizing breast cancer therapy.

Breast Neoplasms --- Molecular Diagnostic Techniques. --- Tumor Markers, Biological. --- Breast --- Tumor markers. --- Sein --- Marqueurs tumoraux --- diagnosis. --- Cancer --- Molecular diagnosis. --- Prognosis. --- Pronostic --- Breast -- Cancer -- Molecular diagnosis. --- Breast -- Cancer -- Prognosis. --- Tumor markers --- Breast Diseases --- Clinical Laboratory Techniques --- Neoplasms by Site --- Genetic Techniques --- Investigative Techniques --- Skin Diseases --- Neoplasms --- Diseases --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Skin and Connective Tissue Diseases --- Molecular Diagnostic Techniques --- Medicine --- Health & Biological Sciences --- Oncology --- Molecular diagnosis --- Prognosis --- Biological markers (Oncology) --- Cancer markers --- Markers, Tumor --- Tumor associated markers --- Medicine. --- Cancer research. --- Biomedicine. --- Cancer Research. --- Cancer research --- Clinical sciences --- Medical profession --- Human biology --- Life sciences --- Medical sciences --- Pathology --- Physicians --- Biochemical markers --- Tumors

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Biomarkers are of critical medical importance for oncologists, allowing them to predict and detect disease and to determine the best course of action for cancer patient care. Prognostic markers are used to evaluate a patient’s outcome and cancer recurrence probability after initial interventions such as surgery or drug treatments and, hence, to select follow-up and further treatment strategies. On the other hand, predictive markers are increasingly being used to evaluate the probability of benefit from clinical intervention(s), driving personalized medicine. Evolving technologies and the increasing availability of “multiomics” data are leading to the selection of numerous potential biomarkers, based on DNA, RNA, miRNA, protein, and metabolic alterations within cancer cells or tumor microenvironment, that may be combined with clinical and pathological data to greatly improve the prediction of both cancer progression and therapeutic treatment responses. However, in recent years, few biomarkers have progressed from discovery to become validated tools to be used in clinical practice. This Special Issue comprises eight review articles and five original studies on novel potential prognostic and predictive markers for different cancer types.

Medicine --- MSI2 --- OSCC --- oral cancer --- musashi 2 --- prognosis --- N-cadherin --- EMT --- breast cancer --- new metastasis --- eribulin --- blood --- biomarker --- bladder cancer --- immune checkpoint inhibitor --- CD8+ T effector cells --- microRNA --- biomarkers --- head and neck cancer --- laryngeal cancer --- prediction --- metastasis --- lifestyle habit --- chemo-/radio resistance --- therapeutic target --- AKT --- AR --- castration-resistant prostate cancer (CRPC) --- MAPK --- mTOR --- PI3K --- prostate cancer --- therapeutic resistance --- WNT --- miRNA --- melanoma --- melanoma resistance to MAPK/MEK inhibitors --- resistance to immune checkpoint inhibitors --- TNBC --- BRCA1/2 --- HRR --- PDL1 --- TILs --- PI3KCA --- PTEN --- CTCs --- CSC --- pancreatic cancer --- K-RAS oncogene --- oncogene dependency --- targeted therapies --- genomic mutations --- transcriptomics --- metabolomics --- selenoproteins --- cancer --- HUB nodes --- major histocompatibility complex (MHC) --- human leukocyte antigen (HLA) --- antigen processing machinery (APM) molecules --- carcinogenesis --- tumor predisposition --- cancer immunotherapy --- pheochromocytoma --- paraganglioma --- head and neck neoplasms --- head and neck tumors --- genetic syndromes --- mutations --- hyperglycemia --- cardioncology --- nivolumab --- cytokines --- cardiotoxicity --- acetyltransferase --- cancer prognosis --- NAA10 --- n/a

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Biomarkers are of critical medical importance for oncologists, allowing them to predict and detect disease and to determine the best course of action for cancer patient care. Prognostic markers are used to evaluate a patient’s outcome and cancer recurrence probability after initial interventions such as surgery or drug treatments and, hence, to select follow-up and further treatment strategies. On the other hand, predictive markers are increasingly being used to evaluate the probability of benefit from clinical intervention(s), driving personalized medicine. Evolving technologies and the increasing availability of “multiomics” data are leading to the selection of numerous potential biomarkers, based on DNA, RNA, miRNA, protein, and metabolic alterations within cancer cells or tumor microenvironment, that may be combined with clinical and pathological data to greatly improve the prediction of both cancer progression and therapeutic treatment responses. However, in recent years, few biomarkers have progressed from discovery to become validated tools to be used in clinical practice. This Special Issue comprises eight review articles and five original studies on novel potential prognostic and predictive markers for different cancer types.

MSI2 --- OSCC --- oral cancer --- musashi 2 --- prognosis --- N-cadherin --- EMT --- breast cancer --- new metastasis --- eribulin --- blood --- biomarker --- bladder cancer --- immune checkpoint inhibitor --- CD8+ T effector cells --- microRNA --- biomarkers --- head and neck cancer --- laryngeal cancer --- prediction --- metastasis --- lifestyle habit --- chemo-/radio resistance --- therapeutic target --- AKT --- AR --- castration-resistant prostate cancer (CRPC) --- MAPK --- mTOR --- PI3K --- prostate cancer --- therapeutic resistance --- WNT --- miRNA --- melanoma --- melanoma resistance to MAPK/MEK inhibitors --- resistance to immune checkpoint inhibitors --- TNBC --- BRCA1/2 --- HRR --- PDL1 --- TILs --- PI3KCA --- PTEN --- CTCs --- CSC --- pancreatic cancer --- K-RAS oncogene --- oncogene dependency --- targeted therapies --- genomic mutations --- transcriptomics --- metabolomics --- selenoproteins --- cancer --- HUB nodes --- major histocompatibility complex (MHC) --- human leukocyte antigen (HLA) --- antigen processing machinery (APM) molecules --- carcinogenesis --- tumor predisposition --- cancer immunotherapy --- pheochromocytoma --- paraganglioma --- head and neck neoplasms --- head and neck tumors --- genetic syndromes --- mutations --- hyperglycemia --- cardioncology --- nivolumab --- cytokines --- cardiotoxicity --- acetyltransferase --- cancer prognosis --- NAA10 --- n/a

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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.

cancer treatment --- extreme learning --- independent prognostic power --- AID/APOBEC --- HP --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- artificial intelligence --- epigenetics --- comorbidity score --- denoising autoencoders --- protein --- single-biomarkers --- gene signature extraction --- high-throughput analysis --- concatenated deep feature --- feature selection --- differential gene expression analysis --- colorectal cancer --- ovarian cancer --- multiple-biomarkers --- gefitinib --- cancer biomarkers --- classification --- cancer biomarker --- mutation --- hierarchical clustering analysis --- HNSCC --- cell-free DNA --- network analysis --- drug resistance --- hTERT --- variable selection --- KRAS mutation --- single-cell sequencing --- network target --- skin cutaneous melanoma --- telomeres --- Neoantigen Prediction --- datasets --- clinical/environmental factors --- StAR --- PD-L1 --- miRNA --- circulating tumor DNA (ctDNA) --- false discovery rate --- predictive model --- Computational Immunology --- brain metastases --- observed survival interval --- next generation sequencing --- brain --- machine learning --- cancer prognosis --- copy number aberration --- mutable motif --- steroidogenic enzymes --- tumor --- mortality --- tumor microenvironment --- somatic mutation --- transcriptional signatures --- omics profiles --- mitochondrial metabolism --- Bufadienolide-like chemicals --- cancer-related pathways --- intratumor heterogeneity --- estrogen --- locoregionally advanced --- RNA --- feature extraction and interpretation --- treatment de-escalation --- activation induced deaminase --- knockoffs --- R package --- copy number variation --- gene loss biomarkers --- cancer CRISPR --- overall survival --- histopathological imaging --- self-organizing map --- Network Analysis --- oral cancer --- biostatistics --- firehose --- Bioinformatics tool --- alternative splicing --- biomarkers --- diseases genes --- histopathological imaging features --- imaging --- TCGA --- decision support systems --- The Cancer Genome Atlas --- molecular subtypes --- molecular mechanism --- omics --- curative surgery --- network pharmacology --- methylation --- bioinformatics --- neurological disorders --- precision medicine --- cancer modeling --- miRNAs --- breast cancer detection --- functional analysis --- biomarker signature --- anti-cancer --- hormone sensitive cancers --- deep learning --- DNA sequence profile --- pancreatic cancer --- telomerase --- Monte Carlo --- mixture of normal distributions --- survival analysis --- tumor infiltrating lymphocytes --- curation --- pathophysiology --- GEO DataSets --- head and neck cancer --- gene expression analysis --- erlotinib --- meta-analysis --- traditional Chinese medicine --- breast cancer --- TCGA mining --- breast cancer prognosis --- microarray --- DNA --- interaction --- health strengthening herb --- cancer --- genomic instability