Salivary biomarker development using genomic, proteomic and metabolomic approaches.

The use of saliva as a diagnostic sample provides a non-invasive, cost-efficient method of sample collection for disease screening without the need for highly trained professionals. Saliva collection is far more practical and safe compared with invasive methods of sample collection, because of the infection risk from contaminated needles during, for example, blood sampling. Furthermore, the use of saliva could increase the availability of accurate diagnostics for remote and impoverished regions. However, the development of salivary diagnostics has required technical innovation to allow stabilization and detection of analytes in the complex molecular mixture that is saliva. The recent development of cost-effective room temperature analyte stabilization methods, nucleic acid pre-amplification techniques and direct saliva transcriptomic analysis have allowed accurate detection and quantification of transcripts found in saliva. Novel protein stabilization methods have also facilitated improved proteomic analyses. Although candidate biomarkers have been discovered using epigenetic, transcriptomic, proteomic and metabolomic approaches, transcriptomic analyses have so far achieved the most progress in terms of sensitivity and specificity, and progress towards clinical implementation. Here, we review recent developments in salivary diagnostics that have been accomplished using genomic, transcriptomic, proteomic and metabolomic approaches

Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients

Peer reviewe

Feasibility of Desorption Electrospray Ionization Mass Spectrometry for Diagnosis of Oral Tongue Squamous Cell Carcinoma

Rationale Desorption electrospray ionization mass spectrometry (DESI-MS) has demonstrated utility in differentiating tumor from adjacent normal tissue in both urologic and neurosurgical specimens. We sought to evaluate if this technique had similar accuracy in differentiating oral tongue squamous cell carcinoma (SCC) from adjacent normal epithelium due to current issues with late diagnosis of SCC in advanced stages. Methods Fresh frozen samples of SCC and adjacent normal tissue were obtained by surgical resection. Resections were analyzed using DESI-MS sometimes by a blinded technologist. Normative spectra were obtained for separate regions containing SCC or adjacent normal epithelium. Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) of spectra were used to predict SCC versus normal tongue epithelium. Predictions were compared with pathology to assess accuracy in differentiating oral SCC from adjacent normal tissue. Results Initial PCA score and loading plots showed clear separation of SCC and normal epithelial tissue using DESI-MS. PCA-LDA resulted in accuracy rates of 95% for SCC versus normal and 93% for SCC, adjacent normal and normal. Additional samples were blindly analyzed with PCA-LDA pixel-by-pixel predicted classifications as SCC or normal tongue epithelial tissue and compared against histopathology. The m/z 700–900 prediction model showed a 91% accuracy rate. Conclusions DESI-MS accurately differentiated oral SCC from adjacent normal epithelium. Classification of all typical tissue types and pixel predictions with additional classifications should increase confidence in the validation model

Early detection and personalized treatment in oral cancer: the impact of omics approaches

BACKGROUND: Oral cancer is one of the most common malignant lesions of the head and neck. This cancer is an aggressive and lethal disease with no significant improvements in the overall survival in the last decades. Moreover, the incidence of oral HPV-positive tumors is rising, especially in young people. This oral neoplasm develops through numerous molecular imbalances that affect key genes and signaling pathways; however, the molecular mechanisms involved in the pathogenesis and progression of oral tumors are still to be fully determined. In order to improve the quality of life and long-term survival rate of these patients, it is vital to establish accurate biomarkers that help in the early diagnosis, prognosis and development of target treatments. Such biomarkers may possibly allow for selection of patients that will benefit from each therapy modality, helping in the optimization of intensity and sequence of the treatments in order to decrease side effects and improve survival. CONCLUSION: In this review we discuss the current knowledge of oral cancer and the potential role of omics approaches to identify molecular biomarkers in the improvement of early diagnosis, treatment and prognosis. The pursuit to improve the quality of life and decrease mortality rates of the oral patients needs to be centralized on the identification of critical genes in oral carcinogenesis. Understanding the molecular biology of oral cancer is vital for search new therapies, being the molecular-targeted therapies the most promising treatment for these patients.info:eu-repo/semantics/publishedVersio

Biomarcadores prognósticos e papel de agrina na progressão do câncer oral

Orientador: Adriana Franco Paes LemeTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Odontologia de PiracicabaResumo: O carcinoma de células escamosas da cavidade oral (CECO) é o câncer mais comum na região da cabeça e pescoço. O CECO representa a neoplasia maligna mais importante para os cirurgiões dentistas. É detectado geralmente em estágios clínicos avançados e, apesar dos progressos da medicina, os pacientes ainda apresentam um prognóstico clínico desfavorável. Esse contexto justifica a busca de biomarcadores prognósticos que representem o estadiamento da doença, além de compreender os mecanismos moleculares que controlam sua fisiopatologia. Os objetivos desta tese de doutorado foram identificar os potenciais biomarcadores prognósticos para CECO descritos na literatura biomédica, bem como aprofundar a compreensão do papel do proteoglicano agrina. Essa proteína mostrou-se relevante em vários eventos oncogênicos em estudos prévios publicados por nosso grupo. Para alcançar nosso primeiro objetivo, realizamos uma revisão sistemática da literatura. Nesse estudo, utilizamos a base de dados MEDLINE/PubMed e palavras-chave associando o risco dos pacientes com os seguintes desfechos clínicos para CECO: sobrevida global, sobrevida livre de doença e sobrevida doença-específica. Essa abordagem resultou na publicação de um artigo que identificou 41 potenciais biomarcadores prognósticos em CECO, principalmente proteínas avaliadas por inmunohistoquímica. Esses potenciais biomarcadores devem ser avaliados em novos estudos clínicos. Para o segundo objetivo desta tese, silenciamos a expressão de agrina (utilizando a tecnologia shRNA) em diferentes linhagens celulares e identificamos a rede de proteínas associadas a agrina através de proteômica baseada em espectrometria de massas. O valor prognóstico desta rede foi avaliado utilizando ferramentas de bioinformática e bancos de dados públicos. Nossos resultados sugerem que a proteína agrina é essencial para os eventos oncogênicos associados à progressão do CECO, tanto in vivo como in vitro, o que nos leva a concluir que agrina é um forte candidato como alvo terapêutico para o CECOAbstract: Oral squamous cell carcinoma (OSCC) is the most common cancer of the head and neck. It represents the most prominent malignant neoplasm for dental surgeons. It is usually detected in advanced clinical stages, and despite the medical advances, patients still have a poor clinical prognosis. It justifies the search for prognostic biomarkers that represent the staging of the disease, as well as understand molecular mechanisms that control its physiopathology. The objective of this doctoral thesis was to identify the prognostic biomarkers provided in biomedical literature, as well as to deepen the understanding of the role of a proteoglycan named agrin. Agrin proved to be relevant in several oncogenic events in previous studies published by our group. To achieve our first objective, we performed a systematic review of the literature. In this study, we used the MEDLINE/PubMed database and keywords associated with patient risk and common OSCC clinical endpoints: overall survival, disease-free survival and cause-specific survival. This approach produced an article that identified 41 potential prognostic biomarkers, mainly proteins evaluated by immunohistochemistry. These potential biomarkers must be clinically evaluated in new clinical studies. For the second aim of this thesis, we silenced the expression of agrin (using shRNA technology) in different cell lines and identified the protein network associated with agrin through mass spectrometry-based proteomics. We assessed the prognostic value of this network using bioinformatics tools and public databases. Our results suggest that agrin is essential for the oncogenic events associated with the progression of the OSCC, both in vivo and in vitro, which led us to conclude that agrin is a strong candidate as a therapeutic target for OSCCDoutoradoEstomatologiaDoutor em Estomatopatologi

A REVIEW ON CURRENT SCENARIO OF ORAL CANCER IN INDIA WITH SPECIAL EMPHASIS ON MODERN DETECTION SYSTEMS AND BIOMARKERS

Oral squamous cell carcinoma is a major public health concern worldwide and a growing threat for rapidly developing economies such as India, where it ranks among the top three cancers. This review aims to discuss the national status of oral cancer in terms of incidences and mortality. We have added the emphasis on clinical characteristics of oral potentially malignant disorders and emerging optical diagnostic techniques to detect oral lesions which would otherwise go undetected by a conventional oral examination. Modern detection systems such as autofluorescence, chemiluminiscence, Narrowband imaging and Raman spectroscopy will definitely aid Conventional oral examination for diagnosis. Definitive diagnosis of oral cancer by using saliva and serum-based noninvasive biomarkers can minimize the need of tissue biopsies and patient discomfort. Urgent research efforts are required to find new ways to identify and examine high-risk population for the early diagnosis and prevention of Oral squamous cell carcinoma

Deep learning integrates histopathology and proteogenomics at a pan-cancer level

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models

clinical prognostic factors in patients pts with recurrent and or metastatic rm head and neck carcinoma hnc treated with cetuximab plus chemotherapy

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Squamous Cell Carcinoma: Biomarkers and Potential Therapeutic Targets

Squamous cell carcinoma (SCC) is the second most frequent non-melanoma skin cancer (NMSC) and carries with it a significant psychosocial and economic burden for both patients and health-care systems. Known risk factors for SCC include chronic ultraviolet (UV) exposure, chronic wounds and inflammation, exposure to certain chemicals and immunosuppression. The considerable risk of SCC recurrence and metastasis has driven the need for the discovery of new molecules that could explain the initiation and biological behavior of this type of NMSC. In this respect, proteomic research techniques have rapidly evolved and adapted in order to connect missing links and single out distinctive skin cancer biosignatures. Proteomic analysis of normal, dysplastic, and malignant keratinocytes appears to be promising in respect to SCC biomarker discovery, with the potential to aid in risk assessment, early detection, disease progression and development of novel targeted therapeutic agents. Identifying changes in the keratinocyte proteome pattern from normal to inflammatory and malignant cells will lead to the discovery of novel SCC biomarkers that could represent valuable tools for patient screening, diagnosis, management and follow-up