A Molecular Pathological Method And Its Application In The Diagnosis Of Thyroid Tumor Based On Mass Spectrometry Imaging Metabolomics

Thyroid cancer(TC)is the most common malignancy in the head and neck cancer.Its incidence has continuously increased in the last three decades globally.Differentiated carcinoma derives from deteriorative follicular epithelial cells.Papillary thyroid carcinomas(PTC)and follicular thyroid carcinomas(FTC)are both well-differentiated carcinoma,which represent 80%and 14%of all thyroid malignancies respectively.While,medullary carcinoma derives from deteriorative para-follicular C cells,which accounts for 4%of all thyroid malignancies.The remaining 2%are highly invasive anaplastic carcinomas.Different thyroid tumors show much differences in pathological characteristics,genetic characteristics and biological behavior.Even if two similar thyroid tumors with the same pathological type but different subtypes will be treated with different clinical therapies.Therefore,correct diagnosis of thyroid tumors will help to match the best treatment for each individual so as to achieve precise treatment.Histopathology examination is regarded as the gold standard in TC diagnosis.However,sole morphological assessment based on histological or cytological pattern is not always reliable when diagnosing tumor types with similar features.Here,molecular biomarkers should be considered being introduced to support classical histopathological approach.In this study,air flow-assisted desorption electrospray ionization mass spectrometry imaging(AFADESI-MSI)was used to in situ profile the metabolome of different thyroid tumors types.Combined with self-developed MSI data processing software,we studied four types of thyroid tumors(FA,FTC,cvPTC and fvPTC)based on AFADESI-MSI,not only to find diagnostic biomarkers,but also to achieve model predictive analysis of tumor focuses based on full metabolic profile.We also performed mass spectrometry metabolomics analysis of PTC.Distinct metabolic changes were found in PTC and the expression of its upstream enzymes were also verified.Thus,from the level of endogenous molecules,it is feasible to discriminate different pathological types of thyroid tumors and to help histopathological diagnosis of clinically indeterminate cases using MSI technique.Disturbed metabolic pathways can be speculated from changes of metabolites,which might be a clue for new therapeutic targets for thyroid tumors.The main content of this thesis includes the following three parts:1.Software development for interactive and in-depth analysis of mass spectrometry imaging dataBased on C++ programming language and OpenGL 3D visualization technology,our group developed a powerful and easy-to-use MSI data processing software called"MassImager".It focuses on interactive visualization,in situ biomarker discovery and artificial intelligent pathological diagnosis.Simplified data preprocessing and high-throughput MSI data exchange,serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets.It also offers diverse self-defined operations for visual processing,including multiple ion visualization,multiple channel superposition,image normalization,visual resolution enhancement and image filter.Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra,also the overlaid optical image guide,to directly find out the region-specific biomarkers.Moreover,automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling.Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image,which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer.All the features are integrated together in Masslmager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis.2.Molecular pathological diagnosis of thyroid tumor based on ambient mass spectrometry imaging metabolomicsThis study used AFADESI-MSI technique to do mass spectrometry imaging analysis of thyroid tumors,including follicular thyroid cancer(FTC),classical variant of papillary thyroid cancer(cvPTC),follicular variant of papillary thyroid cancer(fvPTC),follicular adenoma(FA),nodular goiter(NG).Upon multivariate statistical analysis based on OPLS-DA model,pathological differentiation of thyroid tumors can be successfully achieved from two perspectives:biomarker panel and overall metabolic fingerprint.First,a panel of in situ diagnostic biomarkers were discovered to be able to discriminate differentiated thyroid carcinoma(FTC、cvPTC and fvPTC)and benign follicular adenoma(FA).Besides,a well-validated diagnostic model can also be implemented to achieve automatic prediction of interested focuses in tumor tissues.Both ways jointly realized rapid diagnosis of indeterminate thyroid tumor cases.These findings together proved that ambient mass spectrometry imaging metabolomics may provide a promising solution for molecular pathological diagnosis of clinical thyroid tumor.3.In situ metabolomics study of papillary thyroid carcinoma based on ambient mass spectrometry imaging techniqueThis study used AFADESI-MSI technique to do in situ metabolomics research of 39 papillary thyroid carcinomas.Through comparative analysis between cancerous and para-cancerous tissues,several metabolic markers associated with diagnosis or tumorigenesis were discovered and identified.Most of them were significantly up-regulated in cancerous tissue,and only a few metabolites,such as citric acid,were down-regulated.The result of pathway analysis showed that 8 metabolic pathways are closely related to the occurrence and development mechanism of papillary thyroid carcinoma.They are alanine,aspartate and glutamate metabolism,arginine and proline metabolism,glycine,serine and threonine metabolism,lysine synthesis and degradation,glutamine and glutamate metabolism,histidine metabolism and glycerophospholipid metabolism.Moreover,expression of relevant metabolic enzymes in the glycerophospholipid pathway were further validated by immunohistochemistry assay.