Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study.

BACKGROUND: While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes. METHODS: A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. RESULTS: Statistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites. CONCLUSIONS: The results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach.

Lipid profiling of electrosurgical vapors for real-time assistance of soft tissue sarcoma resection.

BACKGROUND: Soft tissue sarcomas (STS) constitute a heterogeneous group of rare tumor entities. Treatment relies on challenging patient-tailored surgical resection. Real-time intraoperative lipid profiling of electrosurgical vapors by rapid evaporative ionization mass spectrometry (REIMS) may aid in achieving successful surgical R0 resection (i.e., microscopically negative-tumor margin resection). Here, we evaluate the ex vivo accuracy of REIMS to discriminate and identify various STS from normal surrounding tissue. METHODS: Twenty-seven patients undergoing surgery for STS at Maastricht University Medical Center+ were included in the study. Samples of resected STS specimens were collected and analyzed ex vivo using REIMS. Electrosurgical cauterization of tumor and surrounding was generated successively in both cut and coagulation modes. Resected specimens were subsequently processed for gold standard histopathological review. Multivariate statistical analysis (principal component analysis-linear discriminant analysis) and leave-one patient-out cross-validation were employed to compare the classifications predicted by REIMS lipid profiles to the pathology classifications. Electrosurgical vapors produced during sarcoma resection were analyzed in vivo using REIMS. RESULTS: In total, 1200 histopathologically-validated ex vivo REIMS lipid profiles were generated from 27 patients. Ex vivo REIMS lipid profiles classified STS and normal tissues with 95.5% accuracy. STS, adipose and muscle tissues were classified with 98.3% accuracy. Well-differentiated liposarcomas and adipose tissues could not be discriminated based on their respective lipid profiles. Distinction of leiomyosarcomas from other STS could be achieved with 96.6% accuracy. In vivo REIMS analyses generated intense mass spectrometric signals. CONCLUSION: Lipid profiling by REIMS is able to discriminate and identify STS with high accuracy and therefore constitutes a potential asset to improve surgical resection of STS in the future.

Evaluation of the Sensitivity of Metabolic Profiling by Rapid Evaporative Ionization Mass Spectrometry: Toward More Radical Oral Cavity Cancer Resections.

Radical resection for patients with oral cavity cancer remains challenging. Rapid evaporative ionization mass spectrometry (REIMS) of electrosurgical vapors has been reported for real-time classification of normal and tumor tissues for numerous surgical applications. However, the infiltrative pattern of invasion of oral squamous cell carcinomas (OSCC) challenges the ability of REIMS to detect low amounts of tumor cells. We evaluate REIMS sensitivity to determine the minimal amount of detected tumors cells during oral cavity cancer surgery. A total of 11 OSCC patients were included in this study. The tissue classification based on 185 REIMS ex vivo metabolic profiles from five patients was compared to histopathology classification using multivariate analysis and leave-one-patient-out cross-validation. Vapors were analyzed in vivo by REIMS during four glossectomies. Complementary desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) was employed to map tissue heterogeneity on six oral cavity sections to support REIMS findings. REIMS sensitivity was assessed with a new cell-based assay consisting of mixtures of cell lines (tumor, myoblasts, keratinocytes). Our results depict REIMS classified tumor and soft tissues with 96.8% accuracy. In vivo REIMS generated intense mass spectrometric signals. REIMS detected 10% of tumor cells mixed with 90% myoblasts with 83% sensitivity and 82% specificity. DESI-MSI underlined distinct metabolic profiles of nerve features and a metabolic shift phosphatidylethanolamine PE(O-16:1/18:2))/cholesterol sulfate common to both mucosal maturation and OSCC differentiation. In conclusion, the assessment of tissue heterogeneity with DESI-MSI and REIMS sensitivity with cell mixtures characterized sensitive metabolic profiles toward in vivo tissue recognition during oral cavity cancer surgeries.

Real-time lipid patterns to classify viable and necrotic liver tumors.

Real-time tissue classifiers based on molecular patterns are emerging tools for fast tumor diagnosis. Here, we used rapid evaporative ionization mass spectrometry (REIMS) and multivariate statistical analysis (principal component analysis-linear discriminant analysis) to classify tissues with subsequent comparison to gold standard histopathology. We explored whether REIMS lipid patterns can identify human liver tumors and improve the rapid characterization of their underlying metabolic features. REIMS-based classification of liver parenchyma (LP), hepatocellular carcinoma (HCC), and metastatic adenocarcinoma (MAC) reached an accuracy of 98.3%. Lipid patterns of LP were more similar to those of HCC than to those of MAC and allowed clear distinction between primary and metastatic liver tumors. HCC lipid patterns were more heterogeneous than those of MAC, which is consistent with the variation seen in the histopathological phenotype. A common ceramide pattern discriminated necrotic from viable tumor in MAC with 92.9% accuracy and in other human tumors. Targeted analysis of ceramide and related sphingolipid mass features in necrotic tissues may provide a new classification of tumor cell death based on metabolic shifts. Real-time lipid patterns may have a role in future clinical decision-making in cancer precision medicine.

Stromal vapors for real-time molecular guidance of breast-conserving surgery.

Achieving radical tumor resection while preserving disease-free tissue during breast-conserving surgery (BCS) remains a challenge. Here, mass spectrometry technologies were used to discriminate stromal tissues reported to be altered surrounding breast tumors, and build tissue classifiers ex vivo. Additionally, we employed the approach for in vivo and real-time classification of breast pathology based on electrosurgical vapors. Breast-resected samples were obtained from patients undergoing surgery at MUMC+. The specimens were subsequently sampled ex vivo to generate electrosurgical vapors analyzed by rapid evaporative ionization mass spectrometry (REIMS). Tissues were processed for histopathology to assign tissue components to the mass spectral profiles. We collected a total of 689 ex vivo REIMS profiles from 72 patients which were analyzed using multivariate statistical analysis (principal component analysis-linear discriminant analysis). These profiles were classified as adipose, stromal and tumor tissues with 92.3% accuracy with a leave-one patient-out cross-validation. Tissue recognition using this ex vivo-built REIMS classification model was subsequently tested in vivo on electrosurgical vapors. Stromal and adipose tissues were classified during one BCS. Complementary ex vivo analyses were performed by REIMS and by desorption electrospray ionization mass spectrometry (DESI-MS) to study the potential of breast stroma to guide BCS. Tumor border stroma (TBS) and remote tumor stroma (RTS) were classified by REIMS and DESI-MS with 86.4% and 87.8% accuracy, respectively. We demonstrate the potential of stromal molecular alterations surrounding breast tumors to guide BCS in real-time using REIMS analysis of electrosurgical vapors.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations.

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.