MALDI imaging mass spectrometry in clinical proteomics research of gastric cancer tissues

In the presented thesis, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry was used for the proteomic analysis of gastric cancer tissue samples, with the aims of 1) identifying proteins that predict disease outcome of patients with intestinal-type gastric cancer after surgical resection, and 2) generating a proteomic classifier that determines HER2-status in order to aid in therapy decision with regard to trastuzumab (Herceptin) administration. In the first study, a seven-protein signature was found to be associated with an unfavorable overall survival independent of major clinical covariates after analyzing 63 intestinal-type primary resected gastric cancer samples by MALDI imaging. Of these seven proteins, three could be identified as CRIP1, HNP-1, and S100-A6, and validated immunohistochemically on tissue microarrays of an independent validation cohort (n=118). While HNP-1 and S100-A6 were found to further subdivide early (UICC-I) and late stage (UICC-II-III) patients into different prognostic groups, CRIP1, a protein previously unknown in gastric cancer, was confirmed as a novel and independent prognostic factor for all patients in the validation cohort. The protein pattern described here serves as a new independent indicator of patient survival complementing the previously known clinical parameters in terms of prognostic relevance. In the second study, we hypothesized that MALDI imaging mass spectrometry may be useful for generating a classifier that may determine HER2-status in gastric cancer. This assumption was based on previous results where HER2-status could be reliably predicted in breast cancer patients. Here, 59 gastric cryo tissue samples were analyzed by MALDI imaging and the obtained proteomic profiles were used to create HER2 prediction models using different classification algorithms. Astonishingly, the breast cancer proteomic classifier from the previous study was able to correctly predict HER2-status in gastric cancers with a sensitivity of 65% and a specificity of 92%. In order to create a universal classifier for HER2-status, breast and non-breast cancer samples were combined, which increased sensitivity to 78%; specificity was 88%. This study provides evidence that HER2-status can be identified on a proteomic level across different cancer types suggesting that HER2 overexpression may constitute a widely spread molecular event independent of the tumor entity.Im Rahmen dieser Doktorarbeit wurden zwei Arbeiten publiziert, in denen die bildgebende Massenspektrometrie als zentrale Methode zur proteomischen Analyse von Magenkarzinomgeweben eingesetzt wurde. Dabei wurden folgende Ziele verfolgt: 1. die Identifizierung prognostischer Proteinmarker für Patienten mit intestinalem Magenkarzinom, und 2. die Generierung eines proteomischen Klassifikators zur Bestimmung des HER2-Status zur Entscheidungshilfe für eine Behandlung mit Trastuzumab (Herzeptin). In der ersten Studie wurde eine Signatur bestehend aus sieben Proteinsignalen gefunden, deren Überexpression unabhängig von anderen klinischen Parametern ein schlechtes Gesamtüberleben der Patienten indizieren. Hierzu wurden 63 Gewebeproben von Patienten mit Magenkarzinom intestinalen Typs mittels MALDI Imaging analysiert. Drei der sieben Proteinsignale konnten als CRIP1, HNP-1 und S100-A6 identifiziert werden. Diese wurden anschließend an einem unabhängigen Patientenkollektiv (n=118) immunhistochemisch anhand von Tissue Microarrays validiert. Dabei zeigte sich, dass die beiden Proteine HNP-1 und S100-A6 bestehende klinische Gruppen nach ihrem Risiko weiter aufstratifizieren konnten; HNP-1 Magenkarzinompatienten im frühen Stadium (UICC I) und S100-A6 Patienten im fortgeschrittenen Stadium (UICC II-III). Darüber hinaus konnte CRIP1 als unabhängiger prognostischer Faktor für alle Patienten des Validierungskollektives bestätigt werden. Perspektivisch könnte die hier beschriebene Proteinsignatur vorhandene klinische Parameter als neuer und unabhängiger Indikator für das Überleben von Magenkrebspatienten ergänzen. In der zweiten Studie wurden Proteinexpressionsmuster benutzt, um den HER2-Status in Magenkrebsgeweben vorauszusagen; denn seit kurzem ist der epidermale Wachstumsfaktor-Rezeptor HER2 eine wichtige tumorbiologische Zielstruktur bei der Behandlung von Magenkrebspatienten mit dem therapeutischen Antikörper Trastuzumab. In einer vorherigen Studie konnten wir die Machbarkeit der HER2-Status-Bestimmung durch MALDI Imaging erfolgreich anhand von Brustkrebsproben demonstrieren. Unter der Annahme, dass der HER2-Überexpression – unabhängig vom Tumortyp – charakteristische molekulare Veränderungen zugrunde liegen, wurde untersucht, ob eine Bestimmung des HER2-Status in Magenkrebspatienten mit Hilfe von Proteinexpressionsmustern aus Brustkrebspatienten erfolgen kann. Hierzu wurden, zusätzlich zu den bereits vorhandenen 48 Brustkrebsgeweben, 59 Magenkrebsfälle mittels MALDI Imaging analysiert und verschiedene HER2-Klassifikationsmodelle erstellt und verglichen. Der HER2-Status in Magenkrebsfällen konnte mit einem Mammakarzinom-spezifischen Profil mit einer Sensitivität von 65% und einer Spezifität von 92% bestimmt werden. Zusätzlich wurden die Expressionsprofile aller vorhandenen Tumorarten zusammengeführt, um einen universellen HER2-Klassifikator zu erstellen. Dies führte zu einer verbesserten Vorhersagequalität (Sensitivität: 78%, Spezifität: 88%). Dass sich der HER2-Status über verschiedene Tumorentitäten hinweg auf proteomischer Ebene bestimmen lässt, legt nahe, dass die Überexpression von HER2 ein unabhängiges molekulares Ereignis darstellt, ungeachtet der Herkunft des Tumors. Zudem unterstreichen die Ergebnisse das diagnostische Potential der bildgebenden Massenspektrometrie zur schnellen und zuverlässigen Bestimmung von tumorbiologischen Zielstrukturen, wie HER2

CRIP1 expression is correlated with a favorable outcome and less metastases in osteosarcoma patients

Predicting the clinical course of osteosarcoma patients is a crucial prerequisite for a better treatment stratification in these highly aggressive neoplasms of bone. In search of new and reliable biomarkers we recently identified cysteine-rich intestinal protein 1 (CRIP1) to have significant prognostic impact in gastric cancer and therefore decided to investigate its role also in osteosarcoma. For this purpose we analyzed 223 pretherapeutic and well characterized osteosarcoma samples for their immunohistochemical expression of CRIP1 and correlated our findings with clinico-pathological parameters including follow-up, systemic spread and response to chemotherapy. Interestingly and contrarily to gastric cancer, we found CRIP1 expression more frequently in patients with long-term survival (10-year survival 73% in positive vs. 54% in negative cases, p = 0.0433) and without metastases (p = 0.0108) indicating a favorable prognostic effect. CRIP1 therefore seems to represent a promising new biomarker in osteosarcoma patients which should be considered for a prospective validation

Performance of Epigenetic Markers SEPT9 and ALX4 in Plasma for Detection of Colorectal Precancerous Lesions

BACKGROUND: Screening for colorectal cancer (CRC) has shown to reduce cancer-related mortality, however, acceptance and compliance to current programmes are poor. Developing new, more acceptable non-invasive tests for the detection of cancerous and precancerous colorectal lesions would not only allow preselection of individuals for colonoscopy, but may also prevent cancer by removal of precancerous lesions. METHODS: Plasma from 128 individuals (cohort I - exploratory study: 73 cases / 55 controls) was used to test the performance of a single marker, SEPT9, using a real-time quantitative PCR assay. To validate performance of SEPT9, plasma of 76 individuals (cohort II - validation study: 54 cases / 22 controls) was assessed. Additionally, improvement of predictive capability considering SEPT9 and additionally ALX4 methylation was investigated within these patients. RESULTS: In both cohorts combined, methylation of SEPT9 was observed in 9% of controls (3/33), 29% of patients with colorectal precancerous lesions (27/94) and 73% of colorectal cancer patients (24/33). The presence of both SEPT9 and ALX4 markers was analysed in cohort II and was observed in 5% of controls (1/22) and 37% of patients with polyps (18/49). Interestingly, also 3/5 (60%) patients with colorectal cancer were tested positive by the two marker panel in plasma. CONCLUSIONS: While these data confirm the detection rate of SEPT9 as a biomarker for colorectal cancer, they also show that methylated DNA from advanced precancerous colorectal lesions can be detected using a panel of two DNA methylation markers, ALX4 and SEPT9. If confirmed in larger studies these data indicate that screening for colorectal precancerous lesions with a blood-based test may be as feasible as screening for invasive cancer

Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research

Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies

Mass spectrometry imaging of metabolites

Mass spectrometry imaging (MSI) is a technique which is gaining increasing interest in biomedical research due to its capacity to visualize molecules in tissues. First applied to the field of clinical proteomics, its potential for metabolite imaging in biomedical studies is now being recognized. Here we describe how to set up experiments for mass spectrometry imaging of metabolites in clinical tissues and how to tackle most of the obstacles in the subsequent analysis of the data

vorgelegt von

MALDI imaging mass spectrometry in clinical proteomics research of gastric cancer tissue

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

Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients.

The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared with histopathological response assessment, which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9% to 93.3% using the metabolic classifier and from 62.2% to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI 1.40-8.12, p = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI 0.67-1.53, p = 0.968) or stromal classifier (HR 1.86, 95% CI 0.81-4.25, p = 0.143). The classifier even allowed us to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment have been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of the tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland