Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment

Orthotopic Glioblastoma Models for Evaluation of the Clinical Target Volume Concept

Simple Summary

The accurate and precise definition of the target volume is of enormous importance for the treatment success of radiotherapy. In glioblastoma, the microscopic tumor extension is unclear, which limits the specificity of irradiation leading to either increased risk of local failure or enhanced toxicity rates. In this study, we investigated the microscopic tumor extensions of two different untreated and irradiated orthotopic brain tumor models and correlated this with histologically stained cancer stem cell markers as well as invasion markers and analyses using Matrix-Assisted Laser Desorption/Ionization (MALDI). We found specific MALDI peaks as potential markers for normal brain tissue but also others for demarcation of tumor areas. Furthermore, MMP14 staining revealed mainly positive cells in the tumor border, which could reflect the invasive front in both models. Altogether, the results of this study indicate that an individualized target volume definition for radiotherapy based on biological tumor characteristics in glioblastoma models seems possible.

Abstract

In times of high-precision radiotherapy, the accurate and precise definition of the primary tumor localization and its microscopic spread is of enormous importance. In glioblastoma, the microscopic tumor extension is uncertain and, therefore, population-based margins for Clinical Target Volume (CTV) definition are clinically used, which could either be too small—leading to increased risk of loco-regional recurrences—or too large, thus, enhancing the probability of normal tissue toxicity. Therefore, the aim of this project is to investigate an individualized definition of the CTV in preclinical glioblastoma models based on specific biological tumor characteristics. The microscopic tumor extensions of two different orthotopic brain tumor models (U87MG_mCherry; G7_mCherry) were evaluated before and during fractionated radiotherapy and correlated with corresponding histological data. Representative tumor slices were analyzed using Matrix-Assisted Laser Desorption/Ionization (MALDI) and stained for putative stem-like cell markers as well as invasion markers. The edges of the tumor are clearly shown by the MALDI segmentation via unsupervised clustering of mass spectra and are consistent with the histologically defined border in H&E staining in both models. MALDI component analysis identified specific peaks as potential markers for normal brain tissue (e.g., 1339 m/z), whereas other peaks demarcated the tumors very well (e.g., 1562 m/z for U87MG_mCherry) irrespective of treatment. MMP14 staining revealed only a few positive cells, mainly in the tumor border, which could reflect the invasive front in both models. The results of this study indicate that MALDI information correlates with microscopic tumor spread in glioblastoma models. Therefore, an individualized CTV definition based on biological tumor characteristics seems possible, whereby the visualization of tumor volume and protein heterogeneity can be potentially used to define radiotherapy-sensitive and resistant areas.

Glioma-specific antigens for immune tumor therapy

This review describes glioma-specific antigens important in immunotherapy of glioma tumors. The structure and function of these antigens and recent immunotherapy data are summarized. Also, some important aspects of tumor formation are outlined. The roles of neuronal precursor cells and tumor stroma cells are discussed. The stroma cells of the tumor may be of interest as a target for tumor therapy, especially since they are less heterogeneous than the tumor cells. To date, the clinical benefit of immunotherapy has been very limited. Immunotherapy is, however, still an extremely promising approach to tumor therapy and it will most likely be implemented as a future treatment option for many types of tumors. The current shortcomings of immunotherapy will probably diminish as we start to understand and are able to modulate tumor-induced immunosuppression. There is also a need for a continued search for new tumor-specific antigens and to optimize protocols for vaccine administration.

Protein expression profiling of brain tumor tissue using SELDI-MS

Surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS) is an established, chip-based method for protein profiling, typically used for biomarker discovery. By combining retention chromatography and mass spectrometry on the same analytical platform, it allows for reliable analyses of small sample quantities in a high-throughput fashion. As such, it is a highly useful tool for a wide range of research fields. We have successfully applied it on brain tumor tissue samples to screen for differences in protein expression between invasive and noninvasive benign meningioma. This chapter lays out the details of the protocols we used, and can serve as a guide for protein expression profiling experiments on brain tumor tissue using SELDI-MS.

Proteomics of gliomas: initial biomarker discovery and evolution of technology

Gliomas are a group of aggressive brain tumors that diffusely infiltrate adjacent brain tissues, rendering them largely incurable, even with multiple treatment modalities and agents. Mostly asymptomatic at early stages, they present in several subtypes with astrocytic or oligodendrocytic features and invariably progress to malignant forms. Gliomas are difficult to classify precisely because of interobserver variability during histopathologic grading. Identifying biological signatures of each glioma subtype through protein biomarker profiling of tumor or tumor-proximal fluids is therefore of high priority. Such profiling not only may provide clues regarding tumor classification but may identify clinical biomarkers and pathologic targets for the development of personalized treatments. In the past decade, differential proteomic profiling techniques have utilized tumor, cerebrospinal fluid, and plasma from glioma patients to identify the first candidate diagnostic, prognostic, predictive, and therapeutic response markers, highlighting the potential for glioma biomarker discovery. The number of markers identified, however, has been limited, their reproducibility between studies is unclear, and none have been validated for clinical use. Recent technological advancements in methodologies for high-throughput profiling, which provide easy access, rapid screening, low sample consumption, and accurate protein identification, are anticipated to accelerate brain tumor biomarker discovery. Reliable tools for biomarker verification forecast translation of the biomarkers into clinical diagnostics in the foreseeable future. Herein we update the reader on the recent trends and directions in glioma proteomics, including key findings and established and emerging technologies for analysis, together with challenges we are still facing in identifying and verifying potential glioma biomarkers.

Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study

We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.

Glioma proteomics: status and perspectives

High grade gliomas are the most common brain tumors in adults and their malignant nature makes them the fourth biggest cause of cancer death. Major efforts in neuro-oncology research are needed to reach similar progress in treatment efficacy as that achieved for other cancers in recent years. In addition to the urgent need to identify novel effective drug targets against malignant gliomas, the search for glioma biomarkers and grade specific protein signatures will provide a much needed contribution to diagnosis, prognosis, treatment decision and assessment of treatment response. Over the past years glioma proteomics has been attempted at different levels, including proteome analysis of patient biopsies and bodily fluids, of glioma cell lines and animal models. Here we provide an extensive review of the outcome of these studies in terms of protein identifications (protein numbers and regulated proteins), with an emphasis on the methods used and the limitations of the studies with regard to biomarker discovery. This is followed by a perspective on novel technologies and on the potential future contribution of proteomics in a broad sense to understanding glioma biology.

Improved ability of biological and previous caries multimarkers to predict caries disease as revealed by multivariate PLS modelling

BACKGROUND

Dental caries is a chronic disease with plaque bacteria, diet and saliva modifying disease activity. Here we have used the PLS method to evaluate a multiplicity of such biological variables (n = 88) for ability to predict caries in a cross-sectional (baseline caries) and prospective (2-year caries development) setting.

METHODS

Multivariate PLS modelling was used to associate the many biological variables with caries recorded in thirty 14-year-old children by measuring the numbers of incipient and manifest caries lesions at all surfaces.

RESULTS

A wide but shallow gliding scale of one fifth caries promoting or protecting, and four fifths non-influential, variables occurred. The influential markers behaved in the order of plaque bacteria > diet > saliva, with previously known plaque bacteria/diet markers and a set of new protective diet markers. A differential variable patterning appeared for new versus progressing lesions. The influential biological multimarkers (n = 18) predicted baseline caries better (ROC area 0.96) than five markers (0.92) and a single lactobacilli marker (0.7) with sensitivity/specificity of 1.87, 1.78 and 1.13 at 1/3 of the subjects diagnosed sick, respectively. Moreover, biological multimarkers (n = 18) explained 2-year caries increment slightly better than reported before but predicted it poorly (ROC area 0.76). By contrast, multimarkers based on previous caries predicted alone (ROC area 0.88), or together with biological multimarkers (0.94), increment well with a sensitivity/specificity of 1.74 at 1/3 of the subjects diagnosed sick.

CONCLUSION

Multimarkers behave better than single-to-five markers but future multimarker strategies will require systematic searches for improved saliva and plaque bacteria markers.

Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype

Meningiomas of WHO grade I can usually be cured by surgical resection. However, some tumors may, despite their benign appearance, display invasive growth behavior. These tumors constitute a difficult clinical problem to handle. By histology alone, bone invasive meningiomas may be indistinguishable from their noninvasive counterparts. In this study we have examined the protein spectra in a series of meningiomas in search of protein expression patterns that may distinguish between bone invasive and noninvasive meningiomas. Tumor tissue from 13 patients with fibrous (6 invasive and 7 noninvasive) and 29 with meningothelial (10 invasive and 19 noninvasive) grade I meningiomas were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI). Multivariate statistical methods were applied for data analyses. Comparing the protein spectra from invasive and noninvasive fibrous meningioma we found 22 peaks whose intensities were significantly different between the two groups (P < 0.001). Based on the expression pattern of these peaks we were able to perfectly separate the two entities (area under ROC curve = 1.0). In meningothelial meningioma the same comparison yielded six significantly differentially expressed peaks (P < 0.001), which to a large degree separated the invasive from noninvasive tissue (area under ROC curve = 0.873). By analyzing the protein spectra in benign meningiomas we could differentiate between invasive and noninvasive growth behavior in both fibrous and meningothelial meningiomas of grade I. A possibility for early identification of invasive grade I meningiomas may have a strong influence on the follow-up policy and the issue of early or late radiotherapy.

Glucose metabolites, glutamate and glycerol in malignant glioma tumours during radiotherapy

OBJECTIVE

The metabolism of malignant glioma was studied in 13 patients. The main objective was to perform a study of the metabolic pattern of glucose, lactate, pyruvate, glutamate and glycerol in tumour tissue during base-line conditions and to detect any changes in the metabolism during radiotherapy.

METHOD

During a stereotactic biopsy, two microdialysis catheters were implanted: one in tumour and one in peri-tumoural tissue. Fasting samples were analysed daily, before and during 5 days of radiotherapy given with 2 Gy fractions.

RESULTS

Base-line levels of glucose and pyruvate were significantly lower in tumour compared to peri-tumoural tissue (P = 0.04 and 0.023, respectively). The lactate/pyruvate ratio was significantly higher in tumour tissue (P = 0.022). In general, the levels of lactate, glutamate and glycerol were higher in tumour tissue, although not statistically significant. Further, we could not detect any significant changes during the 5 days of radiotherapy in any of the metabolites analysed.

CONCLUSION

Radiotherapy up to 10 Gy given in five fractions does not influence the glucose metabolism nor does it induce any acute cytotoxic effect detected with glutamate or glycerol in malignant glioma, as assessed by microdialysis. The study confirms the glycolytic properties of glucose metabolism in malignant glioma.

Proteomic data analysis workflow for discovery of candidate biomarker peaks predictive of clinical outcome for patients with acute myeloid leukemia

Our goal in this paper is to show an analytical workflow for selecting protein biomarker candidates from SELDI-MS data. The clinical question at issue is to enable prediction of the complete remission (CR) duration for acute myeloid leukemia (AML) patients. This would facilitate disease prognosis and make individual therapy possible. SELDI-mass spectrometry proteomics analyses were performed on blast cell samples collected from AML patients pre-chemotherapy. Although the biobank available included approximately 200 samples, only 58 were available for analysis. The presented workflow includes sample selection, experimental optimization, repeatability estimation, data preprocessing, data fusion, and feature selection. Specific difficulties have been the small number of samples and the skew distribution of the CR duration among the patients. Further, we had to deal with both noisy SELDI-MS data and a diverse patient cohort. This has been handled by sample selection and several methods for data preprocessing and feature detection in the analysis workflow. Four conceptually different methods for peak detection and alignment were considered, as well as two diverse methods for feature selection. The peak detection and alignment methods included the recently developed annotated regions of significance (ARS) method, the SELDI-MS software Ciphergen Express which was regarded as the standard method, segment-wise spectral alignment by a genetic algorithm (PAGA) followed by binning, and, finally, binning of raw data. In the feature selection, the "standard" Mann-Whitney t test was compared with a hierarchical orthogonal partial least-squares (O-PLS) analysis approach. The combined information from all these analyses gave a collection of 21 protein peaks. These were regarded as the most potential and robust biomarker candidates since they were picked out as significant features in several of the models. The chosen peaks will now be our first choice for the continuing work on protein identification and biological validation. The identification will be performed by chromatographic purification and MALDI MS/MS. Thus, we have shown that the use of several data handling methods can improve a protein profiling workflow from experimental optimization to a predictive model. The framework of this methodology should be seen as general and could be used with other one-dimensional spectral omics data than SELDI MS including an adequate number of samples.

Applications of SELDI-MS technology in oncology

Considerable interest, speculation and controversy have been generated utilising surface-enhanced laser desorption/ionization in conjunction with mass spectrometry (SELDI-MS) for the diagnosis, prognosis and therapeutic monitoring of cancer and offers an attractive approach to cancer biomarker discovery from tissues and biological fluids. This technology utilises a combination of mass spectrometry and chromatography to facilitate protein profiling of complex biological mixtures. Compared to some other more traditional proteomic platforms, such as 2D polyacrylamide gel electrophoresis, it has a high-throughput capability and can resolve low-mass proteins. However, a considerable number of challenging issues related to the design of studies, including reproducibility, sensitivity, specificity, variation in sample collection, processing and storage, have been reported as problematic with this technology; albeit some of these concerns could perhaps also be lauded against other proteomic approaches that have attempted to address complex protein mixtures, such as plasma. Applications, successes and limitations of SELDI-MS in both clinical and basic science arenas will be reviewed in this article.

SELDI-TOF MSversus prostate specific antigen analysis of prospective plasma samples in a nested case–control study of prostate cancer

There is an urgent need for better biomarkers for detection of clinically significant prostate cancer (PCa). Recent studies suggest that surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) analysis of serum may provide diagnostic information. The aim of this study was to investigate if PCa cases could be identified by applying predefined SELDI-TOF analysis conditions on prospectively, uniformly collected plasma samples from PCa cases and matched controls. Prospective samples from 387 incident PCa cases and an equal number of controls, matched for age and time for recruitment, were analyzed by SELDI-TOF MS (IMAC30/Cu chip) and multivariate classification analysis. Prospective prostate specific antigen levels were subjected to ROC curve analysis giving an AUC of 0.87 for the total cohort with a median lag time between blood sampling and diagnosis of 6.1 years. No markers were found by SELDI-TOF MS that significantly discriminated between cases and controls in the total cohort or in subanalysis of cases with less than 2 years between blood donation and diagnosis (n = 42). Cases with aggressive disease at the time of diagnosis who gave blood less than 4 years prior to diagnosis (n = 23) could however be separated from their controls (sensitivity 70%, specificity 83%) by a model based on SELDI-TOF MS and OPLS-DA data analysis. We were thus not able to confirm previous results with SELDI-TOF MS identifying men with PCa from healthy individuals, but we report an optimal experimental set-up for verification of markers for early detection of cancer in prospectively collected samples.