Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges.

Studying the proteome-the entire set of proteins in cells, tissues, organs and body fluids-is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific intrinsic and extrinsic signals. Discovering protein signatures/pathways responsible for cancer transformation may lead to a better understanding of tumor biology and to a more effective diagnosis, prognosis, recurrence and response to therapy. Moreover, proteins can act as a biomarker or potential drug targets. Hence, it is of major importance to implement proteomic, particularly mass spectrometric, approaches in cancer research, to provide new crucial insights into tumor biology. Recently, mass spectrometry imaging (MSI) approaches were implemented in cancer research, to provide individual molecular characteristics of each individual tumor while retaining molecular spatial distribution, essential in the context of personalized disease management and medicine.

Mass Spectrometry Imaging Reveals Neutrophil Defensins as Additional Biomarkers for Anti-PD-(L)1 Immunotherapy Response in NSCLC Patients.

(1) Background: Therapeutic blocking of the interaction between programmed death-1 (PD-1) with its ligand PD-L1, an immune checkpoint, is a promising approach to restore the antitumor immune response. Improved clinical outcomes have been shown in different human cancers, including non-small cell lung cancer (NSCLC). Unfortunately, still a high number of NSCLC patients are treated with immunotherapy without obtaining any clinical benefit, due to the limitations of PD-L1 protein expression as the currently sole predictive biomarker for clinical use; (2) Methods: In this study, we applied mass spectrometry imaging (MSI) to discover new protein biomarkers, and to assess the possible correlation between candidate biomarkers and a positive immunotherapy response by matrix-assisted laser desorption/ionization (MALDI) MSI in 25 formalin-fixed paraffin-embedded (FFPE) pretreatment tumor biopsies (Biobank@UZA); (3) Results: Using MALDI MSI, we revealed that the addition of neutrophil defensin 1, 2 and 3 as pretreatment biomarkers may more accurately predict the outcome of immunotherapy treatment in NSCLC. These results were verified and confirmed with immunohistochemical analyses. In addition, we provide in-vitro evidence of the immune stimulatory effect of neutrophil defensins towards cancer cells; and (4) Conclusions: With proteomic approaches, we have discovered neutrophil defensins as additional prospective biomarkers for an anti-PD-(L)1 immunotherapy response. Thereby, we also demonstrated that the neutrophil defensins contribute in the activation of the immune response towards cancer cells, which could provide a new lead towards an anticancer therapy.

MALDI Mass Spectrometry Imaging Linked with Top-Down Proteomics as a Tool to Study the Non-Small-Cell Lung Cancer Tumor Microenvironment.

Advanced non-small-cell lung cancer (NSCLC) is generally linked with a poor prognosis and is one of the leading causes of cancer-related deaths worldwide. Since only a minority of the patients respond well to chemotherapy and/or targeted therapies, immunotherapy might be a valid alternative in the lung cancer treatment field, as immunotherapy attempts to strengthen the body's own immune response to recognize and eliminate malignant tumor cells. However, positive response patterns to immunotherapy remain unclear. In this study, we demonstrate how immune-related factors could be visualized from single NSCLC tissue sections (Biobank@UZA) while retaining their spatial information by using matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), in order to unravel the molecular profile of NSCLC patients. In this way, different regions in lung cancerous tissues could be discriminated based on the molecular composition. In addition, we linked visualization (MALDI MSI) and identification (based on liquid chromatography higher resolution mass spectrometry) of the molecules of interest for the correct biological interpretation of the observed molecular differences within the area in which these molecules are detected. This is of major importance to fully understand the underlying molecular profile of the NSCLC tumor microenvironment.

Diagnostic considerations regarding pediatric delirium: a review and a proposal for an algorithm for pediatric intensive care units.

CONTEXT: If delirium is not diagnosed, it is unlikely that any effort will be made to reverse it. Given evidence for under-diagnosis, tools that aid recognition are required. OBJECTIVE: Relating three presentations of pediatric delirium (PD) to standard criteria and developing a diagnostic algorithm. RESULTS: Delirium-inducing factors, disturbance of consciousness and inattention are common in PICU patients: a pre-delirious state is present in most. An algorithm is introduced, containing (1) evaluation of the sedation-agitation level, (2) psychometric assessment of behavior and (3) opinion of the caregivers. DISCUSSION: It may be argued that the behavioral focus of the algorithm would benefit from the inclusion of neurocognitive measures. LIMITATIONS: No sufficiently validated diagnostic instrument covering the entire algorithm is available yet. CONCLUSION: This is the first proposal for a PD diagnostic algorithm. Given the high prevalence of predelirious states at the PICU, daily evaluation is mandatory. Future algorithmic refinement is urgently required.

Pediatric illness severity measures predict delirium in a pediatric intensive care unit.

CONTEXT: Delirium in children is a serious but understudied neuropsychiatric disorder. So there is little to guide the clinician in terms of identifying those at risk. OBJECTIVE: To study, in a pediatric intensive care unit (PICU), the predictive power of widely used generic pediatric mortality scoring systems in relation to the occurrence of pediatric delirium (PD). DESIGN AND METHODS: Four-year prospective observational study, 2002-2005. Predictors used were the Pediatric Index of Mortality (PIM) and Pediatric Risk of Mortality (PRISM II). SETTING: A tertiary 8-bed PICU in The Netherlands. PATIENTS: 877 critically ill children who were acutely, nonelectively, and consecutively admitted. MAIN OUTCOME MEASURE: Pediatric delirium. MAIN RESULTS: Out of 877 children with mean age 4.4 yrs, 40 were diagnosed with PD (Cumulative incidence: 4.5%), 85% of whom (versus 40% with nondelirium) were mechanically ventilated. The area under the curve was 0.74 for PRISM II and 0.71 for the PIM, with optimal cut-off points at the 60th centile (PRISM: sensitivity: 76%; specificity: 62%; PIM: sensitivity: 82%; specificity: 62%). A PRISM II or PIM score above the 60th centile was strongly associated with later PD in terms of relative risk (PRISM II: risk ratio = 4.9; 95% confidence interval: 2.3-10.1; PIM: RR = 6.7; 95% confidence interval: 3.0-15.0). Given the low incidence of PD, values for positive predictive value were lower (PRISM II: 8.3%; PIM: 8.9%, rising to, respectively, 10.1% and 10.6% in mechanically ventilated patients) and values for negative predictive value were higher (PRISM II: 98.3%; PIM: 98.7%). LIMITATIONS: Given the relatively low incidence of delirium, a low detection rate biased toward the most severe cases cannot be excluded. CONCLUSIONS: Given the fact that PIM and PRISM II are widely used mortality scoring instruments, prospective associations with PD suggest additional value for ruling in, or out, patients at risk of PD.