Use of proteomics to identify biomarkers associated with chronic kidney disease and long-term outcomes in patients with myocardial infarction.

BACKGROUND: Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long-term outcomes. METHODS: A total of 175 different biomarkers from MI patients enrolled in the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes. RESULTS: A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min-1 /1.73 m2 were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor-1, adipocyte fatty acid-binding protein-4, TNF-related apoptosis-inducing ligand receptor 2, growth differentiation factor-15 and TNF receptor-2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization. CONCLUSION: In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long-term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD.

Proteomic analysis of brain metastatic lung adenocarcinoma reveals intertumoral heterogeneity and specific alterations associated with the timing of brain metastases.

BACKGROUND: Brain metastases are associated with considerable negative effects on patients' outcome in lung adenocarcinoma (LADC). Here, we investigated the proteomic landscape of primary LADCs and their corresponding brain metastases. MATERIALS AND METHODS: Proteomic profiling was conducted on 20 surgically resected primary and brain metastatic LADC samples via label-free shotgun proteomics. After sample processing, peptides were analyzed using an Ultimate 3000 pump coupled to a QExactive HF-X mass spectrometer. Raw data were searched using PD 2.4. Further data analyses were carried out using Perseus, RStudio and GraphPad Prism. Proteomic data were correlated with clinical and histopathological parameters and the timing of brain metastases. Mass spectrometry-based proteomic data are available via ProteomeXchange with identifier PXD027259. RESULTS: Out of the 6821 proteins identified and quantified, 1496 proteins were differentially expressed between primary LADCs and corresponding brain metastases. Pathways associated with the immune system, cell-cell/matrix interactions and migration were predominantly activated in the primary tumors, whereas pathways related to metabolism, translation or vesicle formation were overrepresented in the metastatic tumors. When comparing fast- versus slow-progressing patients, we found 454 and 298 differentially expressed proteins in the primary tumors and brain metastases, respectively. Metabolic reprogramming and ribosomal activity were prominently up-regulated in the fast-progressing patients (versus slow-progressing individuals), whereas expression of cell-cell interaction- and immune system-related pathways was reduced in these patients and in those with multiple brain metastases. CONCLUSIONS: This is the first comprehensive proteomic analysis of paired primary tumors and brain metastases of LADC patients. Our data suggest a malfunction of cellular attachment and an increase in ribosomal activity in LADC tissue, promoting brain metastasis. The current study provides insights into the biology of LADC brain metastases and, moreover, might contribute to the development of personalized follow-up strategies in LADC.

Localization of sunitinib, its metabolites and its target receptors in tumour-bearing mice: a MALDI-MS imaging study.

BACKGROUND AND PURPOSE: The clinical effects of anti-angiogenic agents remain controversial. Therefore, elucidating the pharmacological properties of these compounds is a pivotal issue. EXPERIMENTAL APPROACH: The effects of treatment with sunitinib on tumour and normal tissues of mice bearing C-26 adenocarcinoma cells were analysed by matrix-assisted laser desorption ionization MS imaging (MALDI-MSI). Expression of the key targets of sunitinib--angiogenic receptors--was studied by immunofluorescent labelling. KEY RESULTS: MALDI-MS assays showed that sunitinib and its fragment ions were present throughout tumour and normal tissues. Major metabolites were identified in blood and solid tissues, while minor drug metabolites were detectable only in blood. Tumour growth and intratumour VEGF receptor-2 expressions were significantly reduced in sunitinib-treated mice, while the expression of the other targeted receptors, PDGF receptor -α or -ß and fibroblast growth factor receptor-1, remained unaffected. Within tumour tissue, the close proximity of sunitinib metabolites to the precursor ion suggested in situ metabolism of the administered drug. There were intratumour areas where the signal intensity of sunitinib correlated with expression of VEGF receptor-2. CONCLUSIONS AND IMPLICATIONS: This is the first study that demonstrates MALDI-MSI is a versatile platform to study the intratumour localization of an unlabelled anti-angiogenic drug. The combination of MALDI-MSI and immunofluorescence analysis can provide further insights into the molecular interaction of drug compounds and their targets within tumour tissue.