Tumor monocyte content predicts immunochemotherapy outcomes in esophageal adenocarcinoma.

For inoperable esophageal adenocarcinoma (EAC), identifying patients likely to benefit from recently approved immunochemotherapy (ICI+CTX) treatments remains a key challenge. We address this using a uniquely designed window-of-opportunity trial (LUD2015-005), in which 35 inoperable EAC patients received first-line immune checkpoint inhibitors for four weeks (ICI-4W), followed by ICI+CTX. Comprehensive biomarker profiling, including generation of a 65,000-cell single-cell RNA-sequencing atlas of esophageal cancer, as well as multi-timepoint transcriptomic profiling of EAC during ICI-4W, reveals a novel T cell inflammation signature (INCITE) whose upregulation correlates with ICI-induced tumor shrinkage. Deconvolution of pre-treatment gastro-esophageal cancer transcriptomes using our single-cell atlas identifies high tumor monocyte content (TMC) as an unexpected ICI+CTX-specific predictor of greater overall survival (OS) in LUD2015-005 patients and of ICI response in prevalent gastric cancer subtypes from independent cohorts. Tumor mutational burden is an additional independent and additive predictor of LUD2015-005 OS. TMC can improve patient selection for emerging ICI+CTX therapies in gastro-esophageal cancer.

Identification of protein biomarkers in human serum using iTRAQ and shotgun mass spectrometry.

Blood serum is one of the easiest accessible sources of biomarkers and its proteome presents a significant parcel of immune system proteins. These proteins can provide not only biological explanation but also diagnostic and drug response answers independently of the type of disease or condition in question. Shotgun mass spectrometry has profoundly contributed to proteome analysis and is presently considered as an indispensible tool in the field of biomarker discovery. In addition, the multiplexing potential of isotopic labeling techniques such as iTRAQ can increase statistical relevance and accuracy of proteomic data through the simultaneous analysis of different biological samples. Here, we describe a complete protocol using iTRAQ in a shotgun proteomics workflow along with data analysis steps, customized for the challenges associated with the serum proteome.

Application of meta-analysis methods for identifying proteomic expression level differences.

We present new statistical approaches for identification of proteins with expression levels that are significantly changed when applying meta-analysis to two or more independent experiments. We showed that the Euclidean distance measure has reduced risk of false positives compared to the rank product method. Our Ψ-ranking method has advantages over the traditional fold-change approach by incorporating both the fold-change direction as well as the p-value. In addition, the second novel method, Π-ranking, considers the ratio of the fold-change and thus integrates all three parameters. We further improved the latter by introducing our third technique, Σ-ranking, which combines all three parameters in a balanced nonparametric approach.

Proteomic analysis of the maternal protein restriction rat model for schizophrenia: identification of translational changes in hormonal signaling pathways and glutamate neurotransmission.

Previous studies have found that some first onset schizophrenia patients show signs of impaired insulin signaling. Also, epidemiological studies have shown that periods of suboptimal nutrition including protein deficiencies during pregnancy can lead to increased incidence of metabolic conditions and psychiatric disorders in the offspring. For these reasons, we have carried out a molecular profiling analysis of blood serum and brain tissues from adult offspring produced by the maternal low protein (LP) rat model. The results showed similar changes to those seen in schizophrenia. Multiplex immunoassay profiling identified changes in the levels of insulin, adiponectin, and leptin along with alterations in inflammatory and vascular system-related proteins such as osteopontin, macrophage colony-stimulating factor 1, and vascular cell adhesion molecule 1. LC-MS(E) proteomic profiling showed that glutamatergic pathways were altered in frontal cortex, while signaling pathways and cytoskeletal proteins involved in hormonal secretion and synaptic remodeling were altered in the hypothalamus. Taken together, these studies indicate that the LP rat model recapitulates several pathophysiological attributes seen in schizophrenia patients. We propose that the LP model may have utility for drug discovery efforts, especially to identify compounds that modulate the metabolic and glutamatergic systems.

Analysis of the rat hypothalamus proteome by data-independent label-free LC-MS/MS.

Studies of neuronal, endocrine, and metabolic disorders would be facilitated by characterization of the hypothalamus proteome. Protein extracts prepared from 16 whole rat hypothalami were measured by data-independent label-free nano LC-MS/MS. Peptide features were detected, aligned, and searched against a rat Swiss-Prot database using ProteinLynx Global Server v.2.5. The final combined dataset comprised 21 455 peptides, corresponding to 622 unique proteins, each identified by a minimum of two distinct peptides. The majority of the proteins (69%) were cytosolic, and 16% were membrane proteins. Important proteins involved in neurological and synaptic function were identified including several members of the Ras-related protein family and proteins involved in glutamate biosynthesis.

The application of selective reaction monitoring confirms dysregulation of glycolysis in a preclinical model of schizophrenia.

BACKGROUND: Establishing preclinical models is essential for novel drug discovery in schizophrenia. Most existing models are characterized by abnormalities in behavioral readouts, which are informative, but do not necessarily translate to the symptoms of the human disease. Therefore, there is a necessity of characterizing the preclinical models from a molecular point of view. Selective reaction monitoring (SRM) has already shown promise in preclinical and clinical studies for multiplex measurement of diagnostic, prognostic and treatment-related biomarkers. METHODS: We have established an SRM assay for multiplex analysis of 7 enzymes of the glycolysis pathway which is already known to be affected in human schizophrenia and in the widely-used acute PCP rat model of schizophrenia. The selected enzymes were hexokinase 1 (Hk1), aldolase C (Aldoc), triosephosphate isomerase (Tpi1), glyceraldehyde-3-phosphate dehydrogenase (Gapdh), phosphoglycerate mutase 1 (Pgam1), phosphoglycerate kinase 1 (Pgk1) and enolase 2 (Eno2). The levels of these enzymes were analyzed using SRM in frontal cortex from brain tissue of PCP treated rats. RESULTS: Univariate analyses showed statistically significant altered levels of Tpi1 and alteration of Hk1, Aldoc, Pgam1 and Gapdh with borderline significance in PCP rats compared to controls. Most interestingly, multivariate analysis which considered the levels of all 7 enzymes simultaneously resulted in generation of a bi-dimensional chart that can distinguish the PCP rats from the controls. CONCLUSIONS: This study not only supports PCP treated rats as a useful preclinical model of schizophrenia, but it also establishes that SRM mass spectrometry could be used in the development of multiplex classification tools for complex psychiatric disorders such as schizophrenia.