Olink (United States)

To facilitate personalized treatment of diabetic kidney disease (DKD), we developed the Joslin Kidney Panel (JKP) of 21 circulating proteins associated with progression to end-stage kidney disease (ESKD). Prognostic models using baseline concentrations of JKP proteins in circulation and clinical markers were then developed to stratify individuals according to ESKD risk and according to response to fenofibrate, a potential reno-protective drug. The custom-made Joslin OLINK multipurpose proteomics platform was used to quantify JKP proteins. Association between baseline serum/plasma concentrations of these proteins and kidney outcomes was examined in five independent study groups. In Type 1 diabetes individuals from Joslin (N=59), FinnDiane (N=389), and Steno (N=283), all JKP proteins were good discriminators of ESKD risk during 10-year follow-up. Baseline concentrations of KIM-1 and WFDC2 performed the best, matching or outperforming the clinical markers. An optimal model to discriminate ESKD risk that included three clinical markers and eight JKP proteins (KIM1, TNF-R2, TNF-R3, TNF-R19L, PVRL4, WFDC2, DLL1, SYND1), was developed using the FinnDiane cohort (C-index 0.868, SE±0.019) and validated in the Steno cohort (C-index 0.913, SE±0.104) and in Type 2 diabetes Joslin study (C-index 0.807, SE±0.036). In each of these studies the optimal model performed better than models based solely on three clinical markers. In a subgroup of 450 individuals with Type 2 diabetes from the ACCORD-Lipid trial, high levels of three JKP proteins (EFNA4, DLL1, IL-1RT1) predicted amelioration of fast kidney function decline during four years of follow-up in those treated with fenofibrate compared to placebo. Quantification of circulating JKP proteins using the Joslin OLINK platform discriminates ESKD risk in individuals with diabetes and their response to reno-protective drugs. Use of this multi-purpose precision medicine tool should facilitate studies on the etiology of DKD and enable the development of effective personalized treatment protocols for individuals with DKD.

ABSTRACT Cancer immunotherapy approaches target signaling pathways that are highly synonymous between CD4 and CD8 T-cell subsets and, therefore, often stimulate nonspecific lymphocyte activation, resulting in cytotoxicity to otherwise healthy tissue. The goal of our study was to identify intrinsic modulators of basic T lymphocyte activation pathways that could discriminately bolster CD8 anti-tumor effector responses. Using a Tbc1d10c null mouse, we observed marked resistance to a range of tumor types conferred by Tbc1d10c deficiency. Moreover, tumor-bearing Tbc1d10c null mice receiving PD-1 or CTLA-4 monotherapy exhibited a 33% or 90% cure rate, respectively. While Tbc1d10c was not expressed in solid tumor cells, Tbc1d10c disruption selectively augmented CD8 T-cell activation and cytotoxic effector responses and adoptive transfer of CD8 T cells alone was sufficient to recapitulate Tbc1d10c null tumor resistance. Mechanistically, Tbc1d10c suppressed CD8 T-cell activation and anti-tumor function by intersecting canonical NF-κB pathway activation via regulation of Map3k3-mediated IKKβ phosphorylation. Strikingly, none of these cellular or molecular perturbations in the NF-κB pathway were featured in Tbc1d10c null CD4 T cells. Our findings identify a Tbc1d10c-Map3k3-NF-κB signaling axis as a viable therapeutic target to promote CD8 T-cell anti-tumor immunity while circumventing CD4 T cell-associated cytotoxicity and NF-κB activation in tumor cells.

Abstract Most patients treated with immune checkpoint blockade (ICB) do not have durable treatment responses. Therefore, there is a critical need to identify early non-invasive biomarkers of response. We performed plasma proteomic analysis (>700 proteins) at three timepoints on 174 metastatic melanoma patients treated with ICB. We leverage independent training and testing cohorts to build a predictor of immunotherapy response that outperforms several tissue-based approaches. We found 217 differentially expressed proteins between ICB responders (R) and non-responders (NR), including a co-regulated module of proteins enriched in certain NR patients. By analyzing single-cell RNA-sequencing data of tumor biopsies from 32 patients, we dissected the relative contribution of cells in the tumor to proteins in circulation. The majority of proteins in the co-regulated NR module derived from tumor and myeloid cells. Amongst myeloid cells, we identified a subset of tumor-associated macrophages (TAMs) with a suppressive phenotype that expressed high levels of the co-regulated NR module, thus suggesting they are key drivers of non-response signatures. Together, our data demonstrates the utility of plasma proteomics in biomarker discovery and in understanding the biology of host response to tumors.

We report a non-s initial-state angle-resolved photoemission extended fine-structure (ARPEFS) study of clean surfaces for the purpose of further understanding the technique. The surface structure sensitivity of ARPEFS applied to clean surfaces and to arbitrary initial states is studied using normal photoemission data taken from the Ni 3p core levels of a Ni(111) single crystal and the Cu 3s and the Cu 3p core levels of a Cu(111) single crystal. The Fourier transforms of these clean surface data are dominated by backscattering. Unlike the s initial-state data, the p initial-state data show a peak in the Fourier transform corresponding to in-plane scattering from the six nearest neighbors to the emitter. Evidence was seen for single-scattering events from the same plane as the emitters and double-scattering events. Using a recently developed, multiple-scattering calculation program, ARPEFS data from clean surfaces and from p initial states can be modeled to high precision. Although there are many layers of emitters when measuring photoemission from a clean surface, test calculations show that the ARPEFS signal is dominated by photoemission from atoms in the first two crystal layers. Thus ARPEFS applied to clean surfaces is sensitive to surface reconstruction. The best-fit calculation for clean Ni(111) indicatesmore » an expansion of the first two layers. {copyright} {ital 1997} {ital The American Physical Society}« less