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Biology subjects

Lundqvist, A.

Publications and source records attributed to Lundqvist, A..

3 recordsLinked to original sources

CD4 T cell surface architecture is associated with response to anti-PD-1 therapy in non-small cell lung cancer

Anti-PD-1 therapy has substantially improved outcomes for patients with advanced non-small cell lung cancer (NSCLC), but clinical responses remain highly heterogeneous and are difficult to predict. Current immune profiling approaches primarily characterize immune cell composition and protein abundance, whereas the spatial organization of proteins at the cell surface has remained largely unexplored. Here, we demonstrate for the first time that cell surface protein organization is associated with clinical response to immune checkpoint inhibition therapy. Using the Proximity Network Assay (PNA), we simultaneously quantified cell frequencies, protein abundance, and protein organization in pretreatment peripheral blood mononuclear cells from 62 patients with advanced NSCLC treated with anti-PD-1 agents. While most cell populations were unchanged between responders and non-responders, pronounced response-associated alterations were identified within the CD4 T-cell compartment. Responders exhibited elevated expression of adhesion-associated proteins, and remodeling of a CD5-containing membrane domain. Increased colocalization within this domain was associated with poor clinical outcome and reduced survival. Machine learning analysis further identified CD4 T cell features as the strongest predictors of treatment response and demonstrated that protein organization provided greater predictive power than protein abundance alone. Together, these findings identify cell surface protein organization as a previously unexplored source of biomarkers and highlight its potential to improve immune profiling and biomarker discovery in cancer immunotherapy.

immunology↗

The solid tumor microenvironment changes the hierarchy of CD155 and CD112 receptors, shaping checkpoint blockade outcome

Reproducing a physiologically relevant tumor microenvironment in vitro is essential for developing effective immunotherapeutic treatments. By integrating the use of combinatorial receptor blockade and organoid models we provide a deep functional understanding of CD155 and CD112 receptors in solid tumors and their impact on cellular immunotherapy and infiltration. CD226 showed plasticity in response to the environment, being able to switch between CD155 and CD112 depending on the ligand availability. In addition, CD226 drove NK cell infiltration into tumor tissues via CD155 and CD112 ligation, with CD226-CD112 interaction specifically promoting migration from the periphery to the core. Downregulation of CD155 and TIGIT induced by the tumor microenvironment and previous drug exposure reduced the long-term efficacy of TIGIT blockade. Taken together, our findings point towards using CD112R blockade in primary tumors to simultaneously enhance NK cell killing activity and promote infiltration into the tumor core via CD226 and CD112 interaction. ONE SENTENCE SUMMARYTumors shape the hierarchy of CD155-CD112 receptors, reducing TIGIT blockade efficacy, while CD226 drives NK infiltration and shows binding plasticity

immunology↗

Systems analysis of metabolic responses to a mixed meal test in an obese cohort reveals links between tissue metabolism and the gut microbiota

Individuals with prediabetes and type 2 diabetes mellitus (T2DM) have poor ability to adapt to diet-triggered perturbations. We investigated global metabolic responses to a mixed meal test (MMT) in morbidly obese individuals with different diabetic status by performing plasma metabolomic profiling. Abnormal metabolism of carbohydrates, (branched-chain) amino acids, fatty acids and acylcholines in individuals with (pre)diabetes was observed. Moreover, differences in metabolic responses were associated with altered fecal metagenomics and transcriptomes of liver, jejunum and adipose tissues, which revealed a modified gut microbiome and multi-tissue metabolism in individuals having insulin resistance. Finally, using integrative machine learning models, we built a predictive model based on metabolomics data after 2h MMT, and identified possible new biomarkers for glycemic control including N-acetylaspartate and phenylalanine-derived metabolites that may be useful for diagnosis, intervention and prevention of T2DM.

systems biology↗