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Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow samples from 70 patients with MM using multiplexed immunofluorescence, automated microscopy and deep-learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA repair pathway and EYA3 expression in proteasome inhibitor sensitivity and major histocompatibility complex class II expression in the response to elotuzumab. Globally, ex vivo drug sensitivity associated with bone marrow microenvironmental signatures reflecting treatment stage, clonality and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for patients with MM., Nature Cancer, 4 (5), ISSN:2662-1347

Supplementary Methods, Tables and Figures

The plasma–protein histidine-rich glycoprotein (HRG) is implicated in phenotypic switching of tumor-associated macrophages, regulating cytokine production and phagocytotic activity, thereby promoting vessel normalization and antitumor immune responses. To assess the therapeutic effect of HRG gene delivery on CNS tumors, we used adenovirus-encoded HRG to treat mouse intracranial GL261 glioma. Delivery of Ad5-HRG to the tumor site resulted in a significant reduction in glioma growth, associated with increased vessel perfusion and increased CD45+ leukocyte and CD8+ T-cell accumulation in the tumor. Antibody-mediated neutralization of colony-stimulating factor-1 suppressed the effects of HRG on CD45+ and CD8+ infiltration. Using a novel protein interaction–decoding technology, TRICEPS-based ligand receptor capture (LRC), we identified Stanniocalcin-2 (STC2) as an interacting partner of HRG on the surface of inflammatory cells in vitro and colocalization of HRG and STC2 in gliomas. HRG reduced the suppressive effects of STC2 on monocyte CD14+ differentiation and STC2-regulated immune response pathways. In consequence, Ad5-HRG–treated gliomas displayed decreased numbers of IL35+ Treg cells, providing a mechanistic rationale for the reduction in GL261 growth in response to Ad5-HRG delivery. We conclude that HRG suppresses glioma growth by modulating tumor inflammation through monocyte infiltration and differentiation. Moreover, HRG acts to balance the regulatory effects of its partner, STC2, on inflammation and innate and/or acquired immunity. HRG gene delivery therefore offers a potential therapeutic strategy to control antitumor immunity. Mol Cancer Ther; 17(9); 1961–72. ©2018 AACR.

Purpose:The clinical standard treatment for patients with malignant pleural mesothelioma (MPM) includes a cisplatin-based chemotherapy, leading to reduction of tumor size in only a minority of patients. Predicting response to chemotherapy in patients with MPM by using a genetic marker would, therefore, enable patient stratification.Experimental Design:In this retrospective biomarker study, eligible patients had resectable MPM, measurable disease, and available primary MPM tissue. All patients underwent first-line treatment with cisplatin and pemetrexed, followed by surgery. Thorough molecular analysis was performed (whole-exome and targeted deep sequencing, and copy-number analyses), and also mechanistic in vitro data (viability assays, Western blots, and immunoprecipitation) using mesothelioma cell lines with and without siRNA-mediated BRCA1-associated protein 1 (BAP1) knockdown were provided.Results:In a training cohort of patients with MPM (n = 28), mutations or deletions of BAP1 each predicted resistance to chemotherapy in patients with primary MPM. The negative predictive value of BAP1 loss in patients with MPM was confirmed by amplicon sequencing and copy-number array technology in an independent test cohort (n = 39). Preliminary mechanistic studies using siRNA-based knockdown of BAP1 in MPM cell culture models along with immunoprecipitation assays confirmed chemoresistance in vitro, possibly through inhibition of apoptosis and transcriptional regulation of the BAP1/HCF1/E2F1 axis.Conclusions:Alterations in BAP1 in MPM were a negative predictor for response to chemotherapy and could possibly be used as a companion biomarker for treatment decision.

Most, if not all, proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing the majority of cellular processes in health and disease. Ever-advancing analytical capabilities promise to pinpoint lesions in proteome modularity driving disease phenotypes. Affinity purification of the protein of interest combined with LC-MS/MS (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can however not be resolved from a single AP-MS experiment but requires computational inference from multiple time-and resource-intensive reciprocal AP-MS experiments.In this study we introduce Deep Interactome Profiling by Mass Spectrometry (DIP-MS) which combines affinity enrichment with BN-PAGE separation, DIA mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment.We applied DIP-MS to probe the organisation of the human prefoldin (PFD) family of complexes, resolving distinct PFD holo- and sub-complex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution and thus represents a critical steppingstone to relate a proteome state to phenotype in both healthy and diseased conditions.

Heterogeneous high-density lipoprotein (HDL) particles, which can contain hundreds of proteins, affect human health and disease through dynamic molecular interactions with cell surface proteins. How HDL mediates its long-range signaling functions and interactions with various cell types is largely unknown. Due to the complexity of HDL, we hypothesize that multiple receptors engage with HDL particles resulting in condition-dependent receptor-HDL interaction clusters at the cell surface. Here we used the mass spectrometry-based and light-controlled proximity labeling strategy LUX-MS in a discovery-driven manner to decode HDL-receptor interactions. Surfaceome nanoscale organization analysis of hepatocytes and endothelial cells using LUX-MS revealed that the previously known HDL-binding protein scavenger receptor SCRB1 is embedded in a cell surface protein community, which we term HDL synapse. Modulating the endothelial HDL synapse, composed of 60 proteins, by silencing individual members showed that the HDL synapse can be assembled in the absence of SCRB1 and that the members are interlinked. The aminopeptidase AMPN (also known as CD13) was identified as an HDL synapse member that directly influences HDL uptake into the primary human aortic endothelial cells (HAECs). Our data indicate that preformed cell surface residing protein complexes modulate HDL function and suggest new theragnostic opportunities.

Glioblastoma is the most common yet deadliest primary brain cancer1. The neural behavior of glioblastoma, including the formation of synaptic circuitry and tumor microtubes, is increasingly understood to be pivotal for disease manifestation2–8. Nonetheless, the few approved treatments for glioblastoma target its oncological nature, while its neural vulnerabilities remain incompletely mapped and clinically unexploited. Here, we systematically survey the neural molecular dependencies and cellular heterogeneity across glioblastoma patients and diverse model systems. In 27 patient tumour samples taken directly after surgery, we identify a spectrum of cancer cell morphologies indicative of poor prognosis and discover a set of repurposable neuroactive drugs with consistent anti-glioblastoma efficacy. Glioblastoma cells exhibit functional dependencies on highly expressed neuroactive drug targets, while interpretable molecular machine learning (COSTAR) reveals their downstream convergence on AP-1-driven tumour suppression. This drug-target connectivity signature is confirmed by highly accurate in silico drug screening on >1 million compounds using COSTAR, as well as by multi-omic profiling of drug-treated glioblastoma cells. Thus, Ca2+-driven AP-1 pathway induction represents a tumour-intrinsic vulnerability at the intersection of oncogenesis and neural activity-dependent signaling. Opportunities for clinical translation of this neural vulnerability are epitomized by the antidepressant Vortioxetine synergizing with current standard of care treatments in vivo. Together, the results presented here provide a mechanistic foundation and conceptual framework for the treatment of glioblastoma based on its neural origins.

High-density lipoprotein (HDL) is a mixture of complex particles mediating reverse cholesterol transport (RCT) and several cytoprotective activities. Despite its relevance for human health, many aspects of HDL-mediated lipid trafficking and cellular signaling remain elusive at the molecular level. During HDL's journey throughout the body, its functions are mediated through interactions with cell surface receptors on different cell types. To characterize and better understand the functional interplay between HDL particles and tissue, we analyzed the surfaceome-residing receptor neighborhoods with which HDL potentially interacts. We applied a combination of chemoproteomic technologies including automated cell surface capturing (auto-CSC) and HATRIC-based ligand-receptor capturing (HATRIC-LRC) on four different cellular model systems mimicking tissues relevant for RCT. The surfaceome analysis of EA.hy926, HEPG2, foam cells, and human aortic endothelial cells (HAECs) revealed the main currently known HDL receptor scavenger receptor B1 (SCRB1), as well as 155 shared cell surface receptors representing potential HDL interaction candidates. Since vascular endothelial growth factor A (VEGF-A) was recently found as a regulatory factor of transendothelial transport of HDL, we next analyzed the VEGF-modulated surfaceome of HAEC using the auto-CSC technology. VEGF-A treatment led to the remodeling of the surfaceome of HAEC cells, including the previously reported higher surfaceome abundance of SCRB1. In total, 165 additional receptors were found on HAEC upon VEGF-A treatment representing SCRB1 co-regulated receptors potentially involved in HDL function. Using the HATRIC-LRC technology on human endothelial cells, we specifically aimed for the identification of other bona fide (co-)receptors of HDL beyond SCRB1. HATRIC-LRC enabled, next to SCRB1, the identification of the receptor tyrosine-protein kinase Mer (MERTK). Through RNA interference, we revealed its contribution to endothelial HDL binding and uptake. Furthermore, subsequent proximity ligation assays (PLAs) demonstrated the spatial vicinity of MERTK and SCRB1 on the endothelial cell surface. The data shown provide direct evidence for a complex and dynamic HDL receptome and that receptor nanoscale organization may influence binding and uptake of HDL., International Journal of Molecular Sciences, 23 (16), ISSN:1422-0067

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein–protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography–MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics. ISSN:1548-7105 ISSN:1548-7091

Purpose: The clinical standard treatment for patients with malignant pleural mesothelioma (MPM) includes a cisplatin-based chemotherapy, leading to reduction of tumor size in only a minority of patients. Predicting response to chemotherapy in patients with MPM by using a genetic marker would, therefore, enable patient stratification. Experimental Design: In this retrospective biomarker study, eligible patients had resectable MPM, measurable disease, and available primary MPM tissue. All patients underwent first-line treatment with cisplatin and pemetrexed, followed by surgery. Thorough molecular analysis was performed (whole-exome and targeted deep sequencing, and copy-number analyses), and also mechanistic in vitro data (viability assays, Western blots, and immunoprecipitation) using mesothelioma cell lines with and without siRNA-mediated BRCA1-associated protein 1 (BAP1) knockdown were provided. Results: In a training cohort of patients with MPM (n = 28), mutations or deletions of BAP1 each predicted resistance to chemotherapy in patients with primary MPM. The negative predictive value of BAP1 loss in patients with MPM was confirmed by amplicon sequencing and copy-number array technology in an independent test cohort (n = 39). Preliminary mechanistic studies using siRNA-based knockdown of BAP1 in MPM cell culture models along with immunoprecipitation assays confirmed chemoresistance in vitro, possibly through inhibition of apoptosis and transcriptional regulation of the BAP1/HCF1/E2F1 axis. Conclusions: Alterations in BAP1 in MPM were a negative predictor for response to chemotherapy and could possibly be used as a companion biomarker for treatment decision.

Listeria monocytogenesis an opportunistic foodborne pathogen responsible for listeriosis, a potentially fatal foodborne disease. Many differentListeriastrains and serotypes exist, but a proteogenomic resource that bridges the gap in our molecular understanding of the relationships between theListeriagenotypes and phenotypes via proteotypes is still missing. Here we devised a next-generation proteogenomics strategy that enables the community to rapidly proteotypeListeriastrains and relate this information back to the genotype. Based on sequencing andde novoassembly of the two most commonly usedListeriamodel strains, EGD-e and ScottA, we established two comprehensiveListeriaproteogenomic databases. A genome comparison established core- and strain-specific genes potentially responsible for virulence differences. Next, we established a DIA/SWATH-based proteotyping strategy, including a new and robust sample preparation workflow, that enables the reproducible, sensitive, and relative quantitative measurement ofListeriaproteotypes. This reusable and publically available DIA/SWATH library covers 70% of open reading frames ofListeriaand represents the most extensive spectral library forListeriaproteotype analysis to date. We used these two new resources to investigate theListeriaproteotype in states mimicking the upper gastrointestinal passage. Exposure ofListeriato bile salts at 37 °C, which simulates conditions encountered in the duodenum, showed significant proteotype perturbations including an increase of FlaA, the structural protein of flagella. Given thatListeriais known to lose its flagella above 30 °C, this was an unexpected finding. The formation of flagella, which might have implications on infectivity, was validated by parallel reaction monitoring and light and scanning electron microscopy.flaAtranscript levels were not significantly different with and without exposure to bile salts at 37 °C, suggesting regulation at the post-transcriptional level. Together, these analyses provide a comprehensive proteogenomic resource and toolbox for theListeriacommunity enabling the analysis ofListeriagenotype-proteotype-phenotype relationships.

Multi-layered omics approaches can help define relationships between genetic factors, biochemical processes and phenotypes thus extending research of inherited diseases beyond identifying their monogenic cause 1. We implemented a multi-layered omics approach for the inherited metabolic disorder methylmalonic aciduria (MMA). We performed whole genome sequencing, transcriptomic sequencing, and mass spectrometry-based proteotyping from matched primary fibroblast samples of 230 individuals (210 affected, 20 controls) and related the molecular data to 105 phenotypic features. Integrative analysis identified a molecular diagnosis for 84% (177/210) of affected individuals, the majority (148) of whom had pathogenic variants in methylmalonyl-CoA mutase (MMUT). Untargeted analysis of all three omics layers revealed dysregulation of the TCA cycle and surrounding metabolic pathways, a finding that was further corroborated by multi-organ metabolomics of a hemizygous Mmut mouse model. Integration of phenotypic disease severity indicated downregulation of oxoglutarate dehydrogenase and upregulation of glutamate dehydrogenase, two proteins involved in glutamine anaplerosis of the TCA cycle. The relevance of disturbances in this pathway was supported by metabolomics and isotope tracing studies which showed decreased glutamine-derived anaplerosis in MMA. We further identified MMUT to physically interact with both, oxoglutarate dehydrogenase complex components and glutamate dehydrogenase providing evidence for a multi-protein metabolon that orchestrates TCA cycle anaplerosis. This study emphasizes the utility of a multi-modal omics approach to investigate metabolic diseases and highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.Take home messageCombination of integrative multi-omics technologies with clinical and biochemical features leads to an increased diagnostic rate compared to genome sequencing alone and identifies anaplerotic rewiring as a targetable feature of the rare inborn error of metabolism methylmalonic aciduria.Graphical abstract

The molecular nanoscale organization of the surfaceome is a fundamental regulator of cellular signaling in health and disease. Technologies for mapping the spatial relationships of cell surface receptors and their extracellular signaling synapses would unlock theranostic opportunities to target protein communities and the possibility to engineer extracellular signaling. Here, we develop an optoproteomic technology termed LUX-MS that enables the targeted elucidation of acute protein interactions on and in between living cells using light-controlled singlet oxygen generators (SOG). By using SOG-coupled antibodies, small molecule drugs, biologics and intact viral particles, we demonstrate the ability of LUX-MS to decode ligand receptor interactions across organisms and to discover surfaceome receptor nanoscale organization with direct implications for drug action. Furthermore, by coupling SOG to antigens we achieved light-controlled molecular mapping of intercellular signaling within functional immune synapses between antigen-presenting cells and CD8+ T cells providing insights into T cell activation with spatiotemporal specificity. LUX-MS based decoding of surfaceome signaling architectures thereby provides a molecular framework for the rational development of theranostic strategies., Nature Communications, 12 (1), ISSN:2041-1723

High-density lipoprotein (HDL) is a heterogeneous mixture of blood-circulating multimolecular particles containing many different proteins, lipids, and RNAs. Recent advancements in mass spectrometry-based proteotype analysis show promise for the analysis of proteoforms across large patient cohorts. In order to create the required spectral libraries enabling these data-independent acquisition (DIA) strategies, HDL was isolated from the plasma of more than 300 patients with a multiplicity of physiological HDL states. HDL proteome spectral libraries consisting of 296 protein groups and more than 786 peptidoforms were established, and the performance of the DIA strategy was benchmarked for the detection of HDL proteotype differences between healthy individuals and a cohort of patients suffering from diabetes mellitus type 2 and/or coronary heart disease. Bioinformatic interrogation of the data using the generated spectral libraries showed that the DIA approach enabled robust HDL proteotype determination. HDL peptidoform analysis enabled by using spectral libraries allowed for the identification of post-translational modifications, such as in APOA1, which could affect HDL functionality. From a technical point of view, data analysis further shows that protein and peptide quantities are currently more discriminative between different HDL proteotypes than peptidoforms without further enrichment. Together, DIA-based HDL proteotyping enables the robust digitization of HDL proteotypes as a basis for the analysis of larger clinical cohorts.

All poxviruses contain a set of proteinaceous structures termed lateral bodies (LB) that deliver viral effector proteins into the host cytosol during virus entry. To date, the spatial proteotype of LBs remains unknown. Using the prototypic poxvirus, vaccinia virus (VACV), we employed a quantitative comparative mass spectrometry strategy to determine the poxvirus LB proteome. We identified a large population of candidate cellular proteins, the majority being mitochondrial, and 15 candidate viral LB proteins. Strikingly, one-third of these are VACV redox proteins whose LB residency could be confirmed using super-resolution microscopy. We show that VACV infection exerts an anti-oxidative effect on host cells and that artificial induction of oxidative stress impacts early and late gene expression as well as virion production. Using targeted repression and/or deletion viruses we found that deletion of individual LB-redox proteins was insufficient for host redox modulation suggesting there may be functional redundancy. In addition to defining the spatial proteotype of VACV LBs, these findings implicate poxvirus redox proteins as potential modulators of host oxidative anti-viral responses and provide a solid starting point for future investigations into the role of LB resident proteins in host immunomodulation., PLoS Pathogens, 18 (7), ISSN:1553-7374, ISSN:1553-7366

Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology., In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data.

Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

Loss of myelin underlies the pathology of several neurological disorders of diverse etiology. CNS remyelination by adult oligodendrocyte progenitor cells (OPCs) can occur but it differs from developmental myelination carried out by neonatal OPCs. We asked whether the myelin proteome of remyelinated regions is changed. We compared the myelin proteome formed during development to the remyelination proteome attained after lysolecithin-induced demyelination in the mouse spinal cord. Mass-spectrometry analysis of iTRAQ labelled myelin protein lysates showed that the proteome of remyelination is different from that of developmental myelination, leading to profound changes in myelin protein content. Aside from known mediators of oligodendrocyte differentiation, we found proteome alterations included modulators of metabolism, cell signaling and actin cytoskeleton dynamics. Downregulating one candidate (FSCN1/Fascin1) was sufficient to partially hamper oligodendrocytes in-vitro. In summary, we identify the difference in the proteome of remyelinating oligodendrocytes as a novel potential contributor to the pathophysiology of demyelinating disorders, thus providing new potential therapeutic targets for future studies.

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays., Nature Communications, 12 (1), ISSN:2041-1723

CD20 is a B cell-specific membrane protein and represents an attractive target for therapeutic antibodies. Despite widespread usage of anti-CD20 antibodies for B cell depletion therapies, the biological function of their target remains unclear. Here, we demonstrate that CD20 controls the nanoscale organization of receptors on the surface of resting B lymphocytes. CRISPR/Cas9-mediated ablation of CD20 in resting B cells resulted in relocalization and interaction of the IgM-class B cell antigen receptor with the coreceptor CD19. This receptor rearrangement led to a transient activation of B cells, accompanied by the internalization of many B cell surface marker proteins. Reexpression of CD20 restored the expression of the B cell surface proteins and the resting state of Ramos B cells. Similarly, treatment of Ramos or naive human B cells with the anti-CD20 antibody rituximab induced nanoscale receptor rearrangements and transient B cell activation in vitro and in vivo. A departure from the resting B cell state followed by the loss of B cell identity of CD20-deficient Ramos B cells was accompanied by a PAX5 to BLIMP-1 transcriptional switch, metabolic reprogramming toward oxidative phosphorylation, and a shift toward plasma cell development. Thus, anti-CD20 engagement or the loss of CD20 disrupts membrane organization, profoundly altering the fate of human B cells., Proceedings of the National Academy of Sciences of the United States of America, 118 (7), ISSN:0027-8424, ISSN:1091-6490

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

CD8+ tumor-infiltrating T cells can be regarded as one of the most relevant predictive biomarkers in immune-oncology. Highly infiltrated tumors, referred to as inflamed (clinically “hot”), show the most favorable response to immune checkpoint inhibitors in contrast to tumors with a scarce immune infiltrate called immune desert or excluded (clinically “cold”). Nevertheless, quantitative and reproducible methods examining their prevalence within tumors are lacking. We therefore established a computational diagnostic algorithm to quantitatively measure spatial densities of tumor-infiltrating CD8+ T cells by digital pathology within the three known tumor compartments as recommended by the International Immuno-Oncology Biomarker Working Group in 116 prospective metastatic melanomas of the Swiss Tumor Profiler cohort. Workflow robustness was confirmed in 33 samples of an independent retrospective validation cohort. The introduction of the intratumoral tumor center compartment proved to be most relevant for establishing an immune diagnosis in metastatic disease, independent of metastatic site. Cut-off values for reproducible classification were defined and successfully assigned densities into the respective immune diagnostic category in the validation cohort with high sensitivity, specificity, and precision. We provide a robust diagnostic algorithm based on intratumoral and stromal CD8+ T-cell densities in the tumor center compartment that translates spatial densities of tumor-infiltrating CD8+ T cells into the clinically relevant immune diagnostic categories “inflamed”, “excluded”, and “desert”. The consideration of the intratumoral tumor center compartment allows immune phenotyping in the clinically highly relevant setting of metastatic lesions, even if the invasive margin compartment is not captured in biopsy material., The authors present a robust diagnostic algorithm based on digital pathology and image analysis that quantifies intratumoral and stromal CD8+ T-cell densities in the tumor center and invasive margin compartment in metastatic melanoma. Spatial CD8+ T-cell densities are translated into the clinically relevant immune diagnostic categories “inflamed”, “excluded”, and “desert”. Their approach also allows efficient immune phenotyping of metastatic lesions, on biopsy material or even in the absence of material from the invasive margin.

Treatment of glioblastoma multiforme (GBM), the most aggressive form of primary brain cancer, has essentially not advanced over the past few decades. Numerous challenges hinder the successful development of new therapies, including drug delivery across the blood-brain barrier (BBB), the complexity of the tumor microenvironment (TME), and the lack of clinically predictive cancer models. Here, we present the results of an image-based ex vivo drug-testing platform that addresses these therapeutic roadblocks. To demonstrate the clinical utility of our platform, in a retrospective cohort of 14 GBM patients, we show that ex vivo sensitivity to Temozolomide (TMZ, 1st-line GBM chemotherapy), is associated with longer progression free survival (PFS) and overall survival (OS). Next, by screening 150 clinically approved drugs across 27 GBM surgical patient samples, we identify a set of BBB-permeable neuroactive drugs with anti-glioma activity. These neurological drugs display remarkably consistent on-target killing of cancer cells with minimal toxicity to non-malignant TME cells across both primary and recurrent GBM samples. Single-cell transcriptional profiling of GBM patient samples and functional genetics reveals novel glioma-dependencies on neurological drug-target expression. Furthermore, a drug-target network enrichment analysis uncovers an AP1/BTG/TP53 gene signature associated with the anti-glioma activity of neurological drugs. In silico screening of over 1 million compounds for this common gene signature identified additional drug hits that could be validated in patient samples with 90% accuracy. Multiplexed transcriptomics revealed AP-1 transcription factor family activation to be the common underlying feature of neurological drugs with anti-glioma activity. Among the most promising candidate drugs, we identify the atypical antidepressant Vortioxetine as the strongest inducer of this gene signature, and confirm its efficacy in vivo across multiple mouse models. Vortioxetine in combination with Temozolomide or Lomustine further increased median survival in vivo compared to single agents alone. This study thus provides a clinically predictive and personalized drug-testing platform that identifies new treatment opportunities for GBM, warranting further investigation. Citation Format: Sohyon Lee, Tobias Weiss, Marcel Bühler, Rebekka Wegmann, Julien Mena, Michel Bihl, Sandra Goetze, Audrey van Drogen, Elisabeth J. Rushing, Bernd Wollscheid, Michael Weller, Berend Snijder. Image-based functional precision medicine for repurposing neuroactive drugs in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5325.