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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

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.

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

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.

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.

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.

Nature Biotechnology, 37 (3), ISSN:1546-1696, ISSN:1087-0156

Summary Protein kinases are essential for signal transduction and control of most cellular processes, including metabolism, membrane transport, motility, and cell cycle. Despite the critical role of kinases in cells and their strong association with diseases, good coverage of their interactions is available for only a fraction of the 535 human kinases. Here, we present a comprehensive mass-spectrometry-based analysis of a human kinase interaction network covering more than 300 kinases. The interaction dataset is a high-quality resource with more than 5,000 previously unreported interactions. We extensively characterized the obtained network and were able to identify previously described, as well as predict new, kinase functional associations, including those of the less well-studied kinases PIM3 and protein O-mannose kinase (POMK). Importantly, the presented interaction map is a valuable resource for assisting biomedical studies. We uncover dozens of kinase-disease associations spanning from genetic disorders to complex diseases, including cancer., Graphical Abstract, Highlights • AP-MS confirms over 1,300 known and reveals over 5,000 kinase protein interactions • Functional contexts and regulatory circuits for poorly studied kinases • Kinases are biochemically linked to a broad range of Mendelian disease modules • Biochemical context for kinase complexes containing human cancer proteins, In this issue of Molecular Cell, Buljan et al. present a comprehensive account of human kinase complexes for inferring new kinase functions, kinase signaling modules, and kinase complexes linked to genetic diseases.

STAT3 mediates inflammation and modulates immunity. We found that in addition to its role in autoimmunity, over-activated STAT3 interferes in early erythro-myeloid precursor development and reduces commitment towards the erythroid lineage by interfering with the erythropoietin-STAT5 signalling pathway.

The characterization of all protein complexes of human cells under defined physiological conditions using affinity purification-mass spectrometry (AP-MS) is a highly desirable step in the quest to understand the phenotypic effects of genomic information. However, such a challenging goal has not yet been achieved, as it requires reproducibility of the experimental workflow and high data consistency across different studies and laboratories. We systematically investigated the reproducibility of a standardized AP-MS workflow by performing a rigorous interlaboratory comparative analysis of the interactomes of 32 human kinases. We show that it is possible to achieve high interlaboratory reproducibility of this standardized workflow despite differences in mass spectrometry configurations and subtle sample preparation-related variations and that combination of independent data sets improves the approach sensitivity, resulting in even more-detailed networks. Our analysis demonstrates the feasibility of obtaining a high-quality map of the human protein interactome with a multilaboratory project.

Coordinated activities of protein kinases and phosphatases ensure phosphorylation homeostasis, which, when perturbed, can instigate diseases, including cancer. Yet, in contrast to kinases, much less is known about protein phosphatase functions and their interactions and complexes. Here, we used quantitative affinity proteomics to assay protein-protein interactions for 54 phosphatases distributed across the three major protein phosphatase families, with additional analysis of their 12 co-factors. We identified 838 high-confidence interactions, of which 631, to our knowledge, have not been reported before. We show that inhibiting the activity of phosphatases PP1 and PP2A by okadaic acid disrupts their specific interactions, supporting the potential of therapeutics that target these proteins. Additional analyses revealed candidate physical and functional interaction links to phosphatase-based regulation of several signaling pathways and to human cancer. Our study provides an initial glimpse of the protein interaction landscape of phosphatases and their functions in cellular regulation.

Tissue homeostasis is controlled by signaling systems that coordinate cell proliferation, cell growth and cell shape upon changes in the cellular environment. Deregulation of these processes is associated with human cancer and can occur at multiple levels of the underlying signaling systems. To gain an integrated view on signaling modules controlling tissue growth, we analyzed the interaction proteome of the human Hippo pathway, an established growth regulatory signaling system. The resulting high‐resolution network model of 480 protein‐protein interactions among 270 network components suggests participation of Hippo pathway components in three distinct modules that all converge on the transcriptional co‐activator YAP1. One of the modules corresponds to the canonical Hippo kinase cassette whereas the other two both contain Hippo components in complexes with cell polarity proteins. Quantitative proteomic data suggests that complex formation with cell polarity proteins is dynamic and depends on the integrity of cell‐cell contacts. Collectively, our systematic analysis greatly enhances our insights into the biochemical landscape underlying human Hippo signaling and emphasizes multifaceted roles of cell polarity complexes in Hippo‐mediated tissue growth control., Molecular Systems Biology, 9 (1), ISSN:1744-4292

This study investigates the role of a newly identified gene called hCDC4 in prostate cancer. The hCDC4/FBXW7 gene encodes a protein that functions in a cellular process called proteolysis, or protein degradation. hCdc4 degrades a protein called cyclin E1, which is a central component of the cell division machinery. Cyclin E1 is involved in initiating DNA replication in cells. However, in many types of human tumors cyclin E1 protein level is aberrant and this phenotype has been shown in vitro and in vivo to be oncogenic. Very little is known regarding cyclin E1/hCdc4 in prostate tumors. We are exploring whether hCDC4/FBXW7 functions as a tumor suppressor gene in prostate cancer. We have completed a genetic screen of prostate tumors and found an hCDC4/FBXW7 gene mutation. We have shown that this mutant hCdc4 cannot bind cyclin E1 substrate in vivo and mislocalizes in cells. We are currently exploring the effects of hCDC4/FBXW7 knockdown in prostate cancer progression and identifying substrates of dysregulation of cyclin E1 kinase activity due to hCDC4/FBXW7 inactivation using protein array technology.

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules is therefore of major significance in basic and translational biology. To date, the systematic detection and quantification of protein complexes has remained technically challenging. The chromatographic separation of native protein complexes followed by the mass spectrometric analysis of the proteins contained in sequential fractions results in potentially thousands of protein elution profiles from which, in principle, the presence of specific complexes can be inferred. However, the de novo inference of protein complexes from such datasets has so far remained limited with regard to selectivity and the retrieval of quantitative information.We recently developed a variant of this strategy, complex-centric proteome profiling, which extends the concepts of targeted proteomics to the level of native protein complex analysis. The complex-centric workflow consists of size exclusion chromatography (SEC) to fractionate native protein complexes, DIA/SWATH mass spectrometry to precisely quantify the proteins in each SEC fraction based on a consistent set of peptides, and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler. Complex-centric proteome profiling captures the majority of proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to genetically unaltered tissue cultures and adaptable to primary tissue. At present it requires approximately 8 days of wet-lab work, 15 days of MS measurement time and 7 days of computational analysis.