Tumour metastasis is a complex process involving reciprocal interplay between cancer cells and host stroma at both primary and secondary sites, and is strongly influenced by microenvironmental factors such as hypoxia1. Tumour-secreted proteins play a crucial role in these interactions2-5 and present strategic therapeutic potential. Metastasis of breast cancer to the bone affects approximately 85% of patients with advanced disease and renders them largely untreatable6. Specifically, osteolytic bone lesions, where bone is destroyed, lead to debilitating skeletal complications and increased patient morbidity and mortality6,7. The molecular interactions governing the early events of osteolytic lesion formation are currently unclear. Here we show hypoxia to be specifically associated with bone relapse in ER-negative breast cancer patients. Global quantitative analysis of the hypoxic secretome identified Lysyl Oxidase (LOX) as significantly associated with bone-tropism and relapse. High expression of LOX in primary breast tumours or systemic delivery of LOX leads to osteolytic lesion formation whereas silencing or inhibition of LOX activity abrogates tumour-driven osteolytic lesion formation. We identify LOX as a novel regulator of NFATc1-driven osteoclastogenesis, independent of RANK Ligand, which disrupts normal bone homeostasis leading to the formation of focal pre-metastatic lesions. We show that these lesions subsequently provide a platform for circulating tumour cells to coloniseCorrespondence and requests for materials should be addressed to janine.erler@bric.ku.dk and a.gartland@shef.ac.uk.

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Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.

SummaryCancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks.

SummaryTo investigate miRNA function in human acute myeloid leukemia (AML) stem cells (LSC), we generated a prognostic LSC-associated miRNA signature derived from functionally validated subpopulations of AML samples. For one signature miRNA, miR-126, high bioactivity aggregated all in vivo patient sample LSC activity into a single sorted population, tightly coupling miR-126 expression to LSC function. Through functional studies, miR-126 was found to restrain cell cycle progression, prevent differentiation, and increase self-renewal of primary LSC in vivo. Compared with prior results showing miR-126 regulation of normal hematopoietic stem cell (HSC) cycling, these functional stem effects are opposite between LSC and HSC. Combined transcriptome and proteome analysis demonstrates that miR-126 targets the PI3K/AKT/MTOR signaling pathway, preserving LSC quiescence and promoting chemotherapy resistance.

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Key words: acute myeloid leukemia / computational biology / proteomics / single-cell approaches / tandem mass tags (TMT)Final character count: 47,782 AbstractIn recent years, cellular life science research has experienced a significant shift, moving away from conducting bulk cell interrogation towards single-cell analysis. It is only through single cell analysis that a complete understanding of cellular heterogeneity, and the interplay between various cell types that are fundamental to specific biological phenotypes, can be achieved. Single-cell assays at the protein level have been predominantly limited to targeted, antibody-based methods. However, here we present an experimental and computational pipeline, which establishes a comprehensive single-cell mass spectrometry-based proteomics workflow.By exploiting a leukemia culture system, containing functionally-defined leukemic stem cells, progenitors and terminally differentiated blasts, we demonstrate that our workflow is able to explore the cellular heterogeneity within this aberrant developmental hierarchy. We show our approach is capable to quantifying hundreds of proteins across hundreds of single cells using limited instrument time. Furthermore, we developed a computational pipeline (SCeptre), that effectively clusters the data and permits the extraction of cell-specific proteins and functional pathways. This proof-of-concept work lays the foundation for future global single-cell proteomics studies. Duployez et al, 2019;Ng et al, 2016), their accuracy has proven limitation when used as a proxy for protein levels (Vogel & Marcotte, 2012; Khan et al, 2013). Therefore, to gain a thorough understanding of what occurs in a cell at the protein level, on a global scale, MSbased approaches are the sole way to accomplish this. Being the cellular workhorses, there is much knowledge to be gained from mechanisms occurring at the protein level, either through enzyme activity, post-translational modifications or protein degradation/proteolysis; hence the great need for protein level approaches at the single-cell level.A few years ago, a novel type of flow cytometry was established; by combining traditional flow cytometry workflows with mass spectrometry, a new analysis method termed Mass Cytometry was developed, more commonly referred to as CyTOF (Newell et al, 2012;Bodenmiller et al, 2012). This allows the simultaneous readout of tens of markers simultaneously, allowing single-cell analysis of pre-defined sets of proteins or posttranslational modifications (PTMs). This method, however, relies heavily on previously