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As interactions between the immune system and tumour cells are governed by a complex network of cell–cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient’s response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication-specific and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types, as well as three T cell subtypes. Using the tumour-derived RGEPs, we can estimate the content of many tumours associated immune and stromal cell types, their therapeutically relevant ratios, as well as an improved gene expression profile of the malignant cells. Mathematical approaches can be used to assess immune cell composition from the tumour's bulk expression data. Here the authors optimise the CYBERSORT-based deconvolution algorithm by including cell type-specific reference gene expression profiles generated from tumour-derived single-cell RNA sequencing data.

SummaryBackground Overactivation of human epidermal growth factor receptor 3 (HER3) triggers multiple intracellular pathways resulting in tumor cell survival. This Phase 1 study assessed the safety, efficacy, and pharmacokinetics (PK) of seribantumab, a fully human anti-HER3 monoclonal antibody. Methods Adult patients with advanced or refractory solid tumors were treated in six dose cohorts of seribantumab: 3.2, 6, 10, 15, or 20 mg/kg weekly, or 40 mg/kg loading dose followed by 20 mg/kg weekly maintenance dose (40/20 mg/kg) using a modified 3 + 3 dose escalation strategy with cohort expansion. Primary objectives were identification of a recommended Phase 2 dose (RP2D) and determination of objective response rate. Secondary objectives were assessment of safety, dose-limiting toxicities, and PK. Results Forty-four patients (26 dose escalation; 18 dose expansion) were enrolled. Seribantumab monotherapy was well tolerated with most adverse events being transient and mild to moderate (grade 1 or 2) in severity; maximum tolerated dose was not reached. The highest dose, 40/20 mg/kg, was identified as RP2D. Best response was stable disease, reported in 24% and 39% of patients during the dose escalation and expansion portions of the study, respectively. Seribantumab terminal half-life was ≈100 h; steady state concentrations were reached after 3–4 weekly doses. Conclusions Seribantumab monotherapy was well tolerated across all dose levels. Safety and PK data from this study support further seribantumab investigations in genomically defined populations.Clinical trial registration NCT00734305. August 12, 2008.

Although epidermal growth factor receptor (EGFR; also called ErbB1) and its relatives initiate one of the most well-studied signalling networks, there is not yet a genome-wide view of even the earliest step in this pathway: recruitment of proteins to the activated receptors. Here we use protein microarrays comprising virtually every Src homology 2 (SH2) and phosphotyrosine binding (PTB) domain encoded in the human genome to measure the equilibrium dissociation constant of each domain for 61 peptides representing physiological sites of tyrosine phosphorylation on the four ErbB receptors. This involved 77,592 independent biochemical measurements and provided a quantitative protein interaction network that reveals many new interactions, including ones that fall outside of our current view of domain selectivity. By slicing through the network at different affinity thresholds, we found surprising differences between the receptors. Most notably, EGFR and ErbB2 become markedly more promiscuous as the threshold is lowered, whereas ErbB3 does not. Because EGFR and ErbB2 are overexpressed in many human cancers, our results suggest that the extent to which promiscuity changes with protein concentration may contribute to the oncogenic potential of receptor tyrosine kinases, and perhaps other signalling proteins as well.

Systematic efforts are currently under way to construct defined sets of cloned genes for high-throughput expression and purification of recombinant proteins. To facilitate subsequent studies of protein function, we have developed miniaturized assays that accommodate extremely low sample volumes and enable the rapid, simultaneous processing of thousands of proteins. A high-precision robot designed to manufacture complementary DNA microarrays was used to spot proteins onto chemically derivatized glass slides at extremely high spatial densities. The proteins attached covalently to the slide surface yet retained their ability to interact specifically with other proteins, or with small molecules, in solution. Three applications for protein microarrays were demonstrated: screening for protein-protein interactions, identifying the substrates of protein kinases, and identifying the protein targets of small molecules.

A generalized platform for introducing a diverse range of biomolecules into living cells in high-throughput could transform how complex cellular processes are probed and analyzed. Here, we demonstrate spatially localized, efficient, and universal delivery of biomolecules into immortalized and primary mammalian cells using surface-modified vertical silicon nanowires. The method relies on the ability of the silicon nanowires to penetrate a cell's membrane and subsequently release surface-bound molecules directly into the cell's cytosol, thus allowing highly efficient delivery of biomolecules without chemical modification or viral packaging. This modality enables one to assess the phenotypic consequences of introducing a broad range of biological effectors (DNAs, RNAs, peptides, proteins, and small molecules) into almost any cell type. We show that this platform can be used to guide neuronal progenitor growth with small molecules, knock down transcript levels by delivering siRNAs, inhibit apoptosis using peptides, and introduce targeted proteins to specific organelles. We further demonstrate codelivery of siRNAs and proteins on a single substrate in a microarray format, highlighting this technology's potential as a robust, monolithic platform for high-throughput, miniaturized bioassays.

PDZ domains have long been thought to cluster into discrete functional classes defined by their peptide-binding preferences. We used protein microarrays and quantitative fluorescence polarization to characterize the binding selectivity of 157 mouse PDZ domains with respect to 217 genome-encoded peptides. We then trained a multidomain selectivity model to predict PDZ domain-peptide interactions across the mouse proteome with an accuracy that exceeds many large-scale, experimental investigations of protein-protein interactions. Contrary to the current paradigm, PDZ domains do not fall into discrete classes; instead, they are evenly distributed throughout selectivity space, which suggests that they have been optimized across the proteome to minimize cross-reactivity. We predict that focusing on families of interaction domains, which facilitates the integration of experimentation and modeling, will play an increasingly important role in future investigations of protein function.

As an alternative to conventional, target-oriented drug discovery, we report a strategy that identifies compounds on the basis of the state that they induce in a signaling network. Immortalized human cells are grown in microtiter plates and treated with compounds from a small-molecule library. The target network is then activated and lysates derived from each sample are arrayed onto glass-supported nitrocellulose pads. By probing these microarrays with antibodies that report on the abundance or phosphorylation state of selected proteins, a global picture of the target network is obtained. As proof of concept, we screened 84 kinase and phosphatase inhibitors for their ability to induce different states in the ErbB signaling network. We observed functional connections between proteins that match our understanding of ErbB signaling, indicating that state-based screens can be used to define the topology of signaling networks. Additionally, compounds sort according to the multidimensional phenotypes they induce, suggesting that state-based screens may inform efforts to identify the targets of biologically active small molecules.

Current regulation of T cell receptor (TCR)-based therapeutics may require repeated testing of patients for specific HLA alleles as well as companion diagnostics development, despite the invariant nature of the HLA genotype and availability of robust clinical HLA tests. This increases the burden on patients and the organizations developing these products. We propose regulatory flexibility to facilitate the development of and access to TCR-based therapeutics.Current regulation of T cell receptor (TCR)-based therapeutics may require repeated testing of patients for specific HLA alleles as well as companion diagnostics development, despite the invariant nature of the HLA genotype and availability of robust clinical HLA tests. This increases the burden on patients and the organizations developing these products. We propose regulatory flexibility to facilitate the development of and access to TCR-based therapeutics.

Signaling mediated by the Epidermal Growth Factor Receptor (EGFR) is crucial in normal development, and aberrant EGFR signaling has been implicated in a wide variety of cancers. Here we find that the high- and low-affinity interactions between EGFR and its ligands activate different signaling pathways. While high-affinity ligand binding is sufficient for activation of most canonical signaling pathways, low-affinity binding is required for the activation of the Signal transducers and activators of transcription (Stats) and Phospholipase C-gamma 1 (PLCγ1). As the Stat proteins are involved in many cellular responses including proliferation, migration and apoptosis, these results assign a function to low-affinity interactions that has been omitted from computational models of EGFR signaling. The existence of receptors with distinct signaling properties provides a way for EGFR to respond to different concentrations of the same ligand in qualitatively different ways.

Activation of the Met receptor tyrosine kinase, either by its ligand, hepatocyte growth factor (HGF), or via ligand-independent mechanisms, such as MET amplification or receptor overexpression, has been implicated in driving tumor proliferation, metastasis, and resistance to therapy. Clinical development of Met-targeted antibodies has been challenging, however, as bivalent antibodies exhibit agonistic properties, whereas monovalent antibodies lack potency and the capacity to down-regulate Met. Through computational modeling, we found that the potency of a monovalent antibody targeting Met could be dramatically improved by introducing a second binding site that recognizes an unrelated, highly expressed antigen on the tumor cell surface. Guided by this prediction, we engineered MM-131, a bispecific antibody that is monovalent for both Met and epithelial cell adhesion molecule (EpCAM). MM-131 is a purely antagonistic antibody that blocks ligand-dependent and ligand-independent Met signaling by inhibiting HGF binding to Met and inducing receptor down-regulation. Together, these mechanisms lead to inhibition of proliferation in Met-driven cancer cells, inhibition of HGF-mediated cancer cell migration, and inhibition of tumor growth in HGF-dependent and -independent mouse xenograft models. Consistent with its design, MM-131 is more potent in EpCAM-high cells than in EpCAM-low cells, and its potency decreases when EpCAM levels are reduced by RNAi. Evaluation of Met, EpCAM, and HGF levels in human tumor samples reveals that EpCAM is expressed at high levels in a wide range of Met-positive tumor types, suggesting a broad opportunity for clinical development of MM-131.