Anti-cancer immunotherapy is encountering its own checkpoint. Responses are dramatic and long lasting but occur in a subset of tumors and are largely dependent upon the pre-existing immune contexture of individual cancers. Available data suggest that three landscapes best define the cancer microenvironment: immune-active, immune-deserted and immune-excluded. This trichotomy is observable across most solid tumors (although the frequency of each landscape varies depending on tumor tissue of origin) and is associated with cancer prognosis and response to checkpoint inhibitor therapy (CIT). Various gene signatures (e.g. Immunological Constant of Rejection - ICR and Tumor Inflammation Signature - TIS) that delineate these landscapes have been described by different groups. In an effort to explain the mechanisms of cancer immune responsiveness or resistance to CIT, several models have been proposed that are loosely associated with the three landscapes. Here, we propose a strategy to integrate compelling data from various paradigms into a “Theory of Everything”. Founded upon this unified theory, we also propose the creation of a task force led by the Society for Immunotherapy of Cancer (SITC) aimed at systematically addressing salient questions relevant to cancer immune responsiveness and immune evasion. This multidisciplinary effort will encompass aspects of genetics, tumor cell biology, and immunology that are pertinent to the understanding of this multifaceted problem.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0355-5) contains supplementary material, which is available to authorized users.

In this contribution we analyse the probability distribution of rare first passage times corresponding to transitions between product and reactant states in a kinetic transition network. The mean first passage times and corresponding rate constants are analysed in detail for two model landscapes and the double funnel landscape corresponding to an atomic cluster. Evaluation schemes based on eigendecomposition and kinetic path sampling, which both allow access to the passage time distribution, are benchmarked against mean first passage times calculated used graph transformation. Numerical precision issues severely limit the useful temperature range for eigendecomposition, but kinetic path sampling is capable of extending the first passage time analysis to lower temperatures, where the kinetics of interest constitute rare events. We then investigate the influence of free energy based state regrouping schemes for the underlying network. Alternative formulations of the effective transition rates for a given regrouping are compared in detail, to determine their numerical stability and capability to reproduce the true kinetics, including recent coarse-graining approaches that preserve occupancy cross correlation functions. We find that appropriate regrouping of states under the simplest local equilibrium approximation can provide reduced transition networks with useful accuracy at somewhat lower temperatures. Finally, a method is provided to systematically interpolate between the local equilibrium approximation and exact intergroup dynamics. Spectral analysis is applied to each grouping of states, employing a moment-based mode selection criteria to produce a reduced state space, which does not require any spectral gap to exist, but reduces to gap-based coarse graining as a special case. Implementations of the developed methods are freely available online.

Dysregulated activity of the protease matriptase is a key contributor to aggressive tumor growth, cancer metastasis, and osteoarthritis. Methods for the detection and quantification of matriptase activity and inhibition would be useful tools. To address this need, we developed a matriptase-sensitive protein biosensor based on a dimerization-dependent red fluorescent protein (ddRFP) reporter system. In this platform, two adjoining protein domains, connected by a protease-labile linker, produce fluorescence when assembled and are nonfluorescent when the linker is cleaved by matriptase. A panel of ddRFP-based matriptase biosensor designs was created that contained different linker lengths between the protein domains. These constructs were characterized for linker-specific cleavage, matriptase activity, and matriptase selectivity; a biosensor containing a RSKLRVGGH linker (termed B4) was expressed at high yields and displayed both high catalytic efficiency and matriptase specificity. This biosensor detects matriptase inhibition by soluble and yeast cell surface expressed inhibitor domains with up to a 5-fold dynamic range and also detects matriptase activity expressed by human cancer cell lines. In addition to matriptase, we highlight a strategy that can be used to create effective biosensors for quantifying activity and inhibition of other proteases of interest.

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Discrete state Markov chains in discrete or continuous time are widely used to model phenomena in the social, physical and life sciences. In many cases, the model can feature a large state space, with extreme differences between the fastest and slowest transition timescales. Analysis of such ill-conditioned models is often intractable with finite precision linear algebra techniques. In this contribution, we propose a solution to this problem, namely partial graph transformation, to iteratively eliminate and renormalize states, producing a low-rank Markov chain from an ill-conditioned initial model. We show that the error induced by this procedure can be minimized by retaining both the renormalized nodes that represent metastable superbasins, and those through which reactive pathways concentrate, i.e. the dividing surface in the discrete state space. This procedure typically returns a much lower rank model, where trajectories can be efficiently generated with kinetic path sampling. We apply this approach to an ill-conditioned Markov chain for a model multi-community system, measuring the accuracy by direct comparison with trajectories and transition statistics. This article is part of a discussion meeting issue ‘Supercomputing simulations of advanced materials’.