Michael Jendrusch

The immune system can recognize and attack cancer cells, especially those with a high load of mutation-induced neoantigens. Such neoantigens are abundant in DNA mismatch repair (MMR)-deficient, microsatellite-unstable (MSI) cancers. MMR deficiency leads to insertion/deletion (indel) mutations at coding microsatellites (cMS) and to neoantigen-inducing translational frameshifts. Here, we develop a tool to quantify frameshift mutations in MSI colorectal and endometrial cancer. Our results show that frameshift mutation frequency is negatively correlated to the predicted immunogenicity of the resulting peptides, suggesting counterselection of cell clones with highly immunogenic frameshift peptides. This correlation is absent in tumors with Beta-2-microglobulin mutations, and HLA-A*02:01 status is related to cMS mutation patterns. Importantly, certain outlier mutations are common in MSI cancers despite being related to frameshift peptides with functionally confirmed immunogenicity, suggesting a possible driver role during MSI tumor evolution. Neoantigens resulting from shared mutations represent promising vaccine candidates for prevention of MSI cancers.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection of MSI and other biomarkers. We hypothesized that histology images synthesized by conditional generative adversarial networks (CGANs) retain information about genetic alterations. To test this, we developed a 'histology CGAN' which was trained on 256 patients (training cohort 1) and 1457 patients (training cohort 2). The CGAN synthesized 10 000 synthetic MSI and non-MSI images which contained a range of tissue types and were deemed realistic by trained observers in a blinded study. Subsequently, we trained a deep learning detector of MSI on real or synthetic images and evaluated the performance of MSI detection in a held-out set of 142 patients. When trained on real images from training cohort 1, this system achieved an area under the receiver operating curve (AUROC) of 0.742 [0.681, 0.854]. Training on the larger cohort 2 only marginally improved the AUROC to 0.757 [0.707, 0.869]. Training on purely synthetic data resulted in an AUROC of 0.743 [0.658, 0.801]. Training on both real and synthetic data further increased AUROC to 0.777 [0.715, 0.821]. We conclude that synthetic histology images retain information reflecting underlying genetic alterations in colorectal cancer. Using synthetic instead of real images to train deep learning systems yields non-inferior classifiers. This approach can be used to create large shareable data sets or to augment small data sets with rare molecular features. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

De novo protein design is a longstanding fundamental goal of synthetic biology, but has been hindered by the difficulty in reliable prediction of accurate high-resolution protein structures from sequence. Recent advances in the accuracy of protein structure prediction methods, such as AlphaFold (AF), have facilitated proteome scale structural predictions of monomeric proteins. Here we develop AlphaDesign, a computational framework for de novo protein design that embeds AF as an oracle within an optimisable design process. Our framework enables rapid prediction of completely novel protein monomers starting from random sequences. These are shown to adopt a diverse array of folds within the known protein space. A recent and unexpected utility of AF to predict the structure of protein complexes, further allows our framework to design higher-order complexes. Subsequently a range of predictions are made for monomers, homodimers, heterodimers as well as higher-order homo-oligomers - trimers to hexamers. Our analyses also show potential for designing proteins that bind to a pre-specified target protein. Structural integrity of predicted structures is validated and confirmed by standard ab initio folding and structural analysis methods as well as more extensively by performing rigorous all-atom molecular dynamics simulations and analysing the corresponding structural flexibility, intramonomer and interfacial amino-acid contacts. These analyses demonstrate widespread maintenance of structural integrity and suggests that our framework allows for fairly accurate protein design. Strikingly, our approach also reveals the capacity of AF to predict proteins that switch conformation upon complex formation, such as involving switches from α -helices to β -sheets during amyloid filament formation. Correspondingly, when integrated into our design framework, our approach reveals de novo design of a subset of proteins that switch conformation between monomeric and oligomeric state.

Electrical impedance is an established technique used for cell and particle characterization. The temporal and spectral resolution of electrical impedance have been used to resolve basic cell characteristics like size and type, as well as to determine cell viability and activity. Such electrical impedance measurements are typically performed across the entire sample volume and can only provide an overall indication concerning the properties and state of that sample. For the study of heterogeneous structures such as cell layers, biological tissue, or polydisperse particle mixtures, an overall measured impedance value can only provide limited information and can lead to data misinterpretation. For the investigation of localized sample properties in complex heterogeneous structures/mixtures, the addition of spatial resolution to impedance measurements is necessary. Several spatially resolved impedance measurement techniques have been developed and applied to cell and particle research, including electrical impedance tomography, scanning electrochemical microscopy, and microelectrode arrays. This review provides an overview of spatially resolved impedance measurement methods and assesses their applicability for cell and particle characterization.