Neoantigen quality predicts immunoediting in survivors of pancreatic cancer.

Cancer immunoediting1 is a hallmark of cancer2 that predicts that lymphocytes kill more immunogenic cancer cells to cause less immunogenic clones to dominate a population. Although proven in mice1,3, whether immunoediting occurs naturally in human cancers remains unclear. Here, to address this, we investigate how 70 human pancreatic cancers evolved over 10 years. We find that, despite having more time to accumulate mutations, rare long-term survivors of pancreatic cancer who have stronger T cell activity in primary tumours develop genetically less heterogeneous recurrent tumours with fewer immunogenic mutations (neoantigens). To quantify whether immunoediting underlies these observations, we infer that a neoantigen is immunogenic (high-quality) by two features-'non-selfness'  based on neoantigen similarity to known antigens4,5, and 'selfness'  based on the antigenic distance required for a neoantigen to differentially bind to the MHC or activate a T cell compared with its wild-type peptide. Using these features, we estimate cancer clone fitness as the aggregate cost of T cells recognizing high-quality neoantigens offset by gains from oncogenic mutations. With this model, we predict the clonal evolution of tumours to reveal that long-term survivors of pancreatic cancer develop recurrent tumours with fewer high-quality neoantigens. Thus, we submit evidence that that the human immune system naturally edits neoantigens. Furthermore, we present a model to predict how immune pressure induces cancer cell populations to evolve over time. More broadly, our results argue that the immune system fundamentally surveils host genetic changes to suppress cancer

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that na\"ive pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization

Reaction Dynamics of Rocket Propellant with Magnesium Oxide Nanoparticles

The combustion behavior of rocket propellant grade 2 (RP-2) was investigated as a function of magnesium oxide (MgO) nanoparticles (i.e., 20 nm diameter) added at varied concentrations. The MgO nanoparticles were surface-treated with a long-chain carboxylic acid to aid their dispersion in RP-2. The fuel droplet regression rate, surface tension, and heat of combustion of RP-2 with MgO nanoparticle additives were measured to characterize combustion behavior. Heat of combustion and surface tension measurements varied negligibly among all samples indicating that calorific output and surface tension are not controlling parameters influencing fuel combustion behavior. However, fuel droplet regression rates were considerably increased by adding 0.5 wt % MgO from 0.225 to 66.16 mm/s, which is an improvement by 2 orders of magnitude. Further analysis showed that MgO particles enhance diffusive heat transfer, which promotes nucleation and disruptive burning throughout the three stages of regression, heating/evaporation (stage 1), combustion of RP-2 (stage 2), and combustion of carboxylic acid dispersant (stage 3), and, thus, lead to improved fuel droplet combustion

Germ cell tumors and associated hematologic malignancies evolve from a common shared precursor

Germ cell tumors (GCTs) are the most common cancer in men between the ages of 15 and 40. Although most patients are cured, those with disease arising in the mediastinum have distinctly poor outcomes. One in every 17 patients with primary mediastinal nonseminomatous GCTs develop an incurable hematologic malignancy and prior data intriguingly suggest a clonal relationship exists between hematologic malignancies and GCTs in these cases. To date, however, the precise clonal relationship between GCTs and the diverse additional somatic malignancies arising in such individuals have not been determined. Here, we traced the clonal evolution and characterized the genetic features of each neoplasm from a cohort of 15 patients with GCTs and associated hematologic malignancies. We discovered that GCTs and hematologic malignancies developing in such individuals evolved from a common shared precursor, nearly all of which harbored allelically imbalanced p53 and/or RAS pathway mutations. Hematologic malignancies arising in this setting genetically resembled mediastinal GCTs rather than de novo myeloid neoplasms. Our findings argue that this scenario represents a unique clinical syndrome, distinct from de novo GCTs or hematologic malignancies, initiated by an ancestral precursor that gives rise to the parallel evolution of GCTs and blood cancers in these patients