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The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcusbromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

AbstractAmyloid fibrillation is the root cause of several neuro as well as non-neurological disorders. Understanding the molecular basis of amyloid aggregate formation is crucial for deciphering various neurodegenerative diseases. In our study, we have examined the lysozyme fibrillation process using nano-infrared spectroscopy (nanoIR). NanoIR enabled us to investigate both structural and chemical characteristics of lysozyme fibrillar species concurrently. The spectroscopic results indicate that lysozyme transformed into a fibrillar structure having mainly parallel β-sheets, with almost no antiparallel β-sheets. Features such as protein stiffness have a good correlation with obtained secondary structural information showing the state of the protein within the fibrillation state. The structural and chemical details were compared with transmission electron microscopy (TEM) and circular dichroism (CD). We have utilized nanoIR and measured infrared spectra to characterize lysozyme amyloid fibril structures in terms of morphology, molecular structure, secondary structure content, stability, and size of the cross-β core. We have shown that the use of nanoIR can complement other biophysical studies to analyze the aggregation process and is particularly useful for studying proteins involved in aggregation to help in designing molecules against amyloid aggregation. Specifically, the nanoIR spectra afford higher resolution information and a characteristic fingerprint for determining states of aggregation.Communicated by Ramaswamy H. Sarma

Data-driven approaches for materials design and selection have accelerated materials discovery along with the upsurge of machine learning applications. We report here a prediction-to-lab-scale synthesis of cubic phase triple-cation lead halide perovskites guided by a machine learning perovskite stability predictor. The starting double-cation perovskite resulting from the incorporation of 15% dimethylammonium (DMA) in methylammonium lead triiodide suffers from significant deviation from the perovskite structure. By analyzing the X-ray diffraction and scanning electron microscopy, we confirmed that it is possible to recover the perovskite structure with the cubic phase at room temperature (RT) while minimizing the iterations of trial-and-error by adding <10 mol % of cesium cation additives, as guided by the machine learning predictor. Our conclusions highly support the cubic-phase stabilization at RT by controlling the stoichiometric ratio of various sized cations. This prediction-to-lab-scale synthesis approach also enables us to identify room for improvements of the current machine learning predictor to take into consideration the cubic phase stability as well as phase segregation.

Forecasting the structural stability of hybrid organic/inorganic compounds, where polyatomic molecules replace atoms, is a challenging task; the composition space is vast, and the reference structure for the organic molecules is ambiguously defined. In this work, we use a range of machine-learning algorithms, constructed from state-of-the-art density functional theory data, to conduct a systematic analysis on the likelihood of a given cation to be housed in the perovskite structure. In particular, we consider both ABC3chalcogenide (I–V–VI3) and halide (I–II–VII3) perovskites. We find that the effective atomic radius and the number of lone pairs residing on the A-site cation are sufficient features to describe the perovskite phase stability. Thus, the presented machine-learning approach provides an efficient way to map the phase stability of the vast class of compounds, including situations where a cation mixture replaces a single A-site cation. This work demonstrates that advanced electronic structure theory combined with machine-learning analysis can provide an efficient strategy superior to the conventional trial-and-error approach in materials design.

Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrelated) imputation methods is well established. However, the performance of family- and population-based imputation methods on family data has been subject to much less scrutiny. Here, we extensively compare several family- and population-based imputation methods on family data of large pedigrees with both European and African ancestry. Our comparison includes many widely used family- and population-based tools and another method, Ped_Pop, which combines family- and population-based imputation results. We also compare four subject selection strategies for full sequencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Moreover, we compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation R2for predicting power of association analysis using imputation results. Our results show that (1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for rare variants but not for common ones; (3) combining family- and population-based imputation outperforms all imputation approaches for all minor allele frequencies; (4) GIGI-Pick gives the best selection strategy based on the R2criterion; and (5) R2is the best measure of imputation accuracy. Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools on the same family data and provides guidelines for future studies.

Chromosomal translocations that generate in-frame oncogenic gene fusions are notable examples of the success of targeted cancer therapies. We have previously described gene fusions of FGFR3-TACC3 (F3–T3) in 3% of human glioblastoma cases. Subsequent studies have reported similar frequencies of F3–T3 in many other cancers, indicating that F3–T3 is a commonly occuring fusion across all tumour types. F3–T3 fusions are potent oncogenes that confer sensitivity to FGFR inhibitors, but the downstream oncogenic signalling pathways remain unknown. Here we show that human tumours with F3–T3 fusions cluster within transcriptional subgroups that are characterized by the activation of mitochondrial functions. F3–T3 activates oxidative phosphorylation and mitochondrial biogenesis and induces sensitivity to inhibitors of oxidative metabolism. Phosphorylation of the phosphopeptide PIN4 is an intermediate step in the signalling pathway of the activation of mitochondrial metabolism. The F3–T3–PIN4 axis triggers the biogenesis of peroxisomes and the synthesis of new proteins. The anabolic response converges on the PGC1α coactivator through the production of intracellular reactive oxygen species, which enables mitochondrial respiration and tumour growth. These data illustrate the oncogenic circuit engaged by F3–T3 and show that F3–T3-positive tumours rely on mitochondrial respiration, highlighting this pathway as a therapeutic opportunity for the treatment of tumours with F3–T3 fusions. We also provide insights into the genetic alterations that initiate the chain of metabolic responses that drive mitochondrial metabolism in cancer.

Type I interferons (IFNs) are essential innate immune proteins that maintain tissue homeostasis through tonic expression and can be upregulated to drive antiviral resistance and inflammation upon stimulation. However, the mechanisms that inhibit aberrant IFN upregulation in homeostasis and the impacts of tonic IFN production on health and disease remain enigmatic. Here, we report that caspase-8 negatively regulates type I IFN production by inhibiting the RIPK1-TBK1 axis during homeostasis across multiple cell types and tissues. When caspase-8 is deleted or inhibited, RIPK1 interacts with TBK1 to drive elevated IFN production, leading to heightened resistance to norovirus infection in macrophages but also early onset lymphadenopathy in mice. Combined deletion of caspase-8 and RIPK1 reduces the type I IFN signaling and lymphadenopathy, highlighting the critical role of RIPK1 in this process. Overall, our study identifies a mechanism to constrain tonic type I IFN during homeostasis which could be targeted for infectious and inflammatory diseases.

Least squares support vector machines (LSSVMs) have been widely applied for classification and regression with comparable performance with SVMs. The LSSVM model lacks sparsity and is unable to handle large-scale data due to computational and memory constraints. A primal fixed-size LSSVM (PFS-LSSVM) introduce sparsity using Nyström approximation with a set of prototype vectors (PVs). The PFS-LSSVM model solves an overdetermined system of linear equations in the primal. However, this solution is not the sparsest. We investigate the sparsity-error tradeoff by introducing a second level of sparsity. This is done by means of L0 -norm-based reductions by iteratively sparsifying LSSVM and PFS-LSSVM models. The exact choice of the cardinality for the initial PV set is not important then as the final model is highly sparse. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to LSSVM models. The approximations of the two models allow to scale the models to large-scale datasets. Experiments on real-world classification and regression data sets from the UCI repository illustrate that these approaches achieve sparse models without a significant tradeoff in errors.

We propose a novel algorithm, FURS (Fast and Unique Representative Subset selection) to deterministically select a set of nodes from a given graph which retains the underlying community structure. FURS greedily selects nodes with high-degree centrality from most or all the communities in the network. The nodes with high-degree centrality for each community are usually located at the center rather than the periphery and can better capture the community structure. The nodes are selected such that they are not isolated but can form disconnected components. The FURS is evaluated by quality measures, such as coverage, clustering coefficients, degree distributions and variation of information. Empirically, we observe that the nodes are selected such that most or all of the communities in the original network are retained. We compare our proposed technique with state-of-the-art methods like SlashBurn, Forest-Fire, Metropolis and Snowball Expansion sampling techniques. We evaluate FURS on several synthetic and real-world networks of varying size to demonstrate the high quality of our subset while preserving the community structure. The subset generated by the FURS method can be effectively utilized by model-based approaches with out-of-sample extension properties for inferring community affiliation of the large-scale networks. A consequence of FURS is that the selected subset is also a good candidate set for simple diffusion model. We compare the spread of information over time using FURS for several real-world networks with random node selection, hubs selection, spokes selection, high eigenvector centrality, high Pagerank, high betweenness centrality and low betweenness centrality-based representative subset selection.