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Summary: Immune‐related toxicities, otherwise known as immune‐related adverse events (irAEs), occur in a substantial fraction of cancer patients treated with immune checkpoint inhibitors (ICIs). Ranging from asymptomatic to life‐threatening, ICI‐induced irAEs can result in hospital admission, high‐dose corticosteroid treatment, ICI discontinuation, and in some cases, death. A deeper understanding of the factors underpinning severe irAE development will be essential for improved irAE prediction and prevention, toward maximizing the benefits and safety profiles of ICIs. In recent work, we applied mass cytometry, single‐cell RNA sequencing, single‐cell V(D)J sequencing, bulk RNA sequencing, and bulk T‐cell receptor (TCR) sequencing to identify pretreatment determinants of severe irAE development in patients with advanced melanoma. Across 71 patients separated into three cohorts, we found that two baseline features in circulation—elevated activated CD4 effector memory T‐cell abundance and TCR diversity—are associated with severe irAE development, independent of the affected organ system within 3 months of ICI treatment initiation. Here, we provide an extended perspective on this work, synthesize and discuss related literature, and summarize practical considerations for clinical translation. Collectively, these findings lay a foundation for data‐driven and mechanistic insights into irAE development, with the potential to reduce ICI morbidity and mortality in the future. [ABSTRACT FROM AUTHOR]

Considering that the current cost control algorithm has the problems of poor control effect and high cost of manpower and material resources in enterprise cost control, a target cost control algorithm based on Hypercycle model is proposed. By analyzing the relationship between capital, price, technology and target cost control, this paper analyzes the influence of uncertain factors on target cost, and constructs a cost control super cycle model to realize enterprise cost control. Examples show that the algorithm can effectively improve the cost effect of enterprises, and has the practicability of reducing enterprise costs and improving enterprise profits. [ABSTRACT FROM AUTHOR]

Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution. CytoSPACE maps individual cells from a reference single-cell RNA sequencing atlas to spatial transcriptomics data. [ABSTRACT FROM AUTHOR]

Despite growing numbers of immune checkpoint blockade (ICB) trials with available omics data, it remains challenging to evaluate the robustness of ICB response and immune evasion mechanisms comprehensively. To address these challenges, we integrated large-scale omics data and biomarkers on published ICB trials, non-immunotherapy tumor profiles, and CRISPR screens on a web platform TIDE (http://tide.dfci.harvard.edu). We processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these data on the TIDE web platform with three interactive analysis modules, we demonstrate the utility of public data reuse in hypothesis generation, biomarker optimization, and patient stratification. [ABSTRACT FROM AUTHOR]

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis -effects and distal trans -effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types. [Display omitted] • Multi-omic clusters reveal shared oncogenic driver pathways across ten cancer types • Genetic changes correlate with altered, tumor-specific protein-protein interactions • cis / trans -effects and kinase activities show driver heterogeneity and druggability • Proteomic integration with genomic drivers resolves distinct cancer hallmark patterns A multi-omics analysis-based resource across ten cancer types from more than 1,000 patients provides pan-cancer insights into shared oncogenic driver mechanisms and pathways. [ABSTRACT FROM AUTHOR]

Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample. [ABSTRACT FROM AUTHOR]

Nitrosomonas europaea , an ammonia oxidizing bacterium, was chosen as a research model to study the alteration of cell membrane in the presence of sulfurous acid and biodegradation of acetochlor. Significant changes of the outer cell membrane were observed in the presence of sulfurous acid using scanning electron microscopy (SEM) and Atomic Force Microscopy (AFM). The fluorescence polarization has shown a significant decrease in membrane fluidity and the increase of permeability of cell membrane. Lysozyme experiment show the cell becomes easily influenced by substance in medium. Inductively Coupled Plasma–Mass Spectrometry (ICP–MS) measurements show considerable amount of Ca 2+ and Mg 2+ in the supernatant from the sulfurous acid exposed cells. Sulfurous acid treatment enhanced the ability of N. europaea to degrade acetochlor. On this basis, it can be concluded that the increased cell permeability is favor for the absorbability of nutrition. As a result, N. europaea grows faster and the biodegradation efficiency was improved. [ABSTRACT FROM AUTHOR]

The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel cancer therapies, but the precise UPS genes and substrates important for cancer growth are incompletely understood. Leveraging multi-omics data across more than 9,000 human tumors and 33 cancer types, we found that over 19% of all cancer driver genes affect UPS function. We implicate transcription factors as important substrates and show that c-Myc stability is modulated by CUL3. Moreover, we developed a deep learning model (deepDegron) to identify mutations that result in degron loss and experimentally validated the prediction that gain-of-function truncating mutations in GATA3 and PPM1D result in increased protein stability. Last, we identified UPS driver genes associated with prognosis and the tumor microenvironment. This study demonstrates the important role of UPS dysregulation in human cancer and underscores the potential therapeutic utility of targeting the UPS. [Display omitted] • The ubiquitin-proteasome system (UPS) represents ∼19% of mutated cancer driver genes • A machine learning approach, deepDegron, reveals de novo degron motifs • Truncating mutations in GATA3 and PPM1D increase protein expression via degron loss • ChIP-seq data can help infer transcription factor substrates of mutated UPS in cancer The mechanisms underlying oncogenic mutations in cancer remain incompletely understood. By leveraging machine learning, Tokheim et al. find that ∼19% of cancer driver genes affect protein degradation, systematically revealing transcription factors as important substrates. They also validate an unconventional role of truncating mutations to increase stability of GATA3 and PPM1D. [ABSTRACT FROM AUTHOR]