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Abstract TP53 is one of the most ubiquitously altered genes in human cancer. The biological impact of rare variants, particularly those located within noncoding regions, remains poorly understood. From interrogation of clinical massively parallel sequencing data from over 55,000 tumors, which included 23,330 tumors with known TP53 mutations, TP53 intron 4 nucleotide substitutions at position c.375+5G were identified in 45 tumors (0.2% of TP53‐mutated cancers), comprising cancers of different organ sites. Loss‐of‐heterozygosity or a second‐hit somatic TP53 mutation was observed in 34 of 40 (85%) informative cases. RT‐PCR analysis showed the c.375+5G>T variant to be associated with aberrantly spliced TP53 mRNA transcripts with concomitant loss of the normal transcript. Immunohistochemical staining for p53 was performed on a representative subset of tumors with TP53 c.375+5G variants (n = 14), all of which showed loss of protein expression (100%; n = 13 complete loss, n = 1 subclonal loss). Our data are consistent with classification of TP53 c.375+5G variants as deleterious intronic mutations that interfere with proper mRNA splicing, ultimately resulting in loss of expression of functional p53 protein. The clinical scenario of a tumor with loss of p53 immunohistochemical staining, yet lacking a detectable TP53 exonic mutation, should therefore prompt consideration of splice‐altering intronic variants.

Abstract Mucinous carcinomas can arise in any organ with epithelial cells that produce mucus. While mucinous tumors from different organs are histologically similar, it remains to be elucidated whether they share molecular alterations. Here we analyzed a total of 902 patients across six cancer types by comparing mucinous and non-mucinous samples, integrating text mining of pathology reports, gene expression, methylation, mutational and copy-number profiling. We found that, in addition to genes involved in mucin processing and secretion, MUC2 up-regulation is a multi-cancer biomarker of mucinous histology and is regulated by DNA methylation in colorectal, breast and stomach cancer. The majority of carcinomas with mucinous differentiation had fewer DNA copy-number alterations than non-mucinous tumors. The tumor mutational burden was lower in breast and lung with mucinous differentiation compared to their non-mucinous counterparts. We found several differences in the frequency of oncogenic gene and pathway alterations between mucinous and non-mucinous carcinomas, including a lower frequency of p53 pathway alterations in colorectal and lung cancer, and a lower frequency of PI-3-Kinase/Akt pathway alterations in breast and stomach cancer with mucinous differentiation. This study shows that carcinomas with mucinous differentiation originating from different organs share transcriptomic and genomic similarities. These results might pave the way for a more biologically relevant taxonomy for these rare cancers.

As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve patient outcomes. Existing prognostic models mostly consider the impact of biomarkers at presentation on the risk of a single patient outcome at a single follow up time. We collected data for 553 Polymerase Chain Reaction (PCR)-positive COVID-19 patients admitted to hospital whose eventual outcomes were known. The data collected for the patients included demographics, comorbidities and laboratory values taken at admission and throughout the course of hospitalization. We trained multivariate Markov prognostic models to identify high-risk patients at admission along with a dynamic measure of risk incorporating time-dependent changes in patients’ laboratory values. From the set of factors available upon admission, the Markov model determined that age >80 years, history of coronary artery disease and chronic obstructive pulmonary disease increased mortality risk. The lab values upon admission most associated with mortality included neutrophil percentage, red blood cells (RBC), red cell distribution width (RDW), protein levels, platelets count, albumin levels and mean corpuscular hemoglobin concentration (MCHC). Incorporating dynamic changes in lab values throughout hospitalization lead to dramatic gains in the predictive accuracy of the model and indicated a catalogue of variables for determining high-risk patients including eosinophil percentage, white blood cells (WBC), platelets, pCO2, RDW, large unstained cells (LUC) count, alkaline phosphatase and albumin. Our prognostic model highlights the nuance of determining risk for COVID-19 patients and indicates that, rather than a single variable, a range of factors (at different points in hospitalization) are needed for effective risk stratification.

A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient's hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. Our main focus was to quantify the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation, by so doing, to explain the observed patterns of mortality. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.3% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (78.7%) versus those who died (61.8%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.

Lithium metal anodes have again attracted widespread attention due to the continuously growing demand of cells with higher energy density. However, the lithium deposition mechanism and the affecting process of influencing factors, such as temperature, cycling current density, and electrolyte composition are not fully understood and require further investigation. In this article, the behavior of lithium metal anode at different temperatures (25, 40, and 60 ∘C), lithium salts, electrolyte concentrations (1 and 2 M), and the applied cell current (equivalent to 0.5 C, 1 C, and 2 C). is investigated. Two different salts were evaluated: lithium bis(fluorosulfonyl)imide (LiFSI) and lithium bis(trifluoromethanesul-fonyl)imide (LiTFSI). The cells at a medium temperature (40 ∘C) show the highest Coulombic efficiency (CE). However, shorter cycle life is observed compared to the experiments at room temperature (25 ∘C). Regardless of electrolyte type and C-rate, the higher temperature of 60 ∘C provides the worst Coulombic efficiency and cycle life among those at the examined temperatures. A higher C-rate has a positive effect on the stability over the cycle life of the lithium cells. The best performance in terms of long cycle life and relatively good Coulombic efficiency is achieved by fast charging the cell with high concentration LiFSI in 1,2-dimethoxyethane (DME) electrolyte at a temperature of 25 ∘C. The cell has an average Coulombic efficiency of 0.987 over 223 cycles. In addition to galvanostatic experiments, Electrochemical Impedance Spectroscopy (EIS) measurements were performed to study the evolution of the interface under different conditions during cycling.

Trastuzumab has demonstrated clinical efficacy in the treatment of HER2‐positive serous endometrial cancer (EC), which led to its incorporation into standard‐of‐care management of this aggressive disease. Acquired resistance remains an important challenge, however, and its underlying mechanisms in EC are unknown. To define the molecular changes that occur in response to anti‐HER2 therapy in EC, targeted next‐generation sequencing (NGS), HER2 immunohistochemistry (IHC), and fluorescence in situ hybridization (FISH) were performed on pre‐ and post‐treatment tumour samples from 14 patients with EC treated with trastuzumab or trastuzumab emtansine. Recurrent tumours after anti‐HER2 therapy acquired additional genetic alterations compared with matched pre‐treatment ECs and frequently showed decreased HER2 protein expression by IHC (7/14, 50%). Complete/near‐complete absence of HER2 protein expression (score 0/1+) observed post‐treatment (4/14, 29%) was associated with retained HER2 gene amplification (n = 3) or copy number neutral status (n = 1). Whole‐exome sequencing performed on primary and recurrent tumours from the latter case, which exhibited genetic heterogeneity of HER2 amplification in the primary tumour, revealed selection of an early HER2‐non‐amplified clone following therapy. Our findings demonstrate that loss of target expression, by selection of HER2‐non‐amplified clones or, more commonly, by downregulation of expression, may constitute a mechanism of resistance to anti‐HER2 therapy in HER2‐positive EC. © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]