Metabolomics studies to date have described widespread metabolic reprogramming events during the development of non-squamous non-small cell lung cancer (NSCLC). Extending far beyond the Warburg effect, not only is carbohydrate metabolism affected, but also metabolism of amino acids, cofactors, lipids, and nucleotides.We evaluated the clinical impact of metabolic reprogramming. We performed comparative analysis of publicly available data on non-squamous NSCLC, to identify concensus altered metabolic pathways. We investigated whether alterations of metabolic genes controlling those consensus metabolic pathways impacted clinical outcome. Using the clinically annotated lung adenocarcinoma (LUAD) cohort from The Cancer Genome Atlas, we surveyed the distribution and frequency of function-altering mutations in metabolic genes and their impact on overall survival (OS).We identified 42 metabolic genes of clinical significance, the majority of which (37 of 42) clustered across three metabolic superpathways (carbohydrates, amino acids, and nucleotides) and most functions (40 of 42) were associated with shorter OS. Multivariate analyses showed that dysfunction of carbohydrate metabolism had the most profound impact on OS [hazard ratio (HR) =5.208; 95% confidence interval (CI): 3.272 to 8.291], false discovery rate (FDR)-P≤0.0001, followed by amino acid metabolism (HR =3.346; 95% CI: 2.129 to 5.258), FDR-P≤0.0001 and nucleotide metabolism (HR =2.578; 95% CI: 1.598 to 4.159), FDR-P=0.0001. The deleterious effect of metabolic reprogramming on non-squamous NSCLC was observed independently of disease stage and across treatments groups.By providing a detailed landscape of metabolic alterations in non-squamous NSCLC, our findings offer new insights in the biology of the disease and metabolic adaptation mechanisms of clinical significance.
Drug-induced liver injury (DILI) with features of autoimmunity (AI) represents an important category of hepatotoxicity due to medication exposure. Drugs repeatedly associated with AI-DILI include diclofenac, α-methyl DOPA, hydralazine, nitrofurantoin, minocycline, and more recently statins and anti-TNF-α agents. Usually, symptoms of acute liver injury occur within a few months after initiation of a culprit medication, but a longer latency period is possible. Like idiopathic autoimmune hepatitis, circulating autoantibodies and a hypergammaglobulinemia are frequently present in sera from patients with AI-DILI. If performed, a liver biopsy should demonstrate interface hepatitis with a prominent plasma cell infiltrate. The severity of AI-DILI is variable, but a complete resolution after withdrawal of the offending medication is the expectation. A response to corticosteroid therapy supports the diagnosis, whereas a lack of recurrence of symptoms or signs following corticosteroid cessation distinguishes AI-DILI from idiopathic autoimmune hepatitis.
Abstract Identification of early immune signatures associated with acute myeloid leukemia (AML) relapse following hematopoietic stem cell transplant (HSCT) is critical for patient outcomes. We analyzed PBMCs from 58 patients with AML undergoing HSCT, focusing on T cell subsets and functional profiles. High-dimensional flow cytometry coupled with Uniform Manifold Approximation and Projection dimensionality reduction and PhenoGraph clustering revealed distinct changes in CD4+ and CD8+ T cell populations in 16 patients who relapsed within 1 y of HSCT. We observed increased IL-2, IL-10, and IL-17–producing CD4+ T cells, alongside decreased CD8+ T cell function early in relapsing patients. Notably, relapsing patients exhibited increased TCF-1intermediate cells, which lacked granzyme B or IFN-γ production in the CD4+ T cell compartment. We then developed a supervised machine learning algorithm that predicted AML relapse with 90% accuracy within 30 d after HSCT using high-throughput assays. The algorithm leverages condensed immune phenotypic data, alongside the ADASYN algorithm, for data balancing and 100 rounds of XGBoost supervised learning. This approach holds potential for detecting relapse-associated immune signatures months before clinical manifestation. Our findings demonstrate a distinct immunological signature potentially capable of predicting AML relapse as early as 30 d after HSCT.
