The need for a better classification system for gastric neoplasms
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Comprehensive discovery and functional characterization of the noncanonical proteome
The systematic identification and functional characterization of noncanonical translation products, such as novel peptides, will facilitate the understanding of the human genome and provide new insights into cell biology. Here, we constructed a high-coverage peptide sequencing reference library with 11,668,944 open reading frames and employed an ultrafiltration tandem mass spectrometry assay to identify novel peptides. Through these methods, we discovered 8945 previously unannotated peptides from normal gastric tissues, gastric cancer tissues and cell lines, nearly half of which were derived from noncoding RNAs. Moreover, our CRISPR screening revealed that 1161 peptides are involved in tumor cell proliferation. The presence and physiological function of a subset of these peptides, selected based on screening scores, amino acid length, and various indicators, were verified through Flag-knockin and multiple other methods. To further characterize the potential regulatory mechanisms involved, we constructed a framework based on artificial intelligence structure prediction and peptide‒protein interaction network analysis for the top 100 candidates and revealed that these cancer-related peptides have diverse subcellular locations and participate in organelle-specific processes. Further investigation verified the interacting partners of pep1-nc-OLMALINC, pep5-nc-TRHDE-AS1, pep-nc-ZNF436-AS1 and pep2-nc-AC027045.3, and the functions of these peptides in mitochondrial complex assembly, energy metabolism, and cholesterol metabolism, respectively. We showed that pep5-nc-TRHDE-AS1 and pep2-nc-AC027045.3 had substantial impacts on tumor growth in xenograft models. Furthermore, the dysregulation of these four peptides is closely correlated with clinical prognosis. Taken together, our study provides a comprehensive characterization of the noncanonical proteome, and highlights critical roles of these previously unannotated peptides in cancer biology.
Myeloid neoplasms with PHF6 mutations: context-dependent genomic and prognostic characterization in 176 informative cases
Recent reports suggest a favorable prognosis for PHF6 mutation (PHF6MUT) in chronic myelomonocytic leukemia (CMML) and unfavorable in acute myeloid leukemia (AML). We accessed 176 consecutive patients with a spectrum of myeloid neoplasms with PHF6MUT, including AML (N = 67), CMML (N = 49), myelodysplastic syndromes (MDS; N = 36), myeloproliferative neoplasms (MPN; N = 16), and MDS/MPN (N = 8). PHF6 mutations were classified as nonsense (43%) or frameshift (30%) with the PHD2 domain being the most frequently (64%) affected region. Median follow-up was 25 months with 110 (63%) deaths and 44 allogenic transplants. Our top-line observations include (a) a distinctly superior overall survival (OS; 81 vs. 18 months; p < 0.01) and blast transformation-free survival (BTFS; “not reached” vs. 44 months; p < 0.01) in patients with CMML vs. those with other myeloid neoplasms, (ii) a higher than expected frequency of isolated loss of Y chromosome, in the setting of CMML (16% vs. expected 6%) and MDS (8% vs expected 2.5%), (iii) a significant association, in MDS, between PHF6MUT variant allele fraction (VAF) > 20% and inferior OS (HR 3.0, 95% CI 1.1–8.1, multivariate p = 0.02) as well as female gender and inferior BTFS (HR 26.8, 95% CI 1.9–368.3, multivariate p = 0.01), (iv) a relatively favorable median post-transplant survival of 46 months. Multivariable analysis also identified high-risk karyotype (HR 5.1, 95% CI 1.2–20.9, p = 0.02), and hemoglobin <10 g/dL (HR 2.7, 95% CI 1.0–7.2, p = 0.04), as independent predictors of inferior OS in patients with MDS. The current study provides disease-specific information on genotype and prognosis of PHF6-mutated myeloid neoplasms.
Rapid brain tumor classification from sparse epigenomic data
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.
Raptin, a sleep-induced hypothalamic hormone, suppresses appetite and obesity
Sleep deficiency is associated with obesity, but the mechanisms underlying this connection remain unclear. Here, we identify a sleep-inducible hypothalamic protein hormone in humans and mice that suppresses obesity. This hormone is cleaved from reticulocalbin-2 (RCN2), and we name it Raptin. Raptin release is timed by the circuit from vasopressin-expressing neurons in the suprachiasmatic nucleus to RCN2-positive neurons in the paraventricular nucleus. Raptin levels peak during sleep, which is blunted by sleep deficiency. Raptin binds to glutamate metabotropic receptor 3 (GRM3) in neurons of the hypothalamus and stomach to inhibit appetite and gastric emptying, respectively. Raptin-GRM3 signaling mediates anorexigenic effects via PI3K-AKT signaling. Of note, we verify the connections between deficiencies in the sleeping state, impaired Raptin release, and obesity in patients with sleep deficiency. Moreover, humans carrying an RCN2 nonsense variant present with night eating syndrome and obesity. These data define a unique hormone that suppresses food intake and prevents obesity.
MIML: multiplex image machine learning for high precision cell classification via mechanical traits within microfluidic systems
Label-free cell classification is advantageous for supplying pristine cells for further use or examination, yet existing techniques frequently fall short in terms of specificity and speed. In this study, we address these limitations through the development of a novel machine learning framework, Multiplex Image Machine Learning (MIML). This architecture uniquely combines label-free cell images with biomechanical property data, harnessing the vast, often underutilized biophysical information intrinsic to each cell. By integrating both types of data, our model offers a holistic understanding of cellular properties, utilizing cell biomechanical information typically discarded in traditional machine learning models. This approach has led to a remarkable 98.3% accuracy in cell classification, a substantial improvement over models that rely solely on image data. MIML has been proven effective in classifying white blood cells and tumor cells, with potential for broader application due to its inherent flexibility and transfer learning capability. It is particularly effective for cells with similar morphology but distinct biomechanical properties. This innovative approach has significant implications across various fields, from advancing disease diagnostics to understanding cellular behavior.
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