Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.

BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. METHODS: Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N = 546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validations to assess generalization performance. RESULTS: In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. CONCLUSIONS: A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

Quantitative criticism of literary relationships.

Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term "quantitative criticism," focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca's main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions.

Actin-myosin network influences morphological response of neuronal cells to altered osmolarity.

Acute osmotic fluctuations in the brain occur during a number of clinical conditions and can result in a variety of adverse neurological symptoms. Osmotic perturbation can cause changes in the volumes of intra- and extracellular fluid and, due to the rigidity of the skull, can alter intracranial pressure thus making it difficult to analyze purely osmotic effects in vivo. The present study aims to determine the effects of changes in osmolarity on SH-SY5Y human neuroblastoma cells in vitro, and the role of the actin-myosin network in regulating this response. Cells were exposed to hyper- or hypoosmotic media and morphological and cytoskeletal responses were recorded. Hyperosmotic shock resulted in a drop in cell body volume and planar area, a persisting shape deformation, and increases in cellular translocation. Hypoosmotic shock did not significantly alter planar area, but caused a transient increase in cell body volume and an increase in cellular translocation via the development of small protrusions rich in actin. Disruption of the actin-myosin network with latrunculin and blebbistatin resulted in changes to volume and shape regulation, and a decrease in cellular translocation. In both osmotic perturbations, no apparent disruptions to cytoskeletal integrity were observed by light microscopy. Overall, because osmotically induced changes persisted even after volume regulation occurred, it is possible that osmotic stress may play a larger role in neurological dysfunction than currently believed.