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.

Coliform Bacteria and Staphylococcus aureus in Retail Natural Cheeses.

Fifty commercial retail samples of natural cheeses (American and non-American-type) were examined for coliform bacteria, Escherichia coli , E. coli O157:H7, and thermonuclease-positive S. aureus . Nine cheese samples had coliform group bacteria ranging from 10 to 1.1 × 103 CFU/ml by the violet red bile agar procedure. Twenty-four or 48% of the cheeses were positive for coliform group bacteria when a 25-g sample was used in an enrichment broth. One sample had a confirmed E. coli by both the violet red bile agar and enrichment methods. No E. coli O157:H7 were found in the retail natural cheeses examined. Also, no thermonuclease-positive S. aureus were isolated. The levels of potentially pathogenic microorganisms in cheeses were lower in this study than reported in a 1974 to 1976 Canadian study.