Bertin, M. | bioRxiv Science

bioRxiv2023

Dynamic X-chromosomal reactivation enhances female brain resilience

While random X-chromosome inactivation in female cells of placental mammalians silences one allele of the majority of X-chromosomal genes, a considerable fraction is only incompletely and variably inactivated resulting in a tissue-specific pattern of mono- and biallelic expression. Here we used clonal human female induced pluripotent stem cells (iPSCs) allowing to trace the (in)activation status of the two X-chromosomes individually along neural differentiation trajectories. We discovered X-chromosome-wide locus- and lineage-specific dynamic usage of the two X-chromosomal alleles in female cells induced by differentiation. Leveraging iPSCs derived from patients with an X-linked neurodevelopmental disorder, we demonstrate that activation of alleles on the inactive X-chromosome can exert protective effects on the manifestation of disease phenotypes in female neural cells and tissue. Taken together, our data demonstrate that alleles on the inactive X-chromosome can serve as a critical reservoir reactivated during differentiation, thereby enhancing resilience of female neural tissue.

neuroscience↗

bioRxiv2018

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

BackgroundBlood-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.\n\nMethodsWhole-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-validation to assess generalization performance.\n\nResultsIn 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.\n\nConclusionsA 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.

cancer biology↗