BACKGROUND: Liquid biopsy is a minimally-invasive method of sampling bodily fluids, capable of revealing evidence of cancer. The distribution of cell-free DNA (cfDNA) fragment lengths has been shown to differ between healthy subjects and cancer patients, whereby the distributional shift correlates with the sample's tumour content. These fragmentomic data have not yet been utilised to directly quantify the proportion of tumour-derived cfDNA in a liquid biopsy. RESULTS: We used statistical learning to predict tumour content from Fourier and wavelet transforms of cfDNA length distributions in samples from 118 cancer patients. The model was validated on an independent dilution series of patient plasma. CONCLUSIONS: This proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies.

MOTIVATION: The high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intratumor heterogeneity (ITH) by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq datasets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods. RESULTS: Here, we introduce BnpC, a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles. We benchmarked our method comprehensively against state-of-the-art methods on simulated data using various data sizes, and applied it to three cancer scDNA-seq datasets. On simulated data, BnpC compared favorably against current methods in terms of accuracy, runtime and scalability. Its inferred genotypes were the most accurate, especially on highly heterogeneous data, and it was the only method able to run and produce results on datasets with 5000 cells. On tumor scDNA-seq data, BnpC was able to identify clonal populations missed by the original cluster analysis but supported by Supplementary Experimental Data. With ever growing scDNA-seq datasets, scalable and accurate methods such as BnpC will become increasingly relevant, not only to resolve ITH but also as a preprocessing step to reduce data size. AVAILABILITY AND IMPLEMENTATION: BnpC is freely available under MIT license at https://github.com/cbg-ethz/BnpC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.