Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on sensitivity for detecting genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for non-invasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 cancer patients using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90–150 bp, and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90–150 bp improved detection of tumor DNA, with more than 2-fold median enrichment in >95% of cases, and more than 4-fold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with AUC>0.99 compared to AUC<0.80 without fragmentation features. Increased identification of cfDNA from patients with glioma, renal, and pancreatic cancer was achieved with AUC>0.91, compared to AUC<0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cell-free DNA for clinical applications, earlier diagnosis and study of tumor biology.

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.

Genomic complexity from profound copynumber aberration has prevented effective molecular stratification of ovarian and other cancers. Here we present a method for copynumber signature identification that decodes this complexity. We derived eight signatures using 117 shallow wholegenome sequenced highgrade serous ovarian cancer cases, which were validated on a further 497 cases. Mutational processes underlying the copynumber signatures were identified, including breakagefusionbridge cycles, homologous recombination deficiency and wholegenome duplication. We show that most tumours are heterogeneous and harbour multiple signature exposures. We also demonstrate that copy number signatures predict overall survival and changes in signature exposure observed in response to chemotherapy suggest potential treatment strategies. 2. CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/174201 doi: bioRxiv preprint first posted online Aug. 9, 2017;The discrete mutational processes that drive copynumber change in human cancers are not readily identifiable from genomewide sequence data. This presents a major challenge for the development of precision medicine for cancers that are strongly dominated by copynumber changes, including highgrade serous ovarian (HGSOC), oesophageal, nonsmallcell lung and triple negative breast cancers 1 . These tumours have low frequency of recurrent oncogenic mutations, few recurrent copy number alterations and highly complex genomic profiles 2 .HGSOCs are poor prognosis carcinomas with ubiquitous TP53 mutation 3 . Despite efforts to discover new molecular subtypes and targeted therapies, overall survival has not improved over two decades 4 . Current genomic stratification is limited to defining homologous recombinationdeficient (HRD) tumours 57 , and classification using gene expression does not currently have clinical utility 8,9 . Detailed genomic analysis using whole genome sequencing has shown frequent loss of RB1, NF1 and PTEN by gene breakage events 10 and enrichment of amplification associated foldback inversions in nonHRD tumours 11 . However, none of these approaches has provided a broad mechanistic understanding of HGSOC, reflecting the challenges of detecting classifiers in extreme genomic complexity.Recent algorithmic advances have enabled interpretation of complex genomic changes by identifying mutational signatures genomic patterns that are the imprint of mutagenic processes accumulated over the lifetime of a cancer cell . Importantly, these studies show that tumours typically harbour multiple mutational processes requiring computational approaches that can robustly identify coexistent mutational signatures. Quantification of the exposure of a tumour to specific mutational signatures provides a rational framework to personalise therapy 14 but currently is not readily applicable to copynumber driven tumours. We hypothesized that specific feat...