Chromosomal instability (CIN) results in the accumulation of large-scale losses, gains and rearrangements of DNA 1 . The broad genomic complexity caused by CIN is a hallmark of cancer 2 ; however, there is no systematic framework to measure different types of CIN and their effect on clinical phenotypes pan-cancer. Here we evaluate the extent, diversity and origin of CIN across 7,880 tumours representing 33 cancer types. We present a compendium of 17 copy number signatures that characterize specific types of CIN, with putative aetiologies supported by multiple independent data sources. The signatures predict drug response and identify new drug targets. Our framework refines the understanding of impaired homologous recombination, which is one of the most therapeutically targetable types of CIN. Our results illuminate a fundamental structure underlying genomic complexity in human cancers and provide a resource to guide future CIN research.CIN has complex consequences, including loss or amplification of driver genes, focal rearrangements, extrachromosomal DNA, micronuclei formation and activation of innate immune signalling 1 . This leads to associations with disease stage, metastasis, poor prognosis and therapeutic resistance 3 . The causes of CIN are also diverse and include mitotic errors, replication stress, homologous recombination deficiency (HRD), telomere crisis and breakage fusion bridge cycles, among others 1,4 .Because of the diversity of these causes and consequences, CIN is generally used as an umbrella term. Measures of CIN either divide tumours into broad categories of high or low CIN 5 , are restricted to a single aetiology such as HRD 6 , are limited to a particular genomic feature such as whole-chromosome-arm changes 7 , or can only be quantified in specific cancer types 8,9 . As a result, there is no systematic framework to comprehensively characterize the diversity, extent and origins of CIN pan-cancer, or to define how different types of CIN within a tumour relate to clinical phenotypes. Here we present a robust analysis framework to quantitatively measure different types of CIN across cancer types.

High-grade serous ovarian carcinoma (HGSOC) is the most genomically complex cancer, characterized by ubiquitous TP53 mutation, profound chromosomal instability, and heterogeneity. The mutational processes driving chromosomal instability in HGSOC can be distinguished by specific copy number signatures. To develop clinically relevant models of these mutational processes we derived 15 continuous HGSOC patient-derived organoids (PDOs) and characterized them using bulk transcriptomic, bulk genomic, single-cell genomic, and drug sensitivity assays. We show that HGSOC PDOs comprise communities of different clonal populations and represent models of different causes of chromosomal instability including homologous recombination deficiency, chromothripsis, tandem-duplicator phenotype, and whole genome duplication. We also show that these PDOs can be used as exploratory tools to study transcriptional effects of copy number alterations as well as compound-sensitivity tests. In summary, HGSOC PDO cultures provide validated genomic models for studies of specific mutational processes and precision therapeutics.

Chromosomal instability is a common characteristic of many cancers. Chromosomally instable tumour cells exhibit frequent copy number aberrations (CNAs) and a wide variation in the amount of DNA in cancer cells, referred to as cell ploidy. High levels of ploidy, in particular, are associated with whole genome doubling (WGD), a widespread macro-evolutionary event in tumour history. Individual cells' genomes are also undergoing replication as part of the cell cycle, and this constitutes an important covariate for single-cell genome analysis. Accurate and unbiased measurement of single-cell ploidy and replication status, including WGDs, based on DNA sequencing data is important for many downstream applications, such as detecting genomic variants, quantifying intratumour heterogeneity, and reconstructing tumour evolutionary phylogenies. Here we present scAbsolute, an approach to measure ploidy and replication status in single cells using scalable stochastic variational inference with a constrained Dirichlet Process Gaussian Mixture Model. We demonstrate its accuracy across three sequencing technologies (10X, DLP, ACT) and different cell lines and tumour samples. We address the problem of identifying cells with double the amount of DNA, but otherwise identical copy number profiles as is the case after WGD, solely based on sequencing information. Finally, we provide a robust and general method for identifying cells undergoing DNA replication. scAbsolute provides a scalable and unbiased way of ascertaining single-cell ploidy and replication status, paving the way for accurate detection of CNAs and WGDs in single-cell DNA sequencing data.