Kocher, Jean-Pierre

Summary Cancer genome sequencing is essential for understanding tumor evolution and advancing precision medicine. 1 However, reference gaps and germline variants obscure detection of small and large somatic variants and methylation in repetitive regions. 1–3 It is common for tumor cells to gain or lose chromosome arms due to somatic structural changes that occur inside highly repetitive satellite DNA sequences in the centromeres. 4 To identify the full spectrum of somatic variants, including complex rearrangements, we construct and curate near-complete, haplotype-resolved assemblies of the most recent common ancestor of an early-passage broadly-consented hypodiploid pancreatic cancer cell line and matched normal tissues. The tumor assembly completely recapitulates all 35 tumor chromosomes observed with karyotyping, with multiple translocation-induced hybrid chromosomes. The hybrid chromosomes contain putative functional dicentric and fused centromeres, nested foldback inversions causing 14 breakpoints with a haplotype switch in a single event, and centromeric satellite tandem duplications up to 136 kbp. Direct comparison of tumor and normal assembly haplotypes uncovers >7,000 variants altering >1 Mbp of sequence in repetitive regions that have been hidden by reference gaps and germline variants. 44 % of somatic small variants change representation because they alter germline variants on GRCh38, impacting mutational signatures and kataegis/omikli clusters. Most somatic LINE insertions originate from two hypomethylated non-reference germline LINE insertions, highlighting their impact on insertion mutation burden. These assemblies demonstrate that centromeric, acrocentric, and telomeric regions conventionally excluded from analysis harbor extensive somatic and epigenetic changes. Resolving complete tumor genomes enables a deeper understanding of cancer structural plasticity and the endpoints of breakage-fusion-bridge cycles. These assembled, curated paired normal-tumor benchmarks will serve as a critical foundation for developing future algorithms to characterize the most intractable regions of cancer genomes.

Abstract We present chromosome-level, phased diploid genome assemblies of two widely used human fibroblast cell lines: BJ (46,XY) and IMR-90 (46,XX). Using Oxford Nanopore, PacBio HiFi, and Hi-C sequencing data, we generated assemblies spanning 5.9 and 6.0 Gbp with diploid quality values exceeding QV 60. To validate structural integrity, we developed KaryoScope, an alignment-free tool for generating computational karyograms from k-mer feature databases. We identify >50 000 structural variants relative to T2T-CHM13v2.0, the majority of which are heterozygous and cell-line-specific. Combining reference-based and de novo gene annotation, we uncover a previously unreported 1 Mbp homozygous duplication at the 16p11.2 locus in BJ, demonstrating that even karyotypically normal cell lines can harbor clinically relevant submicroscopic rearrangements. We show that mapping publicly available short-read, RNA-seq, and ChIP-seq data to sample-matched diploid assemblies substantially improves read alignment and enables haplotype phasing of 23%–28% of short reads. The BJ and IMR-90 assemblies and associated variant calls are publicly available as a resource for the research community.

AbstractMotivation: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis.Results: We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets.Availability: Supplementary results and source code are available at http://compbio.cs.umn.edu/Feature_Class.Contact: kuang@cs.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.