Breakage-Fusion-Bridge (BFB) is a mechanism of genomic instability characterized by the joining and subsequent tearing apart of sister chromatids. When this process is repeated during multiple rounds of cell division, it leads to patterns of copy number increases of chromosomal segments as well as fold-back inversions where duplicated segments are arranged head-to-head. These structural variations can then drive tumorigenesis.
BFB can be observed in progress using cytogenetic techniques, but generally BFB must be inferred from data like microarrays or sequencing collected after BFB has ceased. Making correct inferences from this data is not straightforward, particularly given the complexity of some cancer genomes and BFB's ability to generate a wide range of rearrangement patterns.
Here we present algorithms to aid the interpretation of evidence for BFB. We first pose the BFB count vector problem: given a chromosome segmentation and segment copy numbers, decide whether BFB can yield a chromosome with the given segment counts. We present the first linear-time algorithm for the problem, improving a previous exponential-time algorithm. We then combine this algorithm with fold-back inversions to develop tests for BFB. We show that, contingent on assumptions about cancer genome evolution, count vectors and fold-back inversions are sufficient evidence for detecting BFB. We apply the presented techniques to paired-end sequencing data from pancreatic tumors and confirm a previous finding of BFB as well as identify a new chromosomal region likely rearranged by BFB cycles, demonstrating the practicality of our approach.
Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware
Hierarchical Pooling and Explainability in Graph Neural Networks for Tumor and Tissue-of-Origin Classification Using RNA-seq Data
Institute of Informatics（信息学院）；Universidade Federal do Rio Grande do Sul（鲁汶联邦大学）；Bioinformatics Core（生物信息学核心）；Hospital de Clínicas de Porto Alegre（波多利亚临床医院）
Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Evolution Online LLC（Evolution Online有限责任公司）
