Biomedical subjects

Weini Huang

Publications and source records attributed to Weini Huang.

3 recordsLinked to original sources

PubMed2025

Extrachromosomal DNA-Driven Oncogene Dosage Heterogeneity Promotes Rapid Adaptation to Therapy in MYCN-Amplified Cancers.

UNLABELLED: Extrachromosomal DNA (ecDNA) amplification enhances intercellular oncogene dosage variability and accelerates tumor evolution by violating foundational principles of genetic inheritance through its asymmetric mitotic segregation. Spotlighting high-risk neuroblastoma, we demonstrate how ecDNA amplification undermines the clinical efficacy of current therapies in cancers with extrachromosomal MYCN amplification. Integrating theoretical models of oncogene copy number-dependent fitness with single-cell ecDNA quantification and phenotype analyses, we reveal that ecDNA copy-number heterogeneity drives phenotypic diversity and determines treatment sensitivity through mechanisms unattainable by chromosomal oncogene amplification. We demonstrate that ecDNA copy number directly influences cell fate decisions in cancer cell lines, patient-derived xenografts, and primary neuroblastomas, illustrating how extrachromosomal oncogene dosage-driven phenotypic diversity offers a strong evolutionary advantage under therapeutic pressure. Furthermore, we identify senescent cells with reduced ecDNA copy numbers as a source of treatment resistance in neuroblastomas and outline a strategy for their targeted elimination to improve the treatment of MYCN-amplified cancers. SIGNIFICANCE: ecDNA-driven tumor genome evolution provides a major challenge to curative cancer therapies. We demonstrate that ecDNA copy-number dynamics drives treatment resistance by promoting oncogene dosage-dependent phenotypic heterogeneity in MYCN-amplified cancers. Exploiting phenotype-specific vulnerabilities of ecDNA cells, therefore, presents a powerful strategy to overcome treatment resistance. See related commentary by Korsah, p. 1979.

Humans↗

PubMed2025

Single-cell mutational burden distributions in birth-death processes.

Genetic mutations are footprints of cancer evolution and reveal critical dynamic parameters of tumour growth, which otherwise are hard to measure in vivo. The mutation accumulation in tumour cell populations has been described by various statistics, such as site frequency spectra (SFS), single-cell division distributions (DD) and mutational burden distributions (MBD). While DD and SFS have been intensively studied in phylogenetics especially after the development of whole genome sequencing technology of bulk samples, MBD has drawn attention more recently with the single-cell sequencing data. Although those statistics all arise from the same somatic evolutionary process, an integrated understanding of these distributions is missing and requires novel mathematical tools to better inform the ecological and evolutionary dynamics of tumours. Here we introduce dynamical matrices to analyse and unite the SFS, DD and MBD and derive recurrence relations for the expectations of these three distributions. While we successfully recover classic exact results in pure-birth cases for the SFS and the DD through our new framework, we derive a new expression for the MBD and approximate all three distributions when death is introduced. We demonstrate a natural link between the SFS and the single-cell MBD, and show that the MBD can be regenerated through the DD. Counter-intuitively, the single-cell MBD is mainly driven by the stochasticity arising in the DD, rather than the extra stochasticity in the number of mutations at each cell division.

Single-Cell Analysis↗

PubMed2022

The evolutionary dynamics of extrachromosomal DNA in human cancers.

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance-random identity by descent-is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer.

Biological Evolution↗