Scholarlink

Clonal evolution of cancer results in intratumor heterogeneity (ITH), making treatment and cure challenging. Single-cell sequencing has advanced our understanding of ITH, but tracing subclonal evolution using mutational profiles of cells is limited by scale and noise. Moreover, available tumor progression tree inference methods usually offer a single tree to explain the progression of a tumor, and do not inform about alternative evolutionary scenarios. In this paper we introduce the partition function for a tumor progression tree, to assess the reliability of any proposed subclonal structure in a single-cell sequenced tumor. Given that tumor progression can be represented as a perfect phylogeny, the partition function for a given set of cells, R, and a given mutation, \(\rho \) , is defined as the likelihood that \(\rho \) is present exclusively in R. As such, the partition function evaluates whether R forms a subclone with \(\rho \) as a possible subclonal driver, which is especially useful if the cells of R are biologically or clinically significant, e.g., have aggressive growth, therapy resistance, or metastatic potential. We also present an algorithm to estimate the partition function and provide theoretical proofs that our algorithm asymptotically approaches the ground truth. In addition, we present empirical evidence of its accuracy on simulated data, as well as its practical value in analyzing the progression history of real tumors. The implementation is available at: https://github.com/algo-cancer/Partition-Function/ .

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Partition Function Algorithm to Evaluate Inferred Subclonal Structures in Single-Cell Sequencing Data

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Farid Rashidi Mehrabadi,
  • Erfan Sadeqi Azer,
  • John D. Bridgers,
  • Eva Pérez-Guijarro,
  • Kerrie L. Marie,
  • Howard H. Yang,
  • Charli Gruen,
  • Chih Hao Wu,
  • Welles Robinson,
  • Huaitian Liu,
  • Can Kizilkale,
  • Michael C. Kelly,
  • Cari Smith,
  • Sung Chin,
  • Jessica Ebersole,
  • Sandra Burkett,
  • Aydin Buluc,
  • Maxwell P. Lee,
  • Erin K. Molloy,
  • Teresa M. Przytycka,
  • Glenn Merlino,
  • Chi-Ping Day,
  • Salem Malikić,
  • Funda Ergun,
  • S. Cenk Sahinalp

摘要

Clonal evolution of cancer results in intratumor heterogeneity (ITH), making treatment and cure challenging. Single-cell sequencing has advanced our understanding of ITH, but tracing subclonal evolution using mutational profiles of cells is limited by scale and noise. Moreover, available tumor progression tree inference methods usually offer a single tree to explain the progression of a tumor, and do not inform about alternative evolutionary scenarios. In this paper we introduce the partition function for a tumor progression tree, to assess the reliability of any proposed subclonal structure in a single-cell sequenced tumor. Given that tumor progression can be represented as a perfect phylogeny, the partition function for a given set of cells, R, and a given mutation, \(\rho \) , is defined as the likelihood that \(\rho \) is present exclusively in R. As such, the partition function evaluates whether R forms a subclone with \(\rho \) as a possible subclonal driver, which is especially useful if the cells of R are biologically or clinically significant, e.g., have aggressive growth, therapy resistance, or metastatic potential. We also present an algorithm to estimate the partition function and provide theoretical proofs that our algorithm asymptotically approaches the ground truth. In addition, we present empirical evidence of its accuracy on simulated data, as well as its practical value in analyzing the progression history of real tumors. The implementation is available at: https://github.com/algo-cancer/Partition-Function/ .