Publications

Preprints

1. bioRxiv

   

   Accurate detection of tumor clonality and ongoing expansion mode from genomic data

   Yanjie Chen, Roman Jaksik, Peter Terranova, and 6 more authors

   bioRxiv, Jun 2026

   Abs DOI Bib HTML

   Recent evidence shows that despite considerable effort, currently available algorithms for estimating intra-tumor heterogeneity (ITH) remain limited. We developed DECODE (Deciphering Cancer Origin from DNA Evolution), a novel mutation clustering method that incorporates the impact of sample-specific sequencing coverage and mutation calling biases. On synthetic data, DECODE outperformed existing methods across multiple clonality metrics and accurately detected and characterized the neutral tail in the site frequency spectrum (SFS), which encodes the tumor’s ongoing expansion mode. In acute myeloid leukemia, accounting for the neutral tail enabled DECODE to yield more parsimonious clonal decompositions that align more closely with known subclonal dynamics that drive relapse. Applied to data from The Cancer Genome Atlas, DECODE not only detected a neutral SFS tail in most samples across tumor types but also uncovered a clinically meaningful link between ITH and survival in low-grade glioma. By jointly inferring clonality and expansion mode, DECODE provides two complementary and prognostically relevant readouts of tumor evolution from single tumor genomic samples.

   @article{chen2026accurate,
     title = {Accurate detection of tumor clonality and ongoing expansion mode from genomic data},
     author = {Chen, Yanjie and Jaksik, Roman and Terranova, Peter and El Baghdadi, Sara and Koval, Andrew and Kurpas, Monika K. and Tavar{\'e}, Simon and Kimmel, Marek and Dinh, Khanh N.},
     journal = {bioRxiv},
     year = {2026},
     month = jun,
     publisher = {Cold Spring Harbor Laboratory},
     doi = {10.64898/2026.06.15.732415},
     url = {https://pubmed.ncbi.nlm.nih.gov/42367953/},
   }

In preparation

1. 

   Automatic perturbation kernels for biological applications of Approximate Bayesian Computation sequential Monte Carlo with random forests

   Yanjie Chen and Khanh N. Dinh

   Accepted by FedCSIS

2. 

   Refining breast cancer stratification through improved mutation clustering of genomic samples

   Yanjie Chen and Khanh N. Dinh

   Submitted

3. 

   Generative data augmentation methods for miRNA/RNA-seq

   Collaborators from Li-Xuan Qin Lab

   Preparing manuscript