Seung Jun Shin

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

The paper develops a Bayesian semi-parametric model to predict cancer recurrence risk in survivors, showing good predictive performance for second primary cancers.

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.

The paper estimates penetrance for first and second primary cancer diagnoses in Li-Fraumeni syndrome families and validates a risk prediction model with an AUC of 0.77.

The paper estimates cancer-specific penetrance in Li-Fraumeni syndrome using a statistical model and validates it across multiple cohorts, showing age-dependent risk patterns for different cancer types.