Statistical methods and software for precision oncology

I am an associate professor in the Department of Biostatistics at the UNC Gillings School of Global Public Health, with a joint appointment at the Lineberger Comprehensive Cancer Center.

Our lab develops statistical and machine learning methods for precision oncology, including adaptive clinical trial designs that integrate real-time biomarker data, deep learning methods for tumor subtyping and missing data, and open-source R packages for cancer genomics.

Research interests: adaptive trial design, Bayesian methods, nonnegative matrix factorization, deep learning, missing data, cancer genomics, precision oncology

Training: Postdoctoral fellow, Harvard School of Public Health & Dana-Farber Cancer Institute (2014). PhD, Biostatistics, UNC Chapel Hill (2013). BS, Duke University (2006).

Research · CV (PDF) · Publications

Recent News

Representative Translational Work

Pancreatic oncologists at UNC Lineberger needed a way to classify individual tumors into molecular subtypes from a single biopsy, without requiring a reference cohort. We developed PurIST, a rank-based classifier that handles tumor purity variation, validated it across international cohorts, and worked with the Yeh laboratory to bring it through CLIA certification. It is currently being evaluated prospectively in several clinical trials and has been licensed to Tempus, making it available at hospitals nationwide. That cycle (clinical need, statistical method, validated software, deployed tool) is how most of our projects begin.

Current Funding

  • MPI, ARPA-H ADAPT program grant (metastatic breast cancer)
  • MPI, NCI U01 (pancreatic cancer)
  • PI, DOD-funded LLM clinical trial navigation tool

See full funding portfolio for details.

Selected Awards

  • 2025 Gillings Research Excellence Award
  • 2024 James E. Grizzle Distinguished Alumnus Award
  • 2023 Teaching Innovation Award, UNC Gillings
  • 2021 Delta Omega Faculty Award, Gillings School of Global Public Health
  • 2017 IBM and R.J. Reynolds Junior Faculty Development Award, UNC-CH
  • 2013 Barry H. Margolin Dissertation Award for best doctoral dissertation

Service & Leadership

  • Breast SPORE Core B Co-Director (P50-CA058223, 2024–2029)
  • Pancreatic SPORE Core C Co-Director (P50-CA257911, 2022–2027)
  • Lineberger LCCC Biostatistics Shared Resource Associate Director (P30-CA016086)
  • Nature Medicine Statistical Advisory Panel (2023–)
  • Associate Editor, Annals of Applied Statistics (2022–)
  • V Foundation Scientific Advisory Board (2023–)
  • TBCRC Statistical Working Group (2017–)
  • Faculty Executive Committee, Department of Biostatistics (2025–)
  • Gillings Research Council (2023–)
  • Chair, Applied Doctoral Exam Committee, Department of Biostatistics (2015–)

Recent Invited Talks

June 2026 NCI GI SPORE Meeting

Adaptive Trial Design for the RAS-Inhibitor Era: Combinations, Resistance, and Biomarker-Guided Therapy

Adaptive and biomarker-guided designs for RAS-inhibitor combination trials: dose optimization, resistance-aware randomization, and real-time biomarker integration in GI cancers.

View slides
May 2026 StatsUp.AI Webinar, American Statistical Association

Staying in the Driver's Seat: Calibrating AI Use in Statistical Research, Training, and Practice

Practical guardrails and habits for using AI in statistical research while keeping scientific ownership. The companion repository includes templates, checklists, and the full lab guide, free to adapt and use.

View slides Companion resources
May 2026 FDA CDRH OSEL-wide AI Seminar

When Biomarkers, Assays, and Protocols Co-evolve: Cross-platform Reproducibility, Validation, and Adaptive Design

Cross-platform reproducibility and validation of biomarker assays, and adaptive design considerations when biomarkers, assays, and protocols evolve together during a trial.

View slides
January 2026 Lineberger Innovate Cancer Data Science Symposium Chapel Hill, NC

Leveraging Artificial Intelligence in Modern Biomarker-Driven Clinical Trial Design: the ARPA-H ADAPT Program

How AI and adaptive statistical design come together in the ARPA-H ADAPT metastatic breast cancer platform.

View slides

News

Lab

Dec 18, 2024

Congratulations to Euphy Wu on successfully defending her PhD in Biostatistics! Euphy’s dissertation, co-mentored by Drs. Naim Rashid and Mike Love, developed methods for allele-specific expression analysis and topic-model-based single-cell clustering.

Latest Updates

Selected Publications

  1. Cancer Cell
    The ADAPT learning cancer treatment system: ARPA-H’s initiative to revolutionize cancer therapy
    Andrea H Bild, Michelle C Sangar, Jasmine A McQuerry, Trey Ideker, Scott Kopetz, and 15 more authors
    Cancer Cell, 2026
  2. Cell Rep Med
    DeCAF defines clinical fibroblast subtypes and multidimensional tumor-stroma crosstalk shaping prognosis and immunotherapy response
    Xianlu Laura Peng, Ian C McCabe, Elena V Kharitonova, Yi Xu, Ryan T Zhao, and 23 more authors
    Cell Reports Medicine, 2026
  3. Stat Med
    Efficient Computation of High‐Dimensional Penalized Piecewise Constant Hazard Random Effects Models
    Hillary M Heiling, Naim U Rashid, Quefeng Li, Xianlu L Peng, Jen Jen Yeh, and 1 more author
    Statistics in Medicine, 2025
  4. JCO
    DNA mutational profiling in patients with colorectal cancer treated with standard of care reveals differences in outcome and racial distribution of mutations
    Federico Innocenti, Wancen Mu, Xueping Qu, Fang-Shu Ou, Omar Kabbarah, and 4 more authors
    Journal of Clinical Oncology, 2024
  5. Biometrics
    Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects
    Hillary M Heiling, Naim U Rashid, Quefeng Li, Xianlu L Peng, Jen Jen Yeh, and 1 more author
    Biometrics, 2024
  6. JCGS
    Deeply learned generalized linear models with missing data
    David K Lim, Naim U Rashid, Junier B Oliva, and Joseph G Ibrahim
    Journal of Computational and Graphical Statistics, 2024
  7. JASA
    High-dimensional precision medicine from patient-derived xenografts
    Naim U Rashid, Daniel J Luckett, Jingxiang Chen, Michael T Lawson, Longshaokan Wang, and 6 more authors
    Journal of the American Statistical Association, 2021
  8. CCR
    Purity independent subtyping of tumors (PurIST), a clinically robust, single-sample classifier for tumor subtyping in pancreatic cancer
    Naim U Rashid, Xianlu L Peng, Chong Jin, Richard A Moffitt, Keith E Volmar, and 14 more authors
    Clinical Cancer Research, 2020
  9. JASA
    Modeling between-study heterogeneity for improved replicability in gene signature selection and clinical prediction
    Naim U Rashid, Quefeng Li, Jen Jen Yeh, and Joseph G Ibrahim
    Journal of the American Statistical Association, 2020
  10. Nat Genet
    Virtual microdissection identifies distinct tumor-and stroma-specific subtypes of pancreatic ductal adenocarcinoma
    Richard A Moffitt, Raoud Marayati, Elizabeth L Flate, Keith E Volmar, S Gabriela Herrera Loeza, and 13 more authors
    Nature genetics, 2015