David Woods, Ph.D.

Full-stack Ph.D. biomedical scientist with expertise spanning wet-lab research, biostatistics, computational biology, and software development.

Former NCI funded Assistant Professor and expert in translational oncology and immunology, with 17+ years experience turning data into discoveries; scientific leader and mentor experienced in securing research funding, building and directing multi-disciplinary teams, managing biopharma collaborations, and bridging dry-lab analytics with wet-lab execution.

Software

mouseExperiment

An open-source R package and accompanying web-based dashboard for statistical analysis of preclinical tumor-growth studies.

  • R
  • Bayesian (brms)
  • Survival
  • Shiny
  • Docker

PhenoComb

A combinatorics tool for discovering complex phenotypes in high-dimension single-cell data. Peer-reviewed in Bioinformatics Advances. A next-generation Python rewrite is in development.

  • R
  • Single-cell
  • Flow cytometry
  • Biomarker discovery

Probability Bio

I founded Probability Bio and built a modular Python ecosystem for running in silico clinical trials on multi-modal cancer patient data.

  • Python
  • Systems biology
  • Survival ML
  • Real-world data

Impact

Research output

2,600+citations (Google Scholar)
18h-index
6 + 6first-author and senior-author papers

Publications in high-impact journals including Clinical Cancer Research, Cancer Immunology Research, Nature Communications, Blood, JCI Insight, and Journal for ImmunoTherapy of Cancer.

Biomarker discoveries

  1. CD4+CD38+CD39+ T cells predict checkpoint resistance Discovered and validated across clinical specimens, murine models, multispectral imaging, CITE-seq, and public scRNA-seq cohorts (Clinical Cancer Research, 2023; Journal for ImmunoTherapy of Cancer, 2022; JCI Insight, 2020).
  2. CD4 polarization phenotypes associated with reduced TIL expansion In melanoma patients treated with adoptive cell therapy (The Journal of Immunology, 2023).
  3. Serum IL-6 and CRP as prognostic biomarkers Associated with survival in checkpoint-treated melanoma (Journal for ImmunoTherapy of Cancer, 2020; 270+ citations).
  4. Ipilimumab associated immune changes are associated with nivolumab resistance Response-associated immunophenotypes in metastatic melanoma (JCI Insight, 2020).
  5. T-cell pSTAT3 expression and Treg function associated with benefit from adjuvant nivolumab PD-1 blockade induces pSTAT3 expression and results in reduced suppressive function (Clinical Cancer Research, 2018).

Clinical trials built on this research

NCT02032810

Phase I of Histone Deacetylase (HDAC) Inhibitor Panobinostat With Ipilimumab With Unresectable III/IV Melanoma.

NCT02935790

Selective HDAC6 Inhibitor ACY-241 in Combination With Ipilimumab and Nivolumab.

NCT03565406

A Phase 1b Study of the Selective HDAC Inhibitor Mocetinostat in Combination With Ipilimumab and Nivolumab in Patients With Unresectable Stage III or Stage IV Melanoma.

NCT03999749

A Phase II Study of the Interleukin-6 Receptor Inhibitor Tocilizumab in Combination With Ipilimumab and Nivolumab in Patients With Unresectable Stage III or Stage IV Melanoma.

Funding

$10M+ competitive grant funding secured across federal, institutional, and industry sources.

Federal (NIH/NCI)

  • K99/R00 Pathway to Independence Award
  • T32 Postdoctoral Trainee Grant
  • Loan Repayment Program and renewals

Institutional

  • NYU Langone
  • Moffitt Cancer Center
  • University of Colorado Anschutz Medical Campus

Industry

  • Three sponsored research agreements, leading to initiation of two phase I clinical trials
  • Research reagent funding

Selected talks

  • 2022
    Society for Immunotherapy of Cancer Annual MeetingEctoenzyme-expressing T cells associated with checkpoint immunotherapy resistance in metastatic melanoma
  • 2019
    International Society for Advancement of Cytometry (CYTO) Annual MeetingHigh-parameter flow cytometry and computational analysis for predictive melanoma biomarkers
  • 2017
    American Association of Immunologists Annual MeetingPD-1 blockade induces phosphorylated STAT3 and modulates Treg suppressive function
  • 2016
    Society for Immunotherapy of Cancer Annual MeetingIncreased STAT3 signaling and decreased suppressive function of regulatory T cells are biomarkers of positive patient outcome to nivolumab therapy
  • 2016
    American Society of Clinical Oncology Annual MeetingEpigenetic control of lineage commitment and resistance to TIL adoptive cell therapy
  • 2012
    American Society of Hematology Annual MeetingHistone deacetylase 11 (HDAC11) regulates cytotoxic T-cell function and memory phenotype

Publications

Also on Google Scholar and the NCBI bibliography.

Skills

Biology

  • Cancer biology
  • Immunology
  • Immunotherapy
  • Biomarkers
  • Multi-omics
  • Epigenetics
  • HDACs
  • T-cell biology
  • Melanoma
  • Solid tumors
  • Immune checkpoint blockade
  • Adoptive cell therapy (TIL)
  • Resistance mechanisms
  • Bench-to-bedside
  • Preclinical murine models
  • Clinical trial specimens
  • In vitro functional assays
  • Tumor spheroids/organoids

Data

  • Next Generation Sequencing (NGS)
  • Bulk & single-cell RNA-seq
  • CITE-seq
  • DNA methylation
  • ChIP-seq
  • ATAC-seq
  • Flow cytometry
  • Multi-omic integration
  • Public consortia data
  • Real world data

Statistics

  • Experimental design
  • Bayesian inference & MCMC
  • Generalized linear models
  • Mixed-effects models
  • Survival analysis
  • Causal inference
  • Network analysis

Machine Learning & Deep Learning

  • Dimension Reduction: PCA / UMAP / PLS
  • Clustering
  • Regularized regression
  • Random forest
  • Gradient boosting
  • Bagging
  • MLPs & CNNs
  • Representation learning (CVAE)
  • Imputation

Programming

  • Python
  • R
  • Bash
  • Git
  • SQL
  • Snakemake
  • Docker
  • Tidyverse
  • scikit-learn
  • PyTorch
  • CUDA / Apple MPS
  • Scanpy
  • Seurat
  • DESeq2
  • ggplot2
  • Shiny
  • Ollama