About

Trudy G Oliver is a Professor of Pharmacology and Cancer Biology at Duke University. She is a member of the Duke Cancer Institute and is based at the Duke Department of Pharmacology and Cancer Biology located at 308 Research Drive, LSRC C138B, Durham, NC 27708. Her role involves research and teaching within the department, contributing to the academic and scientific community at Duke University.

Research topics

• Cancer research
• Genetics
• Biology
• Cell biology
• Oncology
• Internal medicine
• Medicine

Selected publications

• Bcl-xL blockade targets neutrophils and synergizes with chemotherapy in lung squamous cell carcinoma

  EMBO Molecular Medicine · 2026-03-31

  articleOpen access

  Abstract Tumor-associated neutrophils (TANs) represent a large fraction of immune cells in tumors, but how their regulation and function vary in distinct cancer subtypes remains unknown. In Kras LSL-G12D/WT ; p53 fl/fl mouse models of lung adenocarcinoma (LUAD), TANs have an increased lifespan compared to normal neutrophils. Specifically, TANs upregulate the anti-apoptotic protein Bcl-xL, whose blockade by a BH3 mimetic selectively kills ageing TANs and diminishes tumor growth. Here, we have addressed this issue in lung squamous cell carcinoma (LUSC) using the Rosa26 LSL-Sox2-IRES-GFP ; Nkx2-1 fl/fl ; Lkb1 fl/fl mouse model, where we demonstrate increased TAN survival with a rise in Bcl-xL similarly to LUAD. However, unlike in LUAD, inhibiting Bcl-xL alone was insufficient to alter tumor progression in LUSC. After carboplatin and paclitaxel treatment, a combination chemotherapy used in human LUSC, we detected increased neutrophils in circulation, spleen and tumors, and increased Bcl-xL in neutrophils and TANs. Bcl-xL blockade decreased the pool of Bcl-xL-high TANs and synergized with chemotherapy. Altogether, our results suggest distinct outcomes for targeting TANs in different tumor types and reinforce the concept of repurposing BH3 mimetics against cancer.

  PublisherDOI

• Code and data associated with Izzo et al journal submission

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-15

  otherOpen accessSenior author

  Lung squamous cell carcinoma (LUSC) is a basal-like subtype of lung cancer with limited treatment options. While prior studies have identified tumour-propagating cell states in squamous tumours, the broader landscape of intra-tumoural heterogeneity within LUSC remains poorly understood. Here, we employ Sox2-driven mouse models, organoid cultures, and single-cell transcriptomic analyses to uncover previously unrecognized levels of cell fate diversity within LUSC. Specifically, we identify a KRT13+ hillock-like population of slower-dividing tumour cells characterized by immunomodulatory gene expression signatures. The tumour hillock-like state is conserved across multiple animal and human-derived models and is present in the majority of human LUSCs as well as head and neck and esophageal squamous tumours. Our findings shed light on the cellular origins of tumour hillock-like states: lung club cells give rise to tumours with luminal hillock-like populations, while basal-like tumour-propagating cells transition into basal hillock-like states, resembling lineage plasticity trajectories of the normal lung. Mechanistically, KLF4 promotes KRT13, a broadly conserved hillock-like state with enrichment of potential therapeutic targets, and resistance to platinum-based chemotherapy. Together, these results provide molecular insights into the lineage plasticity underlying intra-tumoural heterogeneity within LUSC, offering potential avenues for new therapeutic strategies.

  PublisherDOI

• Code and data associated with Izzo et al journal submission

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-15

  otherOpen accessSenior author

  Lung squamous cell carcinoma (LUSC) is a basal-like subtype of lung cancer with limited treatment options. While prior studies have identified tumour-propagating cell states in squamous tumours, the broader landscape of intra-tumoural heterogeneity within LUSC remains poorly understood. Here, we employ Sox2-driven mouse models, organoid cultures, and single-cell transcriptomic analyses to uncover previously unrecognized levels of cell fate diversity within LUSC. Specifically, we identify a KRT13+ hillock-like population of slower-dividing tumour cells characterized by immunomodulatory gene expression signatures. The tumour hillock-like state is conserved across multiple animal and human-derived models and is present in the majority of human LUSCs as well as head and neck and esophageal squamous tumours. Our findings shed light on the cellular origins of tumour hillock-like states: lung club cells give rise to tumours with luminal hillock-like populations, while basal-like tumour-propagating cells transition into basal hillock-like states, resembling lineage plasticity trajectories of the normal lung. Mechanistically, KLF4 promotes KRT13, a broadly conserved hillock-like state with enrichment of potential therapeutic targets, and resistance to platinum-based chemotherapy. Together, these results provide molecular insights into the lineage plasticity underlying intra-tumoural heterogeneity within LUSC, offering potential avenues for new therapeutic strategies.

  PublisherDOI

• Multiomic characterization of small cell lung cancer: Real‐world insights into therapeutic opportunities

  Cancer · 2026-02-28

  articleOpen access

  BACKGROUND: The dominant expression of lineage-related transcription factors (TFs)-ASCL1, NEUROD1, POU2F3, and, controversially, YAP1-has enabled the classification of small cell lung cancer (SCLC) into distinct subtypes (SCLC-A/N/P/Y, respectively). Emerging evidence suggests that a T cell-inflamed phenotype characterizes an SCLC subset. A large-scale multiomic analysis of samples from real-world patients with SCLC was conducted to examine the expression of clinically relevant biomarkers across SCLC subtypes. METHODS: Comprehensive molecular profiling of patient samples (N = 944) was performed via next-generation DNA sequencing (592-gene panel or whole exome), RNA sequencing (whole transcriptome), and immunohistochemistry. Tumors were stratified on the basis of the dominant expression of an individual TF (SCLC-A/N/Y/P subtypes), coexpression of multiple TFs (mixed), or low expression of all four TFs (TF-) for characterization of immune-related gene signatures (T-cell inflamed, natural killer cell, and Stimulator of Interferon Genes pathway) and clinically relevant target genes. RESULTS: The cohort was composed of 25.6% SCLC-A, 10.2% SCLC-N, 12.5% SCLC-Y, 4.3% SCLC-P, 19.5% SCLC TF-, and 27.9% mixed subtypes. The SCLC-Y subtype exhibited the highest expression of immune-related gene signatures, with comparable expression observed in mixed samples expressing YAP1. Additionally, expression of clinically relevant target genes found in SCLC-A (DLL3, SEZ6, and BCL2) and SCLC-N (SSTR2) was increased in mixed samples expressing ASCL1 and NEUROD1. The TF- subtype was not associated with increased immune-related signatures or other target genes. CONCLUSIONS: This large-scale multiomic analysis revealed significant associations between SCLC subtypes and specific immune signatures and comutations. These findings provide insights into the molecular heterogeneity of SCLC, and highlight potential biomarkers for targeted therapies.

  PublisherDOI

• Data from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <div>Abstract<p>Lung cancer, the leading cause of cancer mortality, exhibits diverse histologic subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. In this study, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMM), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCAMGDB aligned 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in the GEMMs. To accompany this resource, a web application was developed that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCAMGDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance.</p><p><b>Significance:</b> The Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB) provides a comprehensive and accessible resource for the research community to investigate lung cancer biology in mouse models.</p></div>

  PublisherDOI

• Figure S7 from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <p>Ascl1 expression across reprocessed RNA-seq datasets</p>

  PublisherDOI

• Figure S2 from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <p>Top 100 frequent genetic mutations in realworld lung cancer patients</p>

  PublisherDOI

• Table S2 from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <p>Depositor communications, describing input from data depositors</p>

  PublisherDOI

• Supplementary Figure S2 from KSR2 Promotes Self-Renewal and Clonogenicity of Small Cell Lung Carcinoma

  2025-07-02

  preprintOpen access

  <p>Figure S2. Proliferation, apoptosis, and subtype are not affected by KSR2 disruption in H209 tumor xenografts.</p>

  PublisherDOI

• Data from KSR2 Promotes Self-Renewal and Clonogenicity of Small Cell Lung Carcinoma

  2025-07-02

  preprintOpen access

  <div>Abstract<p>Small cell lung carcinoma (SCLC) tumors are heterogeneous, with a subpopulation of cells primed for tumor initiation. In this study, we show that kinase suppressor of Ras 2 (KSR2) promotes the self-renewal and clonogenicity of SCLC cells. KSR2 is a molecular scaffold that promotes Raf/MEK/ERK signaling. KSR2 is preferentially expressed in the ASCL1 subtype of SCLC (SCLC-A) tumors and is expressed in pulmonary neuroendocrine cells, one of the identified cells of origin for SCLC-A tumors. The expression of KSR2 in SCLC and pulmonary neuroendocrine cells was previously unrecognized and serves as a novel model for understanding the role of KSR2-dependent signaling in normal and malignant tissues. Disruption of KSR2 in SCLC-A cell lines inhibits the colony-forming ability of tumor-propagating cells <i>in vitro</i> and their tumor-initiating capacity <i>in vivo.</i> The effect of KSR2 depletion on self-renewal and clonogenicity is dependent on the interaction of KSR2 with ERK. These data indicate that the expression of KSR2 is an essential driver of SCLC-A tumor–propagating cell function and therefore may play a role in SCLC tumor initiation. These findings shed light on a novel effector promoting initiation of SCLC-A tumors and a potential subtype-specific therapeutic target.</p>Implications:<p>Manipulation of the molecular scaffold KSR2 in SCLC-A cells reveals its contribution to self-renewal, clonogenicity, and tumor initiation.</p></div>

  PublisherDOI

Recent grants

• NIH Grant R01CA187457

  NIH · $1.2M · 2020

• Coordinating center for the NCI small cell lung cancer research consortium

  NIH · $11.7M · 2022–2027

• NIH Grant R01CA18745701

  NIH · $309k · 2019

• Genomic and Functional Identification of Chemotherapy Resistance Mechanisms in Small Cell Lung Cancer

  NIH · $3.1M · 2018–2025

• NIH Grant R21CA216504

  NIH · $358k · 2019

Frequent coauthors

• Tyler Jacks

  Massachusetts Institute of Technology

  130 shared

• David M. Sabatini

  Czech Academy of Sciences, Institute of Organic Chemistry and Biochemistry

  102 shared

• Ömer Yılmaz

  Istanbul Eye Hospital

  80 shared

• Mari Mino–Kenudson

  Massachusetts General Hospital

  80 shared

• Nada Y. Kalaany

  Boston Children's Museum

  73 shared

• Natasha Curry

  Nuffield Trust

  71 shared

• Jade Y. Moon

  71 shared

• Vedat O. Yilmaz

  71 shared

Labs

• Cancer Genetics and EpigeneticsPI