Investigator

Aik Tan

Senior Director of Data Science · Huntsman Cancer Institute, Department of Oncological Sciences

ATAik Tan
Papers(1)
Differential Infiltra…
Collaborators(10)
Craig ShriverDaniel SpakowiczGeorge WeinerHoward ColmanIgor PuzanovIslam EljilanyJose Conejo-GarciaJulian Marin-AcevedoMartin McCarterSusanne Arnold
Institutions(9)
Huntsman Cancer Insti…Uniformed Services Un…The Ohio State Univer…University Of Iowa He…Roswell Park Cancer I…H. Lee Moffitt Cancer…Indiana UniversityUniversity of Colorad…University of Kentucky

Papers

Differential Infiltration of Key Immune T-Cell Populations Across Malignancies Varying by Immunogenic Potential and the Likelihood of Response to Immunotherapy

Background: Solid tumors vary by the immunogenic potential of the tumor microenvironment (TME) and the likelihood of response to immunotherapy. The emerging literature has identified key immune cell populations that significantly impact immune activation or suppression within the TME. This study investigated candidate T-cell populations and their differential infiltration within different tumor types as estimated from mRNA co-expression levels of the corresponding cellular markers. Methods: We analyzed the mRNA co-expression levels of cellular biomarkers that define stem-like tumor-infiltrating lymphocytes (TILs), tissue-resident memory T-cells (TRM), early dysfunctional T-cells, late dysfunctional T-cells, activated-potentially anti-tumor (APA) T-cells and Butyrophilin 3A (BTN3A) isoforms, utilizing clinical and transcriptomic data from 1892 patients diagnosed with melanoma, bladder, ovarian, or pancreatic carcinomas. Real-world data were collected under the Total Cancer Care Protocol and the Avatar® project (NCT03977402) across 18 cancer centers. Furthermore, we compared the survival outcomes following immune checkpoint inhibitors (ICIs) based on immune cell gene expression. Results: In melanoma and bladder cancer, the estimated infiltration of APA T-cells differed significantly (p = 4.67 × 10−12 and p = 5.80 × 10−12, respectively) compared to ovarian and pancreatic cancers. Ovarian cancer had lower TRM T-cell infiltration than melanoma, bladder, and pancreatic (p = 2.23 × 10−8, 3.86 × 10−28, and 7.85 × 10−9, respectively). Similar trends were noted with stem-like, early, and late dysfunctional T-cells. Melanoma and ovarian expressed BTN3A isoforms more than other malignancies. Higher densities of stem-like TILs; TRM, early and late dysfunctional T-cells; APA T-cells; and BTN3A isoforms were associated with increased survival in melanoma (p = 0.0075, 0.00059, 0.013, 0.005, 0.0016, and 0.041, respectively). The TRM gene signature was a moderate predictor of survival in the melanoma cohort (AUROC = 0.65), with similar findings in testing independent public datasets of ICI-treated patients with melanoma (AUROC 0.61–0.64). Conclusions: Key cellular elements related to immune activation are more heavily infiltrated within ICI-responsive versus non-responsive malignancies, supporting a central role in anti-tumor immunity. In melanoma patients treated with ICIs, higher densities of stem-like TILs, TRM T-cells, early dysfunctional T-cells, late dysfunctional T-cells, APA T-cells, and BTN3A isoforms were associated with improved survival.

250Works
1Papers
11Collaborators
Tumor MicroenvironmentNeoplasmsBiomarkers, TumorHead and Neck NeoplasmsNeoplasm MetastasisCell Line, TumorCarcinoma, Squamous Cell

Positions

2022–

Senior Director of Data Science

Huntsman Cancer Institute · Department of Oncological Sciences

2019–

Vice-Chair, Professor and Senior Member

Moffitt Cancer Center · Biostatistics and Bioinformatics

2013–

Associate Professor

University of Colorado Denver School of Medicine · Medicine/Medical Oncology

2009–

Assistant Professor

University of Colorado Denver School of Medicine · Medicine/Medical Oncology

2004–

Post-Doctoral Research Fellow

Johns Hopkins School of Medicine · Oncology

Education

2005

Ph.D.

University of Glasgow · Computing Science

Country

US

Keywords
Translational BioinformaticsCancer Systems BiologyComputational Drug RepurposingKinase Signaling NetworksResistance MechanismsDevelopmental TherapeuticsCancer Data ScienceComputational Immuno-Oncology