Faculty

Alexander T Pearson, MD PhD

Associate Professor of Medicine
Committee on Cancer Biology
Committee on Clinical Pharmacology and Pharmacogenomics
Committee on Genetics, Genomics and Systems Biology
Clinical Interests: Clinical Trials, Head and Neck Cancer, Immunotherapy, Salivary Gland Cancers
Research and Scholarly Interests: Artificial Intelligence, Education, Graduate Medical, Head and Neck Cancer, Mathematical Model, Models, Biologic, Neural Network Models, Salivary Gland Cancers, Statistical Models, Therapies, Combined Modality, Tumor Cell Lines, Tumor Immune Escape
Websites: Center for Computational Medicine and Clinical Artificial Intelligence, Pearson Lab, Research Network Profile
Contact: apearson5@uchicago.edu
Graduate Programs: Cancer Biology, Genetics, Genomics & Systems Biology

The Pearson Lab integrates clinical expertise, mathematical modeling, high dimensional statistics, and basic tumor biology methods to investigate and propose new treatments for head and neck cancer.

University of Michigan
Ann Arbor, MI
Fellowship - Hematology/Oncology
2016

University of Michigan
Ann Arbor, MI
Postdoctoral Fellowship - Nor Lab (2013-2017)
2016

University of Michigan
Ann Arbor, MI
Residency - Internal Medicine
2012

University of Rochester
Rochester, NY
MD - Medicine
2010

University of Rochester
Rochester, NY
PhD - Statistics
2009

Cornell University
Ithaca, NY
BS - Biometry and Statistics
2002

Virtual multiplex immunofluorescence identifies lymphocyte subsets predictive of response to neoadjuvant therapy.
Virtual multiplex immunofluorescence identifies lymphocyte subsets predictive of response to neoadjuvant therapy. Ther Adv Med Oncol. 2025; 17:17588359251379411.
PMID: 41142474

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer.
Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer. medRxiv. 2025 Aug 27.
PMID: 40909848

Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.
Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine. Genome Med. 2025 Aug 07; 17(1):87.
PMID: 40775734

Early Dynamics of Circulating Tumor HPV-DNA with Neoadjuvant Chemotherapy and Response-Adapted De-escalation in Human Papillomavirus-Associated Oropharyngeal Cancer.
Early Dynamics of Circulating Tumor HPV-DNA with Neoadjuvant Chemotherapy and Response-Adapted De-escalation in Human Papillomavirus-Associated Oropharyngeal Cancer. Clin Cancer Res. 2025 Aug 01; 31(15):3150-3159.
PMID: 40423530

Corrigendum to "Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas". [Oral Oncol. 163 (2025) 107207].
Corrigendum to "Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas". [Oral Oncol. 163 (2025) 107207]. Oral Oncol. 2025 Oct; 169:107510.
PMID: 40721367

Counterfactual Diffusion Models for Interpretable Morphology-based Explanations of Artificial Intelligence Models in Pathology.
Counterfactual Diffusion Models for Interpretable Morphology-based Explanations of Artificial Intelligence Models in Pathology. bioRxiv. 2025 Jul 22.
PMID: 39554184

Tumor-intrinsic and immune-related features associated with treatment failure in human papillomavirus-related oropharyngeal cancer.
Tumor-intrinsic and immune-related features associated with treatment failure in human papillomavirus-related oropharyngeal cancer. J Natl Cancer Inst. 2025 Jul 01; 117(7):1366-1376.
PMID: 40069922

Analytical and clinical performance validation of HPV-SEQ, a novel NGS-based liquid biopsy platform for detection and quantification of human papilloma virus circulating tumor DNA.
Analytical and clinical performance validation of HPV-SEQ, a novel NGS-based liquid biopsy platform for detection and quantification of human papilloma virus circulating tumor DNA. Oral Oncol. 2025 Aug; 167:107445.
PMID: 40578243

End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning.
End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning. BMC Artif Intell. 2025; 1(1):3.
PMID: 40605934

Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional study.
Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional study. Lancet Reg Health Am. 2025 Jul; 47:101138.
PMID: 40519355

View All Publications

Mark Roth Award
University of Michigan
2015

Chief Fellow in Hematology/Oncology
University of Michigan
2014 - 2015