About

Peter Kuhn, Ph.D., is a University Professor and Dean’s Professor of Biological Sciences, Medicine, Biomedical Engineering, Aerospace and Mechanical Engineering, and Urology at the USC Dornsife College of Letters, Arts and Sciences. He is a scientist and entrepreneur with a long-standing commitment to personalized medicine and individualized patient care, focusing on the redesign of cancer care. His research team in physics oncology has discovered new ways of understanding how cancer spreads to the human body and is using those breakthroughs to impact patient care. Dr. Kuhn is a founding member of the Michelson Center for Convergent Biosciences, a co-founder of the USC Michelson Center for Convergent Bioscience Bridge Institute, and the director of the Convergent Science Institute in Cancer (CSI-Cancer). His overarching strategy is to advance understanding of the human body to improve the human condition, with research shedding new light on the mechanisms of cancer spread to develop biologically informed and clinically actionable personalized care strategies.

Research topics

• Computer Science
• Information Retrieval
• Internal medicine
• Medicine
• Data Mining
• Genetics
• Virology
• Cancer research
• Biology
• Oncology
• Intensive care medicine
• Database
• Pediatrics
• Pathology

Selected publications

• OpenIMC Data Submission

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  datasetOpen access

  The datasets associated with OpenIMC. Includes datasets and outputs for various analyses as part of the OpenIMC manuscript. Figure2_BloodBased: 4 patients with late-stage breast cancer IMC data collected from their peripheral blood, including masks and output data. HR.zip: high-resolution IMC data and deconvolved examples Patient2_IMMUcan.zip: Breast tissue data from the IMMUcan study and outputs, original data provided by Windhager et al (2023) SegmentationEval.zip: Masks generated by CellSAM, Cellpose, and original masks provided by Jackson et al., 2019Scalability_and_Steinbock.zip: Performance metrics, scalabality (RAM, CPU), and comparison to steinbock for thisFeature_Benchmark.zip: comaprison of OpenIMC and Steinbock for featuresReproducibility.zip: GUI vs CLI reproducibility

  PublisherDOI

• OpenIMC

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  otherOpen access

  OpenIMC is a software tool to analyze Imaging Mass Cytometry data. Here we provide a tagged release consistent with the code at the time of submission for publication.

  PublisherDOI

• Abstract 3746: Single-cell liquid biopsy profiling in mCRPC receiving PSCA-targeted CAR-T cell therapy

  Cancer Research · 2026-04-03

  articleSenior author

  Abstract Metastatic castration-resistant prostate cancer (mCRPC) represents an aggressive, treatment-refractory stage of prostate cancer with limited treatment options and poor prognosis. Chimeric antigen receptor (CAR)-T cell therapy has demonstrated success in hematological malignancies, but its efficacy in solid tumors is limited by tumor microenvironment (TME) barriers and tumor cell heterogeneity. In this study, we applied a single-cell enrichment-free liquid biopsy platform to monitor disease progression and CAR-T cell response in mCRPC patients enrolled in a Phase 1 clinical trial (NCT03873805). Using fluorescent whole-slide imaging (fWSI), we analyzed peripheral blood (PB) and bone marrow aspirate (BMA) from eight patients (four responders and four nonresponders) collected longitudinally before, during, and after therapy. Two key findings emerged: 1) lymphodepletion mobilized circulating tumor cells (CTCs) from bone marrow into PB, altering compartment-specific cellularity regardless of response, and 2) clearance of clonal CTCs after CAR-T cell infusion occurred in responders but not in nonresponders. Single-cell analyses further revealed that PB and BMA captured distinct CTC subtypes, underscoring the complementary value of both compartments for monitoring. This multi-omic analysis leverages high-resolution single-cell liquid biopsies to characterize circulating rare cells, such as CTCs and their subtypes, to correlate them with clinically observed responses to CAR-T cell therapy in mCRPC. Citation Format: Doanna Minh Pham, Stephanie Nicole Shishido, Saul J. Priceman, Tanya B. Dorff, Peter Kuhn. Single-cell liquid biopsy profiling in mCRPC receiving PSCA-targeted CAR-T cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3746.

  PublisherDOI

• OpenIMC Data Submission

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  datasetOpen access

  The datasets associated with OpenIMC. Includes datasets and outputs for various analyses as part of the OpenIMC manuscript. Figure2_BloodBased: 4 patients with late-stage breast cancer IMC data collected from their peripheral blood, including masks and output data. HR.zip: high-resolution IMC data and deconvolved examples Patient2_IMMUcan.zip: Breast tissue data from the IMMUcan study and outputs, original data provided by Windhager et al (2023) SegmentationEval.zip: Masks generated by CellSAM, Cellpose, and original masks provided by Jackson et al., 2019Scalability_and_Steinbock.zip: Performance metrics, scalabality (RAM, CPU), and comparison to steinbock for thisFeature_Benchmark.zip: comaprison of OpenIMC and Steinbock for featuresReproducibility.zip: GUI vs CLI reproducibility

  PublisherDOI

• OpenIMC

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-23

  otherOpen access

  OpenIMC is a software tool to analyze Imaging Mass Cytometry data. Here we provide a tagged release consistent with the code at the time of submission for publication.

  PublisherDOI

• Automated Single-Cell Analysis in the Liquid Biopsy of Breast Cancer

  Cancers · 2025-08-26 · 1 citations

  articleOpen access

  Background/Objectives: Breast cancer (BC) is the most prevalent cancer worldwide, with approximately 40% of early-stage BC patients developing recurrence despite initial treatments. Current diagnostic methods, such as mammography and solid tissue biopsies, face limitations in sensitivity, accessibility, and the ability to characterize tumor heterogeneity or monitor systemic disease progression. Methods: To address these gaps, this study investigates a fully automated analysis workflow using data derived from fluorescent Whole-Slide Imaging (fWSI) for detecting and classifying rare cells (circulating tumor and tumor microenvironment cells) in peripheral blood samples. Our methodology integrates supervised machine learning algorithms for rare event detection, immunofluorescence-based classification, and statistical quantification of cellular features. Results: Using a fWSI dataset of 534 cancer and non-cancer peripheral blood samples, the automated model demonstrated high concordance with manual annotation, achieving up to 98.9% accuracy and a precision-sensitivity AUC of 83.2%. Morphometric analysis of rare cells identified significant differences between normal donors, early-stage BC, and late-stage BC cohorts, with distinct clusters emerging in late-stage BC. Conclusions: These findings highlight the potential of liquid biopsy and algorithmic approaches for improving BC diagnostics and staging, offering a scalable, minimally invasive solution to enhance clinical decision-making. Future work aims to refine the automated framework to minimize errors and improve the robustness across diverse cohorts.

  PublisherOA PDFDOI

• Cellular senescence in precancer lesions and early-stage cancers

  Cancer Cell · 2025-11-06 · 4 citations

  article
  PublisherDOI

• Anti-tumour effects of perioperative ketamine in lung cancer: a non-randomised, exploratory biomarker study

  British Journal of Anaesthesia · 2025-09-11

  letterSenior author
  PublisherDOI

• Characterizing circulating rare cells in peripheral blood for detecting and monitoring multiple myeloma and precursor states

  npj Precision Oncology · 2025-12-02

  articleOpen accessSenior authorCorresponding

  Multiple myeloma (MM) arises from abnormal plasma cells (PCs) progressing from precursor states, including monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Understanding this transition and progression to overt MM requires improved non-invasive strategies. We employed a liquid biopsy approach to detect and characterize circulating PCs across disease states in 68 patients (MGUS = 11, SMM = 21, NDMM = 19, RRMM = 17) using multi-channel immunofluorescence staining and machine learning-assisted rare event detection. PCs were identified by CD138 and B-cell maturation antigen (BCMA) expressions, with distinct phenotypic subpopulations stratifying disease states. The D | CD138 | BCMA-Memb phenotype was the most predictive, with incidence increasing from MGUS to SMM and overt MM (p < 0.005). Multivariate modeling distinguished precursors from overt disease with 86% accuracy. Shifts in BCMA and CD45 expression suggested immune cell profile alterations with progression and treatment. These findings underscore PB-based liquid biopsy as a promising tool for MM detection and monitoring, revealing circulating PC heterogeneity.

  PublisherOA PDFDOI

• Aqueous Humor Liquid Biopsy Identifies Murine Double Minute 4 Segmental Gain in Retinoblastoma: Implications for Chemotherapy Response and Precision Oncology

  JCO Precision Oncology · 2025-08-01 · 2 citations

  articleOpen access
  PublisherDOI

Recent grants

• NIH Grant P50GM062411

  NIH · $42.0M · 2006

• NIH Grant U54CA143906

  NIH · $4.2M · 2015

• NIH Grant U54GM074961

  NIH · $34.5M · 2010

• NIH Grant R33CA173373

  NIH · $1.4M · 2016

• NIH Grant U54GM094618

  NIH · $34.2M · 2016

Frequent coauthors

• Ashley M. Deacon

  367 shared

• Keith O. Hodgson

  Stanford Synchrotron Radiation Lightsource

  349 shared

• Mitchell D. Miller

  Rice University

  345 shared

• Henry van den Bedem

  University of California, San Francisco

  327 shared

• Guenter Wolf

  Stanford Synchrotron Radiation Lightsource

  326 shared

• John Wooley

  University of California, San Diego

  325 shared

• Scott A. Lesley

  293 shared

• Raymond C. Stevens

  ShanghaiTech University

  288 shared