Enhancing Oncology Treatment through Plasma Proteomics and Machine Learning

K. McGregor [1], [6], M. Harel [2], A. P. Dicker [3], J. Subramanian [4], E.S. Santos [5], I. Sela. Y. Elon [2], D.R. Gandara [7]

[1] Intermountain Health, Grand Junction, CO, [2] OncoHost, Binyamina, Israel, [3] Thomas Jefferson University, Philadelphia, PA, [4] Inova Schar, Fairfax, VA, [5] The Oncology Institute of Hope and Innovation, Fort Lauderdale, FL, [6] OncoHost, Cary, NC, [7] UC Davis, Sacramento, CA

NARRATION

K. McGregor [1], [6], M. Harel [2], A. P. Dicker [3], J. Subramanian [4], E.S. Santos [5], I. Sela. Y. Elon [2], D.R. Gandara [7]. Enhancing Oncology Treatment through Plasma Proteomics and Machine Learning. Uploaded to https://www.posterpresentations.com/research/posters/VH-57113/. Submitted on March 19, 2025.

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Poster - #VH-57113 - Keywords: oncology proteomics machine learning liquid biopsy bioinformatics metastatic non-small cell lung cancer monotherapy chemotherapy PD-L1

Enhancing Oncology Treatment through Plasma Proteomics and Machine Learning

K. McGregor [1], [6], M. Harel [2], A. P. Dicker [3], J. Subramanian [4], E.S. Santos [5], I. Sela. Y. Elon [2], D.R. Gandara [7]
[1] Intermountain Health, Grand Junction, CO, [2] OncoHost, Binyamina, Israel, [3] Thomas Jefferson University, Philadelphia, PA, [4] Inova Schar, Fairfax, VA, [5] The Oncology Institute of Hope and Innovation, Fort Lauderdale, FL, [6] OncoHost, Cary, NC, [7] UC Davis, Sacramento, CA

ABSTRACT:
Liquid biopsy offers unique opportunities to examine biological processes in patients, enabling the optimization of treatment. The PROphet platform uses bioinformatic proteomic pattern recognition and machine learning to create algorithms to predict the probability of clinical benefit from immunotherapy.