Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy

Joseph D. Butner, Dalia Elganainy, Charles X. Wang, Zhihui Wang, Shu Hsia Chen, Nestor F. Esnaola, Renata Pasqualini, Wadih Arap, David S. Hong, James Welsh, Eugene J. Koay, Vittorio Cristini

Research output: Contribution to journalArticle

Abstract

We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.

Original languageEnglish (US)
Article numbereaay6298
JournalScience advances
Volume6
Issue number18
DOIs
StatePublished - Apr 2020

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy'. Together they form a unique fingerprint.

Cite this