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 W. Welsh, Eugene J. Koay, Vittorio Cristini
Abstract
= 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.
Topics & Concepts
ImmunotherapyCancer immunotherapyMedicineOncologyCancerInternal medicineCancer researchCancer Immunotherapy and BiomarkersMathematical Biology Tumor GrowthPancreatic and Hepatic Oncology Research