PPubMed25 Oct 2021Cited 19×

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Joseph D Butner, Geoffrey V Martin, Zhihui Wang, Bruna Corradetti, Mauro Ferrari, Nestor Esnaola, Caroline Chung, David S Hong, James W Welsh, Naomi Hasegawa, Elizabeth A Mittendorf, Steven A Curley, Shu-Hsia Chen, Ping-Ying Pan, Steven K Libutti, Shridar Ganesan, Richard L Sidman, Renata Pasqualini, Wadih Arap, Eugene J Koay, Vittorio Cristini, Caigang Liu, Mone Zaidi

Abstract

The translational mathematical model introduced here retrospectively investigates the molecular, cellular, and biophysical mechanism(s) behind patient response to immune checkpoint inhibitor therapy, and it explores the potential clinical value of the early prediction of treatment effectiveness. The reductionist approach introduced here simplifies the rather complex biological cross-talk between the immune system with cancer cells to a set of differential equations ultimately dependent on three intrinsic parameters that represent tumor proliferation, intratumoral immune cell presence/killing e

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A mechanistic mathematical model using early biopsy-derived parameters can predict individual patient response to checkpoint inhibitor therapy.

What’s new

i.Developed a mechanistic mathematical model to predict checkpoint inhibitor response.
ii.Established methods to infer model parameters directly from early biopsy samples.
iii.Demonstrated that model parameters (Λ and μ) can serve as early, robust biomarkers of therapy efficacy.
iv.Calibrated the model using multiple clinical trial datasets and validated it on an independent cohort.
v.Compared model‑derived intratumoral CD8+ T‑cell estimates with literature values.

How they did it

The authors fitted the model to clinical trial data by first estimating a tumor proliferation constant (α) from pre‑treatment imaging, then using α to compute immune‑related parameters (Λ and μ) from biopsy measurements. Calibration used six trials (n≈189 patients) and validation used a separate cohort (n=64). Model predictions were compared to RECIST v1.1 response categories.

What they found

Calibration cohort: 55 responders vs 134 non‑responders.
Validation cohort: 25 objective responders vs 39 stable/progressive disease patients.
Predicted CD8+ T‑cell density in responders: 3970 ± 1429 cells/mm² (calibration) and 4871 ± 567 cells/mm² (validation).
Predicted CD8+ T‑cell density in non‑responders: 553 ± 147 cells/mm² (calibration) and 165 ± 2604 cells/mm² (validation).
Values of 1.000 in early‑time analyses stem from only four patients imaged before 30 days.

Caveats

✕Early‑time analyses rely on a very small number of patients (n = 4), limiting statistical robustness.
✕Findings are based on specific trial cohorts and require broader validation across additional tumor types and treatment settings.

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