Related Experiment Video
Updated: Apr 6, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Longitudinal Blood Immune-Inflammatory and Radiomic Profiling to Decode Different Patterns of Acquired Resistance to
Giulia Mazzaschi1,2, Cristina Marrocchio2,3, Lucas Moron Dalla Tor2
1Medical Oncology Unit, University Hospital of Parma, Parma, Italy.
Purpose:
To uncover the underpinnings of acquired resistance (AR) to immunotherapy (IO), we determined whether distinctive clinicopathologic, radiomic, and peripheral blood (PB) immune-inflammatory features reflect oligo- and systemic (sys)-AR in patients with advanced non-small cell lung cancer (NSCLC) undergoing immune checkpoint inhibitor therapy.
Experimental Design:
On 105 consecutive patients with IO-treated advanced NSCLC, PB immunophenotypes, cytokines, and CT-derived radiomic features (RF), extracted from primary and merged metastatic lesions, were prospectively collected at baseline (T0) and at the first disease assessment (T1, 9-12 weeks), and their Δ variation [(T1-T0)/T0] was computed. AR, defined as progression after the initial response (complete/partial) or stable disease ≥6 months, was subdivided according to the number of new and/or progressive lesions in oligoAR (≤3) and sysAR (>3). Clinicopathologic, PB, and radiomic parameters and survival outcomes were statistically correlated to AR patterns.
Results:
OligoAR and sysAR involved 24% and 12.4% of cases, respectively. Whereas baseline PB immune profiles were comparable, a Δpos cytotoxic (NK, CD8+granzyme B+) and Δneg immunosuppressive (CD14+ monocytes) dynamic coupled with different modulations of IL-6, TGF-β1, TNFα, and soluble PD-L1 represented distinctive features of oligoAR versus sysAR (P < 0.05). Significantly longer postprogression survival characterized oligoAR versus sysAR (median 20.3 vs. 5.6 months; HR, 0.22; P < 0.001). The number and sites of oligoAR involvement appeared to condition the blood immune background (P < 0.05) and survival. ΔRFs outperformed baseline RFs, with 15 ΔRFs sharply discriminating oligoAR from sysAR (P range: <0.001-0.04). ROC analysis confirmed the optimal performance of top-ranked ΔRFs (AUC range: 0.88-0.99).
Conclusions:
Longitudinal analysis of blood-immune hallmarks and radiomic descriptors may decipher distinct patterns of AR to IO in advanced patients with NSCLC. See related commentary by Jeng and Schoenfeld, p. 1381.
