Longitudinal Blood Immune-Inflammatory and Radiomic Profiling to Decode Different Patterns of Acquired Resistance to Immunotherapy in Patients with NSCLC.

Abstract

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. 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. 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). 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.