In a retrospective study design, we analysed analysed 130 patients (age 68 ± 7 y) with NSCLC stage IV. Only patients with lung adenocarcinoma were included. Patients with ALK, ROS1, RET, MET, NTRK, EGFR, BRAF mutation were excluded. All patients were treated in our lung cancer centre in the Florence-Nightingale hospital in Düsseldorf / Germany. We collected data from 06/2017 to 01/22.

Patients were treated according to the guidelines with either CPI alone (pembrolizumab, nivolumab, atezolizumab, cemiplimab) or CIT (Carboplatin/Pemetrexed/Pembrolizumab, Carboplatin/Paclitaxel/Atezolizumab).

We registered patients’ characteristics including TTF-1 expression and comorbidities.

TTF-1 expression was measured immunohistochemically using the SPT24 antibody. We accepted prior therapy lines of NSCLC according to guidelines.

We chose variables, based on their prognostic impact in NSCLC and risk factors for infections. This included age, sex, BMI, ECOG performance status, lung cancer pathology (squamous and non-squamous NSCLC), comorbidities (COPD, diabetes mellitus, hypertension), PD-L1 status, AMT use and timing of AMT.

The AMT prescription and the timing of its intake were recorded in the medical history.

Group 1 consisted of 40 patients with CPI and TTF-1 expression, group 2 were 26 patients with CPI and with no TTF-1 expression. Group 3 consisted of 41 patients with CIT and TTF-1 expression, group 4 were 23 patients with CIT and with no TTF-1 expression. Patient characteristics are shown in Table 1.

Information about TTF-1 expression and the other variables were taken from our electronic patient database. Only patients with all available data about TTF-1 expression and PFS were included in the analysis.

PFS was determined according to classical RECIST criteria in repeated restaging with computed tomography during antineoplastic therapy.

Continuous variables are expressed as mean ± SD or median and compared using t-test unless stated otherwise. Statistical analysis was performed using SPSS (Version 28, IBM, Armonk, NY).

Cox proportional-hazards regression was used to analyse the effect of several factors on progression free survival in uni- and multivariable analyses.

Cox regression survival curves were generated to visualize the distribution of times from baseline to disease progression. All statistical tests were 2-tailed and a p-value < 0.05 was considered statistically significant.