The efficacy of checkpoint inhibitors for non-small cell lung cancer (NSCLC) with MET exon 14 skipping (METΔ14ex) remains controversial. 110 consecutive METΔ14ex NSCLC patients receiving first-line chemotherapy (CHT) and/or immunotherapy (IO) in 10 German centers between 2016-2022 were analyzed. Combined CHT-IO was given to 35/110 (32%) patients, IO alone to 43/110 (39%), and CHT to 32/110 (29%) upfront. Compared to CHT, CHT-IO showed longer progression-free survival (median PFS 6 vs. 2.5 months, p = 0.004), more objective responses (ORR 49% vs. 28%, p = 0.086) and numerically longer overall survival (OS 16 vs. 10 months, p = 0.240). For IO monotherapy, OS (14 vs. 16 months) and duration of response (26 vs. 22 months) were comparable to those of CHT-IO. Primary progressive disease (PD) was more frequent with IO compared to CHT-IO (13/43 vs. 3/35, p = 0.018), particularly for never-smokers (p = 0.041). Higher PD-L1 TPS were not associated with better IO outcomes, but TP53 mutated tumors showed numerically improved ORR (56% vs. 32%, p = 0.088) and PFS (6 vs. 3 months, p = 0.160), as well as longer OS in multivariable analysis (HR=0.54, p = 0.034) compared to their wild-type counterparts. Any second-line treatment was administered to 35/75 (47%) patients, with longer survival for capmatinib or tepotinib compared to crizotinib (PFS 10 vs. 3 months, p = 0.013; OS 16 vs. 13 months, p = 0.270). CHT-IO is superior to CHT, and IO alone also effective for METΔ14ex NSCLC, especially in the presence of TP53 mutations and independent of PD-L1 expression, but never-smokers are at higher risk of primary PD.

Despite routine use of DNA-hypomethylating agents (HMAs) in AML/MDS therapy, their mechanisms of action are not yet unraveled. Pleiotropic effects of HMAs include global methylome and transcriptome changes. We asked whether in blasts and T-cells from AML patients HMA-induced in vivo demethylation and remethylation occur randomly or non-randomly, and whether gene demethylation is associated with gene induction. Peripheral blood AML blasts from patients receiving decitabine (20 mg/m² day 1–5) were serially isolated for methylome analyses (days 0, 8 and 15, n = 28) and methylome-plus-transcriptome analyses (days 0 and 8, n = 23), respectively. T-cells were isolated for methylome analyses (days 0 and 8; n = 16). We noted massive, non-random demethylation at day 8, which was variable between patients. In contrast, T-cells disclosed a thousand-fold lesser, random demethylation, indicating selectivity of the demethylation for the malignant blasts. The integrative analysis of DNA demethylation and transcript induction revealed 87 genes displaying a significant inverse correlation, e.g. the tumor suppressor gene IFI27, whose derepression was validated in two AML cell lines. These results support HMA-induced, non-random early in vivo demethylation events in AML blasts associated with gene induction. Larger patient cohorts are needed to determine whether a demethylation signature may be predictive for response to this treatment.

UICC stage IV small-cell lung cancer (SCLC) is a highly aggressive malignancy without curative treatment options. Several randomized trials have demonstrated improved survival rates through the addition of checkpoint inhibitors to first-line platin-based chemotherapy. Consequently, a combination of chemo- and immunotherapy has become standard palliative treatment. However, no reliable predictive biomarkers for treatment response exist. Neither PD-L1 expression nor tumor mutational burden have proven to be effective predictive biomarkers. In this study, we compared the cellular immune statuses of SCLC patients to a healthy control cohort and investigated changes in peripheral blood B, T, and NK lymphocytes, as well as several of their respective subsets, during treatment with immunochemotherapy (ICT) using flow cytometry. Our findings revealed a significant decrease in B cells, while T cells showed a trend to increase throughout ICT. Notably, high levels of exhausted CD4+ and CD8+ cells, alongside NK subsets, increased significantly during treatment. Furthermore, we correlated decreases/increases in subsets after two cycles of ICT with survival. Specifically, a decrease in Th17 cells indicated a better overall survival. Based on these findings, we suggest conducting further investigation into Th17 cells as a potential early predictive biomarkers for response in patients receiving palliative ICT for stage IV SCLC.

Introduction In older patients (pts), host factors such as functional deficits, comorbidities and other age-related factors are increasingly recognized as predictors for outcome of leukemia treatment. Thus, prospective clinical trials increasingly implement functional studies (often termed geriatric assessment) in their pretreatment diagnostic workup. In older, fit AML pts receiving standard chemotherapy, Klepin et al. demonstrated a predictive value of this approach for overall survival (OS) in a single-center study (Blood 2013). We also developed a Frailty Score, assessed at 3 study centers and predicting OS of elderly AML/MDS pts receiving either hypomethylating agents (n=66) or sole best supportive care (n=35; Deschler et al., Haematologica 2013). To validate this score, which is composed of performance status (PS), activities of daily living (ADL) and fatigue, we prospectively assessed these parameters in the DECIDER trial (AMLSG 14-09, NCT00867672). Methods In the DECIDER trial, 200 non-fit AML pts aged >60 years (yr) were randomized between four treatment arms with either decitabine (DAC) alone, or DAC plus all-trans retinoid acid (ATRA) or DAC plus valproic acid (VPA) or DAC plus ATRA and VPA. We assessed PS via ECOG, ADL via Barthel index and fatigue via the EORTC QLQ-C30 questionnaire, which were available for 200, 175 and 156 pts, respectively. Pts with missing ADL and/or fatigue assessments tended to have a lower PS than those with complete data, which may explain at least in part why the assessments could not be performed. The Frailty Score was calculated using ECOG PS as a substitute for the Karnofsky Index (KI, as applied when establishing this score), with ECOG 0-1 taken as corresponding to KI ≥80, and ECOG 2-3 to KI <80%. The influence of the single factors (ECOG, ADL, fatigue) and of the Frailty Score on OS was investigated with Cox regression models adjusted for treatment, and in analyses adjusted additionally for established disease-related prognostic factors. Results For 141 pts with complete data in all 3 parameters building the Frailty Score, median follow-up time was 28 months, and 122 pts had died. The median age of pts at AML diagnosis was 76 yr (range 61-90) and 96 pts (68.1%) were males. Median WBC was 3.100/µl (interquartile range [IQR] 1.400 to 11.500/µl), median platelet count 49.000/µl (IQR 30.000 to 98.000/µl), median hemoglobin (Hb) 9.2g/dl (IQR 8.3 to 10.1g/dl), with median peripheral blasts of 48% (IQR 25 to 76%) and median LDH 287 U/l (IQR 203 to 459 U/l). 32.6% of the pts had adverse genetic risk according to ELN 2010 criteria. 56.7% had relevant comorbidities (HCT-CI ≥3). The majority of pts had a reduced PS (21.3% ECOG 0, 62.4% ECOG 1 and 16.3% ECOG 2-3). When assessing ADL, 30.5% had limitations (ADL 1, ADL <100 and fatigue ≥50 had significant adverse effects on OS, with estimated hazard ratios (HRs) of 1.78, 1.91, and 1.62, respectively (Table 1). When the analyses were additionally adjusted for the established disease-related factors HCT-CI, LDH, hemoglobin, and genetics, effects were reduced with estimated HRs of 1.38, 1.37, and 1.40, respectively. Our Frailty Score, applied to this cohort of AML pts, showed that with the defined cut-off values of PS via ECOG, ADL and fatigue, pts can be grouped into three risk categories (combining pts with score 2 and 3 into one group) with significantly different OS (adjusted for treatment, trend test p<0.0001, Table 1, Figure 1). When the analysis was adjusted for the stated disease-related prognostic factors, this effect was also reduced, but still present with an estimated HR of 1.99 comparing 1 vs. 0 points, and of 1.70 comparing 3 vs. 0 points, trend test p=0.033. Conclusions This previously described pt fitness assessment to calculate the Frailty Score could now be implemented in a multicenter academic trial setting. In our independent, large AML cohort, the score was able to separate pts into groups with different OS, underlining the emerging roles of ADL and fatigue as predictors of outcome, in addition to the already well-established role of PS. A second prospective validation study, in AML pts aged ≥ 60 yr receiving standard "7+3" induction therapy or 10-day DAC (EORTC AML21 "inDACtion vs. induction" trial, NCT02172872), is ongoing.