IGHV-associated methylation signatures more accurately predict clinical outcomes of chronic lymphocytic leukemia patients than IGHV mutation load.

Currently, no molecular biomarker indices are used in standard care to make treatment decisions at diagnosis of chronic lymphocytic leukemia (CLL). We used Infinium MethylationEPIC array data from diagnostic blood samples of 114 CLL patients and developed a procedure to stratify patients based on methylation signatures associated with mutation load of the IGHV gene. This procedure allowed us to predict the time to treatment with a hazard ratio (HR) of 8.34 (95% confidence interval [CI]: 4.54-15.30), as opposed to a HR of 4.35 (95% CI: 2.60-7.28) using IGHV mutation status. Detailed evaluation of 17 cases for which the two classification procedures gave discrepant results showed that these cases were incorrectly classified using IGHV status. Moreover, methylation-based classification stratified patients with different overall survival (HR=1.82; 95% CI: 1.07-3.09), which was not possible using IGHV status. Furthermore, we assessed the performance of the developed classification procedure using published HumanMethylation450 array data for 159 patients for whom information on time to treatment, overall survival and relapse was available. Despite 450K array methylation data not containing all the biomarkers used in our classification procedure, methylation signatures again stratified patients with significantly better accuracy than did IGHV mutation load regarding all available clinical outcomes. Thus, stratification using IGHV-associated methylation signatures may provide better prognostic power than IGHV mutation status.

Plasma Immune Proteins and Circulating Tumor DNA Predict the Clinical Outcome for Non-Small-Cell Lung Cancer Treated with an Immune Checkpoint Inhibitor.

Immunotherapy has altered the therapeutic landscape for patients with non-small-cell lung cancer (NSCLC). The immune checkpoint inhibitor pembrolizumab targets the PD-1/PD-L1 signaling axis and produces durable clinical responses, but reliable biomarkers are lacking. Using 115 plasma samples from 42 pembrolizumab-treated patients with NSCLC, we were able to identify predictive biomarkers. In the plasma samples, we quantified the level of 92 proteins using the Olink proximity extension assay and circulating tumor DNA (ctDNA) using targeted next-generation sequencing. Patients with an above-median progression-free survival (PFS) had significantly higher expressions of Fas ligand (FASLG) and inducible T-cell co-stimulator ligand (ICOSLG) at baseline than patients with a PFS below the median. A Kaplan-Meier analysis demonstrated that high levels of FASLG and ICOSLG were predictive of longer PFS and overall survival (OS) (PFS: 10.83 vs. 4.49 months, OS: 27.13 vs. 18.0 months). Furthermore, we identified a subgroup with high expressions of FASLG and ICOSLG who also had no detectable ctDNA mutations after treatment initiation. This subgroup had significantly longer PFS and OS rates compared to the rest of the patients (PFS: 25.71 vs. 4.52 months, OS: 34.62 vs. 18.0 months). These findings suggest that the expressions of FASLG and ICOSLG at baseline and the absence of ctDNA mutations after the start of treatment have the potential to predict clinical outcomes.

Blood tumor mutational burden and dynamic changes in circulating tumor DNA predict response to pembrolizumab treatment in advanced non-small cell lung cancer.

Background: The use of immunotherapy targeting the programmed cell death protein-1 (PD-1) and its ligand (PD-L1) has provided new hope for patients with non-small cell lung cancer (NSCLC). However, good biomarkers are needed to identify which patients will benefit from the treatment. In this study, we investigated if circulating tumor DNA (ctDNA) could predict response to pembrolizumab. Methods: Plasma samples from patients with NSCLC treated with pembrolizumab were collected immediately before and after one or two cycles of treatment. ctDNA was isolated and analyzed using targeted next-generation sequencing with a lung cancer gene panel. Results: Mutations were detected in ctDNA in 83.93% of patients before treatment initiation. High blood tumor mutational burden (bTMB), measured as the number of different mutations per Mb panel, correlated to longer progression-free survival (PFS) (10.45 vs. 2.30 months) and overall survival (OS) (21.80 vs. 12.20 months), whereas no predictive value was found in the number of mutant molecules per mL of plasma. The absence of mutations just after treatment initiation correlated with improved PFS (20.25 vs. 4.18 months) and OS (28.93 vs. 15.33 months). High bTMB before treatment was associated with a decreasing ctDNA level after treatment initiation. Importantly, a subgroup of patients experienced an increase in the ctDNA level after treatment initiation, and this correlated with inferior PFS (2.19 vs. 11.21 months) and OS (7.76 vs. 24.20 months). All patients in the subgroup with increased ctDNA level progressed within 10 months. Conclusions: Monitoring of ctDNA contains vital information about response to therapy, where the bTMB and the dynamics in the initial part of treatment are particularly important for response. Increasing ctDNA levels after treatment initiation are significantly correlated with inferior survival.