Background: Liquid biopsy has the potential to monitor biological effects of treatment. KRAS represents the most commonly mutated oncogene in Caucasian non-small-cell lung cancer (NSCLC). The aim of this study was to explore association of dynamic plasma KRAS genotyping with outcome in advanced NSCLC patients. Methods: Advanced NSCLC patients were prospectively enrolled. Plasma samples were collected at baseline (T1), after 3 or 4 weeks, according to treatment schedule (T2) and at first radiological restaging (T3). Patients carrying KRAS mutation in tissue were analysed in plasma with droplet digital PCR. Semi-quantitative index of fractional abundance of mutated allele (MAFA) was used. Results: KRAS-mutated cohort included 58 patients, and overall 73 treatments (N = 39 chemotherapy and N = 34 immune checkpoint inhibitors) were followed with longitudinal liquid biopsy. Sensitivity of KRAS detection in plasma at baseline was 48.3% (95% confidence interval (CI): 35.0–61.8). KRAS mutation at T2 was associated with increased probability of experiencing progressive disease as best radiological response (adjusted odds ratio: 7.3; 95% CI: 2.1–25.0, p = 0.0016). Increased MAFA (T1–T2) predicted shorter progression-free survival (adjusted hazard ratio (HR): 2.1; 95% CI: 1.2–3.8, p = 0.0142) and overall survival (adjusted HR: 3.2; 95% CI: 1.2–8.4, p = 0.0168). Conclusions: Longitudinal analysis of plasma KRAS mutations correlated with outcome: its early assessment during treatment has great potentialities for monitoring treatment outcome in NSCLC patients.

High heterogeneity in clinical benefit characterizes the use of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC). We prospectively enrolled 113 advanced NSCLC patients treated with ICIs and performed liquid biopsy at the time of ICI start (T1), after 3 weeks (T2) and at the time of radiological evaluation (T3). Molecular variables were associated with outcome endpoints: cfDNA quantification, its dynamic change (∆T2–T1), variant allele frequency (VAF) of the gene with the highest frequency detected at baseline with NGS (maxVAF) and its dynamic change (∆T2–T1). At multivariate analysis, PD-L1 negativity, T1 cfDNA, cfDNA increase (∆T2–T1), and T2 VAF were significantly associated with shorter progression-free survival (PFS); PD-L1 negativity, squamous histology, T1 cfDNA, cfDNA (∆T2–T1) increase, and T2 maxVAF affected overall survival (OS). Among high PD-L1 expressing patients treated in first-line, elevated T2 maxVAF and cfDNA increase (∆T2–T1) correlated with worse PFS; higher T2 maxVAF and cfDNA increase (∆T2–T1) with worse OS. Derived integrated models were used to build nomograms and categorize different risk groups.

Introduction: In ovarian cancer, expression of metabolism-related markers has been investigated in several studies focusing on individual markers; however, a parallel quantitative evaluation of markers mapping to distinct metabolic processes and their prognostic value in large patient cohorts is still lacking. Methods: Here, by using immunohistochemistry followed by digital pathology, we investigated the expression of several markers related to glycolysis including monocarboxylate transporter 1 and 4 (MCT1, MCT4), glutamine metabolism (glutaminase, GLS) and hypoxia/acidosis (carbonic anhydrase 9, CA IX) in tissue microarrays of > 300 patients recruited in the MITO16A clinical trial, which involved treatment of ovarian cancer patients with carboplatin/taxol plus bevacizumab. Results: Regarding the prognostic impact of these markers, results indicate that GLS expression correlated with progression-free survival, but this effect disappeared when data were corrected for multiple testing. All other markers showed no correlation with clinical outcome. Conclusion: These results indicate marked heterogeneity of expression of metabolism-associated markers in ovarian cancer; however, there was a lack of association with clinical benefit after chemotherapy/anti-vascular endothelial growth factor treatment. Notwithstanding the lack of prognostic value, knowledge of the pattern of expression of these biomarkers in tumors can be useful for patient stratification purposes when new drugs targeting these metabolic pathways will be tested.

Background: Immune checkpoint inhibitors (ICIs) are widely used in non-oncogene addicted Non-small cell lung cancer and characterized by high heterogeneity in clinical benefit. Different combination strategies are available in first-line setting and PD-L1 is the only predictive marker used in clinical practice. Methods: Patients with NSCLC treated with ICI single-agent according to clinical practice were prospectively enrolled. Liquid biopsy was performed at the time of first dose administration (T1), after 3 weeks (T2) and at the time of radiological evaluation (T3). Cell free DNA (cfDNA) was quantified (ng/ml) by qPCR and analysed by NGS targeted panel. Molecular variables used for association with outcome endpoints were: cfDNA quantification as static parameter, dynamic cfDNA change (DT2-T1), variant allele frequency of the gene with the highest frequency at base line (MaxVAF) and dynamic maxVAF change (DT2-T1). Cox regression analysis was used to build integrated predictive models. Results: 113 patients were included. At multivariate analysis, PD-L1 negativity, T1 cfDNA, cfDNA increase (DT2-T1), and maxVAF at T2 were significantly associated with shorter PFS; PD-L1 negativity, squamous histology, T1 cfDNA, increase of cfDNA (DT2-T1), and maxVAF at T2 were significantly associated with worse OS. Integrated model permitted to build a nomogram and establish three groups of patients deriving different clinical benefit from ICI. The model was tested in patients expressing PD-L1 ³50% and treated with first-line pembrolizumab (n=57) and was able to identify elevated maxVAF at T2 and increase (DT2-T1) of cfDNA as independently associated with worse PFS; higher levels of maxVAF at T2 and increase (DT2-T1) of cfDNA with worse OS. Derived integrated model was able to identify patients with different clinical benefit (high, intermediate, low risk). Conclusions: We developed an integrated nomogram to stratify NSCLC patients deriving different clinical benefit from ICIs which outperforms individual predictive markers.

Background : Sensitive detection of EGFR mutations in liquid biopsies of advanced non-small-cell lung cancer (aNSCLC) is vital for guiding targeted treatments. Real-time PCR offers a quick turnaround time but relatively low sensitivity while Next-Generation Sequencing (NGS) offers broader EGFR coverage, co-mutation evaluation and high sensitivity. Methods: This study evaluated the amplicon-based NGS Plasma-SeqSensei™ Solid Cancer In Vitro Diagnostics (IVD) Kit (Sysmex) against the real-time PCR-based cobas^® EGFR Mutation Test v2 (Roche), the current routine standard at the Veneto Institute of Oncology IOV-IRCCS. We enrolled 130 patients with aNSCLC in the RARE Study between April 2022 and August 2025 who were referred to our institute. Liquid biopsies were taken at diagnosis or at progression and analyzed using two methods with the primary objective of assessing diagnostic concordance for EGFR profiling. Sysmex NGS libraries were sequenced on a NextSeq 550 sequencer (Illumina), with the NextSeq 500/550 Mid Output Kit v2.5 (150 Cycles) in single-end mode. Results: Among 129 evaluable samples, the NGS assay demonstrated a marginally higher EGFR mutation detection rate, identifying mutations in 31/129 cases (24.0%, 95% CI: 17–33), versus 28/129 (21.7%, 95% CI: 15–30) by cobas, specifically for variants covered by both assays. Overall concordance was almost perfect (Cohen’s Kappa = 0.89, 95% CI: 0.80–0.98), confirming the high reliability of both methods. Furthermore, we identified a cfDNA input threshold of at least 20 ng as critical for ensuring optimal assay sensitivity and reliable mutation detection (Odds Ratio = 4.41 for inputs ≥ 20 ng, p = 0.006). Conclusions: Ultimately, the Sysmex NGS proved to be a robust and highly sensitive assay. It delivers performance comparable to that of standard RT-PCR while providing the clinical advantage of concurrently detecting a broader spectrum of EGFR variants and actionable mutations in other genes.