Purpose: Metabolic reprogramming is now characterized as one of the core hallmarks of cancer, and it has already been shown that the altered genomic profile of metabolically rewired cancer cells can give valuable information. In this study, we quantified three Metabolism-Related Gene (MRG) transcripts in the circulating tumor cells (CTCs) of early stage NSCLC patients and evaluated their associations with epithelial and EMT markers. Experimental Design: We first developed and analytically validated highly sensitive RT-qPCR assays for the quantification of HK2, MCT1 and PHGDH transcripts, and further studied the expression of MRGs in CTCs that were isolated using a size-dependent microfluidic device (Parsortix, Angle) from the peripheral blood of: (a) 46 NSCLC patients at baseline, (b) 39/46 of these patients one month after surgery, (c) 10/46 patients at relapse and (d) 10 pairs of cancerous and adjacent non-cancerous FFPE tissues from the same NSCLC patients. Epithelial and EMT markers were also evaluated. Results: MCT1 and HK2 were differentially expressed between HD and NSCLC patients. An overexpression of MCT1 was detected in 15/46 (32.6%) and 3/10 (30%) patients at baseline and at progression disease (PD), respectively, whereas an overexpression of HK2 was detected in 30.4% and 0% of CTCs in the same group of samples. The expression levels of all tested MRGs decreased in CTCs one month after surgery, but a significant increase was noticed at the time of relapse for PHGDH and MCT1 only. The expression levels of HK2 and MCT1 were associated with the overexpression of mesenchymal markers (TWIST-1 and VIM). Conclusion: An overexpression of MRGs was observed at a high frequency in the CTCs isolated from early NSCLC patients, thereby supporting the role of MRGs in metastatic processes. The glycolytic and mesenchymal subpopulation of CTCs was significantly predominant compared to CTCs that were glycolytic but not mesenchymal-like. Our data indicate that MRGs merit further evaluation through large and well-defined cohort studies.

Immunoglobulin M (IgM) monoclonal gammopathy of undetermined significance (IgM-MGUS) and asymptomatic Waldenström macroglobulinemia (WM; aWM) are precursor conditions of symptomatic WM with an annual 1.5% to 12% risk of progression. Although clinical prognostic models exist for risk stratification, it remains challenging to distinguish asymptomatic patients who will eventually progress from those who will not. Hence, the characterization of genomic features that shape disease progressors could potentially improve risk stratification. We performed whole-exome sequencing on 229 samples from 139 patients, including 9 patients with sequential samples. We observed an increasing mutation burden through the stages of disease evolution. Genes such as CD79B, ARID1A, and CREBBP were more often mutated in the aWM progressed (aWMpr) compared with the stable aWM (aWMst) group, whereas MYD88L265 variant allele frequency was significantly higher in patients with aWMpr than those with aWMst. In addition, patients with IgM-MGUS with MYD88WT genotype showed a distinct genomic profile compared with the MYD88MUT patients. Furthermore, the presence of more aneuploidies showed a significant association with a higher risk of progression to the symptomatic disease. Overall, our study shows that genomic profiling of patients’ tumor at the time of aWM diagnosis might represent an improved strategy for identifying patients at high risk to progression who could benefit from earlier intervention.

Bottom‐up proteomics relies on efficient and repeatable sample preparation for accurate protein identification and precise quantification. This study evaluates the performance of adapted SPEED (Sample Preparation by Easy Extraction and Digestion) protocol, a simplified, detergent‐free approach tailored for various biological matrices, including lysis‐resistant samples. Protein extraction and denaturation steps were refined for 8 biological matrices enabling standardized, cheap, and scalable proteomics analysis on 96‐well plates. For tissue samples requiring downstream applications like Western blotting, we used a low‐detergent RIPA buffer. Notably, the protocols demonstrate remarkable down‐scalability, enabling robust proteomics measurements from as few as 3000 cells per sample for preparation and even down to 300 cells per LC‐MS/MS analysis. Key advancements include a 30‐min nanoLC‐MS/MS run, achieving a 15–20 samples‐per‐day throughput, and leveraging the power of diaPASEF using thoroughly optimized DIA‐windows to enhance proteome coverage. These adaptations streamline workflows, enabling proteomics analyses in matrices with challenging physical and biochemical properties. This study underscores the importance of early‐stage optimization and feasibility testing in proteomics pipelines to inform study design and sample selection. By showcasing robust, scalable adaptations of the SPEED protocol, we provide a foundation for reproducible, high‐throughput proteomic studies across diverse biological contexts.

Small extracellular vesicles (sEVs) are extracellular vesicles that mediate intercellular communication through the transfer of bioactive molecules, including proteins, lipids, and nucleic acids. Among their cargo, microRNAs (miRNAs) have emerged as critical regulators of gene expression with significant implications in cancer biology. Tumor-derived extracellular vesicle-associated microRNAs (EV-miRNAs) can reprogram recipient cells, promoting oncogenesis, metastasis, angiogenesis, and therapeutic resistance. This review provides a comprehensive overview of EV-miRNAs in cancer, examining their biogenesis, mechanisms of intercellular transfer, and functional roles in tumor progression. We discuss the clinical potential of EV-miRNAs as non-invasive biomarkers for cancer diagnosis and prognosis, as well as their emerging applications in targeted therapeutic strategies. Furthermore, we address current challenges related to isolation techniques, quantification methods, and standardization protocols that hinder clinical translation. Finally, we outline future directions for integrating EV-miRNA analysis into precision oncology frameworks and liquid biopsy platforms, highlighting opportunities to advance personalized cancer care.