Triple-negative breast cancers (TNBCs) lack progesterone and estrogen receptors and do not have amplified human epidermal growth factor receptor 2, the main therapeutic targets for managing breast cancer. TNBCs have an altered metabolism, including an increased Warburg effect and glutamine dependence, making the glutaminase inhibitor CB-839 therapeutically promising for this tumor type. Accordingly, CB-839 is currently in phase I/II clinical trials. However, not all TNBCs respond to CB-839 treatment, and the tumor resistance mechanism is not yet fully understood. Here we classified cell lines as CB-839–sensitive or –resistant according to their growth responses to CB-839. Compared with sensitive cells, resistant cells were less glutaminolytic and, upon CB-839 treatment, exhibited a smaller decrease in ATP content and less mitochondrial fragmentation, an indicator of poor mitochondrial health. Transcriptional analyses revealed that the expression levels of genes linked to lipid metabolism were altered between sensitive and resistant cells and between breast cancer tissues (available from The Cancer Genome Atlas project) with low versus high glutaminase (GLS) gene expression. Of note, CB-839–resistant TNBC cells had increased carnitine palmitoyltransferase 2 (CPT2) protein and CPT1 activity levels. In agreement, CB-839–resistant TNBC cells mobilized more fatty acids into mitochondria for oxidation, which responded to AMP-activated protein kinase and acetyl-CoA carboxylase signaling. Moreover, chemical inhibition of both glutaminase and CPT1 decreased cell proliferation and migration of CB-839–resistant cells compared with single inhibition of each enzyme. We propose that dual targeting of glutaminase and CPT1 activities may have therapeutic relevance for managing CB-839–resistant tumors.

The poor prognosis of head and neck cancer (HNC) is associated with metastasis within the lymph nodes (LNs). Herein, the proteome of 140 multisite samples from a 59-HNC patient cohort, including primary and matched LN-negative or -positive tissues, saliva, and blood cells, reveals insights into the biology and potential metastasis biomarkers that may assist in clinical decision-making. Protein profiles are strictly associated with immune modulation across datasets, and this provides the basis for investigating immune markers associated with metastasis. The proteome of LN metastatic cells recapitulates the proteome of the primary tumor sites. Conversely, the LN microenvironment proteome highlights the candidate prognostic markers. By integrating prioritized peptide, protein, and transcript levels with machine learning models, we identify nodal metastasis signatures in blood and saliva. We present a proteomic characterization wiring multiple sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.

Early detection of oral squamous cell carcinoma (OSCC) significantly enhances treatment outcomes and survival rates, with lymph node metastasis serving as a main prognostic factor. However, current clinical practices rely on TNM classification, including histological confirmation of metastatic disease in lymph nodes, often involving elective neck dissection, a procedure that can cause post‐operative morbidity. Here it is shown that zinc imidazole framework‐8 (ZIF‐8) electrochemical biosensors can effectively distinguish non‐metastatic (N0) from lymph node metastatic (N+) OSCC saliva samples. By monitoring the OSCC biomarkers cystatin B (CSTB), leukotriene A 4 hydrolase (LTA4H), and collagen type VI alpha 1 chain (COL6A1) in human saliva through electrochemical impedance spectroscopy and antigen–antibody immunoreactions, elevated biomarker levels in N0 samples are observed. The biosensor displays high accuracy, specificity, and reproducibility, with limits of detection lower than 0.4 ng mL ⁻¹ . Supervised bioinformatic analysis, using 34 machine learning classifiers, indicates LTA4H as the most accurate biomarker for distinguishing prognostic groups, confirming previous mass spectrometry findings. Notably, the AdaBoost model, integrating the combined detection of biomarkers, achieves a 76% accuracy rate in identifying metastatic saliva samples. This non‐invasive biosensor technology, combined with bioinformatics, presents a sensitive and reliable approach to improve clinical assessments and guiding therapeutic decisions for OSCC patients.

Single-cell proteomics (SCP) has advanced considerably but still is largely limited to homogeneous populations and distant from clinical applicability. We present an SCP workflow for assessing the cellular heterogeneity in saliva and tear fluid. Initially, benchmarks were established using a standard HeLa digestion curve, resulting in more than 5,463 protein groups (PGs) at 50 pg. For single HeLa cells, the workflow was improved to minimize contamination and increase quantitative performance, reaching a maximum of 3,785 PGs per single cell. Following, SCP was benchmarked across heterogenous populations of saliva and tear fluid, collected from 10 healthy individuals. By improving cell isolation, contamination control, and DIA-based search and quantitation, single cells from saliva (n=110) and tear fluid (n=149), with average diameters of 8 and 11 µm, respectively, yielded a maximum of 700 PGs per single cell. Downstream analysis indicated overrepresented protein functions, distinct cluster markers and twenty-three validated therapeutic targets identified from single-cell data. Taken together, this study demonstrates the robustness of our SCP workflow applied to biofluids, driving the discovery of biomarkers and therapeutic targets in complex microenvironments.

1. Single-cell proteomics (SCP) based on mass spectrometry has emerged as a powerful and transformative tool in clinical medicine research, bringing a holistic cellular view and a comprehensive ana...