Comprehensive proteogenomic characterization of rare kidney tumors.

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness.

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

The limitless applications of single-cell metabolomics.

Single-cell metabolomics (SCM) is currently one of the most powerful tools for performing high-throughput metabolic analysis at the cellular level. The power of single-cell metabolomics to determine the metabolic profiles of individual cells makes it very suitable for decoding cell heterogeneity. SCM bears great potential in cell type identification and differentiation within cell colonies. With the development of various equipment and techniques, SCM analysis has become possible for a wide range of biological samples. Many fields have incorporated this cutting-edge analytic tool to generate fruitful findings. This review article pays close attention to the prevalent techniques utilized in SCM and the exciting new findings and applications developed by studies in phytology, neurology, and oncology using SCM.

Bridging the Metabolic Parallels Between Neurological Diseases and Cancer.

Despite the many recent breakthroughs in cancer research, oncology has traditionally been seen as a distinct field from other diseases. Recently, more attention has been paid to repurposing established therapeutic strategies and targets of other diseases towards cancer treatment, with some of these attempts generating promising outcomes [1, 2]. Recent studies using advanced metabolomics technologies [3] have shown evidence of close metabolic similarities between cancer and neurological diseases. These studies have unveiled several metabolic characteristics shared by these two categories of diseases, including metabolism of glutamine, gamma-aminobutyric acid (GABA), and N-acetyl-aspartyl-glutamate (NAAG) [4-6]. The striking metabolic overlap between cancer and neurological diseases sheds light on novel therapeutic strategies for cancer treatment. For example, 2-(phosphonomethyl) pentanedioic acid (2-PMPA), one of the glutamate carboxypeptidase II (GCP II) inhibitors that prevent the conversion of NAAG to glutamate, has been shown to suppress cancer growth [6, 7]. These promising results have led to an increased interest in integrating this metabolic overlap between cancer and neurological diseases into the study of cancer metabolism. The advantages of studying this metabolic overlap include not only drug repurposing but also translating existing knowledge from neurological diseases to the field of cancer research. This chapter discusses the specific overlapping metabolic features between cancer and neurological diseases, focusing on glutamine, GABA, and NAAG metabolisms. Understanding the interconnections between cancer and neurological diseases will guide researchers and clinicians to find more effective cancer treatments.

Mitochondrial copper depletion suppresses triple-negative breast cancer in mice.

Depletion of mitochondrial copper, which shifts metabolism from respiration to glycolysis and reduces energy production, is known to be effective against cancer types that depend on oxidative phosphorylation. However, existing copper chelators are too toxic or ineffective for cancer treatment. Here we develop a safe, mitochondria-targeted, copper-depleting nanoparticle (CDN) and test it against triple-negative breast cancer (TNBC). We show that CDNs decrease oxygen consumption and oxidative phosphorylation, cause a metabolic switch to glycolysis and reduce ATP production in TNBC cells. This energy deficiency, together with compromised mitochondrial membrane potential and elevated oxidative stress, results in apoptosis. CDNs should be less toxic than existing copper chelators because they favorably deprive copper in the mitochondria in cancer cells instead of systemic depletion. Indeed, we demonstrate low toxicity of CDNs in healthy mice. In three mouse models of TNBC, CDN administration inhibits tumor growth and substantially improves survival. The efficacy and safety of CDNs suggest the potential clinical relevance of this approach.