Proteomic Analysis of Lung Cancer Types—A Pilot Study

Proteomic Profiling of Small-Cell Lung Cancer: A Systematic Review

The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Whole-exome and targeted gene sequencing of large-cell lung carcinoma reveals recurrent mutations in the PI3K pathway

BACKGROUND

Large cell lung carcinoma (LCLC) is an exceptionally aggressive disease with a poor prognosis. At present, little is known about the molecular pathology of LCLC.

METHODS

Ultra-deep sequencing of cancer-related genes and exome sequencing were used to detect the LCLC mutational in 118 tumor-normal pairs. The cell function test was employed to confirm the potential carcinogenic mutation of PI3K pathway.

RESULTS

The mutation pattern is determined by the predominance of A > C mutations. Genes with a significant non-silent mutation frequency (FDR) < 0.05) include TP53 (47.5%), EGFR (13.6%) and PTEN (12.1%). Moreover, PI3K signaling (including EGFR, FGRG4, ITGA1, ITGA5, and ITGA2B) is the most mutated pathway, influencing 61.9% (73/118) of the LCLC samples. The cell function test confirmed that the potential carcinogenic mutation of PI3K pathway had a more malignant cell function phenotype. Multivariate analysis further revealed that patients with the PI3K signaling pathway mutations have a poor prognosis (P = 0.007).

CONCLUSIONS

These results initially identified frequent mutation of PI3K signaling pathways in LCLC and indicate potential targets for the treatment of this fatal type of LCLC.

Supervised topological data analysis for MALDI mass spectrometry imaging applications

BACKGROUND

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) displays significant potential for applications in cancer research, especially in tumor typing and subtyping. Lung cancer is the primary cause of tumor-related deaths, where the most lethal entities are adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Distinguishing between these two common subtypes is crucial for therapy decisions and successful patient management.

RESULTS

We propose a new algebraic topological framework, which obtains intrinsic information from MALDI data and transforms it to reflect topological persistence. Our framework offers two main advantages. Firstly, topological persistence aids in distinguishing the signal from noise. Secondly, it compresses the MALDI data, saving storage space and optimizes computational time for subsequent classification tasks. We present an algorithm that efficiently implements our topological framework, relying on a single tuning parameter. Afterwards, logistic regression and random forest classifiers are employed on the extracted persistence features, thereby accomplishing an automated tumor (sub-)typing process. To demonstrate the competitiveness of our proposed framework, we conduct experiments on a real-world MALDI dataset using cross-validation. Furthermore, we showcase the effectiveness of the single denoising parameter by evaluating its performance on synthetic MALDI images with varying levels of noise.

CONCLUSION

Our empirical experiments demonstrate that the proposed algebraic topological framework successfully captures and leverages the intrinsic spectral information from MALDI data, leading to competitive results in classifying lung cancer subtypes. Moreover, the framework's ability to be fine-tuned for denoising highlights its versatility and potential for enhancing data analysis in MALDI applications.

Inter- and intratumoral proteomics and glycosaminoglycan characterization of ALK rearranged lung adenocarcinoma tissues: a pilot study

Lung cancer is one of the most common types of cancer with limited therapeutic options, therefore a detailed understanding of the underlying molecular changes is of utmost importance. In this pilot study, we investigated the proteomic and glycosaminoglycan (GAG) profile of ALK rearranged lung tumor tissue regions based on the morphological classification, mucin and stromal content. Principal component analysis and hierarchical clustering revealed that both the proteomic and GAG-omic profiles are highly dependent on mucin content and to a lesser extent on morphology. We found that differentially expressed proteins between morphologically different tumor types are primarily involved in the regulation of protein synthesis, whereas those between adjacent normal and different tumor regions take part in several other biological processes (e.g. extracellular matrix organization, oxidation-reduction processes, protein folding) as well. The total amount and the sulfation profile of heparan sulfate and chondroitin sulfate showed small differences based on morphology and larger differences based on mucin content of the tumor, while an increase was observed in both the total amount and the average rate of sulfation in tumors compared to adjacent normal regions.

Compositional Analysis of Glycosaminoglycans in Different Lung Cancer Types-A Pilot Study

Lung cancer is one of the most commonly diagnosed cancer types. Studying the molecular changes that occur in lung cancer is important to understand tumor formation and identify new therapeutic targets and early markers of the disease to decrease mortality. Glycosaminoglycan chains play important roles in various signaling events in the tumor microenvironment. Therefore, we have determined the quantity and sulfation characteristics of chondroitin sulfate and heparan sulfate in formalin-fixed paraffin-embedded human lung tissue samples belonging to different lung cancer types as well as tumor adjacent normal areas. Glycosaminoglycan disaccharide analysis was performed using HPLC-MS following on-surface lyase digestion. Significant changes were identified predominantly in the case of chondroitin sulfate; for example, the total amount was higher in tumor tissue compared to the adjacent normal tissue. We also observed differences in the degree of sulfation and relative proportions of individual chondroitin sulfate disaccharides between lung cancer types and adjacent normal tissue. Furthermore, the differences in the 6-O-/4-O-sulfation ratio of chondroitin sulfate were different between the lung cancer types. Our pilot study revealed that further investigation of the role of chondroitin sulfate chains and enzymes involved in their biosynthesis is an important aspect of lung cancer research.

Speciation Analysis Highlights the Interactions of Auranofin with the Cytoskeleton Proteins of Lung Cancer Cells

Two types of lung cells (epithelial cancer lung cells, A-549 and lung fibroblasts MRC-5) were exposed to the clinically established gold drug auranofin at concentrations close to the half-maximal inhibitory drug concentrations (IC₅₀). Collected cells were subjected to speciation analysis using inductively coupled plasma mass spectrometry (ICP-MS). Auranofin showed better affinity toward proteins than DNA, RNA, and hydrophilic small molecular weight compounds. It can bind to proteins that vary in size (~20 kDa, ~75 kDa, and ≥200 kDa) and pI. However, the possibility of dimerization and protein–protein complex formation should also be taken into account. µRPLC/CZE-ESI-MS/MS studies on trypsinized proteins allowed the indication of 76 peptides for which signal intensity was influenced by auranofin presence in cells. Based on it, identity was proposed for 20 proteins. Except for thioredoxin reductase (TrxR), which is directly targeted by gold complex, the proteins were found to be transformed. Five indicated proteins: myosin, plectin, talin, two annexins, and kinase M3K5, are responsible for cell–cell, cell–protein interactions, and cell motility. A wound healing test confirmed their regulation by auranofin as cell migration decreased by 40% while the cell cycle was not interrupted.

Optimized Sample Preparation and Microscale Separation Methods for High-Sensitivity Analysis of Hydrophilic Peptides

The optimization of solid-phase extraction (SPE) purification and chromatographic separation is usually neglected during proteomics studies. However, the effects on detection performance are not negligible, especially when working with highly glycosylated samples. We performed a comparative study of different SPE setups, including an in-house optimized method and reversed-phase chromatographic gradients for the analysis of highly glycosylated plasma fractions as a model sample for glycopeptide analysis. The in-house-developed SPE method outperformed the graphite-based and hydrophilic interaction liquid chromatography (HILIC) purification methods in detection performance, recovery, and repeatability. During optimization of the chromatography, peak distribution was maximized to increase the peptide detection rate. As a result, we present sample purification and chromatographic separation methods optimized for the analysis of hydrophilic samples, the most important of which is heavily N-glycosylated protein mixtures.