Mutations in the epidermal growth factor receptor (EGFR) gene are commonly observed in non-small-cell lung cancer (NSCLC), particularly in tumors of adenocarcinoma (ADC) histology (NSCLC/ADC). Robust data exist regarding the prevalence of EGFR mutations in Western and Asian patients with NSCLC/ADC, yet there is a lack of data for patients of other ethnicities. This review collated available data with the aim of creating a complete, global picture of EGFR mutation frequency in patients with NSCLC/ADC by ethnicity. Worldwide literature reporting EGFR mutation frequency in patients with NSCLC/ADC was reviewed, to create a map of the world populated with EGFR mutation frequency by country (a 'global EGFR mutMap'). A total of 151 worldwide studies (n=33162 patients with NSCLC/ADC, of which 9749 patients had EGFR mutation-positive NSCLC/ADC) were included. There was substantial variation in EGFR mutation frequency between studies, even when grouped by geographic region or individual country. As expected, the Asia-Pacific NSCLC/ADC subgroup had the highest EGFR mutation frequency (47% [5958/12819; 87 studies; range 20%-76%]) and the lowest EGFR mutation frequency occurred in the Oceania NSCLC/ADC subgroup (12% [69/570; 4 studies; range 7%-36%]); however, comparisons between regions were limited due to the varying sizes of the patient populations studied. In all regional (geographic) subgroups where data were available, EGFR mutation frequency in NSCLC/ADC was higher in women compared with men, and in never-compared with ever-smokers. This review provides the foundation for a global map of EGFR mutation frequency in patients with NSCLC/ADC. The substantial lack of data from several large geographic regions of the world, notably Africa, the Middle East, Central Asia, and Central and South America, highlights a potential lack of routine mutation testing and the need for further investigations in these regions.

EGFR mutation incidence in non-small-cell lung cancer of adenocarcinoma histology: a systematic review and global map by ethnicity (mutMapII)

http://li.uscap.org/article/S0023683722002550/pdf

High-throughput proteomics: a methodological mini-review.

Altered expression and function of the transcription factor cyclic AMP response-binding protein (CREB) has been identified to play an important role in cancer and is associated with the overall survival and therapy response of tumor patients. This review focuses on the expression and activation of CREB under physiologic conditions and in tumors of distinct origin as well as the underlying mechanisms of CREB regulation by diverse stimuli and inhibitors. In addition, the clinical relevance of CREB is summarized, including its use as a prognostic and/or predictive marker as well as a therapeutic target.

/pdf/what-turns-creb-on-and-off-and-why-does-it-matter-3ncgh5hqgr.pdf

What turns CREB on? And off? And why does it matter?

In the past decades, several ground breaking discoveries in life science were made. The completion of sequencing the human genome certainly belongs to the key tasks successfully completed, representing a true milestone in the biomedicine. The accomplishment of the complete genome also brings along a new, even more challenging task for scientists: The characterization of the human proteome. Proteomics, the main tool for proteome research, is a relatively new and extremely dynamically evolving branch of science, focused on the evaluation of gene expression at proteome level. Due to the specific properties of proteins, current proteomics deals with different issues, such as protein identification, quantification, characterization of post-translational modification, structure and function elucidation, and description of possible interactions. This field incorporates technologies that can be applied to serum and tissue in order to extract important biological information in the form of biomarkers to aid clinicians and scientists in understanding the dynamic biology of their system of interest, such as a patient with cancer. The present review article provides a detail description of proteomics and its role in cancer research.

Proteomics: A new perspective for cancer

Non-small cell lung cancer (NSCLC) is difficult to cure because of the high recurrence rate and the side effects of current treatments. It is urgent to develop a new treatment that is safer and more effective than current treatments against NSCLC. Herein, we constructed anti-epidermal growth factor receptor (EGFR) peptide-conjugated PEGylated triangular gold nanoplates (TGN-PEG-P75) as a targeting photothermal therapy (PTT) agent to treat NSCLC under the guidance of computed tomography (CT) and photoacoustic (PA) imaging. The surface of TGNs is successfully conjugated with a novel peptide P75 that has the specific affinity to epidermal growth factor receptor (EGFR). It is found that the EGFR is overexpressed in NSCLC cells. The TGN-PEG-P75 has uniform edge length (77.9 ± 7.0 nm) and neutrally charged surface. The cell uptake experiments demonstrate remarkable affinity of the TGN-PEG-P75 to high EGFR expression cells than low EGFR expression cells (5.1-fold). Thanks to the strong near-infrared absorbance, ...

Anti-EGFR Peptide-Conjugated Triangular Gold Nanoplates for Computed Tomography/Photoacoustic Imaging-Guided Photothermal Therapy of Non-Small Cell Lung Cancer

Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc.) and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.

/pdf/proteomics-pathway-array-and-signaling-network-based-3tv8smefjb.pdf

Proteomics, pathway array and signaling network-based medicine in cancer

Lung cancer, specifically non-small cell lung cancer (NSCLC), is a leading cause of mortality worldwide. In China, a dramatic increase in the incidence of NSCLC is expected in the next 20 years (Molina et al. Mayo Clin Proc. 2008;83:584–594). Mutated epidermal growth factor receptor (EGFR) status is a known predictor of response to tyrosine kinase inhibitors (TKIs), and immunohistochemistry may be a less costly way of predicting presence of mutation. In this study, mutation analysis of EGFR in 218 cases of NSCLC was performed. One hundred thirty tissue samples were examined via immunohistochemistry of p-EGFR (Y1045 and Y1068) and correlated with mutation status. Mutations were seen in 29% of patients, and were correlated with female sex, nonsmoking history, and adenocarcinoma histology. Phosphorylation at Y1045 was noted in 52% of cases, but in 71% of cases with EGFR mutation (P = .003). Phosphorylation of Y1068 was seen in 55% of cases but in 73% of cases with EGFR mutation (P = .006). This study correlating EGFR mutation with p-EGFR expression in resected NSCLC is one of the largest to date, although TKI response could not be assessed. The data show that, among Chinese patients, detection of p-1045 and p-1068 expression with immunohistochemistry predicts EGFR mutations. Immunohistochemical analysis of p-EGFR may be useful to predict responses to TKI therapy, although future studies are necessary.

Epidermal growth factor receptor (EGFR) mutation and p-EGFR expression in resected non-small cell lung cancer.

Background: Current histopathological classification and TNM staging have limited accuracy in predicting survival and stratifying patients for appropriate treatment. The goal of the study is to determine whether the expression pattern of functionally important regulatory proteins can add additional values for more accurate classification and prognostication of non-small lung cancer (NSCLC). Methods: The expression of 108 proteins and phosphoproteins in 30 paired NSCLC samples were assessed using Protein Pathway Array (PPA). The differentially expressed proteins were further confirmed using a tissue microarray (TMA) containing 94 NSCLC samples and were correlated with clinical data and survival. Results: Twelve of 108 proteins (p-CREB(Ser133), p-ERK1/2(Thr202/Tyr204), Cyclin B1, p-PDK1(Ser241), CDK4, CDK2, HSP90, CDC2p34, β-catenin, EGFR, XIAP and PCNA) were selected to build the predictor to classify normal and tumor samples with 97% accuracy. Five proteins (CDC2p34, HSP90, XIAP, CDK4 and CREB) were confirmed to be differentially expressed between NSCLC (n=94) and benign lung tumor (n=19). Over-expression of CDK4 and HSP90 in tumors correlated with a favorable overall survival in all NSCLC patients and the over-expression of p-CREB(Ser133) and CREB in NSCLC correlated with a favorable survival in smokers and those with squamous cell carcinoma, respectively. Finally, the four proteins (CDK4, HSP90, p-CREB and CREB) were used to calculate the risk score of each individual patient with NSCLC to predict survival. Conclusion: In summary, our data demonstrated a broad disturbance of functionally important regulatory proteins in NSCLC and some of these can be selected as clinically useful biomarkers for diagnosis, classification and prognosis.

Protein signature for non-small cell lung cancer prognosis.

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Proteomics, pathway array and signaling network-based medicine in cancer

Epidermal growth factor receptor (EGFR) mutation and p-EGFR expression in resected non-small cell lung cancer.

Protein signature for non-small cell lung cancer prognosis.