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      Response and toxicity prediction by MALDI-TOF-MS serum peptide profiling in patients with non-small cell lung cancer

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          Abstract

          We validated a previously reported proteomic signature, associated with treatment outcome, in an independent cohort of patients with non-small cell lung cancer (NSCLC). A novel peptide signature was developed to predict toxicity.

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          Most cited references14

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          Is Open Access

          Non-invasive approaches to monitor EGFR-TKI treatment in non-small-cell lung cancer

          Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are standard treatments for advanced non-small-cell lung cancer (NSCLC) patients harboring activating epidermal growth factor receptor (EGFR) mutations. Nowadays, tumor tissues acquired by surgery or biopsy are the routine materials for EGFR mutation analysis. However, the accessibility of tumor tissues is not always satisfactory in advanced NSCLC. Moreover, a high proportion of NSCLC patients will eventually develop resistance to EGFR-TKIs. Invasive procedures, such as surgery or biopsy, are impractical to be performed repeatedly to assess the evolution of EGFR-TKI resistance. Thus, exploring some convenient and less invasive techniques to monitor EGFR-TKI treatment is urgently needed. Circulating cell-free tumor DNA (ctDNA) has a high degree of specificity to detect EGFR mutations in NSCLC. Besides, ctDNA is capable of monitoring the disease progression during EGFR-TKI treatment. Certain serum microRNAs that correlate with EGFR signaling pathway, such as miR-21 and miR-10b, have been demonstrated to be helpful in evaluating the efficiency of EGFR-TKI therapeutics. A commercialized serum-based proteomic test, named VeriStrat test, has shown an outstanding ability to predict the clinical outcome of NSCLC patients receiving EGFR-TKIs. Analysis of EGFR mutations in circulating tumor cells (CTCs) is feasible, and CTCs represent a promising material to predict EGFR-TKI-treatment efficacy and resistance. These evidences suggested that non-invasive techniques based on serum or plasma samples had a great potential for monitoring EGFR-TKI treatment in NSCLC. In this review, we summarized these non-invasive approaches and considered their possible applications in EGFR-TKI-treatment monitoring.
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            Why have so few proteomic biomarkers "survived" validation? (Sample size and independent validation considerations).

            Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC-MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC-MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.
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              Molecular biology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines.

              Based on recent bench and clinical research, the treatment of lung cancer has been refined, with treatments allocated according to histology and specific molecular features. For example, targeting mutations such as epidermal growth factor receptor (EGFR) with tyrosine kinase inhibitors has been particularly successful as a treatment modality, demonstrating response rates in selected patients with adenocarcinoma tumors harboring EGFR mutations that are significantly higher than those for conventional chemotherapy. However, the development of new targeted therapies is, in part, highly dependent on an improved understanding of the molecular underpinnings of tumor initiation and progression, knowledge of the role of molecular aberrations in disease progression, and the development of highly reproducible platforms for high-throughput biomarker discovery and testing. In this article, we review clinically relevant research directed toward understanding the biology of lung cancer. The clinical purposes of this research are (1) to identify susceptibility variants and field molecular alterations that will promote the early detection of tumors and (2) to identify tumor molecular alterations that serve as therapeutic targets, prognostic biomarkers, or predictors of tumor response. We focus on research developments in the understanding of lung cancer somatic DNA mutations, chromosomal aberrations, epigenetics, and the tumor microenvironment, and how they can advance diagnostics and therapeutics.
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                Author and article information

                Journal
                PROTEOMICS - Clinical Applications
                Prot. Clin. Appl.
                Wiley
                18628346
                July 2016
                July 2016
                May 27 2016
                : 10
                : 7
                : 743-749
                Affiliations
                [1 ]Department of Medical Oncology; VU University Medical Center; Amsterdam The Netherlands
                [2 ]Department of Pulmonary Diseases; VU University Medical Center; Amsterdam The Netherlands
                [3 ]OncoProteomics Laboratory; Department of Medical Oncology; VU University Medical Center; Amsterdam The Netherlands
                Article
                10.1002/prca.201600025
                27040893
                9af6b128-abd3-4258-8cc3-71bfb0e8e866
                © 2016

                http://doi.wiley.com/10.1002/tdm_license_1.1

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