Steve Ruben

Expansion of CD133+ colon cancer cultures retaining stem cell properties to enable cancer stem cell target discovery

Background:Despite earlier studies demonstrating in vitro propagation of solid tumour cancer stem cells (CSCs) as non-adherent tumour spheres, it remains controversial as to whether CSCs can be maintained in vitro. Additional validation of the CSC properties of tumour spheres would support their use as CSC models and provide an opportunity to discover additional CSC cell surface markers to aid in CSC detection and potential elimination.Methods:Primary tumour cells isolated from 13 surgically resected colon tumour specimens were propagated using serum-free CSC-selective conditions. The CSC properties of long-term cultured tumour spheres were established and mass spectrometry-based proteomics performed.Results:Freshly isolated CD133+ colorectal cancer cells gave rise to long-term tumour sphere (or spheroids) cultures maintaining CD133 expression. These spheroid cells were able to self-renew and differentiate into adherent epithelial lineages and recapitulate the phenotype of the original tumour. Relative to their differentiated progeny, tumour spheroid cells were more resistant to the chemotherapeutic irinotecan. Finally, CD44, CD166, CD29, CEACAM5, cadherin 17, and biglycan were identified by mass spectrometry to be enriched in CD133+ tumour spheroid cells.Conclusion:Our data suggest that ex vivo-expanded colon CSCs isolated from clinical specimens can be maintained in culture enabling the identification of CSC cell surface-associated proteins.

Identification and Characterization of Angiogenesis Targets through Proteomic Profiling of Endothelial Cells in Human Cancer Tissues

Genomic and proteomic analysis of normal and cancer tissues has yielded abundant molecular information for potential biomarker and therapeutic targets. Considering potential advantages in accessibility to pharmacological intervention, identification of targets resident on the vascular endothelium within tumors is particularly attractive. By employing mass spectrometry (MS) as a tool to identify proteins that are over-expressed in tumor-associated endothelium relative to normal cells, we aimed to discover targets that could be utilized in tumor angiogenesis cancer therapy. We developed proteomic methods that allowed us to focus our studies on the discovery of cell surface/secreted proteins, as they represent key antibody therapeutic and biomarker opportunities. First, we isolated endothelial cells (ECs) from human normal and kidney cancer tissues by FACS using CD146 as a marker. Additionally, dispersed human colon and lung cancer tissues and their corresponding normal tissues were cultured ex-vivo and their endothelial content were preferentially expanded, isolated and passaged. Cell surface proteins were then preferentially captured, digested with trypsin and subjected to MS-based proteomic analysis. Peptides were first quantified, and then the sequences of differentially expressed peptides were resolved by MS analysis. A total of 127 unique non-overlapped (157 total) tumor endothelial cell over-expressed proteins identified from directly isolated kidney-associated ECs and those identified from ex-vivo cultured lung and colon tissues including known EC markers such as CD146, CD31, and VWF. The expression analyses of a panel of the identified targets were confirmed by immunohistochemistry (IHC) including CD146, B7H3, Thy-1 and ATP1B3. To determine if the proteins identified mediate any functional role, we performed siRNA studies which led to previously unidentified functional dependency for B7H3 and ATP1B3.

Abstract 2813: Serum biomarker panel detects lung cancer in never smokers

Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL Introduction: Lung cancer is the leading cause of cancer mortality worldwide with more than 1 million deaths each year. Although most lung cancers are attributable to smoking tobacco, it is estimated that as many as 25% of all lung cancer cases are in subjects who have never smoked. A number of clinical, epidemiological, and molecular variations suggest that lung cancers that arise in smokers and non-smokers are significantly different. Biomarker panels may have considerable value when combined with imaging protocols in detecting and diagnosing lung cancer. We previously employed a novel mass spectrometry-based approach to identify serum biomarkers which we have previously shown to detect non-small cell lung cancer (NSCLC) in a smoking population representing all 4 stages of disease. In this study we extend these findings to a cohort of lung cancer subjects who have never smoked. Methodology: Initially 9 biomarkers were assayed in serum collected from smoking subjects with NSCLC and appropriate controls. More than 600 specimens collected from 4 independent sites were employed in the study. Samples were randomly divided into a training set (NSCLC n=128, controls n = 191) and a testing set (NSCLC n=141, controls n=175) and used to develop a regression-based algorithm for lung cancer detection. Subsequently, an independent validation study was undertaken in cohort of lung cancer subjects who had never smoked (interview questionnaire). All stages of cancer (stage I n = 8, stage II n = 4, stage III n = 17, stage IV n = 11) and all major histological cell types (adenocarcinoma n = 21, squamous n = 7, bronchioloalveolar = 8, others n=4) were included. Controls were matched on age/gender (n=40). Results: A global 6-marker regression model identified smoking associated cancer cases with good performance (Training AUC=0.877; Testing AUC=0.868). All stages of cancer were distinguished as well as all of the major histological cell types. Fitting of the model to data from the never smoker cohort revealed that the algorithm again discriminated the malignant cases with strong performance AUC=0.906 (sensitivity = 83% at specificity = 83%). Conclusion: Lung cancer biomarkers identified initially through proteomic analysis have shown robust performance in a study cohort of lung cancer subjects who have never smoked. The findings from these studies suggest that these biomarkers may provide suitable performance across all lung cancer populations to design tests for a variety of diagnostic applications. For example, it is possible that these biomarkers could be employed to enhance the discrimination of malignant nodules identified by radiologic imaging. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2813. doi:10.1158/1538-7445.AM2011-2813

Abstract 4568: Multivariate analysis of a panel of protein biomarkers for early detection of non-small cell lung cancer

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC Introduction: Lung cancer is the leading cause of cancer mortality in the US; it has been estimated that 219,440 individuals will be diagnosed with lung cancer and 159,390 will die from the disease in 2009 alone. Low dose CT, while very sensitive at visualizing early stage tumors, also identifies non malignant solitary pulmonary nodules (false positives) in a significant fraction of smokers. Biomarker panels may have considerable value when combined with imaging protocols in helping to discriminate benign from malignant nodules. We have employed a novel mass spectrometry-based approach to identify serum biomarkers which we have previously shown to detect non-small cell lung cancer (NSCLC) representing all 4 stages of disease. In this study we extend these findings to a cohort highly representative of early stage lung cancer. Methodology: Validation of non small cell lung cancer markers was performed by ELISA analysis on a cohort of sera collected at the NYU Langone Medical Center. In this study lung cancer cases represent predominantly early stage disease (65 stage I/ 91 all cancer). Controls comprise current and former healthy smokers (n=90) as well as subjects with non-malignant lung disease (COPD, n=46). Samples were randomly divided into a training set (cancer n=39; smokers n= 38; COPD n=20) and a test set (cancer n=52; smokers n=52; COPD n=26). Logistic regression analysis revealed several multi-marker panels capable of distinguishing malignant samples from matched controls in the training set. A classifier developed for each panel was then applied to the test set. Multivariate approaches were employed to analyze this data together with other key clinical parameters including: tumor stage, size, histology and lung-function data. Results: Markers for the panel were selected based on both individual marker performance and complementation of the performance of the relevant biomarker panel. Logistic regression analysis on the training set revealed a 9-marker panel which resolved malignant samples with 90% sensitivity at 96% specificity (AUC=0.977). The panel resolved malignant lesions under 1 cm and demonstrated good discrimination of COPD patients. A 6-marker panel was identified with similar performance characteristics (AUC=0.974). Applying the classifier generated from the training set to the test set also showed strong performance in this study, with an AUC of 0.92 for the 9 marker set in preliminary analyses. Conclusion: Panels of lung biomarkers identified initially through proteomic analysis have shown robust performance in a study cohort strongly biased toward early-stage disease. The findings from these studies suggest that these biomarker panels provide the performance and flexibility to design tests suitable for a variety of diagnostic applications such as screening high risk subjects prior to CT scanning and improving the discrimination of nodules identified by radiologic imaging. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4568.