Lee Lancashire

Evaluation and Prognostic Significance of Circulating Tumor Cells in Patients With Non–Small-Cell Lung Cancer

PURPOSE Lung cancer is the leading cause of cancer-related death worldwide. Non-small-cell lung cancer (NSCLC) lacks validated biomarkers to predict treatment response. This study investigated whether circulating tumor cells (CTCs) are detectable in patients with NSCLC and what their ability might be to provide prognostic information and/or early indication of patient response to conventional therapy. PATIENTS AND METHODS In this single-center prospective study, blood samples for CTC analysis were obtained from 101 patients with previously untreated, stage III or IV NSCLC both before and after administration of one cycle of standard chemotherapy. CTCs were measured using a semiautomated, epithelial cell adhesion molecule-based immunomagnetic technique. RESULTS The number of CTCs in 7.5 mL of blood was higher in patients with stage IV NSCLC (n = 60; range, 0 to 146) compared with patients with stage IIIB (n = 27; range, 0 to 3) or IIIA disease (n = 14; no CTCs detected). In univariate analysis, progression-free survival was 6.8 v 2.4 months with P < .001, and overall survival (OS) was 8.1 v 4.3 months with P < .001 for patients with fewer than five CTCs compared with five or more CTCs before chemotherapy, respectively. In multivariate analysis, CTC number was the strongest predictor of OS (hazard ratio [HR], 7.92; 95% CI, 2.85 to 22.01; P < .001), and the point estimate of the HR was increased with incorporation of a second CTC sample that was taken after one cycle of chemotherapy (HR, 15.65; 95% CI, 3.63 to 67.53; P < .001). CONCLUSION CTCs are detectable in patients with stage IV NSCLC and are a novel prognostic factor for this disease. Further validation is warranted before routine clinical application.

Analysis of circulating tumor cells in patients with non-small cell lung cancer using epithelial marker-dependent and -independent approaches.

Introduction: Epithelial circulating tumor cells (CTCs) are detectable in patients with non-small cell lung cancer (NSCLC). However, epithelial to mesenchymal transition, a widely reported prerequisite for metastasis, may lead to an underestimation of CTC number. We compared directly an epithelial marker-dependent (CellSearch) and a marker-independent (isolation by size of epithelial tumor cells [ISET]) technology platform for the ability to identify CTCs. Molecular characteristics of CTCs were also explored. Methods: Paired peripheral blood samples were collected from 40 chemonäive, stages IIIA to IV NSCLC patients. CTCs were enumerated by Epithelial Cell Adhesion Molecule-based immunomagnetic capture (CellSearch, Veridex) and by filtration (ISET, RareCell Diagnostics). CTCs isolated by filtration were assessed by immunohistochemistry for epithelial marker expression (cytokeratins, Epithelial Cell Adhesion Molecule, epidermal growth factor receptor) and for proliferation status (Ki67). Results: CTCs were detected using ISET in 32 of 40 (80%) patients compared with 9 of 40 (23%) patients using CellSearch. A subpopulation of CTCs isolated by ISET did not express epithelial markers. Circulating tumor microemboli (CTM, clusters of ≥3 CTCs) were observed in 43% patients using ISET but were undetectable by CellSearch. Up to 62% of single CTCs were positive for the proliferation marker Ki67, whereas cells within CTM were nonproliferative. Conclusions: Both technology platforms detected NSCLC CTCs. ISET detected higher numbers of CTCs including epithelial marker negative tumor cells. ISET also isolated CTM and permitted molecular characterization. Combined with our previous CellSearch data confirming CTC number as an independent prognostic biomarker for NSCLC, we propose that this complementary dual technology approach to CTC analysis allows more complete exploration of CTCs in patients with NSCLC.

Evaluation of Circulating Tumor Cells and Serological Cell Death Biomarkers in Small Cell Lung Cancer Patients Undergoing Chemotherapy

Serological cell death biomarkers and circulating tumor cells (CTCs) have potential uses as tools for pharmacodynamic blood-based assays and their subsequent application to early clinical trials. In this study, we evaluated both the expression and clinical significance of CTCs and serological cell death biomarkers in patients with small cell lung cancer. Blood samples from 88 patients were assayed using enzyme-linked immunosorbent assays for various cytokeratin 18 products (eg, M65, cell death, M30, and apoptosis) as well as nucleosomal DNA. CTCs (per 7.5 ml of blood) were quantified using Veridex CellSearch technology. Before therapeutic treatment, cell death biomarkers were elevated in patients compared with controls. CTCs were detected in 86% of patients; additionally, CD56 was detectable in CTCs, confirming their neoplastic origin. M30 levels correlated with the percentage of apoptotic CTCs. M30, M65, lactate dehydrogenase, and CTC number were prognostic for patient survival as determined by univariate analysis. Using multivariate analysis, both lactate dehydrogenase and M65 levels remained significant. CTC number fell following chemotherapy, whereas levels of serological cell death biomarkers peaked at 48 hours and fell by day 22, mirroring the tumor response. A 48-hour rise in nucleosomal DNA and M30 levels was associated with early response and severe toxicity, respectively. Our results provide a rationale to include the use of serological biomarkers and CTCs in early clinical trials of new agents for small cell lung cancer.

Serum Proteomic Fingerprinting Discriminates Between Clinical Stages and Predicts Disease Progression in Melanoma Patients

PURPOSE Currently known serum biomarkers do not predict clinical outcome in melanoma. S100-beta is widely established as a reliable prognostic indicator in patients with advanced metastatic disease but is of limited predictive value in tumor-free patients. This study was aimed to determine whether molecular profiling of the serum proteome could discriminate between early- and late-stage melanoma and predict disease progression. PATIENTS AND METHODS Two hundred five serum samples from 101 early-stage (American Joint Committee on Cancer [AJCC] stage I) and 104 advanced stage (AJCC stage IV) melanoma patients were analyzed by matrix-assisted laser desorption/ionisation (MALDI) time-of-flight (ToF; MALDI-ToF) mass spectrometry utilizing protein chip technology and artificial neural networks (ANN). Serum samples from 55 additional patients after complete dissection of regional lymph node metastases (AJCC stage III), with 28 of 55 patients relapsing within the first year of follow-up, were analyzed in an attempt to predict disease recurrence. Serum S100-beta was measured using a sandwich immunoluminometric assay. RESULTS Analysis of 205 stage I/IV serum samples, utilizing a training set of 94 of 205 and a test set of 15 of 205 samples for 32 different ANN models, revealed correct stage assignment in 84 (88%) of 96 of a blind set of 96 of 205 serum samples. Forty-four (80%) of 55 stage III serum samples could be correctly assigned as progressors or nonprogressors using random sample cross-validation statistical methodologies. Twenty-three (82%) of 28 stage III progressors were correctly identified by MALDI-ToF combined with ANN, whereas only six (21%) of 28 could be detected by S100-beta. CONCLUSION Validation of these findings may enable proteomic profiling to become a valuable tool for identifying high-risk melanoma patients eligible for adjuvant therapeutic interventions.

An introduction to artificial neural networks in bioinformatics—application to complex microarray and mass spectrometry datasets in cancer studies

Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

Quantitative Proteomics Analysis Demonstrates Post-transcriptional Regulation of Embryonic Stem Cell Differentiation to Hematopoiesis

Embryonic stem (ES) cells can differentiate in vitro to produce the endothelial and hematopoietic precursor, the hemangioblasts, which are derived from the mesoderm germ layer. Differentiation of BryGFP/+ ES cell to hemangioblasts can be followed by the expression of the BryGFP/+ and Flk1 genes. Proteomic and transcriptomic changes during this differentiation process were analyzed to identify mechanisms for phenotypic change during early differentiation. Three populations of differentiating BryGFP ES cells were obtained by flow cytometric sorting, GFP−Flk1− (epiblast), GFP+Flk1− (mesoderm), and GFP+Flk1+ (hemangioblast). Microarray analyses and relative quantification two-dimensional LCLC-MS/MS on nuclear extracts were performed. We identified and quantified 2389 proteins, 1057 of which were associated to their microarray probe set. These included a variety of low abundance transcription factors, e.g. UTF1, Sox2, Oct4, and E2F4, demonstrating a high level of proteomic penetrance. When paired comparisons of changes in the mRNA and protein expression levels were performed low levels of correlation were found. A strong correlation between isobaric tag-derived relative quantification and Western blot analysis was found for a number of nuclear proteins. Pathway and ontology analysis identified proteins known to be involved in the regulation of stem cell differentiation, and proteins with no described function in early ES cell development were also shown to change markedly at the proteome level only. ES cell development is regulated at the mRNA and protein level.

A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks

Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis.

Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach

OBJECTIVE The advent of microarrays has attracted considerable interest from biologists due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Subsequent analysis of the data may identify specific features which correspond to characteristics of interest within the population, for example, analysis of gene expression profiles in cancer patients to identify molecular signatures corresponding with prognostic outcome. These high throughput technologies have resulted in an unprecedented rate of data generation, often of high complexity, highlighting the need for novel data analysis methodologies that will cope with data of this nature. METHODS Stepwise methods using artificial neural networks (ANNs) have been developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. Here these methods have been applied to a gene microarray dataset to identify and validate gene signatures corresponding with estrogen receptor and lymph node status in breast cancer. RESULTS Many gene transcripts were identified whose expression could differentiate patients to very high accuracies based upon firstly whether they were positive or negative for estrogen receptor, and secondly whether metastasis to the axillary lymph node had occurred. A number of these genes had been previously reported to have a role in cancer. Significantly fewer genes were used compared to other previous studies. The models using the optimal gene subsets were internally validated using an extensive random sample cross-validation procedure and externally validated using a follow up dataset from a different cohort of patients on a newer array chip containing the same and additional probe sets. Here, the models retained high accuracies, emphasising the potential power of this approach in analysing complex systems. These findings show how the proposed method allows for the rapid analysis and subsequent detailed interrogation of gene expression signatures to provide a further understanding of the underlying molecular mechanisms that could be important in determining novel prognostic markers associated with cancer.

Diagnostic biomarkers differentiating metastatic melanoma patients from healthy controls identified by an integrated MALDI-TOF mass spectrometry/bioinformatic approach

The prognosis of advanced metastatic melanoma (American Joint Committee on Cancer (AJCC) stage IV) remains dismal with a 5‐year survival rate of 6–18%. In the present study, an integrated MALDI mass spectrometric approach combined with artificial neural networks (ANNs) analysis and modeling has been used for the identification of biomarker ions in serum from stage IV melanoma patients allowing the discrimination of metastatic disease from healthy status with high specificities of 92% for protein ions and 100% for peptide biomarkers. Our ANNs model also correctly classified 98% of a blind validation set of AJCC stage I melanoma samples as nonstage IV samples, emphasizing the power of the newly defined biomarkers to identify patients with late‐stage metastatic melanoma. Sequence analysis identified peptides derived from metastasis‐associated proteins; alpha 1‐acid glycoprotein precursor‐1/2 (AAG‐1/2) and complement C3 component precursor‐1 (CCCP‐1). Furthermore, quantitation of serum AAG by an immunoassay showed a significant (p<0.001) increase in AAG serum concentration in stage IV patients in comparison with healthy volunteers; moreover; the quantity of AAG plotted against MALDI‐MS peak intensity classified the groups into two distinct clusters. Ongoing studies of other disease stages will provide evidence whether our strategy is sufficiently robust to give rise to stage‐specific protein/peptide signatures in melanoma.

Statistical Considerations of Optimal Study Design for Human Plasma Proteomics and Biomarker Discovery.

A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes >2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.