Genomic and Proteomic Profiling of Lung Cancers: Lung Cancer Classification in the Age of Targeted Therapy

A peptide selected by biopanning identifies the integrin alphavbeta6 as a prognostic biomarker for nonsmall cell lung cancer

The development of new modes of diagnosis and targeted therapy for lung cancer is dependent on the identification of unique cell surface features on cancer cells and isolation of reagents that bind with high affinity and specificity to these biomarkers. We recently isolated a 20-mer peptide which binds to the lung adenocarcinoma cell line, H2009, from a phage-displayed peptide library. We show here that the cellular receptor for this peptide, TP H2009.1, is the uniquely expressed integrin, alphavbeta6, and the peptide binding to lung cancer cell lines correlates to integrin expression. The peptide is able to mediate cell-specific uptake of a fluorescent nanoparticle via this receptor. Expression of alphavbeta6 was assessed on 311 human lung cancer samples. The expression of this integrin is widespread in early-stage nonsmall cell lung carcinoma (NSCLC). Log-rank test and Cox regression analyses show that expression of this integrin is significantly associated with poor patient outcome. Preferential expression is observed in the tumors compared with the surrounding normal lung tissue. Our data indicate that alphavbeta6 is a prognostic biomarker for NSCLC and may serve as a receptor for targeted therapies. Thus, cell-specific peptides isolated from phage biopanning can be used for the discovery of cell surface biomarkers, emphasizing the utility of peptide libraries to probe the surface of a cell.

Proteomics in cancer

Proteomic studies have generated numerous datasets of potential diagnostic, prognostic, and therapeutic significance in human cancer. Two key technologies underpinning these studies in cancer tissue are two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS). Although surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF)-MS is the mainstay for serum or plasma analysis, other methods including isotope-coded affinity tag technology, reverse-phase protein arrays, and antibody microarrays are emerging as alternative proteomic technologies. Because there is little overlap between studies conducted with these approaches, confirmation of these advanced technologies remains an elusive goal. This problem is further exacerbated by lack of uniform patient inclusion and exclusion criteria, low patient numbers, poor supporting clinical data, absence of standardized sample preparation, and limited analytical reproducibility (in particular of 2D-PAGE). Despite these problems, there is little doubt that the proteomic approach has the potential to identify novel diagnostic biomarkers in cancer. In therapeutic proteomics, the challenge is significant due to the complexity systems under investigation (i.e., cells generate over 10(5) different polypeptides). However, the most significant contribution of therapeutic proteomics research is expected to derive not from single experiments, but from the synthesis and comparison of large datasets obtained under different conditions (e.g., normal, inflammation, cancer) and in different tissues and organs. Thus, standardized processes for storing and retrieving data obtained with different technologies by different research groups will have to be developed. Shifting the emphasis of cancer proteomics from technology development and data generation to careful study design, data organization, formatting, and mining is crucial to answer clinical questions in cancer research.

KIF14 messenger RNA expression is independently prognostic for outcome in lung cancer

PURPOSE

The mitotic kinesin KIF14 is overexpressed in multiple cancers including lung cancer. Therefore, we investigated KIF14 expression in association with clinical variables and the effect of KIF14 on in vitro colony formation in non-small-cell lung carcinoma.

EXPERIMENTAL DESIGN

RNA was extracted from 129 untreated, resected tumors and KIF14 expression was quantified by real-time reverse transcription-PCR. Associations with clinical variables were determined by standard statistical methods. KIF14 expression was knocked down by small interfering RNA in H1299 and HeLa cells; proliferation and growth in soft agar were assayed.

RESULTS

Squamous cell carcinoma had the highest KIF14 level, followed by large-cell undifferentiated carcinoma, then adenocarcinoma (P = 0.002). KIF14 level decreased with differentiation (P = 0.01) but was not associated with pathologic stage, T or N stage, or sex. When dichotomized about the median, KIF14 overexpression significantly decreased disease-free survival (Kaplan-Meier log-rank, P = 0.01) and trended toward decreasing overall survival (P = 0.08). In a univariate Cox proportional hazard regression, increasing KIF14 expression decreased disease-free survival [P = 0.01; hazard ratio, 1.44 (95% confidence interval, 1.09-1.91)]. In a multivariate Cox regression, including stage, differentiation, histology, and tumor purity as covariates, KIF14 overexpression remained an independent prognostic factor for disease-free survival [P = 0.01; hazard ratio, 1.45 (95% confidence interval, 1.09-1.94)]. Knockdown of KIF14 in non-small-cell lung carcinoma and cervical carcinoma cell lines decreased proliferation and colony formation in soft agar.

CONCLUSIONS

KIF14 expression is independently prognostic for disease-free survival in lung cancer and knockdown decreases tumorigenicity in vitro, showing that it is a clinically relevant oncogene and an exciting therapeutic target for further study.

Use of a cytokine gene expression signature in lung adenocarcinoma and the surrounding tissue as a prognostic classifier

BACKGROUND

A 17-cytokine gene expression signature in noncancerous hepatic tissue from patients with metastatic hepatocellular carcinoma (HCC) was recently found to predict HCC metastasis and recurrence. We examined whether the cytokine gene expression profile of noncancerous lung tissue could predict the metastatic capability of adjacent lung adenocarcinoma.

METHODS

We analyzed a 15-cytokine gene expression profile in noncancerous lung tissue and corresponding lung tumor tissue from 80 US lung adenocarcinoma patients using real-time quantitative reverse transcription-polymerase chain reaction. We then used unsupervised hierarchical clustering and Prediction Analysis of Microarray classification to test the prognostic ability of the 15-cytokine gene profile in the US patients and in an independent validation set comprising 50 Japanese patients with stage I disease. Survival was analyzed by the Kaplan-Meier method using the log-rank test, and univariate and multivariable Cox proportional hazards modeling were used to analyze the association of clinical variables with patient survival. All statistical tests were two-sided.

RESULTS

A 15-cytokine gene signature in noncancerous lung tissue primarily reflected the lymph node status of 80 lung adenocarcinoma patients, whereas the gene signature of the corresponding lung tumor tissue was associated with prognosis independent of lymph node status. Cytokine Lung Adenocarcinoma Survival Signature of 11 genes (CLASS-11), a refined 11-gene signature, accurately classified patients, including those with stage I disease, according to risk of death from adenocarcinoma. CLASS-11 prognostic classification was statistically significantly associated with survival and was an independent prognostic factor for stage I patients (hazard ratio for death in the high-risk CLASS-11 group compared with the low-risk CLASS-11 reference group = 7.46, 95% confidence interval = 2.14 to 26.05; P = .002). CLASS-11 also classified patients in the validation set according to risk of recurrence.

CONCLUSION

CLASS-11, which consists of genes for pro- and anti-inflammatory cytokines, identifies stage I lung adenocarcinoma patients who have a poor prognosis.

Chemical and biological differentiation of three human breast cancer cell types using time-of-flight secondary ion mass spectrometry

We use time-of-flight secondary ion mass spectrometry (TOF-SIMS) to image and classify individual cells on the basis of their characteristic mass spectra. Using statistical data reduction on the large data sets generated during TOF-SIMS analysis, similar biological materials can be differentiated on the basis of a combination of small changes in protein expression, metabolic activity and cell structure. We apply this powerful technique to image and differentiate three carcinoma-derived human breast cancer cell lines (MCF-7, T47D, and MDA-MB-231). In homogenized cells, we show the ability to differentiate the cell types as well as cellular compartments (cytosol, nuclear, and membrane). These studies illustrate the capacity of TOF-SIMS to characterize individual cells by chemical composition, which could ultimately be applied to detect and identify single aberrant cells within a normal cell population. Ultimately, we anticipate characterizing rare chemical changes that may provide clues to single cell progression within carcinogenic and metastatic pathways.

Targeting the epigenome for the treatment of thoracic malignancies

Despite considerable efforts to improve the diagnosis and treatment of lung cancer, this disease remains the leading cause of cancer-related mortality worldwide. Recent elucidation of epigenetic regulation of gene expression during malignant transformation, together with the identification of agents that modulate DNA methylation and histone acetylation, provide new opportunities for the treatment and prevention of lung cancer via chromatin remodeling mechanisms. Further analysis of molecular response in tumor tissues following exposure to chromatin remodeling agents may enable us to identify novel mechanisms pertaining to lung cancer epigenetics, and design more efficacious regimens.

Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung

Non-small-cell lung cancers (NSCLC) compose 80% of all lung carcinomas with squamous cell carcinomas (SCC) and adenocarcinoma representing the majority of these tumors. Although patients with early-stage NSCLC typically have a better outcome, 35% to 50% will relapse within 5 years after surgical treatment. We have profiled primary squamous cell lung carcinomas from 129 patients using Affymetrix U133A gene chips. Unsupervised analysis revealed two clusters of SCC that had no correlation with tumor stage but had significantly different overall patient survival (P = 0.036). The high-risk cluster was most significantly associated with down-regulation of epidermal development genes. Cox proportional hazard models identified an optimal set of 50 prognostic mRNA transcripts using a 5-fold cross-validation procedure. Quantitative reverse transcription-PCR and immunohistochemistry using tissue microarrays were used to validate individual gene candidates. This signature was tested in an independent set of 36 SCC samples and achieved 84% specificity and 41% sensitivity with an overall predictive accuracy of 68%. Kaplan-Meier analysis showed clear stratification of high-risk and low-risk patients [log-rank P = 0.04; hazard ratio (HR), 2.66; 95% confidence interval (95% CI), 1.01-7.05]. Finally, we combined the SCC classifier with our previously identified adenocarcinoma prognostic signature and showed that the combined classifier had a predictive accuracy of 71% in 72 NSCLC samples also showing significant differences in overall survival (log-rank P = 0.0002; HR, 3.54; 95% CI, 1.74-7.19). This prognostic signature could be used to identify patients with early-stage high-risk NSCLC who might benefit from adjuvant therapy following surgery.

Molecular correlates of site-specific metastasis

Metastasis is the spread of tumor cells from a primary site to distant organs. It is the major cause of cancer morbidity and death. In the last few decades, significant advances have been made in surgical techniques, radiation therapy delivery, and chemotherapy including the development of combination regimens and agents inhibiting newly characterized biological targets. Treatment of metastasis, however, remains the most challenging task in cancer therapy because metastatic growth relies on complex interactions between tumor cells and the host and is often resistant to all therapeutic modalities. Management of metastasis in bone is especially challenging given the difficulty of access for therapeutic agents. Contemporary research seeks to explain the striking organ specificity observed in metastasis. In this article, we will examine historic perspectives on site-specific metastasis and review cellular and molecular evidence pertinent to the mechanisms of organ specificity. We will discuss a number of studies that aim to identify gene signatures correlating with organ-selective metastasis using microarray technology. Lastly, we will discuss potential areas of future research including microRNAs, proteomics, and the development of diagnostic and therapeutic interventions.

A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer

BACKGROUND

Clinical trials have indicated a benefit of adjuvant chemotherapy for patients with stage IB, II, or IIIA--but not stage IA--non-small-cell lung cancer (NSCLC). This classification scheme is probably an imprecise predictor of the prognosis of an individual patient. Indeed, approximately 25 percent of patients with stage IA disease have a recurrence after surgery, suggesting the need to identify patients in this subgroup for more effective therapy.

METHODS

We identified gene-expression profiles that predicted the risk of recurrence in a cohort of 89 patients with early-stage NSCLC (the lung metagene model). We evaluated the predictor in two independent groups of 25 patients from the American College of Surgeons Oncology Group (ACOSOG) Z0030 study and 84 patients from the Cancer and Leukemia Group B (CALGB) 9761 study.

RESULTS

The lung metagene model predicted recurrence for individual patients significantly better than did clinical prognostic factors and was consistent across all early stages of NSCLC. Applied to the cohorts from the ACOSOG Z0030 trial and the CALGB 9761 trial, the lung metagene model had an overall predictive accuracy of 72 percent and 79 percent, respectively. The predictor also identified a subgroup of patients with stage IA disease who were at high risk for recurrence and who might be best treated by adjuvant chemotherapy.

CONCLUSIONS

The lung metagene model provides a potential mechanism to refine the estimation of a patient's risk of disease recurrence and, in principle, to alter decisions regarding the use of adjuvant chemotherapy in early-stage NSCLC.

Proteomic analysis in cancer research: potential application in clinical use

The ultimate goal of cancer proteomics is to adapt proteomic technologies for routine use in clinical laboratories for the purpose of diagnostic and prognostic classification of disease states, as well as in evaluating drug toxicity and efficacy. The novel technologies allows researchers to facilitate the comprehensive analyses of genomes, transcriptomes, and proteomes in health and disease. The information that is expected from such technologies may soon exert a dramatic change in cancer research and impact dramatically on the care of cancer patients. Analysis of tumor-specific proteomic profiles may also allow better understanding of tumor development and the identification of novel targets for cancer therapy. The localization of gene products, which is often difficult to deduce from the sequence, can be determined experimentally. Mechanisms, such as regulation of protein function by proteolysis, recycling, and isolation in cell compartments, affect gene products, not genes. Finally, protein-protein interactions and the molecular composition of cellular structures can be determined only at the protein level. The biological variability among patient samples as well as the great dynamic range of biomarker concentrations are currently the main challenges facing efforts to deduce diagnostic patterns that are unique to specific disease states. While several strategies exist to address this problem, we have tried to offer a wide perspective about the current possibilities.

Elevation of serum l-lactate dehydrogenase B correlated with the clinical stage of lung cancer

To identify potential biomarkers related with lung cancer metastasis, conditional media (CM) proteins collected from a primary non-small cell lung cancer (NSCLC) cell line NCI-H226 and its brain metastatic subline H226Br were analyzed by one-dimensional electrophoresis (1-D PAGE) and matrix-assisted laser desorption/time of flight mass spectrometry (MALDI-TOF-MS). Twelve biomarkers were identified, of which l-lactate dehydrogenase B (LDHB) chain was significantly up-regulated in the CM of H226Br cell and was further validated in 105 lung cancer, 93 non-lung cancer, 41 benign lung disease, as well as 65 healthy individuals sera using enzyme-linked immunosorbent assay (ELISA). It was found that the levels of LDHB were specifically elevated in NSCLC sera compared with other groups and were progressively increased with the clinical stage. At the cutoff point 0.260 (OD value) on the receiver operating characteristic (ROC) curve, LDHB could comparatively discriminate lung cancer from benign lung disease and healthy control groups with sensitivity 81%, specificity 70% and total accuracy 76%. These findings demonstrated that secretome could open up a possibility to find, identify, and characterize novel biomarkers related with invasion and metastasis.

The US Food and Drug Administration perspective on cancer biomarker development

Despite the intense interest in biomarker development for cancer management, few biomarker assays for diagnostic uses have been submitted to the US Food and Drug Administration (FDA). What challenges must researchers overcome to bring cancer-detection technologies to the market and, therefore, into clinical use?

Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues

Successful treatment of multiple cancer types requires early detection and identification of reliable biomarkers present in specific cancer tissues. To test the feasibility of identifying proteins from archival cancer tissues, we have developed a methodology, termed direct tissue proteomics (DTP), which can be used to identify proteins directly from formalin-fixed paraffin-embedded prostate cancer tissue samples. Using minute prostate biopsy sections, we demonstrate the identification of 428 prostate-expressed proteins using the shotgun method. Because the DTP method is not quantitative, we employed the absolute quantification method and demonstrate picogram level quantification of prostate-specific antigen. In depth bioinformatics analysis of these expressed proteins affords the categorization of metabolic pathways that may be important for distinct stages of prostate carcinogenesis. Furthermore, we validate Wnt-3 as an upregulated protein in cancerous prostate cells by immunohistochemistry. We propose that this general strategy provides a roadmap for successful identification of critical molecular targets of multiple cancer types.

Past, present and future of gene expression-tailored therapy for lung cancer

“Variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease.” Sir William Osler (1849–1919). All human beings are different and some of these differences are the variations in response to xenobiotics. Personalized medicine means: the right patient population, the right drug, the right dose, the right indication, and administration at the right time. This review provides an update on concepts of personalized therapy for lung cancer.

Tumor transcriptome reveals the predictive and prognostic impact of lysosomal protease inhibitors in non-small-cell lung cancer

PURPOSE

Insight into clinical response to platinum-based chemotherapy (PBC) in non-small-cell lung cancer (NSCLC).

METHODS

Matched tumor and nontumor lung tissues from PBC-treated NSCLC patients (four nonresponders and four responders) and tumor tissue from an independent test set (four nonresponders and four responders), were profiled using microarrays. Lysosomal protease inhibitors SerpinB3 and cystatin C were highly correlated with clinical response and were further evaluated by immunohistochemistry in PBC-treated patients (36 prechemotherapy and 13 postchemotherapy). Investigation of the pathogenic and prognostic significance of SerpinB3 was performed in 251 primary tumors, with 64 regional lymph node pairs, from chemotherapy-naïve NSCLC patients using immunohistochemistry.

RESULTS

Bioinformatic analyses of gene expression in the training set identified a gene set (n = 17) that separated all patients in the training and test sets (n = 16) according to response in hierarchical clustering. Transcriptome profiling revealed that SerpinB3 mRNA was highly correlated with degree of response (r = -0.978; P < .0001) and was a clear outlier (nonresponders:responders > 50-fold). SerpinB3 protein expression was correlated with clinical response in PBC-treated NSCLC patients (P = .045). Expression of SerpinB3 and cystatin C, relative to the target, protease cathepsin B, was independently predictive of response (odds ratio, 17.8; 95% CI, 2.0 to 162.4; P = .01), with an accuracy of 72%. High SerpinB3 expression levels, invariably associated with chemoresistance, had contrasting prognostic impact in untreated squamous cell carcinomas (hazard ratio [HR], 0.43; 95% CI, 0.18 to 0.93) or adenocarcinomas (HR, 2.09; 95% CI, 1.03 to 4.72).

CONCLUSION

This provides the first comprehensive molecular characterization of clinical responsiveness to PBC in NSCLC and reveals the predictive and prognostic impact of two lysosomal protease inhibitors, potentially representing novel targets for NSCLC therapeutics.

Expression profile-defined classification of lung adenocarcinoma shows close relationship with underlying major genetic changes and clinicopathologic behaviors

PURPOSE

This study was conducted to gain insight into the relationship between expression profiles and underlying genetic changes, which are known to be important for the pathogenesis of lung cancers.

METHODS

Expression profiles of 18,175 unique genes and three major targets for genetic changes, p53, epidermal growth factor receptor (EGFR), and K-ras, were investigated in 149 patients with non-small-cell lung cancer, including 90 patients with adenocarcinoma to determine their relationships with various clinicopathologic features and Gene Ontology (GO) terms.

RESULTS

This study successfully established a basis for expression profile-defined classification, which can classify adenocarcinomas into two major types, terminal respiratory unit (TRU) type and non-TRU type. Our GO term-based identifier of particular biologic processes, molecular functions, and cellular compartments clearly showed characteristic retention of normal peripheral lung features in TRU type, in sharp contrast to the significant association of non-TRU type with cell cycling and proliferation-related features. While significantly higher frequency of EGFR mutation was observed in TRU type, we found that the presence of EGFR mutations was a significant predictor of shorter postoperative survival for TRU type, independent of disease stage. We were also able to identify a set of genes in vivo with significant upregulation in the presence of EGFR mutations.

CONCLUSION

This study has shed light on heterogeneity in lung cancers, especially in adenocarcinomas, by establishing a molecularly, genetically, and clinically relevant, expression profile-defined classification. Future studies using independent patient cohorts are warranted to confirm the prognostic significance of EGFR mutations in TRU-type adenocarcinoma.

Unique microRNA molecular profiles in lung cancer diagnosis and prognosis

MicroRNA (miRNA) expression profiles for lung cancers were examined to investigate miRNA's involvement in lung carcinogenesis. miRNA microarray analysis identified statistical unique profiles, which could discriminate lung cancers from noncancerous lung tissues as well as molecular signatures that differ in tumor histology. miRNA expression profiles correlated with survival of lung adenocarcinomas, including those classified as disease stage I. High hsa-mir-155 and low hsa-let-7a-2 expression correlated with poor survival by univariate analysis as well as multivariate analysis for hsa-mir-155. The miRNA expression signature on outcome was confirmed by real-time RT-PCR analysis of precursor miRNAs and cross-validated with an independent set of adenocarcinomas. These results indicate that miRNA expression profiles are diagnostic and prognostic markers of lung cancer.