Connective tissue-activating peptide III: a novel blood biomarker for early lung cancer detection

Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation

INTRODUCTION

Identification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately.

METHODS

Prospective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided.

RESULTS

During follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites.

CONCLUSION

The PLCO lung cancer risk models demonstrate high discrimination and calibration.

Elevated levels of CXC chemokine connective tissue activating peptide (CTAP)-III in lung cancer patients

Despite advances in treatments, lung cancer has been the leading cause of cancer-related deaths in the United States for the past several decades. Recent findings from the National Lung Screening Trial reveal that low-dose helical computed tomography (CT) scan screening of high-risk individuals reduces lung cancer mortality. This suggests that early detection is of key importance to improving patient outcome. However, of those screened with CT scans, 25% had positive scans that require further follow-up studies which often involve more radiation exposure and invasive tests to reduce false positive results. The purpose of this study was to identify candidate plasma biomarkers to aid in diagnosis of lung cancer in at-risk individuals. We found increased expression of the CXC chemokine connective tissue-activating peptide (CTAP)-III from plasma specimens of lung cancer patients compared to at-risk control subjects. Identification of the peptide was confirmed by the addition of an anti-NAP-2 antibody that recognizes CTAP-III and NAP-2. We also quantified and verified the increased levels of plasma CTAP-III with ELISA in patients with lung cancer (mean ± SD, 1859 ± 1219 ng/mL) compared to controls (698 ± 434 ng/mL; P<0.001). Our findings demonstrate elevated plasma levels of CTAP-III occur in lung cancer patients. Further studies are required to determine if this chemokine could be utilized in a blood-based biomarker panel for the diagnosis of lung cancer.

Screening and early detection of lung cancer

Lung cancer with an estimated 342,000 deaths in 2008 (20% of total) is the most common cause of death from cancer, followed by colorectal cancer (12%), breast cancer (8%), and stomach cancer (7%) in Europe. In former smokers, the absolute lung cancer risk remains higher than in never-smokers; these data therefore call for effective secondary preventive measures for lung cancer in addition to smoking cessation programs. This review presents and discusses the most recent advances in the early detection and screening of lung cancer.An overview of randomized controlled computerized tomography-screening trials is given, and the role of bronchoscopy and new techniques is discussed. Finally, the approach of (noninvasive) biomarker testing in the blood, exhaled breath, sputum, and bronchoscopic specimen is reviewed.

Biomarkers in cancer micrometastasis: where are we at?

Despite considerable advances in the field of solid tumors, disseminated malignancy remains the cause of the vast majority of cancer-related deaths. In patients with no overt metastasis, early spread of tumor cells is usually undetected by current imaging technologies. In addition, the metastatic process is complex and depends on multiple interactions (crosstalk) of disseminating tumor cells with the individual homeostatic mechanisms, which the tumor cells can usurp. Despite these many variables, a flurry of surrogate biomarkers to detect micrometastasis has been developed in the last decade. These biomarkers open avenues for understanding cancer dormancy and metastasis, have the potential to provide novel therapeutic targets and may help predict outcome and therapeutic decisions at diagnosis and during follow-up of cancer patients. This review focuses on ongoing efforts to unravel metastasis biology, surrogate biomarkers currently investigated to monitor micrometastasis and tools used to identify, quantify and determine their capacity to efficiently establish metastasis.

Reduced plasma level of CXC chemokine ligand 7 in patients with pancreatic cancer

BACKGROUND

Early detection is essential to improve the outcome of patients with pancreatic cancer. A noninvasive and cost-effective diagnostic test using plasma/serum biomarkers would facilitate the detection of pancreatic cancer at the early stage.

METHODS

Using a novel combination of hollow fiber membrane-based low-molecular-weight protein enrichment and LC-MS-based quantitative shotgun proteomics, we compared the plasma proteome between 24 patients with pancreatic cancer and 21 healthy controls (training cohort). An identified biomarker candidate was then subjected to a large blinded independent validation (n = 237, validation cohort) using a high-density reverse-phase protein microarray.

RESULTS

Among a total of 53,009 MS peaks, we identified a peptide derived from CXC chemokine ligand 7 (CXCL7) that was significantly reduced in pancreatic cancer patients, showing an area under curve (AUC) value of 0.84 and a P value of 0.00005 (Mann-Whitney U test). Reduction of the CXCL7 protein was consistently observed in pancreatic cancer patients including those with stage I and II disease in the validation cohort (P < 0.0001). The plasma level of CXCL7 was independent from that of CA19-9 (Pearson's r = 0.289), and combination with CXCL7 significantly improved the AUC value of CA19-9 to 0.961 (P = 0.002).

CONCLUSIONS

We identified a significant decrease of the plasma CXCL7 level in patients with pancreatic cancer, and combination of CA19-9 with CXCL7 improved the discriminatory power of the former for pancreatic cancer.

IMPACT

The present findings may provide a new diagnostic option for pancreatic cancer and facilitate early detection of the disease.

The grand challenge to decipher the cancer proteome

The quest to decipher protein alterations in cancer has spanned well over half a century. The vast dynamic range of protein abundance coupled with a plethora of isoforms and disease heterogeneity have been formidable challenges. Progress in cancer proteomics has substantially paralleled technological developments. Advances in analytical techniques and the implementation of strategies to de-complex the proteome into manageable components have allowed proteins across a wide dynamic range to be explored. The massive amounts of data that can currently be collected through proteomics allow the near-complete definition of cancer subproteomes, which reveals the alterations in signalling and developmental pathways. This allows the discovery of predictive biomarkers and the annotation of the cancer genome based on proteomic findings. There remains a considerable need for infrastructure development and the organized collaborative efforts to efficiently mine the cancer proteome.

Integrative and comparative genomics analysis of early hepatocellular carcinoma differentiated from liver regeneration in young and old

BACKGROUND

Hepatocellular carcinoma (HCC) is the third-leading cause of cancer-related deaths worldwide. It is often diagnosed at an advanced stage, and hence typically has a poor prognosis. To identify distinct molecular mechanisms for early HCC we developed a rat model of liver regeneration post-hepatectomy, as well as liver cells undergoing malignant transformation and compared them to normal liver using a microarray approach. Subsequently, we performed cross-species comparative analysis coupled with copy number alterations (CNA) of independent early human HCC microarray studies to facilitate the identification of critical regulatory modules conserved across species.

RESULTS

We identified 35 signature genes conserved across species, and shared among different types of early human HCCs. Over 70% of signature genes were cancer-related, and more than 50% of the conserved genes were mapped to human genomic CNA regions. Functional annotation revealed genes already implicated in HCC, as well as novel genes which were not previously reported in liver tumors. A subset of differentially expressed genes was validated using quantitative RT-PCR. Concordance was also confirmed for a significant number of genes and pathways in five independent validation microarray datasets. Our results indicated alterations in a number of cancer related pathways, including p53, p38 MAPK, ERK/MAPK, PI3K/AKT, and TGF-beta signaling pathways, and potential critical regulatory role of MYC, ERBB2, HNF4A, and SMAD3 for early HCC transformation.

CONCLUSIONS

The integrative analysis of transcriptional deregulation, genomic CNA and comparative cross species analysis brings new insights into the molecular profile of early hepatoma formation. This approach may lead to robust biomarkers for the detection of early human HCC.

Applying mass spectrometry based proteomic technology to advance the understanding of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy in adults. It is characterized by clonal proliferation of terminally differentiated B lymphocytes and over-production of monoclonal immunoglobulins. Recurrent genomic aberrations have been identified to contribute to the aggressiveness of this cancer. Despite a wealth of knowledge describing the molecular biology of MM as well as significant advances in therapeutics, this disease remains fatal. The identification of biomarkers, especially through the use of mass spectrometry, however, holds great promise to increasing our understanding of this disease. In particular, novel biomarkers will help in the diagnosis, prognosis and therapeutic stratification of MM. To date, results from mass spectrometry studies of MM have provided valuable information with regards to MM diagnosis and response to therapy. In addition, mass spectrometry was employed to study relevant signaling pathways activated in MM. This review will focus on how mass spectrometry has been applied to increase our understanding of MM.

New biomarkers for lung cancer

IMPORTANCE OF THE FIELD

Despite many efforts to improve early detection, lung cancer remains the leading cause of cancer deaths. Stage is the main determinant of prognosis and the basis for deciding treatment options. Screening tests for lung cancer have not been successful so far.

AREAS COVERED IN THE REVIEW

The article reviews the available literature related to biomarkers in use at present and those that could be used for early diagnosis, staging, prognosis, response to therapy and prediction of recurrence. The single biomarkers are analysed, divided according to the technological methods used and the locations of sampling.

WHAT THE READER WILL GAIN

The reader will gain knowledge on biomarkers in use and those now under study. The reader will also gain insights into the difficulties pertaining to the development of biomarkers, results reproducibility and clinical application.

TAKE HOME MESSAGE

Although some markers seem to be promising, at present there is no consensus on the proven value of their clinical use in lung cancer. The future lies probably in a panel of biomarkers instead of individual assays, or in predictive models derived from the integration of clinical variables and gene expression profiles.