Background: Whether thymidine kinase (TK) is considered a new diagnostic biomarker in lung cancer depends on it being superior to or adding further information to already established tumor markers. Here, we investigated its relevance in diagnosis, therapy monitoring and prognosis of patients with diverse forms of lung cancer. Patients and Methods: Pretherapeutic TK concentrations were analyzed by radioimmunoassay in serum of 181 patients with advanced lung cancer (53 small cell lung cancer (SCLC), 128 non-small cell lung cancer (NSCLC)), 40 with benign lung diseases, 44 with benign non-lung-related diseases and 29 healthy controls. Diagnostic power of TK was compared with that of established lung cancer markers carcinoembryonic antigen (CEA), cytokeratin 19-fragments (CYFRA 21-1), neuron-specific enolase (NSE) and progastrin-releasing peptide (ProGRP). Furthermore, TK courses of 29 NSCLC patients during cytotoxic chemotherapy were recorded and prognostic relevance of pretherapeutic TK levels was tested in 128 NSCLC patients. Results: While healthy controls had low TK serum levels (median 2.5 U/l, 95th percentile 8.8 U/l), they were significantly higher in patients with lung cancer (median 4.2 U/l, p=0.014) and also in patients with benign lung diseases (median 5.7 U/l; p=0.002). Patients with lung cancer and benign lung diseases could not be separated by TK values. No noticeable difference of TK concentrations was further found in NSCLC (median 4.3 U/l) as compared with SCLC patients (median 3.7 U/l) neither in adeno cell carcinomas (median 5.4 U/l) and squamous cell carcinomas (median 3.0 U/l). In NSCLC, the best diagnostic capacity versus benign lung diseases was found for CYFRA 21-1 (AUC 88.2%), NSE (AUC 86.4%), and CEA (AUC 82.9%), while TK reached only an AUC of 45.7%. The best diagnostic profile in SCLC versus benign lung diseases was observed for NSE (AUC 93.9%) and ProGRP (AUC 85.4%), while TK did not have any diagnostic power (AUC 46.6%). Concerning therapy monitoring, TK was unable to discriminate between the various response groups, neither pretherapeutically, nor before therapy cycles 2 and 3. However, pretherapeutic TK levels showed high prognostic value for overall survival in NSCLC patients: While median survival in patients with TK levels >= 20 U/l was only 3.1 months, it was 9.0 months in patients with TK levels < 20 U/l. In multivariate analyses, TK remained an independent prognostic marker, along with the clinical variables stage and performance score. Conclusion: Although the performance of serum TK for diagnosis and therapy monitoring of advanced lung cancer was poor, it has a promising prognostic relevance which will have to be further validated.

OBJECTIVE:The aim of this study was to describe treatment outcomes for multi-drug resistant tuberculosis (MDR-TB) outpatients on a standardized regimen in Nepal.METHODOLOGY:Data on pulmonary MDR-TB patients enrolled for treatment in the Green Light Committee-approved National Programme between 15 September 2005 and 15 September 2006 were studied. Standardized regimen was used (8Z-Km-Ofx-Eto-Cs/16Z-Ofx-Eto-Cs) for a maximum of 32 months and follow-up was by smear and culture. Drug susceptibility testing (DST) results were not used to modify the treatment regimen. MDR-TB therapy was delivered in outpatient facilities for the whole course of treatment. Multivariable analysis was used to explain bacteriological cure as a function of sex, age, initial body weight, history of previous treatment and the region of report.PRINCIPAL FINDINGS:In the first 12-months, 175 laboratory-confirmed MDR-TB cases (62% males) had outcomes reported. Most cases had failed a Category 2 first-line regimen (87%) or a Category 1 regimen (6%), 2% were previously untreated contacts of MDR-TB cases and 5% were unspecified. Cure was reported among 70% of patients (range 38%-93% by Region), 8% died, 5% failed treatment, and 17% defaulted. Unfavorable outcomes were not correlated to the number of resistant drugs at baseline DST. Cases who died had a lower mean body weight than those surviving (40.3 kg vs 47.2 kg, p<0.05). Default was significantly higher in two regions [Eastern OR = 6.2; 95%CL2.0-18.9; Far West OR = 5.0; 95%CL1.0-24.3]. At logistic regression, cure was inversely associated with body weight <36 kg [Adj.OR = 0.1; 95%CL0.0-0.3; ref. 55-75 kg] and treatment in the Eastern region [Adj.OR = 0.1; 95%CL0.0-0.4; ref. Central region].CONCLUSIONS:The implementation of an ambulatory-based treatment programme for MDR-TB based on a fully standardized regimen can yield high cure rates even in resource-limited settings. The determinants of unfavorable outcome should be investigated thoroughly to maximize likelihood of successful treatment.

The increasing panel of systemic therapies enables the individual management of cancer patients, even in advanced stages. However, diagnostic tools indicating early the efficacy of therapy are still needed. In prospectively collected sera of 161 patients with recurrent non-small cell lung cancer (NSCLC) receiving second-line chemotherapy, the courses of nucleosomes, cytokeratin-19 fragments (CYFRA 21-1), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and progastrin-releasing peptide (ProGRP) were investigated and correlated with therapy response. At high specificity for detection of progressive disease, most sensitive biomarkers were identified and included in a combination model. High levels and insufficient decreases of nucleosomes and CYFRA 21-1 during the first cycle of therapy indicated poor outcome. Combination of nucleosome concentrations at day 8 and CYFRA 21-1 before start of the second cycle enabled the early detection of progressive disease with a sensitivity of 34.4% at 95% specificity (AUC 0.79) prior to imaging techniques. When cutoffs were fixed at the 90th percentile of responding patients, the combination model achieved sensitivities of 19% at 100% specificity and of 52% at 88% specificity. Thus, nucleosomes and CYFRA 21-1 showed to be valuable for the individual management of patients with recurrent NSCLC.

Nucleosomes, complexes of DNA and histone proteins, are released during cell death into the blood circulation. Elevated serum and plasma levels have been found in various forms of cancer, but also in autoimmune diseases and acute situations such as stroke, trauma, and during sepsis. Here, the clinical relevance of circulating nucleosomes for diagnosis, staging, prognosis, and therapeutic monitoring of cancer is reviewed. Several studies have shown that levels of nucleosomes are significantly higher in serum and plasma of cancer patients in comparison to healthy controls. However, because of elevations of nucleosome levels in patients with benign diseases relevant for differential diagnosis, they are not suitable for cancer diagnosis. Concerning tumor staging, nucleosome levels correlate with tumor stage and presence of metastases in gastrointestinal cancer, but not in other tumor types. Prognostic value of circulating nucleosomes is found in lung cancer in univariate analyses, but not in multivariate analyses. Circulating nucleosomes are most informative for the monitoring of cytotoxic therapy. Strongly decreasing levels are mainly found in patients with remission of disease, whereas constantly high or increasing values are associated with progressive disease during chemo- and radiotherapy. In addition, therapy outcome is already indicated by the nucleosomal course during the first week of chemo- and radiotherapy in patients with lung, pancreatic, and colorectal cancer as well as in hematologic malignancies. Despite their non-tumor-specificity, kinetics of nucleosomes are valuable markers for the early estimation of therapeutic efficacy and may be helpful to adapting early cancer therapy in the future.

Nucleosomes, complexes of DNA and histone proteins, are released during cell death into the blood circulation. Elevated serum and plasma levels have been found in various forms of cancer, but also in autoimmune diseases and acute situations such as stroke, trauma, and during sepsis. Here, the clinical relevance of circulating nucleosomes for diagnosis, staging, prognosis, and therapeutic monitoring of cancer is reviewed. Several studies have shown that levels of nucleosomes are significantly higher in serum and plasma of cancer patients in comparison to healthy controls. However, because of elevations of nucleosome levels in patients with benign diseases relevant for differential diagnosis, they are not suitable for cancer diagnosis. Concerning tumor staging, nucleosome levels correlate with tumor stage and presence of metastases in gastrointestinal cancer, but not in other tumor types. Prognostic value of circulating nucleosomes is found in lung cancer in univariate analyses, but not in multivariate analyses. Circulating nucleosomes are most informative for the monitoring of cytotoxic therapy. Strongly decreasing levels are mainly found in patients with remission of disease, whereas constantly high or increasing values are associated with progressive disease during chemo‐ and radiotherapy. In addition, therapy outcome is already indicated by the nucleosomal course during the first week of chemo‐ and radiotherapy in patients with lung, pancreatic, and colorectal cancer as well as in hematologic malignancies. Despite their non‐tumor‐specificity, kinetics of nucleosomes are valuable markers for the early estimation of therapeutic efficacy and may be helpful to adapting early cancer therapy in the future.

Purpose: Besides new therapeutic drugs, effective diagnostic tools indicating early the efficacy of therapy are required to improve the individual management of patients with nonoperable cancer diseases. Experimental Design: In prospectively collected sera of 128 patients with newly diagnosed small cell lung cancer receiving first-line chemotherapy, the courses of nucleosomes, progastrin-releasing peptide (ProGRP), neuron-specific enolase (NSE), cytokeratin-19 fragments (CYFRA 21-1), and carcinoembryonic antigen were investigated and correlated with therapy response objectified by computed tomography before start of the third treatment course. Results: In univariate analyses, high levels and insufficient decreases of nucleosomes, ProGRP, NSE, and CYFRA 21-1 during the first and second cycles of therapy correlated with poor outcome. Insufficient response to therapy was most efficiently indicated by the baseline values of nucleosomes, ProGRP, and CYFRA 21-1 before the second therapy cycle reaching areas under the curve (AUC) of 81.8%, 71.3%, and 74.9% in receiver operating characteristic curves, respectively. Combinations of nucleosomes with ProGRP (AUC 84.1%), CYFRA 21-1 (AUC 82.5%), and NSE (AUC 83.6%) further improved the diagnostic power in the high specificity range and yielded sensitivities of 47.1%, 35.3%, and 35.3% at 95% specificity, respectively. In multivariate analyses, including clinical and biochemical variables, only performance score and nucleosomes before cycle 2 were found to independently indicate therapy response. Conclusions: Biochemical markers specifically identified patients with insufficient therapy response at the early treatment phase and showed to be valuable for diseases management of small cell lung cancer.

19010 Background: Biomarker models may be effective for prediction of therapy response and prognosis of overall survival. Methods: 60 clinical factors, “classical” laboratory and oncological biomarkers were prospectively investigated on 300 patients with newly diagnosed advanced NSCLC before and during first-line chemotherapy to test whether i) predictive and prognostic markers are identical, ii) biomarkers have additive prognostic impact to clinical factors, iii) intratherapeutical biomarkers improve prognostic models, iv) biomarkers are useful for early estimation of therapy response? Univariate evaluations were done by Wilcoxon and Logrank tests, multivariate analyses by Cox regression. Results: 30% of patients had progression after 2 cycles of chemotherapy and 57% deceased during observation time (1–29 months). Concerning pretherapeutic markers, strong predictors of therapy response were also highly relevant for prognosis, such as performance score (PS), metastases other than lung (MOL), chemotherapy, WBC, CRP, albumin, CYFRA 21–1, nucleosomes, CA125, CA15–3, and CA72–4. Multivariate analysis revealed PS, MOL, chemotherapy, CRP and CYFRA 21–1 as independent prognostic parameters. When intratherapeutic markers were included, CYFRA 21–1 and nucleosomes before 2nd therapy cycle (BV2), and therapy response also indicated strongly and independently survival and improved the prognostic power of the model. Further, combination of nucleosomes on day 8 and CYFRA 21–1 (BV2) already enabled the detection of insufficient therapy efficacy after one cycle of chemotherapy in 29% of progressive patients with 100% specificity. At 90% specificity, sensitivity rose to 55%. Conclusion: In advanced lung cancer, highly predictive pretherapeutical biomarkers had also high prognostic relevance. Biomarkers determined during 1st therapy cycle improved the prognostic model and enabled the early estimation of therapy response. No significant financial relationships to disclose.

Infection with Mycobacterium tuberculosis is controlled by an efficacious immune response in about 90% of infected individuals who do not develop disease. Although essential mediators of protection, e.g., interferon-γ, have been identified, these factors are insufficient to predict the outcome of M. tuberculosis infection. As a first step to determine additional biomarkers, we compared gene expression profiles of peripheral blood mononuclear cells from tuberculosis patients and M. tuberculosis-infected healthy donors by microarray analysis. Differentially expressed candidate genes were predominantly derived from monocytes and comprised molecules involved in the antimicrobial defense, inflammation, chemotaxis, and intracellular trafficking. We verified differential expression for alpha-defensin 1, alpha-defensin 4, lactoferrin, Fcγ receptor 1A (cluster of differentiation 64 [CD64]), bactericidal permeability-increasing protein, and formyl peptide receptor 1 by quantitative polymerase chain reaction analysis. Moreover, we identified increased protein expression of CD64 on monocytes from tuberculosis patients. Candidate biomarkers were then assessed for optimal study group discrimination. Using a linear discriminant analysis, a minimal group of genes comprising lactoferrin, CD64, and the Ras-associated GTPase 33A was sufficient for classification of (1) tuberculosis patients, (2) M. tuberculosis-infected healthy donors, and (3) noninfected healthy donors.

18079 Background: Currently available data concerning the prognostic relevance of biochemical markers in NSCLC are conflicting. Methods: In a prospective study, 60 pretherapeutic parameters including 23 clinical factors, 22 “classical” laboratory markers, and 15 oncological biomarkers were investigated in 300 patients with newly diagnosed advanced NSCLC undergoing first-line chemotherapy. After selection of prognostically independent, clinical parameters by Cox regression analysis, biochemical markers were separately tested together with these clinical factors, and if independently prognostically relevant, simultaneously analyzed by Cox regression using both forward and backward selection. Dependent on clinical preconditions, multivariate analyses were performed on A) clinical, laboratory and oncological lung parameters and B) clinical, laboratory and all oncological parameters. Results: Concerning clinical factors, performance score (PS), weight loss, metastases other than lung (MOL), and chemotherapy showed independent prognostic relevance. Testing biochemical variables with these clinical factors revealed a plentitude of them remaining prognostically significant. When analyzing all prognostically relevant clinical and biochemical variables simultaneously, multivariate models of independently prognostic parameters included for panel A) PS, MOL, CRP, CYFRA 21–1 and chemotherapy. In panel B) CYFRA 21–1 was replaced by the combination of HCG-beta, CA72–4, CA19–9 and S100. Both models improved the prognostic information within the various PS categories significantly. Conclusion: Depending on the availability of pretherapeutic clinical and laboratory variables, various prognostic models for NSCLC patients undergoing first-line chemotherapy can be established. The resulting multivariate models improve the prognostic power over the currently used performance score and provide a more precise pretherapeutic, prognostic stratification of these patients. No significant financial relationships to disclose.

Abstract: Facing an era of promising new antitumor therapies, predictors of therapy response are needed for the individual management of treatment. In sera collected prospectively from 311 patients with advanced non-small cell lung cancer receiving first-line chemotherapy, changes in nucleosomal DNA fragments, cytokeratin-19 fragments (CYFRA 21–1), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and progastrin-releasing peptide (ProGRP) were investigated and correlated with therapy response. In univariate analysis, high levels, slower and incomplete decline in nucleosomal DNA, CYFRA 21–1, and CEA predicted poor outcome. DNA concentrations at day 8 of the first therapeutic cycle and CYFRA 21–1 before start of the second cycle were identified as best predictive variables. In multivariate analysis, they predicted progression with a specificity of 100% in 29% of the cases earlier than imaging techniques. Thus, nucleosomal DNA and CYFRA 21–1 specifically identify a subgroup of patients with insufficient therapy response at the early treatment phase and showed to be valuable for disease management.

Facing an era of promising new antitumor therapies, predictors of therapy response are needed for the individual management of treatment. In sera collected prospectively from 311 patients with advanced non-small cell lung cancer receiving first-line chemotherapy, changes in nucleosomal DNA fragments, cytokeratin-19 fragments (CYFRA 21-1), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and progastrin-releasing peptide (ProGRP) were investigated and correlated with therapy response. In univariate analysis, high levels, slower and incomplete decline in nucleosomal DNA, CYFRA 21-1, and CEA predicted poor outcome. DNA concentrations at day 8 of the first therapeutic cycle and CYFRA 21-1 before start of the second cycle were identified as best predictive variables. In multivariate analysis, they predicted progression with a specificity of 100% in 29% of the cases earlier than imaging techniques. Thus, nucleosomal DNA and CYFRA 21-1 specifically identify a subgroup of patients with insufficient therapy response at the early treatment phase and showed to be valuable for disease management.

Summary Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type. Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes. Results: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results. Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.