Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.

BACKGROUND: Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts. METHODS: To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model. RESULTS: GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls. CONCLUSIONS: GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.

Optimizing Screening for Colorectal Cancer: An Algorithm Combining Fecal Immunochemical Test, Blood-Based Cancer-Associated Proteins and Demographics to Reduce Colonoscopy Burden.

BACKGROUND: Fecal Immunochemical Test (FIT) is widely used in population-based screening for colorectal cancer (CRC). This had led to major challenges regarding colonoscopy capacity. Methods to maintain high sensitivity without compromising the colonoscopy capacity are needed. This study investigates an algorithm that combines FIT result, blood-based biomarkers associated with CRC, and individual demographics, to triage subjects sent for colonoscopy among a FIT positive (FIT+) screening population and thereby reduce the colonoscopy burden. MATERIALS AND METHODS: From the Danish National Colorectal Cancer Screening Program, 4048 FIT+ (≥100 ng/mL Hemoglobin) subjects were included and analyzed for a panel of 9 cancer-associated biomarkers using the ARCHITECT i2000. Two algorithms were developed: 1) a predefined algorithm based on clinically available biomarkers: FIT, age, CEA, hsCRP and Ferritin; and 2) an exploratory algorithm adding additional biomarkers: TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, B2M and sex to the predefined algorithm. The diagnostic performances for discriminating subjects with or without CRC in the 2 models were benchmarked against the FIT alone using logistic regression modeling. RESULTS: The discrimination of CRC showed an area under the curve (AUC) of 73.7 (70.5-76.9) for the predefined model, 75.3 (72.1-78.4) for the exploratory model, and 68.9 (65.5-72.2) for FIT alone. Both models performed significantly better (P < .001) than the FIT model. The models were benchmarked vs. FIT at cutoffs of 100, 200, 300, 400, and 500 ng/mL Hemoglobin using corresponding numbers of true positives and false positives. All performance metrics were improved at all cutoffs. CONCLUSION: A screening algorithm including a combination of FIT result, blood-based biomarkers and demographics outperforms FIT in discriminating subjects with or without CRC in a screening population with FIT results above 100 ng/mL Hemoglobin.

Pre-analytical considerations in the development of a prototype SARS-CoV-2 antigen ARCHITECT automated immunoassay.

OBJECTIVES: To evaluate pre-analytical challenges related to high-volume central laboratory SARS-CoV-2 antigen testing with a prototype qualitative SARS-CoV-2 antigen immunoassay run on the automated Abbott ARCHITECT instrument. METHODS: Contrived positive and negative specimens and de-identified nasal and nasopharyngeal specimens in transport media were used to evaluate specimen and reagent on-board stability, assay analytical performance and interference, and clinical performance. RESULTS: TCID50/mL values were similar for specimens in various transport media. Inactivated positive clinical specimens and viral lysate (USA-WA1/2020) were positive on the prototype immunoassay. Within-laboratory imprecision was ≤0.10 SD (<1.00 S/C) with a ≤10% CV (≥1.00 S/C). Assay reagents were stable on board the instrument for 14 days. No high-dose hook effect was observed with a SARS-CoV-2 stock of Ct 13.0 (RLU>1.0 × 106). No interference was observed from mucin, whole blood, 12 drugs, and more than 20 cross-reactants. While specimen stability was limited at room temperature for specimens with or without viral inactivation, a single freeze/thaw cycle or long-term storage (>30 days) at -20 °C did not adversely impact specimen stability or assay performance. Specificity of the prototype SARS-CoV-2 antigen immunoassay was ≥98.5% and sensitivity was ≥89.5% across two ARCHITECT instruments. Assay sensitivity was inversely correlated with Ct and was similar to that reported for the Roche Elecsys® SARS-CoV-2 Ag immunoassay. CONCLUSIONS: The prototype SARS-CoV-2 antigen ARCHITECT immunoassay is sensitive and specific for detection of SARS-CoV-2 in nasal and nasopharyngeal specimens. Endogenous proteases in mucus may degrade the target antigen, which limits specimen storage and transport times and complicates assay workflow.

Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Development of exploratory algorithms to aid in risk of malignancy prediction of indeterminate pulmonary nodules.

Early detection of lung cancer allows for earlier stage treatment initiation and improved patient prognosis. This report focuses on utilization of combining patient demographic information with non-invasive biomarkers and their potential ability to predict risk of malignancy of nodules. A pilot study cohort of 141 subjects with IPNs (105 stage I cancer and 36 benign nodules) were collected by RUMC. The demographic variables of gender, age, sex, race, ethnicity, nodule size (mm), and smoking pack years, as well as the plasma levels of CA-125, SCC, CEA, HE4, ProGRP, NSE, Cyfra 21-1, hs-CRP, Ferritin, IgG, IgG1, IgG2, IgG3, IgG4, IgE, IgM, IgA, KFLC, and LFLC, were assessed for this cohort. Multivariable analyses of the previously aforementioned biomarkers and demographic variables yielded a reduced algorithm consisting of CA-125, total IgG, IgA, IgM, IgE, LFLC, nodule size, and smoking pack years with improved performance (AUC 0.82, 95 %CI 0.74-0.90) over the same analysis of the demographic variables (age, nodule size, and smoking pack years) alone (AUC 0.70, 95 %CI 0.61-0.78). This reduced algorithm of biomarkers and demographic variables may aid in assessing the risk of IPN malignancy which could be a useful stratification tool in early detection of lung cancer in high-risk subjects.

Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers.

BACKGROUND: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. METHODS: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index. RESULTS: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy. CONCLUSIONS: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

Biomarker Changes in Response to a 12-Week Supplementation of an Oral Nutritional Supplement Enriched with Protein, Vitamin D and HMB in Malnourished Community Dwelling Older Adults with Sarcopenia.

Malnutrition and sarcopenia commonly overlap and contribute to adverse health outcomes. Previously, chronic supplementation with two oral nutritional supplements (ONS), control (CONS) and experimental ONS enriched with protein, vitamin D and ß-hydroxy ß-methylbutyrate (HMB) (EONS), improved muscle strength and quality in malnourished sarcopenic older adults, with EONS demonstrating early strength benefits at 12 weeks. To understand the underlying biological mechanisms contributing to the observed early strength benefits of EONS, we examined serum biomarker changes in response to 12-week supplementation. Serum samples (EONS (n = 90) and CONS (n = 103)) collected at baseline and 12 weeks were analyzed. Biomarkers (n = 243) were measured using multiplexed immunoassay, commercial immunoassays and ELISAs. Sixty markers were excluded with levels below assay detection limits. Sixteen biomarkers significantly changed in response to both interventions including nutritional and metabolic markers. Thirteen biomarkers significantly changed in response to EONS but not CONS. Increases in immunoglobulins, myoglobin, total protein, vitamin E and magnesium were observed with EONS. Inflammation-related ferritin and osteopontin decreased, while soluble receptors for cytokines increased, suggesting decreased inflammation. Sex hormone-binding globulin associated with sarcopenia also decreased with EONS. Biomarkers reflective of multiple biological systems were impacted by nutritional intervention in sarcopenic older adults. Incremental biomarker changes were observed in response to EONS containing HMB that possibly link to improvements in skeletal muscle health.

Early detection of colorectal neoplasia: application of a blood-based serological protein test on subjects undergoing population-based screening.

BACKGROUND: Blood-based biomarkers used for colorectal cancer screening need to be developed and validated in appropriate screening populations. We aimed to develop a cancer-associated protein biomarker test for the detection of colorectal cancer in a screening population. METHODS: Participants from the Danish Colorectal Cancer Screening Program were recruited. Blood samples were collected prior to colonoscopy. The cohort was divided into training and validation sets. We present the results of model development using the training set. Age, sex, and the serological proteins CEA, hsCRP, TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, ferritin and B2M were used to develop a signature test to discriminate between participants with colorectal cancer versus all other findings at colonoscopy. RESULTS: The training set included 4048 FIT-positive participants of whom 242 had a colorectal cancer. The final model for discriminating colorectal cancer versus all other findings at colonoscopy had an AUC of 0.70 (95% CI: 0.66-0.74) and included age, sex, CEA, hsCRP, HE4 and ferritin. CONCLUSION: The performance of the biomarker signature in this FIT-positive screening population did not reflect the positive performance of biomarker signatures seen in symptomatic populations. Additional biomarkers are needed if the serological biomarkers are to be used as a frontline screening test.

Instability of corticotropin during long-term storage - myth or reality?

OBJECTIVES: Corticotropin is notorious for its instability. Whereas several studies have investigated its short-term stability in plasma following venous blood sampling, studies on long-term stability are lacking. Here we investigated the long-term storage stability of corticotropin in ethylenediaminetetraacetic acid containing plasma. METHODS: Specimens from healthy volunteers (neat, spiked) were stored in polypropylene microcentrifuge tubes with socket screw-caps at -20 °C and -70 °C for up to one and a half years. Corticotropin in plasma was measured using an Abbott research only immunoassay. Separately, specimens from patients were collected during diagnostic routine testing and stored in polystyrene tubes with push-caps at -20 °C for up to 6 years. In these samples corticotropin hormone was measured using the Diasorin corticotropin immunoassay. RESULTS: Storage of specimens at -20 °C or -70 °C for up to one and a half years showed minimal changes (<11%) in corticotropin levels, while storage of patient samples at -20 °C for up to 6 years showed a significant (54%) reduction in corticotropin levels. CONCLUSIONS: Corticotropin levels are stable in plasma when stored at -20 °C for one and a half years using the Abbott research only assay, but with longer storage time a significant reduction in corticotropin levels can be expected. Once specimens are stored for future corticotropin measurements, one should consider storage time, storage temperature and assay differences.

Serum biomarkers that predict lean mass loss over bed rest in older adults: An exploratory study.

BACKGROUND: Lean mass (LM) loss during extended bed rest contributes to long term functional decline in older adults. Identifying blood biomarkers that predict a hospitalized individual's risk of losing LM could allow for timely intervention. METHODS: LM from 19 healthy subjects (age 60-76 y, 4 males, 15 females), who were confined to 10 days of complete bed rest, was measured pre- and post-bed rest. One hundred eighty-seven biomarkers from pre-bed rest fasted serum samples were obtained from all evaluable subjects (n = 18), analyzed using multiplexed immunoassay array and pooled. Decision tree analysis was used to identify pre-bed rest markers that predict LM loss over bed rest. RESULTS: Sixty-three markers were excluded due to being below assay detection limits. One pair of markers, Tissue inhibitor of metalloprotease-1 (TIMP1) and tenascin C (TNC), were found to correlate with percent change in total LM over bed rest: [R2 = 0.71, all subjects; R2 = 0.76, females]. Subjects with pre-bed rest TIMP1 ≥ 141 ng/ml had the highest loss of total LM over bed rest, whereas subjects with pre-bed rest TIMP1 < 141 and TNC ≥ 461 ng/ml maintained total LM over bed rest. An additional marker set was found to correlate with percent change in leg LM loss over bed rest: matrix metalloprotease-3 (MMP3) and apolipoprotein A2 (APOA2) [R2 = 0.59, females]. Females with pre-bed rest MMP3 < 6.93 ng/ml had the highest loss of leg LM over bed rest. Whereas females with pre-bed rest MMP3 ≥ 6.93 and ApoA2 < 276 ng/ml, maintained leg lean mass at the end of bed rest. CONCLUSIONS: Panels of blood biomarkers associated with the muscle extracellular matrix may predict the likelihood for LM loss over extended bed rest.

Development of blood-based biomarker tests for early detection of colorectal neoplasia: Influence of blood collection timing and handling procedures.

INTRODUCTION: Blood-based, cancer-associated biomarkers are susceptible to a variety of well-known preanalytical factors. The influence of bowel preparation before a diagnostic colonoscopy on biomarker levels is, however, poorly investigated. The present study assessed the influence of bowel preparation on colorectal cancer-associated biomarkers. In addition, the effect of single versus double centrifugation of plasma biomarkers was assessed. METHODS: Blood samples were collected pre- and post-bowel preparation from 125 subjects scheduled for first time diagnostic colonoscopy due to symptoms attributable to CRC. The samples were separated into serum and EDTA plasma, and analyzed by four independent collaborators for: 1) the proteins AFP, CA19-9, CEA, hs-CRP, CyFra21-1, Ferritin, Galectin-3 and TIMP-1, 2) the proteins BAG4, IL6ST, vWF, CD44 and EGFR, 3) the glycoprotein Galectin-3 ligand, and 4) cell-free DNA (cfDNA). Statistical analysis of biomarker data has been performed using mixed modelling, including repeated measures. RESULTS: The biomarkers generally showed negligible variation between pre- and post-bowel preparation except for CyFra21-1, Ferritin, BAG4 and cfDNA. CyFra21-1 levels were systematically reduced with 29% (95% CI 21-36%) by bowel preparation (p ≤ 0.0001). Ferritin was not significantly different between pre- and post-bowel preparation (p = 0.07), however the estimated difference (increase) was 18%. BAG4 was systematically reduced by 12% (95% CI 1-22%, p = 0.04), while cfDNA showed a significant increase of 28% (95% CI 17-39%, p < 0.0001). Double centrifugation compared to single centrifugation showed reduced vWF (ratio 0.86, p ≤ 0.0001) and CD44 (ratio 0.85, p = 0.016), but increased IL6ST levels (ratio 1.18, p = 0.014). CONCLUSIONS: Results of the present study demonstrated systematic, statistically significant differences between pre-bowel and post-bowel preparation levels for three independent blood-based biomarkers (BAG4, CyFra21-1, cfDNA), illustrating the importance of timing of sample collection for biomarker analyses.

Evaluation of algorithm development approaches: Development of biomarker panels for early detection of colorectal lesions.

INTRODUCTION: Colorectal cancer (CRC) is the third most common cancer in the U.S. Early detection of CRC can substantially increase survival rates. Test compliance may be improved by offering a blood-based test option. METHODS: Endoscopy II trial specimens were tested for AFP, CA19-9, CEA, hs-CRP, CyFra 21-1, Ferritin, Galectin-3, and TIMP-1 levels. These biomarkers, as well as patient demographic information (e.g., age, gender), were included in algorithm development. Six statistical methods were utilized to develop algorithms that would discriminate cancer vs. noncancers. Statistical methods included logistic regression, adaptive index modeling, partial least-squares discriminant analysis, feature vector (weighted and unweighted), and random forest. The performance of these algorithms was compared against benchmark criteria established for stool-based tests. RESULTS: Using several statistical methods, the presence of CRC and high-risk adenomas was detected with an AUCs of at least 0.65-0.76, with a few of models approaching the stool-based tests benchmark performance. Further, common markers were utilized across the different statistical techniques, with model complexities ranging from 3 to 9 markers. CONCLUSIONS: Predictive models identified subjects with CRC and high-risk adenomas with the similar levels of statistical accuracy. Clinical performance differences were minimal across the statistical techniques, although the intuitive interpretations, model complexity, clinical adoption and implementation varied.

Validation of a novel model for the early detection of hepatocellular carcinoma.

BACKGROUND: The biomarkers alpha-fetoprotein (AFP) and protein induced by vitamin K absence/antagonist-II (PIVKA-II) may be useful for detecting early-stage hepatocellular carcinoma (HCC). We evaluated the performance of AFP and PIVKA-II levels, alone and in combination with clinical factors, for the early detection of HCC. METHODS: In a case-control study, serum AFP and PIVKA-II were measured using the ARCHITECT immunoassay analyzer system in a cohort of 119 patients with HCC, 215 patients with non-malignant liver disease, and 34 healthy subjects. Five predictive models for detecting HCC were developed based on age, gender, AFP, and/or PIVKA-II levels; the best model was validated in an independent cohort of 416 patients with HCC and 412 control subjects with cirrhosis. RESULTS: In both cohorts, AFP and PIVKA-II concentrations were higher in patients with HCC compared to healthy controls and patients with non-malignant liver disease. The model that combined AFP and PIVKA-II, age, and gender had the highest AUC of 0.95 (0.95, 95% CI 0.93-0.98), with a sensitivity of 93% and a specificity of 84% in the development cohort, and an AUC of 0.87 (95% CI 0.85-0.90), sensitivity of 74%, and specificity of 85% in the validation cohort. When limiting the validation cohort to only early-stage HCC, the AUC was 0.85 (95% CI 0.81-0.88), sensitivity was 70%, and specificity was 86%. CONCLUSIONS: Compared to each biomarker alone, the combination of AFP and PIVKA-II with age and gender improved the accuracy of detecting HCC and differentiating HCC from non-malignant liver disease.

Development and analytical performance of a new ARCHITECT automated dipeptidyl peptidase-4 immunoassay.

BACKGROUND: Dipeptidyl peptidase-4 (DPP-4) may be a suitable biomarker to identify people with severe asthma who have greater activation of the interleukin-13 (IL-13) pathway and may therefore benefit from IL-13-targeted treatments. We report the analytical performance of an Investigational Use Only immunoassay and provide data on the biological range of DPP-4 concentrations. METHODS: We assessed assay performance, utilising analyses of precision, linearity and sensitivity; interference from common endogenous assay interferents, and from asthma and anti-diabetic medications, were also assessed. The assay was used to measure the range of serum DPP-4 concentrations in healthy volunteers and subjects with diabetes and severe, uncontrolled asthma. RESULTS: The total precision of DPP-4 concentration measurement (determined using percentage coefficient of variation) was ≤5% over 20 days. Dilution analysis yielded linear results from 30 to 1305 ng/mL; the limit of quantitation was 19.2 ng/mL. No notable endogenous or drug interferences were observed at the expected therapeutic concentration. Median DPP-4 concentrations in healthy volunteers and subjects with asthma or Type 1 diabetes were assessed, with concentrations remaining similar in subjects with diabetes and asthma across different demographics. CONCLUSION: These analyses indicate that the ARCHITECT DPP-4 Immunoassay is a reliable and robust method for measuring serum DPP-4 concentration.

Development of a new ARCHITECT automated periostin immunoassay.

BACKGROUND: Periostin is being investigated as a potential biomarker for T-helper-2 (Th2)-driven asthma or eosinophilic inflammation and may help to identify patients more likely to benefit from interleukin-13-targeted treatments. We report the development and analytic performance of the investigational use only ARCHITECT Periostin Immunoassay, a new automated assay developed to detect serum periostin concentrations. METHODS: We assessed assay performance in terms of precision, sensitivity, linearity, interference from classical immunoassay interferents and representatives of common asthma medications, specimen handling, and isoform reactivity. The assay was also used to assess the biological variability of serum periostin concentrations in samples from healthy volunteers and from subjects with uncontrolled asthma (the intended use population). RESULTS: The percentage CVs for 5-day total precision, assessed using two instruments, was <6% across 2 controls and one serum-based panel. Limit of quantitation was 4ng/mL (dilution adjusted concentration), suiting the needs for this application. Dilution analysis yielded linear results and no endogenous sample or drug interferences were observed. All known periostin isoforms expressed in the mature human lung were detected by the assay. CONCLUSION: Our studies provide support that the ARCHITECT Periostin Immunoassay is a reliable and robust test for measuring serum periostin concentrations.

Increased serological cancer-associated biomarker levels at large bowel endoscopy and risk of subsequent primary cancer (†).

BACKGROUND: Frequently, subjects offered colonoscopy due to symptoms of colorectal neoplasia are diagnosed with diverticula. The symptoms may, however, also be related to extra-colonic neoplasia. The present retrospective study evaluated a possible association between increased levels of predefined biomarkers in subjects diagnosed with diverticula and risk of developing a primary malignant disease. METHODS: During 2004/2005, about 4509 subjects were included in a multicenter study with collection of blood samples before bowel endoscopy. The aim was to evaluate a relation between the protein biomarkers CEA, TIMP-1, CA19-9 and YKL-40 and findings at endoscopy. Diverticula were diagnosed in 1021 subjects. By 31 December 2012, subjects who had developed primary malignancy were identified retrospectively and relation between biomarker levels at endoscopy and risk of developing primary malignancy was calculated. The relation with the four biomarkers was divided into three groups: 0 = none increased; 1 = one increased and 2 = two or more increased. RESULTS: In the observation period, 148 subjects developed a primary malignant disease. Univariable analyzes of the biomarker levels showed that CEA, TIMP-1 and CA19-9 were significantly associated with development of primary malignancy. A multivariable analysis showed that increased levels were associated with development of malignancy (p < 0.0001). The 1- and 5-year cumulative risks of being diagnosed with a primary malignancy were: group 0: 1.1%/5.5%; group 1: 4.2%/10.1% and group 2: 11.4%/18.8%, respectively. CONCLUSION: Increased levels of CEA, TIMP-1 and CA19-9 at endoscopy with findings of diverticula were associated with a significantly increased risk of being diagnosed with a subsequent primary malignant disease.

Challenges in developing a validated biomarker for angiogenesis inhibitors: the motesanib experience.

PURPOSE: We sought to develop placental growth factor as a predictive pharmacodynamic biomarker for motesanib efficacy as first-line therapy in patients with advanced nonsquamous non-small-cell lung cancer. EXPERIMENTAL DESIGN: Placental growth factor was evaluated at baseline and study week 4 (after 3 weeks treatment) in an exploratory analysis of data from a randomized phase 2 study of motesanib 125 mg once daily plus carboplatin/paclitaxel and in a prespecified analysis of data from a randomized, double-blind phase 3 study of motesanib 125 mg once daily plus carboplatin/paclitaxel vs placebo plus carboplatin/paclitaxel (MONET1). Associations between fold-change from baseline in placental growth factor and overall survival were evaluated using Cox proportional hazards models. RESULTS: In the phase 2 study, serum placental growth factor increased from baseline a mean 2.8-fold at study week 4. Patients with ≥2.2-fold change from baseline in placental growth factor (n = 18) had significantly longer overall survival than those with <2.2-fold change (n = 19; 22.9 vs 7.9 months; hazard ratio, 0.30; 95% CI, 0.12-0.74; P = 0.009). Consequently, placental growth factor was investigated as a pharmacodynamic biomarker in the phase 3 MONET1 study. There was no association between log-transformed placental growth factor fold-change from baseline to week 4 (continuous variable) and overall survival (hazard ratio, 0.98; 95% CI, 0.79-1.22; P = 0.868). MONET1 did not meet its primary endpoint of overall survival. Likewise, median overall survival was similar among patients with ≥2.0-fold change in placental growth factor (n = 229) compared with <2.0-fold change (n = 127; 14.8 vs 13.8 months; hazard ratio, 0.88; 95% CI, 0.67-1.15, P = 0.340). CONCLUSIONS: Our results illustrate the challenges of successfully translating phase 2 biomarker results into phase 3 studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT00460317, NCT00369070.

Establishment and characterization of 7 new monoclonal antibodies to tissue inhibitor of metalloproteinases-1.

Tissue inhibitor of metalloproteinases-1 (TIMP-1) plays a pivotal role in tissue remodeling processes, such as inflammation, wound healing and cancer invasion. Experimental results have pointed to a role in angiogenesis, cell proliferation, apoptosis and in malignant transformation. In clinical investigations high tumor tissue or plasma levels of TIMP-1 have been shown to have a strong and independent association with shorter survival time for breast and colorectal cancer patients, respectively. The purpose of this study has been to develop and characterize new anti-TIMP-1 monoclonal antibodies that may be useful in future development of TIMP-1 immunoassays.Peptide-based epitope mapping reveals linear epitopes. Surface plasmon resonance was used to determine antibody affinity and ability of antibodies to sandwich with each other. Antigen recognition was tested using ELISA and a chemiluminescence microtiter immunoassay format. Three antibodies recognized linear peptides. Estimated antibody affinities for TIMP-1 ranged from 6.6 x 10(8) to>10(10) 1/M. Antibodies demonstrated different abilities in 'capture' and 'detection' positions in the sandwich experiment. All antibody pairs bound TIMP-1:ProMMP-9 complexes. TIMP-1:MMP-9 complexes were marginally reactive with five antibody pairs. The results suggest that the antibodies are unique. They may be useful in designing assays that recognize various forms of TIMP-1. Future studies will clarify whether the use of different combinations of antibodies will increase the clinical value of TIMP-1 measurements in the treatment of cancer patients.