High Levels of Expression of P-glycoprotein/Multidrug Resistance Protein Result in Resistance to Vintafolide.

Targeting surface receptors overexpressed on cancer cells is one way to specifically treat cancer versus normal cells. Vintafolide (EC145), which consists of folate linked to a cytotoxic small molecule, desacetylvinblastine hydrazide (DAVLBH), takes advantage of the overexpression of folate receptor (FR) on cancer cells. Once bound to FR, vintafolide enters the cell by endocytosis, and the reducing environment of the endosome cleaves the linker, releasing DAVLBH to destabilize microtubules. Vintafolide has shown efficacy and improved tolerability compared with DAVLBH in FR-positive preclinical models. As the first FR-targeting drug to reach the clinic, vintafolide has achieved favorable responses in phase II clinical trials in FR-positive ovarian and lung cancer. However, some FR-positive patients in these clinical trials do not respond to vintafolide. We sought to identify potential biomarkers of resistance to aid in the future development of this and other FR-targeting drugs. Here, we confirm that high P-glycoprotein (P-gp) expression was the strongest predictor of resistance to DAVLBH in a panel of 359 cancer cell lines. Furthermore, targeted delivery of DAVLBH via the FR, as in vintafolide, fails to overcome P-gp-mediated efflux of DAVLBH in both in vitro and in vivo preclinical models. Therefore, we suggest that patients whose tumors express high levels of P-gp be excluded from future clinical trials for vintafolide as well as other FR-targeted therapeutics bearing a P-gp substrate. Mol Cancer Ther; 15(8); 1998-2008. ©2016 AACR.

An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies.

Combination drug therapy is a widely used paradigm for managing numerous human malignancies. In cancer treatment, additive and/or synergistic drug combinations can convert weakly efficacious monotherapies into regimens that produce robust antitumor activity. This can be explained in part through pathway interdependencies that are critical for cancer cell proliferation and survival. However, identification of the various interdependencies is difficult due to the complex molecular circuitry that underlies tumor development and progression. Here, we present a high-throughput platform that allows for an unbiased identification of synergistic and efficacious drug combinations. In a screen of 22,737 experiments of 583 doublet combinations in 39 diverse cancer cell lines using a 4 by 4 dosing regimen, both well-known and novel synergistic and efficacious combinations were identified. Here, we present an example of one such novel combination, a Wee1 inhibitor (AZD1775) and an mTOR inhibitor (ridaforolimus), and demonstrate that the combination potently and synergistically inhibits cancer cell growth in vitro and in vivo This approach has identified novel combinations that would be difficult to reliably predict based purely on our current understanding of cancer cell biology. Mol Cancer Ther; 15(6); 1155-62. ©2016 AACR.

A candidate plasma protein classifier to identify Alzheimer's disease.

Biomarkers currently used in the aid for the diagnosis of Alzheimer's disease (AD) are cerebrospinal fluid (CSF) protein markers and brain neuroimaging markers. These biomarkers, however, either involve semi-invasive procedures or are costly to measure. Thus, AD biomarkers from more easily accessible body fluids, such as plasma, are very enticing. Using an aptamer-based proteomic technology, we profiled 1,129 plasma proteins of AD patients and non-demented control individuals. A 5-protein classifier for AD identification was constructed in the discovery study with excellent 10-fold cross-validation performance (90.1% sensitivity, 84.2% specificity, 87.9% accuracy, and AUC as 0.94). In an independent validation study, the classifier was applied and correctly predicted AD with 100.0% sensitivity, 80.0% specificity, and 90.0% accuracy, matching or outperforming the CSF Aß42 and tau biomarkers whose performance were assessed in individual-matched CSF samples obtained at the same visit as plasma sample collection. Moreover, the classifier also correctly predicted mild cognitive impairment, an early pre-dementia state of the disease, with 96.7% sensitivity, 80.0% specificity, and 92.5% accuracy. These studies demonstrate that plasma proteins could be used effectively and accurately to contribute to the clinical diagnosis of AD. Although additional and more diverse cohorts are needed for further validation of the robustness, including the support of postmortem diagnosis, the 5-protein classifier appears to be a promising blood test to contribute diagnosis of AD.

Discovery of biomarkers predictive of GSI response in triple-negative breast cancer and adenoid cystic carcinoma.

UNLABELLED: Next-generation sequencing was used to identify Notch mutations in a large collection of diverse solid tumors. NOTCH1 and NOTCH2 rearrangements leading to constitutive receptor activation were confined to triple-negative breast cancers (TNBC; 6 of 66 tumors). TNBC cell lines with NOTCH1 rearrangements associated with high levels of activated NOTCH1 (N1-ICD) were sensitive to the gamma-secretase inhibitor (GSI) MRK-003, both alone and in combination with paclitaxel, in vitro and in vivo, whereas cell lines with NOTCH2 rearrangements were resistant to GSI. Immunohistochemical staining of N1-ICD in TNBC xenografts correlated with responsiveness, and expression levels of the direct Notch target gene HES4 correlated with outcome in patients with TNBC. Activating NOTCH1 point mutations were also identified in other solid tumors, including adenoid cystic carcinoma (ACC). Notably, ACC primary tumor xenografts with activating NOTCH1 mutations and high N1-ICD levels were sensitive to GSI, whereas N1-ICD-low tumors without NOTCH1 mutations were resistant. SIGNIFICANCE: NOTCH1 mutations, immunohistochemical staining for activated NOTCH1, and HES4 expression are biomarkers that can be used to identify solid tumors that are likely to respond to GSI-based therapies.

Clinical validation of a genetic model to estimate the risk of developing choroidal neovascular age-related macular degeneration.

Predictive tests for estimating the risk of developing late-stage neovascular age-related macular degeneration (AMD) are subject to unique challenges. AMD prevalence increases with age, clinical phenotypes are heterogeneous and control collections are prone to high false-negative rates, as many control subjects are likely to develop disease with advancing age. Risk prediction tests have been presented previously, using up to ten genetic markers and a range of self-reported non-genetic variables such as body mass index (BMI) and smoking history. In order to maximise the accuracy of prediction for mainstream genetic testing, we sought to derive a test comparable in performance to earlier testing models but based purely on genetic markers, which are static through life and not subject to misreporting. We report a multicentre assessment of a larger panel of single nucleotide polymorphisms (SNPs) than previously analysed, to improve further the classification performance of a predictive test to estimate the risk of developing choroidal neovascular (CNV) disease. We developed a predictive model based solely on genetic markers and avoided inclusion of self-reported variables (eg smoking history) or non-static factors (BMI, education status) that might otherwise introduce inaccuracies in calculating individual risk estimates. We describe the performance of a test panel comprising 13 SNPs genotyped across a consolidated collection of four patient cohorts obtained from academic centres deemed appropriate for pooling. We report on predictive effect sizes and their classification performance. By incorporating multiple cohorts of homogeneous ethnic origin, we obtained >80 per cent power to detect differences in genetic variants observed between cases and controls. We focused our study on CNV, a subtype of advanced AMD associated with a severe and potentially treatable form of the disease. Lastly, we followed a two-stage strategy involving both test model development and test model validation to present estimates of classification performance anticipated in the larger clinical setting. The model contained nine SNPs tagging variants in the regulators of complement activation (RCA) locus spanning the complement factor H (CFH), complement factor H-related 4 (CFHR4), complement factor H-related 5 (CFHR5) and coagulation factor XIII B subunit (F13B) genes; the four remaining SNPs targeted polymorphisms in the complement component 2 (C2), complement factor B (CFB), complement component 3 (C3) and age-related maculopathy susceptibility protein 2 (ARMS2) genes. The pooled sample size (1,132 CNV cases, 822 controls) allowed for both model development and model validation to confirm the accuracy of risk prediction. At the validation stage, our test model yielded 82 per cent sensitivity and 63 per cent specificity, comparable with metrics reported with earlier testing models that included environmental risk factors. Our test had an area under the curve of 0.80, reflecting a modest improvement compared with tests reported with fewer SNPs.

cDNA targets improve whole blood gene expression profiling and enhance detection of pharmocodynamic biomarkers: a quantitative platform analysis.

BACKGROUND: Genome-wide gene expression profiling of whole blood is an attractive method for discovery of biomarkers due to its non-invasiveness, simple clinical site processing and rich biological content. Except for a few successes, this technology has not yet matured enough to reach its full potential of identifying biomarkers useful for clinical prognostic and diagnostic applications or in monitoring patient response to therapeutic intervention. A variety of technical problems have hampered efforts to utilize this technology for identification of biomarkers. One significant hurdle has been the high and variable concentrations of globin transcripts in whole blood total RNA potentially resulting in non-specific probe binding and high background. In this study, we investigated and quantified the power of three whole blood profiling approaches to detect meaningful biological expression patterns. METHODS: To compare and quantify the impact of different mitigation technologies, we used a globin transcript spike-in strategy to synthetically generate a globin-induced signature and then mitigate it with the three different technologies. Biological differences, in globin transcript spiked samples, were modeled by supplementing with either 1% of liver or 1% brain total RNA. In order to demonstrate the biological utility of a robust globin artifact mitigation strategy in biomarker discovery, we treated whole blood ex vivo with suberoylanilide hydroxamic acid (SAHA) and compared the overlap between the obtained signatures and signatures of a known biomarker derived from SAHA-treated cell lines and PBMCs of SAHA-treated patients. RESULTS: We found cDNA hybridization targets detect at least 20 times more specific differentially expressed signatures (2597) between 1% liver and 1% brain in globin-supplemented samples than the PNA (117) or no treatment (97) method at FDR = 10% and p-value < 3x10-3. In addition, we found that the ex vivo derived gene expression profile was highly concordant with that of the previously identified SAHA pharmacodynamic biomarkers. CONCLUSIONS: We conclude that an amplification method for gene expression profiling employing cDNA targets effectively mitigates the negative impact on data of abundant globin transcripts and greatly improves the ability to identify relevant gene expression based pharmacodynamic biomarkers from whole blood.

Development of a microarray platform for FFPET profiling: application to the classification of human tumors.

BACKGROUND: mRNA profiling has become an important tool for developing and validating prognostic assays predictive of disease treatment response and outcome. Archives of annotated formalin-fixed paraffin-embedded tissues (FFPET) are available as a potential source for retrospective studies. Methods are needed to profile these FFPET samples that are linked to clinical outcomes to generate hypotheses that could lead to classifiers for clinical applications. METHODS: We developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al 1 and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples. RESULTS: When a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples (leave-one-out cross validation) with an error rate of approximately 14% (p-value = 3.7 x 10(-7)). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling. CONCLUSION: We have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for clinical trial enrollment or to guide patient treatment.

Identification of diagnostic biomarkers for infection in premature neonates.

Infection is a leading cause of neonatal morbidity and mortality worldwide. Premature neonates are particularly susceptible to infection because of physiologic immaturity, comorbidity, and extraneous medical interventions. Additionally premature infants are at higher risk of progression to sepsis or severe sepsis, adverse outcomes, and antimicrobial toxicity. Currently initial diagnosis is based upon clinical suspicion accompanied by nonspecific clinical signs and is confirmed upon positive microbiologic culture results several days after institution of empiric therapy. There exists a significant need for rapid, objective, in vitro tests for diagnosis of infection in neonates who are experiencing clinical instability. We used immunoassays multiplexed on microarrays to identify differentially expressed serum proteins in clinically infected and non-infected neonates. Immunoassay arrays were effective for measurement of more than 100 cytokines in small volumes of serum available from neonates. Our analyses revealed significant alterations in levels of eight serum proteins in infected neonates that are associated with inflammation, coagulation, and fibrinolysis. Specifically P- and E-selectins, interleukin 2 soluble receptor alpha, interleukin 18, neutrophil elastase, urokinase plasminogen activator and its cognate receptor, and C-reactive protein were observed at statistically significant increased levels. Multivariate classifiers based on combinations of serum analytes exhibited better diagnostic specificity and sensitivity than single analytes. Multiplexed immunoassays of serum cytokines may have clinical utility as an adjunct for rapid diagnosis of infection and differentiation of etiologic agent in neonates with clinical decompensation.

Protein microarray analysis of disease activity in pediatric inflammatory bowel disease demonstrates elevated serum PLGF, IL-7, TGF-beta1, and IL-12p40 levels in Crohn's disease and ulcerative colitis patients in remission versus active disease.

OBJECTIVES: Cytokines and growth factors play a major role in the dysregulated immune response in inflammatory bowel disease (IBD). We hypothesized that significant differences exist between the serum cytokine and growth factor profiles of pediatric IBD patients with active disease (AD) and those in remission, and that levels of some of these soluble mediators may be used to define regulators in IBD and determine disease activity. METHODS: Eighty-eight consecutive patients with confirmed Crohn's disease (CD) and ulcerative colitis (UC) seen at the Duke Children's Hospital were prospectively enrolled and a serum sample was obtained. Data were recorded at the time of serum collection to calculate disease activity indices. The relative expression of 78 cytokines, growth factors, and soluble receptors was determined using proprietary antibody-based protein microarrays amplified by rolling circle amplification. SPSS 8 (SPSS Inc., Chicago, IL) was used to compare protein profiles for CD and UC patients in clinical remission (CR) versus AD. RESULTS: Sixty-five CD patients and 23 UC patients were enrolled. Forty-one CD patients had available samples and PCDAI results. Twenty-two patients were in remission PCDAI < or = 12.5 (median 5), 19 patients had disease activity >15 (median 30). Univariate analysis revealed that PLGF, IL-7, IL-12p40, and TGF-beta1 cytokine levels were significantly elevated for patients in CR versus AD (p < 0.01). Twelve UC serum samples had Seo/Truelove Witt AI for analysis. Five patients were in remission by TW AI and Seo AI < or =110 and 7 patients had active mild-to-severe disease by TW and Seo AI >110. Only one cytokine, IL12p40, showed significance between CR versus AD (p < 0.02). CONCLUSIONS: Surprisingly, we found no differences in circulating levels of proinflammatory cytokines but found that pediatric IBD patients in remission compared to those with AD had higher levels of specific circulating cytokines, including the regulatory cytokines IL-12p40 and TGF-beta1. It may be that these cytokines directly regulate intestinal inflammation in IBD or reflect the activity of T regulatory cells in negatively regulating the inflammatory response. Further studies will be needed to validate our results to define the molecular pathways involved in the intestinal immune response in man.