Dimeric diarylheptanoids with anti-inflammatory activity from Zingiber officinale

Two previously undescribed chain diarylheptanoid derivatives (2-3), five previously undescribed dimeric diarylheptanoids (4-8), together with one known cyclic diarylheptanoid (1) were isolated from Zingiber officinale. Their structures were elucidated by extensive spectroscopic analyses (HR-ESI-MS, IR, UV, 1D and 2D NMR) and ECD calculations. Biological evaluation of compounds 1-8 revealed that compounds 2, 3 and 4 could inhibit nitrite oxide and IL-6 production in lipopolysaccharide induced RAW264.7 cells in a dose-dependent manner.

Unusual acetylated flavonol glucuronides, oxyphyllvonides A-H with renoprotective activities from the fruits of Alpinae oxyphylla

As a widely consumed spice and Traditional Chinese Medicine, Alpinae oxyphylla has been used to treat conditions such as diarrhea, ulcers, dementia, and enuresis. Fruits of A. oxyphylla were phytochemically studied and the bioactive constituents against renal fibrosis were identified. Eight previously undescribed acetylated flavonol glucuronides named oxyphyllvonides A-H (1-7 and 10), two known acetylated flavonol glucuronides (8 and 9), together with seven known flavone glycosides (11-17) were isolated from the fruits of A. oxyphylla. Among them, flavonol glucuronides were discovered in Zingiberaceae for the first time. The planar structures of 1-7 and 10 were determined using HRESIMS and extensive spectroscopic techniques (UV, IR, 1D-NMR, and 2D-NMR). The absolute configurations of the sugar moiety in these compounds were determined by using LC-MS analysis of acid-hydrolyzed derivatized monosaccharides. Biological evaluation showed that 7-10, 13, 14, 16 and 17 inhibit renal fibrosis in TGF-β1-induced kidney proximal tubular cells. In addition, 7, 8 and 14 were superior to nootkatone in inhibiting Fibronectin expression. The finding has significant relevance to our ongoing research on the anti-renal fibrosis activity of A. oxyphylla.

Succipenoids A-C: A dimeric abietane and nor-abietane diterpenoids from fossil Chinese medicinal succinum and their anti-inflammatory potential

An unprecedented dimeric abietane, succipenoid A (1), and two previously undescribed nor-abietane diterpenoids featuring a rarely occurring naphthalene ring or with a large conjugated system, succipenoids B and C (2 and 3), along with seven known diterpenoids (4-10) were isolated from the CH2Cl2 extract of succinum. The structures of these compounds, including their absolute configurations, were elucidated using spectroscopic and computational techniques. Notably, compounds 1-4 and 6-10 were isolated from succinum for the first time. In order to evaluate their anti-inflammatory potential, in vitro tests were conducted. The results demonstrated that compounds 1, 2, 4, and 6-10 exhibite dose-dependent inhibition of iNOS expression in lipopolysaccharide-induced RAW 264.7 cells.

Nine pairs of undescribed enantiomers from the resin of Styrax tonkinensis (Pierre) Craib ex Hart with anti-inflammatory activity

Nine pairs of undescribed enantiomers, (±)-styraxoids A-I (1-9), were isolated from the resin of Styrax tonkinensis, and their structures were assigned by spectroscopic and computational methods. Compounds (±)-1 are a pair of degraded lignans, and the remaining compounds (±)-(2-9) are phenylpropanoid skeletons. Compounds (±)-8 and (±)-9 feature a 1,3-dioxolane moiety. The biological evaluation showed that both enantiomers of 1 could inhibit LPS-induced INOS and COX-2 in RAW264.7 cells in a dose-dependent manner.

Correction: Non-peptide compounds from Kronopolites svenhedini (Verhoeff) and their antitumor and iNOS inhibitory activities

[This corrects the article DOI: 10.3762/bjoc.19.59.].

Eight new 2-(2-phenylethyl)chromone derivatives from agarwood of Aquilaria sinensis with anti-inflammatory activity

Eight previously unknown 2-(2-phenylethyl)chromone derivatives, called aquichromones A - E (1-3, 5 and 6) and 8-epi-aquichromone C (4), including two pairs of enantiomers [(±)-1 and (±)-2] were isolated from the agarwood of Aquilaria sinensis. The structures and absolute stereochemistry of these natural products were elucidated by using spectroscopic and computational methods. The result of biological assay showed that two members of this group, 4 and 5, have significant dose-dependent anti-inflammatory activity.

Loss of ANCO1 Expression Regulates Chromatin Accessibility and Drives Progression of Early-Stage Triple-Negative Breast Cancer

Mutations in the gene ankyrin repeat domain containing 11 (ANKRD11/ANCO1) play a role in neurodegenerative disorders, and its loss of heterozygosity and low expression are seen in some cancers. Here, we show that low ANCO1 mRNA and protein expression levels are prognostic markers for poor clinical outcomes in breast cancer and that loss of nuclear ANCO1 protein expression predicts lower overall survival of patients with triple-negative breast cancer (TNBC). Knockdown of ANCO1 in early-stage TNBC cells led to aneuploidy, cellular senescence, and enhanced invasion in a 3D matrix. The presence of a subpopulation of ANCO1-depleted cells enabled invasion of the overall cell population in vitro and they converted more rapidly to invasive lesions in a xenograft mouse model. In ANCO1-depleted cells, ChIP-seq analysis showed a global increase in H3K27Ac signals that were enriched for AP-1, TEAD, STAT3, and NFκB motifs. ANCO1-regulated H3K27Ac peaks had a significantly higher overlap with known breast cancer enhancers compared to ANCO1-independent ones. H3K27Ac engagement was associated with transcriptional activation of genes in the PI3K-AKT, epithelial-mesenchymal transition (EMT), and senescence pathways. In conclusion, ANCO1 has hallmarks of a tumor suppressor whose loss of expression activates breast-cancer-specific enhancers and oncogenic pathways that can accelerate the early-stage progression of breast cancer.

Five new sesquiterpenoids from agarwood of Aquilaria sinensis

Five new eudesmane-type sesquiterpenoids (aquisinenoids F-J (1-5)) and five known compounds (6-10) were isolated from the agarwood of Aquilaria sinensis. Their structures, including absolute configurations, were identified by comprehensive spectroscopic analyses and computational methods. Inspired by our previous study on the same kinds of skeletons, we speculated that the new compounds have anticancer and anti-inflammatory activities. The results did not show any activity, but they revealed the structure-activity relationships (SAR).

Non-peptide compounds from Kronopolites svenhedini (Verhoeff) and their antitumor and iNOS inhibitory activities

Six new compounds, including a tetralone 1, two xanthones 2 and 3, a flavan derivative 4, and two nor-diterpenoids 7 and 8, accompanied by two known flavan derivatives 5 and 6 and a known olefine acid (9) were isolated from whole bodies of Kronopolites svenhedini (Verhoeff). The structures of the new compounds were determined by 1D and 2D nuclear magnetic resonance (NMR) and other spectroscopic methods, as well as computational methods. Selected compounds were evaluated for their biological properties against a mouse pancreatic cancer cell line and inhibitory effects on iNOS and COX-2 in RAW264.7 cells.

Six new diterpenoids from the resins of Populus euphratica and their anti-inflammatory activities

Six new diterpenoids, identified as two abietane derivatives, euphraticanoids J and K (1 and 2), two pimarane derivatives, euphraticanoids L and M (3 and 4), and two 9,10-seco-abietane derivatives, euphraticanoids N and O (5 and 6) were isolated from Populus euphratica resins. Their structures including absolute configurations were characterized using spectroscopic, quantum chemical NMR, and ECD calculation methods. The anti-inflammatory activity of the compounds was tested and the results revealed that compounds 4 and 6 inhibited the production of iNOS and COX-2 in a dose-dependent manner in lipopolysaccharide (LPS)-induced RAW 264.7 cells.

Design and modeling for drug combination experiments with order effects

Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order-effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order-effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real-data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration.

Does Chronic Use of High Dose Proton Pump Inhibitors Increase Risk for Pancreatic Cancer?

OBJECTIVES

To analyze whether use of proton pump inhibitors increase the risk for pancreatic cancer in a mouse model and human clinical cohorts.

METHODS

p48-Cre/LSL-KrasG12D mice that develop precancerous pancreatic intraepithelial neoplasia (PanINs) were treated with low- or high-dose proton pump inhibitors (PPIs) orally for 1 and 4 months. The mechanism for the cholecystokinin receptor 2 (CCK-2R) activation was investigated in vitro. Two resources were employed to analyze the risk of pancreatic cancer in human subjects with PPI use.

RESULTS

Serum gastrin levels were increased 8-fold (P < 0.0001) in mice treated with chronic high-dose PPIs, and this change correlated with an increase (P = 0.02) in PanIN grade and the development of microinvasive cancer. The CCK-2R expression was regulated by microRNA-148a in the p48-Cre/LSL-KrasG12D mice pancreas and in human pancreatic cancer cells in vitro. Proton pump inhibitor consumption in human subjects was correlated with pancreatic cancer risk (odds ratio, 1.54). A validation analysis conducted using the large-scale United Kingdom Biobank database confirmed the correlation (odds ratio, 1.9; P = 0.00761) of pancreatic cancer risk with PPI exposure.

CONCLUSIONS

This investigation revealed in both murine models and human subjects, PPI use is correlated with a risk for development of pancreatic cancer.

Diarylheptanoid glycosides from Zingiber officinale peel and their anti-apoptotic activity

Four new diarylheptanoid glycosides (1-4), (1S,3R,5S)-2-(4-hydroxy-3- methoxyphenyl)-6-[2-(4-hydroxyphenyl)ethyl]-tetrahydropyran-4-ol-4'-O-β-D-glucopyranoside (1), (1S,3R,5S)-2-(4,5-dihydroxy-3-methoxyphenyl)-6-[2-(4-hydroxyphenyl) ethyl]-tetrahydropyran-4-ol-4'-O-β-D-glucopyranoside (2), (1S,3R,5S)-2-(4-hydroxy- 3,5-dimethoxyphenyl)-6-[2-(4-hydroxy-3-methoxyphenyl)ethyl]-tetrahydropyran-4-ol-4'-O-β-D-glucopyranoside (3), and (1R,3R,5R)-2-(4-hydroxy-3,5-dimethoxyphenyl)- 6-[2-(4-hydroxy-3-methoxyphenyl)ethyl]-tetrahydropyran-4-ol-3-O-β-D-glucopyranoside (4) were isolated from the 50% ethanol extract of Zingiber officinale peel. The structures of the isolated compounds were determined by HR-ESI-MS and extensive spectroscopic techniques (UV, IR, 1D-NMR, and 2D-NMR). Compounds 1-4 significantly increased the survival rate of human normal lung bronchial epithelial cells (BEAS-2B) induced by lipopolysaccharide (LPS) at the concentration of 10 μM.

Integrative analysis with a system of semiparametric projection non-linear regression models

In integrative analysis parametric or nonparametric methods are often used. The former is easier for interpretation but not robust, while the latter is robust but not easy to interpret the relationships among the different types of variables. To combine the advantages of both methods and for flexibility, here a system of semiparametric projection non-linear regression models is proposed for the integrative analysis, to model the innate coordinate structure of these different types of data, and a diagnostic tool is constructed to classify new subjects to the case or control group. Simulation studies are conducted to evaluate the performance of the proposed method, and shows promising results. Then the method is applied to analyze a real omics data from The Cancer Genome Atlas study, compared the results with those from the similarity network fusion, another integrative analysis method, and results from our method are more reasonable.

External validation of a panel of plasma microRNA biomarkers for lung cancer

Aim: We externally validate plasma miRNAs biomarkers for lung cancer in a large and retrospective sample set collected from a geographically distant population. Methods: Plasma samples are tested blindly to the clinical annotations by using PCR for quantitation of the four miRNAs in cohort 1 consisting of 232 lung cancer cases and 243 controls and cohort 2 comprising 239 cases and 246 controls. Results: Combined use of the four plasma miRNAs has 91% sensitivity and 95% specificity for diagnosis of lung cancer, and 85% sensitivity for early-stage lung cancer, while maintaining a specificity of 95%. Conclusion: The diagnostic values of the biomarkers are reproducibly confirmed in the independent and large sample sets, providing an assay for lung cancer detection.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Therapeutic effect of live combined Bifidobacterium and Lactobacillus tablets for children with diarrhea secondary to pneumonia: Influencing factors

AIM

To observe the clinical effects of combined Bifidob-acterium and Lactobacillus tablets in the treatment of children with diarrhea secondary to pneumonia, and to analyze the related influencing factors.

METHODS

Two hundred and thirty children with secondary diarrhea were selected from 722 children with pneumonia admitted to our hospital between June 2016 and June 2017 and randomly divided into a study group and a control group (n = 115, each). In the control group, only antibiotics were given, and the study group was treated with antibiotics and combined Bifidobacterium and Lactobacillus tablets. The efficacy of the two groups was observed and compared, and the related influencing factors were analyzed.

RESULTS

The total effective rate in the study group was 94.78%, which was significantly higher than that in the control group (71.30%; P < 0.05). The number of bowel movements in the study group was significantly less than that of the control group, and the time to disappearance of diarrhea symptoms was significantly shorter than that of the control group (P < 0.05). Logistics multivariate analysis showed that age, length of hospital stay, use of hormones, use of probiotics, and severity of illness were independent risk factors for secondary diarrhea in children with pneumonia (P < 0.05).

CONCLUSION

Live combined Bifidobacterium and Lactobacillus tablets are effective in the treatment of children with diarrhea secondary to pneumonia and can promote the disappearance of diarrhea symptoms and reduce the number of stools. There are many clinical factors that can affect the development of secondary diarrhea, and early intervention should be targeted at such risk factors to reduce the risk of secondary diarrhea in children with pneumonia.

Prevention of Lipid Peroxidation-derived Cyclic DNA Adduct and Mutation in High-Fat Diet-induced Hepatocarcinogenesis by Theaphenon E

Obesity is associated with cancer risk and its link with liver cancer is particularly strong. Obesity causes non-alcoholic fatty liver disease (NAFLD) that could progress to hepatocellular carcinoma (HCC). Chronic inflammation likely plays a key role. We carried out a bioassay in the high-fat diet (HFD)-fed C57BL/6J mice to provide insight into the mechanisms of obesity-related HCC by studying γ-OHPdG, a mutagenic DNA adduct derived from lipid peroxidation. In an 80-week bioassay, mice received a low-fat diet (LFD), high-fat diet (HFD), and HFD with 2% Theaphenon E (TE) (HFD+TE). HFD mice developed a 42% incidence of HCC and LFD mice a 16%. Remarkably, TE, a standardized green tea extract formulation, completely blocked HCC in HFD mice with a 0% incidence. γ-OHPdG measured in the hepatic DNA of mice fed HFD and HFD+TE showed its levels increased during the early stages of NAFLD in HFD mice and the increases were significantly suppressed by TE, correlating with the tumor data. Whole-exome sequencing showed an increased mutation load in the liver tumors of HFD mice with G>A and G>T as the predominant mutations, consistent with the report that γ-OHPdG induces G>A and G>T. Furthermore, the mutation loads were significantly reduced in HFD+TE mice, particularly G>T, the most common mutation in human HCC. These results demonstrate in a relevant model of obesity-induced HCC that γ-OHPdG formation during fatty liver disease may be an initiating event for accumulated mutations that leads to HCC and this process can be effectively inhibited by TE. Cancer Prev Res; 11(10); 665-76. ©2018 AACR.

Integrating Circulating Immunological and Sputum Biomarkers for the Early Detection of Lung Cancer

We have demonstrated that assessments of microRNA (miRNA) expressions in circulating peripheral blood mononucleated cell (PBMC) and sputum specimens, respectively, may help diagnose lung cancer. To assess the individual and combined analysis of the miRNAs across the different body fluids for lung cancer early detection, we analyse a panel of 3 sputum miRNAs (miRs-21, 31, and 210) and a panel of 2 PBMC miRNAs (miRs-19b-3p and 29b-3p) in a discovery cohort of 68 patients with lung cancer and 66 cancer-free smokers. We find that integrating 2 sputum miRNAs (miRs-31 and 210) and 1 PBMC miRNA (miR-19b-3p) has higher sensitivity (86.8%) and specificity (92.4%) compared with the individual panels. The synergistic value of the integrated panel of 3 biomarkers is confirmed in a validation cohort, independent of stage and histological type of lung cancer, and patients' age, sex, and ethnicity. Integrating circulating immunological and sputum biomarkers could improve the early detection of lung cancer.

A plasma miRNA signature for lung cancer early detection

The early detection of lung cancer continues to be a major clinical challenge. Using whole-transcriptome next-generation sequencing to analyze lung tumor and the matched noncancerous tissues, we previously identified 54 lung cancer-associated microRNAs (miRNAs). The objective of this study was to investigate whether the miRNAs could be used as plasma biomarkers for lung cancer. We determined expressions of the lung tumor-miRNAs in plasma of a development cohort of 180 subjects by using reverse transcription PCR to develop biomarkers. The development cohort included 92 lung cancer patients and 88 cancer-free smokers. We validated the biomarkers in a validation cohort of 64 individuals comprising 34 lung cancer patients and 30 cancer-free smokers. Of the 54 miRNAs, 30 displayed a significant different expression level in plasma of the lung cancer patients vs. cancer-free controls (all P < 0.05). A plasma miRNA signature (miRs-126, 145, 210, and 205-5p) with the best prediction was developed, producing 91.5% sensitivity and 96.2% specificity for lung cancer detection. Diagnostic performance of the plasma miRNA signature had no association with stage and histological type of lung tumor, and patients' age, sex, and ethnicity (all p > 0.05). The plasma miRNA signature was reproducibly confirmed in the validation cohort. The plasma miRNA signature may provide a blood-based assay for diagnosing lung cancer at the early stage, and thereby reduce the associated mortality and cost.

Experimental design for multi-drug combination studies using signaling networks

Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this article. The simulation studies show our proposed methods perform well.

The repurposed anthelmintic mebendazole in combination with trametinib suppresses refractory NRASQ61K melanoma

Structure-based drug repositioning in addition to random chemical screening is now a viable route to rapid drug development. Proteochemometric computational methods coupled with kinase assays showed that mebendazole (MBZ) binds and inhibits kinases important in cancer, especially both BRAF^WT and BRAF^V600E. We find that MBZ synergizes with the MEK inhibitor trametinib to inhibit growth of BRAF^WT-NRAS^Q61K melanoma cells in culture and in xenografts, and markedly decreased MEK and ERK phosphorylation. Reverse Phase Protein Array (RPPA) and immunoblot analyses show that both trametinib and MBZ inhibit the MAPK pathway, and cluster analysis revealed a protein cluster showing strong MBZ+trametinib - inhibited phosphorylation of MEK and ERK within 10 minutes, and its direct and indirect downstream targets related to stress response and translation, including ElK1 and RSKs within 30 minutes. Downstream ERK targets for cell cycle, including cMYC, were down-regulated, consistent with S- phase suppression by MBZ+trametinib, while apoptosis markers, including cleaved caspase-3, cleaved PARP and a sub-G1 population, were all increased with time. These data suggest that MBZ, a well-tolerated off-patent approved drug, should be considered as a therapeutic option in combination with trametinib, for patients with NRAS^Q61mut or other non-V600E BRAF mutant melanomas.

A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules

Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.

A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma

Accurate classification of squamous cell carcinoma (SCC) from adenocarcinoma (AC) of non–small cell lung cancer (NSCLC) can lead to personalized treatments of lung cancer. We aimed to develop a miRNA-based prediction model for differentiating SCC from AC in surgical resected tissues and bronchoalveolar lavage (BAL) samples. Expression levels of seven histological subtype-associated miRNAs were determined in 128 snap-frozen surgical lung tumor specimens by using reverse transcription-polymerase chain reaction (RT-PCR) to develop an optimal panel of miRNAs for acutely distinguishing SCC from AC. The biomarkers were validated in an independent cohort of 112 FFPE lung tumor tissues, and a cohort of 127 BAL specimens by using droplet digital PCR for differentiating SCC from AC. A prediction model with two miRNAs (miRs-205-5p and 944) was developed that had 0.988 area under the curve (AUC) with 96.55% sensitivity and 96.43% specificity for differentiating SCC from AC in frozen tissues, and 0.997 AUC with 96.43% sensitivity and 96.43% specificity in FFPE specimens. The diagnostic performance of the prediction model was reproducibly validated in BAL specimens for distinguishing SCC from AC with a higher accuracy compared with cytology (95.69 vs. 68.10%; P < 0.05). The prediction model might have a clinical value for accurately discriminating SCC from AC in both surgical lung tumor tissues and liquid cytological specimens.

Analysis of small nucleolar RNAs in sputum for lung cancer diagnosis

Molecular analysis of sputum presents a noninvasive approach for diagnosis of lung cancer. We have shown that dysregulation of small nucleolar RNAs (snoRNAs) plays a vital role in lung tumorigenesis. We have also identified six snoRNAs whose changes are associated with lung cancer. Here we investigated if analysis of the snoRNAs in sputum could provide a potential tool for diagnosis of lung cancer. Using qRT-PCR, we determined expressions of the six snoRNAs in sputum of a training set of 59 lung cancer patients and 61 cancer-free smokers to develop a biomarker panel, which was validated in a testing set of 67 lung cancer patients and 69 cancer-free smokers for the diagnostic performance. The snoRNAs were robustly measurable in sputum. In the training set, a panel of two snoRNA biomarkers (snoRD66 and snoRD78) was developed, producing 74.58% sensitivity and 83.61% specificity for identifying lung cancer. The snoRNA biomarkers had a significantly higher sensitivity (74.58%) compared with sputum cytology (45.76%) (P < 0.05). The changes of the snoRNAs were not associated with stage and histology of lung cancer (All P >0.05). The performance of the biomarker panel was confirmed in the testing cohort. We report for the first time that sputum snoRNA biomarkers might be useful to improve diagnosis of lung cancer.

A Prediction Model Based on Biomarkers and Clinical Characteristics for Detection of Lung Cancer in Pulmonary Nodules

Lung cancer early detection by low-dose computed tomography (LDCT) can reduce the mortality. However, LDCT increases the number of indeterminate pulmonary nodules (PNs), whereas 95% of the PNs are ultimately false positives. Modalities for specifically distinguishing between malignant and benign PNs are urgently needed. We previously identified a panel of peripheral blood mononucleated cell (PBMC)-miRNA (miRs-19b-3p and -29b-3p) biomarkers for lung cancer. This study aimed to evaluate efficacy of integrating biomarkers and clinical and radiological characteristics of smokers for differentiating malignant from benign PNs. We analyzed expression of 2 miRNAs (miRs-19b-3p and -29b-3p) in PBMCs of a training set of 137 individuals with PNs. We used multivariate logistic regression analysis to develop a prediction model based on the biomarkers, radiographic features of PNs, and clinical characteristics of smokers for identifying malignant PNs. The performance of the prediction model was validated in a testing set of 111 subjects with PNs. A prediction model comprising the two biomarkers, spiculation of PNs and smoking pack-year, was developed that had 0.91 area under the curve of the receiver operating characteristic for distinguishing malignant from benign PNs. The prediction model yielded higher sensitivity (80.3% vs 72.6%) and specificity (89.4% vs 81.9%) compared with the biomarkers used alone (all P < .05). The performance of the prediction model for malignant PNs was confirmed in the validation set. We have for the first time demonstrated that the integration of biomarkers and clinical and radiological characteristics could efficiently identify lung cancer among indeterminate PNs.

Integrating DNA methylation and microRNA biomarkers in sputum for lung cancer detection

Background

Abnormal microRNA (miRNA) expressions and promoter methylation of genes detected in sputum may provide biomarkers for non-small lung cancer (NSCLC). Here, we evaluate the individual and combined analysis of the two classes of sputum molecular biomarkers for NSCLC detection.

Results

We analyze expression of 3 miRNAs (miR-21, miR-31, and miR-210) and methylation of 3 genes (RASSF1A, PRDM14, and 3OST2), which were previously identified as potential biomarkers for NSCLC, in sputum of a set of 117 stage I NSCLC patients and 174 cancer-free smokers. The results are validated in a different set of 144 stage I NSCLC patients and 171 controls. The panel of 3 miRNA biomarkers has 81.5 % sensitivity and 85.9 % specificity; the panel of 3 methylation biomarkers displays 82.9 % sensitivity and 76.4 % specificity for NSCLC detection. Integrated analysis of 2 miRNAs (miR-31 and miR-210) and 2 genes (RASSF1A and 3OST2) yields higher sensitivity (87.3 %) and specificity (90.3 %) compared with the individual panels of the biomarkers (P < 0.05). Combined analysis of all the 3 miRNAs and 3 genes does not have performance superior to that of the panel of 2 miRNAs and 2 genes (P > 0.05). The performance of combined use of the two classes of biomarkers was confirmed in the validation set.

Conclusions

The integration of two different classes of biomarkers synergistically improves both the sensitivity and the specificity for the early detection of NSCLC.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0275-5) contains supplementary material, which is available to authorized users.

Nonlinear response surface in the study of interaction analysis of three combination drugs

Few articles have been written on analyzing three-way interactions between drugs. It may seem to be quite straightforward to extend a statistical method from two-drugs to three-drugs. However, there may exist more complex nonlinear response surface of the interaction index (II) with more complex local synergy and/or local antagonism interspersed in different regions of drug combinations in a three-drug study, compared in a two-drug study. In addition, it is not possible to obtain a four-dimensional (4D) response surface plot for a three-drug study. We propose an analysis procedure to construct the dose combination regions of interest (say, the synergistic areas with II≤0.9). First, use the model robust regression method (MRR), a semiparametric method, to fit the entire response surface of the II, which allows to fit a complex response surface with local synergy/antagonism. Second, we run a modified genetic algorithm (MGA), a stochastic optimization method, many times with different random seeds, to allow to collect as many feasible points as possible that satisfy the estimated values of II≤0.9. Last, all these feasible points are used to construct the approximate dose regions of interest in a 3D. A case study with three anti-cancer drugs in an in vitro experiment is employed to illustrate how to find the dose regions of interest.

Small non-coding RNA biomarkers in sputum for lung cancer diagnosis

The early detection of lung cancer can reduce the mortality. However, there is no effective means in clinical settings for noninvasively detecting lung cancer. We previously developed 3 sputum miRNA biomarkers and 2 sputum small nucleolar RNA (snoRNA) biomarkers that can potentially be used for noninvasively diagnosing lung cancer. Here we evaluate the individual and combined applications of the two types of biomarkers in different sets of lung cancer patients and controls. Combined analysis of the miRNAs and snoRNAs has a synergistic effect with 89 % sensitivity and 89 % specificity, and may provide a useful tool for lung cancer early detection.

Experimental design and statistical analysis for three-drug combination studies

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose-response shapes of individual constituent drugs. Thus, different classes of drugs of different dose-response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose-response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose-response surface formed by dose ranges of three drugs.

Modeling sustained treatment effects in tumor xenograft experiments

In cancer drug development, demonstrated efficacy in tumor xenograft models is an important step toward bringing a promising compound to human use. A key outcome variable is tumor volume measured over a period of time, while mice are treated with certain treatment regimens. A constrained parametric model has been proposed to account for special features, such as intrinsic tumor growth, or tumor volume truncations due to tumor size being either too large or too small to detect. However, since the drug concentration in the blood of a mouse or its tissues may be stabilized at a certain level and maintained during a period of time, the treatment may have sustained effects. This article extends the constrained parametric model to account for the sustained drug effects. The ECM algorithm for incomplete data is applied to estimating the dose-response relationship in the proposed model. The model selection based on likelihood functions is given and a simulation study is conducted to investigate the performance of the proposed estimator. A real xenograft study on the antitumor agent temozolomide combined with irinotecan against the rhabdomyosarcoma is analyzed using the proposed methods.

Survival analysis with functional covariates for partial follow-up studies

Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of the actual counts, that affect patient's disease-free survival time.

Combination immunotherapy after ASCT for multiple myeloma using MAGE-A3/Poly-ICLC immunizations followed by adoptive transfer of vaccine-primed and costimulated autologous T cells

PURPOSE

Myeloma-directed cellular immune responses after autologous stem cell transplantation (ASCT) may reduce relapse rates. We studied whether coinjecting the TLR-3 agonist and vaccine adjuvant Poly-ICLC with a MAGE-A3 peptide vaccine was safe and would elicit a high frequency of vaccine-directed immune responses when combined with vaccine-primed and costimulated autologous T cells.

EXPERIMENTAL DESIGN

In a phase II clinical trial (NCT01245673), we evaluated the safety and activity of ex vivo expanded autologous T cells primed in vivo using a MAGE-A3 multipeptide vaccine (compound GL-0817) combined with Poly-ICLC (Hiltonol), granulocyte macrophage colony-stimulating factor (GM-CSF) ± montanide. Twenty-seven patients with active and/or high-risk myeloma received autografts followed by anti-CD3/anti-CD28-costimulated autologous T cells, accompanied by MAGE-A3 peptide immunizations before T-cell collection and five times after ASCT. Immune responses to the vaccine were evaluated by cytokine production (all patients), dextramer binding to CD8(+) T cells, and ELISA performed serially after transplant.

RESULTS

T-cell infusions were well tolerated, whereas vaccine injection site reactions occurred in >90% of patients. Two of nine patients who received montanide developed sterile abscesses; however, this did not occur in the 18 patients who did not receive montanide. Dextramer staining demonstrated MAGE-A3-specific CD8 T cells in 7 of 8 evaluable HLA-A2(+) patients (88%), whereas vaccine-specific cytokine-producing T cells were generated in 19 of 25 patients (76%). Antibody responses developed in 7 of 9 patients (78%) who received montanide and only weakly in 2 of 18 patients (11%) who did not. The 2-year overall survival was 74% [95% confidence interval (CI), 54%-100%] and 2-year event-free survival was 56% (95% CI, 37%-85%).

CONCLUSIONS

A high frequency of vaccine-specific T-cell responses were generated after transplant by combining costimulated autologous T cells with a Poly-ICLC/GM-CSF-primed MAGE-A3 vaccine.

Digital PCR quantification of miRNAs in sputum for diagnosis of lung cancer

PURPOSE

MicroRNAs (miRNAs) play important roles in the initiation and progression of lung cancer. Measuring miRNA expression levels in sputum could provide a potential approach for the diagnosis of lung cancer. The emerging digital PCR is a straightforward technique for precise, direct, and absolute quantification of nucleic acids. The objective of the study was to investigate whether digital PCR could be used to quantify miRNAs in sputum for lung cancer diagnosis.

METHODS

We first determined and compared dynamic ranges of digital PCR and conventional quantitative reverse transcriptase PCR (qRT-PCR) for miRNA quantification using RNA isolated from sputum of five healthy individuals. We then used digital PCR to quantify copy number of two lung cancer-associated miRNAs (miR-31 and miR-210) in 35 lung cancer patients and 40 cancer-free controls.

RESULTS

Copy number of the miRNAs measured by digital PCR displayed a linear response to input cDNA amount in a twofold dilution series over seven orders of magnitude. miRNA quantification determined by digital PCR assay was in good agreement with that obtained from qRT-PCR analysis in sputum. Furthermore, combined quantification of miR-31 and miR-210 copy number by using digital PCR in sputum of the cases and controls provided 65.71 % sensitivity and 85.00 % specificity for lung cancer diagnosis.

CONCLUSION

As digital PCR becomes more established, it would be a robust tool for quantitative assessment of miRNA copy number in sputum for lung cancer diagnosis.

Platelet glycoprotein Ibα ectodomain shedding and non-surgical bleeding in heart failure patients supported by continuous-flow left ventricular assist devices

BACKGROUND

Non-surgical bleeding (NSB) is a major complication among heart failure (HF) patients supported by continuous-flow left ventricular assist devices (CF-LVADs). Understanding the hemostatic defects contributing to NSB after CF-LVAD implantation is crucial for prevention of this adverse event. The aim of this study was to examine the link between platelet glycoprotein Ibα (GPIbα) ectodomain shedding and NSB in CF-LVAD recipients and to identify a potential biomarker of NSB.

METHODS

Serial blood samples were collected from 35 HF patients supported with CF-LVADs. Platelet function was evaluated by a platelet function analysis device and thromboelastography (TEG). Platelet GPIbα shedding, von Willebrand factor (vWF) antigen and vWF collagen binding capacity were determined using enzyme-linked immunosorbent assays (ELISAs). The structural analysis of vWF was performed by gel electrophoresis. These platelet function measures with vWF parameters of the patients who had NSB between 4 and 32 days after CF-LVAD implantation (bleeder) were analyzed against those without NSB (non-bleeder). Blood samples from 7 healthy individuals were collected to obtain healthy reference values for the laboratory assays.

RESULTS

Elevated GPIbα shedding was found to be a pre-existing condition in all HF patients prior to CF-LVAD implantation. Post-operative level of GPIbα shedding increased and remained elevated in the bleeder group, whereas a consistent decrease was found in the non-bleeder group. A receiver operating characteristic (ROC) analysis indicated that the level of GPIbα shedding had a predictive power of NSB in patients on CF-LVAD support.

CONCLUSIONS

Platelet GPIbα ectodomain shedding which attenuates platelet reactivity is associated with NSB. Plasma GPIbα level may potentially be used to refine bleeding risk stratification in CF-LVAD patients.

Translational phase I trial of vorinostat (suberoylanilide hydroxamic acid) combined with cytarabine and etoposide in patients with relapsed, refractory, or high-risk acute myeloid leukemia

PURPOSE

To determine the maximum-tolerated dose (MTD) of the histone deacetylase inhibitor vorinostat combined with fixed doses of cytarabine (ara-C or cytosine arabinoside) and etoposide in patients with poor-risk or advanced acute leukemia, to obtain preliminary efficacy data, describe pharmacokinetics, and in vivo pharmacodynamic effects of vorinostat in leukemia blasts.

EXPERIMENTAL DESIGN

In this open-label phase I study, vorinostat was given orally on days one to seven at three escalating dose levels: 200 mg twice a day, 200 mg three times a day, and 300 mg twice a day. On days 11 to 14, etoposide (100 mg/m(2)) and cytarabine (1 or 2 g/m(2) twice a day if ≥65 or <65 years old, respectively) were given. The study used a standard 3+3 dose escalation design.

RESULTS

Eighteen of 21 patients with acute myelogenous leukemia (AML) treated on study completed planned therapy. Dose-limiting toxicities [hyperbilirubinemia/septic death (1) and anorexia/fatigue (1)] were encountered at the 200 mg three times a day level; thus, the MTD was established to be vorinostat 200 mg twice a day. Of 21 patients enrolled, seven attained a complete remission (CR) or CR with incomplete platelet recovery, including six of 13 patients treated at the MTD. The median remission duration was seven months. No differences in percentage S-phase cells or multidrug resistance transporter (MDR1 or BCRP) expression or function were observed in vivo in leukemia blasts upon vorinostat treatment.

CONCLUSIONS

Vorinostat 200 mg twice a day can be given safely for seven days before treatment with cytarabine and etoposide. The relatively high CR rate seen at the MTD in this poor-risk group of patients with AML warrants further studies to confirm these findings.

The novel BCR-ABL and FLT3 inhibitor ponatinib is a potent inhibitor of the MDR-associated ATP-binding cassette transporter ABCG2

Ponatinib is a novel tyrosine kinase inhibitor with potent activity against BCR-ABL with mutations, including T315I, and also against fms-like tyrosine kinase 3. We tested interactions between ponatinib at pharmacologically relevant concentrations of 50 to 200 nmol/L and the MDR-associated ATP-binding cassette (ABC) proteins ABCB1, ABCC1, and ABCG2. Ponatinib enhanced uptake of substrates of ABCG2 and ABCB1, but not ABCC1, in cells overexpressing these proteins, with a greater effect on ABCG2 than on ABCB1. Ponatinib potently inhibited [(125)I]-IAAP binding to ABCG2 and ABCB1, indicating binding to their drug substrate sites, with IC(50) values of 0.04 and 0.63 μmol/L, respectively. Ponatinib stimulated ABCG2 ATPase activity in a concentration-dependent manner and stimulated ABCB1 ATPase activity at low concentrations, consistent with it being a substrate of both proteins at pharmacologically relevant concentrations. The ponatinib IC(50) values of BCR-ABL-expressing K562 cells transfected with ABCB1 and ABCG2 were approximately the same as and 2-fold higher than that of K562, respectively, consistent with ponatinib being a substrate of both proteins, but inhibiting its own transport, and resistance was also attenuated to a small degree by ponatinib-induced downregulation of ABCB1 and ABCG2 cell-surface expression on resistant K562 cells. Ponatinib at pharmacologically relevant concentrations produced synergistic cytotoxicity with ABCB1 and ABCG2 substrate chemotherapy drugs and enhanced apoptosis induced by these drugs, including daunorubicin, mitoxantrone, topotecan, and flavopiridol, in cells overexpressing these transport proteins. Combinations of ponatinib and chemotherapy drugs warrant further testing.

PGC1α promotes tumor growth by inducing gene expression programs supporting lipogenesis

Despite the role of aerobic glycolysis in cancer, recent studies highlight the importance of the mitochondria and biosynthetic pathways as well. PPARγ coactivator 1α (PGC1α) is a key transcriptional regulator of several metabolic pathways including oxidative metabolism and lipogenesis. Initial studies suggested that PGC1α expression is reduced in tumors compared with adjacent normal tissue. Paradoxically, other studies show that PGC1α is associated with cancer cell proliferation. Therefore, the role of PGC1α in cancer and especially carcinogenesis is unclear. Using Pgc1α(-/-) and Pgc1α(+/+) mice, we show that loss of PGC1α protects mice from azoxymethane-induced colon carcinogenesis. Similarly, diethylnitrosamine-induced liver carcinogenesis is reduced in Pgc1α(-/-) mice as compared with Pgc1α(+/+) mice. Xenograft studies using gain and loss of PGC1α expression showed that PGC1α also promotes tumor growth. Interestingly, while PGC1α induced oxidative phosphorylation and tricarboxylic acid cycle gene expression, we also observed an increase in the expression of two genes required for de novo fatty acid synthesis, ACC and FASN. In addition, SLC25A1 and ACLY, which are required for the conversion of glucose into acetyl-CoA for fatty acid synthesis, were also increased by PGC1α, thus linking the oxidative and lipogenic functions of PGC1α. Indeed, using stable (13)C isotope tracer analysis, we show that PGC1α increased de novo lipogenesis. Importantly, inhibition of fatty acid synthesis blunted these progrowth effects of PGC1α. In conclusion, these studies show for the first time that loss of PGC1α protects against carcinogenesis and that PGC1α coordinately regulates mitochondrial and fatty acid metabolism to promote tumor growth.

Creative solution for implementation of experiential, competency-based palliative care training for internal medicine residents

To graduate internal medicine residents with basic competency in palliative care, we employ a two-pronged strategy targeted at both residents and attending physicians as learners. The first prong provides a knowledge foundation using web-based learning programs designed specifically for residents and clinical faculty members. The second prong is assessment of resident competency in key palliative care domains by faculty members using direct observation during clinical rotations. The faculty training program contains Competency Assessment Tools addressing 19 topics distributed amongst four broad palliative care domains designed to assist faculty members in making the clinical competency assessments. Residents are required to complete their web-based training by the end of their internship year; they must demonstrate competency in one skill from each of the four broad palliative care domains prior to graduation. Resident and faculty evaluation of the training programs is favorable. Outcome-based measures are planned to evaluate long-term program effectiveness.

Study of flow-induced hemolysis using novel Couette-type blood-shearing devices

To assist the development and application of blood-contacting medical devices, two novel flow-through Couette-type blood-shearing devices have been developed to study the quantitative relationship between blood damage indexes and flow-dependent parameters. One device is an axial flow-through Couette-type device supported by a pair of pin bearings adapted from the adult Jarvik 2000 blood pump. The other is a centrifugal flow-through Couette-type device supported with magnetic bearings adapted from the CentriMag blood pump. In both devices, a rotor spindle was used to replace the original impeller blades so that a small gap was created between the housing and the rotating spindle surface. Computational fluid dynamics simulations have shown that a uniform, high shear stress region can be generated inside the small gap while the shear stresses elsewhere are relatively low. The possibility of secondary blood damage caused by mechanical seals was eliminated due to the use of a magnetic rotor system. Blood flow through the gap was driven by an externally pressurized reservoir. By adjusting the rotational speed and blood flow rate, shear-induced hemolysis was quantified at a matrix of exposure time (0.039 to 1.48 s) and shear stress (50 to 320 Pa). All of the experiments were conducted at room temperature using heparinized ovine blood with a hematocrit value of 30%. The measured hemolysis levels were much lower than those published in the literature, and the overestimation of those earlier studies may be attributable to device-related secondary blood-damaging effects. A new set of coefficients for the power law model was derived from the regression of the experimental data.

Abstract 5413: Maximal Power Design and Analysis of Drug Combination Studies: Applications to Vorinostat with Cytosine Arabinoside and Etoposide and Other Combination Studies

Drug combinations are the hallmark of cancer therapy. Preclinical experiments on multi-drug combinations are important steps to bring the therapy to clinic. A statistical approach for evaluating the joint effect of the combination is necessary because even in vitro experiments often demonstrate significant variation in dose-effect. Such variation needs to be accounted for in the experimental design and analysis. Our research has developed a maximal power (MP) design and interaction index surface (IIS) analysis methods for in vitro and in vivo (e.g., xenograft) combination studies so that the joint effect of a combination can be estimated efficiently and the most synergistic combination can be identified. We demonstrate that these statistical methods and software have resulted in the identification of highly synergistic dose combinations that could have been missed with classic methods.

The first study is the combination of vorinostat (suberoylanilide hydroxamic acid) combined with ara-C and with etoposide in leukemia cell lines. The doses in the experiment were generated by the MP design and the data analyzed using the IIS approach so that synergistic, additive and antagonistic interaction dose regions are identified. Cytotoxic antagonism resulted when vorinostat was combined concomitantly with ara-C; however, when vorinostat was given first followed by a drug-free interval before ara-C treatment, this sequential combination was mostly synergistic. Etoposide combined with vorinostat was additive to synergistic, and the synergism became more pronounced when etoposide was given post a drug-free interval after vorinostat. These findings are used in designing the CTEP trial (NCI 6829: Phase I trial of vorinostat in combination with cytarabine and etoposide in patients with advanced acute leukemia and high-risk myelodysplastic syndromes, PI: Gojo/Ross). The interim results on toxicity and response have been consistent with the model.

Another study utilized a novel thiazolidine compound plus Sorafnib where initial experiments using classic methods failed to identify synergistic combinations. Subsequent experiments using the MP methodologies demonstrated significant synergistic drug combinations. The SynStat R program for the design and analysis of drug synergy is available at http://www.umgcc.org/research/biostat_software.htm).

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5413.

Efficient experimental design and nonparametric modeling of drug interaction

The design and analysis of drug combination studies continue to be an area requiring further methodological developments. Faessel et al. (1998) studied the joint effects of the combinations of trimetrexate (TMQ) and the GARFT inhibitor AG2034 to inhibit the growth of HCT-8 human ileocecal adenocarcinoma cells. Their experiments provide a rich data resource to validate the performance of new experimental design and analysis methods for future experiments. In this paper, we first re-analyze the same data with a nonparametric model and briefly review the experimental design used in the original paper. By comparing the analysis results, we found that the fixed ratio design and the usage of the parametric model for estimating the interaction index are based on an assumption not supported by the data. We then show how the efficiency of the experiments would be improved had the maximal power experimental design based on uniform measures been used. The usage of the proposed maximal power experimental design is further supported by simulation studies.