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G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs’ largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.

Rumen microbes play an important role in the conversion of indigestible plant material to energy and protein in dairy cows. Sampling for ruminal contents via cannula is considered the gold standard technique for microbial analysis, but the technique requires ruminally cannulated animals and specialized animal facilities. The purpose of this study was to determine whether other sampling methods and locations along the digestive tract may serve as noninvasive proxies to the cannula method for microbial analysis. Six ruminally cannulated lactating Holstein dairy cows were adapted to a standard total mixed ration for 2 wk and sampled during the third week. Sampling locations and methods included salivary content, rumination bolus (regurgitated digesta collected from the cow's mouth), feces, and rumen contents via stomach tube and cannula. Stomach tube and cannula samples differ in proportions of solid and liquid material and were therefore separated into whole (as collected), liquid, and solid fractions. Samples were collected at 0 (before feeding), 2, 4, 6, 8, and 12 h after feeding over 2 d. All samples were extracted for total genomic DNA and selected samples for metabolically active DNA (RNA), PCR-amplified for the V1-V2 region of the 16S rRNA bacterial gene, and analyzed for bacterial diversity using the QIIME2 pipeline followed by statistical analysis in R ( https://www.R-project.org/ ). In DNA-based analysis, at the community level, saliva, rumination bolus, and fecal samples clustered in separate groups, whereas all fractions of stomach tube and cannula samples clustered together, indicating that microbial communities of stomach tube and cannula samples were homogeneous. Rumination bolus samples at 6, 8, and 12 h after feeding clustered with stomach tube and cannula samples, indicating that rumination bolus samples may be an alternative for cannula samples; however, time of sampling is critical for sampling of bolus digesta. Results of the RNA-based analysis of rumination bolus samples and solid samples from cannula and stomach tube at 0 and 6 h after feeding were similar. We concluded that the solid fraction of samples obtained via the stomach tube method may serve as a proxy for the solid fraction of whole ruminal contents obtained via cannula for DNA-based microbial investigations. Both rumination bolus and stomach tube solid samples may serve as proxies for cannula solid samples for RNA-based microbial analysis.

Diarrhea is a major cause of illness and death in preweaned calves and causes significant economic losses to producers. A better understanding of the fecal microbiota in diarrheic and nondiarrheic calves could lead to improved treatment and prevention strategies. The purpose of this study was to compare the fecal microbiota of diarrheic and nondiarrheic calves to improve our understanding of what constitutes a healthy fecal microbiota in preweaned calves. At each of 7 farms, fecal samples were obtained from 1 to 3 diarrheic Holstein dairy calves (2 to 17 d old at sampling time) and age-matched (within 5 d) nondiarrheic controls for a total of 20 samples. Calves were fed either acidified bulk milk, pasteurized or unpasteurized waste milk, or milk replacer depending on farm. Fecal samples were extracted for genomic DNA, PCR-amplified for the V1-V2 region of the 16S rRNA bacterial gene, sequenced on the Illumina MiSeq (Illumina Inc., San Diego, CA) platform, and analyzed using QIIME2. Firmicutes and Bacteroidetes were the most abundant phyla in both groups; Fusobacteria was numerically more abundant in the diarrheic group, whereas Proteobacteria and Actinobacteria were numerically more abundant in the nondiarrheic group. At the genus level, Bacteroides was the most abundant genus in both groups and was numerically more abundant in the nondiarrheic group. Results from the mixed-effects regression model showed that Faecalibacterium and Butyricimonas were more abundant in the nondiarrheic calves, whereas Clostridium and Peptostreptococcus were more abundant in the diarrheic calves. Our results indicate that commensal bacteria acquired in the neonatal period may have been replaced with potential pathogens in diarrheic calves, which may have contributed to the incidence of diarrhea either directly or indirectly.

Microbial syntrophy (obligate metabolic mutualism) is the hallmark of energy-constrained anaerobic microbial ecosystems. For example, methanogenic archaea and fermenting bacteria coexist by interspecies hydrogen transfer in the complex microbial ecosystem in the foregut of ruminants; however, these synergistic interactions between different microbes in the rumen are seldom investigated. We hypothesized that certain bacteria and archaea interact and form specific microbial cohorts in the rumen. To this end, we examined the total (DNA-based) and potentially metabolically active (cDNA-based) bacterial and archaeal communities in rumen samples of dairy cows collected at different times in a 24 h period. Notably, we found the presence of distinct bacterial and archaeal networks showing potential metabolic interactions that were correlated with molar proportions of specific volatile fatty acids (VFAs). We employed hypothesis-driven structural equation modeling to test the significance of and to quantify the extent of these relationships between bacteria-archaea-VFAs in the rumen. Furthermore, we demonstrated that these distinct microbial networks were host-specific and differed between cows indicating a natural variation in specific microbial networks in the rumen of dairy cows. This study provides new insights on potential microbial metabolic interactions in anoxic environments that have broader applications in methane mitigation, energy conservation, and agricultural production.

Objectives Scalable informatics solutions that provide molecularly tailored treatment recommendations to clinicians are needed to streamline precision oncology in care settings. Materials and Methods We developed a cloud-based virtual molecular tumor board (VMTB) platform that included a knowledgebase, scoring model, rules engine, an asynchronous virtual chat room and a reporting tool that generated a treatment plan for each of the 1725 patients based on their molecular profile, previous treatment history, structured trial eligibility criteria, clinically relevant cancer gene-variant assertions, biomarker-treatment associations, and current treatment guidelines. The VMTB systematically allows clinician users to combine expert-curated data and structured data from clinical charts along with molecular testing data to develop consensus on treatments, especially those that require off-label and clinical trial considerations. Results The VMTB was used as part of the cancer care process for a focused subset of 1725 patients referred by advocacy organizations wherein resultant personalized reports were successfully delivered to treating oncologists. Median turnaround time from data receipt to report delivery decreased from 14 days to 4 days over 4 years while the volume of cases increased nearly 2-fold each year. Using a novel scoring model for ranking therapy options, oncologists chose to implement the VMTB-derived therapies over others, except when pursuing immunotherapy options without molecular support. Discussion VMTBs will play an increasingly critical role in precision oncology as the compendium of biomarkers and associated therapy options available to a patient continues to expand. Conclusion Further development of such clinical augmentation tools that systematically combine patient-derived molecular data, real-world evidence from electronic health records and expert curated knowledgebases on biomarkers with computational tools for ranking best treatments can support care pathways at point of care.

Background Multi-omic profiling of pancreatic neuroendocrine tumors (PanNETs) was performed to correlate genomic, proteomic, and molecular pathway alterations with clinicopathologic factors and identify novel co-occurring pathogenic alterations of potential clinical relevance to PanNET management. Methods PanNETs referred to Perthera, Inc. having undergone molecular profiling for precision matched therapeutic purposes were screened. Correlative analyses were performed using Fisher's exact test across individual pathogenic alterations or altered molecular pathways and clinicopathologic variables. Associations were visualized by hierarchical clustering. Prognostic associations with overall survival (OS) were identified using Cox regression for pathogenic alterations and pathway-level alterations. Hazard ratios (HR) and odds ratios (OR) were reported with 95% confidence intervals (CI). Results From 12/2014-1/2019, 46 patients with predominantly locally advanced and metastatic PanNETs were included. MEN1 alterations by next-generation sequencing (NGS) were less associated with having high-grade PanNETs and metastatic disease at diagnosis (p ≤ 0.05). Genomic alterations associated with increased replicative stress (primarily driven by RB1 and TP53) correlated with higher grade (OR 6.87 [95% CI: 1.57-35.18], p = 0.0043) and worse OS (HR 13.62 [95% CI: 1.51-122.5], p = 0.0198). Other significant associations included: ERCC1 protein expression with DAXX or MEN1 alterations (NGS), PTEN (NGS) with ARID1A or TP53 alterations (NGS), and history of diabetes coincided with cell cycle pathway alterations but was mutually exclusive with replicative stress pathway alterations. Conclusions We identified several molecular signatures of potential clinical significance for therapeutic targeting and prognostication in PanNETs warranting prospective validation. Our findings are hypothesis generating and can inform larger molecular profiling efforts in PanNETs.

Antibiotic use data are critical for drawing conclusions about the epidemiological connections between antibiotic use in farms animals, antibiotic resistance, animal health, and human health. The goal of this study was to quantitatively and qualitatively characterize antibiotic use on dairy farms in Pennsylvania, the state with second largest number of dairy farms nationally. A survey was sent to 10% of the 6,580 dairy farms registered in Pennsylvania and completed by 235 producers (response rate of 36%). Data on antibiotic use in the previous month and in the previous 6 mo were collected based on farmer self-report, using either recall or treatment records. Two metrics were used to quantify antibiotic consumption: animal-defined daily doses (ADD) and days of therapy (DOT), a metric used in human medicine for purposes of antimicrobial stewardship. Across all farms, 24,444 ADD and 19,029 DOT were reported, representing treatment incidences of 4.2 ADD/1,000 animal-days and 3.3 DOT/1,000 animal-days. These rates were generally lower than those found in other states and countries. The main indication for antibiotic use was mastitis, and first-generation cephalosporins were the most commonly used class of antibiotic for all indications, followed by penicillins and third-generation cephalosporins. Trends in use were similar for ADD and DOT, but the numbers of recorded DOT and associated treatment incidences were generally lower than the number of ADD and associated treatment incidences. Rates of treatment were significantly associated with herd size. This study is the first to quantify antibiotic use on dairy farms in Pennsylvania and the first to use the DOT metric in a dairy setting.

The industrialization of the agri-food industry and resultant decrease in the number of people employed on farms has contributed to a knowledge gap among consumers about food production processes. A commonly reported concern of dairy consumers is the use of antibiotics in dairy animals, even though these drugs are an important tool for promoting animal health and welfare and food safety. The extent to which consumers are aware of antibiotic residue avoidance practices in dairy production is unknown, and it is unclear whether acquisition of such knowledge could affect purchasing behavior and perceptions of dairy farming. The objectives of this study were to assess consumers' perceptions about the quality and production of dairy products in the United States and determine whether educational materials on processes that limit the occurrence of antibiotic residues in milk can change consumers' perceptions of dairy products and purchasing behaviors. We surveyed 804 consumers and assigned them to 1 of 3 interventions: (1) a control arm (reading the content of the Dairy page of the USDA's myplate.gov website); (2) an educational brochure on the processes that prevent antibiotic residues in milk; and (3) a video on the same processes. We found that a majority (86.1%) of participants believe that the quality of dairy products in the United States is high, although many had concerns about the treatment of dairy animals and chemicals (pesticides, antibiotics, hormones) in dairy products. Compared with the control intervention, the brochure was associated with a significant decrease in the level of concern consumers had about chemicals in their milk [−0.20 points on a Likert scale, 95% confidence interval (CI), −0.32 to −0.08] and a significantly increased comfort in purchasing conventional dairy products (odds ratio 2.43, 95% CI, 1.62 to 3.66). The video was associated with even stronger effects: a 0.29-unit decrease in the level of concern about chemicals in milk (95% CI, −0.42 to −0.016) and 2.94 times greater odds of purchasing conventional dairy products (95%, CI 1.92 to 4.49). Although consumer food decision making is complex and driven by multiple factors, it appears that education about the processes that promote food safety can reassure consumers about their concerns and potentially affect purchasing habits.

Background Antimicrobial resistance is a serious concern. Although the widespread use of antimicrobials in livestock has exacerbated the emergence and dissemination of antimicrobial resistance genes (ARG) in farm environments, little is known about whether antimicrobial use affects distribution of ARG in livestock systems. This study compared the distribution of microbiomes and resistomes (collections of ARG) across different farm sectors in dairy herds that differed in their use of antimicrobials. Feces from heifers, non-lactating, and lactating cows, manure storage, and soil from three conventional (antimicrobials used to treat cows) and three organic (no antimicrobials used for at least four years) farms in Pennsylvania were sampled. Samples were extracted for genomic DNA, processed, sequenced on the Illumina NextSeq platform, and analyzed for microbial community and resistome profiles using established procedures. Results Microbial communities and resistome profiles clustered by sample type across all farms. Overall, abundance and diversity of ARG in feces was significantly higher in conventional herds compared to organic herds. The ARG conferring resistance to betalactams, macrolide-lincosamide-streptogramin (MLS), and tetracyclines were significantly higher in fecal samples of dairy cows from conventional herds compared to organic herds. Regardless of farm type, all manure storage samples had greater diversity (albeit low abundance) of ARG conferring resistance to aminoglycosides, tetracyclines, MLS, multidrug resistance, and phenicol. All soil samples had lower abundance of ARG compared to feces, manure, and lagoon samples and were comprised of ARG conferring resistance to aminoglycosides, glycopeptides, and multi-drug resistance. The distribution of ARG is likely driven by the composition of microbiota in the respective sample types. Conclusions Antimicrobial use on farms significantly influenced specific groups of ARG in feces but not in manure storage or soil samples.

Asparagopsis taxiformis (AT) is a source of multiple halogenated compounds and, in a limited number of studies, has been shown to decrease enteric CH4 emission in vitro and in vivo. Similarly, oregano has been suggested as a potential CH4 mitigating agent. This study consisted of 2 in vitro and 2 in vivo experiments. Experiment (Exp.) 1 was aimed at establishing the effect of AT on CH4 emission in vitro. Two experiments (Exp. 2 and 3) with lactating dairy cows were conducted to determine the antimethanogenic effect of AT and oregano (Exp. 3) in vivo. Another experiment (Exp. 4) was designed to investigate stability of bromoform (CHBr3) in AT over time. In Exp. 3, 20 Holstein cows were used in a replicated 4 × 4 Latin square design with four 28-d periods. Treatments were basal diet (control) or basal diet supplemented with (dry matter basis) 0.25% AT (LowAT), 0.50% AT (HighAT), or 1.77% oregano (Origanum vulgare L.) leaves. Enteric gas emissions were measured using the GreenFeed system (C-Lock Inc., Rapid City, SD), and rumen samples were collected for fermentation analysis using the ororuminal technique. In Exp.1 (in vitro), relative to the control, AT (at 1% dry matter basis, inclusion rate) decreased CH4 yield by 98%. In Exp. 3, HighAT decreased average daily CH4 emission and CH4 yield by 65% and 55%, respectively, in experimental periods 1 and 2, but had no effect in periods 3 and 4. The differential response to AT among experimental periods was likely a result of a decrease in CHBr3 concentration in AT over time, as observed in Exp. 4 (up to 84% decrease in 4 mo of storage). In Exp. 3, H2 emission was increased by AT and, as expected, the proportion of acetate in the total volatile fatty acids in the rumen was decreased and those of propionate and butyrate were increased by HighAT compared with the control. Compared with the control, HighAT decreased dry matter intake, milk yield, and energy-corrected milk yield in Exp. 3. Milk composition was not affected by treatment, except lactose percentage and yield were decreased by HighAT. Concentrations of iodine and bromide in milk were increased by HighAT compared with the control. Milk CHBr3 concentration and its organoleptic characteristics were not different between control and HighAT. Oregano had no effect on CH4 emission or lactational performance of the cows in Exp. 3. Overall, AT included at 0.50% in the ration of dairy cows can have a large mitigation effect on enteric CH4 emission, but dry matter intake and milk production may also decrease. There was a marked decrease in the CH4 mitigation potential of AT in the second half of Exp. 3, likely resulting from CHBr3 decay over time.

The development of a robust microbiome is critical to the health of dairy calves, but relatively little is known about the progression of the microbiome through the weaning transition. In this study, fecal samples were obtained from ten female Holstein calves at 6 timepoints between 2–13 weeks of age. Calves were fed acidified milk until weaning at 8 weeks old and had access to starter grain throughout the study. Fecal samples were extracted for genomic DNA, PCR-amplified for the V1-V2 region of the 16S rRNA bacterial gene, sequenced on the Illumina MiSeq platform, and analyzed using the QIIME2 pipeline. Bacterial richness, estimated by number of observed species, and bacterial diversity, estimated by Shannon diversity index, both differed significantly between timepoints and both increased over time (P

The objective of this experiment was to compare ruminal fluid samples collected through rumen cannula (RC) or using an oral stomach tube (ST) for measurement of ruminal fermentation and microbiota variables. Six ruminally cannulated lactating Holstein cows fed a standard diet were used in the study. Rumen samples were collected at 0, 2, 4, 6, 8, and 12 h after the morning feeding on two consecutive days using both RC and ST techniques. Samples were filtered through two layers of cheesecloth and the filtered ruminal fluid was used for further analysis. Compared with RC, ST samples had 7% greater pH; however, the pattern in pH change after feeding was similar between sampling methods. Total volatile fatty acids (VFA), acetate and propionate concentrations in ruminal fluid were on average 23% lower for ST compared with RC. There were no differences between RC and ST in VFA molar proportions (except for isobutyrate), ammonia and dissolved hydrogen (dH2) concentrations, or total protozoa counts, and there were no interactions between sampling technique and time of sampling. Bacterial ASV richness was higher in ST compared with RC samples; however, no differences were observed for Shannon diversity. Based on Permanova analysis, bacterial community composition was influenced by sampling method and there was an interaction between sampling method and time of sampling. A core microbiota comprised of Prevotella, S24-7, unclassified Bacteroidales and unclassified Clostridiales, Butyrivibrio, unclassified Lachnospiraceae, unclassified Ruminococcaceae, Ruminococcus, and Sharpea was present in both ST and RC samples, although their relative abundance varied and was influenced by an interaction between sampling time and sampling method. Overall, our results suggest that ruminal fluid samples collected using ST (at 180 to 200 cm depth) are not representative of rumen pH, absolute values of VFA concentrations, or bacterial communities >2 h post-feeding when compared to samples of ruminal fluid collected using RC. However, ST can be a feasible sampling technique if the purpose is to study molar proportions of VFA, protozoa counts, dH2, and ammonia concentrations., Frontiers in Microbiology, 11, ISSN:1664-302X

Isocitrate dehydrogenase 1 (IDH1) is a metabolic enzyme implicated in cancer cell metabolic reprogramming. This is underscored by the detection of functional, somatic IDH1 mutations frequently found in secondary glioblastoma. To our knowledge, there has never been a reported, validated case of an IDH1 mutation in a pancreatic ductal adenocarcinoma (PDA). Herein, we present a case of a patient with metastatic PDA that harbored a potentially actionable, albeit rare, IDH1 mutation. As part of the Know Your Tumor project (Pancreatic Cancer Action Network), a 48-year-old female was diagnosed with metastatic PDA and subsequently started on standard of care chemotherapy, during which her hepatic lesions progressed. Detailed molecular profiling was performed on a biopsy from a liver lesion that demonstrated an IDH1 mutation, R132H. This mutation was confirmed by an independent sequencing reaction from the tumor sample, and by immunohistochemistry using an antibody specific for the IDH1 R132H mutation. The patient subsequently received a mutant IDH1 inhibitor (AG-120, Agios Pharmaceuticals, Cambridge, MA), but with no response. IDH1 mutations are common in certain cancer types, but have not been reported in PDA. We report the first case of an IDH1 mutation in this tumor type, perhaps providing a rare opportunity for a targeted therapy as a treatment option for PDA.

Clostridioides difficile causes severe colitis in people and is a significant enteric pathogen in many species of animals, including swine, horses, and potentially cattle. C. difficile is shed in feces, and transmission occurs horizontally via the fecal-oral route. Livestock has been suggested as a potential reservoir for C. difficile, and while studies have shown that swine and farm workers can be colonized with identical clones of C. difficile, the zoonotic transmission of C. difficile from livestock to people has not been definitively demonstrated. The goal of this study was to determine whether dairy calves and dairy farm workers harbored genetically similar isolates of C. difficile. First, we validated a glove juice protocol for detecting C. difficile on farm workers’ hands. We then visited 23 farms and collected 1) fecal samples from 92 dairy calves, 2) hand rinsates from 38 dairy farm workers, and 3) fecal samples from five of the dairy farm workers who were willing to submit them. All samples underwent anaerobic culture and qPCR to detect C. difficile. C. difficile was detected on 15 of the farms (65.2%, 95% confidence interval (CI) 42.7%–83.6%) and in 28 calves (30.4%, 95% CI 21.2–40.9%) but in none of the hand rinsates or human fecal samples. Thus, the zoonotic transmission of C. difficile on dairy farms could not be demonstrated, and dairy farmers did not appear to be at increased risk of acquiring C. difficile via the fecal-oral route.

Background There is a lack of precision medicine in pancreatic ductal adenocarcinoma (PDA) and related cancers, and outcomes for patients with this diagnosis remain poor despite decades of research investigating this disease. Therefore, it is necessary to explore novel therapeutic options for these patients who may benefit from personalized therapies. Objective Molecular profiling of hepatopancreaticobiliary malignancies at our institution, including but not limited to PDA, was initiated to assess the feasibility of incorporating molecular profiling results into patient oncological therapy planning. Methods All eligible patients from Thomas Jefferson University (TJU) with hepatopancreaticobiliary tumors including PDA, who agreed to molecular testing profiling, were prospectively enrolled in a registry study from December 2014 to September 2017 and their tumor samples were tested to identify molecular markers that can be used to guide therapy options in the future. Next generation sequencing (NGS) and protein expression in tumor samples were tested at CLIA-certified laboratories. Prospective clinicopathologic data were extracted from medical records and compiled in a de-identified fashion. Results Seventy eight (78) patients were enrolled in the study, which included 65/78 patients with PDA (local and metastatic) and out of that subset, 52/65 patients had surgically resected PDA. Therapy recommendations were generated based on molecular and clinicopathologic data for all enrolled patients. NGS uncovered actionable alterations in 25/52 surgically resected PDAs (48%) which could be used to guide therapy options in the future. High expression of three proteins, TS (p = 0.005), ERCC1 (p = 0.001), and PD-1 (p = 0.04), was associated with reduced recurrence-free survival (RFS), while TP53 mutations were correlated with longer RFS (p = 0.01). Conclusions The goal of this study was to implement a stepwise strategy to identify and profile resected PDAs at our institution. Consistent with previous studies, approximately half of patients with resected PDA harbor actionable mutations with possible targeted therapeutic implications. Ongoing studies will determine the clinical value of identifying these mutations in patients with resected PDA.

Background Molecular profiling is increasingly used to match patients with metastatic cancer to targeted therapies, but obtaining a high-quality biopsy specimen from metastatic sites can be difficult. Methods Patient samples were received by Perthera to coordinate genomic, proteomic and/or phosphoproteomic testing, using a specimen from either the primary tumour or a metastatic site. The relative frequencies were compared across specimen sites to assess the potential limitations of using a primary tumour sample for clinical decision support. Results No significant differences were identified at the gene or pathway level when comparing genomic alterations between primary and metastatic lesions. Site-specific trends towards enrichment of MYC amplification in liver lesions, STK11 mutations in lung lesions and ATM and ARID2 mutations in abdominal lesions were seen, but were not statistically significant after false-discovery rate correction. Comparative analyses of proteomic results revealed significantly elevated expression of ERCC1 and TOP1 in metastatic lesions. Conclusions Tumour tissue limitations remain a barrier to precision oncology efforts, and these real-world data suggest that performing molecular testing on a primary tumour specimen could be considered in patients with pancreatic adenocarcinoma who do not have adequate tissue readily available from a metastatic site.

The poor norepinephrine innervation and high density of Gi/o-coupled α(2A) and α(2C) adrenoceptors in the striatum and the dense striatal dopamine innervation have prompted the possibility that dopamine could be an effective adrenoceptor ligand. Nevertheless, the reported adrenoceptor agonistic properties of dopamine are still inconclusive. In this study, we analyzed the binding of norepinephrine, dopamine and several compounds reported as selective dopamine D(2)-like receptor ligands, such as the D(3) receptor agonist 7-OH-PIPAT and the D(4) receptor agonist RO-105824, to α(2) adrenoceptors in cortical and striatal tissue, which express α(2A) adrenoceptors and both α(2A) and α(2C) adrenoceptors, respectively. The affinity of dopamine for α(2) adrenoceptors was found to be similar to that for D(1)-like and D(2)-like receptors. Moreover, the exogenous dopamine receptor ligands also showed high affinity for α(2A) and α(2C) adrenoceptors. Their ability to activate Gi/o proteins through α(2A) and α(2C) adrenoceptors was also analyzed in transfected cells with bioluminescent resonance energy transfer techniques. The relative ligand potencies and efficacies were dependent on the Gi/o protein subtype. Furthermore, dopamine binding to α(2) adrenoceptors was functional, inducing changes in dynamic mass redistribution, adenylyl cyclase activity and ERK1/2 phosphorylation. Binding events were further studied with computer modeling of ligand docking. Docking of dopamine at α(2A) and α(2C) adrenoceptors was nearly identical to its binding to the crystallized D(3) receptor. Therefore, we provide conclusive evidence that α(2A) and α(2C) adrenoceptors are functional receptors for norepinephrine, dopamine and other previously assumed selective D(2)-like receptor ligands, which calls for revisiting previous studies with those ligands.

The purpose of our study was to improve our ability to predict response to therapy by integrating mechanistic kinetic data of protein interactions with patient-specific gene and protein expression data. The genetic heterogeneity of cancer results in patient-to-patient variability that makes it difficult to predict whether the patient will respond to a treatment. Molecular biomarkers such as the expression levels of genes or proteins have proven useful in limited cases, for example ErbB2 and trastuzumab, particularly in the epidermal growth factor (EGF) family, but these kinds of markers are still unable to accurately predict all responsive patients. These biomarkers are typically univariate and linear, whereas multivariate, nonlinear biomarkers are needed to adequately describe the network of molecular interactions targeted by a drug. Here, we present a method for building multivariate biomarkers that incorporate BOTH patient-specific transcriptomic/proteomic data AND detailed mechanistic models of the nonlinear interactions between ligands and receptors. The mechanistic models that we use are computational pharmacodynamic models, with multiple compartments, each with multiple cell types that express the ligands and receptors under investigation. The models incorporate detailed protein-protein interaction networks to simulate the complex dynamics of growth factor families and their receptors. By integrating this molecular detail into whole-body simulations with tumors, we can evaluate many different therapeutic approaches – different drugs, doses, schedules, and routes of administration. Our models make predictions of the dynamics of receptor tyrosine kinase activity and of key blood-borne biomarkers following therapeutic intervention. These predictions can and have been validated against clinical experimental data. We applied the method to the EGFR/ErbB family in breast cancer, using individualized data from The Cancer Genome Atlas (TCGA), and showed that the personalized models were able to capture the observed variability in receptor phosphorylation. Before the addition of drugs, the models behaved in a relatively monotonic fashion, with signaling outputs closely following the expression of the key ligands. However, the response to the addition of drugs was much more complex; the baseline expression of genes/proteins was not as good a predictor of the response. We simulated the addition of three antibody drugs that each target one of EGFR, HER2, and HER3. We applied principal component analysis to characterize the output of our simulations – post-therapy time course response. We derived metrics that accounted for both target-specific and off-target effects. We then used multivariate supervised learning methods to develop predictive biomarkers. We found that biomarkers derived from gene expression data were outperformed by biomarkers derived from simulated baseline tumor behavior (i.e. that combined quantitative mechanistic information with gene expression data). This suggested that linear transformations of transcriptomic and proteomic data may not be adequate for predicting drug response; instead, the nonlinear mechanism-based transformation that is central to the computational model is more predictive. In addition, for each of the antibodies investigated, the incorporation of mechanistic protein interactions resulted in the identification of off-target effects with high inter-individual heterogeneity that have the potential to significantly blunt the response. In conclusion, transforming individualized expression data through a detailed kinetic model of molecular interactions improves predictiveness of treatment response. Citation Format: Robert Joseph Bender, Feilim Mac Gabhann. Population pharmacodynamics: Mechanism-based modeling of receptor tyrosine kinase networks in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-36.

// Michael J. Pishvaian 1,2,* , R. Joseph Bender 1,* , Lynn M. Matrisian 3 , Lola Rahib 3 , Andrew Hendifar 4 , William A. Hoos 3 , Sam Mikhail 5 , Vincent Chung 6 , Vincent Picozzi 7 , Craig Heartwell 1 , Kimberly Mason 1 , Katelyn Varieur 1 , Metasebia Aberra 1 , Subha Madhavan 1,2 , Emanuel Petricoin III 1 and Jonathan R. Brody 1,8 1 Perthera, Inc, McLean, VA, USA 2 Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA 3 The Pancreatic Cancer Action Network, Manhattan Beach, CA, USA 4 Cedars-Sinai Medical Center, Los Angeles, CA, USA 5 Ohio State University, Columbus, OH, USA 6 City of Hope Cancer Center, Duarte, CA, USA 7 Virginia Mason Medical Center, Seattle, WA, USA 8 Jefferson Pancreatic, and Related Cancer Center, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA * These authors have contributed equally to this work Correspondence to: Jonathan R. Brody, email: // Keywords : cfDNA, pancreatic cancer, blood-based NGS Received : June 15, 2016 Accepted : September 25, 2016 Published : November 08, 2016 Abstract Recent improvements in next-generation sequencing (NGS) technology have enabled detection of biomarkers in cell-free DNA in blood and may ultimately replace invasive tissue biopsies. However, a better understanding of the performance of blood-based NGS assays is needed prior to routine clinical use. As part of an IRB-approved molecular profiling registry trial of pancreatic ductal adenocarcinoma (PDA) patients, we facilitated blood-based NGS testing of 34 patients from multiple community-based and high-volume academic oncology practices. 23 of these patients also underwent traditional tumor tissue-based NGS testing. cfDNA was not detected in 9/34 (26%) patients. Overall concordance between blood and tumor tissue NGS assays was low, with only 25% sensitivity of blood-based NGS for tumor tissue NGS. Mutations in KRAS, the major PDA oncogene, were only detected in 10/34 (29%) blood samples, compared to 20/23 (87%) tumor tissue biopsies. The presence of mutations in circulating DNA was associated with reduced overall survival (54% in mutation-positive versus 90% in mutation-negative). Our results suggest that in the setting of previously treated, advanced PDA, liquid biopsies are not yet an adequate substitute for tissue biopsies. Further refinement in defining the optimal patient population and timing of blood sampling may improve the value of a blood-based test.

211 Background: We correlated genomic, proteomic, and molecular pathway alterations with clinicopathologic factors and identified novel co-occurring pathogenic alterations of potential clinical relevance to PanNET management through multi-omic profiling. Methods: Perthera, Inc. deploys an IRB-approved registry that was utilized in partnership with PanCAN’s “Know Your Tumor” program. PanNETs having undergone molecular profiling for precision matched therapeutic purposes were screened. We performed correlative analyses by pairwise comparisons between pathogenic alterations or altered molecular pathways and clinicopathologic variables. Hierarchical clustering was used to visualize associations. The Kaplan-Meier method was used to estimate overall survival (OS) and survival differences across variables were analyzed by the log-rank test. Results: We included 33 patients with predominantly locally advanced and metastatic PanNETs from 12/2014-1/2018. Chromatin remodeling pathway and MEN1 alterations by next-generation sequencing (NGS) were less associated with having high-grade PanNETs, while MEN1 alterations were also less associated with metastatic disease at diagnosis (all Fisher’s exact two-tailed p ≤ 0.05). Several molecular pathway or pathogenic alterations correlated with worse OS: DNA repair pathway (log-rank p = 0.0022), RB1 alterations by NGS (p = 0.018), and TP53 alterations by NGS (p = 0.01). There were several significant co-occurring alterations (Fisher’s exact p ≤ 0.05): ERCC1 expression by immunohistochemistry (IHC) and DAXX (NGS), RB1 (NGS) and DNA repair pathway (NGS), and TS (IHC) and cyclin-dependent kinase pathway (NGS). Having an altered chromatin remodeling pathway was less associated with having an altered receptor tyrosine kinase (RTK) signaling pathway (Fisher’s exact p ≤ 0.05). Conclusions: We identified several molecular signatures of potential clinical significance for therapeutic targeting and prognostication in PanNETs warranting prospective validation. Our findings are hypothesis generating and can inform larger molecular profiling efforts in PanNETs.

Background: For metastatic non-small cell lung cancer (NSCLC), guidelines include molecular testing for actionable biomarkers and recommend broad profile testing. Despite these recommendations, previous studies indicate that not all patients with NSCLC are receiving testing, even for actionable mutations in EGFR, ALK, and ROS. There are widespread gaps in the community setting, and Lung Cancer Alliance (LCA) data show that fewer than 50% of callers to the patient HelpLine have had molecular testing on their lung cancer. Methods: To help address this problem, we developed an innovative program that combines direct patient services with increasing enrollment to clinical studies. Patients are recruited to the LungMATCH molecular testing program through conversations on the LCA HelpLine. They are then entered into the Perthera Program to receive a Perthera Report (PR) through consent into an IRB-approved registry protocol. The Program includes tissue acquisition, multi-omic molecular profiling, and collection of patient treatment history followed by integration into a computational pipeline with extensive drug and clinical trial databases to provide ranked therapeutic options matched to the patient. An every-patient, real-time medical review board then reviews and approves the PR. PRs are returned to both treating physicians and patients. Data are being collected longitudinally on treatment decisions, patient outcomes including progression-free and overall survival, and patient experience. Results: In the first nine months, 72 patients were referred into the Program. The majority of the patients (76%) came from community centers across a wide geographic region in the United States. There were a number of barriers to signing informed consent and completing biopsy identified. Most common reasons included patients in poor health, cost concerns, unsupportive doctors, and patient loyalty to the physician (discomfort with advocating for the testing). Of the first 11 patients who received a PR, 9 had actionable alterations that indicated either a standard-of-care agent or a clinical trial. Actionable alterations were identified by next-generation sequencing, in situ hybridization, and immunohistochemistry. Two patients also had high tumor mutation burden. Conclusions: We introduced a nonprofit-corporate partnership that navigates patients and their physicians through a comprehensive precision-therapy program. We have demonstrated that this type of program is feasible and there is broad patient interest, particularly from patients seen in nonacademic settings. A number of barriers were identified and are being addressed. Importantly, the majority of patients who received a completed PR (82%) had actionable molecular alterations, underscoring the potential impact of the program. Citation Format: Jennifer C. King, Andrew Ciupek, Tara Perloff, Ashley Blanchard, Kimberly Mason, Edik Blais, David Halverson, Joseph Bender, Subha Madhavan, Emanuel Petricoin. Addressing gaps in molecular testing for patients with lung cancer [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr B26.

4130Background: Precision oncology is currently being defined mainly by a genomics-oriented view to tumor biology. However, a multi-omic view to tumor biology and more accurate outcome prediction i...

6508Background: A deficiency in current approaches in Precision Oncology is the sole focus on gene mutations while overlooking the patient context, including medical and treatment history, as well ...

4126Background: To democratize the implementation of precision medicine (PM) in the care of pancreatic cancer patients, the Know Your Tumor (KYT) program was initiated US-wide using a turn-key PM o...

e21033Background: For metastatic non-small cell lung cancer (NSCLC) guidelines include molecular testing for actionable biomarkers and recommend broad profile testing. Previous studies indicate tha...

214 Background: The activation of the RAS/RAF/MEK/ERK pathway is critical for the proliferation, survival, and tumorigenesis of PDACs. Oncogenic mutations in KRAS (90%) or BRAF (3%) are recurrent genomic alterations, but their co-occurrence is not well described. We reviewed BRAF alterations and clinical outcomes in consecutive PDAC patients (pts). Methods: Perthera, Inc. deploys an IRB-approved registry that was utilized in partnership with PanCAN’s “Know Your Tumor” program. Perthera uses CAP/CLIA accredited multi-Omic profiling, including next generation DNA sequencing (NGS, Foundation Medicine) and proteomics/immunohistochemistry (Neogenomics Inc. and Caris Life Sciences, Inc.). We used a Fisher's exact test with multiple testing correction to compare frequencies of mutations and protein over-/under-expression between BRAF-mutated (n = 21) and BRAF wild type pts (n = 745). Results: Of 766 pancreatic cancer pts, 21 pts were identified with BRAF mutations. 18 pts were diagnosed with PDAC, 1 pancreatoblastoma, 1 pancreatic acinar cell carcinoma, and 1 mixed acinar neuroendocrine carcinoma. Amongst the 18 PDAC pts with BRAF mutations, nine variations were found: V600E (5/18), BRAF fusion (4/18), N486_P490del (3/18), T310I (1/18), K601N (1/18), G596R (1/18), exon 2-10 deletions & S467L (1/18), equivocal amplification of BRAF (1/18), and a BRAF inframe deletion (1/18). KRAS was mutated in 6% of BRAF mutated PDACs, compared to 94% in BRAF wild type PDACs (p < 0.001). Amongst KRAS wild type PDACs, 53% of CDKN2A mutated pts had BRAF mutations vs. only 15% of CDKN2A wild type cases (p < 0.05). Two BRAF mutated pts from the database were given BRAF inhibitors. A sustained response to the combination dabrafenib and trametinib was observed in a BRAF V600E mutated PDAC pt, while no response was seen in a pt with concurrent KRAS G12A & BRAF K601N mutations who received trametinib. Conclusions: BRAF mutations are significantly and inversely correlated with KRAS alterations. The most common BRAF alteration, V600E mutation, was found to be mutually exclusive with the KRAS mutation. Clinical trials targeting BRAF alterations in KRAS wild type pancreatic cancer appears warranted.

213 Background: Pancreatic cancer metastasizes very early, as evidenced by the fact that > 70% of patients with operable disease ultimately develop metastases. Thus, it is likely that the molecular characteristics of primary pancreatic tumors are similar to metastatic lesions. We compared the frequency of genetic alterations and protein expression from primary vs. metastatic pancreatic tumors, and from metastases from different sites. By focusing on actionable genetic and proteomic information, we sought to explore whether targeted therapies could be tailored to patients at metastatic progression based on primary surgical material. Methods: Next generation DNA sequencing (NGS) data of 208 genes and a limited set of protein markers were analyzed from pancreatic tumors of 431 patients enrolled in the Know Your Tumor initiative. Of the 208 genes tested, mutations in 70 were considered potentially actionable based on preclinical and clinical evidence. We compared 146 primary pancreatic tumors against 285 metastatic lesions, and examined subgroups for liver vs. lung vs. other metastatic lesions. Molecular alterations were compared between independent groups for each gene/protein using Fisher’s exact test. Significance was assessed using a false discovery rate adjusted q-value threshold of 0.05. Results: No differences in the specific mutation or expression pattern were observed between primary vs. metastatic lesions, nor across the site of metastasis after correcting for multiple hypotheses. Even the proportion of actionable alterations (including mutations in the homologous recombination DNA repair pathway) was similar across subgroups. Conclusions: Comparison of the muli-omic profile of primary vs. metastatic pancreatic adenocarcinoma reveals that the molecular architecture is very similar, and that actionable alterations are identified at the same frequency. This is unlike the data observed from other solid tumors, (e.g. colon and breast cancer), in which substantial molecular discordance and heterogeneity exists between primary tumors and metastatic sites, but is consistent with the belief that primary pancreatic cancers metastasize early and thus are molecularly indistinguishable from metastatic lesions.

Background The vascular endothelial growth factor (VEGF) family is central to cancer angiogenesis. However, targeting VEGF as an anti-cancer therapeutic approach has shown success for some tumor types but not others. Here we examine the expression of the expanded VEGF family in prostate cancer, including the Semaphorin (Sema) family members that compete with VEGFs for Neuropilin binding and can themselves have pro- or anti-angiogenic activity. Results First, we used multivariate statistical methods, including partial least squares and clustering, to examine VEGF/Sema gene expression variability in previously published prostate cancer microarray datasets. We show that unlike some cancers, such as kidney cancer, primary prostate cancer is characterized by both a down-regulation of the pro-angiogenic members of the VEGF family and a down-regulation of anti-angiogenic members of the Sema family. We found pro-lymphangiogenic signatures, including the genes encoding VEGFC and VEGFD, associated with primary tumors that ultimately became aggressive. In contrast to primary prostate tumors, prostate cancer metastases showed increased expression of key pro-angiogenic VEGF family members and further repression of anti-angiogenic class III Sema family members. Given the lack of success of VEGF-targeting molecules so far in prostate cancer, this suggests that the reduction in anti-angiogenic Sema signaling may potentiate VEGF signaling and even promote resistance to VEGF-targeting therapies. Inhibition of the VEGF ‘accelerator’ may need to be accompanied by promotion of the Sema ‘brake’ to block cancer angiogenesis. To leverage our mechanistic understanding, and to link multigene expression changes to outcomes, we performed individualized computational simulations of competitive VEGF and Sema receptor binding across many tumor samples. The simulations suggest that loss of Sema expression promotes angiogenesis by lowering plexin signaling, not by potentiating VEGF signaling via relaxation of competition. Conclusions The combined analysis of bioinformatic data with computational modeling of ligand-receptor interactions demonstrated that enhancement of angiogenesis in prostate cancer metastases may occur through two different routes: elevation of VEGFA and reduction of class 3 Semaphorins. Therapeutic inhibition of angiogenesis in metastatic prostate cancer should account for both of these routes. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0201-z) contains supplementary material, which is available to authorized users.

Recent genomic profiling studies in pancreatic adenocarcinoma (PDA) have revealed actionable mutations affecting multiple signaling pathways, but in spite of these mutations, targeted inhibitors of these pathways have low success rates. A possible reason for these failures is that single-gene biomarkers (e.g. a KRAS mutation as an indicator of MEK inhibitor sensitivity) fail to account for crosstalk within and between dysregulated pathways. We have previously curated a knowledgebase of published studies as evidence to support molecular tumor board recommendations to cancer patients after multi-omic profiling. Here we present a computational framework for integrating this knowledgebase with drug response data from cancer cell lines to propose “actionable” biomarkers based on a panel of pathways instead of targeting a single gene mutation. We constructed a computational model encompassing a broad range of cancer-related pathways, including RAS/RAF/MEK/ERK, PI3K/AKT, cell cycle regulation, and DNA repair. The model consisted of a set of ordinary differential equations (ODE) with protein interactions following Hill-type kinetics and the rate of cell division and apoptosis modeled dependent on key signaling nodes, including the level of phosphorylated ERK and AKT. We integrated two sources of publicly available data: 1) published studies correlating phosphoprotein measurements and resistance pathways to targeted inhibitors in clinical development; and 2) mutation data correlated with drug-specific response metrics (e.g. IC50 values), such as CCLE and NCI-60. We systematically screened frequently observed overlapping disrupted signaling pathways (i.e., combinations of mutations) by simulating predicted IC50 values for targeted inhibitors. Based on these simulations, we then simulated the effect of pairs of drugs to explore which drug combinations may be best suited for inhibiting tumor growth when tumors harbor multiple mutations. We present two applications of this computational approach: a comparison of CDK4/6 inhibition in CDKN2A-mutated PDA vs. hormone receptor-positive breast cancer and a comparison of PARP inhibition in BRCA1/2-mutated PDA and ovarian cancer. The predictions generated by our simulations were consistent with clinical observations in that fewer combinations of mutations in PDA were sensitive to these inhibitors than in breast and ovarian cancer, suggesting ways to refine biomarkers for sensitivity to these drugs in PDA. The computational approach presented here takes into account multiple datasets from a knowledgebase to provide a prioritized list of treatments that match a patient’s molecular profile while also providing the rationale for the recommendation. This represents a step toward incorporation of systems biology in precision oncology. Citation Format: R Joseph Bender, Edik Blais, Apoorva Kulkarni, Michael J. Pishvaian, David Halverson, Jonathan R. Brody, Emanuel Petricoin, Subha Madhavan. A computational model for integrating genomic data with public datasets for molecular tumor board recommendations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5553. doi:10.1158/1538-7445.AM2017-5553

e15685 Multi-omic Molecular profiling of Pancreatic Neuroendocrine Tumors Authors: Rishi R Patel, Joseph Bender, Quanlin, Dana Pan, Lynn Matrisian, David Halverson, Emanuel Petricoin, Subha Madhavan, Richard Tuli, Michael Pishvaian, Andrew Hendifar; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA Background: Pancreatic Neuroendocrine Tumors (pNETs) are a rare malignancy with an incidence of 2 per 1,000,000. In 2016, the Pancreatic Cancer Action Network and Perthera initiated the Know Your Tumor (KYT) initiative in an effort to improve coordination across clinical spectrums in regards to multi-omic molecular profiling and clinical outcomes data pertaining to pancreatic tumors. We used data collected as part of the KYT effort to describe demographic, clinical and genomic data for pNETs. Methods: From 2015 - 2016, 15 patients with pNET were enrolled in the KYT program, which helped facilitate tissue acquisition, clinical data collection, and multi-omic molecular profiling. Using the data collected, we performed Fisher’s Exact to assess for statistical significance between genetic alterations and histology. Results: 11/15 of our patients were female. 8/15 had metastatic disease at the time of diagnosis, while 5/15 had locally advanced disease at the time of diagnosis. 29 genetic alterations were pathogenic. KMT2D and MEN1 were jointly found in 6/15 of our patients. 5/15 with pathogenic alterations in p53, 2/15 with DAXX, 5/15 with alteration in RB1, and 2/15 with alterations in PTEN and TSC2. 2 patients had pathologic alterations in mismatch repair genes, MLH1 and MSH1. Two genes had a statistically significant relationship to pNET histology. Alterations in MEN1 (p = 0.0097) and SPTA1 (p = 0.0333) were associated with high grade tumors (p = 0.0097). Of note, both of the patients under the age of 35 shared an alteration in ATR, which none of the other enrollees expressed. Conclusions: In PNETS, multi-omic profiling through the KYT program identified targetable alterations in several key pathways. Outcome data will be explored.

e20671 Background: For metastatic non-small cell lung cancer (NSCLC), guidelines include molecular testing for actionable biomarkers and recommend broad profile testing. Yet previous studies indicate that not all patients with NSCLC receive testing, even for actionable mutations in EGFR, ALK, and ROS. We hypothesized low testing rates for patients calling a community HelpLine and that we could increase rates with one-on-one caller education and free precision medicine services. Methods: Caller statistics were collected on the toll-free Lung Cancer Alliance (LCA) HelpLine from Sept 1, 2016 – Jan 31, 2017. Recruitment to the LungMATCH molecular testing program began Nov 10, 2017. Patients are recruited through conversations on the LCA HelpLine, then consented into Perthera Cancer Analysis (PCA) through an IRB-approved registry protocol. PCA includes tissue acquisition, multi-omic molecular profiling, and medical review of testing results and clinical and treatment history. PCA reports are returned to both treating physicians and patients. Data is being collected longitudinally on treatment decisions, patient outcomes including progression-free and overall survival, and patient experience. Results: Data from the LCA Helpline identified a gap in molecular testing. 50% (57/115) of patients asked if they received any kind of molecular testing replied "No". Of 32 patients who were tested and knew the results, patients indicated potentially actionable changes in EGFR(15), ALK(8), PD-L1(3), RET, MET, BRAF, and HER2, along with KRAS (4). Since LungMATCH launch, 23 interested patients were referred for PCA. Six patients consented and are undergoing PCA with seven more in the consent process. Reasons for non-consent include: doctor refusal, initiation of testing at the treating institution, concern about financial implications, and one death. Updated results will be presented. Conclusions: Our data indicate that patients with lung cancer are not receiving molecular testing in accordance to guidelines. To address this, we created a program through nonprofit-corporate partnership that navigates patients and their physicians through comprehensive precision therapy. This type of program is feasible and there is patient interest.

278 Background: Recent studies have expanded our knowledge of the genomic landscape of PDA. While critical and in some cases, potentially actionable alterations are being identified, limited outcomes data have thus far made it difficult to validate the relevance of these observations. Methods: The Pancreatic Cancer Action Network (PanCAN) and Perthera have facilitated commercial tumor molecular profiling for over 400 PDA pts since 2014 through KYT, and have developed a database of molecular and clinical information useful for data mining of biomarker-survival correlations. The survival significance of biomarkers was assessed using standard statistical methodology including Kaplan-Meier analysis and Cox proportional hazard models. Results: Linked molecular and outcomes data were available for 360 pts, of which 173 had treatment (tx) information available. Pathogenic mutations from targeted NGS, protein expression from IHC, and protein phosphorylation from RPPA were screened for correlations with overall survival (OS) and progression-free survival (PFS) independent of tx received. As shown in the table, mutations in 3 genes were associated with a better OS; while mutations in 8 genes were associated with poorer OS. Only two mutations were correlated with PFS in 1st or 2nd-line tx ( BRCA2 and KDM6A, worse PFS). Positive expression of 7 proteins, including TS, TOP1, and RRM1, were associated with reduced OS but were not correlated with PFS. High levels of phospho-ribosomal protein S6 were associated with both poor OS (HR=10.3, p=0.001) and poor PFS (HR=9.6, p=0.006). Conclusions: Multiple biomarkers had significant correlations with OS in PDA, while fewer were correlated with PFS. Growth of this registry database will further validate tx-specific predictive biomarkers for use in pts with multi-omic profiling data. [Table: see text]

Triple negative breast cancers (TNBC) are difficult to treat due to a lack of targets and heterogeneity. Inhibition of angiogenesis is a promising therapeutic strategy, but has had limited effectiveness so far in breast cancer. To quantify heterogeneity in angiogenesis-related gene expression in breast cancer, we focused on two families – VEGFs and semaphorins – that compete for neuropilin co-receptors on endothelial cells. We compiled microarray data for over 2,600 patient tumor samples and analyzed the expression of VEGF- and semaphorin-related ligands and receptors. We used principal component analysis to identify patterns of gene expression, and clustering to group samples according to these patterns. We used available survival data to determine whether these clusters had prognostic as well as therapeutic relevance. TNBC was highly associated with dysregulation of VEGF- and semaphorin-related genes; in particular, it appeared that expression of both VEGF and semaphorin genes were altered in a pro-angiogenesis direction. A pattern of high VEGFA expression with low expression of secreted semaphorins was associated with 60% of triple-negative breast tumors. While all TNBC groups demonstrated poor prognosis, this signature also correlated with lower 5-year survival rates in non-TNBC samples. A second TNBC pattern, including high VEGFC expression, was also identified. These pro-angiogenesis signatures may identify cancers that are more susceptible to VEGF inhibition.

Commercial genomic and protein-based profiling was performed on tissue from pancreatic cancer patients who enrolled in the Pancreatic Cancer Action Network's Know Your Tumor (KYT) service. The service offers molecular profiling for patients with resectable or metastatic disease, in preparation for their next treatment plan. Patients are eligible to enroll at any institution, clinic, or cancer center in the US. Biopsies, tissue retrieval and testing is coordinated by Perthera, Inc. From 6/2014 to 11/2015 tumor biopsies, resected or archived tissue was obtained from 152 (53% male) pancreatic cancer patients and profiling including NGS/FISH (144 patients) and IHC (143 patients) was performed. The median age at biopsy or resection was 63 (25-83), 55% of the tissue came from liver, 16% from pancreas, 7% lung, 4% peritoneum, and 18% from another type of tissue. 87% of the patients were diagnosed with pancreatic ductal adenocarcinoma and the remaining with another form of confirmed or suspected pancreatic cancer. The most identified pathogenic mutations were KRAS (85%), TP53 (72%), CDKN2A (47%), CDKN2B (24%), SMAD4 (22%), ARID1A (10%), STK11 (7%), and BRCA2 (5%). The average number of genomic alterations (likely but not confirmed to be somatic) detected was five per patient from a panel of 320 genes. Molecular modifications that are linked to a treatment option were observed in 44% of patients. Twenty four of 144 patients (17%) had mutations in at least one DNA repair gene potentially benefiting from platinum or PARP inhibitor-based therapies, including BRCA2 (5%), ATM (4%), BAP1 (4%), SMARCA4 (3%), FANC (1%), CHEK2 (1%), and PALB2 ( To date, 7 of the 152 patients have enrolled in a clinical trial, 4 were treated with an off label treatment, 67 have yet to make their next treatment decision, and 53 are known to be deceased. KYT has successfully profiled tumors and presented targeted treatment options to patients and physicians in 33 states regardless of a patient's socioeconomic background and geographic location, moving the field a step closer towards a democratized, personalized approach to treating pancreatic cancer. Citation Format: Lola Rahib, Anitra Engebretson, Michael J. Pishvaian, Jonathan R. Brody, William A. Hoos, Emily E. Lyons, Joseph Bender, Emanuel F. Petricoin, Subha Madhavan, Craig Heartwell, Lynn M. Matrisian. Molecular profiling of pancreatic cancer patients from a wide geographical distribution across the US. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 93.

The purpose of our study was to improve our ability to predict response to therapy by integrating mechanistic kinetic data of protein interactions with patient-specific gene and protein expression data. The genetic heterogeneity of cancer results in patient-to-patient variability that makes it difficult to predict whether the patient will respond to a treatment. Molecular biomarkers such as the expression levels of genes or proteins have proven useful in limited cases, for example ErbB2 and trastuzumab, particularly in the epidermal growth factor (EGF) family, but these kinds of markers are still unable to accurately predict all responsive patients. These biomarkers are typically univariate and linear, whereas multivariate, nonlinear biomarkers are needed to adequately describe the network of molecular interactions targeted by a drug. Here, we present a method for building multivariate biomarkers that incorporate BOTH patient-specific transcriptomic/proteomic data AND detailed mechanistic models of the nonlinear interactions between ligands and receptors. The mechanistic models that we use are computational pharmacodynamic models, with multiple compartments, each with multiple cell types that express the ligands and receptors under investigation. The models incorporate detailed protein-protein interaction networks to simulate the complex dynamics of growth factor families and their receptors. By integrating this molecular detail into whole-body simulations with tumors, we can evaluate many different therapeutic approaches – different drugs, doses, schedules, and routes of administration. Our models make predictions of the dynamics of receptor tyrosine kinase activity and of key blood-borne biomarkers following therapeutic intervention. These predictions can and have been validated against clinical experimental data. We applied the method to the EGFR/ErbB family in breast cancer, using individualized data from The Cancer Genome Atlas (TCGA), and showed that the personalized models were able to capture the observed variability in receptor phosphorylation. Before the addition of drugs, the models behaved in a relatively monotonic fashion, with signaling outputs closely following the expression of the key ligands. However, the response to the addition of drugs was much more complex; the baseline expression of genes/proteins was not as good a predictor of the response. We simulated the addition of three antibody drugs that each target one of EGFR, HER2, and HER3. We applied principal component analysis to characterize the output of our simulations – post-therapy time course response. We derived metrics that accounted for both target-specific and off-target effects. We then used multivariate supervised learning methods to develop predictive biomarkers. We found that biomarkers derived from gene expression data were outperformed by biomarkers derived from simulated baseline tumor behavior (i.e. that combined quantitative mechanistic information with gene expression data). This suggested that linear transformations of transcriptomic and proteomic data may not be adequate for predicting drug response; instead, the nonlinear mechanism-based transformation that is central to the computational model is more predictive. In addition, for each of the antibodies investigated, the incorporation of mechanistic protein interactions resulted in the identification of off-target effects with high inter-individual heterogeneity that have the potential to significantly blunt the response. In conclusion, transforming individualized expression data through a detailed kinetic model of molecular interactions improves predictiveness of treatment response. Citation Format: Robert Joseph Bender, Feilim Mac Gabhann. Population pharmacodynamics: Mechanism-based modeling of receptor tyrosine kinase networks in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-36.

Four atypical cases of presumed infectious mononucleosis (IM) encephalitis are presented. To establish an etiologic diagnosis, Paul-Bunnell-Davidsohn heterophil titers (PBD), antibody titers to the antigens of the Epstein-Barr virus (EBV), and oropharyngeal excretion of EBV were determined. Criteria for a primary EBV infection are (1) an antiviral capsid antigen titer of 1:160 or greater, (2) the presence of antibody to the diffuse component of the early antigen, (3) absence of antibody to the nuclear antigen, and (4) excretion of the virus from the oropharynx. Three of the four cases met these criteria; of the three, one did not have a positive heterophil titer. The fourth case turned out not to be IM; there was a positive PBD heterophil, but there was no evidence of primary EBV infection. Although the PBD heterophil is usually a reliable test to diagnosis IM, it is not always present in children, and it is sometimes nonspecifically elevated. Some EBV titers can be nonspecifically elevated as well; however, the above criteria are diagnostic of primary EBV infection.