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Background: Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step genomic best linear unbiased prediction (ssGBLUP) model are scarce. We investigated the integration of sequencing SNPs selected by association (1262 SNPs) and bioinformatics (2359 SNPs) analyses into the currently used 54K-SNP chip, using three ssGBLUP models which make different assumptions on the distribution of SNP effects: a basic ssGBLUP model, a so-called featured ssGBLUP (ssFGBLUP) model that considered selected sequencing SNPs as a feature genetic component, and a weighted ssGBLUP (ssWGBLUP) model in which the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, with every 1, 30, or 100 adjacent SNPs within a chromosome region sharing the same variance. We used data on milk production and female fertility in Danish Jersey. In total, 15,823 genotyped and 528,981‬ non-genotyped females born between 1990 and 2013 were used as reference population and 7415 genotyped females and 33,040 non-genotyped females born between 2014 and 2016 were used as validation population., Results: With basic ssGBLUP, integrating SNPs selected from sequencing data improved prediction reliabilities for milk and protein yields, but resulted in limited or no improvement for fat yield and female fertility. Model performances depended on the SNP set used. When using ssWGBLUP with the 54K SNPs, reliabilities for milk and protein yields improved by 0.028 for genotyped animals and by 0.006 for non-genotyped animals compared with ssGBLUP. However, with the SNP set that included SNPs selected from sequencing data, no statistically significant difference in prediction reliability was observed between the three ssGBLUP models., Conclusions: In summary, when using 54K SNPs, a ssWGBLUP model with a common weight on the SNPs in a given region is a feasible approach for single-trait genetic evaluation. Integrating relevant SNPs selected from sequencing data into the standard SNP chip can improve the reliability of genomic prediction. Based on such SNP data, a basic ssGBLUP model was suggested since no significant improvement was observed from using alternative models such as ssWGBLUP and ssFGBLUP.

The availability of whole genome sequencing (WGS) data enables the discovery of causative single nucleotide polymorphisms (SNPs) or SNPs in high linkage disequilibrium with causative SNPs. This study investigated effects of integrating SNPs selected from imputed WGS data into the data of 54K chip on genomic prediction in Danish Jersey. The WGS SNPs, mainly including peaks of quantitative trait loci, structure variants, regulatory regions of genes, and SNPs within genes with strong effects predicted with variant effect predictor, were selected in previous analyses for dairy breeds in Denmark-Finland-Sweden (DFS) and France (FRA). Animals genotyped with 54K chip, standard LD chip, and customized LD chip which covered selected WGS SNPs and SNPs in the standard LD chip, were imputed to 54K together with DFS and FRA SNPs. Genomic best linear unbiased prediction (GBLUP) and Bayesian four-distribution mixture models considering 54K and selected WGS SNPs as one (a one-component model) or two separate genetic components (a two-component model) were used to predict breeding values. For milk production traits and mastitis, both DFS (0.025) and FRA (0.029) sets of additional WGS SNPs improved reliabilities, and inclusions of all selected WGS SNPs generally achieved highest improvements of reliabilities (0.034). A Bayesian four-distribution model yielded higher reliabilities than a GBLUP model for milk and protein, but extra gains in reliabilities from using selected WGS SNPs were smaller for a Bayesian four-distribution model than a GBLUP model. Generally, no significant difference was observed between one-component and two-component models, except for using GBLUP models for milk.

Bioreactors are engineered systems capable of supporting a biologically active situation for conducting aerobic or anaerobic biochemical processes. Stability, operational ease, improved nutrient uptake capacity, time- and cost-effectiveness, and large quantities of biomass production, make bioreactors suitable alternatives to conventional plant tissue and cell culture (PTCC) methods. Bioreactors are employed in a wide range of plant research, and have evolved over time. Such technological progress, has led to remarkable achievements in the field of PTCC. Since the classification of bioreactors has been extensively reviewed in numerous reviews, the current article avoids repeating the same material. Alternatively, it aims to highlight the principal advances in the bioreactor hardware s used in PTCC rather than classical categorization. Furthermore, our review summarizes the most significant steps as well as current state-of-the-art of PTCC carried out in various types of bioreactor.

Purpose: Insulin-like growth factor receptor 1 (IGF-1R) is critically involved in pancreatic cancer pathophysiology, promoting cancer cell survival and therapeutic resistance. Assessment of IGF-1R inhibitors in combination with standard-of-care chemotherapy, however, failed to demonstrate significant clinical benefit. The aim of this work is to unravel mechanisms of resistance to IGF-1R inhibition in pancreatic cancer and develop novel strategies to improve the activity of standard-of-care therapies. Experimental Design: Growth factor screening in pancreatic cancer cell lines was performed to identify activators of prosurvival PI3K/AKT signaling. The prevalence of activating growth factors and their receptors was assessed in pancreatic cancer patient samples. Effects of a bispecific IGF-1R and ErbB3 targeting antibody on receptor expression, signaling, cancer cell viability and apoptosis, spheroid growth, and in vivo chemotherapy activity in pancreatic cancer xenograft models were determined. Results: Growth factor screening in pancreatic cancer cells revealed insulin-like growth factor 1 (IGF-1) and heregulin (HRG) as the most potent AKT activators. Both growth factors reduced pancreatic cancer cell sensitivity to gemcitabine or paclitaxel in spheroid growth assays. Istiratumab (MM-141), a novel bispecific antibody that blocks IGF-1R and ErbB3, restored the activity of paclitaxel and gemcitabine in the presence of IGF-1 and HRG in vitro Dual IGF-1R/ErbB3 blocking enhanced chemosensitivity through inhibition of AKT phosphorylation and promotion of IGF-1R and ErbB3 degradation. Addition of istiratumab to gemcitabine and nab-paclitaxel improved chemotherapy activity in vivo Conclusions: Our findings suggest a critical role for the HRG/ErbB3 axis and support the clinical exploration of dual IGF-1R/ErbB3 blocking in pancreatic cancer. Clin Cancer Res; 24(12); 2873-85. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Identification of the genetic variants associated with calf survival in dairy cattle will aid in the elimination of harmful mutations from the cattle population and the reduction of calf and young stock mortality rates. We used de-regressed estimated breeding values for the young stock survival (YSS) index as response variables in a genome-wide association study with imputed whole-genome sequence variants. A total of 4,610 bulls with estimated breeding values were genotyped with the Illumina BovineSNP50 (Illumina, San Diego, CA) single nucleotide polymorphism (SNP) genotyping array. Genotypes were imputed to whole-genome sequence variants. After quality control, 15,419,550 SNP on 29 Bos taurus autosomes (BTA) were used for association analysis. A modified mixed-model association analysis was used for a genome scan, followed by a linear mixed-model analysis for selected genetic variants. We identified 498 SNP on BTA5 and BTA18 that were associated with the YSS index in Nordic Holstein. The SNP rs440345507 (Chr5:94721790) on BTA5 was the putative causal mutation affecting YSS. Two haplotype-based models were used to identify haplotypes with the largest detrimental effects on YSS index. For each association signal, 1 haplotype region with harmful effects and the lead associated SNP were identified. Detected haplotypes on BTA5 and BTA18 explained 1.16 and 1.20%, respectively, of genetic variance for the YSS index. We examined whether YSS quantitative trait loci (QTL) on BTA5 and BTA18 were associated with stillbirth. YSS QTL on BTA18 overlapped a QTL region for stillbirth, but most likely 2 different causal variants were responsible for these 2 QTL. Four component traits of the YSS index, defined by sex and age, were analyzed separately by the modified mixed-model approach. The same genomic regions were associated with both bull and heifer calf mortality. Several genes (EPS8, LOC100138951, and KLK family genes) contained a lead associated SNP or were included in haplotypes with large detrimental effects on YSS in Nordic Holstein cattle., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The ErbB family of receptor tyrosine kinases comprises four members: epidermal growth factor receptor (EGFR/ErbB1), human EGFR 2 (HER2/ErbB2), ErbB3/HER3, and ErbB4/HER4. The first two members of this family, EGFR and HER2, have been implicated in tumorigenesis and cancer progression for several decades, and numerous drugs have now been approved that target these two proteins. Less attention, however, has been paid to the role of this family in mediating cancer cell survival and drug tolerance. To better understand the complex signal transduction network triggered by the ErbB receptor family, we built a computational model that quantitatively captures the dynamics of ErbB signaling. Sensitivity analysis identified ErbB3 as the most critical activator of phosphoinositide 3-kinase (PI3K) and Akt signaling, a key pro-survival pathway in cancer cells. Based on this insight, we designed a fully human monoclonal antibody, seribantumab (MM-121), that binds to ErbB3 and blocks signaling induced by the extracellular growth factors heregulin (HRG) and betacellulin (BTC). In this article, we present some of the key preclinical simulations and experimental data that formed the scientific foundation for three Phase 2 clinical trials in metastatic cancer. These trials were designed to determine if patients with advanced malignancies would derive benefit from the addition of seribantumab to standard-of-care drugs in platinum-resistant/refractory ovarian cancer, hormone receptor-positive HER2-negative breast cancer, and EGFR wild-type non-small cell lung cancer (NSCLC). From preclinical studies we learned that basal levels of ErbB3 phosphorylation correlate with response to seribantumab monotherapy in mouse xenograft models. As ErbB3 is rapidly dephosphorylated and hence difficult to measure clinically, we used the computational model to identify a set of five surrogate biomarkers that most directly affect the levels of p-ErbB3: HRG, BTC, EGFR, HER2, and ErbB3. Preclinically, the combined information from these five markers was sufficient to accurately predict which xenograft models would respond to seribantumab, and the single-most accurate predictor was HRG. When tested clinically in ovarian, breast and lung cancer, HRG mRNA expression was found to be both potentially prognostic of insensitivity to standard therapy and potentially predictive of benefit from the addition of seribantumab to standard of care therapy in all three indications. In addition, it was found that seribantumab was most active in cancers with low levels of HER2, consistent with preclinical predictions. Overall, our clinical studies and studies of others suggest that HRG expression defines a drug-tolerant cancer cell phenotype that persists in most solid tumor indications and may contribute to rapid clinical progression. To our knowledge, this is the first example of a drug designed and clinically tested using the principles of Systems Biology., Competing Interests: The authors declare no conflict of interest.

Given the bulky nature of nanotherapeutics relative to small molecules, it is hypothesized that effective tumor delivery and penetration are critical barriers to their clinical activity. HER2-targeted PEGylated liposomal doxorubicin (MM-302, HER2-tPLD) is an antibody-liposomal drug conjugate designed to deliver doxorubicin to HER2-overexpressing cancer cells while limiting uptake into nontarget cells. In this work, we demonstrate that the administration and appropriate dose sequencing of cyclophosphamide can improve subsequent MM-302 delivery and enhance antitumor activity in preclinical models without negatively affecting nontarget tissues, such as the heart and skin. We demonstrate that this effect is critically dependent on the timing of cyclophosphamide administration. Furthermore, the effect was found to be unique to cyclophosphamide and related analogues, and not shared by other agents, such as taxanes or eribulin, under the conditions examined. Analysis of the cyclophosphamide-treated tumors suggests that the mechanism for improved MM-302 delivery involves the induction of tumor cell apoptosis, reduction of overall tumor cell density, substantial lowering of interstitial fluid pressure, and increasing vascular perfusion. The novel dosing strategy for cyclophosphamide described herein is readily translatable to standard clinical regimens, represents a potentially significant advance in addressing the drug delivery challenge, and may have broad applicability for nanomedicines. This work formed the basis for clinical evaluation of cyclophosphamide for improving liposome deposition as part of an ongoing phase I clinical trial of MM-302 in HER2-positive metastatic breast cancer., (©2015 American Association for Cancer Research.)

PI3K is frequently mutated in cancer and plays an important role in cell growth and survival. Heregulin (HRG)-mediated autocrine or paracrine signaling through the receptor tyrosine kinase ErbB3 potently activates the PI3K/AKT pathway and has been shown to mediate resistance to a wide variety of anticancer agents. Although PI3K functions downstream of HRG-ErbB3, it is unknown whether activating mutations in PI3K render HRG ineffective. If so, patients with PI3K mutations would not be expected to benefit from ErbB3-directed therapies. Here, we find that a subset of cell lines harboring activating PI3K mutations can be further growth-stimulated by HRG, and this effect is blocked by incubation with seribantumab (MM-121), a monoclonal anti-ErbB3 antibody. Although expression of mutant PI3K in wild-type PI3K cells frequently results in loss of HRG-stimulated growth, some cell lines continue to respond to HRG. In cell lines where HRG-stimulated growth is lost, this loss is invariably accompanied by a reduction in ErbB3 levels, a corresponding increase in basal phosphorylation levels of FOXO-family transcription factors, and a reduction in HRG-induced downstream signaling. Importantly, HRG-stimulated growth is partially rescued by re-expressing ErbB3. This response is blocked by seribantumab, indicating that ErbB3 levels rather than downstream signaling proteins limit HRG-stimulated growth in PI3K mutant cells. Overall, these results suggest that activating mutations in PI3K do not preclude potential benefit from ErbB3-directed therapy, but that it may be important to measure ErbB3 levels in patients with PI3K mutant cancers to determine if they would benefit., (©2015 American Association for Cancer Research.)

Although EGFR is a validated therapeutic target across multiple cancer indications, the often modest clinical responses to current anti-EGFR agents suggest the need for improved therapeutics. Here, we demonstrate that signal amplification driven by high-affinity EGFR ligands limits the capacity of monoclonal anti-EGFR antibodies to block pathway signaling and cell proliferation and that these ligands are commonly coexpressed with low-affinity EGFR ligands in epithelial tumors. To develop an improved antibody therapeutic capable of overcoming high-affinity ligand-mediated signal amplification, we used a network biology approach comprised of signaling studies and computational modeling of receptor-antagonist interactions. Model simulations suggested that an oligoclonal antibody combination may overcome signal amplification within the EGFR:ERK pathway driven by all EGFR ligands. Based on this, we designed MM-151, a combination of three fully human IgG1 monoclonal antibodies that can simultaneously engage distinct, nonoverlapping epitopes on EGFR with subnanomolar affinities. In signaling studies, MM-151 antagonized high-affinity EGFR ligands more effectively than cetuximab, leading to an approximately 65-fold greater decrease in signal amplification to ERK. In cell viability studies, MM-151 demonstrated antiproliferative activity against high-affinity EGFR ligands, either singly or in combination, while cetuximab activity was largely abrogated under these conditions. We confirmed this finding both in vitro and in vivo in a cell line model of autocrine high-affinity ligand expression. Together, these preclinical studies provide rationale for the clinical study of MM-151 and suggest that high-affinity EGFR ligand expression may be a predictive response marker that distinguishes MM-151 from other anti-EGFR therapeutics., (©2015 American Association for Cancer Research.)

Immunoliposomes provide a complementary, and in many instances advantageous, drug delivery strategy to antibody-drug conjugates. Their high carrying capacity of 20,000-150,000 drug molecules/liposome, allows for the use of a significantly broader range of moderate-to-high potency small molecule drugs when compared to the comparably few subnanomolar potency maytansinoid- and auristatin-based immunoconjugates. The multivalent display of 5-100 antibody fragments/liposome results in an avidity effect that can make use of even moderate affinity antibodies, as well as a cross-linking of cell surface receptors to induce the internalization required for intracellular drug release and subsequent activity. The underlying liposomal drug must be effectively engineered for long circulating pharmacokinetics and stable in vivo drug retention in order to allow for the drug to be efficiently delivered to the target tissue and take advantage of the site-specific bioavailability provided for by the targeting arm. In this chapter, we describe the rationale for engineering stable immunoliposome-based therapeutics, methods required for preparation of immunoliposomes, as well as for their physicochemical and in vivo characterization., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Monoclonal antibodies are valuable as anticancer therapeutics because of their ability to selectively bind tumor-associated target proteins like receptor tyrosine kinases. Kinetic computational models that capture protein-protein interactions using mass action kinetics are a valuable tool for understanding the binding properties of monoclonal antibodies to their targets. Insights from the models can be used to explore different formats, to set antibody design specifications such as affinity and valence, and to predict potency. Antibody binding to target is driven by both intrinsic monovalent affinity and bivalent avidity. In this chapter, we describe a combined experimental and computational method of assessing the relative importance of these effects on observed drug potency. The method, which we call virtual flow cytometry (VFC), merges experimental measurements of monovalent antibody binding kinetics and affinity curves of antibody-antigen binding into a kinetic computational model of antibody-antigen interaction. The VFC method introduces a parameter χ, the avidity factor, which characterizes the ability of an antibody to cross-link its target through bivalent binding. This simple parameterization of antibody cross-linking allows the model to successfully describe and predict antibody binding curves across a wide variety of experimental conditions, including variations in target expression level and incubation time of antibody with target. We further demonstrate how computational models of antibody binding to cells can be used to predict target inhibition potency. Importantly, we demonstrate computationally that antibodies with high ability to cross-link antigen have significant potency advantages. We also present data suggesting that the parameter χ is a physical, epitope-dependent property of an antibody, and as a result propose that determination of antibody cross-linking and avidity should be incorporated into the screening of antibody panels for therapeutic development. Overall, our results suggest that antibody cross-linking, in addition to monovalent binding affinity, is a key design parameter of antibody performance., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Signal transduction networks are often described by qualitative models that incorporate experimental findings from different cell types. However, a large body of evidence suggests that cell signaling differs substantially from one cell type to the next. Even in cases in which the topology of signaling networks is conserved, different cell types respond differently under the same experimental conditions. In this work we address the problem of developing quantitative and cell-type specific models of cells signaling that incorporate experimental data obtained from protein arrays and mathematical modeling. As a biological system we examine signaling mediated ErbB receptor family as the current literature is very controversial.

Computational biology is impacting drug design and has the potential to revolutionize the drug development process. Here, we describe how data-driven computational models can optimize the design of targeted therapeutics to yield improved safety and efficacy. We propose that, in the future, this approach will improve drug discovery, clinical development, diagnosis and treatment.

Purpose: Anti-HER2 immunoliposomes combine the tumor-targeting of certain anti-HER2 monoclonal antibodies (MAbs) with the pharmacokinetic and drug delivery capabilities of sterically stabilized liposomes. We previously showed that anti-HER2 immunoliposomes bind efficiently to and internalize in HER2-overexpressing cells in vitro, resulting in intracellular drug delivery., Experimental Design: Here we describe the pharmacokinetics and therapeutic efficacy of anti-HER2 immunoliposomes containing doxorubicin (dox) in a series of animal models., Results: Immunoliposomes displayed long circulation that was identical to that of sterically stabilized liposomes in single- and multiple-dose studies in normal rats. Anti-HER2 immunoliposome-dox produced marked therapeutic results in four different HER2-overexpressing tumor xenograft models, including growth inhibition, regression, and cures. These results demonstrated that encapsulation of dox in anti-HER2 immunoliposomes greatly increased its therapeutic index, both by increasing antitumor efficacy and by reducing systemic toxicity. Immunoliposome-dox was significantly superior to all other treatment conditions tested, including free dox, liposomal dox, and anti-HER2 MAb (trastuzumab). When compared with liposomal dox in eight separate therapy studies in HER2-overexpressing models, immunoliposome delivery produced significantly superior antitumor efficacy in each study (P < 0.0001 to 0.04). Anti-HER2 immunoliposome-dox containing either recombinant human MAb HER2-Fab' or scFv C6.5 yielded comparable therapeutic efficacy. Cure rates for immunoliposome-dox reached 50% (11 of 21) with optimized immunoliposomes and Matrigel-free tumors and overall was 16% (18 of 115) versus no cures (0 of 124) with free dox or liposomal dox. Finally, anti-HER2 immunoliposome-dox was also superior to combinations consisting of free MAb plus free dox or free MAb plus liposomal dox., Conclusions: Anti-HER2 immunoliposomes produced enhanced antitumor efficacy via targeted delivery.