Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis

Dual targeting of HER3 and EGFR in colorectal tumors might overcome anti-EGFR resistance

Multiple genetic alterations have been associated with resistance to anti-EGFR therapy in metastatic colorectal cancer (CRC) patients. Research has been mainly focused on driver mutations in KRAS, NRAS, BRAF and PI3K. However, recent evidence suggests a crucial role for non-genetic mechanisms in conferring resistance to anti-EGFR therapy. Specifically, the HER3 receptor is capable of heterodimerizing with multiple EGFR family members resulting in downstream activation of the PI3K and MAPK pathways. Monoclonal antibodies targeted against the HER3 receptor are being investigated in clinical trials; however, preliminary data has shown limited clinical activity. Thus, given the relevance of the HER3 receptor in activating downstream effector pathways and in conferring resistance to anti-EGFR therapy, the therapeutic targeting of HER3 in combination with primary drivers of the tumor is also being investigated. Here, we review the role of HER3 as a promoter of clinical resistance to EGFR therapy and discuss therapeutic approaches that could potentially overcome this resistance.

Emerging anti-cancer antibodies and combination therapies targeting HER3/ERBB3

Cancer progression depends on stepwise accumulation of oncogenic mutations and a select group of growth factors essential for tumor growth, metastasis and angiogenesis. Agents blocking the epidermal growth factor receptor (EGFR, also called HER1 and ERBB1) and the co-receptor called HER2/ERBB2 have been approved over the last decade as anti-cancer drugs. Because the catalytically defective member of the family, HER3/ERBB3, plays critical roles in emergence of resistance of carcinomas to various drugs, current efforts focus on antibodies and other anti-HER3/ERBB3 agents, which we review herein with an emphasis on drug combinations and some unique biochemical features of HER3/ERBB3.

Engineering approaches to study fibrosis in 3-D in vitro systems

Fibrotic diseases occur in virtually every tissue of the body and are a major cause of mortality, yet they remain largely untreatable and poorly understood on a mechanistic level. The development of anti-fibrotic agents has been hampered, in part, by the insufficient fibrosis biomimicry provided by traditional in vitro platforms. This review focuses on recent advancements toward creating 3-D platforms that mimic key features of fibrosis, as well as the application of novel imaging and sensor techniques to analyze dynamic extracellular matrix remodeling. Several opportunities are highlighted to apply new tools from the fields of biomaterials, imaging, and systems biology to yield pathophysiologically relevant in vitro platforms that improve our understanding of fibrosis and may enable identification of potential treatment targets.

Hepatitis B virus X protein reduces the stability of Nrdp1 to up-regulate ErbB3 in hepatocellular carcinoma cells

Hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) is the most widespread type of liver cancer. However, the underlying mechanism of HCC tumorigenesis is very intricate and HBV-encoded X protein (HBx) has been reported to play a key role in this process. It has been reported that HBx up-regulates the transcription of ErbB3. However, it remains unclear whether HBx can regulate ErbB3 expression at post-translational modification level. In this study, we showed that HBx interacts with ubiquitin ligase Nrdp1 (neuregulin receptor degradation protein 1) and decreases its stability, which results in the up-regulation of ErbB3 and promotion of HCC cells. Moreover, the expression of ErbB3 was almost undetectable in normal liver tissues but was relative abundant in HCC tissues, and the level of ErbB3 and Nrdp1 significantly showed a negative correlation in HCC tissues. Taken together, these findings suggest that HBx promotes the progression of HCC by decreasing the stability of Nrdp1, which results in up-regulation of ErbB3, suggesting that ErbB3 may be a target for HCC therapy.

Ewing sarcoma: The clinical relevance of the insulin-like growth factor 1 and the poly-ADP-ribose-polymerase pathway

BACKGROUND

In the last three decades the outcome for patients with localised Ewing sarcoma (ES) has improved significantly since the introduction of multimodality primary treatment. However, for patients with (extra-) pulmonary metastatic and/or non-resectable relapsed disease the outcome remains poor and new treatment options are urgently needed. Currently the insulin-like growth factor 1 receptor (IGF-1R) pathway and the poly-ADP(adenosinediphosphate)-ribose-polymerase (PARP) pathway are being investigated for potential targeted therapies. IGF-1R: The IGF-1R pathway is known to be deregulated by the EWSR1-FLI1 translocation which makes it a potential target for therapy. Clinical trials have been reported in which only ES patients were treated with an IGF-1R inhibitor, either as single agent or in combination. In total 291 ES patients were included in these trials, in which two (0.7%) complete responses, 32 (11%) partial responses of which some durable, and 61 (21%) stable diseases were observed. PARP: In the presence of a PARP inhibitor DNA strand breaks cannot be efficiently repaired, leading to cell death. The first phase II trial with ES patients was recently published and showed no clinical responses, which may have been due to the drug being non-effective as a single agent.

DISCUSSION

The IGF-1R pathway is an interesting target for ES and should be explored further, as biomarkers to select patients that might benefit from treatment are lacking. PARP inhibitors as single agent have so far failed to show improvement in outcome. Future directions include dual insulin receptor/IGF-1R blockade with linsitinib as well as chemotherapy-PARP combinations. Both therapeutic strategies are currently being explored.

Antibody Therapies in Cancer

Antibody-based immunotherapy has become a standard treatment for a variety of cancers. Many well-developed antibodies disrupt signaling of various growth factor receptors for the treatment of a number of cancers by targeting surface antigens expressed on tumor cells. In recent years, a new family of antibodies is currently emerging in the clinic, which target immune cells rather than cancer cells. These immune-targeted therapies strive to augment antitumor immune responses by antagonizing immunosuppressive pathways or providing exogenous immune-activating stimuli, which have achieved dramatic results in several cancers. The future of cancer therapies is likely to combine these approaches with other treatments, including conventional therapies, to generate more effective treatments.

Use of mechanistic models to integrate and analyze multiple proteomic datasets

Proteins in cell signaling networks tend to interact promiscuously through low-affinity interactions. Consequently, evaluating the physiological importance of mapped interactions can be difficult. Attempts to do so have tended to focus on single, measurable physicochemical factors, such as affinity or abundance. For example, interaction importance has been assessed on the basis of the relative affinities of binding partners for a protein of interest, such as a receptor. However, multiple factors can be expected to simultaneously influence the recruitment of proteins to a receptor (and the potential of these proteins to contribute to receptor signaling), including affinity, abundance, and competition, which is a network property. Here, we demonstrate that measurements of protein copy numbers and binding affinities can be integrated within the framework of a mechanistic, computational model that accounts for mass action and competition. We use cell line-specific models to rank the relative importance of protein-protein interactions in the epidermal growth factor receptor (EGFR) signaling network for 11 different cell lines. Each model accounts for experimentally characterized interactions of six autophosphorylation sites in EGFR with proteins containing a Src homology 2 and/or phosphotyrosine-binding domain. We measure importance as the predicted maximal extent of recruitment of a protein to EGFR following ligand-stimulated activation of EGFR signaling. We find that interactions ranked highly by this metric include experimentally detected interactions. Proteins with high importance rank in multiple cell lines include proteins with recognized, well-characterized roles in EGFR signaling, such as GRB2 and SHC1, as well as a protein with a less well-defined role, YES1. Our results reveal potential cell line-specific differences in recruitment.

HER3/ErbB3, an emerging cancer therapeutic target

HER3 is a member of the HER (EGFR/ErbB) receptor family consisting of four closely related type 1 transmembrane receptors (EGFR, HER2, HER3, and HER4). HER receptors are part of a complex signaling network intertwined with the Ras/Raf/MAPK, PI3K/AKT, JAK/STAT, and PKC signaling pathways. Aberrant activation of the HER receptors and downstream signaling molecules tips the balance on cellular events, leading to various types of cancers. Monoclonal antibodies (mAbs) and small molecule inhibitors targeting EGFR and HER2 tyrosine kinase activities exhibit clinical benefits in the treatment of several types of cancers, but their clinical efficacy is limited by the occurrence of drug resistance. HER3 is the preferred dimerization partner of HER2 and it is well established that HER3 plays an important role in drug resistance to EGFR- and HER2-targeting therapies. Since HER3 has limited kinase activity, mAbs are being explored to target HER3 for cancer therapy. Currently, approximately a dozen of anti-HER3 mAbs are at different stages of clinical development. However, the lack of established biomarkers has made it more challenging to stratify cancer patients to whom HER3-targeting therapies can be more effective. In this review, we focus on the validation of HER3 as a cancer drug target, the recent development in biomarker discovery for anti-HER3 therapies, and the progress made in the clinical development of HER3-targeting mAbs.

Beyond static biomarkers--The dynamic response potential of signaling networks as an alternate biomarker?

In this week's issue of Science Signaling, Fey et al. introduce a new type of biomarker. Using the example of neuroblastoma, the authors demonstrate that patient-specific differences in the computed property (the Hill coefficient) of the dynamics of a pathway involved in cell death signaling outperformed the prognostic capability of any single static biomarker alone or in combination.

High-grade serous ovarian cancer cell lines exhibit heterogeneous responses to growth factor stimulation

Background

The factors driving the onset and progression of ovarian cancer are not well understood. Recent reports have identified cell lines that are representative of the genomic pattern of high-grade serous ovarian cancer (HGSOC), in which greater than 90 % of tumors have a mutation in TP53. However, many of these representative cell lines have not been widely used so it is unclear if these cell lines capture the variability that is characteristic of the disease.

Methods

We investigated six TP53-mutant HGSOC cell lines (Caov3, Caov4, OV90, OVCA432, OVCAR3, and OVCAR4) for migration, MMP2 expression, proliferation, and VEGF secretion, behaviors that play critical roles in tumor progression. In addition to comparing baseline variation between the cell lines, we determined how these behaviors changed in response to four growth factors implicated in ovarian cancer progression: HB-EGF, NRG1β, IGF1, and HGF.

Results

Baseline levels of each behavior varied across the cell lines and this variation was comparable to that seen in tumors. All four growth factors impacted cell proliferation or VEGF secretion, and HB-EGF, NRG1β, and HGF impacted wound closure or MMP2 expression in at least two cell lines. Growth factor-induced responses demonstrated substantial heterogeneity, with cell lines sensitive to all four growth factors, a subset of the growth factors, or none of the growth factors, depending on the response of interest. Principal component analysis demonstrated that the data clustered together based on cell line rather than growth factor identity, suggesting that response is dependent on intrinsic qualities of the tumor cell rather than the growth factor.

Conclusions

Significant variation was seen among the cell lines, consistent with the heterogeneity of HGSOC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12935-015-0263-4) contains supplementary material, which is available to authorized users.

In silico model-based inference: an emerging approach for inverse problems in engineering better medicines

Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.

Elucidation of the molecular mechanisms underlying adverse reactions associated with a kinase inhibitor using systems toxicology

BACKGROUND/OBJECTIVES

Targeted kinase inhibitors are an important class of agents in anticancer therapeutics, but their limited tolerability hampers their clinical performance. Identification of the molecular mechanisms underlying the development of adverse reactions will be helpful in establishing a rational method for the management of clinically adverse reactions. Here, we selected sunitinib as a model and demonstrated that the molecular mechanisms underlying the adverse reactions associated with kinase inhibitors can efficiently be identified using a systems toxicological approach.

METHODS

First, toxicological target candidates were short-listed by comparing the human kinase occupancy profiles of sunitinib and sorafenib, and the molecular mechanisms underlying adverse reactions were predicted by sequential simulations using publicly available mathematical models. Next, to evaluate the probability of these predictions, a clinical observation study was conducted in six patients treated with sunitinib. Finally, mouse experiments were performed for detailed confirmation of the hypothesized molecular mechanisms and to evaluate the efficacy of a proposed countermeasure against adverse reactions to sunitinib.

RESULTS

In silico simulations indicated the possibility that sunitinib-mediated off-target inhibition of phosphorylase kinase leads to the generation of oxidative stress in various tissues. Clinical observations of patients and mouse experiments confirmed the validity of this prediction. The simulation further suggested that concomitant use of an antioxidant may prevent sunitinib-mediated adverse reactions, which was confirmed in mouse experiments.

CONCLUSIONS

A systems toxicological approach successfully predicted the molecular mechanisms underlying clinically adverse reactions associated with sunitinib and was used to plan a rational method for the management of these adverse reactions.

VERO stable cell lines expressing full-length human epidermal growth factor receptors 2 and 3: platforms for subtractive phage display

Cross-talk between human epidermal growth factor receptor 2 and 3 (HER2 and HER3) may potentially contribute to therapeutic resistance in human breast cancer. Subtractive phage display allows highly specific selection for antibody fragments directed against cells surface HER2 and HER3. The strategies to select conformation- and activation-specific antibodies against HER2 and HER3 require tightly regulated HER2 and HER3 expressing cells that allow controlled activation/inactivation of these receptors during panning procedures. To achieve this, first, we found that the VERO cell line is an appropriate cell line for heterogeneous expression of HER2 and HER3, and then we established a panel of VERO stable cell lines expressing high levels of HER2 and HER3 alone and in combination. We also showed that HER2 and HER3 expressed in VERO cells were biologically active and could form heterodimer following neuregulin1 treatment. The cell line established here not only provided platforms for phage display-based methods but also could be used in any HER-related studies.

ErbB3-ErbB2 Complexes as a Therapeutic Target in a Subset of Wild-type BRAF/NRAS Cutaneous Melanomas

The treatment options remain limited for patients with melanoma who are wild-type for both BRAF and NRAS (WT/WT). We demonstrate that a subgroup of WT/WT melanomas display high basal phosphorylation of ErbB3 that is associated with autocrine production of the ErbB3 ligand neuregulin-1 (NRG1). In WT/WT melanoma cells displaying high levels of phospho-ErbB3, knockdown of NRG1 reduced cell viability and was associated with decreased phosphorylation of ErbB3, its coreceptor ErbB2, and its downstream target, AKT. Similar effects were observed by targeting ErbB3 with either siRNAs or the neutralizing ErbB3 monoclonal antibodies huHER3-8 and NG33. In addition, pertuzumab-mediated inhibition of ErbB2 heterodimerization decreased AKT phosphorylation, cell growth in vitro, and xenograft growth in vivo. Pertuzumab also potentiated the effects of MEK inhibitor on WT/WT melanoma growth in vitro and in vivo. These findings demonstrate that targeting ErbB3-ErbB2 signaling in a cohort of WT/WT melanomas leads to tumor growth reduction. Together, these studies support the rationale to target the NRG1-ErbB3-ErbB2 axis as a novel treatment strategy in a subset of cutaneous melanomas.

Modeling Signaling Networks to Advance New Cancer Therapies

Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

Heregulin-ErbB3-Driven Tumor Growth Persists in PI3 Kinase Mutant Cancer Cells

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.

Molecular mechanism matters: Benefits of mechanistic computational models for drug development

Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.

Identification of Anti-EGFR and Anti-ErbB3 Dual Variable Domains Immunoglobulin (DVD-Ig) Proteins with Unique Activities

Epidermal growth factor receptor (EGFR) and receptor tyrosine-protein kinase 3 (ErbB3) are two well-established targets in cancer therapy. There is significant crosstalk among these two receptors and others. To block signaling from both EGFR and ErbB3, we generated anti-EGFR and anti-ErbB3 DVD-Ig proteins. Two DVD-Ig proteins maintained the functions of the combination of the two parental antibodies. The DVD-Ig proteins inhibit cell signaling and proliferation in A431 and FaDu cells while half DVD-Ig proteins lost proliferation inhibition function. Interestingly, in the presence of β-Heregulin (HRG), the DVD-Ig proteins show synergies with respect to inhibiting cell proliferation. The DVD-Ig proteins downregulate EGFR protein expression in the presence of HRG, which may be due to receptor internalization. Furthermore, the DVD-Ig proteins remarkably disrupt β-Heregulin binding to FaDu cells.

ERBB3 knockdown induces cell cycle arrest and activation of Bak and Bax-dependent apoptosis in colon cancer cells

ERBB3 is an emerging target for cancer therapy among the EGFR family. Contrary to resistance against EGFR and ERBB2 targeting, the genetic inhibition of ERBB3 results in anti-tumorigenic in HCT116 colon cancer cells harboring constitutively active KRAS and PIK3CA mutations. Still, the anti-tumorigenic molecular mechanism has not been defined. We demonstrated in this study that ERBB3 knockdown resulted in cell cycle arrest and activation of Bak and Bax-dependent apoptosis. Apoptosis was irrelevant to the majority of BH3-only pro-apoptotic proteins and correlated with the transcriptional upregulation of Bak and p53-dependent Bax translocation. Treatment with LY294002, a PI3K inhibitor, resulted in cell cycle arrest without apoptosis and a concomitant down-regulation of cap-dependent translation by the suppression of the PI3K/AKT/mTOR pathway. However, the inhibition of cap-dependent translation by ERBB3 knockdown occurred without altering the PI3K/AKT/mTOR pathway. In addition, ERBB3 knockdown-induced cell cycle arrest was observed in most colon cancer cells but was accompanied by apoptosis in p53 wild-type cells. These results indicate that ERBB3 is a potential target for EGFR- and ERBB2-resistant colon cancer therapy.

Pan-HER, an Antibody Mixture Simultaneously Targeting EGFR, HER2, and HER3, Effectively Overcomes Tumor Heterogeneity and Plasticity

PURPOSE

Accumulating evidence indicates a high degree of plasticity and compensatory signaling within the human epidermal growth factor receptor (HER) family, leading to resistance upon therapeutic intervention with HER family members.

EXPERIMENTAL DESIGN/RESULTS

We have generated Pan-HER, a mixture of six antibodies targeting each of the HER family members EGFR, HER2, and HER3 with synergistic pairs of antibodies, which simultaneously remove all three targets, thereby preventing compensatory tumor promoting mechanisms within the HER family. Pan-HER induces potent growth inhibition in a range of cancer cell lines and xenograft models, including cell lines with acquired resistance to therapeutic antibodies. Pan-HER is also highly efficacious in the presence of HER family ligands, indicating that it is capable of overcoming acquired resistance due to increased ligand production. All three target specificities contribute to the enhanced efficacy, demonstrating a distinct benefit of combined HER family targeting when compared with single-receptor targeting.

CONCLUSIONS

Our data show that simultaneous targeting of three receptors provides broader efficacy than targeting a single receptor or any combination of two receptors in the HER family, especially in the presence of HER family ligands. Pan-HER represents a novel strategy to deal with primary and acquired resistance due to tumor heterogeneity and plasticity in terms of HER family dependency and as such may be a viable alternative in the clinic.

Cell based advanced therapeutic medicinal products for bone repair: Keep it simple?

The development of cell based advanced therapeutic medicinal products (ATMPs) for bone repair has been expected to revolutionize the health care system for the clinical treatment of bone defects. Despite this great promise, the clinical outcomes of the few cell based ATMPs that have been translated into clinical treatments have been far from impressive. In part, the clinical outcomes have been hampered because of the simplicity of the first wave of products. In response the field has set-out and amassed a plethora of complexities to alleviate the simplicity induced limitations. Many of these potential second wave products have remained "stuck" in the development pipeline. This is due to a number of reasons including the lack of a regulatory framework that has been evolving in the last years and the shortage of enabling technologies for industrial manufacturing to deal with these novel complexities. In this review, we reflect on the current ATMPs and give special attention to novel approaches that are able to provide complexity to ATMPs in a straightforward manner. Moreover, we discuss the potential tools able to produce or predict 'goldilocks' ATMPs, which are neither too simple nor too complex.

Colorectal cancer drug target prediction using ontology-based inference and network analysis

Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study, we present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets. We utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data, we inferred 113 potential CRC drug targets. We further prioritized these genes based on their relationships with CRC disease genes in the context of human protein-protein interaction networks. Thus, among the 113 potential drug targets, 15 were selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDA-approved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics.

Saraca indica bark extract shows in vitro antioxidant, antibreast cancer activity and does not exhibit toxicological effects

Medicinal plants are used as a complementary and alternative medicine in treatment of various diseases including cancer worldwide, because of their ease of accessibility and cost effectiveness. Multicomposed mixture of compounds present in a plant extract has synergistic activity, increases the therapeutic potential many folds, compensates toxicity, and increases bioavailability. Saraca indica (family Caesalpiniaceae) is one of the most ancient sacred plants with medicinal properties, exhibiting a number of pharmacological effects. Antioxidant, antibreast cancer activity and toxicological evaluation of Saraca indica bark extract (SIE) were carried out in the present study. The results of the study indicated that this herbal preparation has antioxidant and antibreast cancer activity. Toxicological studies suggest that SIE is safer to use and may have a potential to be used as complementary and alternative medicine for breast cancer therapy.

Model-Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers

Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six-arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM-111), AKT (MK-2206), and MEK (GSK-1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2-expressing tumors may be more broadly extendable to other human cancers.

Systems biology approaches for advancing the discovery of effective drug combinations

Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations.

Graphical abstract

Clinical Translation and Validation of a Predictive Biomarker for Patritumab, an Anti-human Epidermal Growth Factor Receptor 3 (HER3) Monoclonal Antibody, in Patients With Advanced Non-small Cell Lung Cancer

Background

During early clinical development, prospective identification of a predictive biomarker and validation of an assay method may not always be feasible. Dichotomizing a continuous biomarker measure to classify responders also leads to challenges. We present a case study of a prospective–retrospective approach for a continuous biomarker identified after patient enrollment but defined prospectively before the unblinding of data. An analysis of the strengths and weaknesses of this approach and the challenges encountered in its practical application are also provided.

Methods

HERALD (NCT02134015) was a double-blind, phase 2 study in patients with non-small cell lung cancer (NSCLC) randomized to erlotinib with placebo or with high or low doses of patritumab, a monoclonal antibody targeted against human epidermal growth factor receptor 3 (HER3). While the primary objective was to assess safety and progression-free survival (PFS), a secondary objective was to determine a single predictive biomarker hypothesis to identify subjects most likely to benefit from the addition of patritumab. Although not identified as the primary biomarker in the study protocol, on the basis of preclinical results from 2 independent laboratories, expression levels of the HER3 ligand heregulin (HRG) were prospectively declared the predictive biomarker before data unblinding but after subject enrollment. An assay to measure HRG mRNA was developed and validated. Other biomarkers, such as epidermal growth factor receptor (EGFR) mutation status, were also evaluated in an exploratory fashion. The cutoff value for high vs. low HRG mRNA levels was set at the median delta threshold cycle. A maximum likelihood analysis was performed to evaluate the provisional cutoff. The relationship of HRG values to PFS hazard ratios (HRs) was assessed as a measure of internal validation. Additional NSCLC samples were analyzed to characterize HRG mRNA distribution.

Results

The subgroup of patients with high HRG mRNA levels (“HRG-high”) demonstrated clinical benefit from patritumab treatment with HRs of 0.37 (P = 0.0283) and 0.29 (P = 0.0027) in the high- and low-dose patritumab arms, respectively. However, only 102 of the 215 randomized patients (47.4%) had sufficient tumor samples for HRG mRNA measurement. Maximum likelihood analysis showed that the provisional cutoff was within the optimal range. In the placebo arm, the HRG-high subgroup demonstrated worse prognosis compared with HRG-low. A continuous relationship was observed between increased HRG mRNA levels and lower HR. Additional NSCLC samples (N = 300) demonstrated a similar unimodal distribution to that observed in this study, suggesting that the defined cutoff may be applicable to future NSCLC studies.

Conclusions

The prospective–retrospective approach was successful in clinically validating a probable predictive biomarker. Post hoc in vitro studies and statistical analyses permitted further testing of the underlying assumptions. However, limitations of this analysis include the incomplete collection of adequate tumor tissue and a lack of stratification. In a phase 3 study, findings are being confirmed, and the HRG cutoff value is being further refined.

ClinicalTrials.gov Number

NCT02134015.

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High heregulin levels predict benefit from patritumab treatment in patients with NSCLC.

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A prospective–retrospective approach provisionally validated a predictive biomarker.

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Post hoc analyses can be used to test underlying assumptions in biomarker validation.

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The median may be a reasonable initial cutoff for a unimodal continuous biomarker.

Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells

Human epidermal growth factor receptors (HER, also known as ErbB) drive cellular proliferation, pro-survival and stress responses by activating several downstream kinases, in particular ERK, p38 MAPK, JNK (SAPK), the PI3K/AKT, as well as various transcriptional regulators such as STAT3. When co-expressed, the first three members of HER family (HER1-3) can form homo- and hetero-dimers, and there is considerable evidence suggesting that the receptor dimers differentially activate intracellular signaling pathways. To better understand the interactions in this system, we pursued multi-factorial experiments where HER dimerization patterns and signaling pathways were rationally perturbed. We measured the activation of HER1-3 receptors and of the sentinel signaling proteins ERK, AKT, p38 MAPK, JNK, STAT3 as a function of time in a panel of human mammary epithelial (HME) cells expressing different levels of HER1-3 stimulated with various ligand combinations. We hypothesized that the HER dimerization pattern is a better predictor of downstream signaling than the total receptor activation levels. We validated this hypothesis using a combination of model-based analysis to quantify the HER dimerization patterns, and by clustering the activation data in multiple ways to confirm that the HER receptor dimer is a better predictor of the signaling through p38 MAPK, ERK and AKT pathways than the total HER receptor expression and activation levels. We then pursued combinatorial inhibition studies to identify the causal regulatory interactions between sentinel signaling proteins. Quantitative analysis of the collected data using the modular response analysis (MRA) and its Bayesian Variable Selection Algorithm (BVSA) version allowed us to obtain a consensus regulatory interaction model, which revealed that STAT3 occupies a central role in the crosstalk between the studied pathways in HME cells. Results of the BVSA/MRA and cluster analysis were in agreement with each other.

HER-3 peptide vaccines/mimics: Combined therapy with IGF-1R, HER-2, and HER-1 peptides induces synergistic antitumor effects against breast and pancreatic cancer cells

The human epidermal growth factor receptor 3 (HER-3/ErbB3) is a unique member of the human epidermal growth factor family of receptors, because it lacks intrinsic kinase activity and ability to heterodimerize with other members. HER-3 is frequently upregulated in cancers with epidermal growth factor receptor (EGFR/HER-1/ErbB1) or human epidermal growth factor receptor 2 (HER-2/ErBB2) overexpression, and targeting HER-3 may provide a route for overcoming resistance to agents that target EGFR or HER-2. We have previously developed vaccines and peptide mimics for HER-1, HER-2 and vascular endothelial growth factor (VEGF). In this study, we extend our studies by identifying and evaluating novel HER-3 peptide epitopes encompassing residues 99-122, 140-162, 237-269 and 461-479 of the HER-3 extracellular domain as putative B-cell epitopes for active immunotherapy against HER-3 positive cancers. We show that the HER-3 vaccine antibodies and HER-3 peptide mimics induced antitumor responses: inhibition of cancer cell proliferation, inhibition of receptor phosphorylation, induction of apoptosis and antibody dependent cellular cytotoxicity (ADCC). Two of the HER-3 epitopes 237-269 (domain II) and 461-479 (domain III) significantly inhibited growth of xenografts originating from both pancreatic (BxPC3) and breast (JIMT-1) cancers. Combined therapy of HER-3 (461-471) epitope with HER-2 (266-296), HER-2 (597-626), HER-1 (418-435) and insulin-like growth factor receptor type I (IGF-1R) (56-81) vaccine antibodies and peptide mimics show enhanced antitumor effects in breast and pancreatic cancer cells. This study establishes the hypothesis that combination immunotherapy targeting different signal transduction pathways can provide effective antitumor immunity and long-term control of HER-1 and HER-2 overexpressing cancers.

Antibody targeting of HER2/HER3 signaling overcomes heregulin-induced resistance to PI3K inhibition in prostate cancer

Dysregulated expression and/or mutations of the various components of the phosphoinositide 3-kinase (PI3K)/Akt pathway occur with high frequency in prostate cancer and are associated with the development and progression of castration resistant tumors. However, small molecule kinase inhibitors that target this signaling pathway have limited efficacy in inhibiting tumor growth, primarily due to compensatory survival signals through receptor tyrosine kinases (RTKs). Although members of the epidermal growth factor receptor (EGFR), or HER, family of RTKs are strongly implicated in the development and progression of prostate cancer, targeting individual members of this family such as EGFR or HER2 has resulted in limited success in clinical trials. Multiple studies indicate a critical role for HER3 in the development of resistance against both HER-targeted therapies and PI3K/Akt pathway inhibitors. In this study, we found that the growth inhibitory effect of GDC-0941, a class I PI3K inhibitor, is markedly reduced in the presence of heregulin. Interestingly, this effect is more pronounced in cells lacking phosphatase and tensin homolog function. Heregulin-mediated resistance to GDC-0941 is associated with reactivation of Akt downstream of HER3 phosphorylation. Importantly, combined blockade of HER2 and HER3 signaling by an anti-HER2/HER3 bispecific antibody or a mixture of anti-HER2 and anti-HER3 antibodies restores sensitivity to GDC-0941 in heregulin-treated androgen-dependent and -independent prostate cancer cells. These studies indicate that the combination of PI3K inhibitors with HER2/HER3 targeting antibodies may constitute a promising therapeutic strategy for prostate cancer.

Semantically linking in silico cancer models

Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.

Regulation of ERBB3/HER3 signaling in cancer

ERBB3/HER3 is emerging as a molecular target for various cancers. HER3 is overexpressed and activated in a number of cancer types under the conditions of acquired resistance to other HER family therapeutic interventions such as tyrosine kinase inhibitors and antibody therapies. Regulation of the HER3 expression and signaling involves numerous HER3 interacting proteins. These proteins include PI3K, Shc, and E3 ubiquitin ligases NEDD4 and Nrdp1. Furthermore, recent identification of a number of HER3 oncogenic mutations in colon and gastric cancers elucidate the role of HER3 in cancer development. Despite the strong evidence regarding the role of HER3 in cancer, the current understanding of the regulation of HER3 expression and activation requires additional research. Moreover, the lack of biomarkers for HER3-driven cancer poses a big challenge for the clinical development of HER3 targeting antibodies. Therefore, a better understanding of HER3 regulation should improve the strategies to therapeutically target HER3 for cancer therapy.

Functional aspects of the EGF-induced MAP kinase cascade: a complex self-organizing system approach

The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases. Based on a computational model describing a "surface" and an "internalized" parallel route, we use systems biology techniques to characterize aspects of the network's functional organization. We examine the re-organization of protein groups from low to high external stimulation, define functional groups of proteins within the network, determine the parameter best encoding for input intensity and predict the effect of protein removal to the system's output response. Extensive functional re-organization of proteins is observed in the lower end of stimulus concentrations. As we move to higher concentrations the variability is less pronounced. 6 functional groups have emerged from a consensus clustering approach, reflecting different dynamical aspects of the network. Mutual information investigation revealed that the maximum activation rate of the two output proteins best encodes for stimulus intensity. Removal of each protein of the network resulted in a range of graded effects, from complete silencing to intense activation. Our results provide a new "vista" of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems. Functional grouping of the proteins reveals an organizational scheme contrasting the current understanding of modular topology. The six identified groups may provide the means to experimentally follow the dynamics of this complex network. Also, the vulnerability analysis approach may be used for the development of novel therapeutic targets in the context of personalized medicine.

Therapeutic targeting of erbB3 with MM-121/SAR256212 enhances antitumor activity of paclitaxel against erbB2-overexpressing breast cancer

INTRODUCTION

Elevated expression of erbB3 rendered erbB2-overexpressing breast cancer cells resistant to paclitaxel via PI-3 K/Akt-dependent upregulation of Survivin. It is unclear whether an erbB3-targeted therapy may abrogate erbB2-mediated paclitaxel resistance in breast cancer. Here, we study the antitumor activity of an anti-erbB3 antibody MM-121/SAR256212 in combination with paclitaxel against erbB2-overexpressing breast cancer.

METHODS

Cell growth assays were used to determine cell viability. Cells undergoing apoptosis were quantified by a specific apoptotic ELISA. Western blot analyses were performed to assess the protein expression and activation. Lentiviral vector containing shRNA was used to specifically knockdown Survivin. Tumor xenografts were established by inoculation of BT474-HR20 cells into nude mice. The tumor-bearing mice were treated with paclitaxel and/or MM-121/SAR256212 to determine whether the antibody (Ab) enhances paclitaxel’s antitumor activity. Immunohistochemistry was carried out to study the combinatorial effects on tumor cell proliferation and induction of apoptosis in vivo.

RESULTS

MM-121 significantly facilitated paclitaxel-mediated anti-proliferative/anti-survival effects on SKBR3 cells transfected with a control vector or erbB3 cDNA. It specifically downregulated Survivin associated with inactivation of erbB2, erbB3, and Akt. MM-121 enhances paclitaxel-induced poly(ADP-ribose) polymerase (PARP) cleavage, activation of caspase-8 and −3, and apoptosis in both paclitaxel-sensitive and -resistant cells. Specific knockdown of Survivin in the trastuzumab-resistant BT474-HR20 cells dramatically enhanced paclitaxel-induced apoptosis, suggesting that increased Survivin caused a cross-resistance to paclitaxel. Furthermore, the studies using a tumor xenograft model-established from BT474-HR20 cells revealed that either MM-121 (10 mg/kg) or low-dose (7.5 mg/kg) paclitaxel had no effect on tumor growth, their combinations significantly inhibited tumor growth in vivo. Immunohistochemical analysis showed that the combinations of MM-121 and paclitaxel significantly reduced the cells with positive staining for Ki-67 and Survivin, and increased the cells with cleaved caspase-3.

CONCLUSIONS

The combinations of MM-121 and paclitaxel not only inhibit tumor cell proliferation, but also promote erbB2-overexpressing breast cancer cells to undergo apoptosis via downregulation of Survivin in vitro and in vivo, suggesting that inactivation of erbB3 with MM-121 enhances paclitaxel-mediated antitumor activity against erbB2-overexpressing breast cancers. Our data supports further exploration of the combinatorial regimens consisting of MM-121 and paclitaxel in breast cancer patients with erbB2-overexpressing tumors, particularly those resistant to paclitaxel.

In silico model-based inference: a contemporary approach for hypothesis testing in network biology

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics.

A role for the pseudokinase HER3 in the acquired resistance against EGFR- and HER2-directed targeted therapy

Specific inhibition of members of the EGFR (epidermal growth factor receptor) family, particularly EGFR and HER2 (human epidermal growth factor receptor 2), are an important therapeutic strategy in many human cancers. Compared with classical chemotherapy, these targeted therapeutics are very specific and initially effective, but acquired resistance against these targeted therapies is a recurring threat. A growing body of recent work has highlighted a pseudokinase in the EGFR family, HER3, and its ligand, NRG (neuregulin β1), to be of importance in models of resistant cancers, as well as in patients. In the present article, we describe some of the roles in which HER3 can mediate acquired resistance and discuss the current efforts to target HER3 itself in cancer.

A systems biology approach to personalizing therapeutic combinations

The identification of evidence-based, efficacious drug combinations for each cancer, among thousands of potential permutations, is a daunting task. In this perspective, we propose a systematic approach to defining such combinations by molecularly benchmarking a drug against a desired state of efficacy using model systems.

Toward an integrated software platform for systems pharmacology

Understanding complex biological systems requires the extensive support of computational tools. This is particularly true for systems pharmacology, which aims to understand the action of drugs and their interactions in a systems context. Computational models play an important role as they can be viewed as an explicit representation of biological hypotheses to be tested. A series of software and data resources are used for model development, verification and exploration of the possible behaviors of biological systems using the model that may not be possible or not cost effective by experiments. Software platforms play a dominant role in creativity and productivity support and have transformed many industries, techniques that can be applied to biology as well. Establishing an integrated software platform will be the next important step in the field. © 2013 The Authors. Biopharmaceutics & Drug Disposition published by John Wiley & Sons, Ltd.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

In this manuscript, we introduce ODE constrained mixture models for the analysis of population snapshot data of kinetics and dose responses. Population snapshot data can for instance be derived from flow cytometry or single-cell microscopy and provide information about the population structure and the dynamics of subpopulations. Currently available methods enable, however, only the extraction of this information if the subpopulations are very different. By combining pathway-specific ODE and mixture models, a more sensitive method is obtained, which can simultaneously analyse a variety of experimental conditions. ODE constrained mixture models facilitate the reconstruction of subpopulation sizes and dynamics, even in situations where the subpopulations are hardly distinguishable. This is shown for a simulation example as well as for the process of NGF-induced Erk1/2 phosphorylation in primary sensory neurones. We find that the proposed method allows for a simple but pervasive analysis of heterogeneous cell systems and more profound, mechanistic insights.

Regenerative orthopaedics: in vitro, in vivo...in silico

In silico, defined in analogy to in vitro and in vivo as those studies that are performed on a computer, is an essential step in problem-solving and product development in classical engineering fields. The use of in silico models is now slowly easing its way into medicine. In silico models are already used in orthopaedics for the planning of complicated surgeries, personalised implant design and the analysis of gait measurements. However, these in silico models often lack the simulation of the response of the biological system over time. In silico models focusing on the response of the biological systems are in full development. This review starts with an introduction into in silico models of orthopaedic processes. Special attention is paid to the classification of models according to their spatiotemporal scale (gene/protein to population) and the information they were built on (data vs hypotheses). Subsequently, the review focuses on the in silico models used in regenerative orthopaedics research. Contributions of in silico models to an enhanced understanding and optimisation of four key elements-cells, carriers, culture and clinics-are illustrated. Finally, a number of challenges are identified, related to the computational aspects but also to the integration of in silico tools into clinical practice.

Systems analysis of drug-induced receptor tyrosine kinase reprogramming following targeted mono- and combination anti-cancer therapy

The receptor tyrosine kinases (RTKs) are key drivers of cancer progression and targets for drug therapy. A major challenge in anti-RTK treatment is the dependence of drug effectiveness on co-expression of multiple RTKs which defines resistance to single drug therapy. Reprogramming of the RTK network leading to alteration in RTK co-expression in response to drug intervention is a dynamic mechanism of acquired resistance to single drug therapy in many cancers. One route to overcome this resistance is combination therapy. We describe the results of a joint in silico, in vitro, and in vivo investigations on the efficacy of trastuzumab, pertuzumab and their combination to target the HER2 receptors. Computational modelling revealed that these two drugs alone and in combination differentially suppressed RTK network activation depending on RTK co-expression. Analyses of mRNA expression in SKOV3 ovarian tumour xenograft showed up-regulation of HER3 following treatment. Considering this in a computational model revealed that HER3 up-regulation reprograms RTK kinetics from HER2 homodimerisation to HER3/HER2 heterodimerisation. The results showed synergy of the trastuzumab and pertuzumab combination treatment of the HER2 overexpressing tumour can be due to an independence of the combination effect on HER3/HER2 composition when it changes due to drug-induced RTK reprogramming.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

Targeting of erbB3 receptor to overcome resistance in cancer treatment

The erbB receptors, including the epidermal growth factor receptor (EGFR), erbB2 (also known as HER2/neu), erbB3 (or HER3), and erbB4 (or HER4), are often aberrantly activated in a wide variety of human cancers. They are excellent targets for selective anti-cancer therapies because of their transmembrane location and pro-oncogenic activity. While several therapeutic agents against erbB2 and/or EGFR have been used in the treatment of human cancers with efficacy, there has been relatively less emphasis on erbB3 as a molecular target. Elevated expression of erbB3 is frequently observed in various malignancies, where it promotes tumor progression via interactions with other receptor tyrosine kinases (RTKs) due to its lack of or weak intrinsic kinase activity. Studies on the underlying mechanisms implicate erbB3 as a major cause of treatment failure in cancer therapy, mainly through activation of the PI-3 K/Akt, MEK/MAPK, and Jak/Stat signaling pathways as well as Src kinase. It is believed that inhibition of erbB3 signaling may be required to overcome therapeutic resistance and effectively treat cancers. To date, no erbB3-targeted therapy has been approved for cancer treatment. Targeting of erbB3 receptor with a monoclonal antibody (Ab) is the only strategy currently under preclinical study and clinical evaluation. In this review, we focus on the role of erbB3-initiated signaling in the development of cancer drug resistance and discuss the latest advances in identifying therapeutic strategies inactivating erbB3 to overcome the resistance and enhance efficacy of cancer therapeutics.

Approaches to in vitro tissue regeneration with application for human disease modeling and drug development

Reliable in vitro human disease models that capture the complexity of in vivo tissue behaviors are crucial to gain mechanistic insights into human disease and enable the development of treatments that are effective across broad patient populations. The integration of stem cell technologies, tissue engineering, emerging biomaterials strategies and microfabrication processes, as well as computational and systems biology approaches, is enabling new tools to generate reliable in vitro systems to study the molecular basis of human disease and facilitate drug development. In this review, we discuss these recently developed tools and emphasize opportunities and challenges involved in combining these technologies toward regenerative science.