Cancer cells can assume different biological states, which can affect their resistance to therapies. A mesenchymal phenotype has been associated with drug resistance but the mechanism behind this state is not well understood. Stuart Schreiber and colleagues now show that tumour cells with a mesenchymal phenotype are selectively sensitive to inhibition of GPX4, an enzyme that alters lipid metabolism. GPX4 dissipates lipid peroxides and therefore prevents the iron-mediated reactions which induce ferroptotic cell death. These findings offer new perspectives on targeting cancers that have undergone a transition to a mesenchymal state to evade other therapeutic agents.

Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway

Our understanding of the pathogenesis of diseases has advanced enormously in recent decades. As a consequence, drug discovery has gradually shifted from an entirely humanphenotype-based endeavor to today’s reductionist approach centered on single molecular targets. The focus has shifted from the early animal models to isolated proteins via cellular models. This change has led to a decrease in complexity but also to a decrease in relevance to the human condition. In this context, drug research has become (and still is) aimed mainly at the discovery of small molecules able to modulate the biological function of a single protein target thought to be fully responsible for a certain disease. Much effort has been devoted to achieving selectivity for that given target, and indeed, nowadays, many ligands endowed with outstanding in vitro selectivity are available. This one-molecule, one-target paradigm has led to the discovery of many successful drugs, and it will probably remain a milestone for years to come. However, it should be noted that a highly selective ligand for a given target does not always result in a clinically efficacious drug. This may be because (a) the ligand does not recognize the target in vivo, (b) the ligand does not reach the site of action, or (c) the interaction with the respective target does not have enough impact on the diseased system to restore it effectively. Reasons for the latter might lie in both the multifactorial nature of many diseases and the fact that cells can often find ways to compensate for a protein whose activity is affected by a drug, by taking advantage of the redundancy of the system, i.e., of the existence of parallel pathways. Medicinal chemists are often faced with these frustrating aspects of drug research. Drawbacks a and b can be addressed through the well-established rational ligand modification approaches. But issue c is more problematic and needs to be carefully discussed. This is one of the aims of the present article.

https://pubs.acs.org/doi/pdf/10.1021/jm800210c

Multi-Target-Directed Ligands To Combat Neurodegenerative Diseases

Summary: Evolution of RNA viruses occurs through disequilibria of collections of closely related mutant spectra or mutant clouds termed viral quasispecies. Here we review the origin of the quasispecies concept and some biological implications of quasispecies dynamics. Two main aspects are addressed: (i) mutant clouds as reservoirs of phenotypic variants for virus adaptability and (ii) the internal interactions that are established within mutant spectra that render a virus ensemble the unit of selection. The understanding of viruses as quasispecies has led to new antiviral designs, such as lethal mutagenesis, whose aim is to drive viruses toward low fitness values with limited chances of fitness recovery. The impact of quasispecies for three salient human pathogens, human immunodeficiency virus and the hepatitis B and C viruses, is reviewed, with emphasis on antiviral treatment strategies. Finally, extensions of quasispecies to nonviral systems are briefly mentioned to emphasize the broad applicability of quasispecies theory.

Viral Quasispecies Evolution

https://sorger.med.harvard.edu/wordpress/wp-content/uploads/2014/03/Sequential-application-of-anticancer-drugs-enhances-cell-death-by-rewiring-apoptotic-signaling-networks-Lee-Yaffe.pdf

Sequential Application of Anticancer Drugs Enhances Cell Death by Rewiring Apoptotic Signaling Networks

Monoclonal antibodies are widely used for the treatment of cancer, inflammatory and infectious diseases and other disorders. Most of the marketed antibodies are monospecific and therefore capable of interacting and interfering with a single target. However, complex diseases are often multifactorial in nature, and involve redundant or synergistic action of disease mediators or upregulation of different receptors, including crosstalk between their signaling networks. Consequently, blockade of multiple, different pathological factors and pathways may result in improved therapeutic efficacy. This result can be achieved by combining different drugs, or use of the dual targeting strategies applying bispecific antibodies that have emerged as an alternative to combination therapy. This review discusses the various dual targeting strategies for which bispecific antibodies have been developed and provides an overview of the established bispecific antibody formats.

https://www.tandfonline.com/doi/pdf/10.4161/mabs.4.2.19000

Dual targeting strategies with bispecific antibodies

Combinatorial control of biological processes, in which redundancy and multifunctionality are the norm, fundamentally limits the therapeutic index that can be achieved by even the most potent and highly selective drugs. Thus, it will almost certainly be necessary to use new 'targeted' pharmaceuticals in combinations. Multicomponent drugs are standard in cytotoxic chemotherapy, but their development has required arduous empirical testing. However, experimentally validated numerical models should greatly aid in the formulation of new combination therapies, particularly those tailored to the needs of specific patients. This perspective focuses on opportunities and challenges inherent in the application of mathematical modeling and systems approaches to pharmacology, specifically with respect to the idea of achieving combinatorial selectivity through use of multicomponent drugs.

/pdf/systems-biology-and-combination-therapy-in-the-quest-for-1qojvun9fs.pdf

Systems biology and combination therapy in the quest for clinical efficacy.

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.

A Data-Driven Computational Model of the ErbB Receptor Signaling Network

#4166 ErbB3 has been identified as a preferred dimerization partner of ErbB2, critical for driving the proliferation of ErbB2 over-expressing breast tumors We have designed a bispecific antibody, MM-111, which inhibits ligand-induced phosphorylation of ErbB3 with sub-nanomolar potency by exploiting the abundant expression of its dimerization partner, ErbB2, for specific targeting to cancer cells that express both receptors We employed computational physicochemical modeling to guide the kinetic optimization of the monovalent binding affinities to the ErbB2 and ErbB3 receptors to increase the potency and specificity of MM-111 for tumor cells We have demonstrated that MM-111 inhibits activation of the phosphatidylinositol 3-kinase pathway in vitro and in vivo, resulting in attenuation of tumor proliferation Inhibition of growth by MM-111 has been observed in several murine xenograft models including BT474 and MDA-MB-361 breast tumors While the antitumor activity of MM-111 is positively correlated with ErbB2 expression levels, MM-1119s potent inhibition of ErbB3 phosphorylation and signaling downstream from this receptor differs markedly from currently available therapies targeting ErbB2 over-expressing breast tumors and thus provides a novel approach to treatment for these malignancies In conclusion, our data demonstrate that the combination of computational biology with antibody engineering has resulted in the development of a promising, novel therapeutic, MM-111, that has potent antitumor activity in malignancies driven by the ErbB2/3 oncogenic unit Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4166

MM-111: a novel bispecific antibody targeting ErbB3 with potent anti-tumor activity in ErbB2 over-expressing malignancies.

Currently, clinicians use the levels of the hormone receptors for estrogen (ER) and progesterone (PR) and amplifications of the ErbB2 receptor tyrosine kinase (RTK) to distinguish three groups of breast cancers and guide the choice of therapeutic treatments. In this work we show that a limited number of measurements characterizing breast cancer cell lines at the level of RTKs can be used to predict cell line sensitivity to therapeutic inhibitors. We chose a panel of approximately 40 breast cancer cell lines, treated them with 22 different growth factors and cytokines at a saturating and subsaturating dose for 10, 30, and 90 min and measured the phosphorylation of key kinases downstream of the receptors. In addition, we measured the expression and phosphorylation levels of approximately 20 RTKs under serum starvation conditions. We used the measurements to predict the GI50 values of the cell lines in response to over 70 therapeutic inhibitors by partial least square regression. We found that our measurements are sufficient to predict the response to about half of the drugs studied. The majority of drugs we are able to predict target RTKs directly or kinases immediately downstream of the receptors. In some cases we are able to predict the response to drugs that target processes seemingly unrelated to our measurements. We are currently exploring the data to uncover what drives sensitivity and resistance to drugs, gain mechanistic insight in the action of cancer therapeutics, and find predictors of drug response that may be useful in a clinical setting. Our results suggest that a carefully selected set of measurements might be sufficient to classify responsiveness of breast cancers to a number of cancer drugs currently used in the clinic. This is particularly relevant in the case of cancers like triple-negative breast cancers for which choosing effective treatment has proven difficult. This proffered talk is also presented as Poster A17.This proffered talk is also presented as Poster A17. Citation Format: Mario Niepel, Marc Hafner, Emily Pace, Birgit Schoeberl, Peter K. Sorger. The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR5.

Birgit Schoeberl

Papers

Systems biology and combination therapy in the quest for clinical efficacy.

A Data-Driven Computational Model of the ErbB Receptor Signaling Network

MM-111: a novel bispecific antibody targeting ErbB3 with potent anti-tumor activity in ErbB2 over-expressing malignancies.

Abstract PR5: The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs