Therapeutic Ligands Antagonize Estrogen Receptor Function by Impairing Its Mobility.

Estrogen receptor-positive (ER+) breast cancers frequently remain dependent on ER signaling even after acquiring resistance to endocrine agents, prompting the development of optimized ER antagonists. Fulvestrant is unique among approved ER therapeutics due to its capacity for full ER antagonism, thought to be achieved through ER degradation. The clinical potential of fulvestrant is limited by poor physicochemical features, spurring attempts to generate ER degraders with improved drug-like properties. We show that optimization of ER degradation does not guarantee full ER antagonism in breast cancer cells; ER "degraders" exhibit a spectrum of transcriptional activities and anti-proliferative potential. Mechanistically, we find that fulvestrant-like antagonists suppress ER transcriptional activity not by ER elimination, but by markedly slowing the intra-nuclear mobility of ER. Increased ER turnover occurs as a consequence of ER immobilization. These findings provide proof-of-concept that small molecule perturbation of transcription factor mobility may enable therapeutic targeting of this challenging target class.

Systematic pharmacological analysis of agonistic and antagonistic fibroblast growth factor receptor 1 MAbs reveals a similar unique mode of action.

Fibroblast growth factor receptor 1 (FGFR1) is a receptor tyrosine kinase that plays a major role in developmental processes and metabolism. The dysregulation of FGFR1 through genetic aberrations leads to skeletal and metabolic diseases as well as cancer. For this reason, FGFR1 is a promising therapeutic target, yet a very challenging one due to potential on-target toxicity. More puzzling is that both agonistic and antagonistic FGFR1 antibodies are reported to exhibit similar toxicity profiles in vivo, namely weight loss. In this study, we aimed to assess and compare the mechanism of action of these molecules to better understand this apparent contradiction. By systematically comparing the binding of these antibodies and the activation or the inhibition of the major FGFR1 signaling events, we demonstrated that the molecules displayed similar properties and can behave either as an agonist or antagonist depending on the presence or the absence of the endogenous ligand. We further demonstrated that these findings translated in xenografts mice models. In addition, using time-resolved FRET and mass spectrometry analysis, we showed a functionally distinct FGFR1 active conformation in the presence of an antibody that preferentially activates the FGFR substrate 2 (FRS2)-dependent signaling pathway, demonstrating that modulating the geometry of a FGFR1 dimer can effectively change the signaling outputs and ultimately the activity of the molecule in preclinical studies. Altogether, our results highlighted how bivalent antibodies can exhibit both agonistic and antagonistic activities and have implications for targeting other receptor tyrosine kinases with antibodies.

heredERA Breast Cancer: a phase III, randomized, open-label study evaluating the efficacy and safety of giredestrant plus the fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with previously untreated HER2-positive, estrogen receptor-positive locally advanced or metastatic breast cancer.

BACKGROUND: HER2-positive, estrogen receptor-positive breast cancer (HER2+, ER+ BC) is a distinct disease subtype associated with inferior response to chemotherapy plus HER2-targeted therapy compared with HER2+, ER-negative BC. Bi-directional crosstalk leads to cooperation of the HER2 and ER pathways that may drive treatment resistance; thus, simultaneous co-targeting may optimize treatment impact and survival outcomes in patients with HER2+, ER+ BC. First-line (1L) treatment for patients with HER2+ metastatic BC (mBC) is pertuzumab, trastuzumab, and taxane chemotherapy. In clinical practice, dual HER2 blockade plus a fixed number of chemotherapy cycles are given as induction therapy to maximize tumor response, with subsequent HER2-targeted maintenance treatment given as a more tolerable regimen for long-term disease control. For patients whose tumors co-express ER, maintenance endocrine therapy (ET) can be added, but uptake varies due to lack of data from randomized clinical trials investigating the superiority of maintenance ET plus dual HER2 blockade versus dual HER2 blockade alone. Giredestrant, a novel oral selective ER antagonist and degrader, shows promising clinical activity and manageable safety across phase I-II trials of patients with ER+, HER2-negative BC, with therapeutic potential in those with HER2 co-expression. METHODS: This phase III, randomized, open-label, two-arm study aims to recruit 812 patients with HER2+, ER+ locally advanced (LA)/mBC into the induction phase (fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] plus a taxane) to enable 730 patients to be randomized 1:1 to the maintenance phase (giredestrant plus PH FDC SC or PH FDC SC [plus optional ET]), stratified by disease site (visceral versus non-visceral), type of LA/metastatic presentation (de novo versus recurrent), best overall response to induction therapy (partial/complete response versus stable disease), and intent to give ET (yes versus no). The primary endpoint is investigator-assessed progression-free survival. Secondary endpoints include overall survival, objective response rate, clinical benefit rate, duration of response, safety, and patient-reported outcomes. DISCUSSION: heredERA BC will address whether giredestrant plus dual HER2 blockade is superior to dual HER2 blockade alone, to inform the use of this combination in clinical practice for maintenance 1L treatment of patients with HER2+, ER+ LA/mBC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05296798; registered on March 25, 2022. Protocol version 3.0 (November 18, 2022). SPONSOR: F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124 4070, Basel, Switzerland.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug-response analysis such as tissue distribution of dose-response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug-response phenotypes of cancer models.

Evaluation of statistical approaches for association testing in noisy drug screening data.

BACKGROUND: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

A multitargeted probe-based strategy to identify signaling vulnerabilities in cancers.

Most cancer cells are dependent on a network of deregulated signaling pathways for survival and are insensitive, or rapidly evolve resistance, to selective inhibitors aimed at a single target. For these reasons, drugs that target more than one protein (polypharmacology) can be clinically advantageous. The discovery of useful polypharmacology remains serendipitous and is challenging to characterize and validate. In this study, we developed a non-genetic strategy for the identification of pathways that drive cancer cell proliferation and represent exploitable signaling vulnerabilities. Our approach is based on using a multitargeted kinase inhibitor, SM1-71, as a tool compound to identify combinations of targets whose simultaneous inhibition elicits a potent cytotoxic effect. As a proof of concept, we applied this approach to a KRAS-dependent non-small cell lung cancer (NSCLC) cell line, H23-KRASG12C Using a combination of phenotypic screens, signaling analyses, and kinase inhibitors, we found that dual inhibition of MEK1/2 and insulin-like growth factor 1 receptor (IGF1R)/insulin receptor (INSR) is critical for blocking proliferation in cells. Our work supports the value of multitargeted tool compounds with well-validated polypharmacology and target space as tools to discover kinase dependences in cancer. We propose that the strategy described here is complementary to existing genetics-based approaches, generalizable to other systems, and enabling for future mechanistic and translational studies of polypharmacology in the context of signaling vulnerabilities in cancers.

Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs.

Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.

Design of a brain-penetrant CDK4/6 inhibitor for glioblastoma.

CDK4 and CDK6 are kinases with similar sequences that regulate cell cycle progression and are validated targets in the treatment of cancer. Glioblastoma is characterized by a high frequency of CDKN2A/CCND2/CDK4/CDK6 pathway dysregulation, making dual inhibition of CDK4 and CDK6 an attractive therapeutic approach for this disease. Abemaciclib, ribociclib, and palbociclib are approved CDK4/6 inhibitors for the treatment of HR+/HER2- breast cancer, but these drugs are not expected to show strong activity in brain tumors due to poor blood brain barrier penetration. Herein, we report the identification of a brain-penetrant CDK4/6 inhibitor derived from a literature molecule with low molecular weight and topological polar surface area (MW = 285 and TPSA = 66 Å2), but lacking the CDK2/1 selectivity profile due to the absence of a basic amine. Removal of a hydrogen bond donor via cyclization of the pyrazole allowed for the introduction of basic and semi-basic amines, while maintaining in many cases efflux ratios reasonable for a CNS program. Ultimately, a basic spiroazetidine (cpKa = 8.8) was identified that afforded acceptable selectivity over anti-target CDK1 while maintaining brain-penetration in vivo (mouse Kp,uu = 0.20-0.59). To probe the potency and selectivity, our lead compound was evaluated in a panel of glioblastoma cell lines. Potency comparable to abemaciclib was observed in Rb-wild type lines U87MG, DBTRG-05MG, A172, and T98G, while Rb-deficient cell lines SF539 and M059J exhibited a lack of sensitivity.

GRcalculator: an online tool for calculating and mining dose-response data.

BACKGROUND: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose-response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521-627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose-response assays and the reliability of pharmacogenomic associations (Hafner et al. 500-502, 2017). RESULTS: We describe here an interactive website ( www.grcalculator.org ) for calculation, analysis, and visualization of dose-response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics . CONCLUSIONS: GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose-response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose-response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program ( http://www.lincsproject.org /) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose-response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.

A Quantitative Approach to Screen for Nephrotoxic Compounds In Vitro.

Nephrotoxicity due to drugs and environmental chemicals accounts for significant patient mortality and morbidity, but there is no high throughput in vitro method for predictive nephrotoxicity assessment. We show that primary human proximal tubular epithelial cells (HPTECs) possess characteristics of differentiated epithelial cells rendering them desirable to use in such in vitro systems. To identify a reliable biomarker of nephrotoxicity, we conducted multiplexed gene expression profiling of HPTECs after exposure to six different concentrations of nine human nephrotoxicants. Only overexpression of the gene encoding heme oxygenase-1 (HO-1) significantly correlated with increasing dose for six of the compounds, and significant HO-1 protein deregulation was confirmed with each of the nine nephrotoxicants. Translatability of HO-1 increase across species and platforms was demonstrated by computationally mining two large rat toxicogenomic databases for kidney tubular toxicity and by observing a significant increase in HO-1 after toxicity using an ex vivo three-dimensional microphysiologic system (kidney-on-a-chip). The predictive potential of HO-1 was tested using an additional panel of 39 mechanistically distinct nephrotoxic compounds. Although HO-1 performed better (area under the curve receiver-operator characteristic curve [AUC-ROC]=0.89) than traditional endpoints of cell viability (AUC-ROC for ATP=0.78; AUC-ROC for cell count=0.88), the combination of HO-1 and cell count further improved the predictive ability (AUC-ROC=0.92). We also developed and optimized a homogenous time-resolved fluorescence assay to allow high throughput quantitative screening of nephrotoxic compounds using HO-1 as a sensitive biomarker. This cell-based approach may facilitate rapid assessment of potential nephrotoxic therapeutics and environmental chemicals.

Fractional killing arises from cell-to-cell variability in overcoming a caspase activity threshold.

When cells are exposed to death ligands such as TRAIL, a fraction undergoes apoptosis and a fraction survives; if surviving cells are re-exposed to TRAIL, fractional killing is once again observed. Therapeutic antibodies directed against TRAIL receptors also cause fractional killing, even at saturating concentrations, limiting their effectiveness. Fractional killing arises from cell-to-cell fluctuations in protein levels (extrinsic noise), but how this results in a clean bifurcation between life and death remains unclear. In this paper, we identify a threshold in the rate and timing of initiator caspase activation that distinguishes cells that live from those that die; by mapping this threshold, we can predict fractional killing of cells exposed to natural and synthetic agonists alone or in combination with sensitizing drugs such as bortezomib. A phenomenological model of the threshold also quantifies the contributions of two resistance genes (c-FLIP and Bcl-2), providing new insight into the control of cell fate by opposing pro-death and pro-survival proteins and suggesting new criteria for evaluating the efficacy of therapeutic TRAIL receptor agonists.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

Analysis of growth factor signaling in genetically diverse breast cancer lines.

BACKGROUND: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines. RESULTS: We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways. CONCLUSIONS: Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an "indirect negative regulation" by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.

UGT1A4 Polymorphism is not Associated with a Clinically Relevant Change in Giredestrant Exposure.

PURPOSE: Giredestrant is a potent, orally bioavailable, small-molecule selective estrogen receptor antagonist and degrader (SERD) that is being developed for the treatment of patients with estrogen receptor (ER)-positive breast cancer. In vitro, giredestrant was primarily metabolized by UGT1A4. The goal of this study was to investigate if UGT1A4 polymorphism had a clinically relevant impact on giredestrant exposure. METHODS: Genotyping and pharmacokinetic data were obtained from 118 and 61 patients in two clinical studies, GO39932 [NCT03332797] and acelERA Breast Cancer [NCT04576455], respectively. RESULTS: The overall allelic frequencies of UGT1A4*2 and UGT1A4*3 were 3.3% and 11%, respectively. Giredestrant exposure was consistent between patients with wild-type UGT1A4 and UGT1A4*2 and *3 polymorphisms, with no clinically relevant difference observed. In addition, haplotype analysis indicated that no other UGT1A4 variants were significantly associated with giredestrant exposure. CONCLUSION: Therefore, this study indicates that UGT1A4 polymorphism status is unlikely a clinically relevant factor to impact giredestrant exposure and giredestrant can be administered at the same dose level regardless of patients' UGT1A4 polymorphism status.

CDK4/6i-treated HR+/HER2- breast cancer tumors show higher ESR1 mutation prevalence and more altered genomic landscape.

As CDK4/6 inhibitor (CDK4/6i) approval changed treatment strategies for patients with hormone receptor-positive HER2-negative (HR+/HER2-) breast cancer (BC), understanding how exposure to CDK4/6i affects the tumor genomic landscape is critical for precision oncology. Using real-world data (RWD) with tumor genomic profiling from 5910 patients with metastatic HR+/HER2- BC, we investigated the evolution of alteration prevalence in commonly mutated genes across patient journeys. We found that ESR1 is more often altered in tumors exposed to at least 1 year of adjuvant endocrine therapy, contrasting with TP53 alterations. We observed a similar trend after first-line treatments in the advanced setting, but strikingly exposure to aromatase inhibitors (AI) combined with CDK4/6i led to significantly higher ESR1 alteration prevalence compared to AI alone, independent of treatment duration. Further, CDK4/6i exposure was associated with higher occurrence of concomitant alterations in multiple oncogenic pathways. Differences based on CDK4/6i exposure were confirmed in samples collected after 2L and validated in samples from the acelERA BC clinical trial. In conclusion, our work uncovers opportunities for further treatment personalization and stresses the need for effective combination treatments to address the altered tumor genomic landscape following AI+CDK4/6i exposure. Further, we demonstrated the potential of RWD for refining patient treatment strategy and guiding clinical trial design.

CRISPR activation screens identify the SWI/SNF ATPases as suppressors of ferroptosis.

Ferroptosis is an iron-dependent cell death mechanism characterized by the accumulation of toxic lipid peroxides and cell membrane rupture. GPX4 (glutathione peroxidase 4) prevents ferroptosis by reducing these lipid peroxides into lipid alcohols. Ferroptosis induction by GPX4 inhibition has emerged as a vulnerability of cancer cells, highlighting the need to identify ferroptosis regulators that may be exploited therapeutically. Through genome-wide CRISPR activation screens, we identify the SWI/SNF (switch/sucrose non-fermentable) ATPases BRM (SMARCA2) and BRG1 (SMARCA4) as ferroptosis suppressors. Mechanistically, they bind to and increase chromatin accessibility at NRF2 target loci, thus boosting NRF2 transcriptional output to counter lipid peroxidation and confer resistance to GPX4 inhibition. We further demonstrate that the BRM/BRG1 ferroptosis connection can be leveraged to enhance the paralog dependency of BRG1 mutant cancer cells on BRM. Our data reveal ferroptosis induction as a potential avenue for broadening the efficacy of BRM degraders/inhibitors and define a specific genetic context for exploiting GPX4 dependency.

Mapping behavioral specifications to model parameters in synthetic biology.

With recent improvements of protocols for the assembly of transcriptional parts, synthetic biological devices can now more reliably be assembled according to a given design. The standardization of parts open up the way for in silico design tools that improve the construct and optimize devices with respect to given formal design specifications. The simplest such optimization is the selection of kinetic parameters and protein abundances such that the specified design constraints are robustly satisfied. In this work we address the problem of determining parameter values that fulfill specifications expressed in terms of a functional on the trajectories of a dynamical model. We solve this inverse problem by linearizing the forward operator that maps parameter sets to specifications, and then inverting it locally. This approach has two advantages over brute-force random sampling. First, the linearization approach allows us to map back intervals instead of points and second, every obtained value in the parameter region is satisfying the specifications by construction. The method is general and can hence be incorporated in a pipeline for the rational forward design of arbitrary devices in synthetic biology.

Effect of network architecture on synchronization and entrainment properties of the circadian oscillations in the suprachiasmatic nucleus.

In mammals, the suprachiasmatic nucleus (SCN) of the hypothalamus constitutes the central circadian pacemaker. The SCN receives light signals from the retina and controls peripheral circadian clocks (located in the cortex, the pineal gland, the liver, the kidney, the heart, etc.). This hierarchical organization of the circadian system ensures the proper timing of physiological processes. In each SCN neuron, interconnected transcriptional and translational feedback loops enable the circadian expression of the clock genes. Although all the neurons have the same genotype, the oscillations of individual cells are highly heterogeneous in dispersed cell culture: many cells present damped oscillations and the period of the oscillations varies from cell to cell. In addition, the neurotransmitters that ensure the intercellular coupling, and thereby the synchronization of the cellular rhythms, differ between the two main regions of the SCN. In this work, a mathematical model that accounts for this heterogeneous organization of the SCN is presented and used to study the implication of the SCN network topology on synchronization and entrainment properties. The results show that oscillations with larger amplitude can be obtained with scale-free networks, in contrast to random and local connections. Networks with the small-world property such as the scale-free networks used in this work can adapt faster to a delay or advance in the light/dark cycle (jet lag). Interestingly a certain level of cellular heterogeneity is not detrimental to synchronization performances, but on the contrary helps resynchronization after jet lag. When coupling two networks with different topologies that mimic the two regions of the SCN, efficient filtering of pulse-like perturbations in the entrainment pattern is observed. These results suggest that the complex and heterogeneous architecture of the SCN decreases the sensitivity of the network to short entrainment perturbations while, at the same time, improving its adaptation abilities to long term changes.

Modular design of artificial tissue homeostasis: robust control through synthetic cellular heterogeneity.

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of ß-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.

Proteomic profiling across breast cancer cell lines and models.

We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.