Peter Sorger - US grants

DESCRIPTION (provided by applicant): Kinetochores are multi-protein complexes that assemble on centromeric (CEN) DMA and perform three essential functions in chromosome segregation (i) they bind paired sister chromatids to spindle microtubules (MTs) in a bipolar fashion compatible with disjunction at anaphase (ii) they move chromosomes back and forth along spindle MTs by coupling plus-end dynamics to chromosome movement during metaphase and anaphase (iii) they generate the spindle checkpoint signal that links anaphase onset to the prior completion of chromosome-MT attachment. The long-term goals of this study are to understand these three kinetochore functions in precise molecular terms. Biochemical, genetic and imaging experiments will be performed primarily in the budding yeast S. cerevisiae, an organism whose kinetochores are the simplest known, but key conclusions will also be confirmed in human tissue culture cells. : Aim 1. A key inner kinetochore complex comprising CEN DMA, sequence-specific DMA binding proteins, a specialized histone H3 (CenHS) and associated subunits will be reconstituted in vitro and subjected to detailed functional and biophysical analysis. Aim 2. The role of the inner kinetochore in organizing and assembling a "linker" layer comprising the NdcSO, COMA, MIND and Spc105 complexes will be studied in vitro and in vivo Aim 3. Kinetochore subassemblies active in MT attachment, and comprising DMA-binding and linker proteins in combination with one or more MAPs or motors, will be reconstituted in vitro for detailed analysis of force-generating activities. Aim 4. The role of human homologues of yeast linker proteins will be examined using RNAi-based protein depletion, live-cell imaging and biochemical fractionation. Aim 5. An on-line resource will be created to disseminate information on kinetochore functions, assays and phylogeny (a data sharing resource). The significance of these experiments derives from their aim of providing a functional and mechanistic understanding of a particularly simple kinetochore and of using this information to inform the analysis of kinetochores in human cells. Errors in kinetochore assembly cause chromosome instability (CIN) a nearly universal property of human tumor cells. The origins of CIN, and its precise role in tumor progression remain unknown, but functional studies represent one the best way to understand the origins of CIN and to determine whether its appearance in cancer cells is linked to tumorigenic potential

DESCRIPTION (provided by applicant): The accurate transmission of genetic information during the cell cycle is dependent on the correct operation of a spindle assembly checkpoint the monitors the state of chromosome-microtubule attachment. Signals generated at kinetochores and transmitted via a transduction system comprised of Mad and Bub proteins, act to regulate the activity of the anaphase promoting complex (APC) thereby linking cell cycle progression to the correct execution of the mechanical events of mitosis. The goal of this work is to probe the mechanisms of checkpoint gene activation with the goal of defining the upstream events that initiate checkpoint signaling and the downstream events that control APC. In addition, the role played by checkpoint lesions in genomic instability in mice will be examined through the use of conditional alleles in two checkpoint genes. Specifically: (1) The mechanism by which p53-loss rescues the viability of cells lacking a spindle checkpoint will be analyzed in vitro and in vivo (2) The mechanism of action of a recently discovered negative regulator of Mad2 (CMT2) will be analyzed in detail to uncover key aspects of Mad2-mediated checkpoint signaling. (3) The proteins responsible for recruiting Mad and Bub proteins to kinetochores will be studied, with particular emphasis on the members of the Ndc80 complex (4) Bub1, a kinase with dual functions in checkpoint control and chromosome-microtubule attachment will be analyzed using specific mutations that abrogate various functions (5) The potential role of checkpoint lesions in generating chromosome instability and in promoting cancer will be examined in mice using conditional alleles and mouse model off lung cancer development. The molecular analysis of checkpoints will have an impact on two aspects of cancer biology. First, it should help to reveal the mechanism off action of important anti-microtubule chemotherapeutics such as taxol and the vinca alkaloids. These compounds provoke the spindle checkpoint, and lesions in the checkpoint are very likely to alter the effectiveness of these drugs in the clinic. Second, careful study of the spindle checkpoint should clarify the role of chromosome instability on tumor development.

[unreadable] DESCRIPTION (provided by applicant): The goals of this study are to determine how S. cerevisiae kinetochores bind to microtubules and how the forces required for chromosome movement are generated. This will be accomplished by linking systematic experiments to quantitative analysis. Stochastic models of yeast kinetochores and their ten known MAPs and motors will be generated using automated procedures and then calibrated to live-cell data using the method of indirect inference. Chromosome segregation is the process by which replicated DNA molecules are pulled into daughter cells by attaching to and moving along the microtubules of the mitotic spindle. Chromosome-microtubule attachment is mediated by kinetochores, multi-component protein complexes that form on centromeric DNA. S. cerevisiae is an attractive organism in which to study kinetochores because it contains the simplest of all known centromeres and because its molecular genetics is uniquely powerful. The conservation of kinetochore proteins from yeast to man suggests that principles learned from the study of simpler structures in yeast will be directly applicable to higher cells and to the genomic instability which aberrant kinetochore function causes. Aim 1: For kinetochore gene mutations, autoregressive moving average (ARMA) modeling and fuzzy clustering will quantify phenotypic similarity and heterogeneity. Aim 2: The role of tension in controlling kinetochore-microtubule interaction will be explored genetically and with ARMA having an input for exogenous variables (ARMAX) Aim 3: Stochastic models of kinetochores based on biophysical prior knowledge will be evaluated and calibrated to single-cell data using indirect inference and ARMA descriptors as a source of auxiliary parameters.We propose to study the cellular machinery responsible for dividing chromosomes, the DNA and protein structures that encode and preserve the genomel, into two equal sets upon cell division. Normally this process of "segregation" is very accurate, but it goes awry in cancer and gives rise to the genetic plasticity that makes long-term treatment of cancer problematic. [unreadable] [unreadable] [unreadable] [unreadable]

The overall goal of this project, led by Prof. Peter Sorger, isto delineate, in precise molecular terms, the mechanisms that regulate the onset of apoptosis in mammalian cells following exposure to small molecule and biological therapeutics. Variation from one cell type to the next and one cell to the next will be an area of particular focus, with the eventual goal of developing means to predict patient-specific responses to therapy. As a means to create mechanistic, probabilistic, integrative and predictive understanding of apoptosis we will collect population-level and single-cell data on caspase activation kinetics and construct, calibrate and analyze a series of mathematical models that describe key steps in mammalian cell death. We will focus largely on existing and investigational anti-cancer drugs, but also expect to examine agents that alter inflammatory and immune responses. Our selection of therapeutic agents is guided by (i) the importance of conventional agents in standard of care cancer treatment and the possibility of developing improved clinical protocols (e.g. taxanes) (ii) the extent of patient-patient variation in drug response and the attendant difficulty of identifying patients who might benefit from a particular treatment (iii) the potential of new agents to significantly improve outcomes as demonstrated by pre-clinical and clinical studies (e.g. ABT-737). Four specific aims will be pursued involving (1) predictive and mechanistic analysis of pathways controlling mitochondrial outer membrane permeablization and effector caspase activation in cells exposed to ligands that trigger extrinisic apoptosis (2) direct comparison of cell-to-cell variation in the timing and probability of cell death among members of a clonal cell population and between different tumor cell lines (3) experimental and model-driven analysis of intrinsic apoptosis induced by the microtubule poison paclitaxel and the Bcl2 inhibitor ABT737 and single-cell analysis of chemotherapeutics used in combination on diverse cancer cell lines (4) development and application of methods for intravital imaging of mitosis and apoptosis in cancer in situ in the mouse. Success with these aims will impact not only the study of apoptosis, but also general cancer biology and the use of chemotherapeutic drugs

DESCRIPTION (provided by applicant): Project Summary/Abstract We propose to purchase and operate a high performance storage cluster as a shared resource for investigators at multiple institutions in the Boston area. The storage cluster will be customized to the needs of a growing community of microscopists involved in high throughput live and fixed-cell imaging, cell-based screening ("high content screening") and automated image analysis ("machine vision"). Specific projects include creation of a "digital fish" that captures in electronic form the development history of Zebrafish, rapid cell-based analysis of signal transduction circuits in normal and transformed cells, speckle microscopy of the tubulin and actin cytoskeleton and large-scale RNAi screening for new genes involved in fly development. The major users of the proposed storage cluster currently rely on a general purpose, network attached storage provided by HMS IT that lacks the capacity and performance needed for next-generation imaging projects. The existing infrastructure, consisting of EMC Celerra NAS has reached its absolute design limits and is too cumbersome and expensive to expand. In contrast, the proposed 120 TB IQ12000x-EX Storage Cluster (from Isilon Inc, Seattle WA) represents the ideal tradeoff between sophistication and cost, and is expandable to >3 petabytes. The Isilon achieves its low cost and high performance by linking standard hardware to proprietary, highly optimized operating systems and software stacks that perform the funcitons of a filesystem, volume manager, and RAID manager. The advanced features in the Isilon cluster will make it possible for users at multiple locations to store and manage image data within an intuitive and seamless user environment. The remarkable simplicity of the product will also ease the existing burden on staff scientists and storage managers in HMS IT. The overall benefit of the proposed Isilon cluster is that it will address an acute requirement for more disc-based computer storage among major users, increase dramatically the ability of these users to manage the space that is available and support a growing community of new users. Data storage and processing are increasingly critical for innovation in optical microscopy and image analysis. The scientific rationale for the Isilon Cluster is therefore strong and the need immediate. Moreover, the machine is designed and manufactured in the United States by an industry that is currently experiencing rapidly falling demand;thus, we will help to sustain a US- based employment in a sector of the economy that is critical to the future of scientific computing. PUBLIC HEALTH RELEVANCE: Large scale live and fixed cell microscopy is playing an increasingly important role in cell biology, gene identification and drug discovery. Next generation imaging projects are as dependent on the computer systems that manage and process digital data as they are on the microscopes used to collect data. The sophisticated, expandable and US-manufactured clustered computer storage system requested in this proposal is necessary for continued innovation in the laboratories of a user group known nationally for its expertise in microscopy, and to meet the growing needs of a large community of investigators new to digital microscopy.

DESCRIPTION (provided by applicant): The overall goal of the HMS LINCS Center is to delineate the fundamental principles of cellular response to perturbagens at the level of single-cells and cell populations and to make response data routinely available via web-based browse, query and programmatic interfaces. This will involve the development and testing of new measurement methods, computational algorithms, and response signatures for diverse human cell types exposed to perturbations individually and in combination. We will emphasize the systematic collection of data not currently available in existing public databases including live and fixed-cell imaging, biochemical data on signaling proteins and multi-factorial drug-response phenotypes. A focus on diverse transformed and primary cells, including those derived from healthy and diseased donors, and on clinical grade small molecules (kinase inhibitors and epigenome drugs) will increase the translational impact of our work. The proposed LINCS Center represents a continuation of a program in operation for ~3.5 years under a LINCS pilot phase U54 award. We will expand the scope and sophistication of our Center, devoting significant effort to (i) improving the quality of data analysis and visualization, particularly wih respect to the complexities of perturbagen polypharmacy (ii) accelerating the release of perturbagen-response signatures using methods that have been demonstrated to yield reliable and informative data, with a particular emphasis on primary and non-transformed cells (iii) developing and applying new measurement methods, particularly mass spectrometry for analysis of cell populations and live-cell imaging for analysis of single cells. The work will involve nine complementary but independent aims. In Data Generation, Aim 1 will perform systematic analysis of perturbagen responses at a single-cell level. Aim 2 will collect multiplex protein and mRNA data on perturbagen response using a set of complementary imaging, mass spectrometry and bead-based assays. Aim 3 will apply LINCS methods to non-transformed immune cells and induced pluripotent stem cells, and explore if signatures can guide a detailed medicinal chemistry campaign. In Data Analysis, Aim 4 will construct perturbagen-response signatures using statistical modeling, network inference and machine learning methods. Aim 5 will develop new approaches to understanding and analyzing drug interactions on multiple phenotypes in single cells. Aim 6 will develop a novel compressed sensing framework for analyzing the poly-pharmacology of kinase inhibitors. Aim 7 will enhance the query, browse and explore functions of the HMS LINCS website and database and its integration with the UCSC Genome Browser. In Community Interaction and Outreach, Aim 8 will implement diverse training and outreach activities, including collaboration with LINCS and non-LINCS research groups. In Administration, Aim 9 will ensure effective management of the Center, sustained access to tools and data produced within the LINCS Project, and compliance with program goals.

PROJECT SUMMARY Systems and computational biologists, physician-scientists, pharmacologists, biochemists and cell biologists will collaborate in a new Laboratory of Systems Pharmacology (LSP) to apply a measure-model approach to understanding the mechanisms of action of therapeutic drugs in multiple disease areas. The LSP will pioneer the development of quantitative network-centric approaches to pharmacology and toxicology that include analysis of dose-time-response relationships at a single-cell level, modeling of cellular network dynamics and their perturbation by drugs, development of pharmacokinetic and pharmacodynamic models in mouse and ultimately in man and use of systematic approaches to identify and qualify new drug targets. Our approach combines mathematical modeling with empirical measurement (including work with clinical samples and data) and aims to create quantitative and predictive drug response models at different temporal and physical scales. We aim to reinvigorate pharmacology and toxicology as foundational disciplines of translational medicine, develop the conceptual underpinning for personalized and precision medicine and lower the cost of drug discovery by improving its predictability. Close interactions with industry and the FDA will help address the productivity gap in drug discovery and development. The LSP is innovative with respect to its goal of using problems in basic and translational pharmacology to link three disciplines (cell and molecular biology, computational biology and medicine) and its aim of advancing therapeutic science in the Boston area and beyond. Students and postdocs supervised by 18 faculty members, 2 independent postdoctoral fellows and two PhD-level staff from 7 institutions will work in immediate proximity in a new custom-designed laboratory. The lab will host a new graduate program in therapeutics and multi-factored outreach activities that will promote systems pharmacology internationally. These goals will be achieved through four inter-linked research programs (Aims 1-4), a core dedicated to efficient translation of LSP research (Aim 5), an education core and administrative/outreach activities (Aims 6-8). Aim 1 will focus on the determinants of dose-response at a single-cell level, including the role of cell-to-cell variability in fractional response and of timing and order-of-exposure in combination therapy. Aim 2 will take a network-level approach to understanding therapeutic, toxic and paradoxical drug responses by kinase inhibitors in three types of cancer. The mechanistic basis and consequences of poly-pharmacology will also be examined along with differential drug responsiveness by normal and diseased tissues. Aim 3 will address PK-PD by developing multi-scale models of drug actions at the level of cells, tissues and organisms and new methods for measuring drug distribution at the cellular and subcellular levels. Aim 4 will apply machine learning and causal reasoning to target discovery from clinical records in asthma, inflammatory disease and fibrosis and pursue a structure-guided approach to identifying regulators of undruggable targets.

PROJECT SUMMARY Systems and computational biologists, physician-scientists, pharmacologists, biochemists and cell biologists will collaborate in a new Laboratory of Systems Pharmacology (LSP) to apply a measure-model approach to understanding the mechanisms of action of therapeutic drugs in multiple disease areas. The LSP will pioneer the development of quantitative network-centric approaches to pharmacology and toxicology that include analysis of dose-time-response relationships at a single-cell level, modeling of cellular network dynamics and their perturbation by drugs, development of pharmacokinetic and pharmacodynamic models in mouse and ultimately in man and use of systematic approaches to identify and qualify new drug targets. Our approach combines mathematical modeling with empirical measurement (including work with clinical samples and data) and aims to create quantitative and predictive drug response models at different temporal and physical scales. We aim to reinvigorate pharmacology and toxicology as foundational disciplines of translational medicine, develop the conceptual underpinning for personalized and precision medicine and lower the cost of drug discovery by improving its predictability. Close interactions with industry and the FDA will help address the productivity gap in drug discovery and development. The LSP is innovative with respect to its goal of using problems in basic and translational pharmacology to link three disciplines (cell and molecular biology, computational biology and medicine) and its aim of advancing therapeutic science in the Boston area and beyond. Students and postdocs supervised by 18 faculty members, 2 independent postdoctoral fellows and two PhD-level staff from 7 institutions will work in immediate proximity in a new custom-designed laboratory. The lab will host a new graduate program in therapeutics and multi-factored outreach activities that will promote systems pharmacology internationally. These goals will be achieved through four inter-linked research programs (Aims 1-4), a core dedicated to efficient translation of LSP research (Aim 5), an education core and administrative/outreach activities (Aims 6-8). Aim 1 will focus on the determinants of dose-response at a single-cell level, including the role of cell-to- cell variability in fractional response and of timing and order-of-exposure in combination therapy. Aim 2 will take a network-level approach to understanding therapeutic, toxic and paradoxical drug responses by kinase inhibitors in three types of cancer. The mechanistic basis and consequences of poly-pharmacology will also be examined along with differential drug responsiveness by normal and diseased tissues. Aim 3 will address PK- PD by developing multi-scale models of drug actions at the level of cells, tissues and organisms and new methods for measuring drug distribution at the cellular and subcellular levels. Aim 4 will apply machine learning and causal reasoning to target discovery from clinical records in asthma, inflammatory disease and fibrosis and pursue a structure-guided approach to identifying regulators of ?undruggable? targets.

PROJECT SUMMARY In Aim 2 we will construct computational models of cellular responses to drugs across genetically diverse cancer and normal cell lines. Data will be collected using a variety of single-cell and multiplex biochemical assays including sandwich immunoassays, protein and metabolite mass spectrometry, immunofluorescence microscopy and live-cell imaging of cells carrying fluorescent reporter proteins. These data will be integrated in computational models using a three-part strategy. First, significant connections between data on signaling molecules (e.g. Akt inhibition) and phenotypes (e.g. senescence v. apoptosis) will be discovered using statistical techniques such as partial least squares regression (PLSR), discriminant PLSR and Random Forest analysis. Second, network inference involving logical modeling or dynamic Bayes nets and literature-based priors will be used to determine the approximate topology of drug response networks in specific cell types. Finally, information from statistical modeling and network inference will be used to construct dynamic models in which the biochemistry of drug-target binding and of interacting response networks is rendered in mechanistic detail sufficient to reproduce and explain the observed variation in drug sensitivity and resistance from one tumor to the next. We have consciously chosen to model drug response networks for which genomic data provides clear evidence about which molecules and networks to focus on, and for which multiple precedents exist for translating cell-based studies into drug development and clinical care. Aim 2.1 will focus on measuring and modeling the PI3K/mTOR/Akt kinase network in triple negative breast cancer (TNBC), a disease in which this pathway is frequently mutated and being targeted by multiple kinase inhibitors in clinical development or use. Our translational goal is developing signatures and biomarkers predictive of patient response to mono and combination therapy. Aim 2.2 will develop new approaches to the poly-pharmacology of kinase inhibitors based on compressed algorithms that integrate diverse biochemical and structural data. We will use this information to analyze drug responses as multi-factorial perturbations of multi-component networks. Our translational goal is development of rational approaches to multi-kinase targeting. Aim 2.3 will focus on measuring and modeling the responses of BRAF-V600E melanoma and colon cancers to drugs such as vemurafinib with the primary aim of understanding diversity of genes implicated in acquired drug resistance. Our translational goal is overcoming or mitigating acquired resistance through design of patient-specific combination therapies using new or existing drugs. Aim 2.4 will compare the responses of normal and transformed cells directly with the aim of understanding the mechanistic basis of therapeutic index. We will focus on readily available normal human cells and on stem-cell derived cardiomyocytes, an area of interest for the FDA.

PROJECT SUMMARY Absorption, distribution, metabolism and elimination (collectively called pharmacokinetics, PK), and time-dependent drug actions in target organs (pharmacodynamics, PD) play critical roles in efficacy and toxicity of all drugs. We will develop and implement new methods for modeling PK-PD at multiple scales from cells to patients, new methods for measuring PK at the cellular and subcellular levels, and new cell culture systems that better mimic the tumor environment, thus increasing our ability to predict patient responses from cell culture data. Our translational goal is to create new single-cell resolution methods to integrate a molecular understanding of drug-target interaction with measures of target engagement and induction of drug response in tissues and organisms. Aim 3.1 involves technology development for sub-cellular resolution PK measurement by fluorescence imaging of Companion Imaging Drugs (CIDs), small molecule or protein drugs tagged with a fluorophore for imaging in a manner that retains the bioactivity and pharmacokinetics of the parent compound. The properties of CIDs will be optimized (Aim 3.1.1) and the compounds used for intravital imaging of drug distribution and response in living mice (Aim 3.1.2). CIDs will be used to directly assay drug-target interaction in single cells by fluorescence correlation microscopy (Aim 3.1.3). Aim 3.2 will develop a novel quantitative, multiplexed mass spectrometry method for assaying structure-activity relationships at a cellular and sub-cellular level based on covalent modification of target proteins. This will involve creation of novel chemical drug-like probes (Aim 3.2.1) that will then be subjected to systematic chemical modification to explore the impact of physic0-chemical properties such as cLogP, pKa etc. (Aim 3.2.2). Aim 3.3 will involve development of methods for integrating pathway-level knowledge and biomarkers (both predictive and response) into the kind of PK-PD models that are routinely used for translational pharmacology and clinical trial design in industry. Aim 3.4 will attempt to recreate key features of the tumor microenvironment in culture to increase the predictivity of cell culture models. This will involve reproducing time-varying drug exposure as observed in animals and patients (Aim 3.4.1), systematic variation of the soluble environment (Aim 3.4.2), manipulation of the physical environment through changes in substrate elasticity (Aim 3.4.3) and direct assessment of the relationship between drug response in culture and patients across all the data collected Aims 1-3.

PROJECT SUMMARY The studies in Aim 4 represent stretch goals for the LSP in which we attempt to advance therapeutic discovery by mining electronic medical records (EMRs) using machine learning and causal reasoning systems, dissect the druggable process of cell-to-cell communication in inflammation and fibrosis, and use structure- guided discovery to identify new compounds regulating the SHP2 phosphatase. These are studies that link together investigators who are experts in their respective fields, but who have not previously worked together on these topics; the sub-aims therefore have less project-specific preliminary data than those in Aims 1-3. However, one of the key goals of the LSP is to bring systems pharmacology approaches to new areas of research, and this Aim fulfills that goal as well as addressing areas of weakness identified in the A0 review. AIM 4.1 will join HMS systems biologists Chen, Marks and Sorger and BWH clinician Loscalzo in using machine learning and causal reasoning systems to identify key regulatory networks in Asthma. Using data in the large AsthmaBRIDGE clinical database, we aim to subdivide heterogeneous patient populations by molecular subtypes to improve disease management, identify opportunities for drug repurposing and discover new targets that could be advanced into clinical development. Aim 4.1.1 will apply machine learning methods with input from the OpenBEL Pulmonary knowledge base to identify relationships between patient drug response and clinical and molecular phenotype. Aim 4.1.2 will apply reverse causal reasoning to clinical data to generate candidate lists of important networks and potential targets. Aim 4.1.3 will use patient-derived B- cell and alveolar cell culture models to test hypotheses derived from analysis of AsthmaBRIDGE. AIM 4.2 will join Broad genomicist Hacohen, systems biologists Sorger and Yaffe, and clinician Loscalzo in an analysis of the molecular basis of inflammatory and fibrotic disease. Aim 4.2.1 will take a systems approach to inflammation, with a focus on innate immunity. This is an area with many potentially druggable targets (e.g. kinases and cell surface receptors) but in which poor understanding of networks makes it difficult to match targets to indications. Aim 4.2.2 will tackle the related problem of fibrotic disease, with a key advance being a new approach to disease definition based on network state rather than end-stage phenotype. AIM 4.3 will join structural biologist Blacklow, chemist Gray and systems biologists Sorger and Lauffenburger in an attempt to identify allosteric modulators of the SHP2 protein phosphatase; such molecules would represent a new approach to a classically undruggable target.

PROJECT SUMMARY This aim focuses on determinants of dose-response at a single-cell level. We will test the hypothesis that non-genetic cell-to-cell variability (arising from variation in the relative levels or activities of network components) is critical in determining the shape of dose-response curves and the maximum therapeutic effect that can be achieved at high drug concentrations. The impact of stochastic variation will be contrasted with that of cell cycle state and of special lineages (e.g. tumor stem cells). Experiments in this Aim also examine the importance of timing and order-of-exposure in combination cancer therapy. All but aim 1.4 will be performed using panels of ~10-40 cancer cell lines grown in 2D culture supplemented by a smaller number of patient- derived cultures obtained through the Translational Pharmacology Core (Aim 5). The influence of the tumor microenvironment on dose-response will be examined based on progress with Aim 3.4. Studies in Aim 1 are distinguished from those in Aim 2 by their focus on phenotypes as opposed to modeling intracellular signaling. Aim 1.1 will focus on genetically diverse panels of cancer cell lines and their responses to anti-cancer drugs, primarily investigational and approved kinase inhibitors. Aim 1.1.1 will use fixed cell microscopy to discriminate among drug response phenotypes at a single-cell level using molecular markers of cell division, induction of senescence and apoptosis (and other forms of cell death such as autophagy). Variation in response with time after drug addition, physiological state and genotype will be studied across cell types and within single cells in a genetically homogenous population. Aim 1.1.2. will wills use mutational information (MI) and other methods to associate dose response parameters from Aims 1.1.1-1.1.2 with features of the drug, target of cell type. Aim 1.1.3 will supplement fixed-cell analysis with live-cell imaging of selected drug-cell line combinations to determine how response evolves over time and distinguish among phenotypes that appear similar by endpoint assays. Aim 1.1.4 will extend these studies to patient-derived lines and cultures with the goal of increasing the relevance of our findings to human disease. Aim 1.2 will determine the role of cell-to-cell heterogeneity on fractional response and dose-response curves that are unusually shallow. Mutual information analysis of panels of related kinase inhibitors will reveal whether submaximal and shallow dose-response associates with drug, target or phenotype. Aim 1.3 examines the role of time in pharmacology. Aim 1.3.1 investigates the phenomenon of sequential drug synergy involving EGFR inhibitors and DNA damaging agents. Aim 1.3.2 investigates transient drug resistance induced by paradoxical responses to compounds that are thought to be pro-apoptotic. Aim 1.4 extends the analysis to a different therapeutic area, the response of Mycobacterium tuberculosis (Mtb) to antibiotics; these studies follow up recent data showing that asymmetric division by Mtb results in a cell-to-cell heterogeneity that impacts drug response.

PROJECT SUMMARY ? PROJECT 1 (AIM 4): Multi-scale modeling of adaptive drug resistance in BRAF- mutant melanoma. The overall goal of Project 1 is to develop an integrated, quantitative understanding of adaptive drug resistance to targeted BRAF and MEK kinase inhibitors in melanoma, with comparative studies performed in BRAF mutant thyroid and colorectal cancers. One of the primary challenges in understanding adaptive drug resistance is the sheer diversity of proposed mechanisms, ranging from reactivation of MAPK signaling, to engagement of parallel PI3K/mTOR/AKT signaling cascades and altered receptor trafficking. Individual published studies focus on subsets of these phenomena, often in different cell lines, and it remains unclear whether differences in emphasis reflect differences in the underlying biology, methodology (single cell RNASeq v. proteomics for example) or time scale (hours vs. weeks). One possibility is that the phenomenological diversity masks the operation of a common mechanism, in which feedback pathways, receptor trafficking, and parallel signaling cascades all play a role. However, because a single patient can harbor melanomas each with a different set of resistance mutations, the observed diversity is likely to be meaningful. The other extreme is that every tumor finds a unique way to become drug resistant, and that we will discover few if any underlying principles. We believe that the most likely explanation lies midway between these extremes: adaptation involves a handful of biochemically distinct mechanisms that can have a variety of presentations depending on cell type, microenvironment, assay method and time scale. We will test this hypothesis by studying adaptive resistance with detailed kinetic modeling and single cell data in a few BRAF-mutant cell lines combined with more phenomenological modeling in a wider range of cell types. Aim 4.1 will use single-cell data and ODE networks to study homeostasis in immediate-early BRAF/MEK/ERK (MAPK) signaling in four cell lines to elucidate the role played by negative feedback loops involving phosphatases and adaptor proteins. Aim 4.2 will examine the phenomenon of de-differentiation and the generation of slowly cycling drug-insensitive cells likely to contribute to residual disease. Aim 4.3 will use similar in-depth methods to study changes in ADAM protease activity and receptor shedding that cause sustained autocrine and paracrine signaling and increased MAPK activity. Aim 4.4 will look at the time evolution of adaptation based on preliminary evidence showing that, in a single cell line, adaptations can involve MAPK feedback in the short term (1-2 days) and de-differentiation and changes in receptor biology on a longer term (days to weeks). Aim 4.5 will use multi-omic analysis across a panel of 20 BRAF mutant cells lines to establish the extent of variability in mechanisms analyzed in Aims 4.1 to 4.4. Statistical and machine learning approaches will identify the changes in intracellular and autocrine/endocrine signaling most consequential for phenotype.

SUMMARY- OVERALL COMPONENT We will establish a Center for Cancer Systems Pharmacology (CSP Center) that constructs and applies network-level computational models to understand mechanisms of drug response, resistance and toxicity for targeted small molecule drugs and immune checkpoint inhibitors (ICIs). We hypothesize that improved understanding of fundamental cell signaling pathways and interactions between cancer and immune cells will result in greater efficacy while minimizing toxicity. Intrinsic and acquired drug resistance pose the primary challenges to broader application of all cancer therapies. By systematically dissecting how resistance to targeted therapies and ICIs arises, we aim to understand and overcome resistance mechanisms using new drugs or drug combinations, while simultaneously predicting and balancing potential toxicities. These goals will be accomplished by translating findings from the bedside to the bench and then back to the bedside focusing on melanoma, a type of cancer in which both ICIs and targeted drugs are effective, and triple negative breast cancer (TNBC) and brain cancers (GBM) for which ICIs are not approved but where sporadic responses have been observed. We will develop, validate and apply innovative pharmacological concepts and instantiate these in practical form using computational models. Such models will explicitly consider the impact of mutations, phenotypic variability, cell-to-cell interaction and the composition of the tumor microenvironment in mechanisms of action of sequential or simultaneous combinations of targeted drugs and ICIs. Hypothesis generation will focus on deep phenotyping of patient-derived specimens followed by hypothesis testing in pre- clinical settings using complementary multi-omic and computational methods. We will also create and distribute new measurement and software methods to promote systems pharmacology in other areas of cancer biology. Aim 1 will establish an Administrative Core to oversee and coordinate all center activities. Aim 2 will establish a Systems Pharmacology Core to coordinate experimental and computational resources for proteomic, transcriptomic, metabolomic and imaging assays across all three Projects. Aim 3 will establish an Outreach core that promotes training via a website and seminars and ensures curation and distribution of Center data according to FAIR standards. Aim 4 (Project 1) will develop multi-scale computational models of adaptive drug resistance in melanoma that capture and ultimately explain the wide diversity of changes in cell states associated with resistance to RAF/MEK inhibitors. Aim 5 (Project 2) will measure and model the tumor microenvironment before and during treatment, and at the time of drug resistance using a range of innovative, highly-multiplexed assays for malignant and non-malignant cells. Aim 6 (Project 3) will measure and model cell type-specific metabolic, signaling, and transcriptional mechanisms that contribute to the efficacy of ICI combinations, in order to develop improved therapeutic strategies for patients unresponsive to monotherapy.

Abstract The Center for Cancer Systems Pharmacology (CCSP) constructs and applies network-level computational models to understand mechanisms of drug response, resistance and toxicity for targeted small molecule drugs and immune checkpoint inhibitors (ICIs). We hypothesize that improved understanding of fundamental cell signaling pathways and interactions between cancer and immune cells will result in greater efficacy while minimizing toxicity. Intrinsic and acquired drug resistance pose the primary challenges to broader application of all cancer therapies. These goals are be accomplished by translating findings from the bedside to the bench and then back to the bedside focusing on melanoma, a type of cancer in which both ICIs and targeted drugs are effective, as well as triple negative breast cancer, and brain cancers (GBM). In this supplement, we will develop, validate and apply these innovative pharmacological concepts and instantiate these in practical form using computational models in Alzheimer's disease and related dementias. We have discovered a novel mechanism of neuronal death evoked by cytoplasmic dsRNA, which can trigger a type I interferon (IFN-I) response, in the brains of a subset of patients with Alzheimer's disease and Frontotemporal dementia. In a human neural cell in vitro model, we recapitulate the induction of IFN-I signaling and neuronal death by cytoplasmic dsRNA in a dose dependent manner. FDA-approved JAK inhibitors reverse neuronal death whereas other JAK inhibitors do not reverse neuronal death in spite of inhibiting STAT1 phosphorylation. This drug repurposing opportunity for Alzheimer's disease will be enhanced with a better understanding of the critical kinases that are mediating neuronal rescue and a better understanding of the elements of the IFN-I response in human brains to develop biomarkers that faithfully represent this subtype of neuroinflammation. We will also create and distribute new measurement and software methods to promote systems pharmacology in the area of neurodegenerative disease biology. Aim 1, linked to the Systems Pharmacology Core (Aim 2) and Project 3 of the parent grant, will measure and model cell type-specific signaling through chemoproteomics that contribute to the efficacy of JAK inhibitors to rescue neuronal death evoked by cytoplasmic dsRNA, in order to develop improved therapeutic strategies for patients. Aim 2, linked to the Systems Pharmacology Core and Project 2 of the parent grant, will measure and model the microenvironment around cytoplasmic dsRNA in human brains with Alzheimer's and Frontotemporal dementia using a range of innovative, highly- multiplexed assays for innate immune activation, with a particular emphasis on IFN-I signaling and stress granule composition. These efforts will coordinate with the Outreach Core of the parent grant to ensure curation and distribution of Center data according to FAIR standards.