LSP Research Projects
Individual LSP research projects engage faculty, students and fellows having a wide variety of skills and from multiple institutions. Breakthrough research (followed by publication in leading scientific journals) is a primary goal of the lab, but we also make major investments in the development and maintenance of software and the invention of fundamentally new approaches to pre-clinical and clinical pharmacology. Multiple programs use these methods for the discovery and development of new drugs to treat the most challenging indications (drug-resistant cancer, pain, and neurodegeneration). This page describes some of these projects and more information is available in LSP microsites.
The LSP is strongly committed to FAIR (Findable, Accessible, Interoperable and Reusable) research and we have studied and published on factors that influence the reproducibility of laboratory-based research findings. Whenever feasible the lab’s results and methods are made available as open source software and data are made available under public domain (Creative Commons) licenses. More information on this research can be found in the tools and technologies and software sections of this web site.
In many cases, multiple projects are supported in a coordinated way with funding from multi-investigator “center” grants. These centers have their own web sites, as listed below.
LSP Center Programs
The Center for Cancer Systems Pharmacology (CCSP) is part of the National Cancer Institute Center for Cancer Systems Biology Consortium and studies the responsiveness and resistance of human tumors to anticancer drugs as well as the adverse effects that they cause.
The HMS LINCS Center is part of the NIH Library of Integrated Network-based Cellular Signatures (LINCS) Program that collects and disseminates data and analytical tools needed to understand how human cells respond to perturbation by drugs, the environment, and mutation.
The Ludwig Tumor Atlas is a project supported by Ludwig Cancer Research that is developing methods to precise profile the microenvironments of diverse human tumors, with a focus on tumor-immune interactions.
The HMS PCA Center is a part of the NCI Human Tumor Atlas Network (HTAN) collecting highly multiplexed single cell data on early melanomas to better understand factors that promote their progression.
Kinase inhibitors are an intensively studied class of therapeutics with many remaining unknowns – LSP investigators use them as a laboratory in which to develop new approaches to target identification and study factors determining resistance and sensitivity to existing drugs.
Inhibitors of the roughly 540 human protein and lipid kinases are the most intensively developed and studied class of therapeutic drugs. Kinase inhibitors such as imatinib for treatment of chronic myeloid leukemia (CML), vemurafenib for treatment of BRAF-mutant melanoma, and erlotinib for treatment of EGFR-mutant lung cancer were the first breakthrough “targeted” drugs invented for genetically defined cancers. Kinase inhibitors are also important in the treatment of inflammatory diseases. A primary limitation in the use of these drugs is the frequent development of resistance, which involves both a short-term adaptive resistance and genetically determined long-term resistance. These resistance mechanisms involve many of the homeostatic processes that allow cells to survive and proliferate in the presence of internal and external stress and are therefore of great interest in their own right. By studying precisely how drug resistance arises in cell lines, animal models, and humans we hope to prevent its emergence using newly developed drugs or combination therapies. This project is ideal for cancer biologists and oncologists interested in fundamentally improving our approach to small molecule therapy and increasing the durability of therapeutic responses. Clinical trials are underway making use of some of the insights arising from this project.
Ferguson FM, Doctor ZM, Ficarro SB, Browne CM, Marto JA, Johnson JL, Yaron TM, Cantley LC, Kim ND, Sim T, Berberich MJ, Kalocsay M, Sorger PK, Gray NS. Discovery of Covalent CDK14 Inhibitors with Pan-TAIRE Family Specificity. Cell Chem Biol. 2019 Jun 20;26(6):804-817.e12. PMCID: PMC6588450.
Gerosa L, Chidley C, Fröhlich F, Sanchez G, Lim SK, Muhlich J, Chen J-Y, Vallabhaneni S, Baker GJ, Schapiro D, Atanasova MI, Chylek LA, Shi T, Yi L, Nicora CD, Claas A, Ng TSC, Kohler RH, Lauffenburger DA, Weissleder R, Miller MA, Qian W-J, Wiley HS, Sorger PK. Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells. Cell Syst. 2020 Nov 18;11(5):478-494.e9. PMCID: PMC8009031.
Hafner M, Niepel M, Chung M, Sorger PK. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods. 2016 Jun 1;13(6):521–527. PMCID: PMC4887336.
Hafner M, Mills CE, Subramanian K, Chen C, Chung M, Boswell SA, Everley RA, Liu C, Walmsley CS, Juric D, Sorger PK. Multiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity. Cell Chem Biol. 2019 Aug 15;26(8):1067-1080.e8. PMCID: PMC6936329.
LSP investigators are contributing to and benefiting from the rapid advances in deep learning that are revolutionizing data analysis and protein structure prediction and that promise to fundamentally advance our ability to invent new drugs
The contemporary approach to invention of targeted therapies commonly attempts to block the action of specific disease genes through the development of highly selective inhibitors. In the case of multi-gene families such as kinases it is increasingly evident that approved drugs commonly inhibit multiple proteins and that this poly-pharmacology is important to their mechanism of action. To better understand the spectrum of proteins that drugs bind we are investigating drugs targeting multi-enzyme families (kinases, deubiquitinases, and bromodomain inhibitors) using cell-based assays, mass spectrometry, and deep learning (a type of machine learning and artificial intelligence; (AI). Structure prediction and docking using deep learning plays an increasingly important role and members of our lab are active in building on recent successes in this area (AlphaFold for example). This project is actively recruiting individuals interested in ML/AI, protein structure prediction, and chemical biology.
AlQuraishi M. End-to-End Differentiable Learning of Protein Structure. Cell Syst. 2019 Apr 24;8(4):292-301.e3. PMCID: PMC6513320.
AlQuraishi M, Sorger PK. Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nat Methods. 2021 Oct;18(10):1169–1180. PMID: 34608321.
Chowdhury R, Bouatta N, Biswas S, Rochereau C, Church GM, Sorger PK, AlQuraishi M. Single-sequence protein structure prediction using language models from deep learning. 2021 Aug 4; Available from: http://biorxiv.org/lookup/doi/10.1101/2021.08.02.454840
The LSP has developed new highly multiplexed imaging methods and computational tools to enable deep molecular interrogation of tumor-immune interactions and develop next-generation diagnostics for precision cancer care.
The development of a precise, patient-centered, approach to medicine is critically dependent on being able to more precisely diagnose disease and determine which patients are most likely to benefit from a specific therapy. In oncology, diagnosis and disease management are still dominated by information acquired by pathologists using histology, the examination of tissue and tumor sections in the light microscope. Despite a growing body of knowledge, and the increasing impact of genetic information, the practice of pathology remains largely pre-digital. The LSP is at the forefront of developing a new generation of highly-multiplexed tissue imaging methods that can acquire precise molecular information from single cells in histological specimens and then fuse this with other types of genetic and single-cell data. This approach is fundamentally changing our understanding of tumor immune-interaction and the ways in which therapeutic interventions can be tailored to specific tumor types. The development of AI algorithms and large public data bases (as part of the NCI Cancer Moonshot Program) is a key part of this program and we are optimistic that we will have novel methods in clinical use within two to three years. The LSP created the Harvard Tissue Atlas Project in this area as well as the Ludwig Tumor Atlas. Our tissue imaging projects are actively recruiting staff, fellows, and students interested in cancer biology, immunotherapy, microscopy, optical physics, machine learning/AI, and cloud computing.
Lin J-R, Izar B, Wang S, Yapp C, Mei S, Shah PM, Santagata S, Sorger PK. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. Elife. 2018 Jul 11;7. PMCID: PMC6075866.
Liu D, Lin J-R, Robitschek EJ, Kasumova GG, Heyde A, Shi A, Kraya A, Zhang G, Moll T, Frederick DT, Chen Y-A, Wang S, Schapiro D, Ho L-L, Bi K, Sahu A, Mei S, Miao B, Sharova T, Alvarez-Breckenridge C, Stocking JH, Kim T, Fadden R, Lawrence D, Hoang MP, Cahill DP, Malehmir M, Nowak MA, Brastianos PK, Lian CG, Ruppin E, Izar B, Herlyn M, Van Allen EM, Nathanson K, Flaherty KT, Sullivan RJ, Kellis M, Sorger PK, Boland GM. Evolution of delayed resistance to immunotherapy in a melanoma responder. Nat Med. 2021 Jun;27(6):985–992. PMCID: PMC8474080.
Nirmal AJ, Maliga Z, Vallius T, Quattrochi B, Chen AA, Jacobson CA, Pelletier RJ, Yapp C, Arias-Camison R, Chen Y-A, Lian CG, Murphy GF, Santagata S, Sorger PK. The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution. 2021 May 23; Available from: http://biorxiv.org/lookup/doi/10.1101/2021.05.23.445310
Schapiro D, Sokolov A, Yapp C, Muhlich JL, Hess J, Lin J-R, Chen Y-A, Nariya MK, Baker GJ, Ruokonen J, Maliga Z, Jacobson CA, Farhi SL, Abbondanza D, McKinley ET, Betts C, Regev A, Coffey RJ, Coussens LM, Santagata S, Sorger PK. MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging. 2021 Mar 16; Available from: http://biorxiv.org/lookup/doi/10.1101/2021.03.15.435473
LSP investigators working with the Massachusetts Alzheimer's Disease Research Center at the MGH are developing new approaches to treat Alzheimer's Disease based on the hypothesis that the disease has multiple distinct etiologies some involving degeneration-associated chronic inflammation.
As populations age, Alzheimer's Disease (AD) poses a rapidly increasing burden for healthcare systems and the disease is a tragedy for the individuals involved and their families. The discovery of new drugs for neurodegenerative diseases is challenging because the etiology of these diseases is poorly understood, the diseases frequently progress relatively slowly, and end-stage disease – when symptoms are most obvious - is likely to remain difficult to treat. For these reasons we are pursuing multiple approaches to repurposing drugs that are approved by the Food and Drug Administration (FDA) for others indications, commonly an inflammatory disease. We are investigating repurposing opportunities by mining large-scale clinical databases (in collaboration with colleagues in the UK) and by developing pre-clinical models of neurodegeneration. The machine learning algorithm DRIAD (Drug Repurposing In AD), for example, uses profiling data from human neural cell cultures treated with existing compounds to generate ranked lists of possible repurposing candidates. Some of these candidates have advanced to early-stage clinical trials. However, our long-term goal is not to stick with repurposed drugs but instead to use them to better understood disease mechanisms and guide the development of more effective second generation molecules tailored to specific types of Alzheimer's Disease.
Rodriguez S, Sahin A, Schrank BR, Al-Lawati H, Costantino I, Benz E, Fard D, Albers AD, Cao L, Gomez AC, Evans K, Ratti E, Cudkowicz M, Frosch MP, Talkowski M, Sorger PK, Hyman BT, Albers MW. Genome-encoded cytoplasmic double-stranded RNAs, found in C9ORF72 ALS-FTD brain, propagate neuronal loss. Sci Transl Med. 2021 Jul 7;13(601):eaaz4699. PMCID: PMC8779652.
Rodriguez S, Hug C, Todorov P, Moret N, Boswell SA, Evans K, Zhou G, Johnson NT, Hyman B, Sorger PK, Albers MW, Sokolov A. Machine Learning Identifies Novel Candidates for Drug Repurposing in Alzheimer’s Disease. Nat Commun. Cold Spring Harbor Laboratory; 2020 May 16;2020.05.15.098749. PMCID: PMC7884393.
Song Y, Subramanian K, Berberich MJ, Rodriguez S, Latorre IJ, Luria CM, Everley R, Albers MW, Mitchison TJ, Sorger PK. A dynamic view of the proteomic landscape during differentiation of ReNcell VM cells, an immortalized human neural progenitor line. Sci Data. 2019 Feb 19;6:190016. PMCID: PMC6380223.
Through a close collaboration with Tufts Medical School, LSP scientists are using computational approaches, animal models, and deep profiling of TB granulomas to understand how mechanisms of combination therapy and improve treatment approaches.
Tuberculosis remains a scourge in many parts of the developing world and is also common in disadvantaged and immigrant communities in the US. Treating the disease remains challenging, with too few drugs available, patient-to-patient variability in response, and emergence of drug resistance. LSP scientists are studying TB granulomas - sites of inflammation - that represent sites of active or residual disease in which the host immune system is attempting to eradicate infection. Granulomas evolve over time in an irregular fashion even within a single patient and can become the source of resurgent disease. Developing better treatments for TB faces many of the same hurdles as developing drugs for cancer and many TB granulomas are discovered in the process of screening for lung cancer. LSP investigators are using a wide range of tools to study granulomas in humans and animal models. In collaboration with the Bill and Melinda Gates Foundation the LSP is also developing an on-line digital resource for the TB community that focuses initially on fusing data from advanced tissue imaging, single-cell sequencing and radiological scans.
Cokol M, Kuru N, Bicak E, Larkins-Ford J, Aldridge BB. Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis. Sci Adv. 2017 Oct;3(10):e1701881. PMCID: PMC5636204.
Smith TC, Aldridge BB. Targeting drugs for tuberculosis. Science. 2019 Jun 28;364(6447):1234–1235. DOI: 10.1126/science.aay0211.
LSP investigators are using screens in iPSC-derived neurons, cheminformatics, and sophisticated animal models to develop novel non-opioid treatments for pain with greater long-term efficacy and lower abuse potential than current options.
Existing therapies for pain are often ineffective or liable to abuse, contributing to our current addiction and overdose crisis. However, development of non-opioid analgesics is one of the most challenging problems in contemporary drug discovery. An ambitious multi-institution project funded by DARPA (the Panacea Research Program) at HMS aims to develop novel small molecules and biologics for treatment of pain and inflammation while also developing more effective computation-enabled methods for drug discovery platforms. Our approach is target agnostic with screening centered on cell-based phenotypes that are indicative of nociceptor-specific silencing of channels and other proteins involved in drug sensation. The project uses a diversity of cutting-edge pharmacology tools, including iPSC-derived neuronal drug screening, mass spectrometry proteomics, protein structure prediction and docking, computation assisted synthesis planning, and advanced AI-assisted behavioral assays. Related efforts are applying similar approaches to both chemotherapy-induced and diabetic peripheral neuropathy. This program is actively recruited medicinal chemists, chemical biologists and neurobiologists.
Jayakar S, Shim J, Jo S, Bean BP, Singeç I, Woolf CJ. Developing nociceptor-selective treatments for acute and chronic pain. Sci Transl Med. 2021 Nov 10;13(619):eabj9837. PMID: 34757806.
Lee S, Jo S, Talbot S, Zhang H-XB, Kotoda M, Andrews NA, Puopolo M, Liu PW, Jacquemont T, Pascal M, Heckman LM, Jain A, Lee J, Woolf CJ, Bean BP. Novel charged sodium and calcium channel inhibitor active against neurogenic inflammation. Elife. 2019 25;8. PMCID: PMC6877086.
Yekkirala AS, Roberson DP, Bean BP, Woolf CJ. Breaking barriers to novel analgesic drug development. Nat Rev Drug Discov. 2017 Nov;16(11):810. PMCID: PMC6934078.
The LSP is developing new computing platforms and knowledge assembly systems for dramatically improving how we extract causal and mechanistic information from the published literature, most of which is in a text-based form that is not readily computable.
Approaches to studying biological regulation, disease mechanisms, and drug targets that involve systems pharmacology approaches require computable knowledge about biological networks. Many databases have been developed to curate information on gene functions and protein-protein interactions but much of the information in the literature is not yet recorded in databases in a useful way. We are therefore developing novel approaches to knowledge assembly that combine natural language processing (NLP) with novel domain-specific programming languages such as PySB that can capture complex biological concepts in computable form. Much of this work focuses on advancing the Integrated Network and Dynamical Reasoning Assembler (INDRA), an automated model assembly software system that uses NLP to rapidly scan the published literature and also connects to existing databases to collect knowledge from a wide range of existing database. INDRA then assembles this knowledge into a self-consistent form, enabling the generation of causal graphs and dynamical computational models of disease mechanisms and drug action.
Bachman JA, Gyori BM, Sorger PK. Assembling a phosphoproteomic knowledge base using ProtMapper to normalize phosphosite information from databases and text mining. bioRxiv. 2019 Nov 6;822668. DOI: 10.1101/822668.
Gyori BM, Bachman JA. From knowledge to models: Automated modeling in systems and synthetic biology. Current Opinion in Systems Biology. 2021 Dec;28:100362. DOI: 10.1016/j.coisb.2021.100362.
Gyori BM, Bachman JA, Subramanian K, Muhlich JL, Galescu L, Sorger PK. From word models to executable models of signaling networks using automated assembly. Mol Syst Biol. 2017 Nov 24;13(11):954. PMCID: PMC5731347.
The LSP is engaged in a wide-ranging project to understand the causes of success and failure in cancer clinical trials and developing improved computation methods for early-phase (signal finding) trials that are predictive of success in future trials.
Clinical trials are the most expensive and important stage of drug development and yet the determinants of success and failure remain poorly understood. In part this is because it is so hard to access data on the outcomes of existing trials. We are therefore creating a database of survival curves and individual participant data (IPD) that can be used to test specific hypotheses about drug mechanism of action and improve how early phase trials are scored. We have already determined that mechanisms of combination cancer therapy rarely involve drug interaction (synergy) but are instead based on the mechanism of independent drug action in which each patient benefits from the single agent in the combination with the greatest activity. This approach is already being used by leading pharmaceutical companies to prioritize future immunooncology trials. In related work we have developed methods to increase the statistical power associated with small clinical trials, including the basket trials increasingly used to identify responsive patient populations. Our data suggests that current therapeutic approaches would dramatically benefit from increasing precision based in part on new diagnostics involving next-generation digital histology.
Palmer AC, Sorger PK. Combination Cancer Therapy Can Confer Benefit via Patient-to-Patient Variability without Drug Additivity or Synergy. Cell. 2017 Dec 14;171(7):1678-1691.e13. PMCID: PMC5741091.
Palmer AC, Chidley C, Sorger PK. A curative combination cancer therapy achieves high fractional cell killing through low cross-resistance and drug additivity. Elife. 2019 Nov 19;8. PMCID: PMC6897534.
Plana D, Fell G, Alexander BM, Palmer AC, Sorger PK. Cancer patient survival can be accurately parameterized, revealing time-dependent therapeutic effects and doubling the precision of small trials. 2021 May 17; Available from: http://biorxiv.org/lookup/doi/10.1101/2021.05.14.442837