LSP Research Projects

Individual LSP research projects engage faculty, students, and fellows with diverse skills from multiple institutions. Breakthrough research is a primary goal of the lab (followed by publication in leading scientific journals), but we also invest heavily in the development and maintenance of software and the invention of fundamentally new approaches to pre-clinical and clinical pharmacology. Multiple programs use our methods for discovering and developing new drugs to treat the most challenging indications (e.g., drug-resistant cancer, pain, and neurodegeneration). 

Jump to LSP Projects:
Kinase inhibitors | Docking | Digital Pathology | Alzheimer'sTuberculosis | Chronic Pain | Network Biology | Cancer Trials


LSP Center Programs

Many of our projects are supported with funding from multi-investigator “center” grants:

The Center for Cancer Systems Pharmacology (CCSP) studies the responsiveness and resistance of human tumors to anticancer drugs as well as the adverse effects that they cause. Part of the National Cancer Institute Center for Cancer Systems Biology Consortium.

The HMS LINCS Center collects and disseminates data and analytical tools needed to understand how human cells respond to perturbation by drugs, the environment, and mutation. Part of the NIH Library of Integrated Network-based Cellular Signatures (LINCS) Program.

The Harvard Tissue Atlas is developing methods to precisely profile the microenvironments of diverse human tumors. Supported by the NIH, Ludwig Cancer Research Foundation, the Bill and Melinda Gates Foundation, the Gray Foundation, and the Rossy Foundation.

The HMS PCA Center collects and analyzes highly multiplexed single cell data on early melanomas to better understand factors that promote their progression. Part of the NCI Human Tumor Atlas Network (HTAN).

Determinants of sensitivity and resistance to small molecule kinase inhibitors

Kinase inhibitors are an intensively studied class of therapeutics with many remaining unknowns – LSP investigators use them to develop new approaches for identifying targets and to study the factors determining drug resistance and sensitivity.

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), 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. However, 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. 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, interesting 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.

Fröhlich F, Gerosa L, Muhlich J, Sorger PK. Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance. Mol Syst Biol. 2023 Feb 10;19(2):e10988. PMCID: PMC9912026

Using deep learning to model drug-target engagement

LSP investigators are contributing to and benefiting from the rapid advances in deep learning that are revolutionizing data analysis and protein structure prediction and promise to fundamentally advance our ability to invent new drugs

The contemporary approach to the invention of targeted therapies commonly attempts to block the action of specific disease genes by developing 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 this poly-pharmacology is essential to their mechanism of action. To better understand the spectrum of proteins that drugs bind, we are investigating drugs that target 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). Predicting structure and docking using deep learning plays an increasingly important role, and members of our lab are actively 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, Floristean C, Kharkar A, Roy K, Rochereau C, Ahdritz G, Zhang J, Church GM, Sorger PK, AlQuraishi M. Single-sequence protein structure prediction using a language model and deep learning. Nat Biotechnol. 2022 Nov;40(11):1617–1623. PMID: 36192636

Liu C, Kutchukian P, Nguyen ND, AlQuraishi M, Sorger PK. A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules. J Chem Inf Model. 2023 Aug 18; PMID: 37595065

Developing next generation diagnostics and immunoprofiling methods using digital pathology and AI

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, disease diagnosis and management are 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 single cell molecular information in histological specimens and 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 databases (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 Gray BRCA Atlas and 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.

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 YA, Lian CG, Murphy GF, Santagata S, Sorger PK. The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution. Cancer Discov. 2022 Jun 2;12(6):1518–1541. PMCID: PMC9167783.

Schapiro D, Sokolov A, Yapp C, Chen YA, Muhlich JL, Hess J, Creason AL, Nirmal AJ, Baker GJ, Nariya MK, Lin JR, Maliga Z, Jacobson CA, Hodgman MW, Ruokonen J, Farhi SL, Abbondanza D, McKinley ET, Persson D, Betts C, Sivagnanam S, Regev A, Goecks J, Coffey RJ, Coussens LM, Santagata S, Sorger PK. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat Methods. 2022 Mar;19(3):311–315. PMCID: PMC8916956.

Lin JR, Wang S, Coy S, Chen YA, Yapp C, Tyler M, Nariya MK, Heiser CN, Lau KS, Santagata S, Sorger PK. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell. 2023 Jan 19;186(2):363-381.e19. PMID: 36669472

Lin JR, Chen YA, Campton D, Cooper J, Coy S, Yapp C, Tefft JB, McCarty E, Ligon KL, Rodig SJ, Reese S, George T, Santagata S, Sorger PK. High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers. Nat Cancer. 2023 Jul;4(7):1036–1052. PMCID: PMC10368530

Repurposing drugs to treat Alzheimer’s Disease

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.

Charpignon ML, Vakulenko-Lagun B, Zheng B, Magdamo C, Su B, Evans K, Rodriguez S, Sokolov A, Boswell S, Sheu YH, Somai M, Middleton L, Hyman BT, Betensky RA, Finkelstein SN, Welsch RE, Tzoulaki I, Blacker D, Das S, Albers MW. Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia. Nat Commun. 2022 Dec 10;13(1):7652. PMCID: PMC9741618

Developing Cures for Tuberculosis

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 online 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. PMID: 31249047

Larkins-Ford J, Degefu YN, Van N, Sokolov A, Aldridge BB. Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements. Cell Rep Med. 2022 Sep 20;3(9):100737. PMCID: PMC9512659

Larkins-Ford J, Greenstein T, Van N, Degefu YN, Olson MC, Sokolov A, Aldridge BB. Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis. Cell Syst. 2021 Nov 17;12(11):1046-1063.e7. PMCID: PMC8617591

New non-opioid therapeutics for chronic and inflammatory pain

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) 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.

Jain A, Gyori BM, Hakim S, Bunga S, Taub DG, Ruiz-Cantero MC, Tong-Li C, Andrews N, Sorger PK, Woolf CJ. Nociceptor neuroimmune interactomes reveal cell type- and injury-specific inflammatory pain pathways. bioRxiv; 2023. PMCID: PMC9915698.

Knowledge Assembly to enable Network Biology

The LSP is developing new knowledge assembly systems to dramatically improve how we extract causal and mechanistic information from published literature.

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.

Bachman JA, Gyori BM, Sorger PK. Automated assembly of molecular mechanisms at scale from text mining and curated databases. Mol Syst Biol. 2023 May 9;19(5):e11325. PMCID: PMC10167483.

Improving the performance and interpretation of cancer trials

The LSP is engaged in a wide-ranging project to understand the causes of success and failure in cancer clinical trials and develop improved computational methods for early-phase trials that can help predict trial success. 

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 difficult to access data on the outcomes of existing trials. To help, we are 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 immuno-oncology 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 suggest 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 parametrized to improve trial precision and reveal time-dependent therapeutic effects. Nat Commun. 2022 Feb 15;13(1):873. PMCID: PMC8847344.

Pomeroy AE, Schmidt EV, Sorger PK, Palmer AC. Drug independence and the curability of cancer by combination chemotherapy. Trends Cancer. 2022 Nov;8(11):915–929. PMCID: PMC9588605.