John Quackenbush, Harvard University
One of the central tenets of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that genes and genetic variants work together in complex regulatory networks that help define phenotypes and mediate phenotypic transitions. We have found that the networks, and their structure, provide unique insight into how genetic elements interact with each other and the structure of the network has predictive power for identifying critical processes in health and disease and for identifying potential therapeutic targets. Drawing on examples from TCGA, GTEx, and other large datasets, we will explore the ways in which modeling regulatory networks provides insight into functional changes that can drive cancers and other complex diseases and how they are influenced by biological sex and age.
John Quackenbush is Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics at the Harvard TH Chan School of Public Health and Professor at the Channing Division of Network Medicine at Brigham and Women’s Hospital. John’s PhD was in Theoretical Physics but a fellowship to work on the Human Genome Project led him through the Salk Institute, Stanford University, and The Institute for Genomic Research (TIGR), before joining Harvard in 2005. He explores the ways in which our genes work together to create the diversity of phenotypes we observe and how factors like sex and gender alter the gene regulatory landscape. His published work has more than 98,000 citations. Among his honors is recognition in 2013 as a White House Open Science Champion of Change and election to the National Academy of Medicine in 2022.
TJ Sharpe
T.J. Sharpe is a Stage IV melanoma survivor who began sharing his journey through cancer as a blogger and later a keynote speaker. Diagnosed in August 2012 and given two years to live, he has undergone six surgeries and four immunotherapy treatments over two different clinical trials. His story of overcoming numerous issues to find, select, enroll, and complete two trials illustrates the hurdles many patients encounter when faced with a difficult diagnosis.
The continuation of his journey past treatment and into the advocacy world and business of clinical trials adds the "… and this is what I am doing about it" layer to the "I survived cancer" narrative. Taking his personal journey and the insights gleaned from a decade+ of listening to and working with leaders in healthcare, clinical research, digital medicine technologies, and patient advocacy, he speaks frankly about and works tirelessly towards creating advanced patient engagement to benefit all.
Now a speaker, writer, and patient expert consultant, he has worked with top pharmaceutical companies and clinical research stakeholders to address the power imbalance in patient care through sustained, ingrained, and consistent patient engagement. A South Jersey native, T.J. lives in Fort Lauderdale with his wife Jennifer and two children, Josie and Tommy. He is active on Twitter at @TeamTJSharpe and LinkedIn at https://www.linkedin.com/in/tjsharpe/.
T.J. Sharpe is a keynote speaker, writer, and patient advocate who collaborates with leading pharmaceutical and clinical research companies to embed patient insights in life sciences treatments and trials. Given a Stage IV melanoma diagnosis and two years to live, he went from simply trying to survive cancer to actively shaping the patient engagement industry. T.J. shares his journey with humor, raw insight, and reflections on finding hope in challenging times, highlighting the resilience to overcome obstacles to pursue a first-in-human treatment and commitment to making his positive outcome possible for others.
Panelists: Ravi Radhakrishnan, University of Pennsylvania; Ben Greenbaum, Memorial Sloan Kettering; and Ankur Singh, Georgia Tech
See the detailed plans and outlines for this workshop here.
Sergio Branciamore, COH
The epigenetic landscape has emerged as a robust theoretical framework for understanding cell fate and cancer evolution, integrating various biological factors into a coherent, physics-governed system. We have developed a state-transition framework for time-series -omic data, providing a mechanistic mathematical model to construct a potential landscape that accurately describes multiple cancers and clinically relevant therapies. This framework encodes the potential landscape in multi-modal epigenetic data, including gene-transcriptome, miRNA-transcriptome, and DNA methylation. Our state transition model captures cancer dynamics beyond clonal evolution, encompassing the transformation of non-cancerous cells as the disease progresses. In our study, we demonstrate the feasibility of integrating multi-modal -omics data to construct an AML state-space using a CM mouse model. At each sampled time point, we collected over 1,000,000 individual features, including gene expression levels, miRNA expression, and CpG methylation status. We identified an optimal data-driven representation of the multi-modal AML state-space. The state-space was optimized using a feature selection procedure that maximized the distance between control and end-point leukemic samples. To understand the dependency and interaction between different modalities, we employed Bayesian networks. These probabilistic graphical models achieved two key results: they unveiled the conditional dependency between variables and provided insights into the potential information flow between different omics, demonstrating the capacity to analyze the epigenetic landscape effectively.
Other Authors: Sergio Branciamore, David Frankhouser, Yu-Hsuan Fu, Denis O'Meally, Bin Zhang, Lisa Uechi, Ziang Chen, Xiwei Wu, Konnie Urbaniak, Jihyun Irizarry, Ya-Huei Kuo, Guido Marcucci MD, Russell Rockne
Mehdi Damaghi, Stony Brook University
Within a stable ecosystem, species appear harmoniously synchronized, akin to components of a larger super-organism. Yet, when one species breaks free from its ecological limits and multiplies rapidly, this disrupts the equilibrium, leading to the extinction of other species and potential ecosystem collapse. In the realm of medicine, we term this ecological phenomenon as cancer. The ecological changes of tumor microenvironment will apply novel selection pressure on cancer cells and dictates which changes in cancer cells offer adaptive advantages. While the significance of these evolutionary and ecological processes in cancer is acknowledged, we are still lacking the knowledge on cancer initiation, progression, and metastasis. To address that, we study how various tumors are evolving in their microenvironment from normal to precancer and cancer in clinically meaningful ways. We study how changes in microenvironment of normal, precancer, and cancer cells can change their phenotype adapting to varied microenvironment and how adaptation to it can shape the new ecosystem and evolutionary trajectory of cancer cells. This interplay between tumor cells and the microenvironment plays a fundamental role in the development of an ever-changing tumor ecosystem leading to more genotypic heterogeneity and phenotypic plasticity. We use the integration of spatial single-cell transcriptomics, proteomics, metabolomic and lipidomics, and pathomics machine learning analysis to capture the heterogeneity and plasticity of cancer cells in their natural ecological microenvironment and habitats. We discovered novel metabolic phenotypic switch in cells adapted to early acidosis in mammary ducts leading to pre-cancer and carcinogenesis. We then used these markers in our breast cancer ductal carcinoma cohort to find biomarkers for progression from pre-cancer to cancer and upstaging of DCIS.
Other Authors: Ariosto Silva, Erez Persi
Dennis E Discher, University of Pennsylvania
Macrophages often pervade solid tumors, but their nearest neighbor organization is understudied and potentially enables key functions such as phagocytosis. Motivated by recent spatial profiling and prognoses from clinical samples, we observe dynamic macrophage clusters in tumor models under conditions that maximize cancer cell phagocytosis, and we use reductionist approaches to uncover cluster formation pathways including roles for tumor-intrusive ‘intrudopodia’. Macrophage clusters form over hours on low adhesion substrates after M1 polarization, including with Tcell-derived cytokines, and also sort from M2 macrophages that disperse on the same substrates. M1's upregulate specific cell-cell receptors but suppress actomyosin contractility, and while both pathways contribute to cluster formation, decreased cortical tension was predicted to unleash pseudopodia. Macrophage neighbors in tumor spheroids indeed extend these as intrudopodia between adjacent cancer cell junctions – at least when phagocytosis conditions are maximized, and coordinated intrudopodia help detach and individualize cancer cells for rapid engulfment. Macrophage clusters thereby provide a cooperative advantage for phagocytosis to overcome solid tumor cohesion.
Other Authors: Lawrence J. Dooling, Alişya A. Anlaş, Michael P. Tobin, Nicholas M. Ontko, Tristan Marchena, Maximilian Wang,
Heiko Enderling, The University of Texas MD Anderson Cancer Center
Microscopic tumor-immune interactions and macroscopic tumor growth dynamics are highly nonlinear on multiple temporal and spatial scales. Mathematical modeling is poised to help decipher these complex interactions and guide future developments in cancer biology and clinical oncology. We introduce a high-resolution agent-based model with a staggered lattice architecture to simulate size differences of cancer and immune cells to allow simulation of tumor density dependent immune infiltration of the tumor. This approach simulates complex tumor-immune ecosystem dynamics with biologically realistic emergent population-level properties, including the three E’s of cancer immunoediting – tumor escape, tumor-immune equilibrium, and immune-mediated tumor elimination. We use multiplex immunohistochemistry tissue biopsy data as well as clinical outcomes to calibrate and validate such model for different cancer types. On the macroscopic level, we introduce a novel approach to calibrate mathematical model parameters with retrospective clinical trial Kaplan-Meier analyses. These novel tools help advance our predictive modeling and digital twin frameworks for future treatment personalization.
Other Authors: Thomas Dombrowski, Mohammad Zahid, Maureiq Ojwang’, Shari Pilon-Thomas
Order from disorder: self-organization in development and cancer
Zev Gartner, UCSF
Tissues are wildly complex, with properties that emerge from the interactions of large numbers of cells comprising a dizzying number of heterogeneously expressed gene products. The tools of genomics and big data are increasingly viewed as the solution to understanding this complexity. While the utility of these tools is undeniable, we are exploring a parallel approach. Using bioengineering and human mammary organoids as a model system, we provide evidence that the concepts of equilibrium statistical mechanics can provide surprisingly accurate predictions of steady-state phenomena at the tissue scale from only three measurable parameters — an active surface energy, the magnitude of active mechanical fluctuations, and a configurational entropy associated with composing a tissue from different populations of cells. From these measurements we predict the average structure of a tissue across a range of conditions as well as its microscopic variability. This conceptual formalism also provides insight into how changes to these parameters can drive corresponding changes in tissue structure, for example during development and breast cancer progression. I will discuss some assumptions and limitations of this approach, possible extensions to other systems, and the potential to understand other emergent properties of tissues such as cell plasticity and structural phase transitions.
Other Authors: Vasudha Srivastava, Jennifer L. Hu, James C. Garbe, Boris Veytsman, Sundus F. Shalabi, David Yllanes, Matt Thomson, Mark A. LaBarge, Greg Huber
A Statistical Physics Framework for Understanding the Role of Repeat RNA in Tumor Immunity
Benjamin Greenbaum, Memorial Sloan Kettering Cancer Center
Transcriptional dysregulation in tumors can induce the abundant expression of repetitive elements in cancerous cells compared to normal tissues, where they are often transcriptionally silent. Such transcripts have been associated with better outcomes to cancer immunotherapies, as they can modulate the tumor immune microenvironment and generate an under-quantified source of tumor neoantigens. Therefore, it has been hypothesized that the aberrant transcription of repeat RNA is both a critical mechanism for initiating the immune response in the tumor microenvironment and an untapped source of potential therapeutic targets. Using a set of approaches from statistical physics, our team predicted repetitive element RNA directly stimulates receptors of the innate immune system, confirmed this hypothesis in a key subset of immune cells, and showed repeat expression can correlate with response to checkpoint blockade immunotherapies. Repeat RNA is therefore both a novel biomarker for the innate immune response in cancer and a potential therapeutic target to modulate tumor immunity. We will utilize a set of tools, developed by our team, from statistical physics to characterize repeat RNA recognition by innate immune receptors in silico and their role in tumor-immune co-evolution, both with and without the application of immunotherapy. Next, we will characterize the spatial context of repeat RNAs in the tumor immune microenvironment and the co-localization of predicted immunostimulatory RNA with activation of immune signaling, along with in depth immune-phenotyping of the state of the immune microenvironment in vivo. Finally, we will perform functional validation of our predictions on human immune cells to validate mechanisms of recognition and the specific immune subsets responsible for repeat recognition via a set of in vitro assays. Our goal is to use approaches from statistical physics to quantify the role of repetitive elements in tumor immunology, their rules of recognition by innate immune receptors and their part in facilitating cytolytic T cell activity. In doing so we will combine novel RNA detection technologies to study their spatial distribution and localization in cancers; state of the art immune-phenotyping; and mathematical models to characterize their direct role in tumor evolution. We hypothesize that our approach from statistical physics will identify the key structural and sequence features of repeat mediated immune activation in solid tumors and shed light on their specific consequences for tumor evolution and therapeutic efficacy.
Other Authors: David T. Ting
Sex-distinct Transcriptomic Signatures Underlie MRI-defined Edema Patterns in Human Gliomas
Pamela Jackson, Mayo Clinic
Magnetic resonance imaging (MRI) is key to clinically managing brain tumor patients, however connecting the biology to imaging remains challenging. We developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. The goal of our project was to delineate sex-distinct markers associated with MRI-estimated brain tumor edema abundance. We analyzed 179 bulk RNA-Seq multiregional samples (Female: 75; Male: 104) from 55 high grade glioma patients (Female: 21; Male: 34). Patients’ segmented pre-surgical multiparametric MRIs were utilized in the edema mathematical model to create edema scores. We performed differential expression for high and low edema, gene set enrichment analysis (GSEA) using MSigDB hallmarks, and leading edge interpretation. We found that the fatty acid metabolism (FAM) and oxidative phosphorylation (OxPhos) pathways were sex-distinct, with both pathways amplified for high edema in males and low edema in females. Of the OxPhos pathway leading edge genes, 36 were unique to females, 66 were unique to males and 45 were common. Of the FAM pathway leading edge genes,39 were unique to females, 29 were unique to males, and 8 were common. Notably, expression of both IDH1 and IDH2 were increased for males in regions of high edema in the OxPhos pathway. IDH3a was decreased for females in regions of low edema in the OxPhos pathway. These data suggest that there may be sex-distinct metabolism underlying MRI measurable edema formation.
Other Authors: Lee Curtin, Kyle W. Singleton, Maciej M. Mrugala, Richard S. Zimmerman, Bernard R. Bendok, Peter Canoll, Kristin R. Swanson
Genetic variation drives cancer cell adaptation to extracellular matrix stiffness
Tanmay Lele, Texas A&M University
The progression of many solid tumors is accompanied by alterations to the stiffness of the extracellular matrix (ECM). Cancer cells adapt to soft and stiff ECM through mechanisms that are not fully understood. There is significant genetic heterogeneity from cell to cell in tumors, but how ECM stiffness as a parameter might interact with that genetic variation is not known. Here, we employed experimental evolution to study the response of genetically variable and clonal populations of tumor cells to variable ECM stiffness. Proliferation rates of genetically variable populations cultured on soft ECM increased over a period of several weeks, whereas clonal populations did not evolve. Tracking of DNA barcoded cell lineages revealed that soft ECM consistently selected for the same few variants. These data provide evidence that ECM stiffness exerts natural selection on genetically variable tumor populations. Soft-selected cells were highly migratory, with enriched oncogenic signatures and unusual behaviors such as spreading and traction force generation on ECMs with stiffness as low as 1 kPa. Rho-regulated cell spreading was found to be the directly selected trait, with yes-associated protein 1 translocation to the nucleus mediating fitness on soft ECM. Overall, these data show that genetic variation can drive cancer cell adaptation to ECM stiffness.
Other Authors: Ting-ching Wang, Suchita Sawhney, Daylin Morgan, Richard L Bennett, Richa Rashmi, Marcos R Estecio, Amy Brock, Irtisha Singh, Charles F Baer, Jonathan D Licht
Platelet roles in determining biophysical flow in pre-metastatic niche
Fransisca Leonard, Houston Methodist
The role of platelets in interaction with CTCs and other bone marrow-derived cells to increase tumor cell survival, invasion, and growth have been established. However, the biophysical roles of accumulated and aggregated platelets in the pre-metastatic soil have been elusive.
We hypothesized that primary tumors may alter biophysical transport phenomena in distant organs, promoting the arrest of CTCs on vessel walls via platelet contribution. To test this hypothesis, treated tumor-bearing animals with platelet inhibitors: clopidogrel, eptifibatide, anti-GPIba (CD42b) antibody or aspirin.
Series of studies were conducted using flow cytometry and intravital microscopy (IVM) imaging of tumor imaging of liver capillaries of tumor bearing and non-tumor bearing animals followed by phyton-based computational analysis to understand the flow fundamentals in vessels as a function of development/evolution of the pre-metastatic niche. IVM data showed the irregularities in platelet flow dynamics only in the tumor-bearing animals. Further segmentation of the capillaries revealed distinct platelet population speeds, which may be caused by different populations of platelet that were involved in rolling and adhesion process to the activated endothelial cells.
The study provides new insights into the biophysical roles of platelets in the pre-metastatic niche. We plan to expand to study their effects on CTC circulation, immobilization, interaction with platelets, and the changes of flow biomechanical characteristics in the pre-metastatic niche. By understanding both the biological and physical roles of metastatic initiation, we can develop a new and more effective treatment to reduce the risk of metastasis for cancer patients.
Other authors: Xuewu Liu
Mathematical modeling of the cancer-bone ecosystem
Conor Lynch, Moffitt Cancer Center
In skeletal malignancies such as bone metastatic prostate cancer and multiple myeloma, cancer cells are known to hijack stromal cells to induce extensive bone destruction and formation that in turn provides growth factors and cytokines that support tumor survival and growth. Using experimental data to power a hybrid cellular automata mathematical model, we have captured the key steps in normal homeostatic and cancer induced bone remodeling. The model has allowed for a high resolution view of the spatial dynamics occurring between the cancer and stromal cell populations over time. Building on this platform, we have used the model to study cancer evolution, response to drug treatments, the emergence of resistant subpopulations and how niches in the bone can protect cancer cells from applied therapies. We are now adapting the model to integrate immunological components in a bid to understand the how cancer cells influence the activity and exhaustion of endogenous and adoptively transferred T-cells (e.g. CAR-T cells). Collectively, our work underscores the power of mathematical modeling for analyzing multiple simultaneous cellular interactions in complex tumor microenvironments under control and treatment conditions.
Extracellular vesicles mediate pathological crosstalk within and beyond tumor microenvironment
Ravi Radhakrishnan, UPENN
Extracellular vesicles (EVs) are pivotal drivers of cancer progression, influencing various cancer hallmarks. The remodeling of the extracellular matrix (ECM) within the tumor microenvironment (TME) induces mechanosensitive responses that trigger EV secretion. This study presents a framework for understanding the mechanosensitive release of exosomes, combining data-driven insights and biophysical modeling. Key factors include ECM stiffness and composition, which determine a cell's mechanotype, influencing morphological and mechanical characteristics such as area, shape, stiffness, and motility. These characteristics follow distinct clusters identified through single-cell measurements.
We show that exosome release propensity is regulated by two key axes: the biophysical axis, governed by cortical actin tension, and the signaling axis, mediated by intracellular trafficking regulators such as Rab family GTPases. Both biomechanical and biochemical cues converge on these pathways, influencing EV secretion. Previous studies have demonstrated that exosome-mediated tumor amplification occurs across solid tumors, such as breast, pancreatic, and liver cancers. For example, in liver tumors, exosomes derived from stiff, fibrotic environments amplify tumors via the notch signaling pathway.
Our study further highlights that ECM stiffness promotes EV secretion from breast cancer cells, enhancing tumor growth and metastasis. Mutations in the stiffness-sensing protein talin reduce EV secretion and tumor metastasis. Moreover, EVs secreted in stiff microenvironments alter macrophage transcriptional phenotypes, contributing to metastasis. The study underscores the role of EVs in modulating cancer progression and suggests that EV-based liquid biopsies provide real-time monitoring of tumor evolution, offering personalized diagnostic tools and adaptive treatment strategies.
Other Authors: Kshitiz Parihar, Hassan Ghmkin, Phyoe Kyawe Myint, Paul A. Janmey, Wei Guo, Valerie M. Weaver, Ravi Radhakrishnan
Frequency-Dependent Ecological Interactions Increase the Prevalence, and Shape the Distribution, of Preexisting Drug Resistance
Jacob Scott, Cleveland Clinic
The evolution of resistance remains one of the primary challenges for modern medicine, from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, preexisting resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small-cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms, from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of preexisting resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of preexisting resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe preexisting resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict preexisting resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for preexisting resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of preexisting resistance.
Other Authors: Jeff Maltas, Dagim Shiferaw Tadele, Arda Durmaz, Christopher D. McFarland, Michael Hinczewski, Andriy Marusyk and Jacob G. Scott
T Cells Spatially Regulate B Cell Receptor Signaling in Lymphomas through H3K9me3 Modifications
Ankur Singh, Georgia Tech
Diffuse large B-cell lymphoma (DLBCL) originates from germinal center B cells, where interactions with T follicular helper (Tfh) cells are crucial for B cell maturation. Dysregulation at the Tfh-B cell immune synapse disrupts normal B cell differentiation, contributing to follicular lymphoma (FL) and DLBCL. Our research focuses on how epigenetic changes and receptor-ligand signaling between T and B cells influence lymphoma progression and therapy resistance. We have developed technology platforms to study T-B cell mechanotransduction, enabling in vitro and in vivo reporting of cell-generated forces on specific receptors, including the use of a novel synthetic Notch receptor (SynNotch) system and PMFA assays. Notably, using synthetic hydrogel-based lymphoma organoids, we discovered that T cells and tissue stiffness within the lymphoid tumor microenvironment (Ly-TME) alter B cell receptor (BCR) signaling. These changes also modify histone markers like H3K9me3, reducing the effectiveness of BCR pathway inhibitors. Imaging techniques show that T cells increase the expression of DNA methyltransferase 3A and cytoskeletal proteins in ABC-DLBCL cells, regulated by H3K9me3. Expansion microscopy further reveals that T cells enlarge and increase H3K9me3 clusters in DLBCL cells, suggesting chromatin re-organization contributes to therapy evasion. Importantly, treatment with a G9α histone methyltransferase inhibitor reverses these T cell-mediated changes and restores sensitivity to BCR pathway inhibitors. Our findings underscore the critical role of the Ly-TME in shaping DLBCL progression and suggest targeting abnormal signaling and microenvironment interactions could improve outcomes for high-risk patients.
Other Authors: Lucy Britto, Jean Koff, Jintian Lyu, Kaitao Li, Ameya A. Dravid, Deepali Balasubramani, Menglan Li, Amir H.K. Ashkezari, Hyun-Kyu Choi, Cheng Zhu
Imaging and mathematical modeling to guide photoimmunotherapy of ovarian cancer
Rebecca Harmon, Northeastern University
This talk will explore the development of photoimmunotherapy (PIT) as a spatially-selective, cell-targeted treatment for advanced ovarian cancer, utilizing clinical antibody–photosensitizer conjugates and nanoparticles. In collaboration with Heiko Enderling's team at MD Anderson Cancer Center and researchers from Moffitt Cancer Center, we are investigating tumor immune cell dynamics in response to PIT using in vitro ovarian cancer cell-immune cell co-culture models. Our work also involves parameterizing a mathematical model of the tumor immune microenvironment. Additionally, we will discuss progress in our in vivo studies, with a focus on developing a handheld microscopy device for real-time imaging of tumor-immune interactions.
Other Authors: Heiko Enderling
Mechanical force regulates ligand binding and function of PD-1
Cheng Zhu, Georgia Tech
Despite the success of PD-1 blockade in cancer therapy, how PD-1 initiates signaling remains unclear. Soluble PD-L1 is found in patient sera and can bind PD-1 but fails to suppress T cell function. Here, we show that PD-1 function is reduced when mechanical support on ligand is removed. Mechanistically, cells exert forces to PD-1 and prolong bond lifetime at forces <7 pN (catch bond) while accelerate dissociation at forces >8pN (slip bond). Molecular dynamics of PD-1–PD-L2 complex suggests force may cause relative rotation and translation between the two molecules yielding distinct atomic contacts not observed in the crystal structure. Compared to wild-type, PD-1 mutants targeting the force-induced distinct interactions maintain the same binding affinity but suppressed/eliminated catch bond, lowered rupture force, and reduced inhibitory function. Our results uncover a mechanism for cells to probe the mechanical support of PD-1–PD-Ligand bonds using endogenous forces to regulate PD-1 signaling.
Other Authors: Kaitao Li, Paul Cardenas-Lizana, Jintian Lyu, Anna V Kellner, Menglan Li, Peiwen Cong, Valencia E Watson, Zhou Yuan, Eunseon Ahn, Larissa Doudy, Zhenhai Li, Khalid Salaita, Rafi Ahmed