breast cancer

National Institute of Allergy and Infectious Diseases

Structural and functional characterization of autoimmune antibodies against NMDAR

May 31, 2031

Project Summary. The goal of this project is to understand the origins and molecular mechanisms underlying the anti-cancer autoimmune response against the N-methyl-D-aspartate receptor (NMDAR) and its correlation with anti-N-methyl-D-aspartate receptor autoimmune encephalitis (NMDARAE). While anti-cancer immune responses can promote tumor elimination, they may also lead to the production of self-reactive antibodies that trigger autoimmune diseases. NMDARAE is the most common form of immune-mediated encephalitis, which results in prominent neuropsychiatric symptoms, including seizures, psychosis, and memory deficits. NMDARs belong to a family of ligand-gated ion channels expressed exclusively in the central nervous system. They are involved in various aspects of brain development and function, including learning and memory. They respond to the neurotransmitter glutamate and a co-agonist, glycine or D-serine, to mediate excitatory neurotransmission, which plays a central role in synaptic plasticity. NMDARAE is associated with ovarian teratomas, where aberrant NMDAR expression is believed to trigger an autoimmune response. In NMDARAE, anti-NMDAR antibodies, as well as B cells and antibody-secreting cells, cross the blood-brain barrier via unknown mechanisms, resulting in the presence of anti-NMDAR antibodies at high titers within the brain and cerebrospinal fluid (CSF). These antibodies target NMDARs, modulating their function and contributing to disease pathology. Emerging evidence, supported by our preliminary data, suggests that NMDARs are also expressed in triple-negative breast cancer (TNBC), extending the relevance of anti-NMDAR autoimmunity beyond ovarian teratomas. In our TNBC mouse model, which ectopically expresses NMDARs (TNBC-NMDAR), we observed the onset of anti-NMDAR autoimmunity, where the produced antibodies cause both anti-tumor activity and symptoms such as lowered seizure threshold, mirroring key features of NMDARAE. Here, we will establish this TNBC mouse model as we develop molecular methods to characterize it. Aim 1 will focus on establishing and characterizing the TNBC- NMDAR mouse model. We will develop a detection method utilizing the intact tetrameric NMDAR channel proteins and a method to isolate B cells expressing B cell receptors against NMDAR from biological samples by using fluorescently labeled intact NMDAR proteins, followed by single-cell RNA sequencing. Aim 2 will utilize single-particle cryo-electron microscopy (cryo-EM) to investigate the interactions between NMDAR and the cloned antibodies, providing insights into epitope recognition, NMDAR subtype specificity, and conformational changes induced by antibody binding. Aim 3 will assess the impact of the cloned antibodies on NMDAR channel activity using electrophysiology. We will also assess anti-tumor activity and NMDARAE onset by each antibody clone. Together, the proposed research will gain insights into the link between anti-cancer anti-NMDAR autoimmunity and NMDARAE. It will also elucidate which functional properties of the cloned antibodies promote anti-tumor activity while contributing to NMDARAE, thereby informing potential therapeutic strategies.

Weak Cell Adhesion is a Prognostic Signature of Invasive Cancer

May 31, 2031

Project Summary Despite early detection, low-grade and localized breast cancers such as ductal carcinoma in situ (DCIS) can relapse in up to 20% of cases despite standard of care. For DCIS, relapse affects over 12,000 U.S. women annually and has increased 60% in the last 40 years. Current diagnostic assessments including histopathological markers often miss early disseminating cells, lack specificity, or cannot distinguish cancer from non-cancer cells in the stroma. Hence there is an unmet need for cancer diagnostic technologies that employ radically different characterization methods. For example, significant physical differences exist between metastasizing and benign breast cancer cells, owing to metastasizing cells detaching from the primary tumor, migrating through the surrounding stroma, intravasating and extravasating, and ultimately engrafting in distant tissues. We recently demonstrated that cancer cells with weaker adhesion migrate faster and metastasize more frequently in murine breast cancer models than strongly adherent cells. In a small pilot study of human breast tumors, we also observed that the abundance of weakly adherent (WA) cells scales with disease severity; subpopulations from invasive carcinomas were the least adherent. However, a subset of DCIS cases displayed much less adhesion, suggesting that these patients may have a tumor subpopulation that progresses to metastatic disease despite standard-of-care treatment. Weak adhesion is a defining physical characteristic of tumors, but to establish their role in initiation, metastasis, and patient outcomes, we will leverage model systems and our newly patented adhesion technology to answer these fundamental questions of cancer biology and clinical translation. To understand the impact of adhesion on cancer progression, we will evaluate the tumor-initiating potential of WA versus strongly adherent (SA) tumor cells in a murine breast cancer model before confirming how weak adhesion advantages cells to cause secondary disease using bioengineered in vitro models. In dissecting the stages of metastasis where WA cells exhibit advantages, e.g., recapitulating stromal niche, transendothelial migration, and tissue-specific colonization, we will identify mechanisms that enable WA cells to thrive and evaluate therapeutic targets that disrupt these pathways. Finally, we will analyze the adhesion profiles of resected tumors and stroma from 80 breast cancer patients with DCIS or invasive disease. Adhesion data will be correlated with conventional assessment methods and ultimately with patient outcomes, e.g., disease-free and progression-free intervals. We anticipate that the DCIS subpopulation that aligns with the adhesion signature of invasive carcinomas will have shorter intervals and survival time. This integrated study design bridges mouse models, mechanistic bioengineering assays, and human samples to clarify the metastatic potential and prognostic value of WA breast cancer cells. Our use of mouse models in this grant is required to study the interactions among tumor cells, immune cells, vasculature, and stromal tissues that drive tumor formation in vivo. Bioengineered in vitro systems lack the complexity to ask such questions and using injected tumor cells is not possible in humans.

Personalized Spatial Regulatory Networks to Decode Breast Cancer Microenvironments

PROJECT SUMMARY Triple-negative breast cancer (TNBC) is an aggressive subtype with early recurrence, high metastatic burden, and limited treatment options. While genomic alterations contribute to its progression, epigenetic plasticity and spatial organization within the tumor microenvironment (TME) play critical roles in intra-tumor heterogeneity, immune evasion, and therapy resistance, yet remain poorly understood. To address this, we will develop a cost- effective and scalable methodology that integrates spatial ATAC-seq, spatial in situ transcriptomics (Xenium), and single-nucleus (sn) Epi Multiome sequencing (snRNA-seq + snATAC-seq) from core-needle biopsies, enabling high-resolution mapping of gene regulatory networks within the intact TME. Our preliminary data from six TNBC biopsies demonstrate that spatial in situ transcriptomics and spatial ATAC-seq provide critical insights into tissue architecture but suffer from data sparsity, necessitating the integration of single-nucleus Epi Multiome data to enhance cell-type annotation and impute missing genomic features. In Aim 1, we will establish a multi- modal workflow that maximizes molecular insights from limited biopsy material by optimizing tissue-preserving and multiplexed sequencing approaches. This includes leveraging patient-specific genetic variation to deconvolute nuclei-derived data and linking it to spatial transcriptomic and spatial chromatin accessibility profiles. In Aim 2, we will develop a computational framework to integrate these multi-layered datasets, enabling spatially resolved epigenomic-transcriptomic analysis that identifies key regulatory chromatin elements and transcriptional programs associated with TNBC progression, immune infiltration, and therapy resistance. This project will generate the first comprehensive, patient-specific spatial regulatory atlas of TNBC, providing fundamental insights into how chromatin accessibility and gene expression interact within the TME. Ultimately, this work will pave the way for novel precision oncology strategies, biomarker discovery, and the development of targeted therapies that address TNBC’s spatial and molecular heterogeneity.

RECONJOINT: A Preference Elicitation Tool to Improve Shared Decision Making for Breast Reconstruction Surgery

PROJECT SUMMARY/ABSTRACT Breast reconstruction is a critical component of comprehensive breast cancer care, offering physical and emotional restoration after mastectomy. However, 40% of women undergoing breast reconstruction report dissatisfaction and decisional regret due to low involvement with treatment decisions and poor alignment between treatment preferences and the chosen reconstructive technique. Current approaches to shared decision-making (SDM) often fail to elicit and integrate individual-level preferences into treatment planning. This serves as a barrier to effective SDM and patient-centered care. To address this gap, we developed a web- based decision tool that uses adaptive choice–based conjoint (ACBC) analysis to elicit patient-level preferences for breast reconstruction. Preliminary studies indicate that the decision tool is acceptable and usable; patients wanted to view their results and use the tool in clinic, which we could not accommodate at the time because the decision tool currently lacks a structured method for clinical integration. We propose to develop an implementation toolkit for the decision tool to facilitate clinical integration and then test the feasibility, acceptability, and implementation of the intervention, RECONJOINT (decision tool and toolkit). In Aim 1, we will design an implementation toolkit informed by focus groups and developed with input from key partners, including patients, providers, and patient advocates. Candidate elements for the implementation toolkit include components developed for site-level implementation: treatment preferences report, video introducing the tool and existing evidence, and recommendations for patients and providers to incorporate preferences into SDM. In Aim 2, we will evaluate the feasibility, acceptability, and preliminary efficacy of the intervention in a pilot cluster-randomized hybrid type 1 trial conducted at two cancer centers (Memorial Sloan Kettering and Duke University). Our primary outcome of interest is the feasibility of the intervention. Secondary outcomes include acceptability and preliminary efficacy. Using a hybrid design, we will simultaneously evaluate facilitators, barriers, and strategies to implementation and how these factors influence the feasibility and acceptability of the intervention. The Consolidated Framework for Implementation Research and the Theoretical Domains Framework will serve as conceptual frameworks. This study is innovative as it leverages ACBC analysis to elicit patient preferences, designs an intervention with multilevel input from clinical and community partners, and uses a hybrid trial design to simultaneously evaluate feasibility, acceptability, preliminary efficacy, and implementation. By addressing critical barriers to SDM and enhancing patient–provider communication, this research aligns with the goals of PA-25-253 and the National Cancer Plan to deliver high quality, patient-centered cancer care. Findings from this study will inform a full-scale multi-site trial to evaluate the efficacy of the intervention and implementation outcomes (e.g., reach).

Augmented-reality guided lumpectomy

Abstract Far too many women with a newly diagnosed breast cancer must undergo repeat surgery because positive margins were found at the time of their initial lumpectomy. Supine volumetric MRI has potential to improve surgical accuracy, and reduce re-excision rates by nearly 50%. Spurred by our preliminary results improving depth perception via projected apertures and integrating intra-operative marker tracking into commercial Augmented Reality systems, we have developed a highly accurate initial prototype Augmented Reality system to project volumetric MRI data inside the breast to guide surgery. In Aim 1, we will compare methods of projecting apertures in a phantom model of lumpectomy. In Aim 2, we will test the final prototype system in a pilot study of 30 women with new breast cancer. Standardized use of cavity- and shave-margins will enable paired comparisons between standard and AR-guided techniques in the same patients, including ability to reduce positive margin rates and minimize overexcision. Ultimately the system will be ready for future randomized controlled trials to measure efficacy as the next step toward broad clinical adoption.

I3-BC: Image-Based Infiltrating Immune Cell Detection and Outcomes in Breast Cancer Clinical Trials

PROJECT SUMMARY Tumor infiltrating lymphocytes (TILs) represent an accessible biomarker of the tumor-immune microenvironment (TIME) in breast cancer, demonstrating consistent association with response to neoadjuvant chemotherapy and outcomes in HER2-positive and triple-negative breast cancer. Despite efforts to standardize TIL enumeration from hematoxylin and eosin stained tumor slides, TILs have not gained widespread adoption due to inter- observer variability, and time limitations in pathologic assessment, among others. Further, other key elements of the microenvironment, such as tumor-associated macrophages (TAMs), do not yet have standardized approaches for quantification or characterization. As a result, there is no assessment of the TIME for the vast majority of breast cancers diagnosed in the US and around the world. However, the rapid growth of digital pathology offers the potential to leverage computational approaches to overcome these limitations and democratize access to TIL and TAM enumeration. The overall goal of this project is to determine if computational approaches to TILs (existing) and TAMs (to be developed within this grant) are comparable to pathologist- enumerated TILs and TAMs and, further, associated with relevant patient outcomes from two phase III breast cancer clinical trials. Prior to project initiation, we have developed both a compute-intensive artificial intelligence- based TILs approach, an open source software (QuPath)-based TILs approach, and expertise in RNAseq-based immune quantification. We will first focus on TILs - benchmarking the two computational and RNAseq immune approaches against pathologist TIL counts (‘gold standard’) then evaluating association of each with event-free survival in two completed clinical trials (Aim 1). In parallel, we will develop a novel computational approache to enumerate and phenotype TAMs by using immunohistochemical staining for macrophage markers on the same slide with standard H&E, then apply in the same two clinical trials (Aim 2). Our approach is innovative because we will benchmark diverse approaches at scale in relevant clinical studies. The study is significant because we will determine if computational approaches to TILs/TAMs align with pathologist estimates and clinical outcomes, then ensure these algorithms are available to the community. Our long-term goal is to democratize computational TIL and TAM enumeration as pathology decision-support to facilitate integration of accessible tumor-immune microenvironment into clinical trials and care.

Targeted Prodrug Cytokines for Metastatic Breast Cancer Immunotherapy

Project Summary. Our approach directly addresses key limitations in targeting and treating metastatic breast cancer, where we propose the selective activation of modular immune-modulating cytokines within the hypoxic and ROS-active TME for delivery across the BBB, providing the necessary pre-clinical data for future clinical translation. The in vitro and in vivo investigations of this novel immunotherapeutic in immunocompetent models will allow our team to study the interplay between tumor-driven immune activation, cytokine signaling, and anti-tumor immunity in both primary and metastatic sites, and establish a robust groundwork for subsequent clinical validation within the OSUCCC. This proposal addresses two key challenges in developing a novel immunotherapy strategy for breast cancer by answering two hypotheses: (1) can a modular immunotherapy platform with tumor-selective activation of prodrug recombinant cytokines overcome these limitations in drug delivery, and (2) can the development of nanobody-cytokine fusions that can selectively target primary breast cancer tumors and cross the BBB to reach metastatic tumor sites? The first hypothesis focuses on achieving tumor environment-specific activation of prodrug-based recombinant cytokines. Protein cytokines are highly potent, and while others have tried to block their activity using a fused genetic linker to ‘mask’ functionality, no one has yet attempted to use a non-canonical-based chemical strategy to achieve this inhibition. Immune-modulating cytokines will be recombinantly expressed with integrated ncAAs that block cytokine activity until the function is regenerated in the breast cancer TME. Once the cytokine activity is controlled, our second hypothesis will be to achieve selective delivery of the cytokine via fusion to nanobodies. While success has been found in targeting primary tumors in drug and protein delivery, a key challenge remains in reaching secondary metastatic tumors in hard-to-reach sites (i.e., brain). Engineered nanobodies, with affinity for breast cancer tumors and the ability to bind to BBB transcytosis receptors, will enable selective delivery to metastatic breast-to-brain tumors, resulting in tumor- specific activation, immune responses, and improved therapeutic outcomes. This system can significantly improve therapeutic outcomes for patients with mBC by integrating selective activation and delivery mechanisms to reduce off-target effects and enhance tumor-specific immune responses in both primary and secondary metastatic tumor sites. Optimizing drug delivery systems to tune immune responses could offer more effective and less invasive treatment options when compared to traditional and engineered cell-based approaches. Our momentum towards precision medicine and targeted therapies holds significant promise for improving outcomes for mBC patients, and has the potential to serve as a pan-cancer treatment for aggressive metastatic cancers from the following aims: (1) generating a modular platform for tumor-specific activation of prodrug cytokines, (2) evaluating cytokine delivery and anti-cancer immune phenotypes in mBC.

Overcoming Treatment Resistance by Targeting Polyploid Breast Cancer Cells with AI assisted Single-Cell Analysis