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NIH Research Projects · FY 2025 · 2022-07

Project Summary Despite the establishment of obesity as a risk factor in the development of a multitude of cancer types including breast cancer, there is still a significant insufficiency of genetic evidence for implicating obesity in the initiation and progression of breast cancer development. While immunotherapeutic approaches such as the PD-1/PD-L1 blockade have shown promise in treating breast cancer, obesity causes substantial impairment of anti-tumor immunity. A significant challenge in the development of effective immunotherapeutic strategies for obesity- associated breast cancer lies in the characterization of the bone fide target genes that drive obesity-associated immune resistance. The emergence of noncoding RNAs as important biological molecules has provided the foundation for the development of next-generation treatment strategies. Our preliminary data demonstrated that a small nucleolar RNA (snoRNA), SNORD46, is upregulated in breast cancer and negatively correlated with the infiltration of cytotoxic leukocytes. We also indicated that obesity is associated with a chromatin variant of SNORD46. We collected genetic evidence indicating that the expression of SNORD46 leads to obesity and mammary gland tumors which are resistant to immunotherapy. Therefore, the goal of this proposal is to comprehensively characterize and implicate SNORD46 as a driver of obesity-associated breast cancer and demonstrate that targeting snoRNAs effectively improves the expansion and activation of CD8+ T-cells and NK cells and sensitizes obesity-associate breast cancer in immunotherapy. We will address our central hypothesis that snoRNAs act as essential gene targets that promote obesity and obesity-associated breast cancer initiation and immune resistance, which could be attenuated in vivo by an anti- sense oligonucleotide-based targeted therapy. In Specific Aim 1, we will define the molecular mechanism of SNORD46 inhibited, IL-15-dependent non-classic pathway in adipocytes, and IL-15-dependent expression of stimulatory checkpoints in CD8+ T cell and NK cells. In Specific Aim 2, we will ascertain the functional importance of snoRNAs in obesity-associated tumorigenesis and immune resistance using high-fat diet-induced obese mice and Snord46 mut/mut knockin obese mice. The proposed study will provide initial genetic evidence for defining the crucial role of adipocyte-expressed snoRNAs in promoting obesity and obesity-associated breast cancer. Mechanistically, we will elucidate the molecular mechanisms of the SNORD46-leukocyte interactions that drive obesity-associated breast cancer resistance to immunotherapy. Clinically, the proposed studies will delineate that targeting snoRNAs using locked nucleic acids (LNAs) effectively improves the proliferation/activation of CD8+ T-cells and NK cells in vivo, sensitizing obesity-associated breast cancer to immune checkpoint blockers.

NIH Research Projects · FY 2025 · 2022-07

PROJECT SUMMARY / ABSTRACT Non-Hodgkin lymphoma (NHL) is the most frequent hematologic malignancy, and is divided into B-cell and T- cell lymphoma subtypes. B-cell NHL patient outcomes have improved dramatically with the development of new targeted therapies, but similar progress has not been made against peripheral T-cell (PTCL) and cutaneous T-cell (CTCL) lymphomas. Indeed, patients who relapse after initial PTCL therapy have a less than 1 year median survival, and patients with the CTCL subtype mycosis fungoides that is advanced, or that has Sézary syndrome with skin, blood, and lymph node disease, have a median survival of one year from diagnosis. Thus, the identification of novel targets and drugs with activity against PTCL and CTCL will be critical for this area of unmet medical need to help cure these diseases, which have not been a major focus for industry-sponsored research due to their heterogeneity and lower incidence. To address this challenge, we investigated the cell surface proteome of PTCL and CTCL cell lines and found Heat shock protein 70 (HSP70) to be highly expressed. Then, we developed monoclonal antibodies to human HSP70 and found that one, designated clone 239-87, recognized HSP70 on PTCL and CTCL cells but not on normal T-cells. To convert 239-87 into a drug, we linked it to monomethyl auristatin E (MMAE) to generate an antibody-drug conjugate (ADC), which we found inhibited PTCL and CTCL cell line growth as well as and, in some cases, better than brentuximab vedotin (BV), another ADC already approved for T-cell NHL. Also, 239-87-MMAE showed synergy when combined with other therapies already used against PTCL and CTCL, including the deacetylase inhibitor vorinostat and the ADC BV. Next, we used the 239-87 single chain variable fragment sequence to create chimeric antigen receptor (CAR) guided T-cells, and these were activated in the presence of T-cell NHL cell lines. Finally, in a cell line-based xenograft, the 239-87-MMAE ADC cured mice with an aggressive PTCL variant. Our preliminary data support the central hypothesis that targeting cell surface HSP70 using an ADC or CAR T-cell therapy approach will be both novel and effective against PTCL and CTCL, and could ultimately improve patient outcomes. In order to test this hypothesis, we propose three aims: (1) To investigate the differential expression of HSP70 in PTCL and CTCL models, including in primary patient samples, and to perform studies to identify pathways in these cancer cells that regulate HSP70 expression; (2) To identify the best ADC and CAR T-cell construct based on our 239-87 antibody, and explore which combinations will show greatest synergy; and (3) To use in vivo models, including patient-derived xenografts, to determine effective strategies against these lymphomas that will work best in the clinic. Taken together, successful completion of these studies will increase our understanding of the role of HSP70 in PTCL and CTCL biology, provide the rationale to take these approaches to the clinic for patients with PTCL and CTCL who are looking for novel therapies and, ultimately, improve their outcomes.

NIH Research Projects · FY 2024 · 2022-07

Adoptive cellular therapy and therapeutic cancer vaccines have shown promise in solid tumor malignancies but responses have generally been modest. By combining these two modalities in a rationally designed first-in- human study, we propose to treat patients with a highly-defined T cell product (comprised of PRAME-specific memory T cells) in combination with an antigen- specific vaccine (comprised of a curated cocktail of synthetic long peptides) to augment and sustain the in vivo persistence of transferred PRAME-specific T cells. Addition of an immune checkpoint inhibitor, (anti-CTLA4), further favorably modulates the tumor environment by enabling un-fettered CD28 engagement of B7 (to expand the in vivo population of CD28-hi PRAME-specific memory T cells), lowering the threshold of activation of endogenous tumor-reactive T cells (facilitating antigen- spreading), and modulating inhibitory activity (by engaging CTLA4+ regulatory cells). This study, as proof of concept for this triple T cell-based strategy, will be used to address a critical unmet need for patients with metastatic uveal melanoma, and establish a versatile platform for targeting a broader range of tumor antigens and tumor types. In this study, all 3 modalities (PRAME vaccine, ETC therapy and anti-CTLA), are reduced to clinical practice and by using a highly-defined antigen-specific T cell population for adoptive therapy, allows for rigorous immunologic analysis so that reasons for success or failure can be elucidated. In an effort to enhance efficacy as well as broaden this approach to benefit a larger pool of patients, PRAME epitopes presented by additional HLA Class I and II alleles will be identified. The results of the proposed studies may lead to formal Phase II trials to assess true efficacy, development of a new treatment standard for refractory metastatic uveal melanoma, and refinement of CD4 T cell and combined CD4 and CD8 T cell strategies that can be incorporated into future clinical studies. This proposal leverages the clinically-tested expertise in therapeutic cancer vaccines at ISA Pharma and the pioneering development of ETC therapy in the Yee Lab to explore an opportunity that is timely and ideally suited for an academic – industrial partnership.

NIH Research Projects · FY 2025 · 2022-07

PROJECT SUMMARY/ABSTRACT Among the half a million childhood cancer survivors alive in the US today, the most commonly reported non- cancer severe, life-threatening, or fatal chronic condition is cardiovascular disease (CVD) . It is the leading non- cancer cause of premature death in this population. Heart radiation and anthracycline exposure have been associated with a variety of CVD outcomes including cardiomyopathy, coronary artery disease (CAD), and heart valve disease. Investigations of radiation therapy (RT)-related CVD have typically established associations based solely on whole heart dose metrics; thus, overlooking the heterogeneity of the organ and its substructures. Our team was the first to report data demonstrating substructure-level dose response of CVD risk in childhood cancer survivors. Despite establishing distinct radiosentivities, cardiac substructure dose constraints are not commonly incorporated into RT treatment planning due to the lack o f validated risk prediction models, thus, missing opportunities to prospectively optimize RT planning and retrospectively personalize risk-counseling and long-term cardiovascular surveillance in current and future cancer survivors. The goal of the proposed project is to develop and validate novel CVD risk prediction models that incorporate cardiac substructure doses. Further, we propose to develop tools to clinically translate these models into effective personalized treatment paradigms with prospective and retrospective applications for care providers to reduce CVD risk. We will: (1) develop and validate risk prediction models for cardiomyopathy, CAD, and heart valve disease incorporating cardiac RT substructure doses, adjusting for demographics and chemotherapy exposures; and (2) integrate CVD risk prediction models into commercial RT treatment planning systems and web-based applications, and establish their use via in-silico studies of contemporary patients treated with RT. This will be the first investigation to use the unique radiosensitivity of different cardiac substructures as the foundation for models that can predict the risk of specific types of CVD in children newly diagnosed with cancer as well as among long-term survivors. Incorporating the substructure doses into prediction models will significantly advance clinical care for both prospective RT treatment planning and retrospect ive risk assessments. Prospectively, late CVD risk could be decreased in future survivors by optimizing delivery of chest-directed RT with cardiac substructure dose constraints and selecting the plan that confers the lowest risk, while maintaining optimal clinical target volume coverage. Retrospectively post treatment, the clinical team can provide evidence-based personalized risk mitigation counseling, based on individualized risk profiles determined from delivered cardiac substructure doses adjusted for chemotherapy exposures and demographics. Successful execution of the proposed project has the potential to transform clinical practice for treatment of childhood and adolescent patients with cancer.

NIH Research Projects · FY 2025 · 2022-07

Gastric adenocarcinoma (GAC) remains one of the deadliest forms of cancer due to its rapid progression, resistance to therapy, and a high rate of metastatic spread. A common site of metastases is the peritoneal cavity that occurs in 40-50% of patients and leads to development of peritoneal carcinomatosis (PC). PC is almost a universally lethal diagnosis with survival of less than 6 months due to limited therapeutic options that are currently available. Intratumoral heterogeneity (ITH) is a fundamental property of GAC that contributes to therapy resistance, disease progression and metastasis. We and others have characterized the genomic and molecular ITH in GAC and PC, however, tumor cell lineage plasticity−the non-genetic, cell intrinsic origin of ITH remains poorly understood. In our preliminary efforts to dissect the cellular and molecular ITH using single-cell analysis, we discovered that the diversity in tumor cell lineage/state compositions appears to be an upstream key regulator of phenotypic ITH of PC, beyond the genetic factors. We also find that tumors classified based on tumor cell lineage/state compositions (cellular subtypes) are strongly associated with survival, exhibiting differential activation of oncogenic pathways and distinct immune phenotypes. We therefore hypothesize that tumor cell lineages/states dynamically evolve to resist treatment and promote tumor growth and its composition determines phenotypes and outcomes of GACs. The goal of Aim 1 is to characterize tumor cell lineage/state diversity and compositions in clinically defined GAC cohorts, determine their impacts on tumor cell clonal evolution, and identity lineage features associated with GAC progression and metastasis. We will also profile changes in tumor cell lineages and states in paired baseline and progressive tumors following chemotherapy or immunotherapy, determine their impact on immune phenotypes and patient responses to anti-cancer therapies, and identify lineage features associated with therapy resistance. In Aim 2, we will leverage our genetically engineered mouse models (GEMMs) of GAC, follow the expansion and dissemination of cancer cells in GEMMs over a period of time to longitudinally track and characterize dynamic changes in tumor cell lineage identity and transcriptome states at single-cell resolution. We will investigate the clonal architecture of tumor cells in different lineages/states and examine the dynamics of clonal populations that sustain tumor growth at the primary sites, seed and colonize distant organs; we will also profile how tumor cells in different lineages interact with TIME at the primary tumor site and influence invasion and dissemination. In Aim 3, we will investigate tumor cell lineage plasticity in response to chemotherapy and immunotherapy in GEMMs. We will characterize therapy-induced emergence of resistant cell lineages/states and identify the fundamental pathways and drivers of lineage/state transition. This study will link tumor cell lineage/state plasticity to GAC therapy resistance, progression, and metastasis. A better understanding of sources of phenotype heterogeneity and dynamics underlying GAC progression is paramount to identifying effective treatments.

NIH Research Projects · FY 2026 · 2022-07

Summary Statement/Abstract Multiple sclerosis (MS) is the most common demyelinating disease, affecting approximately 400,000 people in the United States and 2.5 million people worldwide. It is not clear what causes MS, but many believe that it is because our own immune system attacks oligodendrocytes that generate myelin. However, the current therapies that dampen our immune system can only relieve the symptoms but not cure the disease itself. Therefore, it is urgent to find novel therapeutic approaches that can cure the disease, for instance by promoting remyelination. The central nervous system has great potential to regenerate oligodendrocytes and remyelinate in response to myelin damage, however the ability of remyelination is greatly diminished in the MS lesions. Two major reasons are known to prevent efficient remyelination in MS lesions: 1) damaged myelin cannot be efficiently cleared, thereby preventing formation of new oligodendrocytes, and 2) newly generated and/or existing oligodendrocytes have lost the ability to form new myelin. We have identified a key regulator – Quaking (protein name: Qki; gene name: Qk) – that is potent to overcome both obstacles. Firstly, we discovered that Qki is a key regulator of phagocytosis of microglia. Depletion of Qki in microglia greatly reduced the phagocytic activity of microglia, which is critical for clearance of myelin debris and consequently remyelination. Secondly, we discovered that Qki is a major regulator of oligodendrocyte differentiation and myelin homeostasis by regulating lipid metabolism of both newly formed oligodendrocytes and existing oligodendrocytes in the demyelinating lesions. Mature myelin has been considered an inert material for decades. However, our study showed that mature myelin is in fact a very dynamic material through exploiting our genetic systems by depleting Qki in mature myelinating oligodendrocytes of adult mice. The comparative lipidomic and transcriptomic analyses identified Qki as an essential factor for myelin lipid biosynthesis by controlling the transcription of the lipid metabolism genes, particularly those for fatty acid desaturation and elongation, via coactivation of the peroxisome proliferator- activated receptor beta (PPARβ)-retinoid X receptor alpha (RXRα) complex. These findings were corroborated by functional rescue experiments with brain penetrant PPARβ/RXRα agonists, KD3010 and bexarotene. We hypothesize that restoring lipid metabolism by activating PPARβ/RXRα/Qki function will help remyelination in MS through two ways: 1) activating microglia’s function to clear myelin debris, consequently promoting oligodendrocyte regeneration, and 2) enhancing lipid generation of existing and newly generated oligodendrocytes. To test this hypothesis, we propose the following three specific aims. To test this hypothesis, we propose the following three specific aims: 1) To investigate the role of Qki/PPARβ in microglial phagocytosis in clearing myelin debris and promoting remyelination, 2) to investigate the role of Qki/PPARβ in myelin lipid metabolism and remyelination, and 3) to elucidate the mechanism by which Qki functions as a coactivator to enhance PPARβ transcription activity in both microglia and oligodendrocytes. Our studies will not only provide insights into the etiological mechanism for MS, but more importantly, help MS patients find a cure through targeting remyelinating pathway.

NIH Research Projects · FY 2025 · 2022-07

Project Summary In most cancers, heterogeneous cell composition within and between tumor samples is mirrored in complex variations at a molecular level. This molecular complexity includes both transcriptional variation and genomic complexity, since tumors continually evolve and acquire new mutations. Therefore, to further our understanding of tumor evolution, it is essential to study the evolutionary dynamics between cancer genomes and transcriptomes. However, due to the complex interplay between cancer cells and their environment, these dynamics are still poorly understood, which presents a major bottleneck for the advancement of clinical management and treatment of cancer patients. Recent multi-region matched DNA/RNA sequencing studies have made significant advances in our understanding of cancer evolutionary dynamics. However, the analytical tools used in these studies were limited to one molecular data type at a time, representing a missed opportunity for novel biological discovery. The overall objective of this proposal is to 1) quantify, at scale, the evolutionary dynamics between genomic and transcriptomic variations in cancer cells; and 2) link this quantity to cancer prognosis and therapeutic response. On the methodological side, we will develop a suite of integrative deconvolution models for matched genomic and transcriptomic data types. Multiple angles to approach the matched data will be evaluated in separate statistical models. On the applied side, we will focus on the clinical impact of such models on the treatment of prostate (PCa) and thyroid cancers (THCa). These two cancers rank 3rd and 12th in prevalence and are projected by the CDC to present a total of 292,810 new cases in 2021 in the US. For both cancers, overtreatment is the most clinically urgent problem since there is no clear method to differentiate low-risk patients from those at high risk. We hypothesize that biomarkers informed by tumor evolutionary trajectory may identify patients who do not need further treatment. Identification of these biomarkers would significantly improve the efficiency of clinical practice. Our research group consists of experienced investigators with complementary expertise in tumor heterogeneity and clinical management of cancers. Together, we propose the following Aims: 1. Develop integrative deconvolution models to study the evolution of transcriptomes in cancer cells, 1A) at the cell-type and gene levels, 1B) at single-nucleotide-variant level, 1C) genomic heterogeneity over a multi-sample design, and 1D) transcriptomic heterogeneity over a multi-sample design; 2. Apply integrative models to cancer patients for biomarker discovery in 2A) high-risk prostate cancer and 2B) high-risk thyroid cancer; 3. Develop user-friendly and computationally efficient software tools for cancer genomics. The proposed methods and tools are expected to open new avenues to discovery by enabling comprehensive profiling of tumor cell types over evolution and associating these values with clinical outcomes. Our proof-of-principle investigation of prostate and thyroid cancers has the potential to identify new integrative biomarkers predictive of cancer prognosis and response to treatment.

NIH Research Projects · FY 2025 · 2022-07

Summary Brain metastasis (BM) affects millions of cancer patients and represents an unmet clinical challenge. Advances in targeted- and immuno-therapies have prolonged cancer patients’ survival via better control of primary cancers and extracranial metastases, but the incidence of BM is increasing steadily upon disease recurrence. Sadly, patients having symptomatic BMs do not respond well to current treatments and have extremely poor survivals. The brain has unique structural and biological features, thus the interaction of BM tumor cells with the brain physical environment are distinctive and underexplored. Deeper understanding of these unique interactions is critical for developing better therapeutics for BM. Recently, we found that microglia, which are myeloid-derived innate immune cells in the brain, were activated upon BM cell extravasation into the brain parenchyma. Further, Lag3 on microglia binds to the major histocompatibility complex (MHC)-II on BM cancer cells, and this interaction inhibits early-stage BM outgrowth. Interestingly, MHC-II is severely downregulated in human and mice BMs compared to their primary tumors. MHC-II genes are known to be silenced by epigenetic modifications in cancer cells, e.g., EZH2-induced 3meK27H3, or increased histone deacetylase (HDAC) function. Indeed, knockout EZH2 in cancer cells increased BM cell surface MHC-II molecules and decreased BM growth in mice; and treating cancer cells with clinically-applicable EZH2- and/or HDAC-inhibitors increased MHC-II expression. These findings led us to hypothesize that MHC-II on BM cells and Lag3 on microglia dynamically interact to control early-stage BM outgrowth, and restoring MHC-II expression in BM using epigenetic drugs may boost brain innate immune responses and provide novel strategies to treat BM. We will test our hypothesis by interrogating how microglia, a unique innate immune component in the brain, interact with BM tumor cells along the temporo-spatial progression of BM. Also, early-stage BM biology is severely understudied, since most surgically resected patients’ BMs are late-stage lesions. We will explore the interaction between BM and the unique brain environment during BM development and discover novel biological determinants that are critical for early-stage BM using enhanced MRI imaging to precisely locate early stage BM lesions, and by spatial gene expression profiling (Aim 1). To uncover mechanisms that boost the innate immune response in early stage BM, we will assess how the tumoral MHC-II/microglial Lag3 interaction functionally controls BM outgrowth and we will elucidate the epigenetic regulation of MHC-II expression in BM cells (Aim 2). Lastly, we will test whether therapeutically increasing MHC-II with clinically-applicable epigenetic drugs boosts immunity and inhibits BM in preclinical models and test the potential synergy of combining epigenetic modulators with existing immune checkpoint therapies (Aim 3). In summary, our proposed studies focus on revealing the dynamic interactions and crosstalk of BM cells with the innate immune compartment within the brain and developing novel early intervention and therapeutic strategies using epigenetic drugs to enhance the immune response and treat BMs.

NIH Research Projects · FY 2025 · 2022-07

Project Summary Lung cancer is the top cause of cancer mortality. Despite recent advances, the majority of patients with lung cancer lack effective therapeutic options, underscoring the dire need for additional treatment approaches. Genomic studies have identified frequent mutations in subunits of the SWI/SNF chromatin remodeling complex including SMARCA4 and ARID1A in non-small cell lung cancer with a frequency of up to 33% in advanced stage disease, making it the most frequently mutated complex in lung cancer. Recent reports and our own data have identified the paralogue SMARCA2 to be synthetic lethal to SMARCA4. However, identifying selective inhibitors of SMARCA2 has been challenging. Hence, we have developed novel SMARCA2 degrading small molecules based on the proteolysis targeting chimera (PROTAC) technology. We demonstrated that YD23, our lead SMARCA2 PROTAC, potently and selectively induces degradation of SMARCA2. We further showed that YD23 selectively inhibits growth of SMARCA4 mutant lung cancer cells. Mechanistically, we showed that YD23 induces changes in chromatin accessibility only in SMARCA4 deficient cells. Taking these observations together, we hypothesize that SMARCA2 degradation using YD23 is an attractive therapeutic strategy with promising therapeutic index in lung cancers with inactivating mutations in SMARCA4. The major objective of the proposed study is to provide preclinical evidence to guide future development of YD23 (or its analogs) in patients with SMARCA4 mutant lung cancer. While we have shown marked sensitivity of SMARCA4 mutant lung cancer cell lines to YD23, we still do not know the detailed mechanistic basis for this activity. Hence, we intend to perform gene expression, epigenetic and chromatin accessibility studies followed by integrative analysis to triangulate on direct target genes whose chromatin landscape is altered by presence or absence of SMARCA2 in SMARCA4 mutant cancer cells. While our in vitro cancer cell growth inhibitory studies are encouraging, a systematic exploration in vivo using various orthogonal model systems is required to aid the preclinical development of SMARCA2 degraders. Thus, we propose to determine the potential of YD23 mediated SMARCA2 degradation in SMARCA4 mutant xenograft and patient derived xenograft (PDX) model systems. Due to the unique microenvironment of lung cancer we will also test efficacy of YD23 in GEM models of lung cancer. Finally, SMARCA2 as a synthetic lethal partner of SMARCA4 has so far been described by in vitro experiments. In vivo genetic validation is critical to unequivocally demonstrate the requirement of a gene of interest in the development of a genetically defined subtype of cancer. Thus, we aim to perform CRISPR-Cas9 mediated Smarca2 genetic ablation to determine the extent of its involvement in the development and biology of Smarca4 mutant GEM models. In conclusion, our study is expected to provide mechanistic insight into the synthetic lethal genetic relationship between SMARCA2 and SMARCA4 and lay the foundation for future clinical development of SMARCA2 degraders as therapeutics.

NIH Research Projects · FY 2025 · 2022-07

PROJECT SUMMARY / ABSTRACT: Cytotoxic T lymphocyte (CTL)-based immunotherapies have shown great success in the treatment of patients with several different cancer types. CTLs recognize peptide antigens presented at the tumor cell surface by HLA class I molecules, triggering specific tumor cell lysis. Defining the nature of such tumor- associated antigens (TAAs) can directly facilitate therapeutic tumor targeting through a number of interventions, including personalized vaccines, endogenous T cell infusion, or TCR-engineered immunotherapies. However, only a minority of human TAAs can be confidently identified using conventional proteomic methodologies. Recent evidence suggests this may be due to the fact that most of the immunopeptidome is comprised of peptides derived from ‘non-canonical’ sources such as those derived from translated introns, RNA editing, proteasome splicing, or containing post-translational modifications. Although these represent potentially high value tumor targets, few of these have yet been validated as bona fide TAAs. However, overcoming the challenges inherent in non-canonical TAA identification holds the promise of significantly expanding the landscape of targetable antigens for cancer patients. The specific objective of this project is to identify and assess non-canonical TAAs as potential therapeutic targets for melanoma, as a necessary prerequisite and foundation for generating effective CTL- based immunotherapies for treating patients with this disease. It is our central hypothesis that unique, non- canonical TAAs can constitute shared immunotherapeutic CTL targets, and that those TAAs induced downstream of oncogenic driver mutations such as BRAF(V600E) will show greater tumor specificity and refractoriness to antigen loss. We have formulated this hypothesis based on preliminary data showing that potentially targetable non-canonical TAA peptides can be identified using an integration of highly sensitive mass spectrometry (MS) combined with genetic sequencing analysis and a novel, in-house bioinformatics pipeline. We have also shown that constitutive oncogenic MAPK pathway activation leads to dramatic global tumor immunopeptidome shifts that appear to potentially involve thousands of non-canonical TAAs. There is a strong clinical rationale for this antigen discovery work since it will directly facilitate the development of novel, CTL-based therapies with the potential to benefit large numbers of cancer patients. The proposed work is innovative, because it will explore different categories of non-canonical TAAs in cancer and assess their immunogenicity and potential therapeutic value as shared cancer targets. It will also shed light on the transcriptomic and proteomic changes that occur upon oncogenic-mediated MAPK pathway activation, and how this influences the tumor immunopeptidome. Lastly, fulfilling the outlined objectives will have an important positive clinical impact, because they will facilitate development of the next generation of novel antigen-specific CTL-based immunotherapies for melanoma patients, and possibly also patients with other cancer types.

NIH Research Projects · FY 2025 · 2022-07

PROJECT SUMMARY Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer and accounts for most cancer deaths. Improved early detection has increased the number of LUADs diagnosed at earlier pathological stages, thus warranting strategies to treat this growing patient subpopulation. Thwarting these advances is a very poor understanding of early events that drive LUAD development and that thus would guide ideal approaches for interception. While normal lung epithelia of LUAD patients were shown to display tumor-pertinent molecular and inflammatory changes, it is not clear why a LUAD develops within a particular region in the lung. Whereas the lung is ecologically rich with many cell populations that partake in both physiological and pathological processes, we still do not know how the properties and roles of individual cell populations, such as epithelial and immune subsets, co-evolve and interact to instigate LUAD development from a specific niche in the lung. In our preliminary efforts, we found by multi-region single-cell sequencing remarkable evolution of the properties and transcriptomic features of multiple cell subsets and states (e.g., protumor immunosuppressive phenotypes) across macro-space, such that cellular ecosystems and immune cell receptor repertoires were more similar among LUADs and adjacent normal regions than with more distant normal sites. Also, such spatial properties were progressively enriched along the pathologic continuum of matched human normal lung, to preneoplasias, up to invasive LUADs. Our preliminary findings motivate the hypothesis that geospatially and temporally evolving expression programs, properties, and interplay of epithelial and immune cells model early development of LUAD from the normal and premalignant lung. In Aim 1, we will study LUADs and matched multi-region normal tissues with defined spatial proximities from the tumors by single-cell RNA and immune receptor sequencing in conjunction with analysis of mutations in the tumors to establish single-cell maps of LUAD and immune co-evolution in space. Spatially modulated cell properties and states will then be used to feed and train a machine learning model that portrays LUAD development from the lung ecosystem. In Aim 2, we will single-cell decode tumor-immune co-evolution along the pathologic continuum of normal and premalignant lung to LUAD as well as identify cell states and properties that are modulated by early immune intervention. We will use temporal information in mice, along with human matched normal lung tissues, preneoplastic lesions, and invasive LUADs, to iteratively validate and fine-tune the performance of our machine learning model to portray LUAD development in time from the normal and premalignant lung. At the end of our studies, we will have built new models that reliably portray LUAD evolution in space and time. By providing an atlas of LUAD development in an accessible data portal, we also expect that our study will offer scalable roadmaps for the scientific community to develop new strategies for treatment of this fatal disease.

NIH Research Projects · FY 2025 · 2022-07

SUMMARY/ABSTRACT Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to investigate molecular mechanisms and response to therapy in cancer. MD Anderson Cancer Center is a leader in the implementation of this antibody-based technology that can assess many protein markers across large numbers of samples in a cost-effective, sensitive, and high-throughput manner. The platform currently assesses ~500 protein markers, covering all major signaling pathways and most drug targets. Its utility was demonstrated through its selection as the platform for proteomic characterization of ~8,000 patient samples through TCGA and >1,000 cell lines through CCLE, and its designation as one of two NCI Genome Characterization Centers in 2015. It is an approved Cancer Therapy Evaluation Platform site for sample characterization, leading to the implementation of multiple effective clinical trials. With ITCR support, we have developed a major bioinformatics resource dedicated to the analysis, visualization, and dissemination of RPPA data, The Cancer Proteome Atlas (TCPA), which has a community of >80,000 users worldwide. The current objective is to improve the data quality control, to enhance the existing analytic capabilities, and to expand the scope of TCPA by adding new functionalities and datasets. We have formed working relationships to link TCPA with other widely used bioinformatics resources. As an experienced, multidisciplinary team, we will pursue four specific aims: Aim #1. Develop a user-friendly, all-in-one software pipeline for processing RPPA data. We will improve quality control and batch effects adjustment steps of RPPA data processing, enhance the performance of the pipeline and interactivity of the results, and provide a user-friendly, general software package to the scientific community. Aim #2. Expand and enhance our existing web platforms for the analysis of RPPA data. We will extend the scope of RPPA data, incorporate other types of molecular data, especially proteomic data, and enhance the analytic and visualization capabilities. Aim #3. Build a user-friendly, interactive web platform for the analysis of cancer RPPA data from xenograft, PDX, and animal models. We will collect and compile RPPA data of >10,000 such samples and develop related visualization and analytic modules. Aim #4. Promote TCPA and active interaction with the user community. We will enhance the RPPA data repository and promote it as a standard reference database, provide documentation, hands-on workshops, and bug fixes, and build web APIs for interaction with other tools. The expected outcome is a dedicated, comprehensive bioinformatics resource that fully integrates RPPA data generation, analysis, dissemination, and user feedback, allowing for fluent exploration and analysis of high-quality proteomic data in rich contexts. The project is important because it will greatly enhance the quality and reproducibility of RPPA data from important consortium projects; substantially reduce barriers in mining complex functional proteomic data; serve as a hub for integrating high-quality RPPA-based proteomics data into other widely used bioinformatic resources, and directly facilitate the development of protein markers for precision cancer medicine.

NIH Research Projects · FY 2026 · 2022-06

Project Summary/Abstract Lung cancer is the leading cause of cancer related deaths in the United States and worldwide. Most patients are diagnosed with advanced stage disease for which available treatment interventions offer minimal survival benefit. Early detection through screening is vital to achieve cure and minimize lung cancer morbidity and mortality. Low- dose computed tomography (LDCT) has become the standard lung cancer screening modality based on data from randomized clinical trials. In 2021, the US Preventive Services Task Force (USPSTF) relaxed its lung cancer screening eligibility criteria (based on age and smoking history) providing coverage to younger and lighter smokers. Even though the eligibility expansion is expected to enhance benefits in specific population groups, many newly eligible individuals would have low lung cancer risk making it less likely to benefit from screening, but will be subject to potential harms such as false-positive findings and risks from invasive diagnostic procedures, emotional and psychological distress, and cost. Thus, it is imperative to accurately identify individuals that are likely to benefit from screening. Management of indeterminate findings is challenging, given the high rates of benign nodules detected by LDCT. Existing lung cancer screening and diagnostic guidelines ignore important risk-factors, whereas promising risk prediction models assessing screening eligibility of individuals and malignancy of indeterminate findings omit life-expectancy and remain underutilized. This research aims to develop individualized, dynamic risk-based screening and diagnostic strategies through stochastic, dynamic decision models. This project leverages the individualizEd luNG cAncer screeninG dEcisions (ENGAGE) framework – a previously developed and validated framework – that offers individualized screening decisions by dynamically assessing the risk and life expectancy of ever-smoked individuals. We will expand the current version of ENGAGE, which is based on age, sex, and smoking history, to incorporate non-smoking risk factors including race, family history and history of pulmonary disease among others, into the decision-making process. We will develop microsimulation models to simulate the progression of pulmonary nodules and overlay a partially observable Markov decision process to optimize the diagnostic management of pulmonary nodules at the patient level, based on a risk assessment for the nodule’s malignancy and information collected from serial LDCT, biopsy, PET/CT or a diagnostic blood-based biomarker. We will integrate the diagnostic module into ENGAGE to derive state-of-the-art individualized screening and diagnostic recommendations, and compare the effectiveness, efficiency, and cost-effectiveness of the updated ENGAGE framework against current practice. This project presents a new direction in lung cancer screening research paving the road towards individualized secondary cancer prevention. The expansion of the ENGAGE framework to facilitate a personalized risk-based program that integrates smoking and non-smoking risk factors, along with life expectancy, will form the basis for the development of optimal, cost-effective lung cancer screening guidelines tailored to individuals.

NIH Research Projects · FY 2025 · 2022-06

Analgesia, the dampening of nociceptive responses to noxious stimuli capable of damaging tissues, is an adaptive behavioral response for organisms- allowing them to feel less pain in physiologically appropriate situations such as physical trauma or the fight/flight response. Analgesia has traditionally been studied in vertebrate models, where endogenous opioid peptides bind to their cognate receptors to dampen behavioral responses to painful stimuli. Genetically tractable model organisms such as Drosophila have recently been used to explore the molecular/genetic bases of nociception and nociceptive sensitization following tissue injury but have not yet been used to dissect conserved analgesia signaling. Drosophila offer both speed of genetic analysis and a variety of sophisticated genetic tools for analyzing gene expression and function that should prove a valuable complement to existing experimental paradigms for the study of analgesia. Our long-term goal in this basic research project is to identify and characterize analgesic signaling pathways in Drosophila larvae. That such pathways are likely to exist is evidenced by our preliminary findings that the opiate compound morphine is analgesic for Drosophila larvae and that a conserved G-protein coupled receptor (GPCR) is required in this organism for thermal analgesia. Morphine feeding to fly larvae causes transient analgesia that spans multiple sensory modalities (heat, cold, touch, chemical), is mimicked by other opiates (fentanyl) and is partially naloxone reversible. Our short-term goals over the initial project period will be to characterize the GPCR required for thermal analgesia. Using our knowledge of which tissues express the GPCR we will determine which tissues functionally require it for analgesia, and assess whether the putative receptor’s analgesic effects extend to other sensory modalities beyond heat. At the biochemical level we will test whether the GPCR (and it’s clearest human ortholog) directly binds morphine to activate signaling. At the genetic level we will identify endogenous peptide ligand(s) and probe genetic interactions with previously identified nociceptive genes and nociceptive sensitization signaling pathways. In our final aim we will probe cellular effects of morphine administration (calcium changes) and whether known deleterious biological side effects of morphine, development of tolerance and constipation are also observed in our new model. Successful completion of these aims will provide a comprehensive cellular and genetic analysis of analgesic signaling in this new model- basic information that is likely to generate testable hypotheses for ongoing work in vertebrate models. Moving forward, this new model of analgesic signaling will provide the possibility of unbiased gene discovery approaches that should allow for identification of novel conserved genes required for analgesia- targets that may in some cases be potentially relevant to human and health and worthy of further exploration in vertebrate models and clinical settings.

NIH Research Projects · FY 2025 · 2022-06

As biomechanical modeling of the breast is integral to predicting tumor location across multimodal diagnostic imaging and during surgery, surgical planning, generating simulations for physician and patient education, and brassiere and clothing design for optimal breast support, advances in model accuracy have the potential to significantly improve women's health and quality of life. Despite the growing use of breast biomechanical models for different applications, there are persistent knowledge gaps in both the anatomical and biomechanical literature that prevent an accurate model from being developed and deployed to patient-specific applications. Accurate biomechanical models are needed for tracking cancer in diagnostic imaging and surgery. However, the accuracy of biomechanical models is sensitive to the geometrical and structural features used to describe the anatomical features and the constitutive parameters used to describe the behavior of the tissues. For example, small alterations in the stiffness of the various breast tissue properties can displace tissues by more than 10 mm. Thus, thorough characterization of the constitutive properties of individual breast structures are necessary to obtain precise predictions of tissue motion. Furthermore, in the absence of precise knowledge of anatomical geometrical and structural features, biomechanical models have placed an overemphasis on the constitutive parameters of the breast tissue. The long-term goal of our research is to develop an accurate biomechanical model of the breast that transforms the applications of breast modeling for both population models and patient-specific applications. Our vision is to improve the model so that it becomes a reliable and useful tool in the diagnosis and management of breast cancer, surgeon education and training, patient education for better shared decision making, and clothing design, especially in the post mastectomy recovery period. Our present human breast tissue biomechanical model represents the state of the art, as it is based on actual 3D analyses. However, it represents a first step, as clinical translation remains limited by insufficient information about the structural and biomechanical characteristics of the fascial support system and its relationship to the adipose and glandular breast structures in the broader population. Thus, we hypothesize that the accuracy of the biomechanical model may be improved by determining the anatomical and biomechanical characteristics of the fascial support system of the breast, understanding the sensitivity of the patient-specific parameters across the population, and validating the translation of these models, with their inherent uncertainties, into the patient-specific setting. Our multi-disciplinary team of breast reconstructive surgeons, engineers, medical physicists, and pathologists are uniquely poised to perform this innovative research leading to the development of a high-fidelity biomechanical model of the human breast that is capable of reproducing its behavior, both in general and in a patient specific sense.

NIH Research Projects · FY 2026 · 2022-06

Project Summary: The primary objective of this research is to develop novel statistical and computational tools to evaluate new and existing cancer therapies for precision cancer medicine, with a principal focus on integrating multiple data sources including randomized controlled trials (RCT) and real world data (RWD). All of the aims are motivated by multidisciplinary collaboration. Evidence-based clinical decision making involves synthesizing available research evidence from multiple resources, including RCT and RWD. Pivotal RCTs are the primary evidence that established the oncologic equivalence or efficacy of local and systemic treatments. However, a recent systematic review found little agreement between population-based RWD and RCTs when comparing the same oncologic treatment regimens. This difference is thought to stem from the highly selective criteria used for trial enrollment coupled with the rapidly changing nature of multidisciplinary cancer care. Moreover, heterogeneous treatment effects by disease biologic tumor subtype on survival outcomes has not been examined sufficiently in early RCTs. We will develop statistical tools and software to evaluate the agreement of findings from RCTs and the real-world patient population, reassessing standard treatment guidelines on local- regional therapies for early-stage breast cancer by patients’ clinical and tumor subtypes. While the proposed methodology is agnostic to disease type, we will use breast cancer patients as proof of principle for the approaches proposed. The specific aims are: (1) to estimate and assess the agreement of treatment efficacy on survival outcomes across multiple studies (e.g., RCT and RWD) using nonparametric calibration weights to adjust for treatment selection bias and heterogeneity between studies; (2) to test the existence of a subgroup of patients with enhanced treatment effect and predict subgroup membership of a treatment using a semi-parametric isotonic- Cox model, and to develop a concordance-assisted learning tool for threshold identification to guide patient treatment selection; (3) to infer the treatment effects on breast cancer-specific survival when the cause of death is unknown in RWD by integrating data from RCT and RWD; (4) to estimate treatment effect for rare subtypes of breast cancer by combining external aggregate data with individual-level data to improve inference efficiency; and (5) to develop and disseminate publicly available, user-friendly software and facilitate the reproducibility and applications of our methods to multiple existing databases, including large-population-level data and RCT data for breast cancer research. The proposed research will advance general methodologic development in comparative effectiveness and precision medicine research by efficiently integrating multiple data sources. More importantly, the study findings could improve evidence-based treatment recommendations, better informing clinicians to select optimal treatments according to patients’ tumor subtypes and other characteristics, thus furthering clinical care via better integration of clinical science.

NIH Research Projects · FY 2026 · 2022-05

PROJECT SUMMARY/ABSTRACT High-grade serous ovarian cancer (HGSC) metastasizes preferentially to the omentum, which is a well- vascularized fold of peritoneal tissue covered by mesothelial cells and a major site of intra-abdominal fat accumulation. It was reported that HGSC and stromal cell-derived pro-inflammatory cytokines downregulate omentin (ITLN1), a novel mesothelial cell-derived adipokine to promote the invasive potential and proliferation of cancer cells in the omental microenvironment. Omentin has been shown to suppress ovarian cancer invasive potential and cell growth through suppressing MMP1 expression and cell traction force in cancer cells, and inducing a local rapid metabolic coupling between ovarian cancer cells and neighboring adipocytes. Besides, higher levels of pre-operative serum omentin in patients with HGSC were associated with longer survival times. In addition, mice treated with omentin had marked increase in activated CD8+ T cell density compared to the untreated control. These findings suggested that adipocytes play an important role in mediating the suppressive effect of omentin on the malignant phenotype of ovarian cancer cells, and the immune microenvironment. Therefore, we focus on secretory proteins that can be upregulated by omentin in mature adipocytes. A recent proteomic study demonstrated that an anti-inflammatory protein tumor necrosis factor-inducible gene 6 protein (TSG-6) was the top gene upregulated by omentin in adipocytes. Previous studies demonstrated that TSG-6 inhibits infiltration of immune cells during inflammation. It can also bind to specific chemokines such as CXCL12 and prevent them to bind to glycosaminoglycan (GAG)-rich tumor stroma and endothelial cell surface, suggesting that TSG-6-mediated blockade of these cytokines to suppress tumor growth, angiogenesis, and regulatory T cell trafficking. Based on these findings, we hypothesize that omentin normalizes the pro-inflammatory omental microenvironment in ovarian cancer through upregulating anti-inflammatory TSG-6 in adipocytes, which binds to pro-inflammatory cytokines secreted by cancer cells and various stromal cell types, which attenuate the immunosuppressive tumor microenvironment, prevent omental metastasis, and suppress tumor progression, and subsequently improve patients’ survival rate. To test this hypothesis, first, we propose to determine the roles of omentin in normalizing the immunosuppressive microenvironment and preventing tumor development in ovarian cancer. Second, we propose to determine the mechanisms by which omentin reprograms the immune landscape in ovarian tumor tissues and suppresses the invasive potential of ovarian cancer cell. Third, we propose to determine the efficacy of omentin administration alone or in combination with paclitaxel in ovarian cancer treatment, and determine the pharmacokinetics/pharmacodynamics and toxicity of omentin using various mouse models. Our studies will enable us to delineate the immune modulator role of omentin in HGSC pathogenesis, and to further develop omentin as a chemopreventive and therapeutic agent in the treatment of HGSC to improve patients’ survival rates.

NIH Research Projects · FY 2026 · 2022-05

Project Summary/Abstract TP53 is the most common somatically mutated gene among all cancers as it is altered in up to 85% of head and neck squamous cell carcinomas (HNSCC). Although TP53 mutations often lead to a loss of wild-type p53 (wtp53) function, many TP53 mutations confer mutant p53 (mutp53) gain-of-function (GOF), promoting cancer cell genomic instability, proliferation, invasion, metastasis, and cancer inflammation. However, the mechanisms involved in mutp53 GOF activity remain largely elusive, which is a major obstacle to fully understanding and targeting mutp53 to prevent tumorigenesis and tumor progression of HNSCC. Our long-term goal is to understand the role of TP53 mutations in promoting tumorigenesis and tumor progression of HNSCC and to use this knowledge to develop effective targeted therapies for HNSCC. The objective of this proposed research, which is the next step in pursuit of that goal, is to identify the specific role of GOF mutp53 in the promotion of chromosomal instability (CIN), which leads to tumor metastasis and immunosuppression, and to further exploit this to design novel treatment strategies for HNSCC. Our central hypothesis is that by targeting MCM5, a component of the replication licensing factor minichromosome maintenance 2-7 complex (MCM2-7), GOF mutp53 predisposes cells to CIN that leads to a STING-dependent cytosolic DNA response involving downstream activation of non-canonical nuclear factor kappa light chain enhancer of activated B cell (NF-κB) signaling, which, in turn, promotes tumor cell invasion, metastasis, and immunosuppression; therefore, targeting mutp53-mediated signaling can be used as a therapeutic strategy for HNSCC in patients with GOF TP53 mutations. Guided by strong preliminary data, this hypothesis will be tested by pursuing three specific aims: 1) Determine the functional roles of GOF mutp53-MCM5-cGAS/STING-non-canonical NF-κB signaling in the promotion of tumor invasion and metastasis in HNSCC cells; 2) Determine the functional roles of GOF mutp53- MCM5-cGAS/STING-non-canonical NF-κB signaling in the promotion of immunosuppression in HNSCC; 3) Identify novel therapeutic strategies for HNSCC with GOF p53 mutations. The research proposed in this application is highly innovative, given that the proposed mechanisms for studying GOF mutant p53 have never been reported before. Our hypothesis is based on our strong preliminary results from a proteomic screen of the mutp53 interactome, which uncovered a physical interaction between GOF mutp53 proteins and MCM5. We expect that the proposed work will identify intrinsic mechanisms of mutp53-mediated GOF in the promotion of genomic instability, metastasis, and immunosuppression that contribute to tumor development and tumor progression of HNSCC. Given the high incidence of p53 mutations in HNSCC, the proposed research is expected to have a significant impact on the public health burden of this deadly disease, and will help us develop novel therapeutic strategies to treat HNSCC patients with TP53 mutations.

NIH Research Projects · FY 2026 · 2022-04

PROJECT SUMMARY Research: Actinic keratoses (AKs) are pre-cancerous skin lesions that arise in the setting of chronic sun exposure and affect tens of millions of people in the United States each year. AKs have a risk of transformation to cutaneous squamous cell carcinoma. The majority of AK clinical care and research focuses on the individual AK and its risk of malignant transformation. However, there is evidence that the presence of AKs may be associated with increased risk of squamous cell carcinoma and other types of skin cancer (melanoma and basal cell carcinoma) for the patient as a whole. Though they are not typically used this way, AKs may be an important clinical biomarker of skin cancer risk, including melanoma, squamous cell carcinoma, and basal cell carcinoma. Unfortunately, there are no specific recommendations for clinicians to follow for skin cancer surveillance or early detection in patients with AKs. This project will address key knowledge gaps: to examine the absolute risks of skin cancer in patients with AKs, which are unknown (Aim 1); to understand the care currently being provided to patients with AKs, which is uncharacterized (Aim 2); and to develop a skin cancer risk prediction model for patients with AKs, which has not previously been done (Aim 3). The overarching goal is to provide evidence to guide clinical care and form the foundation for future recommendations on skin cancer surveillance and early detection in patients with AKs, a large and high-risk group. Candidate: Dr. Mackenzie Wehner, MD MPhil is an Assistant Professor of Health Services Research and of Dermatology at MD Anderson Cancer Center. She completed her undergraduate degree at Yale University, medical school at Stanford University, a Master's in Epidemiology at the University of Cambridge, and residency and post- doctoral research fellowship at the University of Pennsylvania. Her career goal is to become an independent, R01-funded physician scientist in patient-oriented skin cancer research and through her research to decrease the burden of skin cancer in the United States. Environment: During this award period Dr. Wehner will devote at least 75% of her time to research and 20% to clinical care of patients at high risk of skin cancer, including those with AKs. Her primary mentor, Dr. Sharon Giordano, and co-mentor, Dr. David Margolis, are proven mentors and experts in patient-oriented research in cancer. The exceptional training environment in cancer research at MD Anderson will offer Dr. Wehner an outstanding opportunity to launch her career as an independent physician-scientist. Career Development: Dr. Wehner will have support from an experienced and diverse advisory committee and will focus her training on developing skills and experience in 1) health services research and large administrative datasets, 2) risk prediction modeling and tools development, and 3) incorporating genetic data in epidemiologic analyses. Completion of the proposed research and career development plan will serve as a platform upon which Dr. Wehner can successfully transition to independence and pursue an impactful career focused on patient-oriented skin cancer research.

NIH Research Projects · FY 2025 · 2022-04

PROJECT SUMMARY/ABSTRACT Complications associated with vascular access for hemodialysis represent one of the most important sources of morbidity among patients with end-stage renal disease (ESRD) in the United States today. Among the various types of vascular access, arteriovenous fistula (AVF) is preferred because it has better patency rates and fewer complications than other access types. However, AVF primary failure impeding AVF maturation remains a common problem and adding to patients’ morbidity and mortality. Neointimal hyperplasia (NIH) has been identified as one of the main pathophysiologic culprits underlying AVF failure. Thus, improving AVF maturation, reducing NIH, and optimizing imaging for accurate diagnosis and localization of NIH lesions are critical, as well as understanding the mechanism of failure, so that therapeutic interventions can be executed. Based on our preliminary data, we propose to develop novel, resorbable polymeric scaffolds with varying physico-chemical properties that can be wrapped around the AVF to offer structural support and that can be loaded with multifunctional, photoacoustic (PA)- and computed tomography (CT)-active nanoparticles (to facilitate imaging) and mesenchymal stem cells (MSCs) (to mitigate inflammation and NIH). We will then test their safety and efficacy in vitro and in vivo using a uremic rat and pig animal models. In addition, we will assess the use of ultrasound (US) and PA imaging, in combination with positron emission tomography (PET) imaging techniques for monitoring inflammation and AVF maturation. We hypothesize that this therapeutic strategy once delivered locally and in a sustained manner, will increase the concentration in the AVF without systemic toxicity, as well as provide structural support to enhance outward remodeling. We will test this hypothesis in three specific aims: 1) develop a biodegradable polymeric scaffold containing nanoparticles and MSCs to mitigate inflammation and subsequent pathologic NIH during AVF maturation, 2) assess various imaging techniques for monitoring AVF maturation and integrity, and 3) assess physiologic, radiologic, and pathologic changes following implantation of the engineered polymer in the peri-adventitial tissue surrounding iatrogenic AVFs in rat and pig models. The proposed work is significant and innovative because the step-by-step optimization of the physico-chemical properties of the polymeric scaffold will improve the structure of the AVF, as well as delivery and retention of MSCs, which would yield improved rates of AVF maturation and patency among ESRD patients on hemodialysis. The successful completion of the proposed work will help us understand the mechanism of the pathogenesis of non-maturing AVFs and whether polymeric scaffolds loaded with MSCs can modulate NIH. Furthermore, the development of combined US/PA and PET/CT imaging will elucidate the role of not only inflammation but also other targets in AVF maturation/non-maturation for potential drug and/or device development.

NIH Research Projects · FY 2024 · 2022-04

PROJECT SUMMARY/ABSTRACT Acute myeloid leukemia (AML), an aggressive leukemia characterized by the excessive proliferation of abnormal myeloid progenitor cells in the bone marrow (BM), and carries a 5-year survival rate less than 25%. Overexpression of the oncogene ecotropic viral integration site 1 (EVI1) in AML is associated with shorter survival durations and higher relapse rates. AML-associated relapse is multifactorial, and previous studies have shown that the activation of cell cycle quiescence protects AML subclones during chemotherapy, resulting in their survival. Given the severity of EVI1-overexpressing AML, the lack of an in-depth understanding of the role of EVI1 in AML patients’ shorter survival durations and higher relapse rates, and the inadequacy of current therapeutic strategies, we aim to gain a better understanding of EVI1-associated chemoresistance with the long- term goal of developing novel therapies for this sever form of AML. Our preliminary studies using an EVI1- overexpressing AML mouse model and patient samples indicate that EVI1 overexpression activates quiescence pathways. Our ChIP-seq and ATAC-seq data revealed that the mechanism of EVI1-induced quiescence involves two pathways: 1) the upregulation of cyclin-dependent kinase inhibitor 1C (CDKN1C/P57kip2), a critical activator of hematopoietic stem cell quiescence, whose expression has been linked with AML relapse; and 2) the activation of purine-rich box binding protein 1 (PU.1), a master regulator of myelopoiesis, which is sufficient to trigger cell cycle quiescence in hematopoietic stem cells (HSCs). Thus, we hypothesize that EVI1 overexpression protects AML cells from chemotherapy by activating quiescence through CDKN1C and PU.1 pathways. To test our hypothesis, we will elucidate the mechanism of EVI1-induced CDKN1C expression and its role in quiescence (Aim 1), and investigate the role of PU.1 activation in EVI1-associated quiescence (Aim 2) in EVI1- overexpressing AML. This will be accomplished by integrating data from RNA-seq, ChIP-seq, ATAC-seq, and other techniques to analyze EVI1-overexpressing leukemia cells from our in vitro and in vivo models and from primary human AML samples. To translate the proposed mechanistic insights into clinical settings and therapeutic strategies, we will test new treatment regiments in preclinical studies using EVI1-overexpressing AML patient-derived xenograft (PDX) models (Aim 3). In summary, the proposed work will focus on investigating EVI1-induced quiescence mechanisms, and its findings will not only help explain the shorter survival durations and higher relapse rates associated with EVI1-overexpressing leukemia but also unveil new therapeutic strategies that reactivate the cell cycle and improve the survival of patients with EVI1-overexpressing AML.

NIH Research Projects · FY 2026 · 2022-04

Imaging is a critical component in the detection, characterization and treatment of cancer. The rich information content of advanced imaging methods, combined with the growing capacity to collect multiple types of images from various sources and time points during therapy creates an exciting opportunity for image-based guidance and assessment of interventions for radiation oncology, surgery, and interventional radiology. In addition, the development of novel treatment techniques, including radiation, minimally invasive surgery or targeted ablation, requires an accurate assessment of the treatment results for personalized medicine. The ability to personalize treatment is critically dependent on our ability to precisely measure and relate the delivered treatment with the corresponding outcome. Quantitative assessment of therapeutic response through anatomical, functional, and metabolic imaging has been identified by the NIH has a critical component in the advancement of local therapies. Consistent longitudinal acquisition, correlation, and evaluation of these quantitative imaging biomarkers across multiple therapies has the potential to improve the local control and reduce toxicities, ultimately leading to improved patient outcomes through better cancer control and quality of life. These exciting advances has led MD Anderson Cancer Center to develop an Image Guided Cancer Therapy Research Program, which is empowering multidisciplinary teams of scientists and physicians to address clinical challenges and technology barriers. The goal of this training program is to provide multi-disciplinary research training for tomorrow’s pioneering leaders in image guided cancer therapy, including surgery, interventional and diagnostic radiology, radiation oncology, and correlative pathology, using advanced imaging, navigation, and analysis techniques. This training program is unique in that it trains, within a single program, both scientists and clinician scientists working in image guided cancer therapy in all three focal therapy disciplines of surgery, radiation oncology, and interventional radiology.

NIH Research Projects · FY 2025 · 2022-04

Project Summary/Abstract Allogeneic stem cell transplantation is a life-saving therapy for a variety of blood disorders, but its use is limited by a high rate of serious side effects, including the development of graft-versus-host-disease (GVHD). The gut microbiome, or the composition of microorganisms populating the digestive tract, plays a key role in triggering this inflammatory response, and there is an urgent need to analyze patient microbiome profiles to both predict and mitigate risk of GVHD. However, microbiome data pose a number of statistical challenges not addressed by existing methods due to high dimensionality, heterogeneity across subjects, and complex phylogenetic relationships. In this proposal, we develop new data science approaches to make sense of microbiome data, providing insight that can guide the development of future interventions aimed at reducing GVHD incidence. We will develop accurate and efficient methods for microbiome data analysis and make them available in user-friendly formats. We focus on the development of novel methods for visualization and prediction using microbiome data, as detailed in the following specific aims: Specific Aim 1: To develop and evaluate advanced tools for visualization of microbiome data. The high dimensionality and unique structure of microbiome data present challenges to effective data visualization. In this aim, we will develop approaches for both unsupervised and supervised visualization of microbiome data, along with an RShiny app and QIIME2 plug-in that will make these tools accessible to both clinicians and bioinformaticians. The methods and software resulting from this aim will provide robust approaches to enable researchers to better visualize global microbiome heterogeneity across their study population, enhancing data exploration and identification of potential confounding factors or outliers. Specific Aim 2: To develop predictive modeling approaches for binary and survival outcomes. In this aim, we will focus on selection of predictive microbiome features in the context of regression. We will carry out key advances enabling the effective application of sparse modeling to predict GVHD risk: novel statistical approaches to handle binary and time-to-event outcomes, including those with competing risks, and computationally efficient implementations, to be made freely available as both an R package and RShiny application. Specific Aim 3: To develop methods for understanding the impact of rare features. Current microbiome profiling methods allow for very fine resolution of the strains present in each sample. In this aim, we propose two methods to understand the impact of rare features. We will first develop a method to provide insight into kernel association results, by obtaining estimated effect sizes for individual microbiome features. We will then develop an approach for nonparametric clustering of the regression coefficients, which allows flexible aggregation of the observed rare features. Successful completion of this work will result in new statistical and computational approaches to provide insights into microbiome data, generating hypotheses that can guide the development of future strategies to predict and mitigate GVHD. These methods will be disseminated through easy-to-use and efficient cloud-based software implementations.

NIH Research Projects · FY 2026 · 2022-03

Project Summary Functional localization of eloquent brain areas for patients undergoing surgery for brain tumors, epilepsy, or other neurological diseases is crucial to prevent post-surgical deficits and reduce morbidity. Task-based (tb) functional MRI (fMRI), which detects blood oxygenation level–dependent (BOLD) signal changes while a patient performs task paradigms, is a standard-of-care clinical procedure for presurgical mapping of eloquent cortices. Two major limitations of clinical tb-fMRI are a patient's inability to perform the task and lesion-induced impairment of neurovascular coupling (which drives the BOLD signal). Resting-state (rs) fMRI, which measures synchronized BOLD signal oscillations during rest, can be used to map brain networks with minimal requirements for patient compliance and has been demonstrated to accurately localize motor and language areas for presurgical planning. Cerebrovascular reactivity (CVR) mapping, accessed by dynamic BOLD imaging during a hypercapnia task such as breath-holding, can be used to identify areas with potential false-negative fMRI results due to neurovascular uncoupling (NVU) and has been suggested as an emerging standard to be used with clinical fMRI. Currently, there are no commercially available FDA-cleared software tools for localizing the resting-state networks (RSNs) or CVR. Clinical investigators have relied on research software packages that are either not clinically integrated or not yet optimized and validated in large patient populations. Thus, a vetted software solution is urgently needed to enable these state-of-the-art fMRI methods to benefit patients beyond the limitations of tb-fMRI. We hypothesize that enhancing, optimizing, and validating our preliminary software and integrating it with an established commercial fMRI platform will create robust solutions for clinical mapping of RSN and CVR. Through three specific aims, the software solutions will be optimized and tested with rs-fMRI and CVR datasets from approximately 350 patients with brain tumors or epilepsy at three institutions. Aim 1 is to create the software for mapping RSNs and determine optimized workflows for localizing eloquent areas including primary visual, motor (hands, tongue, and feet), and language (primary and secondary) areas. Both seed-based correlation and independent component analysis will be incorporated. Aim 2 is to create the software for mapping CVR and determine the optimized workflow for identifying and visualizing brain areas with potential false-negative fMRI results. The software will include a multiple-latency general linear model and a unique graphical user interface to visualize the NVU. Aim 3 is to test and validate the software with presurgical fMRI datasets. The results will be compared against those obtained from (1) processed using widely used research software packages, (2) tb- fMRI, and (3) intraoperative direct cortical stimulation. This research is anticipated to create robust and clinically available software that will greatly increase the patient population who can benefit from presurgical fMRI and will improve confidence in functional localization for surgical planning. This will directly benefit patients by preserving their post-surgical functions while allowing surgeons to safely maximize the resection of brain lesions.

NIH Research Projects · FY 2026 · 2022-03

PROJECT SUMMARY Effective treatments are elusive for the majority of patients with neuropathic pain, which is reflective of the incomplete knowledge of the underlying mechanisms. Our proposal will advance mechanistic understanding of peripheral neuropathic pain maintenance by investigating the differentiation and function of B cells after peripheral nerve injury. Our long-term goal is to harness the disease-modifying potential of neuroimmune signaling to treat neuropathic pain. As a step toward achieving this goal, the overall objective of this application is to discover if and how B cells cause neuropathic pain after peripheral nerve injury. Our central hypothesis is that peripheral nerve injury induces differentiation of B cells into plasma cells. The plasma cells secrete autoantibodies that form complexes with autoantigens which maintain neuropathic pain by signaling through the activating Fc gamma receptor (FcγR) subtypes (I, III, IV) along the pain neuraxis. We propose that insufficient efferocytosis (deficient non-inflammatory clearance of apoptotic cells, leading to release of Danger Associated Molecular Patterns (DAMPs)) at the site of nerve injury, triggers autoimmune B cell differentiation. This hypothesis is based on strong evidence that plasma cells are pro-nociceptive, as we show that either constitutive deficiency or pharmacological depletion of differentiating B cells protects male and female mice from neuropathic pain. Our data also reveal that insufficient efferocytosis leads to B cell differentiation, as pharmacological stimulation of efferocytosis at the time of peripheral nerve injury reduces immunoglobulin G (IgG) deposits in the spinal dorsal horn. The rationale for testing our hypothesis is that deciphering this previously overlooked adaptive immune response to peripheral nerve injury will reveal new and tractable therapeutic targets for neuropathic pain. To accomplish the overall objective of this application, we will test the central hypothesis in a mouse model of peripheral nerve injury across the following specific aims: 1) Define the function of B cell differentiation after peripheral nerve injury. Validation studies will be performed in a piglet model of peripheral injury, and in biological samples obtained from patients with lumbar radiculopathy; 2) Identify whether FcγR signaling maintains neuropathic pain; and, 3) Determine whether insufficient efferocytosis induces B cell differentiation leading to neuropathic pain. The proposed studies take a multidisciplinary approach, including pharmacologic and genetic manipulations, flow cytometry, in situ hybridization, adoptive transfer, and assessment of evoked and operant pain behaviors. As efferocytosis and downstream plasma cell differentiation have not been previously implicated in traumatic neuropathic pain, our proposal is highly innovative and is expected to expand our paradigm for neuroimmune regulation of peripheral neuropathic pain. The results will have significant impact on the treatment of peripheral neuropathic pain by revealing new sites for therapeutic intervention.