Browsing by Author "Miller, Lance D."

Now showing 1 - 8 of 8
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    Bulk and Single-Cell Profiling of Breast Tumors Identifies TREM-1 as a Dominant Immune Suppressive Marker Associated With Poor Outcomes
    (Frontiers Media, 2021-12-08) Pullikuth, Ashok K.; Routh, Eric D.; Zimmerman, Kip D.; Chifman, Julia; Chou, Jeff W.; Soike, Michael H.; Jin, Guangxu; Su, Jing; Song, Qianqian; Black, Michael A.; Print, Cristin; Bedognetti, Davide; Howard-McNatt, Marissa; O’Neill, Stacey S.; Thomas, Alexandra; Langefeld, Carl D.; Sigalov, Alexander B.; Lu, Yong; Miller, Lance D.; Biostatistics and Health Data Science, School of Medicine
    Background: Triggering receptor expressed on myeloid cells (TREM)-1 is a key mediator of innate immunity previously associated with the severity of inflammatory disorders, and more recently, the inferior survival of lung and liver cancer patients. Here, we investigated the prognostic impact and immunological correlates of TREM1 expression in breast tumors. Methods: Breast tumor microarray and RNAseq expression profiles (n=4,364 tumors) were analyzed for associations between gene expression, tumor immune subtypes, distant metastasis-free survival (DMFS) and clinical response to neoadjuvant chemotherapy (NAC). Single-cell (sc)RNAseq was performed using the 10X Genomics platform. Statistical associations were assessed by logistic regression, Cox regression, Kaplan-Meier analysis, Spearman correlation, Student's t-test and Chi-square test. Results: In pre-treatment biopsies, TREM1 and known TREM-1 inducible cytokines (IL1B, IL8) were discovered by a statistical ranking procedure as top genes for which high expression was associated with reduced response to NAC, but only in the context of immunologically "hot" tumors otherwise associated with a high NAC response rate. In surgical specimens, TREM1 expression varied among tumor molecular subtypes, with highest expression in the more aggressive subtypes (Basal-like, HER2-E). High TREM1 significantly and reproducibly associated with inferior distant metastasis-free survival (DMFS), independent of conventional prognostic markers. Notably, the association between high TREM1 and inferior DMFS was most prominent in the subset of immunogenic tumors that exhibited the immunologically hot phenotype and otherwise associated with superior DMFS. Further observations from bulk and single-cell RNAseq analyses indicated that TREM1 expression was significantly enriched in polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and M2-like macrophages, and correlated with downstream transcriptional targets of TREM-1 (IL8, IL-1B, IL6, MCP-1, SPP1, IL1RN, INHBA) which have been previously associated with pro-tumorigenic and immunosuppressive functions. Conclusions: Together, these findings indicate that increased TREM1 expression is prognostic of inferior breast cancer outcomes and may contribute to myeloid-mediated breast cancer progression and immune suppression.
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    c-Met Mediated Cytokine Network Promotes Brain Metastasis of Breast Cancer by Remodeling Neutrophil Activities
    (MDPI, 2023-05-05) Liu, Yin; Smith, Margaret R.; Wang, Yuezhu; D’Agostino, Ralph, Jr.; Ruiz, Jimmy; Lycan, Thomas; Kucera, Gregory L.; Miller, Lance D.; Li, Wencheng; Chan, Michael D.; Farris, Michael; Su, Jing; Song, Qianqian; Zhao, Dawen; Chandrasekaran, Arvind; Xing, Fei; Biostatistics and Health Data Science, School of Medicine
    The brain is one of the most common metastatic sites among breast cancer patients, especially in those who have Her2-positive or triple-negative tumors. The brain microenvironment has been considered immune privileged, and the exact mechanisms of how immune cells in the brain microenvironment contribute to brain metastasis remain elusive. In this study, we found that neutrophils are recruited and influenced by c-Met high brain metastatic cells in the metastatic sites, and depletion of neutrophils significantly suppressed brain metastasis in animal models. Overexpression of c-Met in tumor cells enhances the secretion of a group of cytokines, including CXCL1/2, G-CSF, and GM-CSF, which play critical roles in neutrophil attraction, granulopoiesis, and homeostasis. Meanwhile, our transcriptomic analysis demonstrated that conditioned media from c-Met high cells significantly induced the secretion of lipocalin 2 (LCN2) from neutrophils, which in turn promotes the self-renewal of cancer stem cells. Our study unveiled the molecular and pathogenic mechanisms of how crosstalk between innate immune cells and tumor cells facilitates tumor progression in the brain, which provides novel therapeutic targets for treating brain metastasis.
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    Comprehensive and Computable Molecular Diagnostic Panel (C2Dx) From Small Volume Specimens for Precision Oncology: Molecular Subtyping of Non-Small Cell Lung Cancer From Fine Needle Aspirates
    (Frontiers Media, 2021-04-16) Su, Jing; Huang, Lynn S.; Barnard, Ryan; Parks, Graham; Cappellari, James; Bellinger, Christina; Dotson, Travis; Craddock, Lou; Prakash, Bharat; Hovda, Jonathan; Clark, Hollins; Petty, William Jeffrey; Pasche, Boris; Chan, Michael D.; Miller, Lance D.; Ruiz, Jimmy; Biostatistics, School of Public Health
    The Comprehensive, Computable NanoString Diagnostic gene panel (C2Dx) is a promising solution to address the need for a molecular pathological research and diagnostic tool for precision oncology utilizing small volume tumor specimens. We translate subtyping-related gene expression patterns of Non-Small Cell Lung Cancer (NSCLC) derived from public transcriptomic data which establish a highly robust and accurate subtyping system. The C2Dx demonstrates supreme performance on the NanoString platform using microgram-level FNA samples and has excellent portability to frozen tissues and RNA-Seq transcriptomic data. This workflow shows great potential for research and the clinical practice of cancer molecular diagnosis.
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    Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy
    (Frontiers Media, 2021-04-06) Su, Jing; Song, Qianqian; Qasem, Shadi; O’Neill, Stacey; Lee, Jingyun; Furdui, Cristina M.; Pasche, Boris; Metheny-Barlow, Linda; Masters, Adrianna H.; Lo, Hui-Wen; Xing, Fei; Watabe, Kounosuke; Miller, Lance D.; Tatter, Stephen B.; Laxton, Adrian W.; Whitlow, Christopher T.; Chan, Michael D.; Soike, Michael H.; Ruiz, Jimmy; Biostatistics, School of Public Health
    Background: The incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death. Methods: We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization. Results: We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference. Conclusion: A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.
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    The nuclear structural protein NuMA is a negative regulator of 53BP1 in DNA double-strand break repair
    (Oxford University Press, 2019-04-08) Salvador Moreno, Naike; Liu, Jing; Haas, Karen M.; Parker, Laurie L.; Chakraborty, Chaitali; Kron, Stephen J.; Hodges, Kurt; Miller, Lance D.; Langefeld, Carl; Robinson, Paul J.; Lelièvre, Sophie A.; Vidi, Pierre-Alexandre; Physics, School of Science
    P53-binding protein 1 (53BP1) mediates DNA repair pathway choice and promotes checkpoint activation. Chromatin marks induced by DNA double-strand breaks and recognized by 53BP1 enable focal accumulation of this multifunctional repair factor at damaged chromatin. Here, we unveil an additional level of regulation of 53BP1 outside repair foci. 53BP1 movements are constrained throughout the nucleoplasm and increase in response to DNA damage. 53BP1 interacts with the structural protein NuMA, which controls 53BP1 diffusion. This interaction, and colocalization between the two proteins in vitro and in breast tissues, is reduced after DNA damage. In cell lines and breast carcinoma NuMA prevents 53BP1 accumulation at DNA breaks, and high NuMA expression predicts better patient outcomes. Manipulating NuMA expression alters PARP inhibitor sensitivity of BRCA1-null cells, end-joining activity, and immunoglobulin class switching that rely on 53BP1. We propose a mechanism involving the sequestration of 53BP1 by NuMA in the absence of DNA damage. Such a mechanism may have evolved to disable repair functions and may be a decisive factor for tumor responses to genotoxic treatments.
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    Prognostic Mutational Signatures of NSCLC Patients treated with chemotherapy, immunotherapy and chemoimmunotherapy
    (Springer Nature, 2023-03-27) Smith, Margaret R.; Wang, Yuezhu; D’Agostino, Ralph, Jr.; Liu, Yin; Ruiz, Jimmy; Lycan, Thomas; Oliver, George; Miller, Lance D.; Topaloglu, Umit; Pinkney, Jireh; Abdulhaleem, Mohammed N.; Chan, Michael D.; Farris, Michael; Su, Jing; Mileham, Kathryn F.; Xing, Fei; Biostatistics and Health Data Science, School of Medicine
    Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.
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    scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets
    (Elsevier, 2021-04) Song, Qianqian; Su, Jing; Miller, Lance D.; Zhang, Wei; Biostatistics, School of Public Health
    In gene expression profiling studies, including single-cell RNA sequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model (scLM), a gene co-clustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, scLM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.
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    Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment
    (Springer Nature, 2023-07-21) Song, Qianqian; Ruiz, Jimmy; Xing, Fei; Lo, Hui-Wen; Craddock, Lou; Pullikuth, Ashok K.; Miller, Lance D.; Soike, Michael H.; O’Neill, Stacey S.; Watabe, Kounosuke; Chan, Michael D.; Su, Jing; Biostatistics and Health Data Science, School of Medicine
    Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies.